War Strategy Research Articles

Efficient distribution network based on photovoltaic fed electric vehicle charging station using WSO-RBFNN approach

An integral and prevalent aspect of modern life is the electric vehicle (EV).EV charging networks struggle with power losses and high energy costs, particularly as demand rises leading to inefficiencies and potential system overloads. A hybrid WSO-RBFNN approach is proposed for the distribution network's photovoltaic (PV) fed electric vehicle charging stations. The performance of the proposed hybrid strategy is a combination of war strategy optimization (WSO) and Radial basis function neural network (RBFNN). It is hence referred to as the WSO-RBFNN technique. WSO optimizes the distribution network by minimizing power loss, improving voltage sensitivity, and reducing costs. Meanwhile, the RBFNN predicts the load demand. This innovative technique WSO-RBFNN identifies the nearest charging spots that minimize power loss and RBFNN optimizes power flow predicts charging demands and addresses both environmental and electrical grid stability concerns. The proposed technique is implemented in MATLAB. MATLAB is powerful computational software widely used in different fields like numerical computation, visualization, and algorithm development. MATLAB provides powerful tools for data visualization and plotting. The results are compared to various existing Heap-based optimizer (HBO), Wild horse optimizer (WHO), and Salp Swarm Algorithm (SSA) techniques. The proposed approach contributes only 0.59 % of a power loss it is less and the cost of energy is 0.18$ which is lesser and the voltage deviation is 6.6 pu which is less than the existing techniques.

Seni Berperang pada Masa Jawa Kuno: Pola Astrologis-Simbolik dalam Naskah Yoga Catur Dewata

Lontar Yoga Catur Dewata (YCD) is a literature in Old Javanese that discusses yoga for starting a war. The art of war is mostly concerned only with war strategy (Griffith 2005; Suryohadiprojo 2008). This study found that the art of war that developed in ancient Java used yoga practices with astrological patterns. YCD manuscripts as research objects are recorded in the FS UI collection with the code AH.58 LT-142 and are now stored in the UI's Library. The text of YCD describes the strategy of war to defeat the enemy personally with astrological-symbolic aspects. It deals with counting days, mantras, yogic practices of the existence of deities in sthana, svaravyanjana-nyasa 'syllabic applause', and animal symbolism. The main formulation of this research problem is how to reveal the narrative of astrological-symbolic patterns and the exposure of the relationship with astrological patterns by the art of war in ancient Java. Research requires philological work to produce text editions from YCD manuscripts. The methods used for data analysis are qualitative descriptive and Pierce's semiotic theory to analyze interpretive signs. The results of this study show that the astrological narrative found in the YCD text is related to warfare strategies in ancient Java. It is a local genius and folk science that developed in the community and is recorded in the YCD text.

THE FIFTH WAR GENERATION AND ITS GEOSTRATEGIC IMPLICATIONS ON THE BLACK SEA SECURITY DIMENSION: NEW TRENDS, NEW TENDENCIES

Abstract: paper considers and reflects new dimensions of New Cold War geostrategic balance implications at pan-regional (European) and regional (Black Sea) levels. Currently adopted new challenges in the military doctrine of the Russian Federation envisages engagement into two military conflicts simultaneously and constructed its strategic operational framework founded on incorporation of three combat elements: military, non-military and information warfare measures and proper concepts. Hence, it means that Russia can begin war-game scenarios at the same time against two neighboring nations as it occurs at time being against Ukraine and Georgia. A transformation of war strategy and geostrategic culture (it is a new jargon affiliated with cardinal shifting of strategic operational planning in wargame scenarios at global and regional levels). Hence, the process of geostrategic culture transformation occurring currently whilst introducing Fifth War Generation concept replacing the previous one due to the high technical achievements and technological introduction into the neo-modernist war dynamics itself. Generally to say, a fifth generation warfare is an attempt to accomplish strategic objectives through the use of propaganda and information attack vectors. It's carried out by unknown actors for unknown reasons. Even if the core enemy is identified, the victim nation will not be able to understand the purpose or end goal. The goal of the paper is to realize how the geostrategic cultural transformation takes place at regional level and reflection on what type of influence high-level technologies effect on war process itself.

Unpacking the Impact of External Support on Rebel Groups: Does it Fuel or Quell Violence Against Civilians?

ABSTRACT In civil conflicts, the relations of rebel groups with civilians are largely influenced by the rebels’ capabilities. Seeking external resources to enhance their military efforts and resilience, rebel groups’ access to those resources shapes their war strategies, including violence against civilians. Unlike the extant research drawing on highly aggregated designs, we argue that not every form of support affects rebels’ violent behaviour in the same direction. In this study, we test whether different forms of external support are associated with the level of rebel violence against civilians by using comprehensive data on foreign support to rebel groups in intrastate conflicts between 1989 and 2017. The findings support the need for disaggregation of foreign support to understand better the dynamics in this relationship. The results show that external support in the forms of access to infrastructure, from both state and non-state actors, and troops from non-state actors are associated with a decrease in civilian victimisation.

Analysis of War Optimization Algorithm in a Multi-Loop Power System Based on Directional Overcurrent Relays

In electrical power systems, ensuring a reliable, precise, and efficient relay strategy is crucial for safe and trustworthy operation, especially in multi-loop distribution systems. Overcurrent relays (OCRs) have emerged as effective solutions for these challenges. This study focuses on optimizing the coordination of OCRs to minimize the overall operational time of main relays, thereby reducing power outages. The optimization problem is addressed by adjusting the time multiplier setting (TMS) using the War Strategy Optimization (WSO) algorithm, which efficiently solves this constrained problem. This algorithm mimics ancient warfare strategies of attack and defense to solve complex optimization problems efficiently. The results show that WSO provides superior performance in minimizing total operating time and achieving global optimum solutions with reduced computational effort, outperforming traditional optimization methods (i.e., SM, HPSO, GA, RTO, and JAYA). The proposed algorithm shows a net time gains of 7.77 s, 2.57 s, and 0.8484 s when compared to GA, RTO, and JAYA respectively. This robust protection coordination ensures better reliability and efficiency in multi-loop power systems.

The Art of Diplomacy in Modern Military Conflicts

The article reflects the process of forming a new world order in the context of the Global Hybrid War unleashed by the United States and NATO and the active use of its tools against the victims of aggression. The role of diplomacy in managing the processes of escalation and containment of modern military confl icts is shown. The author draws attention to the increasing threat of a global thermonuclear confl ict, the probability of which increases in the context of the use by Western countries of a proxy war strategy, which opens the way to large-scale aggression, including against nuclear states. The article highlights the urgent need to review the Russian strategy of nuclear deterrence in order to adapt it to the realities of our time.

A Greek American Cold Warrior: George E. Phillies and the Greek Civil War, 1946–1949

Abstract: George E. Phillies, a prominent Greek American and chairman of the Justice for Greece Committee, was an ardent anticommunist who took an interest in themes of growing importance in American public debates of the late 1940s: the Greek Civil War (1946–1949) and US intervention in Greece as inaugurated by the Truman Doctrine (1947). Phillies's social status, hybrid identity, and networking with American and Greek elites were decisive for his emergence as an influential cold warrior at a crucial period for both US-Greek relations and US strategy in the Cold War. His public role was important in the legitimization of the Truman administration policy toward Greece. He used his ties to Greece to construct a supposedly factual account of the Greek Civil War that undermined alternative narratives and criticism of US actions. Phillies represents a case of ethnic anticommunism. His discourse highlights the hybrid, transatlantic dimension of Greek American identity in the mid-twentieth century and the interaction between ideological and ethnic conceptions of identity. His case is revealing for the role of anticommunism in the identity formation process and integration practices among migrants in post-World War II American society.

Integrating fuzzy modelling and war strategy optimization for identifying optimal operating factors of direct ethanol fuel cell

-Operating factors, including temperature, pressure, alcohol concentration, and reactant flow rate, significantly impact how much energy is produced by direct alcohol fuel cells. The cell's performance may go down or up depending on how these factors vary. For instance, greater alcohol temperatures and concentrations can speed up reactions and boost fuel cell efficiency, whereas lower alcohol temperatures and concentrations can have the opposite effect. Consequently, optimizing the operating circumstances is essential to getting the most energy possible out of direct alcohol fuel cells. Therefore, the primary goal is to develop a solid fuzzy model to simulate direct ethanol fuel cells (DMFC). Three process variables—ethanol flow rate, ethanol concentration, and temperature—are considered to increase the power density of DEFC. First, a fuzzy model of the DEFC was developed using experimental data. The best operating conditions to increase power density are then determined using war strategy optimization (WSO). Thanks to fuzzy, the RMSE decreased from 0.529 using RSM to 0.0292 using fuzzy (decreased by 94.5 %), compared to 0.529 using RSM. By around 11.7 %, the squared-R for prediction increases from 0.88 (using RSM) to 0.9831 (using fuzzy). The fuzzy model's low RMSE and high R-square values show the successful modelling phase. Compared to measured data and RSM, integrating fuzzy and WSO increased DEFC's power density by 5 % and 7.26 %, respectively. The ideal ethanol flow rates, concentrations, and temperatures are 9.4 ml/min, 0.25 M, and 74C, respectively.

Enhancing connectivity and coverage in wireless sensor networks: a hybrid comprehensive learning-Fick’s algorithm with particle swarm optimization for router node placement

Wireless Sensor Networks (WSNs) are essential for collecting and transmitting data in modern applications that rely on data, where effective network connectivity and coverage are crucial. The optimal placement of router nodes within WSNs is a fundamental challenge that significantly impacts network performance and reliability. Researchers have explored various approaches using metaheuristic algorithms to address these challenges and optimize WSN performance. This paper introduces a new hybrid algorithm, CFL-PSO, based on combining an enhanced Fick’s Law algorithm with comprehensive learning and Particle Swarm Optimization (PSO). CFL-PSO exploits the strengths of these techniques to strike a balance between network connectivity and coverage, ultimately enhancing the overall performance of WSNs. We evaluate the performance of CFL-PSO by benchmarking it against nine established algorithms, including the conventional Fick’s law algorithm (FLA), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), Salp Swarm Optimization (SSO), War Strategy Optimization (WSO), Harris Hawk Optimization (HHO), African Vultures Optimization Algorithm (AVOA), Capuchin Search Algorithm (CapSA), Tunicate Swarm Algorithm (TSA), and PSO. The algorithm’s performance is extensively evaluated using 23 benchmark functions to assess its effectiveness in handling various optimization scenarios. Additionally, its performance on WSN router node placement is compared against the other methods, demonstrating its competitiveness in achieving optimal solutions. These analyses reveal that CFL-PSO outperforms the other algorithms in terms of network connectivity, client coverage, and convergence speed. To further validate CFL-PSO’s effectiveness, experimental studies were conducted using different numbers of clients, routers, deployment areas, and transmission ranges. The findings affirm the effectiveness of CFL-PSO as it consistently delivers favorable optimization results when compared to existing methods, highlighting its potential for enhancing WMN performance. Specifically, CFL-PSO achieves up to a 66.5% improvement in network connectivity, a 16.56% improvement in coverage, and a 21.4% improvement in the objective function value when compared to the standard FLA.

Transformation of Modern Wars in the South Caucasus: Azerbaijan's Hybrid War Against Artsakh

The Third Artsakh war has dramatically transformed the security environment in the South Caucasus. Being a result and indicator of changing world order, it unleashed the parameters of the newly-developing formats of conflicts, confrontations, and wars, called hybrid warfare. From this perspective the paper studies the parameters of hybrid warfare against Artsakh launched by Azerbaijan. Application of hybrid warfare against Artsakh is not a new phenomenon and has being practiced by Azerbaijan at least during the last 10-15 years. However, the results of the 44-day war has provided new framework, opportunities, and instruments to intensify hybrid operations, striving to maximize the results. Hence, the paper discusses the Azerbaijani strategy of hybrid war against Artsakh by analyzing the main strategies, tactics, instruments and tools used by Azerbaijan against Artsakh, specifically, after 44-day war. The paper argues that after the Third Artsakh war of 2020 Azerbaijan has launched a full scale hybrid operation to provide ethnic cleansing of Artsakh and receive ‘Nagorno-Karabakh without Armenians’. The operation was based on the following components: information-psychological campaign/influence operations (against Armenians in Artsakh), economic and energy aspects, ecological issues, blockade, international campaigns, support to split Armenian power (in Armenia and Armenian Diaspora), sabotage against critical infrastructure and population, and so on. Concluding the paper launches, a discussion about Azerbaijani engagement with Armenia after the ethnic cleansing of Artsakh of Autumn of 2023.

Instant Sign Language Recognition by WAR Strategy Algorithm Based Tuned Machine Learning

Sign language serves as the primary means of communication utilized by individuals with hearing and speech disabilities. However, the comprehension of sign language by those without disabilities poses a significant challenge, resulting in a notable disparity in communication across society. Despite the utilization of numerous effective Machine learning techniques, there remains a minor compromise between accuracy rate and computing time when it comes to sign language recognition. A novel sign language recognition system is presented in this paper with an exceptionally accurate and expeditious, which is developed upon the recently devised metaheuristic WAR Strategy optimization algorithm. Following the preprocessing, both of spatial and temporal features has been extracted using the Linear Discriminant Analysis (LDA) and Gray-level cooccurrence matrix (GLCM) methods. Afterward, the WAR Strategy optimization algorithm has been adopted in two procedures, first in optimizing the extracted set of features, and second to fine-tune the hyperparameters of six standard machine learning models in order to achieve precise and efficient sign language recognition. The proposed system was assessed on sign language datasets of different languages (American, Arabic, and Malaysian) containing numerous variations. The proposed system attained a recognition accuracy ranging from 93.11% to 100% by employing multiple optimized machine learning classifiers and training time of 0.038–10.48 s. As demonstrated by the experimental outcomes, the proposed system is exceptionally efficient regarding time, complexity, generalization, and accuracy.

Management of Public Health: A Study on Novel Strategy for Securing Health Data

The effective management and security of sensitive health data is becoming exponentially important to public health administration. This research examines and evaluates innovative ways to improve health data security in public health management systems to provide accessibility for authorized stakeholders. The study introduces the War Strategy Optimized Elliptic Curve Cryptography (WSO-ECC) encryption method for improving the security of primary healthcare medical records. The WSO-ECC technique, based on a war strategy framework, optimizes encryption and decryption processes by balancing exploration and exploitation phases, enhancing security while maintaining efficiency. It significantly reduces encryption and decryption times compared to traditional methods.The proposed method offers a robust solution for safeguarding medical information in public health management systems, demonstrating superior performance of 24.6 in encryption time, 23 decryption times, execution efficiency, and delay.

Public Health Management: Innovative Facility Arrangement Framework Towards Emergency Relief

The better public health organizations are contingent on the healthier distribution of earthquake relief facilities when there is an immediate seismic event. Choosing the perfect site for these emergency relief centers that would suitably integrate cities is a challenging task. Heuristic calculations are practical methods for resolving these kinds of complex optimization problems. This study's objective is to develop a novel method for allocating parcels to assistance facilities and figuring out where they should be placed: swarm-tuned war strategy optimization (SWSO). The SWSO method syndicates the investigative efficiency of the WSO strategy with Particle Swarm Optimization (PSO) to solve the complex elements of this task by humanizing directional skills. Using GIS, possible sites were designated, and letters were brought utilizing a suggested SWSO method that reduced the quantity of trip. The method exceeded present approaches in terms of meeting cumulatively the achievement of emergency managers and public health managers in providing disaster relief and, eventually, maximizing people's seismic resilience.

GSAAN with War Strategy Optimization Algorithm for Cyber Security in a 5G Wireless Communication Network

In this paper, the graph sample and aggregate-attention network with war strategy optimization algorithm for cyber security in the 5G wireless communication network (CS-5GWCN-GSAAN-WSOA) is proposed in 5G mobile networks to identify cyber threats. Initially, the input data are amassed from the 5G-NIDD dataset. Then the input data are fed to preprocessing, here redundancy elimination and missing value replacement are performed utilizing the contrast limited adaptive histogram equalization filtering (CLAHEF) method. After preprocessing, optimal features are selected by the stochastic gradient boosting based recursive feature elimination process. The selected features are fed to the graph sample and aggregate-attention network (GSAAN) to detect cyber-attacks in 5G wireless communication. Generally, the GSAAN approach does not adopt any optimization techniques for determining optimal parameters and guaranteeing accurate attack detection. Therefore, the war strategy optimization algorithm (WSOA) contemplates to optimize the GSAAN weight parameters. The CS-5GWCN-GSAAN-WSOA method is activated on Python. The CS-5GWCN-GSAAN-WSOA method attains 22.51%, 20.35%, and 35.54% higher accuracy, 19.45%, 27.80%, and 18.93% higher sensitivity analysed with existing models, like cyber security attack identification in 5G wireless systems utilizing machine learning (CS-5GWCN-SVM), enhanced dropping attacks detection in 5G networks depending on deep learning models (CS-5GWCN-KNN), and cyber security issues in 5G enabled attack detection through deep learning (CS-5GWCN-RNN), respectively.

Sparse TFM imaging of different-scale phased array based on the DWSO algorithm

ABSTRACT Using sparse matrix for total focusing method (TFM) imaging can improve computational efficiency in non-destructive testing (NDT). In sparse matrix design, binary particle swarm optimisation (BPSO) and genetic algorithm (GA) often fall into local optimal solution, and the computational efficiency decreases significantly with the expansion of array scale. In this paper, a discrete war strategy optimisation (DWSO) is proposed to realise sparse array ultrasonic imaging. This method uses equidistant scatter mapping to real-number encode the array position and limit the search range. Then, the fitness function is constructed with low side lobe peak and narrow main lobe width to obtain the optimal array element distribution. Experiments on standard test block demonstrate the DWSO algorithm has a narrower main lobe width and lower side lobe peaks than BPSO and GA. The imaging time of the sparse array constructed by this algorithm is reduced by more than 65% compared with the full array. As array scale grows, the running time variation of the proposed method is reduced by 73.2% and 77.3% compared with GA and BPSO, which has good adaptability and provides theoretical support for real-time defect detection.

DIWGAN‐WBSN: A novel health monitoring approach for wireless body sensor networks

SummaryWireless body sensor network (WBSN) is essential for monitoring patients' health problems and offers a low‐cost option for various healthcare applications. In this manuscript, a Novel Health Monitoring Approach for WBSNs (DIWGAN‐WBSN) is proposed, which uses Dual Interactive Wasserstein Generative Adversarial Network (DIWGAN) optimized with War Strategy Optimization Algorithm (WSOA). After sensing the aforementioned attribute information, it is the responsibility of WBSN nodes to transfer the sensed data to the sink node. The Volcano Eruption Algorithm (VEA) is applied to select the optimum cluster heads in WBSN. The results from VEA are fed to the target node; it consists of DIWGAN to classify the health records and to portray the patient's health status. Generally, DIWGAN does not adopt any optimization methods for measuring the ideal parameters and guaranteeing accurate health monitoring and risk assessment. So the proposed WSOA is considered to enhance the DIWGAN. The proposed method is activated in MATLAB; its efficacy is estimated under performance metrics, like precision, specificity, accuracy, and energy utilization. The proposed approach attains 23.9%, 21.34%, and 51.09% higher accuracy; 21.45%, 13.94%, and 20.6% higher precision; 31.32%, 29.61%, and 11.03% higher specificity; and 20.9%, 19.87%, and 24.6% lower energy utilization for HD classification using the Cleveland database than the existing methods like back propagation neural network‐based risk detection in WBSN for health monitoring, random forest algorithm–based health monitoring in WBSN, and ensemble deep learning and feature fusion for health monitoring using WBSN methods, respectively.

Central European strategies in the new Cold War: between balancing and bandwagoning

This article argues that the security of Central Europe – defined as stretching from the Baltic Sea to the Black See–is based first and foremost on the viability and engagement of the Western alliance, understood as NATO and the European Union. The security and defense policies in the region are designed in relation to NATO and bilateral relations with the United States, with limited attempts to achieve some degree of self-sufficiency. Therefore, to understand the security of Central Europe and its individual nations, it is essential to understand their attitudes and fears related to alliances as well as their perceptions of Russia’s intentions. It is posited here that the concept that can best explain the behavior of the nations of the region, allowing also for forecasting future behavior, is the balance of threat theory, as put forward by Stephen Walt. According to the theory, the nations facing threat chose to either balance the threatening power by joining forces with other actors and forming alliances or joining in with their predator, which constitutes a bandwagoning behavior. Historically, especially during the interwar period, these strategies have been employed by Central European nations. Today, the security environment is arguably very different– we have NATO, EU, and American engagement in the region. However, the international environment has deteriorated recently, especially since the Russian full-scale invasion of Ukraine in February 2022. Arguably, Central European states have responded by falling, to some extent, into patterns of behavior familiar from the past, with some clinging to closer relations with their external patron (the United States) and some hedging their bets and opting for relative neutrality or even closer relations with Russia. The article argues that four factors determine states’ behavior vis-à-vis the Russian threat: geography, history, predator intentions, and the perception of allied reliability.

Channel estimation for pilot contamination in massive MIMO-NOMA system

Channel estimation is crucial for massive multiple-input multiple-output (MIMO) systems to scale up multi-user (MU) MIMO, providing great improvement in spectral and energy efficiency. The nature of non-orthogonal cause pilot contamination is experienced only while estimating multi-cell MIMO scheme with the training and it is misplaced while narrowing concentration to multi-cell or one-cell setting, where information of the channel is assumed to be obtainable at no cost. Non-orthogonal multiple access (NOMA) serves numerous users concurrently utilizing channel gain differences. The advancement in massive MIMO-NOMA technology has offered diverse techniques recently for reducing pilot contamination in massive MIMO-NOMA based on pilot allocation. Here, a new approach called War Strategy Chimp Optimization+Deep Neuro-Fuzzy Network (WSChO+DNFN) is designed for the estimation of channels to reduce pilot contamination in a massive MIMO-NOMA system. It takes place in two phases, the transmitter and the receiver phase. The channel estimation is conducted by DNFN that is tuned by devised WSChO. Furthermore, WSChO is an amalgamation of War Strategy Optimization (WSO) and Chimp Optimization Algorithm (ChOA). Additionally, the WSChO+DNFN attained minimal values of BER and normalized MSE of 0.000103 and 0.000074, respectively. The proposed method has achieved a performance gain of 44.39%, 19.26%, 9.17%, 5.22%, 9.92%, and 6.03% compared to the Orthogonal Frequency Division Multiplexing (OFDM), Group Successive Interference Cancellation assisted Semi-Blind Channel Estimation Scheme (GSIC_SBCE), Sector-Based Pilot Assignment Scheme (PAS), Convolutional Neural Network (CNN), User Segregation based Channel Estimation (USCE), Optimal Channel Estimation using Hybrid Machine Learning (OCE_HML), respectively.

Hybrid optimization based deep stacked autoencoder for routing and intrusion detection

This research introduced the optimized Deep Stacked Autoencoder (DSA) for performing Intrusion Detection (ID) in the IoT. Firstly, IoT simulation is carried out and then, the information is routed by using the Chronological War Strategy Optimization (CWSO). Here, the CWSO is newly designed by incorporating the chronological concept with the WSO. After the routing, the ID is completed at the Base station (BS) by executing the following steps. Initially, data is obtained from a database, after that, feature normalization is done using min-max normalization. Meanwhile, Canberra distance is applied to execute the feature selection process. Finally, ID is performed using DSA, which is trained using the Competitive Swarm Henry War Strategy Optimization algorithm (CSHWO). The experimental result confirms that the invented scheme accomplished the superior outcome by the energy, f-score, precision, and recall values of 0.379, 0.913, 0.918 and 0.912, respectively.

Under siege: The role of settler colonialism in targeting healthcare systems.

This article looks at the effects of armed conflict on healthcare systems in Gaza at the intersection of international humanitarian aid, settler colonialism and the ethics of war. Since October 7, 2023, there has been a systemic assault on the health services in Gaza, rooted in colonial expansion. I begin with an overview of human rights and the concept of medical neutrality. This is followed by biopolitics within Gaza and the contradictions in international law regarding the ethics of war. Explained through the lens of historical revisionism, postcolonial theory and biopolitics, I attempt to highlight how healthcare systems are increasingly becoming targets of armed conflict in Gaza as a war strategy.