Hasil untuk "Military Science"

Xindi C. Hu, David Q. Andrews, A. Lindstrom et al.

Drinking water contamination with poly- and perfluoroalkyl substances (PFASs) poses risks to the developmental, immune, metabolic, and endocrine health of consumers. We present a spatial analysis of 2013–2015 national drinking water PFAS concentrations from the U.S. Environmental Protection Agency’s (US EPA) third Unregulated Contaminant Monitoring Rule (UCMR3) program. The number of industrial sites that manufacture or use these compounds, the number of military fire training areas, and the number of wastewater treatment plants are all significant predictors of PFAS detection frequencies and concentrations in public water supplies. Among samples with detectable PFAS levels, each additional military site within a watershed’s eight-digit hydrologic unit is associated with a 20% increase in PFHxS, a 10% increase in both PFHpA and PFOA, and a 35% increase in PFOS. The number of civilian airports with personnel trained in the use of aqueous film-forming foams is significantly associated with the detection of PFASs above the minimal reporting level. We find drinking water supplies for 6 million U.S. residents exceed US EPA’s lifetime health advisory (70 ng/L) for PFOS and PFOA. Lower analytical reporting limits and additional sampling of smaller utilities serving <10000 individuals and private wells would greatly assist in further identifying PFAS contamination sources.

Juan-Pablo Rivera, Gabriel Mukobi, Anka Reuel et al.

Governments are increasingly considering integrating autonomous AI agents in high-stakes military and foreign-policy decision-making, especially with the emergence of advanced generative AI models like GPT-4. Our work aims to scrutinize the behavior of multiple AI agents in simulated wargames, specifically focusing on their predilection to take escalatory actions that may exacerbate multilateral conflicts. Drawing on political science and international relations literature about escalation dynamics, we design a novel wargame simulation and scoring framework to assess the escalation risks of actions taken by these agents in different scenarios. Contrary to prior studies, our research provides both qualitative and quantitative insights and focuses on large language models (LLMs). We find that all five studied off-the-shelf LLMs show forms of escalation and difficult-to-predict escalation patterns. We observe that models tend to develop arms-race dynamics, leading to greater conflict, and in rare cases, even to the deployment of nuclear weapons. Qualitatively, we also collect the models’ reported reasoning for chosen actions and observe worrying justifications based on deterrence and first-strike tactics. Given the high stakes of military and foreign-policy contexts, we recommend further examination and cautious consideration before deploying autonomous language model agents for strategic military or diplomatic decision-making.

Eslam Abdelkreem, Sherif Hussein, Ashraf Tammam

Abstract Vehicular Ad Hoc Networks (VANETs) are wireless networks established between vehicles and their surrounding infrastructure, enabling the exchange of information. Consequently, many applications that can enhance passengers’ safety and traffic flow are built upon this information. However, malicious nodes can manipulate the exchanged data to attack other nodes and disrupt the network’s normal behavior. For example, if an attacker broadcasts a falsified location for a vehicle, the functionality of applications that rely on accurate location sharing will be compromised, potentially leading to deadly accidents. Although numerous Misbehavior Detection Schemes (MDSs) have been proposed to detect position falsification attacks, their effectiveness remains limited for certain attack types, raising concerns given the safety-critical nature of VANET applications. This paper proposes a machine learning-based method for detecting position falsification attacks. The proposed approach evaluates four machine-learning algorithms using three feature vectors (FV1, FV2, and FV3) composed of selected and derived features extracted from Basic Safety Messages (BSMs), in addition to a novel confidence-based Received Signal Strength Indicator feature, termed RSSIConf. The RSSIConf feature assesses the reliability of a sender’s claimed position by comparing the measured RSSI with confidence intervals corresponding to the claimed sender–receiver distance. Experimental results show that the Random Forest classifier trained with FV2 features achieves the best overall performance, outperforming existing approaches with improvements ranging from 0.76% to 13.26% in accuracy and from 0.74% to 12.71% in F1-score across different position spoofing attack types. These improvements enhance the reliability of misbehavior detection and contribute to safer and more trustworthy VANET communications.

WANG Bo, JIANG Xuping, HUANG Qihong

Knowledge of weaponry and equipment is a crucial basis for formulating equipment utilization strategies and development pathways. To address issues such as data redundancy, high interaction difficulty, and low match accuracy of question answers, this paper constructs a Q&amp;A system based on a knowledge graph for weaponry and equipment. The system achieves named entity recognition and classification of questions by fine-tuning the BERT model; it generates graph database query statements by filling named entities into question templates and generates answers by filling answer templates. Analysis of test results shows that this system is capable of effectively ranking correct answers at the top and has achieved a good balance between accuracy and comprehensiveness, although there is still room for improvement.

Jie Li, Yuhao Li, Xiaoyang Zhou et al.

Abstract Chronic inflammation in adipose tissue is widely recognized as a pivotal link connecting obesity to a spectrum of related chronic diseases, including type 2 diabetes, non-alcoholic fatty liver disease, and cardiovascular disorders. In this pathogenic process, the dysregulated interaction between adipocytes and adipose-resident immune cells plays a critical regulatory role; however, the underlying mechanisms governing this abnormal interaction remain largely unknown. In this study, we showed that upregulated β2-microglobulin expression in hypertrophic adipocytes during obesity not only mediated the activation of adipose-resident CD8+ T cells in a cell contact-dependent manner but also facilitated iron overload and the ferroptosis of adipocytes, thereby promoting the M1 polarization of adipose tissue macrophages. Conversely, specific ablation of β2-microglobulin in adipocytes effectively suppressed the activation and accumulation of adipose-resident CD8+ T cells, as well as adipocyte ferroptosis and M1 polarization, ultimately preventing high-fat diet-induced obesity and its related inflammation and metabolic disorders. Additionally, adeno-associated virus-mediated adipose-targeted knockdown of β2-microglobulin has been demonstrated to therapeutically alleviate high-fat diet-induced obesity, as well as its related chronic inflammation and metabolic disorders. Furthermore, our bioinformatic analysis of human adipose transcriptome data revealed a strong correlation between adipose β2-microglobulin and obesity. More importantly, β2-microglobulin is significantly upregulated in adipocytes isolated from patients with obesity. Thus, our findings highlight the pivotal role of adipocytes in obesity-associated chronic inflammation and metabolic disorders via β2-microglobulin-dependent mechanisms.

Xue-Geng Hong, Mei-Qi Zhang, Fang Tang et al.

Toscana virus (TOSV), a member of the <i>Phlebovirus</i> genus transmitted by sandflies, is acknowledged for its capacity to cause neurological infections and is widely distributed across Mediterranean countries. The potential geographic distribution and risk to the human population remained obscure due to its neglected nature. We searched PubMed and Web of Science for articles published between 1 January 1971 and 30 June 2023 to extract data on TOSV detection in vectors, vertebrates and humans, clinical information of human patients, as well as the occurrence of two identified sandfly vectors for TOSV. We further predicted the global distribution of the two sandfly vectors, based on which the global risk of TOSV was projected, after incorporating the environmental, ecoclimatic, biological, and socioeconomic factors. A total of 1342 unique studies were retrieved, among which 389 met the selection criteria and were included for data extraction. TOSV infections were documented in 10 sandfly species and 14 species of vertebrates, as well as causing a total of 7571 human infections. The occurrence probabilities of two sandfly vectors have demonstrated the greatest contributions to the potential distribution of TOSV infection risk. This study provides a comprehensive overview of global TOSV distribution and potential risk zones. Future surveillance and intervention programs should prioritize high-risk areas based on updated quantitative analyses.

Yu Jiang Master of Engineering, Kang Wang Master of Medicine, Yu-Ran Wang Master of Engineering et al.

Microvascular invasion of hepatocellular carcinoma is an important factor affecting tumor recurrence after liver resection and liver transplantation. There are many ways to classify microvascular invasion, however, an international consensus is urgently needed. Recently, artificial intelligence has emerged as an important tool for improving the clinical management of hepatocellular carcinoma. Many studies about microvascular invasion currently focus on preoperative and prognosis prediction of microvascular invasion using artificial intelligence. In this paper, we review the definition and staging of microvascular invasion, especially the diagnosis of it by using artificial intelligence. In preoperative prediction, deep learning based on multimodal data modeling of radiomics-screened features, clinical features, and medical images is currently the most effective means. In prognostic prediction, pathology is the gold standard, and the techniques used should more effectively utilize the global features of the pathology images.

J. Karl, Lee M. Margolis, J. Fallowfield et al.

ABSTRACT The importance of diet and nutrition to military readiness and performance has been recognized for centuries as dietary nutrients sustain health, protect against illness, and promote resilience, performance and recovery. Contemporary military nutrition research is increasingly inter-disciplinary with emphasis often placed on the broad topics of (1) determining operational nutrition requirements in all environments, (2) characterizing nutritional practices of military personnel relative to the required (role/environment) standards, and (3) developing strategies for improving nutrient delivery and individual choices. This review discusses contemporary issues shared internationally by military nutrition research programmes, and highlights emerging topics likely to influence future military nutrition research and policy. Contemporary issues include improving the diet quality of military personnel, optimizing operational rations, and increasing understanding of biological factors influencing nutrient requirements. Emerging areas include the burgeoning field of precision nutrition and its technological enablers.

Kayla S McIntire, Kelly A Crawford, P. Perrin et al.

ABSTRACT Primary Objective: Survey TBI literature to identify evidence of risk for post-injury suicide. Literature Selection: Search terms ((traumatic brain injury OR TBI) AND (suicidality OR suicidal behaviour OR suicidal ideation)) entered in PubMed, OVID Medline, PsychInfo, and Web of Science for papers published in print 01/01/1997 to 06/30/2019. Analysis of Literature: Authors screened abstracts, excluding duplicates and articles not meeting inclusion/exclusion criteria. Full papers were reviewed to make final exclusions. Data were extracted from 40 papers included co- and premorbid disorders, demographics, injury-related and psychological factors. Results: Persons with TBI have a higher risk for suicide than the general population. Reviewed articles reported comorbid depression and/or PTSD as risk factors for post-TBI suicide. Co- or premorbid substance misuse, sex, and sleep disturbance moderate risk. Quality of the literature was limited by sample size, the predominance of male participants, and inconsistency in reporting of findings. Conclusions: Comorbid depression and PTSD are significant post-TBI risk factors for suicide. Several variables combine to moderate or mediate TBI’s connection with suicide. Civilian and military clinician cross-talk and consistent reporting of results from reproducible studies of post-TBI suicide risk factors could improve prevention and treatment efforts in veterans and civilians.

Amirhossein Ghaderi, Matthias Niemeier, John Douglas Crawford

Various models (e.g., scalar, state-dependent network, and vector models) have been proposed to explain the global aspects of time perception, but they have not been tested against specific visual phenomena like perisaccadic time compression and novel stimulus time dilation. Here, in two separate experiments (N = 31), we tested how the perceived duration of a novel stimulus is influenced by 1) a simultaneous saccade, in combination with 2) a prior series of repeated stimuli in human participants. This yielded a novel behavioral interaction: pre-saccadic stimulus repetition neutralizes perisaccadic time compression. We then tested these results against simulations of the above models. Our data yielded low correlations against scalar model simulations, high but non-specific correlations for our feedforward neural network, and correlations that were both high and specific for a vector model based on identity of objective and subjective time. These results demonstrate the power of global time perception models in explaining disparate empirical phenomena and suggest that subjective time has a similar essence to time's physical vector.

Inam Bari, Javed Iqbal, Haider Ali et al.

A wideband circularly polarized rectangular dielectric resonator antenna (DRA) fed by a single feeding mechanism has been studied theoretically and experimentally. The purpose of the study is to determine how adding a parasitic strip next to the flat surface metallic feed would affect various far- and near-field antenna characteristics. Initially, the basic antenna design, i.e., the T-shape feed known as antenna A, produced a 4.81% impedance matching bandwidth (|S₁₁| −10 dB). Due to the narrow and undesirable results of the initial antenna design, antenna-A was updated to the antenna-B design, i.e., Yagi-Uda. The antenna-B produced a decent result (7.89% S₁₁) as compared to antenna-A but still needed the bandwidth widened, for this, a parasitic patch was introduced next to the Yagi-Uda antenna on the rectangular DRA at an optimized location to further improve the results. This arrangement produced circular polarization (CP) waves spanning a broad bandwidth of 28.21% (3.59–3.44 GHz) and a broad impedance |S₁₁| bandwidth of around 29.74% (3.71–3.62 GHz). These findings show that, in addition to producing CP, parasite patches also cause the return loss to rise by a factor of almost three times when compared to results obtained with the Yagi-Uda-shape feed alone. Computer simulation technology was used for the simulation (CST-2017). The planned antenna geometry prototype was fabricated and measured. Performance indicators show that the suggested antenna is a good fit for 5G applications. The simulated outcomes and measurements match up reasonably.

Amber Jamshaid

Education for sustainable development is an evolving notion in teacher education that highlights the need for tolerance, acceptance, and diversity. Therefore, there is a greater need to elucidate the contributions of teacher educators in promoting social tolerance through Education for Sustainable Development (ESD). In addition, it is crucial to understand the challenges faced by teacher educators while dealing with the intolerant, extremist views and/or behaviors in a particular social setting. In doing so, this research interviewed teacher educators (N=12) through a purposive sampling strategy to determine the respondents’ views about promoting social tolerance strategies integrated with ESD. The data was analyzed through qualitative data analysis software (NVivo), and themes were extracted based on the analysis plots. The research argues that promoting social tolerance within teacher education through ESD is a multi-layered process and requires attention at the policy level. It suggests that the existing structure of the Teacher Education program and the curricula are barely in line with the indicated ESD contents and strategies (integration with teaching and learning) needed to promote social tolerance and societal peace in Pakistan.

Steven Shepherd, D. Sherman, A. MacLean et al.

Long-standing structural features of the military have created a culture and society that is dramatically different and disconnected from civilian society. Thus, veterans transitioning to civilian society face a number of challenges related to fulfilling basic psychological needs (e.g., need for structure and order, belonging) and civilians’ reliance on stereotypes to understand military veterans. In an attempt to enrich the understanding of these challenges, we integrate social psychological theories and insights with research from sociology, clinical psychology, military psychology, and organizational behavior. Theories of compensatory control, stereotype threat, and stereotyping are drawn on to help explain the psychological challenges that veterans may encounter during their transition to civilian society. We present recent research that leverages these theories to understand issues veterans face. This theoretical integration illustrates the opportunity and potential for psychological researchers to conduct basic and applied research in the context of veterans and for clinicians and managers to draw on basic theory to inform programs and interventions.

ANDRE FRANGULIS COSTA DUARTE, Maria Eugênia Lobato dos Santos

Este artículo tiene como finalidad presentar un panorama sobre la enseñanza de Lengua Española en la Academia Militar das Agulhas Negras (AMAN). Se expone, inicialmente, una justificativa para la enseñanza de este idioma en este establecimiento de enseñanza, acompañado de su histórico durante las últimas décadas. A continuación, se presentan las actividades realizadas por la Cátedra de Español, la documentación curricular específica de este idioma y los aspectos del Proyecto Idiomas – “CertificAMAN” pertinentes. La elaboración de este trabajo se valió del abordaje cualitativo, a partir de datos obtenidos por un estudio de campo que tuvo como enfoque la comunidad de trabajo formada por los profesores de Español.  Una de las conclusiones es que la evolución de la enseñanza de esta lengua extranjera ocurre en sintonía con la propia evolución de la enseñanza militar y con las expectativas de la Fuerza Terrestre en la competencia lingüística de sus oficiales.  

Robert Jane, Tae Young Kim, Emily Glass et al.

To ensure dominance over a multi-domain battlespace, energy and power utilization must be accurately characterized for the dissimilar operational conditions. Using MATLAB/Simulink in combination with multiple neural networks, we created a methodology which was simulated the energy dynamics of a ground vehicle in parallel to running predictive neural network (NN) based predictive algorithms to address two separate research questions: (1) can energy and exergy flow characterization be developed at a future point in time, and (2) can we use the predictive algorithms to extend the energy and exergy flow characterization and derive operational intelligence, used to inform our control based algorithms or provide optimized recommendations to a battlefield commander in real-time. Using our predictive algorithms we confirmed that the future energy and exergy flow characterizations could be generated using the NNs, which was validated through simulation using two separately created datasets, one for training and one for testing. We then used the NNs to implement a model predictive control (MPC) framework to flexibly operate the vehicles thermal coolant loop (TCL), subject to exergy destruction. In this way we could tailor the performance of the vehicle to accommodate a more mission effective solution or a less energy intensive solution. The MPC resulted in a more effective solution when compared to six other simulated conditions, which consumed less exergy than two of the six cases. Our results indicate that we can derive operational intelligence from the predictive algorithms and use it to inform a model predictive control (MPC) framework to reduce wasted energy and exergy destruction subject to the variable operating conditions.

G. Goodwin, Nikki Blacksmith, Meredith R. Coats

Paul A Mele

This chapter focuses on the Reserve Officer Training Corps (ROTC) and how military science programs develop leaders for service in the military profession.

B. Murray, D. Judge, T. Morris et al.

Health care providers need to be able to function and react appropriately and efficiently during a community-wide disaster situation. Traditional health care education is not adequately structured to provide realistic experiences with respect to high-risk or infrequently encountered events such as a disaster. As a result, many healthcare providers graduate into practice with inadequate exposure or skills to intervene in a disastrous event. Previous studies validate that active participation by students during a simulation can translate into positive, meaningful learning applicable to practice. This paper describes how a disaster response simulation can be utilized as an innovative experiential learning technique. Additionally, interprofessional collaboration and positive learning experiences were fostered between military trainees and health care students in nursing and paramedic sciences. The constructivist framework utilized enabled educators to incorporate interprofessional collaboration, clinical reasoning, and technical skills in the safe learning environment of a simulation.

Yongji Wang, Yangyang Qi, Wenguo Xu et al.

Background: Hepatitis A vaccine has been used in mass and routine public vaccination programs in China. Long-term follow-up studies are required to determine the duration of protection and the need for booster vaccinations. Methods: A prospective, randomized, open-label clinical trial was performed to compare the geometric mean concentration (GMC) and seroprotection rates of anti-Hepatitis A virus (HAV) antibodies elicited by the inactivated vaccines Healive and Havrix. 400 healthy children were randomly assigned 3:1 ratio to receive two doses of Healive or Havrix at 0 and 6 months. Persistence of anti-HAV antibodies for 5 years post immunization has been reported The current study reports new data at 11 years post immunization for the purpose of showing antibody persistence. Sensitivity analyzes were performed to assess the results. In addition, predictions for long-term antibody persistence were performed using a statistical model. Two different serological assays were used that were shown to be 98.3% concordant for detecting anit-HAV antibody. Results: GMCs were significantly higher following Healive compared to Havrix at 1, 6, 7, 66, 112 and 138 months post-vaccination. In addition, the GMCs obtained using sensitivity analysis were very similar to those obtained using the original models. Prediction analysis indicated that the duration of protection for both vaccines was at least 30 years after immunization, with a lower limit of the 95% confidence interval for GMC of greater than 20mIU/mL. Conclusions: Healive is more immunogenic than Havrix in children at 11 years post full immunization. Prediction analysis indicated at least 30 years of antibody persistence for both vaccines.