Hasil untuk "Military Science"

E. Brevik, A. Cerdà, J. Mataix‐Solera et al.

Abstract. The holistic study of soils requires an interdisciplinary approach involving biologists, chemists, geologists, and physicists, amongst others, something that has been true from the earliest days of the field. In more recent years this list has grown to include anthropologists, economists, engineers, medical professionals, military professionals, sociologists, and even artists. This approach has been strengthened and reinforced as current research continues to use experts trained in both soil science and related fields and by the wide array of issues impacting the world that require an in-depth understanding of soils. Of fundamental importance amongst these issues are biodiversity, biofuels/energy security, climate change, ecosystem services, food security, human health, land degradation, and water security, each representing a critical challenge for research. In order to establish a benchmark for the type of research that we seek to publish in each issue of SOIL, we have outlined the interdisciplinary nature of soil science research we are looking for. This includes a focus on the myriad ways soil science can be used to expand investigation into a more holistic and therefore richer approach to soil research. In addition, a selection of invited review papers are published in this first issue of SOIL that address the study of soils and the ways in which soil investigations are essential to other related fields. We hope that both this editorial and the papers in the first issue will serve as examples of the kinds of topics we would like to see published in SOIL and will stimulate excitement among our readers and authors to participate in this new venture.

M. Finnemore

J. Lyu, Zengwei Liu, Xiaohan Wu et al.

Infrared (IR) stealth is essential not only in high technology and modern military but also in fundamental material science. However, effectively hiding targets and rendering them invisible to thermal infrared detectors have been great challenges in past decades. Herein, flexible, foldable, and robust Kevlar nanofiber aerogel (KNA) films with high porosity and specific surface area were fabricated first. The KNA films display excellent thermal insulation performance and can be employed to incorporate with phase-change materials (PCMs), such as polyethylene glycol, to fabricate KNA/PCM composite films. The KNA/PCM films with high thermal management capability and infrared emissivity comparable to that of various backgrounds demonstrate high performance in IR stealth in outdoor environments with solar illumination variations. To further realize hiding hot targets from IR detection, combined structures constituted of thermal insulation layers (KNA films) and ultralow IR transmittance layers (KNA/PCM) are proposed. A hot target covered with this combined structure becomes completely invisible in infrared images. Such KNA/PCM films and KNA-KNA/PCM combined structures hold great promise for broad applications in infrared thermal stealth.

Maad M. Mijwil

In the modern era, many terms related to artificial intelligence, machine learning, and deep learning are widely used in domains such as business, healthcare, industries, and military. In these fields, the accurate prediction and analysis of data are crucial, regardless of how large the data are. However, using big data is confusing due to the rapid growth and massive development in public life, which requires a tremendous human effort in order to deal with such type of data and extract worthy information from it. Thus, the role of artificial intelligence begins in analyzing big data based on scientific techniques, especially in machine learning, whereby it can identify patterns of decision-making and reduce human intervention. In this regard, the significance role of artificial intelligence, machine learning and deep learning is growing rapidly. In this article, the authors decide to highlight these sciences by discussing how to develop and apply them in many decision-making domains. In addition, the influence of artificial intelligence in healthcare and the gains this science provides in the face of the COVID-19 pandemic are highlighted. This article concludes that these sciences have a significant impact, especially in healthcare, as well as the ability to grow and improve their methodology in decision-making. Additionally, artificial intelligence is a vital science, especially in the face of COVID-19.

Philip Mirowski

YANG Liangliang, ZHAO Deyong, LIU Xiaoyong

In response to the current issues with the organization of wartime equipment maintenance forces, a force organization method based on mission clustering analysis and an improved NSGA-Ⅱ algorithm is proposed. Based on the mission clustering analysis results obtained from the maintenance task clustering model, a multi-objective optimization model for maintenance force grouping was established with the objectives of minimizing the total maintenance time and the standard deviation of personnel workload. For the solution method of this multi-objective optimization model, the elite retention strategy and crossover operator of the traditional NSGA-Ⅱ algorithm were optimized and improved. The improved NSGA-Ⅱ algorithm was verified through the ZDT test function in terms of its superiority in convergence and solution set distribution. Using the maintenance mission of a certain artillery group as an example, simulation experiments and model algorithm analysis were conducted, resulting in a set of relatively ideal maintenance force organization schemes. This provides methodological and technical support for decision-makers to select schemes based on battlefield requirements and preference differences.

Liangyu Yin, Yu Cao, Mengda Tang et al.

ABSTRACT Background While the Global Leadership Initiative on Sarcopenia (GLIS) is promising to standardize sarcopenia diagnosis, its operational implementation remains largely undefined. This study aims to operationalize GLIS and evaluate its feasibility, diagnostic concordance and clinical relevance. Methods This three‐stage, multicenter study enrolled 12 116 participants for cut‐off development (mean age 58.7 years, 48.2% men) and 11 241 participants for outcome analysis (mean age 58.4 years, 49.4% men) from a national survey in China. Another 504 patients with chronic kidney disease were included for validation. We proposed the lower limb skeletal muscle mass to five‐time chair stand test ratio (LFR) to assess muscle‐specific strength (MSS). The GLIS conceptual framework was instantiated into six diagnostic criteria combinations using handgrip strength (HGS), appendicular skeletal muscle mass index (ASMI, estimated using a validated formula) and MSS: (1) all three criteria being low (HAM); (2) low HGS plus low ASMI (HA); (3) low MSS (M); (4) low HGS plus low ASMI, or low MSS (HA/M); (5) low HGS or low MSS (H/M); and (6) low ASMI or low MSS (A/M). Intercriteria concordance of these definitions, relevance with functional outcomes and their concordance with the Asian Working Group for Sarcopenia 2019 (AWGS) criteria were evaluated. Results Low MSS cut‐offs were established as < 0.74 for men and < 0.47 for women. Sarcopenia prevalence varied significantly across different definitions: 1055 (8.7%, AWGS), 405 (3.3%, HAM), 619 (5.1%, HA), 2409 (19.9%, M), 2623 (21.6%, HA/M), 3184 (26.3%, H/M) and 3868 (31.9%, A/M). The HA method showed the highest concordance with the AWGS (accuracy = 0.964, κ = 0.722, sensitivity = 1.000, specificity = 0.962). The H/M method demonstrated the strongest correlation with functional outcomes and optimal diagnostic performance (AUCs range from 0.566 to 0.729), with superior discrimination for impaired activities of daily living (ADL), other functional measures and global functional scores (p < 0.05). All methods independently predicted poor functional outcomes. External validation in CKD showed that the H/M method was either superior or comparable to other methods in identifying disabilities (e.g., predicting functional measures, AUC = 0.627, 95% CI = 0.582–0.672). Conclusions This study establishes an operational framework for GLIS using nationally representative data from China and validates its effectiveness in a clinical setting. LFR proves to be a feasible method for assessing MSS. The H/M method effectively captures functional impairment, which may serve as a useful approach for diagnosing sarcopenia. These findings provide actionable benchmarks for sarcopenia research and clinical practice, potentially informing more refined prevention and intervention strategies.

SUN Jin, HU Jicheng, JIAO Yaqi, WANG Kun

The research of equipment failure distribution is the basis of reliability engineering. In view of the complex structure and failure mechanism of wireless communication equipment, numerous failure distribution types, difficulty in selecting and evaluating reliability models and other problems, this paper starts from the research on equipment failure distribution at the bottom level, takes typical wireless communication system vehicle self-organizing network equipment as the research object, and compares and analyzes the correlation coefficients and error values of equipment life exponential distribution, lognormal distribution and two-parameter Weibull distribution. By introducing the information criterion and the special goodness of fit test to analyze the fitting effect of the reliability model, the reliability analysis method of wireless communication equipment based on model selection is proposed, and the test and analysis are combined with actual cases to improve the accuracy and effectiveness of model selection.

Alma M. Liezenga, Stefan Wijnja, Puck de Haan et al.

Poisoning attacks pose an increasing threat to the security and robustness of Artificial Intelligence systems in the military domain. The widespread use of open-source datasets and pretrained models exacerbates this risk. Despite the severity of this threat, there is limited research on the application and detection of poisoning attacks on object detection systems. This is especially problematic in the military domain, where attacks can have grave consequences. In this work, we both investigate the effect of poisoning attacks on military object detectors in practice, and the best approach to detect these attacks. To support this research, we create a small, custom dataset featuring military vehicles: MilCivVeh. We explore the vulnerability of military object detectors for poisoning attacks by implementing a modified version of the BadDet attack: a patch-based poisoning attack. We then assess its impact, finding that while a positive attack success rate is achievable, it requires a substantial portion of the data to be poisoned -- raising questions about its practical applicability. To address the detection challenge, we test both specialized poisoning detection methods and anomaly detection methods from the visual industrial inspection domain. Since our research shows that both classes of methods are lacking, we introduce our own patch detection method: AutoDetect, a simple, fast, and lightweight autoencoder-based method. Our method shows promising results in separating clean from poisoned samples using the reconstruction error of image slices, outperforming existing methods, while being less time- and memory-intensive. We urge that the availability of large, representative datasets in the military domain is a prerequisite to further evaluate risks of poisoning attacks and opportunities patch detection.

Riley Simmons-Edler, Jean Dong, Paul Lushenko et al.

Military weapon systems and command-and-control infrastructure augmented by artificial intelligence (AI) have seen rapid development and deployment in recent years. However, the sociotechnical impacts of AI on combat systems, military decision-making, and the norms of warfare have been understudied. We focus on a specific subset of lethal autonomous weapon systems (LAWS) that use AI for targeting or battlefield decisions. We refer to this subset as AI-powered lethal autonomous weapon systems (AI-LAWS) and argue that they introduce novel risks -- including unanticipated escalation, poor reliability in unfamiliar environments, and erosion of human oversight -- all of which threaten both military effectiveness and the openness of AI research. These risks cannot be addressed by high-level policy alone; effective regulation must be grounded in the technical behavior of AI models. We argue that AI researchers must be involved throughout the regulatory lifecycle. Thus, we propose a clear, behavior-based definition of AI-LAWS -- systems that introduce unique risks through their use of modern AI -- as a foundation for technically grounded regulation, given that existing frameworks do not distinguish them from conventional LAWS. Using this definition, we propose several technically-informed policy directions and invite greater participation from the AI research community in military AI policy discussions.

Clara Maathuis, Kasper Cools

In today's evolving threat landscape, ensuring digital sovereignty has become mandatory for military organizations, especially given their increased development and investment in AI-driven cyber security solutions. To this end, a multi-angled framework is proposed in this article in order to define and assess digital sovereign control of data and AI-based models for military cyber security. This framework focuses on aspects such as context, autonomy, stakeholder involvement, and mitigation of risks in this domain. Grounded on the concepts of digital sovereignty and data sovereignty, the framework aims to protect sensitive defence assets against threats such as unauthorized access, ransomware, and supply-chain attacks. This approach reflects the multifaceted nature of digital sovereignty by preserving operational autonomy, assuring security and safety, securing privacy, and fostering ethical compliance of both military systems and decision-makers. At the same time, the framework addresses interoperability challenges among allied forces, strategic and legal considerations, and the integration of emerging technologies by considering a multidisciplinary approach that enhances the resilience and preservation of control over (critical) digital assets. This is done by adopting a design oriented research where systematic literature review is merged with critical thinking and analysis of field incidents in order to assure the effectivity and realism of the framework proposed.

Ali Sanaei, Ali Rajabzadeh

Large language models (LLMs) are increasingly utilized by researchers across a wide range of domains, and qualitative social science is no exception; however, this adoption faces persistent challenges, including interpretive bias, low reliability, and weak auditability. We introduce a framework that situates LLM usage along two dimensions, interpretive depth and autonomy, thereby offering a straightforward way to classify LLM applications in qualitative research and to derive practical design recommendations. We present the state of the literature with respect to these two dimensions, based on all published social science papers available on Web of Science that use LLMs as a tool and not strictly as the subject of study. Rather than granting models expansive freedom, our approach encourages researchers to decompose tasks into manageable segments, much as they would when delegating work to capable undergraduate research assistants. By maintaining low levels of autonomy and selectively increasing interpretive depth only where warranted and under supervision, one can plausibly reap the benefits of LLMs while preserving transparency and reliability.

Zhiyi Chen, Yancheng Tang, Xuerong Liu et al.

Abstract Depression-anxiety comorbidity is commonly attributed to the occurrence of specific symptoms bridging the two disorders. However, the significant heterogeneity of most bridging symptoms presents challenges for psychopathological interpretation and clinical applicability. Here, we conceptually established a common bridging factor (cb factor) to characterize a general structure of these bridging symptoms, analogous to the general psychopathological p factor. We identified a cb factor from 12 bridging symptoms in depression-anxiety comorbidity network. Moreover, this cb factor could be predicted using edge-centric connectomes with robust generalizability, and was characterized by connectome patterns in attention and frontoparietal networks. In an independent twin cohort, we found that these patterns were moderately heritable, and identified their genetic connectome-transcriptional markers that were associated with the neurobiological enrichment of vasculature and cerebellar development, particularly during late-childhood-to-young-adulthood periods. Our findings revealed a general factor of bridging symptoms and its neurobiological architectures, which enriched neurogenetic understanding of depression-anxiety comorbidity.

Bihter Sabanoglu

Out of the East, Darkness. Ex Oriente Tenebrae: Byzantine presence in video games (A chapter in contemporary Orientalism)

Mengna Zhu, Zijie Xu, Kaisheng Zeng et al.

Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis and decision assistance. However, event extraction in the military field faces the data scarcity problem, which impedes the research of event extraction models in this domain. To alleviate this problem, we propose CMNEE, a large-scale, document-level open-source Chinese Military News Event Extraction dataset. It contains 17,000 documents and 29,223 events, which are all manually annotated based on a pre-defined schema for the military domain including 8 event types and 11 argument role types. We designed a two-stage, multi-turns annotation strategy to ensure the quality of CMNEE and reproduced several state-of-the-art event extraction models with a systematic evaluation. The experimental results on CMNEE fall shorter than those on other domain datasets obviously, which demonstrates that event extraction for military domain poses unique challenges and requires further research efforts. Our code and data can be obtained from https://github.com/Mzzzhu/CMNEE.

Ankan Mullick, Akash Ghosh, G Sai Chaitanya et al.

Material science literature is a rich source of factual information about various categories of entities (like materials and compositions) and various relations between these entities, such as conductivity, voltage, etc. Automatically extracting this information to generate a material science knowledge base is a challenging task. In this paper, we propose MatSciRE (Material Science Relation Extractor), a Pointer Network-based encoder-decoder framework, to jointly extract entities and relations from material science articles as a triplet ($entity1, relation, entity2$). Specifically, we target the battery materials and identify five relations to work on - conductivity, coulombic efficiency, capacity, voltage, and energy. Our proposed approach achieved a much better F1-score (0.771) than a previous attempt using ChemDataExtractor (0.716). The overall graphical framework of MatSciRE is shown in Fig 1. The material information is extracted from material science literature in the form of entity-relation triplets using MatSciRE.

Masayoshi Kozai, Yoshimasa Tanaka, Shuji Abe et al.

The AMIDER, Advanced Multidisciplinary Integrated-Database for Exploring new Research, is a newly developed research data catalog to demonstrate an advanced database application. AMIDER is characterized as a multidisciplinary database equipped with a user-friendly web application. Its catalog view displays diverse research data at once beyond any limitation of each individual discipline. Some useful functions, such as a selectable data download, data format conversion, and display of data visual information, are also implemented. Further advanced functions, such as visualization of dataset mutual relationship, are also implemented as a preliminary trial. These characteristics and functions are expected to enhance the accessibility to individual research data, even from non-expertized users, and be helpful for collaborations among diverse scientific fields beyond individual disciplines. Multidisciplinary data management is also one of AMIDER's uniqueness, where various metadata schemas can be mapped to a uniform metadata table, and standardized and self-describing data formats are adopted. AMIDER website (https://amider.rois.ac.jp/) had been launched in April 2024. As of July 2024, over 15,000 metadata in various research fields of polar science have been registered in the database, and approximately 500 visitors are viewing the website every day on average. Expansion of the database to further multidisciplinary scientific fields, not only polar science, is planned, and advanced attempts, such as applying Natural Language Processing (NLP) to metadata, have also been considered.

Emma K. Dahl, Naomi Rowe-Gurney, Glenn S. Orton et al.

The Ice Giants represent a unique and relatively poorly characterized class of planets that have been largely unexplored since the brief Voyager 2 flyby in the late 1980's. Uranus is particularly enigmatic, due to its extreme axial tilt, offset magnetic field, apparent low heat budget, mysteriously cool stratosphere and warm thermosphere, as well as a lack of well-defined, long-lived storm systems and distinct atmospheric features. All these characteristics make Uranus a scientifically intriguing target, particularly for missions able to complete in situ measurements. The 2023-2032 Decadal Strategy for Planetary Science and Astrobiology prioritized a flagship orbiter and probe to explore Uranus with the intent to "...transform our knowledge of Ice Giants in general and the Uranian system in particular" (National Academies of Sciences and Medicine, 2022). In support of this recommendation, we present community-supported science questions, key measurements, and a suggested instrument suite that focuses on the exploration and characterization of the Uranian atmosphere by an in situ probe. The scope of these science questions encompasses the origin, evolution, and current processes that shape the Uranian atmosphere, and in turn the Uranian system overall. Addressing these questions will inform vital new insights about Uranus, Ice Giants and Gas Giants in general, the large population of Neptune-sized exoplanets, and the Solar System as a whole.

Mei-Xian Li, Qian-Qi Wei, Hui-Lin Mo et al.

Abstract The arterial occlusive disease is one of the leading causes of cardiovascular diseases, often requiring revascularization. Lack of suitable small-diameter vascular grafts (SDVGs), infection, thrombosis, and intimal hyperplasia associated with synthetic vascular grafts lead to a low success rate of SDVGs (< 6 mm) transplantation in the clinical treatment of cardiovascular diseases. The development of fabrication technology along with vascular tissue engineering and regenerative medicine technology allows biological tissue-engineered vascular grafts to become living grafts, which can integrate, remodel, and repair the host vessels as well as respond to the surrounding mechanical and biochemical stimuli. Hence, they potentially alleviate the shortage of existing vascular grafts. This paper evaluates the current advanced fabrication technologies for SDVGs, including electrospinning, molding, 3D printing, decellularization, and so on. Various characteristics of synthetic polymers and surface modification methods are also introduced. In addition, it also provides interdisciplinary insights into the future of small-diameter prostheses and discusses vital factors and perspectives for developing such prostheses in clinical applications. We propose that the performance of SDVGs can be improved by integrating various technologies in the near future. Graphical Abstract

Mehmet Fatih Baş

Wargames have been present in various parts of the world in different forms since the early periods of history. The version used today emerged in the early 19th century in Prussia. Until the mid-20th century, wargames were primarily used in professional military education and training. However, with the widespread popularity of hobby games having military history themes, wargames began to make their presence felt in academia in the 1950s and academic wargames began to be played for educational and analytic purposes in the 1970s. Especially from the 2000s onwards, wargames have found a limited but growing place in academia and have become a subject of study in classrooms, moving towards becoming an independent academic discipline. These games are increasingly played for academic purposes, particularly in the fields of military history and international relations. However, Turkish academia has remained somewhat distant from wargames for various reasons. Nevertheless, positive outcomes have been obtained by experimenting with wargames in Turkish academia. This article evaluates the potential methods for Turkish academics to make use of wargames for academic purposes.