Military Sectors Research Articles

Teaching Model of Soccer Training Based on Virtual Simulation Technology

Virtual simulation technology, a cornerstone of modern innovation, revolutionizes the way we interact with virtual environments, offering immersive experiences across various fields. By leveraging computer-generated simulations, this technology replicates real-world scenarios, enabling users to engage in lifelike experiences without physical constraints. From training simulations for professionals in healthcare, aviation, and military sectors to virtual reality gaming and architectural visualization, virtual simulation technology transcends traditional boundaries, fostering experiential learning and enhancing decision-making processes. Its applications extend to education, where students can explore complex concepts in science, history, and art through interactive simulations, promoting active learning and knowledge retention. As technology continues to evolve, virtual simulation remains at the forefront, continually pushing the boundaries of what is possible in immersive experiences. This paper presents a novel teaching model for soccer training, leveraging virtual simulation technology augmented by Hierarchical Training Feature Co-ordinates Recurrent Network (HTF-RN). The proposed model aims to enhance the effectiveness and efficiency of soccer training programs by providing immersive and interactive experiences in virtual environments. Through simulated experiments and empirical validations, the efficacy of the HTF-RN-enhanced virtual simulation teaching model is evaluated. Results demonstrate significant improvements in player performance, tactical understanding, and decision-making abilities compared to traditional training methods. For instance, players trained using the HTF-RN model exhibited a 25% increase in goal-scoring accuracy and a 30% improvement in passing precision. Additionally, the model enabled coaches to customize training sessions based on individual player strengths and weaknesses, leading to more targeted and impactful training interventions. These findings underscore the potential of virtual simulation technology with HTF-RN in revolutionizing soccer training methodologies, paving the way for more effective player development and performance enhancement.

Застосування ArcGIS для оптимізації військового планування

The purpose of the article is to substantiate the advantages of using this software in comparison with a paper map. Theoretical justification of practical software studies. In modern conditions of military conflict, where strategic management and decision-making determine the effectiveness of actions on the battlefield, the use of advanced technologies becomes an integral part of the functioning of the Armed Forces. One of the key areas of innovation in this context is the use of geographic information systems (GIS), which provide unit commanders with the means to obtain, analyze, and use spatial information. This article is intended to review the practical experience of using GIS in the Armed Forces, particularly focusing on important aspects related to unit management. The basis of our research is the experience and use of the ArcGIS geoinformation system, which was used in the military. The main advantages and capabilities of this system from the point of view of the unit commander are considered, as well as how these technologies can improve leadership and interaction on the battlefield. Method. Methods of analysis of key system functions, including ArcMap, ArcScene, and other modules, were used to understand their potential for unit management. A comparison with paper maps was made: a comparative analysis of the effectiveness of the implementation of regulations on paper topographic maps and in ArcGIS software. Methods of systematization and generalization of scientific information: the advantages of 3D Visualization are highlighted, the study of ArcScene capabilities to improve understanding of the terrain in a three-dimensional format, details and advantages of using special tools for military analysis and management. The results. As a result of the study of the use of the ArcGIS geoinformation system by the unit commander, it was established that the implementation of ArcGIS significantly accelerates a number of actions related to the deployment of artillery, the time required to hit an unplanned target is reduced. Tactical planning is improved: the use of Military Tools for ArcGIS allows the commander to effectively manage and control the actions of his units and the enemy, the creation of tactical symbols and artificial maps simplifies the planning of tasks and responding to unexpected circumstances. The use of 3D Visualization in ArcScene allows you to get a three-dimensional overview of the area, improving the perception of the commander of the unit of the situation. Built-in functions such as centering and zooming in on the target make it easy to monitor objects in real time. The obtained results indicate the great potential and advantages of using the ArcGIS geographic information system in military management, which can lead to an increase in the efficiency and success of military operations. The scientific novelty is that although the ArcGIS software is in service with many NATO countries and is being implemented in the Armed Forces of Ukraine, it is determined that only this program should be used in the Armed Forces of Ukraine for digital maps and direct research that would determine the advantages of this product and analyze there are no particularities of application. The main focus of the research is on the features of ArcGIS application. The components of ArcGIS were identified, their analysis and prospects in the military sector were carried out. Practical meaning. As a result of the study of the use of the geographic information system ArcGIS by the commander of the unit, a significant potential for improving operational management and decision-making in the conditions of military operations was revealed. The use of ArcGIS allows you to speed up actions, improve tactical planning, provide three-dimensional visualization of the terrain and optimize geodetic calculations. The obtained results confirm the importance and necessity of using modern geoinformation tools in the military leadership to ensure the efficiency and success of military operations.

Design of multifunctional polymethacrylimide foam sandwich with excellent radar absorption capability and compressive performance

A composite multifunctional radar absorption polymethacrylimide (RAPMI) foam sandwich (MRAPS) was constructed. Analytical models for out-of-plane and in-plane compression of the MRAPS were established. Three-dimensional failure mechanism maps and specific strength cubic cloud maps were drawn. Finally, a multiobjective particle swarm optimization (MOPSO) algorithm was used to achieve an integrated design, and a specific calculation example was tested and verified through experiments. The resulting sandwich with a 43.0 mm thickness core and 0.13 g/cm3 density achieves 90% effective absorption above broadband (RL ≤ −10 dB) in the 2–18 GHz range. As a radar absorption absorber, the MRAPS increased the engineering value and strategic importance of PMI foam for structural components in the military and aerospace sectors.

The Role of AI in Hybrid Warfare: A Case Study of Pakistan's Cybersecurity Landscape

Technological advancements driven by artificial intelligence (AI) impact social, economic, and military sectors. This research paper explores the relationship between AI and cybersecurity in hybrid warfare. While AI boosts economic development and social betterment, its militarization poses significant risks, especially in asymmetric warfare, where AI-guided attacks can undermine targeted states' economies, infrastructure, and institutions. The paper emphasizes the role of neural network technologies in cyber operations and defense strategies. The absence of legal norms for AI in military actions exacerbates the threat of cyber-terrorism. Focusing on Pakistan's weak cyberinfrastructure, the paper highlights the challenges and opportunities for nuclear-armed states. To strengthen the argument this study implies the case study of Pakistan which faces serious cyber-attacks and cyber operations. Pakistan is known as a theater of Hybrid warfare. So these cyber-attacks and other hybrid strategies can be used against Pakistan to paralyze it.

Civil-military cooperation in the field of additive manufacturing technologies in military logistics

Technological progress opens up opportunities for the development of many areas, including military activities. Industry 4.0 develops technologies that are used in military activities. The potential for supporting military logistics is created by additive manufacturing technology. Development and implementation of innovations should be conducted in a way that ensures interoperability, which means multi-stakeholder and multifaceted cooperation. However, achieving the benefits resulting from this technology requires integrated, systemic, regulated cooperation between the civilian sector - the creator of the technology and the military sector - its beneficiary. This article focuses on issues related to civil-military cooperation in the field of adapting additive manufacturing technologies (3D printing, AMT) to military logistics. The purpose of this paper is to identify the challenges and directions of development of military logistics and presenting the essence and role of AMT, and to define the key factors of civil-military cooperation in the field of implementing AMT in the military logistics system. To solve the research problem, a qualitative research approach was used, referring to the relevant literature and participant observation. Analysis allows to create a proposal of a conceptual model of cooperation between military and civilian systems in the field of implementation and development of AMT in the field of military logistics. The obtained results, discussion and conclusions focus on the conditions for building a civilian-military partnership in the field of targeted development and safe, efficient implementation of AMT in military logistics.

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar.

Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition. However, these approaches have limitations, particularly in complex scenarios. In response to this challenge, this paper proposes a cross-modal supervised through-wall radar human body pose recognition method. By integrating information from both cameras and radar, a cross-modal dataset was constructed, and a corresponding deep learning network architecture was designed. During training, the network effectively learned the pose features of targets obscured by walls, enabling accurate pose recognition (e.g., standing, crouching) in scenarios with unknown wall obstructions. The experimental results demonstrated the superiority of the proposed method over traditional approaches, offering an effective and innovative solution for practical through-wall radar applications. The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition.

What does recovery mean to Jordanian mental health service users and their families? A descriptive qualitative study

Background The concept of recovery in mental health has been extensively documented in the Western context. Yet, little is known about this concept in the Arab context, particularly in Jordan. Aim The aim of this study was to explore the conceptuality of recovery in mental health from the perspectives of both Jordanian service users and their families. Methods A qualitative descriptive design was used, and semi structured interviews were conducted with 16 service users and 16 family members, selected from three different mental health sectors in Jordan: Government, Military and University Sectors. The data were transcribed verbatim, translated into English, and then analyzed using an inductive thematic analysis approach. Results Three main themes were identified as a result of the qualitative data analysis: (1) the definition of recovery implies functional recovery, (2) the definition of recovery implies symptomatic recovery, and (3) the definition of recovery from a religious perspective. Conclusions The findings of the present study shed light on how recovery in mental health is perceived by service users and their families in the Jordanian context, and they generate insight into what to consider when targeting the development and implementation of recovery-supporting care within the mental health services (MHSs) in Jordan.

Specific emitter identification based on ensemble domain adversarial neural network in multi-domain environments

Specific emitter identification is pivotal in both military and civilian sectors for discerning the unique hardware distinctions inherent to various launchers, it can be used to implement security in wireless communications. Recently, a large number of deep learning-based methods for specific emitter identification have been proposed, achieving good performance. However, these methods are trained based on a large amount of data and the data are independently and identically distributed. In actual complex environments, it is very difficult to obtain reliable labeled data. Aiming at the problems of difficulty in data collection and annotation, and the large difference in distribution between training data and test data, a method for individual radiation source identification based on ensemble domain adversarial neural network was proposed. Specifically, a domain adversarial neural network is designed and a Transformer encoder module is added to make the features obey Gaussian distribution and achieve better feature alignment. Ensemble classifiers are then used to enhance the generalization and reliability of the model. In addition, three real and complex migration environments, Alpine–Montane Channel, Plain-Hillock Channel, and Urban-Dense Channel, were constructed, and experiments were conducted on WiFi dataset. The simulation results show that the proposed method exhibits superior performance compared to the other six methods, with an accuracy improvement of about 3%.

Remote Sensing Image Segmentation for Aircraft Recognition Using U-Net as Deep Learning Architecture

Recognizing aircraft automatically by using satellite images has different applications in both the civil and military sectors. However, due to the complexity and variety of the foreground and background of the analyzed images, it remains challenging to obtain a suitable representation of aircraft for identification. Many studies and solutions have been presented in the literature, but only a few studies have suggested handling the issue using semantic image segmentation techniques due to the lack of publicly labeled datasets. With the advancement of CNNs, researchers have presented some CNN architectures, such as U-Net, which has the ability to obtain very good performance using a small training dataset. The U-Net architecture has received much attention for segmenting 2D and 3D biomedical images and has been recognized to be highly successful for pixel-wise satellite image classification. In this paper, we propose a binary image segmentation model to recognize aircraft by exploiting and adopting the U-Net architecture for remote sensing satellite images. The proposed model does not require a significant amount of labeled data and alleviates the need for manual aircraft feature extraction. The public dense labeling remote sensing dataset is used to perform the experiments and measure the robustness and performance of the proposed model. The mean IoU and pixel accuracy are adopted as metrics to assess the obtained results. The results in the testing dataset indicate that the proposed model can achieve a 95.08% mean IoU and a pixel accuracy of 98.24%.

Enhancing operational rations for Special Operations Forces: A qualitative analysis and roadmap for product improvement

AbstractNinety‐eight (n = 98) military personnel from the Brazilian Army Special Operations Forces (SOF) participated in 10 Focus Groups to discuss the suitability of Combat Operational Rations with the service doctrine. The operational rations provided partially meet the operational and doctrinal demands of the SOF. Although the military personnel felt secure with the rations provided, the need to customize kits with locally sourced food items before the start of the mission and complaints about the excessive packaging volume were the categories that showed the highest level of engagement. The insights obtained for improving the rations indicated the need for adjustments in palatability, menu variety, modularization, practicality, and an increase in the nutritional content of macro and micronutrients. Further studies should be conducted in partnership with military technical sectors and strategic defense companies to assess the feasibility of developing rations better aligned with the SOF doctrine.Practical ApplicationsAddressing the challenges identified in the Rations used by the Brazilian Army Special Operations Forces (SOF) is crucial for improving the readiness and performance of elite military units and offers valuable insights for academic research. This benefits the academic community by demonstrating the real‐world relevance of nutrition science, food technology, and logistics in military settings. Furthermore, the collaboration between military technical sectors and strategic defense companies presents an opportunity for industrial innovation. Companies involved in producing and supplying rations can use these findings to develop more suitable, efficient, and cost‐effective products tailored to the unique demands of SOF units.

A review of intentional electromagnetic interference in power electronics: Conducted and radiated susceptibility

AbstractWith the advancements in Industry 4.0 and the global demand for sustainable energy, power electronics devices are emerging as pivotal components for modern industrial, transportation, and energy delivery. Ensuring the security of these devices is of paramount importance for safeguarding the reliable operation of critical systems against any threats. In recent years, the issue of intentional electromagnetic interference (IEMI) attack and their related countermeasures have gained widespread attention in both military and civilian power electronics sectors. Here, a review of IEMI in power electronics is provided. Starting from the electromagnetic compatibility standards, the test setups for evaluating the device immunity are introduced. Due to different attack modes and propagation paths, IEMI illuminations are typically categorized into conduction and radiation, where their susceptibility studies, applications, and defence strategies are discussed, respectively. Finally, the existing challenges, ongoing trends, and future topics in this field are discussed.

Do military expenditures have an impact on the adoption of renewable energy in OECD nations? Evidence from a panel cointegration test approach

PurposeToday, many industries are implementing creative approaches in response to increasing environmental awareness. It is of great importance to answer the question of whether the military sector, one of the most important sectors, can support renewable energy (RE) adaptation. This study aims to examine how military spending affects the supply of RE in 27 Organization for Economic Cooperation and Development (OECD) nations as well as the regulatory function of factors such as innovation, international trade and oil prices between 1990 and 2021.Design/methodology/approachThe study examines the effects of military spending, income, green innovation, international trade, oil prices and the human development index on the supply of RE using various econometric approaches, which are the cointegration test, moments quantile regression and robustness test.FindingsThe findings demonstrate that all factors, excluding military spending, quite likely affect the expansion of the renewable supply. Military spending negatively influences the RE supply; specifically, a 1% increase in military spending results in a 0.88 reduction in the renewable supply. In addition, whereas income elasticity, trade and human development index in OECD nations are higher in the last quantiles of the regression than in the first quantiles, the influence of military spending and innovation on renewable supply is about the same in all quantiles.Practical implicationsOECD nations must consider the practical implications, which are essential to assess and update the military spending of OECD countries from a green energy perspective to transition to clean energy. Based on the study’s overall findings, the OECD countries should incorporate the advantages of innovation, economic growth and international trade into their clean energy transition strategies to lessen the impact of military spending on renewables.Originality/valueThe study aims to fill a gap in the literature regarding the role of military expenditures in the RE development of an OECD country. In addition, the results of the methodological analysis can be used to guide policymakers on how military spending should be in the field of RE.

DETECTION OF CRIME SCENE OBJECTS FOR EVIDENCE ANALYSIS USING DEEP LEARNING TECHNIQUES

Research on the detection of objects at crime scenes has flourished in the last two decades. Researchers have been concentrating on colour pictures, where lighting is a crucial component, since this is one of the most pressing issues in computer vision, with applications spanning surveillance, security, medicine, and more. However, nighttime monitoring is crucial since most security problems cannot be seen by the naked eye. That's why it's crucial to record a dark scene and identify the things at a crime scene. Even when its dark out, infrared cameras are indispensable. Both military and civilian sectors will benefit from the use of such methods for nighttime navigation. On the other hand, IR photographs have issues with poor resolution, lighting effects, and other similar issues. Surveillance cameras with infrared (IR) imaging capabilities have been the focus of much study and development in recent years. This research work has attempted to offer a good model for object recognition by using IR images obtained from crime scenes using Deep Learning. The model is tested in many scenarios including a central processing unit (CPU), Google COLAB, and graphics processing unit (GPU), and its performance is also tabulated.

Analysis of the new market trends of UAVS for safe BVLOS operations

UAVs are currently conquering the skies as prominent tools for various data-intensive applications, in the economic, transport, military and civil sector. While initially found application in the military sector, technology progression allowed them to enter the recreational sector and are now gaining ground in the fringes of the commercial environment. In parallel, technical components and subsystems that are application-optimised focus on highly automated drones, benefit from expertise in other domains, especially when it comes to Electronic Components and Systems (ECS), such as the automotive one, to operate beyond the visual line of sight (BVLOS) with a rather high degree of autonomy. Such technological developments, as well as currents trends and societal needs have opened the way for an unparalleled expansion in the use of UAS for a great number of applications, where humans cannot reach or are unable to perform in a timely and efficient manner. This work aims to present a in-depth analysis of the current market trends that shape the existing landscape for the development of safe and reliable BVLOS operations.

Unveiling the Impact of Extreme Learning Machine in the Defence and Military Sector

Among the most well-known machine learning algorithms, Extreme Learning Machine (ELM) has seen widespread use across a variety of fields, including the defence and military industries. For problems like sluggish technique and iteratively altering the hidden layer’s parameters to optimise the efficiency of the gradient descent approach, a cutting-edge machine learning algorithm known as the ELM has been developed. Depending on the specific objective and circumstance, the Extreme Learning Machine (ELM) may be more appropriate than Deep Neural Network (DNN) techniques. The models constructed in this manner perform quite well in generalisation. The following three goals are emphasised in its unconventional structure: 1) A great degree of accuracy in learning 2) less need for direct human involvement 3) an extremely rapid rate of learning, and moreover, it provides an optimal response for the whole world. As a result of its quick training, flexibility, and resilience, the Extreme Learning Machine (ELM) has several uses in this field, including target detection and tracking, image and signal processing, cybersecurity and intrusion detection, decision support systems, pattern recognition and classification, etc. According to our findings, the ELM approach was used with low training time and the testing accuracy is excellent. Also, this study presents the contribution of the revolutionary machine learning algorithm ELM to the defence and military sectors.

The asymmetric impact of government expenditure on economic growth: Evidence from a NARDL model

Since the 2011 Revolution, Tunisia has faced significant economic development challenges. Government spending plays a crucial role in fostering economic growth. This study focuses on Tunisia from 1980 to 2022, considering factors like foreign direct investment (FDI), trade openness, capital, and labor. It particularly examines spending in four government sectors: agriculture, education, health, and military. Using the Non-linear Autoregressive Distributed Lag (NARDL) model, the study investigates how these sectoral government expenditures relate to Tunisia's gross domestic product (GDP). The findings suggest that the relationship between these factors and GDP is complex. For instance, increases in trade openness and FDI generally lead to GDP growth. Similarly, changes in the labor force impact GDP differently in the short and long term, with negative and positive changes eventually benefiting the economy, but positive changes can initially decrease GDP. The study also finds that government spending on agriculture and health positively affects GDP, whereas spending on military and education has a negative impact. To enhance government spending and stimulate economic growth in Tunisia, the study recommends addressing corruption, inefficiency, and waste. It emphasizes the importance of directing public funds towards infrastructure, particularly in the education and military sectors, to improve welfare and support productive activities.

Job satisfaction in the armed forces: Differences among ranks

Human resource management has attracted significant attention among researchers of public management. It is increasingly accepted that committed and satisfied employees are strategic resources in any organization. The idiosyncrasies of the armed forces and the professionalization processes they have undergone necessitate a study of the attitudes of military personnel towards work in the sector. This paper analyses the determinants of job satisfaction in the military. Three factors (“Work itself”, “Economic aspects” and “Personal relationships”) are identified based on data from the International Social Survey Programme (ISSP). Both the composition and the impact of these factors on job satisfaction vary according to the hierarchical rank of a worker. The findings confirm the importance of differentiated human resources policies that take into account the hierarchical diversity of the military. Our results provide new evidence and valuable practical information for human resource management in the armed forces. We provide policymakers and managers of the military sector with recommendations that allow them to reinforce the ability of the armed forces to attract, retain and motivate talent.

Assault Type Detection in WSN Based on Modified DBSCAN with Osprey Optimization Using Hybrid Classifier LSTM with XGBOOST for Military Sector

Assault Type Detection in WSN Based on Modified DBSCAN with Osprey Optimization Using Hybrid Classifier LSTM with XGBOOST for Military Sector

SYSTEMIC FEATURES OF INNOVATION DEVELOPMENT IN THE USA

Due to the importance of innovations as a factor for economic growth and competitiveness, ensuring their sustainable development is a universal task for the country. The most intense race for innovation is observed among countries striving for global dominance. This article is dedicated to the study of the specific features of innovation development in the United States, where innovative potential has become one of the cornerstones of their economic strategy and a key driver of their success. The United States has significant distinctions from other countries in terms of culture, institutions, economic organizations, economic regulation, etc., which together create the basic conditions for innovation and require consideration. The focus on innovation development in the United States is driven by intensified competition with other global players, particularly from China.The main goal of the research is to summarize and characterize the systemic features of innovation development in the United States, as well as to identify the key factors intensifying innovation productivity in the country.The study of the systemic features of innovation development in the United States covered the analysis of 1) civilizational factors; 2) institutional system; 3) economic system; 4) innovation system; 5) state innovation policy. The main factors intensifying innovation productivity in the United States were identified as the combination of entrepreneurial and large-scale capitalism, venture capital investment, and territorial agglomerations of innovative structures. The need to consider the peculiarities of innovation generation in the military sector, universities, and non-profit organizations has been justified. The strength of the United States innovation system has been statistically demonstrated, however, the increasing competition in science and innovation from China has been confirmed. This has led to the intensification and change in the type of state innovation policy in the United States, with a focus on targeted support for innovation in industry in the context of the green transition.

Characterization and optimization of a quantitative colorimetric acetylcholine esterase inhibition assay for biochip integration demonstrated by neurotoxicity evaluation of malathion

Organophosphates (OPs) and carbamates as insecticides, nematicides, fungicides, and herbicides are constantly increasing. Their neurotoxic nature requires careful usage, and misuse can lead to fatalities. OPs classified as 'Class 1′ toxic compounds irreversibly inhibit cholinesterases due to their molecular structure resembling the natural acetylcholine substrate, leading to toxic events in the human brain. Monitoring such chemicals is relevant for agricultural applications and essential for the military sector to ensure the safety of personnel and civilian populations. State-of-the-art analytical detection methods require time-consuming pre-treatments and costly reagents and face challenges associated with pesticide properties like thermal lability, low volatility, and high polarity, which can compromise analysis performance. Advanced systems like electrophoresis or liquid chromatography are used to address these, but these are not well suited for field analysis. Miniaturized colorimetric assays are becoming more popular for various portable devices and kits (i.e., metabolic or blood cell assays) due to their ease of use and practicality. Here, we aimed to establish and optimize a straight-forward paper-based microfluidic acetylcholine esterase inhibition assay for mobile organophosphate detection, laying the groundwork for future microdevice modules to be used in environmental monitoring, public health, and CBRN applications.