futures research facility

About the FFRC

Finland Futures Research Centre (FFRC) is one of the few university departments devoted to futures research in the world. Jointly founded by three universities in Turku in 1992, the FFRC is a department within the Turku School of Economics at the University of Turku, Finland.

The FFRC works with a transdisciplinary approach in an international environment

The cornerstones of its activities are on developing academic futures studies, critical interdisciplinary research, high quality education, strategic and business foresight and insightfully produced futures knowledge.

All of the FFRC’s primary activities are focused on the promotion of a sustainable future, which is understood as being economically efficient, socially secure, fair, and culturally representative of our world society.

With its internationally active and multidisciplinary personnel and through co-creation with its societal partners, the FFRC is expertly able to meet the needs of its partners and customers, which range from academic institutions to public organizations and the business world.

Nationally, the FFRC carries out its special task as a developer and innovator of futures research and foresight in Finland. In addition, it serves as the permanent advisor of the Committee for the Future of the Finnish Parliament. This provides an excellent way to keep in touch with Finnish decision-makers.

The annual Futures Conferences of the FFRC bring together world-renowned futurists and researchers from various fields as well as Finnish citizens and decision-makers.

Member of the AACSB International

Turku School of Economics is an accredited member of the AACSB International, which is a guarantee of continuous development of excellence in education and research.

Founded 1992

Department at the Turku School of Economics, University of Turku

Offices at Turku – Helsinki – Tampere

Personnel 50+, personnel years in 2021 total 41

Masters students 77, Doctoral researchers 26

Projects annually ca. 35–40

Turnover in 2021: 2.74 M€ 

External funding 75%

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Center for Future Readiness

The Center for Future Readiness generates and translates career and workforce development research into practice and policy.

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University of Minnesota Research and Innovation Office Looks Back on Two Years of Progress

Dear Colleagues,

It has been two years since I had the honor of being appointed the University of Minnesota’s senior research officer, and through the retrospective article that follows, I am proud to look back on the work we have done together to strengthen and grow the University’s already impressive research enterprise. The successes we’ve had could only have happened with the help of many talented people in my office and across the University. I want to thank all of our partners who have made these past two years successful, and I look forward to an exciting new era under the leadership of President Rebecca Cunningham. 

Shashank Priya Vice President for Research and Innovation  

Operational Changes

To recognize the increasingly important role of innovation and entrepreneurialism in the University of Minnesota’s research ecosystem and to respond to feedback from key UMN research constituencies, the Office of the Vice President for Research (OVPR) changed its name to the Research and Innovation Office (RIO) in December 2023 .

In June 2024, RIO also consolidated what were previously geographically dispersed support units into a single operational headquarters in McNamara Alumni Center . The move grew out of RIO's Toward a New Normal effort that focused on post-pandemic flexible/hybrid work arrangements for its employees and space-use optimization. With the move, RIO seeks to boost staff cohesion and coordination, improve the employee experience when onsite, as well as free up space for growing divisions such as Research Computing.  

Research Infrastructure Investments

Over the past two years, RIO has reinvested in key research services and critical research infrastructure to help fill long term gaps and to catch up with needs created when University of Minnesota sponsored research award amounts jumped 30 percent between 2019 and 2023. Those investments include:

The MN-GEMS grant management system went live in April 2024 to replace a previous system that dated back to 1998. MN-GEMS offers better transparency, faster award setup, and enhanced reporting and interconnectivity with other research systems. 

Research Services Staffing

RIO added new staff across nearly all research support units to address increasing needs in grant administration, regulatory requirements, research compliance, and external partnerships. 

RIO also stabilized staffing in RAR, the department that provides animal care and use support for pre-clinical researchers, back to pre-pandemic levels. RIO provided RAR staff salary increases and other improvements based on feedback from RAR employees and the research community. 

Preclinical Research Infrastructure

One of VP Priya’s highest priorities has been to upgrade and maintain the University’s infrastructure and services related to preclinical research. RIO is investing in a new electronic management system for research with animals, a cage wash system to replace an antiquated predecessor, and other improvements to the East Bank vivarium.

University of Minnesota Genomics Center (UMGC) Investments 

RIO has prioritized genomics research at the UMN by stabilizing the UMGC’s finances with a three-year funding commitment, positioning the UMGC to emerge as one of the country’s most powerful technological genomics hubs. Initiatives already underway include the acquisition of new large-scale and cutting-edge sequencers, purchased in part with support from the Medical School and a matching RIO Research Infrastructure grant, and the build-out of the UMGC “CoLab,” a user-accessible facility for training and services in single-cell and spatial genomics. Already, support from RIO has resulted in two rounds of dramatic reductions in the cost of sequencing for investigators.   

Strategic Initiatives

Research 2030.

RIO is working to anticipate trends in research and our society so that University of Minnesota researchers can maximize their participation in addressing the big questions for research in the not-to-distant future—the year 2030. In April 2023, RIO organized the Research 2030 workshop, where the University research community, along with invited national leaders in academia, government, industry, and nonprofits, reflected on emerging discoveries and challenges.

These discussions have helped frame a research strategy into 14 high impact research themes for addressing new social, economic, and global challenges before they arise. They will allow the University to lead rather than follow when it comes to the interests of our research community, as well as potential funders and partners.

The UMN initiatives highlighted below were started based upon or validated by the themes that rose to the top at Research 2030. 

Biotechnology and Biomanufacturing Innovation Center (BBIC) 

BBIC seeks to build a biomanufacturing ecosystem in Minnesota that fosters cross-disciplinary research, innovation, entrepreneurship and education, training, outreach, engagement, and partnership—an ecosystem that is well aligned with anticipated federal investments (e.g., the National Biotechnology and Biomanufacturing Initiative). Six BBIC seed projects have been awarded funding over the next two years, with representation of five colleges/units and two UMN campuses. 

Sustainable GeoCommunities

The University of Minnesota recently co-organized a planning workshop for Sustainable GeoCommunities (SGC), a community-based program that will leverage the power of research to help local communities become more healthy, equitable, and prosperous guided by the UN Sustainable Development Goals. A workshop was hosted on June 24-25 by the Indian Institute of Technology (IIT), Kanpur at its Outreach Center in Noida and was attended by participants from University at Buffalo, IIT, Bombay; IIT, Delhi; IIT, Goa; Amrita Vishwa Vidyapeetham; and more than 20 other organizations. SGC aligns with a broader US-India initiative to expand bilateral research and higher education partnerships.

Data Science Initiative

Minnesota has historic roots in supercomputing, and the Minnesota Supercomputing Institute at UMN is at the cutting edge of big data fields such AI/ML and genomics/proteomics. The Data Science Initiative (DSI) aims to make a mark in data science research, nationally and internationally, by charting new research directions and enabling the development of new methods, data sets, and software that are used to address grand challenges facing our state, nation, and the world. For 2023/24, DSI’s seed funding focus areas are foundational data science, digital health and personalized healthcare delivery, and agriculture and the environment.

National Security Research Initiative

National security agencies support research into nearly all fields of interest to UMN scholars, and increased collaboration with defense and security-related agencies has long been identified as an area of potential growth for the U’s research enterprise. A National Security Research and Innovation Day planned for February 2025 will initially bring together UMN researchers who will explore problems that agencies and companies are seeking to solve across six high interest topics and where that work might overlap with existing UMN research directions. Attendees will explore how collaborations could be built around hypersonics, medical solutions for service members, microelectronics, geospatial imaging, materials in extreme environments, and artificial intelligence.

International Institute for Biosensing 

Biosensing is a fundamental global technology for the 21st century with tremendous applications for humans and the environment. Biosensors serve as critical monitors in a broad spectrum of applications, including food safety, agriculture, the environment, healthcare, animal health, national security and water quality. The International Institute for Biosensing (IIB) is supporting a cohort of five UMN graduate students working on interdisciplinary biosensing projects over three years. The students and their advisers and IIB leadership are also building a network of national and international peers and partners to increase access and lower barriers to physical and intellectual resources needed to accelerate biosensing innovation. This growing network of biosensing resources will give UMN graduate students a jumpstart in their careers by linking them to global resources by the time they graduate.   

Internal Funding Opportunities

In the past two years, RIO has launched two new programs and restarted another: 

Artist-in-Residence

Now in its second year, the Artist-in-Residence program allows artists to collaborate with scientists to benefit both science and art, spurring creative thinking and innovation. The program supports creative interpretation of scientific research and discoveries at the University, and artists are selected for a residency to produce science-based artwork and visuals, display their projects, and participate in a series of public talks to explain their work. 

North Carolina Agricultural and Technical State University (NC A&T) and UMN Research Partnership

This partnership pairs the University with North Carolina Agricultural and Technical State University (N.C. A&T), the largest of the nation’s Historically Black Colleges and Universities (HBCUs), located in Greensboro, NC. In the partnership’s first phase, RIO will award five proposals annually to initiate collaboration with N.C. A&T peer researchers. The second phase is planned to start in year three, when additional funding will be allocated to initiate at least three research programs per year between the two institutions that build on phase one projects, with the potential for undergraduate research opportunities, and the development of 3 + 2 programs in disciplines that are available at only N.C. A&T or UMN. 

UMN Social Justice Impact Grants

In 2023, RIO restarted the Social Justice Impact Grants (SJIG), funding rounds in 2023 and 2024. The SJIG aims to catalyze rigorous, solution-oriented research on social justice topics, including criminal justice reform, housing segregation/gentrification, systemic racism, achievement gaps, health disparities, environmental justice, and related topics. SJIG funds support research that holds high potential for building a more equitable and just society, future external funding, and career advancement.  

Strategic Partnerships at UMN

Research centers.

RIO provides administrative support to 12 University-wide centers and institutes . To help grow and coordinate research and innovation work across the University of Minnesota, RIO recently established closer relationships with two key research centers, the Natural Resources Research Institute (NRRI) and Minnesota Sea Grant . RIO leaders have worked with leaders at these two centers to help create new partnerships for research projects and academic programs.

Corporate Engagement Center

The Corporate Engagement Center (CEC) is a partnership between RIO and the University of Minnesota Foundation’s Corporate Foundation Engagement team. CEC connects leading researchers, University programs and strategic initiatives at the U with corporations locally, nationally and globally. CEC’s focus is a portfolio of 70+ strategic companies, including Minnesota’s 17 Fortune 500 Companies. In 2024, CEC helped secure more than $69 million in investments from the CEC Strategic Portfolio companies, and delivered more than a dozen million dollar plus proposals to portfolio companies, among other accomplishments. 

In partnership with UMF, VP Priya has also convened Innovation Minnesota, a leadership council made up of key technology leaders and officers that will help identify emerging technologies and workforce needs that will help grow existing companies, attract new companies to the state, and launch more startups. Innovation Minnesota will also identify barriers that are impeding innovation in the state.

Technology Commercialization: Discover → Advance → Impact ™

UMN Technology Commercialization is a highly regarded tech transfer shop among its university peers nationwide, with more than 260 startup companies launched since 2006, more than 3,200 current licenses, and more than 500 active patents. RIO and CEC are supporting a new Tech Comm initiative to take that work to the next level. The Discover → Advance → Impact program aims to grow the level of commercializable UMN inventions through gap funding to help develop early technologies and an angel investor network to boost UMN startup company success, among other efforts.  

External Engagement

Federal agency outreach.

In order to grow research opportunities for faculty and students, RIO began creating events for visiting officials and traveling to meet with potential funders and partners across the country as well as abroad. To provide just a couple of recent examples, RIO welcomed 28 researchers and leaders from Sandia National Laboratories for an entire day of tours, presentations, and networking to build on 11 existing research collaborations. Dr. Kimberly Sablon, who leads a key AI and autonomous systems development unit at the Department of Defense (DoD), visited campus and learned about UMN research directions in AI and robotics. Other visitors brought to campus by RIO include delegations and officials from NSF, including NSF director Sethuraman Panchanathan, US Department of Energy Advanced Research Projects Agency-Energy (ARPA-E), Army Research Office (ARO), US Census Bureau, and DoD Strategic Environmental Research and Development Program (SERDP) and the Environmental Security Technology Certification Program (ESTCP).

Greater MSP

RIO has deepened the University's partnership with Greater MSP , a partnership that promotes regional competitiveness, helping to ensure that the partnerships's strategic directions are aligned with the strengths and potential future growth areas of the University's research and innovation enterprise, such as semiconductors and microelectronics, sustainable plastics, hypersonic aviation, sustainable aviation fuel and biomanufacturing, and regenerative agriculture. VP Priya sits on the executive council of Greater MSP's MBOLD initiative, which develops practical solutions to global challenges facing the food and agriculture sectors.

Microelectronics Research

To help advance microelectronics research at the U and the regional microelectronics industry, RIO appointed the University’s first Chief Semiconductor Officer (CSO) , Steven Koester, and launched a website devoted to the University’s semiconductor and microelectronics research and education: chips.umn.edu . The CSO’s responsibilities include coordinating University research and education initiatives and investments related to microelectronics and representing the University within larger efforts to advance Minnesota’s microelectronics industry. Most recently, the CSO has helped facilitate a new training program for 60 employees of Minnesota microelectronics companies and helped launch Minnesota’s participation in SCALE, a defense semiconductor workforce development program sponsored by DoD. The CSO position provides leadership in coordinating state-wide team building activities in order to respond to federal and other opportunities under the bipartisan US CHIPS and Science Act.

International Partnerships

In addition to the Sustainable GeoCommunities work described previously, RIO leaders are also exploring potential health care, medical device and material science collaborations in Bangalore, which Priya visited in February and signed a general Memorandum of Understanding with the prestigious Indian Institute of Science (IISc) and met with leading hospitals. RIO leaders also visited the aforementioned Amrita University in June and signed an MOU with Amrita academic leadership in Chicago in August. 

In June, Governor Margaret Gardner of the Australian state of Victoria toured UMN’s Nano Center, met with Minnesota med tech leaders, and heard a presentation on IIB. Priya visited research university partners and national laboratories in Australia last year as part of a Minnesota trade delegation, including Monash University in Melbourne, Victoria, which he sees as a potential research partner in areas such as biosensing, microelectronics, and clinical research.

RIO and other UMN partners also continue to work with Kiel University in Germany on potential academic and research partnerships that may include student and staff exchanges, collaboration in innovation and technology transfer, and joint work in common fields of interest such as biosensing, material sciences, food and agriculture, life sciences, neuroimaging, cultural studies and marine science and technology. To strengthen these collaborative efforts, more than 20 scientists from Kiel U. are scheduled to visit UMN in September for a workshop on Precision Sensing, organized by IIB. This three-day program will feature presentations by leading researchers in the fields of precision sensing for biomedical, agricultural, and environmental applications, as well as lab tours and a strategic session aimed at securing funding for future international collaborations.

Under the leadership of VP Priya, RIO has significantly strengthened the University's partnerships with leading institutions in South Korea. Building upon the historic Minnesota Project , our collaboration with Seoul National University (SNU) continues with new areas of common interest. Initiatives led by VP Priya have culminated in an MOU with SNU, the hosting of 16 SNU undergraduates in January 2024, and the launch of a collaborative RFP between the IIB and SNU, aimed at advancing biosensing research collaboration and mutual visits. Additionally, we have established MOUs with Hanyang University—where President Ki-Jeong Lee is a UMN alumnus—as well as the Korea Institute of Science and Technology (KIST) in Seoul.  

Vision for the Future

For the future, VP Priya sees great potential for new work in areas such as microelectronics, data science, extreme materials, cybersecurity, and climate; alignment with defense agencies in the areas of robotics, autonomous systems, and secure AI tools; expanded opportunities to pursue research on new biotechnologies and biomanufacturing; under SCALE, new research into specialized semiconductors with applications in satellite communications, among other uses; and geospatial technologies under the SGC program and the NSRI.

“We’ve spent two years both reinforcing the foundation and seeding the future for cutting-edge and impactful research at the University,” said VP Priya. “We’re making new connections and opening doors to new resources that will allow researchers to address grand challenges at the pace and scale needed to make a transformative societal and environmental impact.”  

Standards of Futures Research

Guidelines for Practice and Evaluation

• © 2022
• Lars Gerhold 0 ,
• Dirk Holtmannspötter 1 ,
• Christian Neuhaus 2 ,
• Elmar Schüll 3 ,
• Beate Schulz-Montag 4 ,
• Karlheinz Steinmüller 5 ,
• Axel Zweck 6

Research Forum on Public Safety and Security, Freie Universität Berlin, Berlin, Germany

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VDI Technologiezentrum GmbH, Düsseldorf, Germany

Futuresaffairs, büro für aufgeklärte zukunftsforschung, berlin, germany, salzburg university of applied sciences, salzburg, austria, foresightlab, berlin, germany, z_punkt gmbh, berlin, germany, innovations- und zukunftsforschung, rwth aachen university, aachen, germany.

• Quality of results and processes in futures studies
• Research perspective directed towards the future
• Guidance for scientists and practitioners

Part of the book series: Zukunft und Forschung (ZUFORSCH)

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Future-Oriented Technology Analysis: A Classification Framework

On some fundamental methodological aspects in foresight processes

Foresight: Turning Challenges into Opportunities

• Futures Research
• Technology Assessment
• Futures Studies
• Quality Criteria

Table of contents (18 chapters)

Front matter, standards of group 1: the future as a subject of inquiry, images of the future.

Christian Neuhaus

Karlheinz Steinmüller

Validation by Argumentation

• Armin Grunwald

Aligning the Research with Ambitions for Action

• Gereon Uerz, Christian Neuhaus

Interdisciplinarity

Elmar Schüll

Transdisciplinarity

• Hans-Liudger Dienel

Standards of Group 2: Good Research Practice

Objectives and framework conditions.

• Kerstin Cuhls

Transparency

• Elmar Schüll, Lars Gerhold

Theoretical Foundations

Method selection.

Lars Gerhold

Producing Quality Research

• Roman Peperhove, Tobias Bernasconi

Scientific Relevance

• Birgit Weimert, Axel Zweck

Codes of Conduct and Scientific Integrity

• Andreas Weßner, Elmar Schüll

Standards of Group 3: Practical Relevance and Effectiveness

Practical relevance, usefulness, and effectiveness, understanding the type, role, and specificity of the research audience, transferability and communication of results.

Beate Schulz-Montag

Editors and Affiliations

Dirk Holtmannspötter

About the editors

Dr. Christian Neuhaus, Futuresaffairs, Berlin, Germany

Beate Schulz-Montag, foresightlab, Berlin, Germany

Bibliographic Information

Book Title : Standards of Futures Research

Book Subtitle : Guidelines for Practice and Evaluation

Editors : Lars Gerhold, Dirk Holtmannspötter, Christian Neuhaus, Elmar Schüll, Beate Schulz-Montag, Karlheinz Steinmüller, Axel Zweck

Series Title : Zukunft und Forschung

DOI : https://doi.org/10.1007/978-3-658-35806-8

Publisher : Springer VS Wiesbaden

eBook Packages : Behavioral Science and Psychology , Behavioral Science and Psychology (R0)

Copyright Information : The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022

Softcover ISBN : 978-3-658-35805-1 Published: 14 July 2022

eBook ISBN : 978-3-658-35806-8 Published: 13 July 2022

Series ISSN : 2629-8279

Series E-ISSN : 2945-7874

Edition Number : 1

Number of Pages : XVI, 158

Number of Illustrations : 1 b/w illustrations, 3 illustrations in colour

Topics : Science and Technology Studies , Sociology, general , Artificial Intelligence , Social Sciences, general

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• Based on ‘hope and love,’ UB celebrates opening of FOXG1 Research Center

research news

Based on ‘hope and love,’ UB celebrates opening of FOXG1 Research Center

Members of the FOXG1 Research Center pose for a photo after a ribbon cutting officially opening the new center. Photo: Douglas Levere

By TOM DINKI

Published September 26, 2024

Hope and love aren’t often mentioned in the same breath as scientific research, but they’re exactly what UB’s FOXG1 Research Center (FRC) was founded on. 

“Love for every individual living with FOXG1 syndrome and other neurodevelopmental disorders, as well as their caregivers, and hope that we can find a cure for FOXG1 syndrome and related disorders on the basis of scientific discoveries we are making,” Soo-Kyung Lee, director of the FRC and the parent of a child with FOXG1 syndrome, told a crowd gathered to celebrate the center’s official opening Tuesday. 

The occasion included a ceremonial ribbon cutting and science symposium in the Buffalo Room of Capen Hall, as well as a tour of the Cooke Hall lab where the center’s researchers are developing a promising viral gene therapy against the rare disorder.

“This research center really represents a bold step forward, not only for the University at Buffalo but for the future of science,” said Robin Schulze, dean of the College of Arts and Sciences. “The center means we are that much closer to finding a cure for the neurodevelopmental disorder FOXG1 syndrome. Today’s ribbon cutting clears the way for innovative research and creative collaborations.”

The center is led by Lee, Empire Innovation Professor and Om P. Bahl Endowed Professor in the Department of Biological Sciences, College of Arts and Sciences, and her husband, Jae Lee, professor of biological sciences.

The Lees’ teenage daughter, Yuna , is one of only about 1,000 people in the world diagnosed with FOXG1 syndrome. It’s caused by a mutation of the FOXG1 gene, one of the most important genes for early brain development, and causes cognitive and physical disabilities, as well as life-threatening seizures. Those affected require 24/7 care.  

Soo-Kyung Lee (seated, left) and Jae Lee (right) listen to speakers during the ceremonial ribbon cutting for the FOXG1 Research Center. Photo: Douglas Levere

Since Yuna was diagnosed in 2012, the Lees, who joined UB in 2019, have dedicated their careers to studying the disorder. They’ve found that the FOXG1 gene and protein remain active in mice after birth, providing hope that some symptoms can be alleviated, and hope that therapy of the FOXG1 gene may be transferable to more common disorders like autism and Alzheimer’s disease.

This new center will support the Lees’ ongoing development of a viral gene therapy . A postnatal injection of the therapy into day-old mice rescued abnormalities in parts of the brain responsible for language, memory and social interaction. Their goal is to begin human clinical trials as early as spring 2026, pending U.S. Food and Drug Administration approval.

“[The Lees’] groundbreaking viral therapy offers renewed hope for patients and families affected by this rare disorder. Their personal journey and unwavering dedication to this cause are truly inspiring,” said Venu Govindaraju, vice president for research and economic development, whose office is supporting the center. 

Govindaraju added that between the FRC and the National AI Institute for Exceptional Education — which will use artificial intelligence to assist children with speech and language disorders — UB is dedicated to addressing the unique challenges faced by individuals with disabilities.

“The potential of artificial intelligence in drug discovery and personalized medicine is immense,” he said. “As a leading scientific research community and our excellence in AI and life sciences, we are committed to exploring innovative approaches that can accelerate the development of effective treatments for FOXG1 syndrome and other neurodevelopmental disorders.”

The Lees take ribbon-cutting attendees on a tour of the research center in Cooke Hall. Photo: Douglas Levere

The FRC is also supported by the FOXG1 Research Foundation, where Soo-Kyung Lee is chief scientific officer. The foundation’s CEO, Nasha Fitter, who is also the parent of a child with FOXG1 syndrome, recalled first connecting with Soo-Kyung Lee via a Facebook group in 2017. 

“I honestly almost fell off my chair that we had a neuroscientist who was also a parent and no one had told me this,” Fitter said. “And then I learned that her husband was also a neuroscientist.”

Fitter and others co-founded the FOXG1 Research Foundation shortly after. She traced the organization’s rise, from its humble first symposium in 2018 to now having raised more than $7 million toward human clinical trials, and credited a large part of that success to its partnership with the Lees’ lab.

“They have built an entire gene therapy lab at the University at Buffalo. It is exceptional for an academic medical center to develop treatments,” Fitter said. “That does not usually happen at university campuses, and that is what is happening here.

“We’re literally creating the drug that will be given to our children in a clinical trial,” she added. “It’s a monumental step that this has happened in seven years.”

Jae Lee concluded the day by thanking children with FOXG1 syndrome for making their families whole.

“I feel we are closer to the finish line to find the cure for FOXG1 syndrome,” he said.

Microsoft Research Lab – Asia

Probts: unified benchmarking for time-series forecasting, share this page.

Author: Machine Learning Group

Time-series forecasting is crucial across various industries, including health, energy, commerce, climate, etc. Accurate forecasts over different prediction horizons are essential for both short-term and long-term planning needs across these domains. For instance, during a public health emergency such as the COVID-19 pandemic, projections of infected cases and fatalities over one to four weeks are essential for allocating medical and societal resources effectively. In the energy sector, precise forecasts of electricity demand on an hourly, daily, weekly, and even monthly basis are crucial for power management and renewable energy scheduling. Logistics relies on forecasting short-term and long-term cargo volumes for adaptive route scheduling and efficient supply chain management.

Beyond covering various prediction horizons, accurate forecasting must extend beyond point estimates to include distributional forecasts that quantify estimation uncertainty. Both the expected estimates and the associated uncertainties are indispensable for subsequent planning and optimization, providing a comprehensive view that informs better decision-making.

Given the critical need for accurate point and distributional forecasting across diverse prediction horizons, researchers from Microsoft Research Asia revisited existing time-series forecasting studies to assess their effectiveness in meeting these essential demands. The review encompasses state-of-the-art models developed across various research threads:

• Classical time-series models : These models typically require training from scratch on each dataset, focusing on either long-term point forecasting (e.g., PatchTST, iTransformer) or short-term distributional forecasting (e.g., CSDI, TimeGrad).
• Recent time-series foundation models : These models involve universal pre-training across extensive datasets and are developed by both industrial labs (e.g., TimesFM, MOIRAI, Chronos) and academic institutions (e.g., Timer, UniTS).

Despite the advancements, researchers find that existing approaches often lack a holistic consideration of all essential forecasting needs. This limitation results in “biased” methodological designs and unverified performance in untested scenarios.

To address the gaps identified in existing time-series forecasting studies, researchers developed the ProbTS tool. ProbTS serves as a unified benchmarking platform designed to evaluate how well current approaches meet essential forecasting needs. By highlighting crucial methodological differences, ProbTS provides a comprehensive understanding of the strengths and weaknesses of advanced time-series models and unveils opportunities for future research and innovation.

Repo: https://github.com/microsoft/ProbTS (opens in new tab)

Paper: https://arxiv.org/abs/2310.07446v4 (opens in new tab)

Paradigm differences: Methodological analysis of time series forecasting

The benchmark study using ProbTS highlights two crucial methodological differences found in contemporary research: the forecasting paradigms for point and distributional estimation, and the decoding schemes for variable-length forecasting across different horizons.

Forecasting paradigms for point and distributional estimation

• Point forecasting only: Approaches that support only point forecasting, providing expected estimates without uncertainty quantification.
• Predefined distribution heads: Methods that use predefined distribution heads to generate distributional forecasts, offering a fixed structure for uncertainty estimation.
• Neural distribution estimation modules: Techniques employing neural network-based modules to estimate distributions, allowing for more flexible and potentially more accurate uncertainty quantification.

Decoding schemes for variable-length forecasting across different horizons

• Autoregressive (AR) methods: These methods generate forecasts step-by-step, using previous predictions as inputs for future time steps. They are suitable for scenarios where sequential dependencies are crucial.
• Non-Autoregressive (NAR) methods: These methods produce forecasts for all time steps simultaneously, offering faster predictions and potentially better performance for long-term forecasting.

The research results under the ProbTS framework reveals several key insights:

Firstly, due to customized neural architectures, long-term point forecasting approaches excel in long-term scenarios but struggle in short-term cases and with complex data distributions. The lack of uncertainty quantification leads to significant performance gaps compared to probabilistic models when dealing with complex data distributions. Conversely, short-term probabilistic forecasting methods are proficient in short-term distributional forecasting but exhibit performance degradation and efficiency issues in long-term scenarios.

Secondly, regarding the characteristics of different decoding schemes, NAR decoding is predominantly used in long-term point forecasting models, while short-term probabilistic forecasting models do not show such a biased preference. Meanwhile, AR decoding suffers from error accumulation over extended horizons but may perform better with strong seasonal patterns.

Lastly, for current time-series foundation models, the limitations of AR decoding are reaffirmed in long-term forecasting. Additionally, foundation models show limited support for distributional forecasting, highlighting the need for improved modeling of complex data distributions.

Detailed results and analysis on classical time-series models

Researchers benchmark classical time-series models across a wide range of forecasting scenarios, encompassing both short and long prediction horizons. The evaluation includes both point forecasting metrics (Normalized Mean Absolute Error, NMAE) and distributional forecasting metrics (Continuous Ranked Probability Score, CRPS). Additionally, researchers calculate a non-Gaussianity score to quantify the complexity of data distribution for each forecasting scenario.

Based on the data presented in Figure 2, several noteworthy observations emerge:

• Limitations of long-term point forecasting models: Customized neural architectures for time-series, primarily designed for long-term point forecasting, excel in long-term scenarios. However, their architectural benefits significantly diminish in short-term cases (see Figure 2(a) and 2(c)). Furthermore, their inability to quantify forecasting uncertainty results in larger performance gaps compared to probabilistic models, especially when the data distribution is complex (see Figure 2(c) and 2(d)).
• Weaknesses of short-term probabilistic forecasting models: Current probabilistic forecasting models, while proficient in short-term distributional forecasting, face challenges in long-term scenarios, as evidenced by significant performance degradations (see Figure 2(a) and 2(b)). In addition to unsatisfactory performance, some models experience severe efficiency issues as the prediction horizon increases.

These observations yield several important implications. Firstly, effective architecture designs for short-term forecasting remain elusive and warrant further research. Secondly, the ability to characterize complex data distributions is crucial, as long-term distributional forecasting presents significant challenges in both performance and efficiency.

Following that, researchers compare Autoregressive (AR) and Non-Autoregressive (NAR) decoding schemes across various forecasting scenarios, highlighting their respective pros and cons in relation to forecasting horizons, trend strength, and seasonality strength.

Researchers find that nearly all long-term point forecasting models use the NAR decoding scheme for multi-step outputs, whereas probabilistic forecasting models exhibit a more balanced use of AR and NAR schemes. Researchers aim to elucidate this disparity and highlight the pros and cons of each scheme, as shown in Figure 3.

• Error accumulation in AR decoding: Figure 3(a) shows that existing AR models experiences a larger performance gap compared to NAR methods as the prediction horizon increases, suggesting that AR may suffer from error accumulation.
• Impact of trend strength: Figure 3(b) connects the performance gap with trend strength, indicating that strong trending effects can lead to significant performance differences between NAR and AR models. However, there are exceptions where strong trends do not cause substantial performance degradation in AR-based models.
• Impact of seasonality strength: Figure 3(c) explains these exceptions by introducing seasonality strength as a factor. Surprisingly, AR-based models perform better in scenarios with strong seasonal patterns, likely due to their parameter efficiency in such contexts.
• Combined effects of trend and seasonality: Figure 3(d) demonstrates the combined effects of trend and seasonality on performance differences.

Based on these analyses, researchers point out that the choice between AR and NAR decoding schemes in different research branches is primarily driven by the specific data characteristics in their focused forecasting scenarios. This explains the preference for the NAR decoding paradigm in most long-term forecasting models. However, this preference for NAR may overlook the advantages of AR, particularly its effectiveness in handling strong seasonality. Since both NAR and AR have their own strengths and weaknesses, future research should aim for a more balanced exploration, leveraging their unique advantages and addressing their limitations.

Detailed results and analysis on time-series foundation models

Researchers then extend the analysis framework to include recent time-series foundation models and examine their distributional forecasting capabilities.

The results show that:

• AR vs. NAR decoding in long-term forecasting: Figure 4(a) reaffirms the limitations of AR decoding over extended forecasting horizons. This suggests that time-series data, due to its continuous nature, may require special adaptations beyond those used in language modeling (which operates in a discrete space). Additionally, it is confirmed that AR-based and NAR-based models can deliver comparable performance in short-term scenarios, with AR-based models occasionally outperforming their NAR counterparts.
• Distributional forecasting capabilities: Figure 4(b) compares the distributional forecasting capabilities of foundation models with CSDI, underscoring the importance of capturing complex data distributions. Current foundation models demonstrate limited support for distributional forecasting, typically using predefined distribution heads (e.g., MOIRAI) or approximated distribution modeling in a value-quantized space (e.g., Chronos).

These observations lead to several important conclusions: While AR-based models can be effective in short-term scenarios, their performance diminishes over longer horizons, highlighting the need for further refinement. Time-series data may require unique treatments to optimize AR decoding, particularly for long-term forecasting. The ability to accurately model complex data distributions remains a critical area for improvement in time-series foundation models.

Future directions: Evolving perspectives, models, and tools

Based on the evaluation and analysis of existing methods, researchers have proposed several important future directions for time series prediction models. These directions, if pursued, could significantly impact key scenarios across various industry sectors.

Future direction 1: Adopting a comprehensive perspective of forecasting demands. One primary future direction is to adopt a holistic perspective of essential forecasting demands when developing new models. This approach can help rethink the methodological choices of different models, understand their strengths and weaknesses, and foster more diverse research explorations.

Future direction 2: Designing a universal model. A fundamental question raised by these results is whether we can develop a universal model that fulfills all essential forecasting demands or if we should treat different forecasting demands separately, introducing specific techniques for each. While it is challenging to provide a definitive answer, the ultimate goal could be to create a universal model. When developing such a model, it is necessary to consider issues such as input representation, encoding architecture, decoding scheme, and distribution estimation module, etc. Additionally, future research is needed to address the challenge of distributional forecasting in high-dimensional and noisy scenarios, particularly for long horizons, and to leverage the advantages of both AR and NAR decoding schemes while avoiding their weaknesses.

Future direction 3: Developing tools for future research. To support future research in these directions, researchers have made the ProbTS tool publicly available, hoping this tool will facilitate advancements in the field and encourage collective efforts from the research community.

By addressing these future directions, researchers aim to push the boundaries of time-series forecasting, ultimately developing models that are more robust, versatile, and capable of handling a wide range of forecasting challenges. This progress holds the potential to significantly impact numerous industries, leading to better decision-making, optimized operations, and improved outcomes across critical sectors.

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Future Biosimilar Trends, Challenges, and Opportunities

Ivo Abraham, PhD, RN, University of Arizona Cancer Center, explains how biosimilars are becoming more accessible but face challenges like price erosion and potential shortages.

Ivo Abraham, PhD, RN, director of the Center for Health Outcomes and PharmacoEconomic Research at the University of Arizona Cancer Center, discusses the evolvement of the biosimilar landscape throughout the years in an interview with The Center for Biosimilars®.

Abraham addresses both biosimilars and generics, highlighting their potential to provide more access to patients at lower costs while recognizing the challenges of marketing biosimilars and the need for volume contracts to stay competitive.

This transcript was lightly edited for clarity.

The biosimilar landscape is constantly evolving. What emerging trends in health economics or clinical research do you see impacting the future of biosimilar development and access?

Price erosion is one because it can only go so low. We need to make sure that this does not threaten the biosimilar manufacturing side of the formulary. They have more costs to recover, so there needs to be some way, and it's probably going be more of a coordinated way of limiting the price erosion that we see for the reference products. We know a lot of those costs have already been recouped, whereas a biosimilar company still has to do that. Also, we are seeing the high volume agents that the manufacturer or the reference product is willing to go very, very low in terms of their price because it's made up in part by volume. That's an important aspect.

The other aspect that we need to take into account is we're not going to have influence over which biosimilar you're administered. It's the same thing as with a generic. You can go to the pharmacy and receive a 3-month supply from one company, and 3 months later, it may be another company just because your payer has different contracts or your pharmacy provider.

Dealing with the fact that we're talking about commodity markets, and as we've seen shortages in generic cancer drugs, for instance, manufacturers of generics are pulling out of producing certain treatments because of the margin being so minimal. This is not treating hypertensive patients, where you have millions and millions and millions. Just in the US, you may be talking about a much smaller patient population.

Another thing that is important is that, in the US, we need to let go of our xenophobism, that things that come from abroad are not good. When we talk about biosimilars, we're going to have to take into account that we have now entered also an era of bioparallels. These are products developed somewhere else, are of very high quality, very well researched, with clinical trials and so on, and will enter the US market or are entering the US market at a significant price discount relative to other very well-established products—and I'm referring to PD1 inhibitors—and will provide more access to more patients at potentially lower cost.

What we also need to take into account is that especially the manufacturers of biosimilars that also manufacture generics bundle their products. For instance, have a bundle of chemotherapy agents, not biologic agents, but chemotherapy agents with then a biosimilar of a biological product.

For instance, the first line of breast cancer treatment is highly myelotoxic. Patients are at very high risk of developing neutropenia or febrile neutropenia. Why not, as a manufacturer of generics and biosimilars, create a breast cancer bundle of products? Then the products could be priced accordingly and be sold to payers, providers, and patients.

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Oryol Oblast

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Orlovskoye Polesye National Park

• Type: State with 747,000 residents
• Description: administrative division (oblast) in central Russia
• Neighbors: Bryansk Oblast , Kaluga Oblast , Kursk Oblast , Lipetsk Oblast and Tula Oblast
• Categories: oblast of Russia and locality
• Location: Chernozemye , Russia , Eastern Europe , Europe
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Oryol Oblast

Oryol Oblast ( Russian : Орло́вская о́бласть , romanized :   Orlovskaya oblast' ), also known as Orlovshchina ( Орловщина ), is a federal subject of Russia (an oblast ). Its administrative center is the city of Oryol . Population: 713,374   ( 2021 Census ) ; [9] 786,935   ( 2010 Census ) ; [10]

Hydrography

Legislature, administrative divisions, agriculture, demographics, external links.

It is located in the southwestern part of the Central Federal District , in the Central Russian Upland .

In terms of area, at 24,652   km 2 (9,518   sq   mi) it is one of the smallest federal subjects. [11] From north to south, it extends for more than 150   km (93   mi) , and from west to east—for over 200   km (120   mi) .

Kaluga Oblast border it to the north-west; Tula Oblast is located to the north; Lipetsk Oblast — to the east; Kursk Oblast — to the south, and Bryansk Oblast is to the west.

There are 4,800   km 2 (1,900   sq   mi) of black earth soils ( chernozems ) in the oblast, which amounts to three-quarters of the world chernozem reserves. [11]

The climate is temperate ( Köppen : Dfb ). The winter is moderately cold, with an average January temperature from −9 to −11   °C (16 to 12   °F) . Summers are warm and humid, with an average July temperature from 19 to 21   °C (66 to 70   °F) . Rainfall averages 520 to 630   mm (20 to 25   in) , and snow cover averages 120   days.

On the territory of the Oryol region there are more than 2 thousand rivers and streams with a total length of 9,100   km (5,700   mi) , but there are no navigable water ways. The rivers of the region belong to the basins of three rivers: Volga , Don , Dnieper .

The Oka river , one of Europe's largest rivers, flows through the oblast for part of its course (190   km) and the source of it is in the south of the region. Main tributaries: Zusha (with tributary Neruch ), Vytebet , Nugr , Tson , Orlik , Rybnitsa , Kroma .

Sosna flows in the eastern part of the region. Main tributaries: Trudy , Tim , Lyubovsha , Kshen , Olym .

In the west of the region originate rivers Nerussa , Navlya , Swapa .

1100 lakes and artificial reservoirs of the region cover a total area of about 55   km 2 (21   sq   mi) (0,22%).

In the 12th century, chronicles mention Mtsensk , known as Novosil then. Then modern Orlovschina was part of the Chernigov Principality . After the death of Mikhail of Chernigov Novosil Principality was formed on these territories. By the end of the 15th century it had disintegrated into four separate principalities and, along with all the other fragments of the Chernigov principality, became a part of Grand Duchy of Lithuania . In the 16th century, the fortress town of Oryol was founded, and the town of Livny , destroyed in the 13th century, was restored. In the 16th and 17th centuries, the territory of modern Oryol was the borderland of the Tsardom of Russia , with many fortifications of the Great Abatis Line . With the reduction of the threat posed by the Tatars , agricultural activity of the area had intensified. It was created in 1937 by uniting a selection of territories of three other oblasts: Kursk Oblast , Western Oblast , and Voronezh Oblast . It also included present Bryansk Oblast between 1937 and 1944. In 1941-3 The Region was partly or fully occupied by Germany.

During the Soviet period, the high authority in the oblast was shared between three persons: The first secretary of the Oryol CPSU Committee (who in reality had the biggest authority), the chairman of the oblast Soviet (legislative power), and the Chairman of the oblast Executive Committee (executive power). Since 1991, CPSU lost all the power, and the head of the Oblast administration, and eventually the governor was appointed/elected alongside elected regional parliament .

The Charter of Oryol Oblast is the fundamental law of the region. The Oryol Oblast Council of People's Deputies is the province's standing legislative (representative) body. The Legislative Assembly exercises its authority by passing laws, resolutions, and other legal acts and by supervising the implementation and observance of the laws and other legal acts passed by it. The highest executive body is the Oblast Government, which includes territorial executive bodies such as district administrations, committees, and commissions that facilitate development and run the day to day matters of the province. The Oblast administration supports the activities of the Governor of Oryol Oblast , who is the highest official and acts as guarantor of the observance of the oblast Charter in accordance with the Constitution of Russia .

The head of administration of Oryol Oblast between 1993 and 2009 was Yegor Stroyev . Stroyev led the region for more than 20 years. In 1985 he became the first secretary of the regional committee of the CPSU , and after three years (in 1989-1991 he worked as secretary of the Central Committee of the CPSU ), in 1991 he returned to Oryol, worked as the director of the Institute of Horticultural Crops Selection, and later was elected governor. On February 16, 2009 Russian President Dmitry Medvedev accepted Stroyev's voluntary retirement and nominated Alexander Kozlov as his replacement, which was approved by the Oryol Regional Council of People's Deputies.

The Oryol Oblast Council of People's Deputies is a permanent representative and legislative body of state power in the Oryol Oblast. It consists of 50 deputies elected by residents of the region for 5 year terms according to a mixed-member proportional representation : 25 deputies according to party lists ( proportional representation ) and 25 in single-mandate constituencies ( majoritarian representation ) on the basis of universal equal and direct suffrage by secret ballot.

The current 7th Council of People's Deputies was elected in the 2021 Russian regional elections and will last until 2026. Of the 50 deputies, 27 are from United Russia , 11 from the Communist Party of the Russian Federation , 6 from A Just Russia – For Truth , 3 from the Liberal Democratic Party of Russia , 1 from New People , 1 from the Russian Party of Pensioners for Social Justice , and 1 independent. Leonid Muzalevsky (United Russia) was elected Chairman of the Council.

• 3 cities under the oblast's jurisdiction
• 13 urban-type settlements

The main industries in Oryol Oblast are the food and light industries, engineering and metalworking, and ferrous and nonferrous metallurgy. The engineering and metalworking industries manufacture production equipment for various industries, forklift trucks , construction and agricultural equipment , and machinery for municipal services. Numerous companies in the instrument-making and electronics sectors maintain high scientific and technical potential with the latest high-end technologies and experienced specialists. [12] First digital telephone exchange was introduced in the oblast in 1998. [13]

Most of the oblast's agricultural land is used for plant cultivation. Grain growing is very important, with winter wheat and rye being the main crops. Buckwheat , oats , barley , and potatoes are also grown, and sugar beets are in great demand. The area planted in feed grains is increasing due to the expansion of livestock farming, which includes beef and dairy cattle farming, pig farming, sheep farming for meat and wool , poultry farming , and horse breeding . [14]

Pipelines and power transmission lines are routed through the region's largest oil-trunk pipeline Druzhba (202   km in area). In the southwestern part of the area lies a small section of the Urengoy - Pomary - Uzhgorod pipeline .

Oryol is a major hub of pipelines exporting to Belarus, Western Ukraine and the Baltic states , with branches passing through Bryansk and Kursk .

As of 2016, the motorization level of the area was of 314 cars per 1000 people, which is the 15th of any region of Russia and above the national average (285).

Main roads of the region:

• 54А-1 Oryol — Yefremov (158   km through Zalegoshch , Novosil )

The main line is the double track electrified main line Moscow - Kharkiv - Simferopol (136   km through Mtsensk , Oryol , Zmievka and Glazunovka ).

Other lines:

• branch in Livny and Dolgoye
• historical Riga — Oryol (64   km through Naryshkino and Khotynets )
• Oryol — Dmitriyev (83   km through Kromy )
• Kursk Oblast — Kolpna (20   km)

Population: 713,374   ( 2021 Census ) ; [9] 786,935   ( 2010 Census ) ; [10] 860,262   ( 2002 Census ) ; [15] 890,636   ( 1989 Soviet census ) . [16]

Vital statistics for 2022: [17] [18]

• Births: 4,973 (7.0 per 1,000)
• Deaths: 12,247 (17.2 per 1,000)

Total fertility rate (2022): [19] 1.21 children per woman

Life expectancy (2021): [20] Total — 68.97 years (male   — 64.04, female   — 73.81)

• Russians - 96.1%
• Ukrainians - 1%
• Others - 2.9%
• 17,468 people were registered from administrative databases, and could not declare an ethnicity. It is estimated that the proportion of ethnicities in this group is the same as that of the declared group. [21]

According to a 2012 survey, [22] 40.9% of the population of Oryol Oblast adheres to the Russian Orthodox Church , 5% are unaffiliated generic Christians , 1% are Orthodox Christian believers who don't belong to church or belong to non-Russian Orthodox churches , 1% are adherents of the Rodnovery (Slavic native faith) movement, and 1% are Old Believers . In addition, 34% of the population declares to be " spiritual but not religious ", 8% is atheist , and 9.1% follows other religions or did not give an answer to the question. [22]

Related Research Articles

Voronezh Oblast is a federal subject of Russia. Its administrative center is the city of Voronezh. Its population was 2,308,792 as of the 2021 Census.

Volgograd Oblast is a federal subject of Russia, located in the lower Volga region of Southern Russia. Its administrative center is Volgograd. The population of the oblast was 2,500,781 in the 2021 Census.

Belgorod Oblast is a federal subject of Russia. Its administrative center is the city of Belgorod. As of 2021, the population is 1,540,486.

Kursk Oblast is a federal subject of Russia. Its administrative center is the city of Kursk. As of the 2021 census, Kursk Oblast had a population of 1,082,458.

Lipetsk Oblast is a federal subject of Russia. Its administrative center is the city of Lipetsk. As of the 2021 Census, its population was 1,143,224.

Tula Oblast is a federal subject of Russia. It is geographically located in European Russia and is administratively part of the Central Federal District, covering an area of 25,700 square kilometers (9,900 sq mi). It has a population of 1,553,925 (2010 Census) . Tula is the largest city and the administrative center of the oblast.

Mtsensk is a town in Oryol Oblast, Russia, located on the Zusha River 49 kilometers (30 mi) northeast of Oryol, the administrative center of the oblast. Population: 43,222 (2010 Census) ; 47,807 (2002 Census) ; 48,400 (1989 Soviet census) ; 28,000 (1970).

Cheremisinovsky District is an administrative and municipal district (raion), one of the twenty-eight in Kursk Oblast, Russia. It is located in the northeast of the oblast. The area of the district is 813 square kilometers (314 sq mi). Its administrative center is the urban locality of Cheremisinovo. Population: 7,804 (2021 Census) ; 10,347 (2010 Census) ; 12,431 ; 14,160 (1989 Soviet census) . The population of Cheremisinovo accounts for 43.1% of the district's population.

Ponyrovsky District is an administrative and municipal district (raion), one of the twenty-eight in Kursk Oblast, Russia. It is located in the north of the oblast. The area of the district is 690 square kilometers (270 sq mi). Its administrative center is the urban locality of Ponyri. Population: 10,893 (2021 Census) ; 11,778 (2010 Census) ; 13,553 ; 15,694 (1989 Soviet census) . The population of Ponyri accounts for 45.1% of the district's total population.

Zheleznogorsky District is an administrative and municipal district (raion), one of the twenty-eight in Kursk Oblast, Russia. It is located in the north of the oblast. The area of the district is 991 square kilometers (383 sq mi). Its administrative center is the town of Zheleznogorsk. Population: 15,478 (2021 Census) ; 16,289 (2010 Census) ; 18,192 ; 19,571 (1989 Soviet census) .

Zolotukhinsky District is an administrative and municipal district (raion), one of the twenty-eight in Kursk Oblast, Russia. It is located in the north of the oblast. The area of the district is 1,150 square kilometers (440 sq mi). Its administrative center is the urban locality of Zolotukhino. Population: 21,151 (2021 Census) ; 22,914 (2010 Census) ; 26,800 ; 31,564 (1989 Soviet census) . The population of Zolotukhino accounts for 21.2% of the district's total population.

Livensky District is an administrative and municipal district (raion), one of the twenty-four in Oryol Oblast, Russia. It is located in the southwest of the oblast. The area of the district is 1,806.3 square kilometers (697.4 sq mi). Its administrative center is the town of Livny. Population: 32,791 ; 34,200 (2002 Census) ; 34,503 (1989 Soviet census) .

Mtsensky District is an administrative and municipal district (raion), one of the twenty-four in Oryol Oblast, Russia. It is located in the north of the oblast. The area of the district is 1,665.8 square kilometers (643.2 sq mi). Its administrative center is the town of Mtsensk. Population: 19,233 ; 20,757 (2002 Census) ; 22,317 (1989 Soviet census) .

Novosilsky District is an administrative and municipal district (raion), one of the twenty-four in Oryol Oblast, Russia. It is located in the northeast of the oblast. The area of the district is 778.3 square kilometers (300.5 sq mi). Its administrative center is the town of Novosil. Population: 8,561 ; 10,591 (2002 Census) ; 16,800 (1989 Soviet census) . The population of Novosil accounts for 42.7% of the district's total population.

Lenina is a rural locality in Verkhnekhotemlsky Selsoviet Rural Settlement, Fatezhsky District, Kursk Oblast, Russia. Population: 6 (2010 Census) ; 19 (2002 Census) ;

2nd Shemyakino or Vtoroye Shemyakino is a rural locality in Nizhnemedveditsky Selsoviet Rural Settlement, Kursky District, Kursk Oblast, Russia. Population: 140 (2010 Census) ; 174 (2002 Census) ;

Nizhnyaya Zabolot is a rural locality in Nizhnemedveditsky Selsoviet Rural Settlement, Kursky District, Kursk Oblast, Russia. Population: 116 (2010 Census) ; 120 (2002 Census) ;

Dronyayevo is a rural locality in Brezhnevsky Selsoviet Rural Settlement, Kursky District, Kursk Oblast, Russia. Population: 53 (2010 Census) ; 81 (2002 Census) ;

Alexandrovka is a rural locality in Brezhnevsky Selsoviet Rural Settlement, Kursky District, Kursk Oblast, Russia. Population: 23 (2010 Census) ; 41 (2002 Census) ;

Pakhomovo is a rural locality in Brezhnevsky Selsoviet Rural Settlement, Kursky District, Kursk Oblast, Russia. Population: 15 (2010 Census) ; 19 (2002 Census) ;

• ↑ Президент Российской Федерации.   Указ   №849   от   13 мая 2000 г. «О полномочном представителе Президента Российской Федерации в федеральном округе». Вступил в силу   13 мая 2000 г. Опубликован: "Собрание законодательства РФ", No.   20, ст. 2112, 15 мая 2000 г. (President of the Russian Federation.   Decree   # 849   of   May 13, 2000 On the Plenipotentiary Representative of the President of the Russian Federation in a Federal District . Effective as of   May 13, 2000.).
• ↑ Госстандарт Российской Федерации.   №ОК 024-95   27 декабря 1995 г. «Общероссийский классификатор экономических регионов. 2.   Экономические районы», в ред. Изменения №5/2001 ОКЭР. ( Gosstandart of the Russian Federation.   # OK 024-95   December 27, 1995 Russian Classification of Economic Regions. 2.   Economic Regions , as amended by the Amendment   # 5/2001 OKER. ).
• ↑ rbc.ru Putin Replaces Oryol Oblast Governor (in Russian)
• ↑ "Оценка численности постоянного населения по субъектам Российской Федерации" . Federal State Statistics Service . Retrieved 1 September 2022 .
• ↑ http://www.gks.ru/free_doc/new_site/population/demo/Popul2018.xls . {{ cite web }} : Missing or empty | title= ( help )
• ↑ "Об исчислении времени" . Официальный интернет-портал правовой информации (in Russian). 3 June 2011 . Retrieved 19 January 2019 .
• ↑ Official throughout the Russian Federation according to Article   68.1 of the Constitution of Russia .
• 1 2 Russian Federal State Statistics Service. Всероссийская перепись населения 2020 года. Том 1 [ 2020 All-Russian Population Census, vol. 1 ] (XLS) (in Russian). Federal State Statistics Service .
• 1 2 3 Russian Federal State Statistics Service (2011). Всероссийская перепись населения 2010 года. Том   1 [ 2010 All-Russian Population Census, vol.   1 ] . Всероссийская перепись населения 2010   года [2010 All-Russia Population Census] (in Russian). Federal State Statistics Service .
• 1 2 "Orel Region" . Retrieved 2006-11-29 .
• ↑ "Oryol Oblast" . kommersant.com .
• ↑ «Ростелеком» в Орле переводит абонентов на цифровые АТС
• ↑ "Oryol Region" . kommersant.com .
• ↑ Federal State Statistics Service (21 May 2004). Численность населения России, субъектов Российской Федерации в составе федеральных округов, районов, городских поселений, сельских населённых пунктов   – районных центров и сельских населённых пунктов с населением 3   тысячи и более человек [ Population of Russia, Its Federal Districts, Federal Subjects, Districts, Urban Localities, Rural Localities—Administrative Centers, and Rural Localities with Population of Over 3,000 ] (XLS) . Всероссийская перепись населения 2002   года [All-Russia Population Census of 2002] (in Russian).
• ↑ Всесоюзная перепись населения 1989   г. Численность наличного населения союзных и автономных республик, автономных областей и округов, краёв, областей, районов, городских поселений и сёл-райцентров [ All Union Population Census of 1989: Present Population of Union and Autonomous Republics, Autonomous Oblasts and Okrugs, Krais, Oblasts, Districts, Urban Settlements, and Villages Serving as District Administrative Centers ] . Всесоюзная перепись населения 1989   года [All-Union Population Census of 1989] (in Russian). Институт демографии Национального исследовательского университета: Высшая школа экономики [Institute of Demography at the National Research University: Higher School of Economics]. 1989 – via Demoscope Weekly .
• ↑ "Information on the number of registered births, deaths, marriages and divorces for January to December 2022" . ROSSTAT . Archived from the original on 2 March 2023 . Retrieved 21 February 2023 .
• ↑ "Birth rate, mortality rate, natural increase, marriage rate, divorce rate for January to December 2022" . ROSSTAT . Archived from the original on 2 March 2023 . Retrieved 21 February 2023 .
• ↑ Суммарный коэффициент рождаемости [ Total fertility rate ] . Russian Federal State Statistics Service (in Russian). Archived from the original (XLSX) on 10 August 2023 . Retrieved 10 August 2023 .
• ↑ "Демографический ежегодник России" [ The Demographic Yearbook of Russia ] (in Russian). Federal State Statistics Service of Russia (Rosstat) . Retrieved 2022-06-01 .
• ↑ "ВПН-2010" . www.perepis-2010.ru .
• 1 2 3 "Arena: Atlas of Religions and Nationalities in Russia" . Sreda, 2012.
• ↑ 2012 Arena Atlas Religion Maps . "Ogonek", № 34 (5243), 27/08/2012. Retrieved 21/04/2017. Archived .
• Kropotkin, Peter Alexeivitch ; Bealby, John Thomas (1911). "Orel (government)"   . Encyclopædia Britannica . Vol.   20 (11th   ed.). pp.   250–251.
• (in English) Overview of Oryol Oblast ( Kommersant newspaper)
• (in English) Central Eurasian Information Resource: Images of Oryol Oblast - University of Washington Digital Collection

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Drones reportedly hit oil depot, energy provider in Russia’s Oryol region

Several drones struck "facilities of a fuel and energy complex" in Russia’s Oryol region on the afternoon of Jan. 9, the regional governor, Andrey Klychkov, claimed on Telegram.

According to Russian pro-Kremlin media outlet Mash, a Ukrainian kamikaze drone allegedly hit the Orelnefteprodukt oil depot, and another drone struck the building of a local energy provider, Oryolenergo.

Three people were reportedly injured, and two of them refused hospitalization, Klychkov said. First responders are working on the scene.

Later, Klychkov claimed that the third drone fell on a non-residential building near the village of Malaya Sakhanka near Oryol. No casualties were reported.

The Kyiv Independent couldn’t verify any of the claims above.

Klychkov attributed the attack to Ukraine, while Kyiv hasn’t claimed responsibility. Ukrainian authorities rarely comment on attacks inside Russia.

Explosions were reported on Jan. 8 at a railway track by an oil depot on the outskirts of the Russian city of Nizhny Tagil.

A fire erupted in a substation in Moscow on Jan. 4, causing electricity and heating outages in dozens of multi-story buildings, a few days after Russian attacks against Kyiv Oblast left 260,000 Ukrainian citizens temporarily without power.

Russia began intensifying its attacks against Ukraine's cities and critical infrastructure as the temperatures dropped at the end of 2023, mirroring its strategy from last year.

Over the winter of 2022-2023, Russia engaged in a persistent campaign to target Ukraine's energy infrastructure, causing large-scale outages and damage to the grid.

As the cold weather season began and Ukraine prepared itself for a likely repeat of the strategy, Zelensky said in late October that this year, Ukraine would "not only defend itself but also respond" to Russia's "terrorist attacks" on critical infrastructure.

Read also: Ukrainian energy company on Russia’s attacks on infrastructure: ‘No system in the world has faced the same’

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