Hasil untuk "Military Science"

Nicole Sackley

J. Fearon, D. Laitin

B. Latour

D. Badgujar, M. B. Talawar, S. N. Asthana et al.

B. Place, J. Field

Faisal Al-Kamali, Francois Chan, Hussein A. Ammar et al.

Relays are pivotal in military communication networks, expanding coverage and ensuring reliable connectivity in challenging operational environments. While traditional terrestrial relays (TR) are constrained by fixed locations and vulnerability to physical obstructions, unmanned aerial vehicle (UAV)-mounted aerial relays (AR) offer a dynamic and flexible alternative by operating above obstacles and adapting to changing battlefield conditions. This paper provides a comprehensive survey of AR systems in military communications, presenting a detailed comparison between AR and TR paradigms and examining two specific AR technologies: active aerial relays (AAR) and aerial reconfigurable intelligent surface (ARIS) relays. The survey delves into their operation, benefits, challenges, and military applications, supported by a qualitative analysis across metrics such as coverage, flexibility, security, and cost. A novel multi-dimensional metric, the mission-critical relay effectiveness score (MCRES), is introduced as a quantitative method for evaluating relay suitability based on mission-specific weights for critical attributes like mobility, jamming resilience, deployment speed, stealth, coverage, and autonomy. Furthermore, we present Algorithm 1, a decision-making framework that leverages the MCRES to guide the systematic selection of the optimal relay type, AR or TR, and subsequently AAR or ARIS, tailored to the unique demands of a given military scenario, such as dynamic battlefield operations, electronic warfare, or covert missions. Finally, the paper addresses current implementation challenges and outlines promising future research directions to advance the deployment of robust and resilient UAV-mounted relay systems in contested military environments.

Nebojša N. Gaćeša

All submitted manuscripts relevant to the journal’s profile are reviewed. All referees are eminent external experts in relevant fields, with papers published in these fields in the last three years. The reviewers must not be from the authors' own institution and they should not have recent joint publications with any of the authors. Editorial Board applies the iThenticate (CrossRef and CrossCheck) service for verifying the originality of submitted papers and for preventing duplicate publishing and plagiarism. Journal applies a „double blind peer review process“ for papers. Authors and reviewers are anonymous to each other in the process of review. Reviews after the article has been published are allowed and encouraged for subsequent evaluation of papers and authors.

Si‐Yuan Chen, Peng‐Peng Xu, Ru Feng et al.

Abstract Background Diffuse large B‐cell lymphoma (DLBCL) is the most common subtype of aggressive non‐Hodgkin's lymphoma with distinct clinical and molecular heterogeneity. DLBCL that arises in extranodal organs is particularly linked to poor prognosis. This study aimed to determine the clinical and molecular characteristics of extranodal involvement (ENI) in DLBCL and assess the actual survival status of the patients. Methods In this population‐based cohort study, we investigated the clinical features of 5,023 patients newly diagnosed with DLBCL. Their clinical conditions, eligibility criteria, and sociodemographic details were recorded and analyzed. Gene panel sequencing was performed on 1,050 patients to discern molecular patterns according to ENI. Results The 2‐year overall survival (OS) rate was 76.2% [95% confidence interval (CI), 74.0%‐78.2%], and the 5‐year OS rate was 67.9% (95% CI, 65.2%‐70.4%). The primary treatment was immunochemotherapy with rituximab. Specific lymphoma involvement sites, especially the bones, bone marrow, and central nervous system, were identified as independent adverse prognostic factors. A high prevalence of non‐germinal center B‐cell (non‐GCB) phenotype and myeloid differentiation primary response 88 (MYD88)/CD79B mutations were noted in lymphomas affecting the breasts, skin, uterus, and immune‐privileged sites. Conversely, the thyroid and gastrointestinal tract showed a low occurrence of non‐GCB phenotype. Remarkably, patients with multiple ENIs exhibited a high frequency of MYD88, tet methylcytosine dioxygenase 2 (TET2), CREB binding protein (CREBBP) mutations, increased MYD88L265P and CD79B mutation (MCD)‐like subtypes, and poor prognosis. Genetic subtype‐guided immunochemotherapy showed good efficacy in subgroup analyses after propensity score matching with 5‐year OS and progression‐free survival rates of 85.0% (95% CI, 80.6%‐89.5%) and 72.1% (95% CI, 67.3%‐76.7%). Conclusions In the rituximab era, this large‐scale retrospective analysis from Asia confirmed the poor prognosis of DLBCL with multiple ENIs and underscored the efficacy of genetic subtype‐guided immunochemotherapy in treating extranodal DLBCL.

Shangbin Yang, Xiangzhen Kong, Qin Fang et al.

With the application of hypervelocity weapons in warfare, comprehensively evaluating their destructive effects is of particular interest for protective engineering. Existing studies mostly focused on the depth of penetration by hypervelocity projectile, while investigation on stress waves associated with hypervelocity penetration was very limited. To clarify the generation and propagation of stress waves in concrete targets induced by hypervelocity projectile penetration, in the present study, six spherical projectile penetration tests on concrete targets were firstly conducted with projectile velocity ranged from 1875 m/s to 3940 m/s, in which the stress waves were carefully measured by the PVDF transducers. Then corresponding numerical models were developed and validated, and based on the validated numerical model the mechanisms of generation and propagation of stress waves were clarified. It was found that the stress waves observed during hypervelocity penetration are generated by the continuous interactions of projectile and target during penetration, and have unique characteristics such as the directionality and the ''two peaks'' phenomenon when compared with the stress waves generated by charge explosion. Finally, the effects of projectile velocity, projectile material, and target strength on the stress waves below the penetration depth were numerically investigated, and two important indexes for evaluating the stress waves by hypervelocity penetration were proposed.

Clara Maathuis, Kasper Cools

In an era where AI (Artificial Intelligence) systems play an increasing role in the battlefield, ensuring responsible targeting demands rigorous assessment of potential collateral effects. In this context, a novel collateral damage assessment model for target engagement of AI systems in military operations is introduced. The model integrates temporal, spatial, and force dimensions within a unified Knowledge Representation and Reasoning (KRR) architecture following a design science methodological approach. Its layered structure captures the categories and architectural components of the AI systems to be engaged together with corresponding engaging vectors and contextual aspects. At the same time, spreading, severity, likelihood, and evaluation metrics are considered in order to provide a clear representation enhanced by transparent reasoning mechanisms. Further, the model is demonstrated and evaluated through instantiation which serves as a basis for further dedicated efforts that aim at building responsible and trustworthy intelligent systems for assessing the effects produced by engaging AI systems in military operations.

John Beirne, Haroon Mumtaz, Donghyun Park et al.

This paper investigates the heterogeneous effects of military spending news shocks on household income and wealth inequality for a large, panel of advanced and emerging economies. Confirming prior literature, we find that military spending news shocks lead to persistent increases in aggregate output and Total Factor Productivity. Our primary contribution is documenting contrasting distributional impacts. We find that expansionary military spending is associated with a mitigation of income inequality, as income gains are disproportionately larger at the left tail of the distribution, primarily driven by a rise in labour income and employment in industry. Conversely, the shock is found to increase wealth inequality, particularly in high-income countries, by raising the wealth share of the top decile via effects on business asset holdings.

Maxim Chupilkin

Which factors determine AI's propensity to support military intervention? While the use of AI in high-stakes decision-making is growing exponentially, we still lack systematic analysis of the key drivers embedded in these models. This paper conducts a conjoint experiment in which large language models (LLMs) from leading providers (OpenAI, Anthropic, Google) are asked to decide on military intervention across 128 vignettes, with each vignette run 10 times. This design enables a systematic assessment of AI decision-making in military contexts. The results are remarkably consistent across models: all models place substantial weight on the probability of success and domestic support, prioritizing these factors over civilian casualties, economic shock, or international sanctions. The paper then tests whether LLMs are sensitive to context by introducing different motivations for intervention. The scoring is indeed context-dependent; however, probability of victory remains the most important factor in all scenarios. Finally, the paper evaluates numerical sensitivity and finds that models display some responsiveness to the scale of civilian casualties but no detectable sensitivity to the size of the economic shock.

Niels Taubert

This paper reviews research literature on Diamond Open Access (DOA) journals - sometimes also called Platinum Open Access - that was produced after this journal segment started to become a priority in European research policy around 2020. It contextualizes the current science policy debate, critically examines different understandings of DOA, and reviews studies on the role of such journals in scholarly communication. Most existing research consists of quantitative studies focusing on aspects such as the number of DOA journals, their publication output, the diversity of the landscape in terms of subject areas, languages, publishing entities, indexing in major databases, awareness and perception among scholars, cost analyses, as well as insights into the internal operations of DOA journals. The review shows that research on DOA journals is partly influenced by the science policy discourse in at least two ways: first, through the normativity inherent in that discourse, and second, through the temporality of policy-driven research of practical relevance, which leaves important aspects of the phenomenon understudied. Moreover, research on the DOA journal landscape has implications beyond understanding this particular journal segment, as it also challenges established views of the global system of scholarly communication.

Hang Jiang, Tal August, Luca Soldaini et al.

The field of Computer science (CS) has rapidly evolved over the past few decades, providing computational tools and methodologies to various fields and forming new interdisciplinary communities. This growth in CS has significantly impacted institutional practices and relevant research communities. Therefore, it is crucial to explore what specific research values, known as basic and fundamental beliefs that guide or motivate research attitudes or actions, CS-related research communities promote. Prior research has manually analyzed research values from a small sample of machine learning papers. No prior work has studied the automatic detection of research values in CS from large-scale scientific texts across different research subfields. This paper introduces a detailed annotation scheme featuring ten research values that guide CS-related research. Based on the scheme, we build value classifiers to scale up the analysis and present a systematic study over 226,600 paper abstracts from 32 CS-related subfields and 86 popular publishing venues over ten years.

Joseph E. McCarthy, Mathieu Dahan, Chelsea C. White

We study a two-player discounted zero-sum stochastic game model for dynamic operational planning in military campaigns. At each stage, the players manage multiple commanders who order military actions on objectives that have an open line of control. When a battle over the control of an objective occurs, its stochastic outcome depends on the actions and the enabling support provided by the control of other objectives. Each player aims to maximize the cumulative number of objectives they control, weighted by their criticality. To solve this large-scale stochastic game, we derive properties of its Markov perfect equilibria by leveraging the logistics and military operational command and control structure. We show the consequential isotonicity of the optimal value function with respect to the partially ordered state space, which in turn leads to a significant reduction of the state and action spaces. We also accelerate Shapley's value iteration algorithm by eliminating dominated actions and investigating pure equilibria of the matrix game solved at each iteration. We demonstrate the computational value of our equilibrium results on a case study that reflects representative operational-level military campaigns with geopolitical implications. Our analysis reveals a complex interplay between the game's parameters and dynamics in equilibrium, resulting in new military insights for campaign analysts.

Christian Nitzl, Achim Cyran, Sascha Krstanovic et al.

It is beyond dispute that the potential benefits of artificial intelligence (AI) in military intelligence are considerable. Nevertheless, it remains uncertain precisely how AI can enhance the analysis of military data. The aim of this study is to address this issue. To this end, the AI demonstrator deepCOM was developed in collaboration with the start-up Aleph Alpha. The AI functions include text search, automatic text summarization and Named Entity Recognition (NER). These are evaluated for their added value in military analysis. It is demonstrated that under time pressure, the utilization of AI functions results in assessments clearly superior to that of the control group. Nevertheless, despite the demonstrably superior analysis outcome in the experimental group, no increase in confidence in the accuracy of their own analyses was observed. Finally, the paper identifies the limitations of employing AI in military intelligence, particularly in the context of analyzing ambiguous and contradictory information.

Keisuke Okamura

The evolving landscape of interinstitutional collaborative research across 15 natural science disciplines is explored using the open data sourced from OpenAlex. This extensive exploration spans the years from 1971 to 2020, facilitating a thorough investigation of leading scientific output producers and their collaborative relationships based on coauthorships. The findings are visually presented on world maps and other diagrams, offering a clear and insightful portrayal of notable variations in both national and international collaboration patterns across various fields and time periods. These visual representations serve as valuable resources for science policymakers, diplomats and institutional researchers, providing them with a comprehensive overview of global collaboration and aiding their intuitive grasp of the evolving nature of these partnerships over time.

Saadeh R, Khader Y, Alyahya M et al.

Rami Saadeh,1 Yousef Khader,1 Mohammad Alyahya,2 Majid Al-Samawi,1 Mohammed Z Allouh3,4 1Department of Public Health and Community Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan; 2Department of Health Management and Policy, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan; 3Department of Anatomy, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates; 4Department of Anatomy, Faculty of Medicine, Jordan University of Science and Technology, Irbid, JordanCorrespondence: Mohammed Z Allouh, Department of Anatomy, College of Medicine and Health Sciences, United Arab Emirates University, P. O. Box: 15551, Al Ain, United Arab Emirates, Tel +97 137 137 551, Email m_allouh@uaeu.ac.aePurpose: To identify areas that need improvement in Jordanian health centers regarding infection prevention and control (IPC) programs; water, sanitation, and hygiene (WASH) services; and other protective measures, especially in the context of coronavirus disease (COVID-19).Methods: This is a national assessment study that comprised hospitals of different sectors in Jordan, including, Ministry of Health (MoH), private, and military hospitals. The study included 23 Jordanian hospitals. Assessment tools were developed and adapted mainly from the WASH Facility Improvement Tool (WASH FIT) and other tools. Hospitals were assessed to meet targets based on whether indicators were fully met, partially met, or not met.Results: The mean percentage of the 150 indicators that met the standards was 83.2% (72.6% for MoH, 84.5% for private, and 90.4% for military hospitals). The percentage of indicators, both WASH/IPC and training and education indicators, that met the targets were higher in military hospitals than in MoH and private hospitals. However, in context of COVID-19, only 64.7% of indicators related to precautionary measures were met by all hospitals.Conclusion: The data available on WASH/IPC in Jordan are scarce, and the study findings will help in preventing severe consequences of the COVID-19 pandemic. There is scope for improvement in many WASH/IPC aspects, and urgent actions should be taken, especially to fill the gaps in COVID-19 precautionary measures.Keywords: COVID-19, healthcare, hospitals, infection control, waste management

І. О. Падалка

Прогнозування особливих випадків у польоті для підвищення оперативності прийняття рішення екіпажем на основі аналізу діагностичних даних технологічного обладнання повітряного судна є актуальним науковим завданням. Для його вирішення запропонована модель представлення часового процесу функціонування технологічного обладнання повітряного судна на основі комплексної обробки інформації параметричної діагностики, що заснована на об'єднанні марковської моделі і продукційних правил, що дозволить коригувати ймовірнісні характеристики діагностичних даних при нетиповому розвитку процесу. Для попередження особливих випадків у польоті запропоновано метод виявлення аномальних послідовностей у діагностичних даних технологічного обладнання повітряного судна, який заснований на використанні моделі представлення часового процесу функціонування технологічного обладнання повітряного судна, що дозволяє підвищити достовірність прийняття рішень екіпажем щодо виявлення, розпізнання та недопущення негативних наслідків особливих випадків у польоті. Запропоновано метод передбачення особливих випадків у польоті, який базується на завчасному виявленні аномальних послідовностей у діагностичних даних технологічного обладнання повітряного судна та враховує спостереження за процесом роботи технологічного обладнання, що дозволяє підвищити оперативність та достовірність прийняття рішень екіпажем щодо виявлення, розпізнання та недопущення негативних наслідків особливих випадків у польоті.

Houwu Gong, Houwu Gong, Xiong You et al.

The research on microbe association networks is greatly significant for understanding the pathogenic mechanism of microbes and promoting the application of microbes in precision medicine. In this paper, we studied the prediction of microbe-disease associations based on multi-data biological network and graph neural network algorithm. The HMDAD database provided a dataset that included 39 diseases, 292 microbes, and 450 known microbe-disease associations. We proposed a Microbe-Disease Heterogeneous Network according to the microbe similarity network, disease similarity network, and known microbe-disease associations. Furthermore, we integrated the network into the graph convolutional neural network algorithm and developed the GCNN4Micro-Dis model to predict microbe-disease associations. Finally, the performance of the GCNN4Micro-Dis model was evaluated via 5-fold cross-validation. We randomly divided all known microbe-disease association data into five groups. The results showed that the average AUC value and standard deviation were 0.8954 ± 0.0030. Our model had good predictive power and can help identify new microbe-disease associations. In addition, we compared GCNN4Micro-Dis with three advanced methods to predict microbe-disease associations, KATZHMDA, BiRWHMDA, and LRLSHMDA. The results showed that our method had better prediction performance than the other three methods. Furthermore, we selected breast cancer as a case study and found the top 12 microbes related to breast cancer from the intestinal flora of patients, which further verified the model’s accuracy.