Hasil untuk "Military Science"

Kovačević Nenad V., Blagovčanin Dejan D., Milić Aleksandar M.

The destructive impact on human populations has been a common characteristic of all wars throughout history - not only during their course but long after combat operations have ceased. One of the most insidious legacies of modern warfare is the presence of landmines and unexploded ordnance. Many countries on whose territories combat operations were conducted continue to face this problem. This issue represents a specific security threat as it simultaneously endangers people, the environment, as well as natural, material, and other assets, while also being a latent cause of socio-economic harm to society as a whole. In the context of Southeast Europe, Bosnia and Herzegovina ranks among the countries most affected by the presence of mines and unexploded ordnance. The aim of this paper is to present practical experiences regarding the etiology of mine action and the capacities of Bosnia and Herzegovina to conduct humanitarian demining operations as a segment of mine action. The paper focuses on explaining one component - the military sector - of the unified system established at the state level and aimed at solving the problem of residual mines and unexploded ordnance. Accordingly, the initial hypothesis posits that the capacities of the Armed Forces of Bosnia and Herzegovina enable them to conduct humanitarian demining operations. The paper employs specific scientific methods: induction-deduction, analysis-synthesis, and definition-classification.

A Akmam, Serli Ahzari, E Emiliannur et al.

This experimental study investigates the effectiveness of the Cognitive Conflict-Based Generative Learning Model (GLBCC) in enhancing science literacy among high school physics students. The novelty of this research lies in the innovative integration of cognitive conflict strategies with generative learning principles through a six stage structured framework, specifically designed to address persistent misconceptions in physics education while systematically developing scientific literacy competencies. The research employed pretest-posttest control group design involving 167 Grade XI students from three schools. Students were randomly assigned to experimental groups (n = 83) that received GLBCC instruction and control groups (n = 84) that used the expository learning model. Science literacy was measured using validated instruments assessing scientific knowledge, inquiry processes, and application skills across six key indicators. Statistical analysis using ANOVA with Tukey HSD post-hoc tests revealed significant improvements in science literacy scores for students receiving GLBCC instruction compared to traditional methods (p < 0.001). This study makes a unique contribution to physics education by demonstrating how the deliberate creation of cognitive conflict, combined with authentic real-world physics phenomena, can effectively restructure students conceptual understanding and enhance their scientific thinking capabilities. Factor analysis identified four critical implementation factors: science literacy development components, learning stages and orientation, and objectives, and knowledge construction processes. The findings provide empirical evidence supporting the integration of cognitive conflict strategies with generative learning approaches in physics education, offering practical implications

K. Larsen, R. Lukyanenko, Roland M. Mueller et al.

Researchers must ensure that the claims about the knowledge produced by their work are valid. However, validity is neither well-understood nor consistently established in design science, which involves the development and evaluation of artifacts (models, methods, instantiations, and theories) to solve problems. As a result, it is challenging to demonstrate and communicate the validity of knowledge claims about artifacts. This paper defines validity in design science and derives the Design Science Validity Framework and a process model for applying it. The framework comprises three high-level claim and validity types-criterion, causal, and context-as well as validity subtypes. The framework guides researchers in integrating validity considerations into projects employing design science and contributes to the growing body of research on design science methodology. It also provides a systematic way to articulate and validate the knowledge claims of design science projects. We apply the framework to examples from existing research and then use it to demonstrate the validity of knowledge claims about the framework itself.

Ibrahim K. Alsulami, Shittu Abdullahi, Ahmed Alshahrie et al.

Tan Chong, Wen Zhengji, Zhang Jinguo et al.

Camouflage is a common technique in nature, enabling organisms to protect themselves from predators. The development of novel camouflage technologies, not only in fundamental science, but also in the fields of military and civilian applications, is of great significance. In this study, we propose a new type of deep-subwavelength four-layered meta-coating consisting of Si, Bi, Si, and Cr from top to bottom with total thickness of only ∼355 nm for visible-infrared compatible camouflage. The visible color and the infrared emission properties of the meta-coating can be independently adjusted. Colorful meta-coating for visible camouflage can be obtained by changing the thickness of top Si layer, while the selective high emissivity in non-atmospheric window for infrared camouflage remains. Due to the deep-subwavelength properties, the meta-coating shows high angle tolerance in both visible and infrared regions. The compatible camouflage capability of our proposed meta-coating in the visible-infrared region is validated under different environments. The deep-subwavelength, angular insensitivity, visible-infrared compatibility and large-area fabrication feasibility promise the meta-coating an effective solution for camouflage in various applications such as military weapons and anti-counterfeiting.

Kira HORIACHEVA, Vadym RYZHYKOV

In the realm of professional military education, innovative pedagogical methodologies are pivotal for fostering strategic thinking and adaptive leadership qualities among cadets. This paper delves into the effectiveness of role-play games as a means of experiential learning within military training, with a particular emphasis on the Ukrainian defense and security sector amidst the ongoing conflict.Drawing from insights gleaned from research papers and practical applications this study examineshow role-playing games intersect with cadet training. By analyzing various approaches and practical examples, we aim to illuminate the potential of role-play games in enhancing strategic mindsets and decision-making skills among future military leaders. Understanding the dynamics of incorporating role-playing into military pedagogy is crucial for optimizing the educational experience of cadets and preparing them to navigate the complexities of contemporary warfare effectively.

Simon Maksour, Rocio K. Finol-Urdaneta, Amy J. Hulme et al.

Alzheimer’s disease (AD) is a devastating neurodegenerative condition that affects memory and cognition, characterized by neuronal loss and currently lacking a cure. Mutations in PSEN1 (Presenilin 1) are among the most common causes of early-onset familial AD (fAD). While changes in neuronal excitability are believed to be early indicators of AD progression, the link between PSEN1 mutations and neuronal excitability remains to be fully elucidated. This study examined iPSC-derived neurons (iNs) from fAD patients with PSEN1 mutations S290C or A246E, alongside CRISPR-corrected isogenic cell lines, to investigate early changes in excitability. Electrophysiological profiling revealed reduced excitability in both PSEN1 mutant iNs compared to their isogenic controls. Neurons bearing S290C and A246E mutations exhibited divergent passive membrane properties compared to isogenic controls, suggesting distinct effects of PSEN1 mutations on neuronal excitability. Additionally, both PSEN1 backgrounds exhibited higher current density of voltage-gated potassium (Kv) channels relative to their isogenic iNs, while displaying comparable voltage-gated sodium (Nav) channel current density. This suggests that the Nav/Kv imbalance contributes to impaired neuronal firing in fAD iNs. Deciphering these early cellular and molecular changes in AD is crucial for understanding disease pathogenesis.

І.В. Рогозін, С.М. Новічонок, О.М. Леоненко et al.

У статті запропоновано та обґрунтовано потенційна доцільність напрямку сумісного розвитку військових транспортних засобів та пересувних джерел електричної енергії, який полягає у застосуванні та розвитку конструкції гібридних силових агрегатів автомобільних шасі. В результаті застосування автомобільних шасі з гібридними силовими агрегатами, що мають властивості пересувних джерел електричної енергії, очікується забезпечення виконання вимог сучасної воєнної логістики щодо збільшення енергозабезпеченості підрозділу та послаблення протиріччя між потребою підвищення швидкості пересування для зменшення часу знаходження поза межами захищених споруд при одночасному збільшенні спроможності задовольняти потребу в наданні електричної енергії військовим споживачам.

Yuge Zhang, Qiyang Jiang, Xingyu Han et al.

In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for specialists. Large Language Models (LLMs) have emerged as promising aids as data science agents, assisting humans in data analysis and processing. Yet their practical efficacy remains constrained by the varied demands of real-world applications and complicated analytical process. In this paper, we introduce DSEval -- a novel evaluation paradigm, as well as a series of innovative benchmarks tailored for assessing the performance of these agents throughout the entire data science lifecycle. Incorporating a novel bootstrapped annotation method, we streamline dataset preparation, improve the evaluation coverage, and expand benchmarking comprehensiveness. Our findings uncover prevalent obstacles and provide critical insights to inform future advancements in the field.

Huan Zhang, Yu Song, Ziyu Hou et al.

The emergence of specialized large language models (LLMs) has shown promise in addressing complex tasks for materials science. Many LLMs, however, often struggle with distinct complexities of material science tasks, such as materials science computational tasks, and often rely heavily on outdated implicit knowledge, leading to inaccuracies and hallucinations. To address these challenges, we introduce HoneyComb, the first LLM-based agent system specifically designed for materials science. HoneyComb leverages a novel, high-quality materials science knowledge base (MatSciKB) and a sophisticated tool hub (ToolHub) to enhance its reasoning and computational capabilities tailored to materials science. MatSciKB is a curated, structured knowledge collection based on reliable literature, while ToolHub employs an Inductive Tool Construction method to generate, decompose, and refine API tools for materials science. Additionally, HoneyComb leverages a retriever module that adaptively selects the appropriate knowledge source or tools for specific tasks, thereby ensuring accuracy and relevance. Our results demonstrate that HoneyComb significantly outperforms baseline models across various tasks in materials science, effectively bridging the gap between current LLM capabilities and the specialized needs of this domain. Furthermore, our adaptable framework can be easily extended to other scientific domains, highlighting its potential for broad applicability in advancing scientific research and applications.

Ijaz Bashir, Muhammad Zaheer Sajid, Rizwana Kalsoom et al.

A well-known eye disorder called diabetic retinopathy (DR) is linked to elevated blood glucose levels. Cotton wool spots, confined veins in the cranial nerve, AV nicking, and hemorrhages in the optic disc are some of its symptoms, which often appear later. Serious side effects of DR might include vision loss, damage to the visual nerves, and obstruction of the retinal arteries. Researchers have devised an automated method utilizing AI and deep learning models to enable the early diagnosis of this illness. This research gathered digital fundus images from renowned Pakistani eye hospitals to generate a new “DR-Insight” dataset and known online sources. A novel methodology named the residual-dense system (RDS-DR) was then devised to assess diabetic retinopathy. To develop this model, we have integrated residual and dense blocks, along with a transition layer, into a deep neural network. The RDS-DR system is trained on the collected dataset of 9860 fundus images. The RDS-DR categorization method demonstrated an impressive accuracy of 97.5% on this dataset. These findings show that the model produces beneficial outcomes and may be used by healthcare practitioners as a diagnostic tool. It is important to emphasize that the system’s goal is to augment optometrists’ expertise rather than replace it. In terms of accuracy, the RDS-DR technique fared better than the cutting-edge models VGG19, VGG16, Inception V-3, and Xception. This emphasizes how successful the suggested method is for classifying diabetic retinopathy (DR).

Julius Granlund, Heikki Kyröläinen, Matti Santtila et al.

The objective of the present review was to evaluate the time-course of recovery of biochemical marker levels and physical performance after strenuous military training, and identify which biomarkers are affected. A systematic literature search was conducted using the databases MedLine (Ovid) and Web of Science (WoS) to identify studies until January 2023. Varying relevant search terms were used, related to military training, Special Forces, physical performance, and biomarkers. Records were based on strict inclusion and exclusion criteria. Twelve studies met the inclusion criteria and were selected for this review. A variety of physiological and psychological markers were measured, and military training lasted from 4 to 62 days, with recovery periods varying from 24 h to 6 weeks. Among these studies, full recovery was observed in two studies, while seven studies showed almost full (79–90%) recovery, and in three studies, 44–63% of markers recovered after the measured recovery period. However, in some studies, additional markers could be defined as recovered, depending on the criterion for recovery. In the majority of the studies, most of the measured variables recovered during the follow-up, but often, some variables remained unrecovered, and at times, only modest recovery was seen. It is important to point out that recovery duration depends on the duration and intensity of the military training stressor. Overall, resolution varies between the markers, and sometimes, recovery might not occur, even after prolonged recovery. Therefore, it is important to measure the recovery status of soldiers with both biomarkers and physical performance markers, especially after strenuous training, to maximize operational capability during prolonged missions.

David Oniani, Jordan Hilsman, Yifan Peng et al.

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has captivated the research community, leading to debates about its application in healthcare, mainly due to concerns about transparency and related issues. Meanwhile, concerns about the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied, and decision-makers often fail to consider the significance of generative AI. In this paper, we propose GREAT PLEA ethical principles, encompassing governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy, for generative AI in healthcare. We aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI in healthcare.

Edoardo Gramigna, Riccardo Lasagni Manghi, Marco Zannoni et al.

Hera represents the European Space Agency's inaugural planetary defense space mission and plays a pivotal role in the Asteroid Impact and Deflection Assessment international collaboration with NASA DART mission that performed the first asteroid deflection experiment using the kinetic impactor techniques. With the primary objective of conducting a detailed post-impact survey of the Didymos binary asteroid following the DART impact on its small moon called Dimorphos, Hera aims to comprehensively assess and characterize the feasibility of the kinetic impactor technique in asteroid deflection while conducting an in-depth investigation of the asteroid binary, including its physical and compositional properties as well as the effect of the impact on the surface and shape of Dimorphos. In this work, we describe the Hera radio science experiment, which will allow us to precisely estimate critical parameters, including the mass, which is required to determine the momentum enhancement resulting from the DART impact, mass distribution, rotational states, relative orbits, and dynamics of the asteroids Didymos and Dimorphos. Through a multi-arc covariance analysis, we present the achievable accuracy for these parameters, which consider the full expected asteroid phase and are based on ground radiometric, Hera optical images, and Hera to CubeSats InterSatellite Link radiometric measurements. The expected formal uncertainties for Didymos and Dimorphos GM are better than 0.01% and 0.1%, respectively, while their J2 formal uncertainties are better than 0.1% and 10%, respectively. [...]

Chao Wu, Ping Wu, Pei Li et al.

Abstract Aim and objective This study was to establish an index system for the evaluation of Chinese infectious disease specialist nurses’ core competence. Background The index system for the evaluation of infectious disease specialist nurses’ core competence has not been established. Design A two-round Delphi survey was conducted to seek opinions from experts about the index system for the evaluation of infectious disease specialist nurses’ core competence. Methods The study adopted several research methods, including literature retrieval, theoretical analysis and qualitative research. Based on the above method, the draft of core competence evaluation index system of infectious disease specialist nurses was constructed. A Delphi survey was used for the study of 30 infectious disease experts from 8 provinces and cities around China. A modified recommendation for the Conducting and Reporting of Delphi studies (CREDES) was also used to guide this study. A STROBE checklist was used. Results The Core Competence Evaluation Index System of Infectious Disease Nurses is composed of 6 primary indicators, namely, Nursing Abilities for Infectious Diseases, Infection Prevention and Control Abilities, Responsiveness to Infectious Diseases, Professional Development Abilities, Communication and Management Abilities, and Professionalism and Humanistic Accomplishment, 16 secondary indicators and 47 tertiary indicators. The authority coefficient, judgment coefficient and familiarity degree of Delphi experts were 0.923, 0.933 and 0.913 respectively. Conclusions The evaluation index system of core competence of diseases specialist nurses is scientific and reliable. It can be reference for future training and assessment of Chinese infectious disease specialist nurses. Relevance to clinical practice Infectious disease specialist nurses are the main force for infectious disease nursing. Their core competence is related to the quality of infectious disease nursing and treatment. The core competence of the nurses is important for identification of training strategies and can be regarded as reference for nurse assessment and promotion. The construction of the index system is based on the consensus of infectious disease experts, which is not only helpful to standardize the training strategies and selection standards of infectious disease specialist nurses in the future, but also meet the society’s needs in clinical infectious disease nursing.

Heng Mei, Xiaofan Liu, Yan Li et al.

Abstract Background Hetrombopag, a novel thrombopoietin receptor agonist, has been found in phase I studies to increase platelet counts and reduce bleeding risks in adults with immune thrombocytopenia (ITP). This phase III study aimed to evaluate the efficacy and safety of hetrombopag in ITP patients. Methods Patients who had not responded to or had relapsed after previous treatment were treated with an initial dosage of once-daily 2.5 or 5 mg hetrombopag (defined as the HETROM-2.5 or HETROM-5 group) or with matching placebo in a randomized, double-blind, 10-week treatment period. Patients who received placebo and completed 10 weeks of treatment switched to receive eltrombopag, and patients treated with hetrombopag in the double-blind period continued hetrombopag during the following open-label 14-week treatment. The primary endpoint was the proportion of responders (defined as those achieving a platelet count of ≥ 50 × 109/L) after 8 weeks of treatment. Results The primary endpoint was achieved by significantly more patients in the HETROM-2.5 (58.9%; odds ratio [OR] 25.97, 95% confidence interval [CI] 9.83–68.63; p < 0.0001) and HETROM-5 (64.3%; OR 32.81, 95% CI 12.39–86.87; p < 0.0001) group than in the Placebo group (5.9%). Hetrombopag was also superior to placebo in achieving a platelet response and in reducing the bleeding risk and use of rescue therapy throughout 8 weeks of treatment. The durable platelet response to hetrombopag was maintained throughout 24 weeks. The most common adverse events were upper respiratory tract infection (42.2%), urinary tract infection (17.1%), immune thrombocytopenic purpura (17.1%) and hematuria (15%) with 24-week hetrombopag treatment. Conclusions In ITP patients, hetrombopag is efficacious and well tolerated with a manageable safety profile. Trial registration Clinical trials.gov NCT03222843 , registered July 19, 2017, retrospectively registered.

Jorabar Singh Nirwan, Shan Lou, Saqib Hussain et al.

Electrically tunable lenses (ETLs) are those with the ability to alter their optical power in response to an electric signal. This feature allows such systems to not only image the areas of interest but also obtain spatial depth perception (depth of field, DOF). The aim of the present study was to develop an ETL-based imaging system for quantitative surface analysis. Firstly, the system was calibrated to achieve high depth resolution, warranting the accurate measurement of the depth and to account for and correct any influences from external factors on the ETL. This was completed using the Tenengrad operator which effectively identified the plane of best focus as demonstrated by the linear relationship between the control current applied to the ETL and the height at which the optical system focuses. The system was then employed to measure amplitude, spatial, hybrid, and volume surface texture parameters of a model material (pharmaceutical dosage form) which were validated against the parameters obtained using a previously validated surface texture analysis technique, optical profilometry. There were no statistically significant differences between the surface texture parameters measured by the techniques, highlighting the potential application of ETL-based imaging systems as an easily adaptable and low-cost alternative surface texture analysis technique to conventional microscopy techniques.

Heidi Fuqua Haviland, Renee C. Weber, Clive R. Neal et al.

The Lunar Geophysical Network (LGN) mission is proposed to land on the Moon in 2030 and deploy packages at four locations to enable geophysical measurements for 6-10 years. Returning to the lunar surface with a long-lived geophysical network is a key next step to advance lunar and planetary science. LGN will greatly expand our primarily Apollo-based knowledge of the deep lunar interior by identifying and characterizing mantle melt layers, as well as core size and state. To meet the mission objectives, the instrument suite provides complementary seismic, geodetic, heat flow, and electromagnetic observations. We discuss the network landing site requirements and provide example sites that meet these requirements. Landing site selection will continue to be optimized throughout the formulation of this mission. Possible sites include the P-5 region within the Procellarum KREEP Terrane (PKT; (lat:$15^{\circ}$; long:$-35^{\circ}$), Schickard Basin (lat:$-44.3^{\circ}$; long:$-55.1^{\circ}$), Crisium Basin (lat:$18.5^{\circ}$; long:$61.8^{\circ}$), and the farside Korolev Basin (lat:$-2.4^{\circ}$; long:$-159.3^{\circ}$). Network optimization considers the best locations to observe seismic core phases, e.g., ScS and PKP. Ray path density and proximity to young fault scarps are also analyzed to provide increased opportunities for seismic observations. Geodetic constraints require the network to have at least three nearside stations at maximum limb distances. Heat flow and electromagnetic measurements should be obtained away from terrane boundaries and from magnetic anomalies at locations representative of global trends. An in-depth case study is provided for Crisium. In addition, we discuss the consequences for scientific return of less than optimal locations or number of stations.

Iñigo Martinez, Elisabeth Viles, Igor G. Olaizola

Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise when executing a data science project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects and the ambiguity of roles in data science are among these challenges. Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies. In addition, fewer methodologies offer a complete guideline across team, project and data & information management. In this article we would like to explore the necessity of developing a more holistic approach for carrying out data science projects. We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information management. Finally, we propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have. This framework can be used by other researchers as a roadmap for the design of new data science methodologies or the updating of existing ones.

Wojciech Sójka