Hasil untuk "Military Science"

Volodymyr Hurkovskyi, Yevhen Romanenko, Karel Nedbalek et al.

The article is devoted to the research of the main challenges in military pedagogy and psychology in the context of the russian-Ukrainian war, which has been ongoing since 2014 and escalated in 2022, with an emphasis on foresight strategies for addressing them. The research focuses on three main areas: psychological support for the Armed Forces of Ukraine, preparing citizens for national resistance, and the social and psychological reintegration of veterans. The aim of the research is to develop a scientifically grounded foresight strategy for the advancement of military pedagogy and psychology under the conditions of the russian-Ukrainian war. This includes identifying key challenges and gaps in the system of training military educators and psychologists, justifying the need to establish an institutional framework for coordinating scientific, educational, and practical initiatives, and preparing recommendations for the establishment of a specialised department and research institution on military pedagogy and psychology within the structure of the National Academy of Educational Sciences of Ukraine.The methodological foundation includes an interdisciplinary approach, foresight methodology, content analysis of publications, as well as structural and functional and comparative analysis of the experience from NATO countries (the United States, Israel, Finland, Canada, Australia). The analysis demonstrates the effectiveness of integrating psychological and pedagogical technologies into national security systems. It is proposed to establish a Department and an Institute of Military Pedagogy and Psychology within the National Academy of Educational Sciences of Ukraine to coordinate research, develop the “Military Psychologist” professional standard, educational programmes, and rehabilitation technologies. The foresight analysis outlines baseline, optimistic, and crisis scenarios through to 2030, with the optimistic forecast anticipating a 20% reduction in cases of absence without leave (AWOL), high-quality student training, and a 15% decrease in requests for assistance.The authors highlight systemic shortcomings such as the high rate of absence without leave (80,000 cases in 2024), insufficient psychological training of personnel, and limited stress management competencies among commanders. It is noted that the basic general military training for 70,000 students, introduced under the Law No. 3633-IX from 2025, is hindered by a shortage of personnel and methodologies. The rehabilitation of 1.2 million veterans, including 500,000 combatants, is slowed by the lack of comprehensive programmes, as evidenced by 18,000 requests for psychological assistance in April 2024.The recommendations imply introducing adaptive educational programmes for basic general military training and involving international experts to adapt best practices in the sphere of stress management, resistance training, and veteran rehabilitation.

Renqi Chen, Haoyang Su, Shixiang Tang et al.

The Science of Science (SoS) explores the mechanisms underlying scientific discovery, and offers valuable insights for enhancing scientific efficiency and fostering innovation. Traditional approaches often rely on simplistic assumptions and basic statistical tools, such as linear regression and rule-based simulations, which struggle to capture the complexity and scale of modern research ecosystems. The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS, enabling the automation of large-scale pattern discovery and uncovering insights previously unattainable. This paper offers a forward-looking perspective on the integration of Science of Science with AI for automated research pattern discovery and highlights key open challenges that could greatly benefit from AI. We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them. Additionally, we present a preliminary multi-agent system as an illustrative example to simulate research societies, showcasing AI's ability to replicate real-world research patterns and accelerate progress in Science of Science research.

William F. Godoy, Oscar Hernandez, Paul R. C. Kent et al.

We characterize the GPU energy usage of two widely adopted exascale-ready applications representing two classes of particle and mesh solvers: (i) QMCPACK, a quantum Monte Carlo package, and (ii) AMReXCastro, an adaptive mesh astrophysical code. We analyze power, temperature, utilization, and energy traces from double-/single (mixed)-precision benchmarks on NVIDIA's A100 and H100 and AMD's MI250X GPUs using queries in NVML and rocm_smi_lib, respectively. We explore application-specific metrics to provide insights on energy vs. performance trade-offs. Our results suggest that mixed-precision energy savings range between 6-25% on QMCPACK and 45% on AMReX-Castro. Also, we found gaps in the AMD tooling used on Frontier GPUs that need to be understood, while query resolutions on NVML have little variability between 1 ms-1 s. Overall, application level knowledge is crucial to define energy-cost/science-benefit opportunities for the codesign of future supercomputer architectures in the post-Moore era.

Shufeng Shi, Xiangzhen Kong, Junyu Fan et al.

Concrete material model plays an important role in numerical predictions of its dynamic responses subjected to projectile impact and charge explosion. Current concrete material models could be distinguished into two kinds, i.e., the hydro-elastoplastic-damage model with independent equation of state and the cap-elastoplastic-damage model with continuous cap surface. The essential differences between the two kind models are vital for researchers to choose an appropriate kind of concrete material model for their concerned problems, while existing studies have contradictory conclusions. To resolve this issue, the constitutive theories of the two kinds of models are firstly overviewed. Then, the constitutive theories between the two kinds of models are comprehensively compared and the main similarities and differences are clarified, which are demonstrated by single element numerical examples. Finally, numerical predictions for projectile penetration and charge explosion experiments on concrete targets are compared to further demonstrate the conclusion made by constitutive comparison. It is found that both the two kind models could be used to simulate the dynamic responses of concrete under projectile impact and blast loadings, if the parameter needed in material models are well calibrated, although some discrepancies between them may exist.

Liqiang Jing, Zhehui Huang, Xiaoyang Wang et al.

Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) have demonstrated impressive language/vision reasoning abilities, igniting the recent trend of building agents for targeted applications such as shopping assistants or AI software engineers. Recently, many data science benchmarks have been proposed to investigate their performance in the data science domain. However, existing data science benchmarks still fall short when compared to real-world data science applications due to their simplified settings. To bridge this gap, we introduce DSBench, a comprehensive benchmark designed to evaluate data science agents with realistic tasks. This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions. DSBench offers a realistic setting by encompassing long contexts, multimodal task backgrounds, reasoning with large data files and multi-table structures, and performing end-to-end data modeling tasks. Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG). These findings underscore the need for further advancements in developing more practical, intelligent, and autonomous data science agents.

Сергій ГОЛОВНЯ, Віталій КІРЕТОВ, Олег ЧИЖ

У статті проведено аналіз керівних документів щодо підтримання рівня технічної готовності органу охорони державного кордону. Встановлено зв’язок між поняттями: технічна готовність, бойова готовність та боєздатність озброєння і техніки органу охорони державного кордону. Досліджено складові, що формують технічну готовність озброєння і техніки органів охорони державного кордону, а також показники, що дозволяють її вимірювати. Визначено взаємозв’язки між організаційними заходами з питань комплектування, техобслуговування, відновлення, технічної підготовки і рівнем технічної готовності парку озброєння і техніки. Встановлено принципи та пріоритети комплектування озброєння і техніки, які не знижують рівень готовності органу охорони державного кордону. Надано рекомендації начальникам органі охорони державного кордону щодо принципів та заходів, спрямованих на підтримання парку озброєння і техніки на заданому рівні готовності; щодо принципів формування штатів водіїв, спеціалістів та техніки як бойових підрозділів, так і підрозділів забезпечення. Рекомендації спрямовано на максимальне фокусування начальника органу охорони державного кордону з питань, що формують технічно готовий парк озброєння і техніки. Під час розгляду рекомендацій здійснено розмежування між озброєнням і технікою бойових і стройових груп експлуатації, які призначені для забезпечення дій в умовах загострення обстановки, та озброєнням і технікою транспортних, навчальних груп експлуатації, що забезпечують діяльність військової частини в мирний час. Визначено аспекти, що є ключовими під час організації діяльності начальниками органів охорони державного кордону на період організації оперативно-службової діяльності. Запропоновано заходи, які дозволять начальнику органу охорони державного кордону своєчасно діагностувати рівень готовності парку озброєння і техніки та впливати на відповідність його нормативним вимогам.

Alexandre Riot, Enrico Panettieri, Antonio Cosculluela et al.

Modern additive manufacturing processes enable fabricating architected cellular materials of complex shape, which can be used for different purposes. Among them, lattice structures are increasingly used in applications requiring a compromise among lightness and suited mechanical properties, like improved energy absorption capacity and specific stiffness-to-weight and strength-to-weight ratios. A dedicated modeling strategy to assess the energy absorption capacity of lattice structures under uni-axial compression loading is presented in this work. The numerical model is developed in a non-linear framework accounting for the strain rate effect on the mechanical responses of the lattice structure. Four geometries, i.e., cubic body centered cell, octet cell, rhombic-dodecahedron and truncated cuboctahedron 2+, are investigated. Specifically, the influence of the relative density of the representative volume element of each geometry, the strain-rate dependency of the bulk material and of the presence of the manufacturing process-induced geometrical imperfections on the energy absorption capacity of the lattice structure is investigated. The main outcome of this study points out the importance of correctly integrating geometrical imperfections into the modeling strategy when shock absorption applications are aimed for.

О.М. Компанієць

В статті запропоновано застосування теоретико-ігрового підходу для управління роями безпілотних літальних апаратів (БпЛА) в умовах обмеженої інформації. Зокрема, використовуються методи непараметричного машинного навчання для формування ймовірнісних переконань апаратів щодо бойової обстановки. Кожен апарат може робити прогнози про наслідки своїх дій на основі інформації, отриманої від сусідніх дронів. Це дозволяє приймати рішення, спрямовані на максимізацію загальної ефективності рою. Представлено модель динамічного розподілу цілей, яка передбачає, що БпЛА можуть приєднуватися до рою або залишати його з певною ймовірністю, що моделюється процесом Бернуллі. Такий підхід дозволяє краще враховувати динамічність чисельності рою та змінність пріоритетності цілей на полі бою. Вирішення практичної проблеми розподілу цілей на полі бою знаходиться у площині застосування децентралізованого управління роєм БпЛА, що демонструє свою ефективність у випадках, коли доступ до повної інформації про бойову обстановку обмежений, а рішення повинні прийматися на основі прогнозів та даних, отриманих від сусідніх апаратів. У проведеному дослідженні запропоновано новий підхід до вирішення задач децентралізованого управління роєм на основі динамічних, адаптивних моделей та непараметричних методів навчання, що підвищує ефективність виконання бойових завдань.

A. Newhouse, K. Arrow, S. Karlin et al.

The Assessment Division of The Applied Physics Laboratory has undertaken new responsibilities and is expanding its Senior Analytical Staff. Senior Scientists in such fields as Mathematics, Physics and Physical Chemistry have in the past proven very effective in solving the types of problems involved which include analyses of tactical situations, the employment of future weapon systems and the application of the most recent advances in science and technology. Performance of the work requires close association with scientists of other laboratories, operations research personnel of all branches of the Armed Services, and with senior military and civilian personnel. Studies undertaken by this group will provide guide lines for the hardware research of future years. Staff members are expected to initiate ideas in support of a broad program of National Defense needs and carry them through appropriate analyses with assurance that sound results will be given consideration by the responsible agencies. The Laboratory's locale, equidistant between Baltimore and Washington, D. C , allows staff members to select urban, suburban or rural living and either of these two outstanding centers of culture as a focal point for fine living. These appointments offer exceptional opportunities. For information and arrangements for interview, write in confidence to:

Michael L Brodie

Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it, vital to realizing its potential and managing its risks. Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, it is a fundamentally new field of inquiry, one of the most active, powerful, and rapidly evolving 21st century innovations. Due to its value, power, and applicability, it is emerging in over 40 disciplines, hundreds of research areas, and thousands of applications. Millions of data science publications contain myriad definitions of data science and data science problem solving. Due to its infancy, many definitions are independent, application specific, mutually incomplete, redundant, or inconsistent, hence so is data science. This research addresses this data science multiple definitions challenge by proposing the development of coherent, unified definition based on a data science reference framework using a data science journal for the data science community to achieve such a definition. This paper provides candidate definitions for essential data science artifacts that are required to discuss such a definition. They are based on the classical research paradigm concept consisting of a philosophy of data science, the data science problem solving paradigm, and the six component data science reference framework (axiology, ontology, epistemology, methodology, methods, technology) that is a frequently called for unifying framework with which to define, unify, and evolve data science. It presents challenges for defining data science, solution approaches, i.e., means for defining data science, and their requirements and benefits as the basis of a comprehensive solution.

N. Werner, J. Řípa, C. Thöne et al.

This is the first in a collection of three papers introducing the science with an ultra-violet (UV) space telescope on an approximately 130~kg small satellite with a moderately fast re-pointing capability and a real-time alert communication system approved for a Czech national space mission. The mission, called Quick Ultra-Violet Kilonova surveyor - QUVIK, will provide key follow-up capabilities to increase the discovery potential of gravitational wave observatories and future wide-field multi-wavelength surveys. The primary objective of the mission is the measurement of the UV brightness evolution of kilonovae, resulting from mergers of neutron stars, to distinguish between different explosion scenarios. The mission, which is designed to be complementary to the Ultraviolet Transient Astronomy Satellite - ULTRASAT, will also provide unique follow-up capabilities for other transients both in the near- and far-UV bands. Between the observations of transients, the satellite will target other objects described in this collection of papers, which demonstrates that a small and relatively affordable dedicated UV-space telescope can be transformative for many fields of astrophysics.

Ahmed Abbasi, Roger H. L. Chiang, Jennifer J. Xu

Data science has been described as the fourth paradigm for scientific discovery. The latest wave of data science research, pertaining to machine learning and artificial intelligence (AI), is growing exponentially and garnering millions of annual citations. However, this growth has been accompanied by a diminishing emphasis on social good challenges - our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been. At the same time, the proliferation of machine learning and generative AI have sparked debates about the socio-technical prospects and challenges associated with data science for human flourishing, organizations, and society. Against this backdrop, we present a framework for "data science for social good" (DSSG) research that considers the interplay between relevant data science research genres, social good challenges, and different levels of socio-technical abstraction. We perform an analysis of the literature to empirically demonstrate the paucity of work on DSSG in information systems (and other related disciplines) and highlight current impediments. We then use our proposed framework to introduce the articles appearing in the special issue. We hope that this article and the special issue will spur future DSSG research and help reverse the alarming trend across data science research over the past 30-plus years in which social good challenges are garnering proportionately less attention with each passing day.

Efrat Levy, Nadav Maman, Asaf Shabtai et al.

MIL-STD-1553, a standard that defines a communication bus for interconnected devices, is widely used in military and aerospace avionic platforms. Due to its lack of security mechanisms, MIL-STD-1553 is exposed to cyber threats. The methods previously proposed to address these threats are very limited, resulting in the need for more advanced techniques. Inspired by the defense in depth principle, we propose AnoMili, a novel protection system for the MIL-STD-1553 bus, which consists of: (i) a physical intrusion detection mechanism that detects unauthorized devices connected to the 1553 bus, even if they are passive (sniffing), (ii) a device fingerprinting mechanism that protects against spoofing attacks (two approaches are proposed: prevention and detection), (iii) a context-based anomaly detection mechanism, and (iv) an anomaly explanation engine responsible for explaining the detected anomalies in real time. We evaluate AnoMili's effectiveness and practicability in two real 1553 hardware-based testbeds. The effectiveness of the anomaly explanation engine is also demonstrated. All of the detection and prevention mechanisms employed had high detection rates (over 99.45%) with low false positive rates. The context-based anomaly detection mechanism obtained perfect results when evaluated on a dataset used in prior work.

Haosen Ge, In Young Park, Xuancheng Qian et al.

High-quality text data has become an important data source for social scientists. We have witnessed the success of pretrained deep neural network models, such as BERT and RoBERTa, in recent social science research. In this paper, we propose a compact pretrained deep neural network, Transformer Encoder for Social Science (TESS), explicitly designed to tackle text processing tasks in social science research. Using two validation tests, we demonstrate that TESS outperforms BERT and RoBERTa by 16.7% on average when the number of training samples is limited (<1,000 training instances). The results display the superiority of TESS over BERT and RoBERTa on social science text processing tasks. Lastly, we discuss the limitation of our model and present advice for future researchers.

Mass Soldal Lund

This article presents a case study of a cyber security exercise in military education, and uses this case study to reflect on some challenges with cyber security exercise for educational purposes. The case study discusses central decisions in the design of the exercise, the evaluation of the exercise, as well as challenges with the exercise concept. Through a survey of the literature, we compare the exercise with similar exercises, and have a look at how these exercises are evaluated. Finally, we use the case study and the literature survey to reflect on how further investigations into cyber security exercise could be made.   Samandrag Denne artikkelen presenterer ein casestudie av ei cybersikkerheitsøving i militær utdanning, og nyttar denne casestudien til å drøfte nokre utfordringar med cybersikkerheitsøvingar til utdanningsføremål. Casestudien gjer greie for sentrale avgjerder i designet av øvinga, evaluering av øvinga og utfordringar i øvingskonseptet. Gjennom ein litteraturgjennomgang samanliknar vi øvinga med liknande øvingar, og ser på korleis desse øvingane har blitt evaluert. Avslutningsvis nyttar vi casestudien og litteraturgjennomgangen til å gjere betraktningar om vidare undersøkingar av cybersikkerheitsøvingar.   Nøkkelord: Utdanning; øving; cybersikkerheit; defensive cyberoperasjonar; CDX; cyberrange

О. П. Кулик, І. Л. Костенко, В. Г. Кубрак et al.

У статті обґрунтовується доцільність та загальні принципи побудови апаратури ретрансляції радіосигналів для розміщення на безпілотному літальному апараті для забезпечення радіозв’язку між наземним пунктом управління і екіпажами повітряних суден за межами дальності прямої видимості. Окреслюється необхідний склад приймально-передавальної апаратури бортового автоматичного ретранслятора радіосигналів. Обґрунтовуються значення його основних технічних характеристик для забезпечення ретрансляції радіосигналів між наземною і бортовими радіостанціями, що працюють в діапазоні ультракоротких хвиль.

G. Hardin

Susannah Kate Devitt, Damian Copeland

Australia is a leading AI nation with strong allies and partnerships. Australia has prioritised the development of robotics, AI, and autonomous systems to develop sovereign capability for the military. Australia commits to Article 36 reviews of all new means and methods of warfare to ensure weapons and weapons systems are operated within acceptable systems of control. Additionally, Australia has undergone significant reviews of the risks of AI to human rights and within intelligence organisations and has committed to producing ethics guidelines and frameworks in Security and Defence. Australia is committed to OECD's values-based principles for the responsible stewardship of trustworthy AI as well as adopting a set of National AI ethics principles. While Australia has not adopted an AI governance framework specifically for the Australian Defence Organisation (ADO); Defence Science and Technology Group (DSTG) has published 'A Method for Ethical AI in Defence' (MEAID) technical report which includes a framework and pragmatic tools for managing ethical and legal risks for military applications of AI. Australia can play a leadership role by integrating legal and ethical considerations into its ADO AI capability acquisition process. This requires a policy framework that defines its legal and ethical requirements, is informed by Defence industry stakeholders, and provides a practical methodology to integrate legal and ethical risk mitigation strategies into the acquisition process.

Pedro Hernandez, Edinson Espitia

This systematic mapping study consisted of tracking the scientific literature that addresses the issue of analogies as a didactic strategy in science teaching. An analogy can be understood as comparing an existing knowledge with a new knowledge to achieve a better understanding of the new knowledge as a result of the comparison of similarities; or in other words, use students' own concepts to introduce new concepts using comparisons between the two. The purpose of this study was to identify, analyze, synthesize and evaluate research works that touched on this topic, with this, to have knowledge about the models of uses of analogies, most used didactic strategies, research methodologies in this field and how to evaluate the learning effectiveness of working with analogies. The methodology that was used is the systematic mapping study; Five questions were posed that guided the information tracking process. Later, the electronic documents in English for the last twenty years were traced in five databases related to the educational field. Finally, it is concluded by responding to the purpose of the study where it is evident that, broadly speaking, the research methodologies in this field are quantitative as well as qualitative, to implement analogies, resources such as images, illustrations, textual indications and audiovisual aids are used, it is usually evaluated the effectiveness of using analogies with multiple choice tests, oral tests of creating analogies by students.

Alberto Sesana

I review the scientific potential of the Laser Interferometer Space Antenna (LISA), a space-borne gravitational wave (GW) observatory to be launched in the early 30s'. Thanks to its sensitivity in the milli-Hz frequency range, LISA will reveal a variety of GW sources across the Universe, from our Solar neighbourhood potentially all the way back to the Big Bang, promising to be a game changer in our understanding of astrophysics, cosmology and fundamental physics. This review dives in the LISA Universe, with a specific focus on black hole science, including the formation and evolution of massive black holes in galaxy centres, the dynamics of dense nuclei and formation of extreme mass ratio inspirals, and the astrophysics of stellar-origin black hole binaries.