Hasil untuk "Military Science"

Toby Drinkall

As military organisations consider integrating large language models (LLMs) into command and control (C2) systems for planning and decision support, understanding their behavioural tendencies is critical. This study develops a benchmarking framework for evaluating aspects of legal and moral risk in targeting behaviour by comparing LLMs acting as agents in multi-turn simulated conflict. We introduce four metrics grounded in International Humanitarian Law (IHL) and military doctrine: Civilian Target Rate (CTR) and Dual-use Target Rate (DTR) assess compliance with legal targeting principles, while Mean and Max Simulated Non-combatant Casualty Value (SNCV) quantify tolerance for civilian harm. We evaluate three frontier models, GPT-4o, Gemini-2.5, and LLaMA-3.1, through 90 multi-agent, multi-turn crisis simulations across three geographic regions. Our findings reveal that off-the-shelf LLMs exhibit concerning and unpredictable targeting behaviour in simulated conflict environments. All models violated the IHL principle of distinction by targeting civilian objects, with breach rates ranging from 16.7% to 66.7%. Harm tolerance escalated through crisis simulations with MeanSNCV increasing from 16.5 in early turns to 27.7 in late turns. Significant inter-model variation emerged: LLaMA-3.1 selected an average of 3.47 civilian strikes per simulation with MeanSNCV of 28.4, while Gemini-2.5 selected 0.90 civilian strikes with MeanSNCV of 17.6. These differences indicate that model selection for deployment constitutes a choice about acceptable legal and moral risk profiles in military operations. This work seeks to provide a proof-of-concept of potential behavioural risks that could emerge from the use of LLMs in Decision Support Systems (AI DSS) as well as a reproducible benchmarking framework with interpretable metrics for standardising pre-deployment testing.

XIONG Zhenkai, YANG Haotian, SUN Yinyin, LIN Xinhai, LIU Jiasheng

Aiming at the transportation problems of large cargo and carrier aircraft on the deck of large ships, a multi vehicle formation cooperative transportation strategy based on improved pilot following algorithm is proposed. The stability and accuracy of the traditional navigation following algorithm for multi vehicle operation can not fully meet the needs of large ship deck operation. The multi vehicle steering kinematics model is established, the traditional navigation following algorithm is improved, the virtual pose and linear feedback control are added to eliminate the formation error, the stability is verified and the emergency control strategy is designed. The lateral error of the improved formation is reduced from 0.01 m to 0.004 m under the circumferential condition. The lateral error of formation is reduced from ± 0.01 m to ± 0.002 m under sinusoidal condition. The results show that the improved lateral tracking accuracy of formation motion is improved, which is more conducive to formation transportation.

Sasiprapa Ninwattana, Spencer L. Sterling, Khwankamon Rattanatumhi et al.

Community-based serosurveillance for emerging zoonotic viruses can provide a powerful and cost-effective measurement of cryptic spillovers. Betacoronaviruses, including SARS-CoV, SARS-CoV-2 and MERS-CoV, are known to infect bats and can cause severe respiratory illness in humans, yet remain under-surveyed in high-risk populations. This study aimed to determine the seroprevalence of betacoronaviruses in an occupational cohort in contact with bats before and after the emergence of SARS-CoV-2. Serum samples from pre- and post-COVID-19 pandemic were screened using antigen-based multiplex microsphere immunoassays (MMIAs) and a multiplex surrogate virus neutralization test (sVNT). Pre-pandemic samples showed no SARS-CoV-2 antibodies, while post-pandemic samples from vaccinated participants displayed binding and neutralizing antibodies against SARS-CoV-2 and a related bat CoV. Furthermore, one participant (1/237, 0.43%) had persistent antibodies against MERS-CoV in 2017, 2018 and 2021 but was seronegative in 2023, despite reporting no history of traveling abroad or severe pneumonia. The observed sustained antibody levels indicate a possible exposure to MERS-CoV or a MERS-CoV-like virus, although the etiology and clinical relevance of this finding remains unclear. Ongoing surveillance in high-risk populations remains crucial for understanding virus epidemiology and mitigating zoonotic transmission risk.

Luca Foppiano, Guillaume Lambard, Toshiyuki Amagasa et al.

This study is dedicated to assessing the capabilities of large language models (LLMs) such as GPT-3.5-Turbo, GPT-4, and GPT-4-Turbo in extracting structured information from scientific documents in materials science. To this end, we primarily focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. Due to the evident lack of datasets within Materials Informatics (MI), we evaluated using SuperMat, based on superconductor research, and MeasEval, a generic measurement evaluation corpus. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline). We introduce a novel methodology for the comparative analysis of intricate material expressions, emphasising the standardisation of chemical formulas to tackle the complexities inherent in materials science information assessment. For NER, LLMs fail to outperform the baseline with zero-shot prompting and exhibit only limited improvement with few-shot prompting. However, a GPT-3.5-Turbo fine-tuned with the appropriate strategy for RE outperforms all models, including the baseline. Without any fine-tuning, GPT-4 and GPT-4-Turbo display remarkable reasoning and relationship extraction capabilities after being provided with merely a couple of examples, surpassing the baseline. Overall, the results suggest that although LLMs demonstrate relevant reasoning skills in connecting concepts, specialised models are currently a better choice for tasks requiring extracting complex domain-specific entities like materials. These insights provide initial guidance applicable to other materials science sub-domains in future work.

Richard E. Zeebe, Ilja J. Kocken

Astronomical solutions provide calculated orbital and rotational parameters of solar system bodies based on the dynamics and physics of the solar system. Application of astronomical solutions in the Earth sciences has revolutionized our understanding in at least two areas of active research. (i) The Astronomical (or Milankovic) forcing of climate on time scales > ~10 kyr and (ii) the dating of geologic archives. The latter has permitted the development of the astronomical time scale, widely used today to reconstruct highly accurate geological dates and chronologies. The tasks of computing vs. applying astronomical solutions are usually performed by investigators from different backgrounds, which has led to confusion and recent inaccurate results on the side of the applications. Here we review astronomical solutions and Milankovic forcing in the Earth sciences, primarily aiming at clarifying the astronomical basis, applicability, and limitations of the solutions. We provide a summary of current up-to-date and outdated astronomical solutions and their valid time span. We discuss the fundamental limits imposed by dynamical solar system chaos on astronomical calculations and geological/astrochronological applications. We illustrate basic features of chaotic behavior using a simple mechanical system, i.e., the driven pendulum. Regarding so-called astronomical "metronomes", we point out that the current evidence does not support the notion of generally stable and prominent metronomes for universal use in astrochronology and cyclostratigraphy. We also describe amplitude and frequency modulation of astronomical forcing signals and the relation to their expression in cyclostratigraphic sequences. Furthermore, the various quantities and terminology associated with Earth's axial precession are discussed in detail. Finally, we provide some suggestions regarding practical considerations.

С. І. Поплавець, А. А. Нікітін, І. С. Мещеряков

У статті розглянутий методичний підхід до оцінювання ефективності проведення спеціальної обробки військової техніки під час виникнення радіаційної, хімічної, біологічної небезпечної обстановки з використанням методів теорії масового обслуговування. Застосування методів теорії масового обслуговування під час оцінювання ефективності проведення спеціальної обробки військової техніки дозволяє: визначати потрібну кількість сил та засобів для проведення спеціальної обробки військової техніки; обґрунтовувати потрібний час виконання заходів спеціальної обробки військової техніки; враховувати рівень підготовки особового складу через середні значення показників виконання нормативів зі спеціальної обробки; визначити характер вхідного потоку; встановити середній час обслуговування одного об’єкта одним засобом та середнє число об’єктів, яке надходить на обслуговування за одиницю часу; розподілити розподіл часу та кількість засобів, які виділяються для обслуговування, а також враховувати велику кількість факторів, в тому числі і випадкових. Це обумовлює доцільність використання методів теорії масового обслуговування під час оцінювання ефективності спеціальної обробки військової техніки та потребує розробки відповідного методичного апарата.

Hyun Kwon, Dooseo Park, Ohyun Jo

In this paper, we propose a method for creating a hidden voice that is perceived as silence by a human. The proposed method creates a silent hidden voice that is mistakenly classified as a target phrase by the target model; it does this by configuring the loss function so that the probability of classification into the target phrase by the target model is highest. In an experimental evaluation using the Mozilla Common Voice dataset as the test data source and TensorFlow as the machine learning library, the proposed method created a silent hidden voice that had a 100% attack success rate for a target phrase on a target model while minimizing the average distortion to 187.81.

С.В. Горбенко

В умовах повномасштабної збройної агресії Росії проти України одним з найбільш важливих завдань є своєчасне і якісне комплектування Збройних Сил України військовозобов’язаними, у тому числі офіцерами. У статті проведений аналіз досвіду комплектування збройних сил провідних країн світу офіцерським складом. Проаналізовані останні дослідження і публікації з цього питання. Водночас проведений аналіз свідчить про те, що питання комплектування збройних сил провідних країн світу саме офіцерським складом в сучасних умовах досліджено не повністю та потребує більш детального вивчення. Визначені основні джерела комплектування збройних сил офіцерським складом та надані відповідні пропозиції.

Jianghua Liu, Jiang Zhu, Karim Khayati et al.

Abstract The study focuses on the control of nonlinear dynamic systems in the presence of parameter uncertainties, unmodeled dynamics, and external disturbances. The lumped perturbation is assumed to be bounded within a polynomial in the system state with the polynomial parameters and degrees unknown a priori such that it accommodates a quite wider range dynamic systems. Based on the studies in recent super-twisting algorithm designs and the idea from adaptive sliding mode control for nonlinear systems with uncertainties, we propose a novel adaptive super-twisting algorithm with exponential reaching law, or exponential super-twisting algorithm (ESTA), for the high-stability and acceptable accuracy control of the aimed nonlinear dynamics. The stability analysis and practical finite-time (PFT) convergence are proven using Lyapunov theory and an intuitive analysis of the control behaviour. Simulations are performed to compare the proposed ESTA with the existing super-twisting method and the traditional proportional integral differential control. The simulation results demonstrate the effectiveness of the proposed ESTA in terms of the fastest settling time and the smallest overshoot.

A. Moullet, T. Kataria, D. Lis et al.

PRIMA (The PRobe for-Infrared Mission for Astrophysics) is a concept for a far-infrared (IR) observatory. PRIMA features a cryogenically cooled 1.8 m diameter telescope and is designed to carry two science instruments enabling ultra-high sensitivity imaging and spectroscopic studies in the 24 to 235 microns wavelength range. The resulting observatory is a powerful survey and discovery machine, with mapping speeds better by 2 - 4 orders of magnitude with respect to its far-IR predecessors. The bulk of the observing time on PRIMA should be made available to the community through a General Observer (GO) program offering 75% of the mission time over 5 years. In March 2023, the international astronomy community was encouraged to prepare authored contributions articulating scientific cases that are enabled by the telescope massive sensitivity advance and broad spectral coverage, and that could be performed within the context of GO program. This document, the PRIMA General Observer Science Book, is the edited collection of the 76 received contributions.

Michael L. Brodie

Data science is not a science. It is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery that is not otherwise possible and can be beyond human reasoning. It is changing our world practically and profoundly already widely deployed in tens of thousands of applications in every discipline in an AI Arms Race that, due to its inscrutability, can lead to unfathomed risks. This paper presents an axiology of data science, its purpose, nature, importance, risks, and value for problem solving, by exploring and evaluating its remarkable, definitive features. As data science is in its infancy, this initial, speculative axiology is intended to aid in understanding and defining data science to recognize its potential benefits, risks, and open research challenges. AI based data science is inherently about uncertainty that may be more realistic than our preference for the certainty of science. Data science will have impacts far beyond knowledge discovery and will take us into new ways of understanding the world.

Kashif Shaheed, Imran Qureshi, Fakhar Abbas et al.

The primary objective of this study is to develop an advanced, automated system for the early detection and classification of leaf diseases in potato plants, which are among the most cultivated vegetable crops worldwide. These diseases, notably early and late blight caused by <i>Alternaria solani</i> and <i>Phytophthora infestans</i>, significantly impact the quantity and quality of global potato production. We hypothesize that the integration of Vision Transformer (ViT) and ResNet-50 architectures in a new model, named EfficientRMT-Net, can effectively and accurately identify various potato leaf diseases. This approach aims to overcome the limitations of traditional methods, which are often labor-intensive, time-consuming, and prone to inaccuracies due to the unpredictability of disease presentation. EfficientRMT-Net leverages the CNN model for distinct feature extraction and employs depth-wise convolution (DWC) to reduce computational demands. A stage block structure is also incorporated to improve scalability and sensitive area detection, enhancing transferability across different datasets. The classification tasks are performed using a global average pooling layer and a fully connected layer. The model was trained, validated, and tested on custom datasets specifically curated for potato leaf disease detection. EfficientRMT-Net’s performance was compared with other deep learning and transfer learning techniques to establish its efficacy. Preliminary results show that EfficientRMT-Net achieves an accuracy of 97.65% on a general image dataset and 99.12% on a specialized Potato leaf image dataset, outperforming existing methods. The model demonstrates a high level of proficiency in correctly classifying and identifying potato leaf diseases, even in cases of distorted samples. The EfficientRMT-Net model provides an efficient and accurate solution for classifying potato plant leaf diseases, potentially enabling farmers to enhance crop yield while optimizing resource utilization. This study confirms our hypothesis, showcasing the effectiveness of combining ViT and ResNet-50 architectures in addressing complex agricultural challenges.

Галина КУШНАРЬОВА, Олександр ФРОЛОВ, Ірина РАДЧЕНКО et al.

Метою статті є проведення дослідів із замоченою основою та визначення коефіцієнта умов роботи для підошви квадратного фундаменту залежно від глибини шару ґрунту. У сучасних умовах реконструкція та будівництво будинків і споруд спеціального та військового призначення, можуть проводитись на слабких ґрунтах. До того ж у цих ґрунтах можуть бути високі ґрунтові води, протікання із водопроводу, каналізації, які можуть замочувати основу фундаменту. Тимчасові підземні води та повільне зниження рівня ґрунтової води (підтоплення) можуть призвести до створення вогкості в підземних приміщеннях будинків і споруд спеціального та військового призначення або навіть до їх затоплення. Волога під впливом капілярних сил може підніматися вгору до нещільності мурування, що спричиняє вогкість у нижніх поверхнях будов. Усе це погіршує санітарні умови приміщень, а іноді викликає необхідність вжиття термінових заходів для водозахисту. Особливо гостро проблема захисту будівель від затоплення постає на ділянках, де під час проєктування та будівництва рівень ґрунтової води був значно нижчий від рівня підошви фундаменту, через що серйозний водозахист не передбачався, а в роки експлуатації він значно наблизився до поверхні внаслідок підтоплених територій. Якщо ж підземні води мають агресивні властивості відносно будівельних матеріалів, то інтенсивність руйнування фундаментів та інших частин будівлі різко зростає. За експлуатацію, утримання казармено-житлового фонду, об’єктів соціально-культурного призначення, комунальних споруд та інженерних мереж військових містечок відповідає начальник квартирно-експлуатаційної служби (КЕС). У разі появи в конструкціях будівель, споруд, приміщень будь-яких дефектів конструкцій, начальник КЕС зобов’язаний негайно забезпечити огляд дефектних конструкцій відповідними фахівцями військової частини, вжити заходів з усунення недоліків, якщо виникли аварійні ситуації. У статті розглянуто експериментальне дослідження осідання фундаменту на замоченій основі І типу та визначено зміну величини коефіцієнта умов роботи залежно від тиску на підошві фундаменту та глибини шару просадочного ґрунту.

Daniel C. Mattingly

: The leaders of authoritarian states face a dilemma between building a loyal military to guard against domestic threats and a professional military that can guard against foreign threats. In this article, I argue that leaders respond to domestic threats by promoting loyal officers and to foreign threats by promoting experienced officers. I draw on a new dataset, the first of its kind, of over 12,000 appointments to the People’s Liberation Army of China. The data show that career ties and combat experience are critical for officer promotion to key military and party offices. However, in periods of high domestic threat, party leaders promote unusually large numbers of officers with personal ties to the top leader. In periods of foreign threat, on the other hand, leaders are more likely to promote officers with prior combat experience. The article challenges the conventional wisdom, showing how autocrats face a trade-off between guarding against internal and external threats. Verification Materials: The data and materials required to verify the computational reproducibility of the results, procedures, and analyses in this article are available on the American Journal of Political Science Dataverse within the Harvard Dataverse Network, at: https://doi.org/10.7910/DVN/R3XPEJ.

Ana Belén Sainz

Quantum information is a rapidly-growing interdisciplinary field at the intersection of information science, computer science, mathematics, philosophy, and quantum science, working at the core of our quantum technologies. Regardless of its scientific success, quantum information is not exempt from the intrinsic features that come from the fact that scientists are humans and members of society: both the good and the bad of our social practices leak into the scientific activity. In our scientific community, diversity and equal opportunity problems are particularly difficult to observe due to social, economic, or cultural barriers, often remaining invisible. How can our lack of awareness negatively influence the progress of science in the long term? How can our community grow into a better version of itself? This article reflects on how research events - such as conferences - can contribute to a shift in our culture. This reflection draws on what we learn from Q-Turn: an initiative triggered by postdoctoral researchers to discuss these questions. One of Q-turn's main missions is to foster an inclusive community and highlight outstanding research that may be under-appreciated in other high-impact venues due to systemic biases. As well as a scientific program, Q-turn features talks and discussions on issues that affect the quantum information community, ranging from diversity and inclusion, health and mental health, to workers' rights. In this perspective article, we will consider Q-Turn as an example of how a research community can work to tackle systematic biases, review the successes, and identify further points for development.

Łukasz Potocki

The main objective of the present study is to analyze the European Union defense policy on the example of Permanent Structured Cooperation (PESCO). It will be possible thanks to a comprehensive analysis of individual PESCO projects, participating countries, determinants and perspectives for the development of defense policy. The main hypothesis of the article is that although the strongest military European Union countries, relying on PESCO, seek to expand defense cooperation, this cooperation has no visible impact on increasing their armed forces. The article contains information about the genesis of the European Union Security and Defense Policy and the legal basis of Permanent Structured Cooperation, as well as a detailed list of all PESCO projects with the countries participating in them. An analysis of the implementation of PESCO projects indicates that they are dependent on external factors. This study uses methods appropriate to the science of international relations. Its research tools include an analysis of the literature on the subject, documents, and statistical data.

Asmaa Daoud Haddad , Manal Fathi Anbtawi

Objectives: This study aimed to reveal the role of military science course in enhancing the contents of security education among students at the University of Jordan. Method: The researcher chose (638) university student using the accessible sample Method during the second semester of the academic year 2019/2020. Students completed Security education scale, which consists of five dimensions: psychological security, social security, preventive security, political security, and intellectual security. Results: The results of the study revealed the following. First, the role of the military science course in enhancing the contents of security education was high. Second, the study also indicated statistically significant difference due to gender. Third, the significant difference due to females, the study did not indicate any statistically significant difference due to major, or to interaction between gender and major. Conclusion: The study recommended emphasizing the importance of the dimensions of security education by including them in more than one of the University's compulsory courses, as the Military Sciences course due to the large numbers of students and lack of time may not be able to cover all aspects of security education. The results of the current study showed that the role of the Military Sciences course in enhancing the contents of security education among students of the University of Jordan was high.

P. Edwards

Bin Yu, Chandan Singh

In this article, we take a step back to distill seven principles out of our experience in the spring of 2020, when our 12-person rapid-response team used skills of data science and beyond to help distribute Covid PPE. This process included tapping into domain knowledge of epidemiology and medical logistics chains, curating a relevant data repository, developing models for short-term county-level death forecasting in the US, and building a website for sharing visualization (an automated AI machine). The principles are described in the context of working with Response4Life, a then-new nonprofit organization, to illustrate their necessity. Many of these principles overlap with those in standard data-science teams, but an emphasis is put on dealing with problems that require rapid response, often resembling agile software development.

P. Erickson, Judy L. Klein, L. Daston et al.