International Journal of Applied Mathematics, Sciences, and Technology for National Defense
International Journal of Applied Mathematics, Sciences, and Technology for National Defense [e-ISSN: 2985-9352, p_ISSN: 2986-0776] is a journal published by the Foundation of Advanced Education. International Journal of Applied Mathematics, Sciences, and Technology for National Defense is an independent, interdisciplinary and international journal which is mainly concerned with the National Defense, Security, and Resilience issues cover both military and non-military domains. App.Sci.Def. aims to bridge the gap between theoretical and practical development, thereby contributing to the academic and practitioner communities. Authors are invited to submit articles that have not been previously published and are not being considered elsewhere. We are grateful for the support and constributions from researchers and scholars that allow us to continue sharing valuable knowledge.
A nonlinear multispecies fisheries model for Namibian coastal waters: Implications for food security and marine ecosystem resilience
Background: The Benguela Current Large Marine Ecosystem supports major fisheries that are essential to Namibia's food security and economy. The dynamics of Cape hake, horse mackerel, and sardine are strongly influenced by ecological interactions and harvesting, highlighting the need for mathematically rigorous multispecies fisheries models. Aims: This study aims to develop an analytically tractable nonlinear multispecies fisheries model that describes the ecological interactions among Cape hake, horse mackerel, and sardine under harvesting pressure, while providing a rigorous mathematical framework for evaluating ecosystem stability and sustainable fisheries management. Method: The proposed model integrates logistic growth, predator–prey interactions, interspecific competition, and species-specific harvesting mortality. Qualitative analysis is performed using nonlinear dynamical systems theory to establish existence and uniqueness, positivity, boundedness, equilibrium conditions, and local and global stability. Local stability is analyzed using linearization and the Routh–Hurwitz criterion, whereas global stability is established through a Volterra-type Lyapunov function and LaSalle’s Invariance Principle. In addition, a harvesting-rate estimation framework based on fisheries catch, effort, and biomass data is formulated to facilitate future model calibration. Result: The mathematical analysis establishes the well-posedness of the proposed model and derives analytical conditions for biologically feasible coexistence and system stability. Numerical simulations validate the theoretical results by demonstrating convergence toward a stable coexistence equilibrium under biologically realistic parameter values. The simulations further show that increasing harvesting intensity reduces equilibrium biomass and ecosystem resilience, while excessive exploitation of sardine populations indirectly destabilizes higher trophic levels by reducing prey availability for predator species. Conclusion: The proposed model provides a mathematically rigorous framework for analyzing multispecies fisheries dynamics and offers quantitative insights into the ecological consequences of harvesting. The results support ecosystem-based fisheries management by providing a scientific basis for evaluating sustainable harvesting strategies, establishing science-based catch quotas, identifying precautionary harvesting thresholds, and informing evidence-based policies to strengthen the long-term resilience and sustainability of Namibia's marine fisheries.
Copyright (c) 2026 Philipus Nghifikepunye Nangolo, David I. Iiyambo, Adetayo S. Eegunjobi https://creativecommons.org/licenses/by-sa/4.0
Sparsity-based memory scalability analysis of association rule mining algorithms using e-commerce heterogeneous multi-datasets for decision support systems
Background: Many studies have compared the Apriori, FP-Growth, and ECLAT algorithms. Most of the previous literature focuses on runtime evaluation (execution speed) on homogeneous datasets. Research specifically mapping the visualization of memory curves against the level of data sparsity on common e-commerce heterogeneous. Aims: This study aims to analyze the relationship between sparsity and memory usage of three classic Association Rule Mining algorithms such as Apriori, FP-Growth and ECLAT using a heterogeneous e-commerce dataset. Method: A quantitative approach using Association Rule Mining was applied to different raw datasets from 721 to 30000 transactions. Transactions focused on various item types. The Apriori, FP-Growth, and ECLAT algorithms are implemented in Python, using grid search for minimum support and confidence adjustment to generate frequent item sets and association rules for performance comparison. Result: The results show that the relationship between sparsity and memory usage differs significantly in the three classical association rule mining algorithms. The ECLAT algorithm shows low memory consumption because it uses a vertical TID-list, while the Apriori and FP-Growth algorithms show high memory consumption because they are related to their pattern search methods (Candidate Generation and FP-Tree). Conclusion: This study shows that memory usage is not only influenced by sparsity, but also by the characteristics of the dataset. Then the results of this analysis provide an overview of heterogeneous e-commerce datasets when used in the classical association rule mining algorithm.
Copyright (c) 2026 Esti Fadhilah, Diandra Chika Fransisca https://creativecommons.org/licenses/by-sa/4.0
Numerical design and optoelectronic simulation of a germanium-on-silicon PIN photodetector for high-speed optical communication systems
Background: The rising demand for fast optical communication systems in the telecommunications industry, data centers, aerospace, and defense applications has made it imperative to design photodetectors with high responsivity, minimum dark current, and broad bandwidth. Germanium on Silicon (Ge-on-Si) PIN photodetectors are potential contenders in this regard due to their high optical absorption capability at the telecommunication wavelengths as well as CMOS silicon photonics compatibility. Optimizing their optoelectronic performance is an important issue, however. Aims: In this research, we intend to numerically design, optimize, and evaluate a Ge-on-Si PIN photodetector via a coupled optoelectronic simulation technique for high optical absorption, high responsivity, low dark current, and high speed operation in advanced optical communication systems. Method: The designed photodetector was modeled and simulated by using Lumerical FDTD Solutions and Lumerical DEVICE software. A combined simulation method of optical-electrical was utilized to simulate optical field distribution, optical absorption, carrier generation, photocurrent, dark current, and 3dB-bandwidth of the device. Structural parameters were carefully optimized to enhance the performance of the device for telecommunication operation. Result: The optimized Ge-on-Si PIN photodetector resulted in the optical absorption of about 92% at 1550 nm wavelength, peak responsivity of 0.84 A/W, and the minimum value of the dark current was 0.9 μA at reverse bias voltage V. The calculated 3-dB bandwidth was 45 GHz. The increase in dark current with temperature due to the generation of carriers from thermal energy was also observed through simulations. Conclusion: The designed Ge-on-Si PIN detector offers a well-balanced structure, providing excellent responsivity, low dark current, and large bandwidth, which makes it a very promising device for silicon photonics and high-speed optical communication in the future. Besides, there is a great possibility to apply this detector for defense and aerospace applications such as optical communication for military and satellites and other systems requiring high-performance optical connections.
Copyright (c) 2026 Ashenafi Abera Gebre, Kedir Botamo Adem https://creativecommons.org/licenses/by-sa/4.0
Lee-Carter–ARIMA hybrid approach and machine learning for mortality rate forecasting in the United States: Implications for national defense and population risk assessment
Background: The Accuracy of mortality rate forecasting plays an important role in various decision-making processes in the life insurance sector, including determining premium amounts. In addition, it also contributes to assessing the readiness of human resources to support national defense, as well as to conducting risk assessments aimed at maintaining demographic stability. The United States mortality data was selected as the study object due to the availability of comprehensive and high-quality. Aims: This study explores five hybrid approaches that combine stochastic models and machine learning, along with one non-hybrid approach to assess their potential to improve forecasting accuracy. Method: In this study, the Lee-Carter–ARIMA, Lee-Carter–Random Forest, Lee-Carter–ANN, Lee-Carter–ARIMA–Random Forest, Lee-Carter–ARIMA–ANN, and ANN models were evaluated. These models were applied to mortality rate data from nine divisions in the United States (US), stratified by gender, using training data from 1966 to 2005 and test data from 2006 to 2015. The best model is determined based on the smallest Mean Absolute Percentage Error (MAPE) value while also considering the interpretability of the model. Result: The study's results show that, across the number of divisions, the Lee-Carter–ARIMA–Random Forest model produces the smallest MAPE values most often. However, in terms of average MAPE, the Lee-Carter–ARIMA–ANN model performs better, with MAPEs of 9.66% for females and 9.28% for males. Furthermore, neither of these models yields a substantial improvement in predictive accuracy compared with the Lee-Carter–ARIMA model. Conclusion: Considering the relatively small decrease in MAPE and the difficulty of interpreting machine learning models due to their black box nature, the Lee-Carter–ARIMA model demonstrates the best overall performance relative to the other models. Nevertheless, the Lee-Carter–ARIMA–Random Forest and Lee-Carter–ARIMA–ANN models show potential as alternative approaches that merit further investigation and may contribute to national defense planning and support the maintenance of demographic stability.
Copyright (c) 2026 Vita Nuarini, Mahmudi, Nina Fitriyati, Madona Yunita Wijaya, Irma Fauziah https://creativecommons.org/licenses/by-sa/4.0
On the normal product and modular product of product fuzzy graphs with applications to defense communication networks
Background: This study focuses on the development of new operations of product fuzzy graphs, specifically the normal product and modular product operations. The normal product of product fuzzy graphs is used to build infrastructure with maximum path redundancy. Meanwhile, the modular product operation of product fuzzy graphs is implemented to optimize the alignment of operational processes and information security. Aims: This study aims to define the normal product and modular product of product fuzzy graphs, define the properties of the normal product and modular product of product fuzzy graphs, and to apply to defense communication networks. Method: The research stages consist of formulating the definition of the normal product and modular product of product fuzzy graphs along with its properties and applying to defense communication networks. Results: The results of the study show that the normal product and modular product of product fuzzy graph is fuzzy graph, the normal product and modular product of strong product fuzzy graph is strong, consequently, the normal product and modular product of complete product fuzzy graph is strong. Conclusion: The application of the normal product and modular product of product fuzzy graph can produce a more compact defense communication network.
Copyright (c) 2026 Fery Firmansah, Niken Retnowati, Tasari https://creativecommons.org/licenses/by-sa/4.0
Hybrid random forest–catboost ensemble for heart disease prediction on imbalanced datasets: Toward applications in military health systems
ackground: Heart disease is one of the main causes of death worldwide, with cases increasing every year. This situation highlights the urgent need for early detection systems that are not only fast but also accurate and reliable. In recent years, machine learning has emerged as a promising alternative approach for analyzing medical data, particularly for disease classification and risk prediction tasks. Aims: This study aims to develop a heart disease prediction model by integrating Random Forest and CatBoost in a hybrid ensemble framework and evaluating its performance on an imbalanced medical dataset. Method: This study employs a quantitative approach based on supervised learning using the Behavioral Risk Factor Surveillance System (BRFSS) 2021 dataset, which consists of more than 300,000 observations. Data preprocessing includes duplicate removal, BMI categorization, encoding of categorical variables, and exploratory analysis. To address class imbalance, the Borderline-SMOTE technique was applied before splitting the dataset using an 80:20 train-test split. Random Forest and CatBoost models were trained and combined using a soft voting ensemble. Result: The evaluation results indicate that Random Forest achieved the highest accuracy of 0.94, with well-balanced precision and recall across all classes. CatBoost demonstrated relatively stable performance with accuracy around 0.84. The ensemble approach achieved an accuracy of 0.91 with strong metric stability and good sensitivity to positive cases. Conclusion: The results indicate that Random Forest performs best for the dataset used in this study, while the ensemble model provides a balanced compromise between predictive performance and robustness. The analysis also shows that Age Category, General Health, and BMI are the most influential predictors of heart disease risk. This model can support early cardiovascular risk detection in military personnel, contributing to maintaining operational readiness in defense systems. Furthermore, the proposed approach provides a reliable decision-support tool for large-scale medical screening in resource-constrained healthcare environments.
Copyright (c) 2026 Mahyus Ihsan, Zahnur, Iftahul Fadlan, Ikhsan Maulidi https://creativecommons.org/licenses/by-sa/4.0
A human–animal interaction model for monkeypox transmission dynamics: Implications for public health resilience
Background: Monkeypox is a zoonotic infectious disease transmitted between humans and animal reservoirs that poses a growing public health and biosecurity concern. Its transmission dynamics involve both human-to-human interactions and cross-species transmission, which may contribute to sustained outbreaks. Mathematical modeling provides a systematic framework for analyzing these dynamics and supporting strategic intervention planning. Aims: This study aims to develop and analyze a deterministic compartmental model describing monkeypox transmission dynamics between human and rodent populations and to evaluate the impact of key epidemiological parameters on disease spread. Methods: A system of nonlinear differential equations is formulated to represent the transmission process. The human population is classified into seven compartments , while the rodent population is divided into four compartments . The basic reproduction number is derived using the next-generation matrix approach. Equilibrium analysis is conducted to determine the stability of disease-free and endemic states. Sensitivity analysis and numerical simulations are performed to assess the influence of model parameters. Results: The analysis shows that the disease-free equilibrium is locally stable when , indicating that the infection will die out, whereas endemic transmission persists when . Sensitivity analysis reveals that transmission parameters, particularly and , have the strongest influence on . Numerical simulations demonstrate that reducing transmission rates and increasing intervention-related parameters significantly decrease the number of infected individuals and the epidemic peak. Control measures targeting rodent populations also contribute to reducing sustained transmission. Conclusion: The proposed model emphasizes the critical role of integrating human health interventions with zoonotic reservoir control in mitigating monkeypox outbreaks. The findings provide a mathematical basis for supporting public health policy and strengthening national defense preparedness against emerging infectious diseases. Future research may incorporate optimal control strategies, spatial heterogeneity, and real outbreak data calibration to enhance model applicability and predictive performance.
Copyright (c) 2026 Vina Lusiana, Suhaila Saidat, Yohana Herlina Putri https://creativecommons.org/licenses/by-sa/4.0
Average-based fuzzy time series for forecasting blood bag availability: Implications for health resilience and emergency preparedness in Banda Aceh, Indonesia
Background: Blood availability remains a major challenge in healthcare systems, particularly in developing countries where the demand for blood often exceeds the available supply. Accurate forecasting of blood collection is therefore important to support effective blood inventory management at blood transfusion units. Aims: This study aims to apply the Average-Based Fuzzy Time Series method to forecast the number of collected blood bags at the Blood Transfusion Unit (UTD) of the Indonesian Red Cross in Banda Aceh, both in total and by blood type. Method: Monthly blood collection data from January 2016 to September 2020 were analyzed using the Average-Based Fuzzy Time Series model. The forecasting procedure involved constructing fuzzy intervals using the average-based approach, forming fuzzy logical relationships, and performing defuzzification. Model performance was evaluated using Mean Squared Error (MSE) and Average Forecasting Error Rate (AFER). Result: The second-order model provided the best forecasting performance with an AFER value of 13.67% and an accuracy of approximately 86.33%, producing a prediction of 2054 blood bags for October 2020. Forecasting by blood type yielded predictions of 529 (A), 702 (O), 738 (B), and 154 (AB) blood bags. Conclusion: The results indicate that the Average-Based Fuzzy Time Series method is effective for forecasting blood bag availability and can support planning and management of blood supply at blood transfusion units. Furthermore, the proposed approach has potential applications in defense and emergency contexts by supporting medical logistics planning, improving preparedness, and enhancing the resilience of blood supply systems during military operations and disaster response.
Copyright (c) 2026 Ikhsan Maulidi, Nurafni Fazriani, Radhiah Radhiah, Vina Apriliani https://creativecommons.org/licenses/by-sa/4.0
Impact of SGLT2 inhibitors on heart failure risk and kidney function in type 2 diabetic mellitus patients: A literature review
Background: Type 2 diabetes mellitus is known to be closely associated with an increased risk of heart failure and chronic kidney disease, both of which are major causes of illness and death in diabetic patients. In recent years, sodium-glucose cotransporter-2 inhibitors (SGLT2i), which were initially developed as blood glucose-lowering drugs, have been shown to provide additional benefits beyond glycemic control, particularly in protecting the heart and kidneys. Aims: This literature review aims to comprehensively evaluate the impact of SGLT2 inhibitor use on the risk of heart failure and renal function outcomes in patients with type 2 diabetes mellitus (T2DM). Method: A literature search was conducted through the PubMed, Scopus, and ScienceDirect databases for publications from 2015 to 2025, including randomized controlled trials (RCTs), meta-analyses, and systematic reviews using the keywords "SGLT2 inhibitors," "heart failure," "renal outcomes," and "type 2 diabetes." Results: from various large clinical trials and meta-analyses show that SGLT2 inhibitors consistently reduce the risk of hospitalization due to heart failure and cardiovascular death, with a hazard ratio generally below 0.80. In addition, this therapy also slows the decline in glomerular filtration rate (eGFR), thereby reducing deaths due to renal causes. Conclusion: SGLT-2i has been shown to provide strong cardio-renal protection in patients with T2DM, especially in individuals at high risk of heart failure or chronic kidney disease, in accordance with current clinical guidelines.
Copyright (c) 2026 Chelsy Vanya Br. Meliala, Syahrul Tuba, Adi Priyono, Syed Azhar Syed Sulaiman, Budi Sumaryono, Endah Permata Sari https://creativecommons.org/licenses/by-sa/4.0
Model-prototype based flight test: Bringing flight control engineering to life for a reusable rocket with flightgear
ackground: Reusable rocket technology has gained significant attention due to its potential to reduce launch costs, development time, and environmental impact. However, conventional development and flight testing of reusable rockets require substantial financial resources and carry high risks of material loss, particularly during early-stage experimentation and in resource-constrained research or defense education environments. Aims: This study proposes a low-cost and accessible approach for reusable rocket development through a Model–Prototype Based Flight Test (MPBFT) method. The aim is to integrate mathematical flight control modeling, prototype-level implementation, and real-time three-dimensional visualization into a single co-simulation framework that enables virtual flight testing without physical launch, specifically for reusable rocket descent and landing maneuvers. Method: The MPBFT framework integrates a dynamic model of the reusable rocket developed in MATLAB/Simulink based on rigid-body flight dynamics, with explicit mathematical formulations including governing differential equations, state-space representation, and closed-loop transfer function. A derivative-based proportional–integral–derivative (PID) controller was designed to regulate the rocket's pitch attitude. Real-time 3D visualization was achieved through co-simulation with FlightGear via UDP communication at 50 Hz. Controller gains were tuned iteratively (Kp=12.5, Ki=0.08, Kd=5.2), and robustness was assessed under parameter perturbations (+20% moment of inertia, +15% damping, step disturbance). Results: Simulation results demonstrated stable closed-loop attitude control with the following quantitative metrics: 8.2% maximum overshoot, 3.87 s settling time (±2%), 0.014° steady-state error, damping ratio of 0.62, integral absolute error of 8.42°·s, and control effort (RMS) of 3.21 N·m. Robustness analysis confirmed that all perturbed configurations remained stable and within acceptable thresholds (overshoot <15%, settling time <6 s, steady-state error <0.1°). The co-simulation successfully provided synchronized real-time 3D visualization of rocket pitch motion during descent. Conclusion: The MPBFT framework is an effective and economical alternative for reusable rocket flight control testing, particularly suitable for research, defense aerospace education, and resource-constrained environments. Future work will extend the framework to multi-axis 6-degree-of-freedom dynamics, hardware-in-the-loop simulation, actuator and sensor modeling, and physical subscale flight validation to further enhance fidelity and applicability for advanced reusable rocket control system development.
Model–Prototype Based Flight Test (MPBFT)
Copyright (c) 2026 Aditya Tri Oktaviana, Mochammad Ibnu Alwan, Adhi Kusumadjati, Mikael Syväjärvi https://creativecommons.org/licenses/by-sa/4.0
Pandemic shock and regional economic resilience in Indonesia: A linear mixed model
Background: The COVID-19 pandemic has emerged as a major non-traditional security threat, generating substantial economic disruptions and destabilizing labor markets worldwide. In Indonesia, the surge in open unemployment during the pandemic has raised concerns regarding regional economic resilience and its broader implications for national economic security. As unemployment can exacerbate social vulnerability and weaken adaptive capacity, understanding regional labor market dynamics is critical for strengthening national resilience. Aims: This study aims to examine the impact of pandemic-induced shocks on provincial open unemployment rates in Indonesia and assess regional heterogeneity in economic resilience. Method: The study employs provincial level panel data and applies a Linear Mixed Model (LMM) to capture both temporal effects and regional heterogeneity. The model incorporates pandemic indicators alongside structural economic variables, including informal employment and commodity distribution dynamics, to evaluate their roles as vulnerability factors or resilience buffers during the crisis period. Results: The findings show that the COVID-19 pandemic has significantly increased the open unemployment rate in all provinces in Indonesia. This study also shows that the percentage of trade and transportation margins (MTT) for shallots influences the open unemployment rate with a p-value of 0.017 and a variable coefficient of 0.021. In addition, it was also found that the proportion of informal workers in the total national workforce also has a significant effect on changes in the open unemployment rate (p-value: 0.001, coefficient: -0.077). Another finding from this study is that the level of high school education does not have a significant effect on the open unemployment rate. Conclusion: This study on pandemic induced unemployment shocks contributes by integrating regional heterogeneity into economic resilience analysis using a multilevel modeling framework. Strengthening regional economic resilience through labor market flexibility, supply chain stability, and adaptive policy coordination is essential to safeguarding socioeconomic stability and reinforcing Indonesia’s national resilience against large-scale non-traditional threats.
Copyright (c) 2026 Novalia, Bima Ramadhan, Kenny Candra Pradana, Ahmad Naqiyuddin Bakar https://creativecommons.org/licenses/by-sa/4.0
Towards a standard framework for cybersecurity readiness for Nigerian universities
niversities rely substantially on digital infrastructure and host vast amounts of data that are of interest to malicious actors. Thus, they are vulnerable to cyber-attacks, making their protection a significant issue. While technology continues to improve, educational system infrastructures and system security lag behind. However, to ensure the continuous availability of university digital assets, the infrastructure must be prepared to withstand cyber-attacks. The results of research indicate that several models exist for evaluating the readiness of universities for cybersecurity. These models have been established for developed nations with more sophisticated and mature cyber networks, and may not be directly applicable to developing economies like Nigeria. Therefore, the Design Science Research strategy was adapted to develop a framework for assessing cybersecurity readiness in Nigerian universities. Taking into account pre-event, event management, and post-event factors, the concept of Cybersecurity Readiness Tiers (CRT) was developed to compare the cybersecurity readiness of universities. The Cybersecurity Readiness Framework was evaluated using data collected from candidate universities sampled for this study; Principal Component Analysis was carried out on the dataset to reduce the dimension. The Cybersecurity Readiness Index (CRI) scores, along with their respective distributions, indicate that 14 (65%) of the twenty universities fall in T1, while 6 (35%) fall in T2. The grouping was based on their overall cybersecurity readiness, as computed using the mathematical equations of the framework. Thus, it implies universities in T2 have a high level of readiness to resist cybersecurity incidents, while universities in T1 are at a very low level of readiness due to the weak and inconsistent cybersecurity controls implemented. This study suggests that these universities have gaps in event management and post-event capabilities that require attention. Therefore, a holistic web-based Cybersecurity Assessment tool that will incorporate all security and privacy regulations and best practices can be considered for future studies.
Copyright (c) 2025 Olusegun Hamed Olugbile, J. A. Ojeniyi, Tochukwu Kene Anyachebelu https://creativecommons.org/licenses/by-nc-sa/4.0
Shielding innovation for health security: A PHITS-based optimization of Portland material for proton therapy
Proton therapy is an advanced treatment method for cancer that uses protons to irradiate tumors with high precision. However, the high energy of protons requires effective shielding to protect the surrounding environment and personnel from radiation exposure. In this research, the radiation shielding performance of Portland material was evaluated using the PHITS version 3.351 simulation software. The study focuses on assessing the attenuation of radiation within the cyclotron room under various operational conditions. The effectiveness of radiation shielding made from Portland material in a 230 MeV, 300 NA cyclotron room for a proton therapy facility was investigated. The results from PHITS simulations provide insights into the potential of Portland material in reducing radiation levels in proton therapy rooms, contributing to the safety and efficiency of such facilities. This analysis is essential for optimizing shielding design and ensuring compliance with safety regulations in proton therapy facilities.
Copyright (c) 2025 Damar Adhiwidya Suyanto, Aditya Tri Oktaviana, Mikael Syväjärvi, Yohannes Sardjono, Gede Sustresna Wijaya, Isman Mulyadi Triatmoko https://creativecommons.org/licenses/by-nc-sa/4.0
Cybersecurity and AI as enablers of economic resilience: A framework for sustainable growth in developing countries
Digital transformation offers unprecedented opportunities for sustainable economic growth in developing countries. However, these benefits are accompanied by increased cybersecurity risks and challenges in integrating emerging technologies like artificial intelligence (AI). This paper proposes a conceptual framework that positions cybersecurity and AI as dual enablers of economic resilience aligned with Sustainable Development Goal 8 (SDG 8). The framework comprises three interconnected pillars: cybersecurity infrastructure, AI-driven economic transformation, and AI-enhanced cybersecurity mechanisms. A case study of Sudan illustrates how tailored interventions can foster secure, inclusive, and resilient digital economies in low- and middle-income contexts. The study highlights the importance of ethical governance, multi-stakeholder collaboration, and capacity building to maximize the benefits of AI while mitigating risks. Future research directions include empirical validation and policy experimentation to refine the framework and accelerate digital-led economic resilience in the Global South.
Copyright (c) 2025 Ali Siraj Ahmed, Rahmat Budiarto https://creativecommons.org/licenses/by-nc-sa/4.0
Single nucleotide polymorphism analysis of the peroxisome proliferator-activated receptor gamma gene in type 2 diabetes mellitus patients at Ministry of Defense Hospital, RSPPN Soedirman Jakarta
Type 2 diabetes mellitus is a metabolic disorder influenced by genetic and environmental factors. The Peroxisome Proliferator-Activated Receptor Gamma gene is known to play a role in T2DM pathogenesis, but its polymorphism profile in Indonesian patients remains poorly characterized. This study aimed to analyze single nucleotide polymorphisms in the PPAR-γ gene of T2DM patients at Rumah Sakit Pusat Pertahanan Negara (RSPPN) Soedirman Jakarta using adaptive sampling with nanopore sequencing. Eighteen DNA samples that passed quality control were analyzed using UGENE v52.1. A total of 26 SNPs were identified in 8 subjects, including C161T, which has a reported protective effect in T2DM. The clinical significance of the remaining SNPs remains unclear. This study provides initial insights into genetic variation in T2DM, which may contribute to personalized medical strategies for military personnel and support the development of a more resilient national defense health system by improving disease prevention and management in high-risk populations.
Copyright (c) 2025 Luluk Sakdiyyah, Apip Hadi, Yustinus Maladan, Syahrul Tuba https://creativecommons.org/licenses/by-nc-sa/4.0
Vision stick: An intelligent assistive device to support soldier mobility in visually impaired conditions
The Vision Stick is an innovative assistive device designed to enhance the mobility and independence of visually impaired individuals by integrating AI-powered vision models and ultrasonic sensors into a traditional white cane. Visually impaired individuals face significant challenges in navigating their surroundings, often relying on conventional mobility aids that lack advanced environmental awareness. To address this, the Vision Stick operates in two modes: an online AI vision mode and an offline ultrasonic mode. In online mode, a camera and AI algorithms analyze the surroundings, providing real-time voice descriptions of obstacles, landmarks, and hazards, improving navigation and safety. In offline mode, an ultrasonic sensor detects nearby objects and provides audio feedback, ensuring uninterrupted guidance without internet access. The device retains the familiar structure of a white cane while incorporating lightweight embedded components for ease of use. By combining AI-driven environmental awareness with ultrasonic obstacle detection, the Vision Stick enhances safety, confidence, and autonomy for visually impaired individuals in diverse environments.
Copyright (c) 2025 Muthumanickam K, Logeshwaran V, Sanjay B, Simon Marshal I https://creativecommons.org/licenses/by-nc-sa/4.0
Cadmium telluride (CdTe) thin-film photovoltaics: A sustainable energy solution to support national energy resilience
Cadmium Telluride (CdTe) is one of the most promising materials in thin-film solar cell technology; however, its application faces challenges related to efficiency limitations and environmental risks. This study aims to evaluate the performance characteristics, environmental impact, and sustainability potential of CdTe in Solar Power Plant, known in Indonesia as Pembangkit Listrik Tenaga Surya (PLTS) applications. A systematic literature review was conducted, analyzing more than 40 scientific articles and industrial reports published over the last 15 years, covering efficiency, material availability, recycling strategies, and lifecycle assessments. The results indicate that commercial CdTe modules achieve efficiencies between 7–10%, while laboratory devices exceed 22%. CdTe demonstrates superior performance under high temperatures and low-light conditions, but concerns remain regarding cadmium toxicity and the limited availability of tellurium. The study concludes that CdTe holds significant potential as a cost-effective and efficient solar technology, provided that robust recycling systems and responsible material sourcing practices are implemented.
Copyright (c) 2025 Annisa Mutiara Putri Abdul, Adhi Kusumadjati, Gustav Irgi Aldhiantoro, Dinda Rahma Dewi, Moh. Hisni Alfan Baarik, Cornelia Victoria Anghel https://creativecommons.org/licenses/by-nc-sa/4.0
Evaluating multiple time series models for consumer price index forecasting to support national defense decision-making
Price stability, as reflected in the Consumer Price Index (CPI), plays a crucial role in supporting economic resilience and national defense readiness. This study evaluates multiple time series forecasting models, including Error-Trend-Seasonal (ETS), Holt, Holt–Winter, SARIMA, SARIMAX with exogenous variables, and hybrid approaches combining Holt/Holt–Winter with SARIMA, to identify the most accurate method for predicting Indonesia’s CPI. Monthly data from 2017–2022 were analyzed using a training–testing split, and forecasting accuracy was assessed based on RMSE. The results show that the Holt–Winter model outperforms all other approaches, achieving the lowest RMSE value of 1.9159. Residual diagnostics confirm that the Holt–Winter model effectively captures trend and seasonal patterns, with errors behaving close to white noise. These findings highlight the superiority of Holt–Winter in providing reliable CPI forecasts, offering significant implications for economic policy formulation and strategic planning in the context of national resilience.
Copyright (c) 2025 Muhammad Yusuf Al Habsy, Ro'fah Nur Rachmawati, Jumadil Saputra https://creativecommons.org/licenses/by-sa/4.0
Power and performance analysis of UUV motor systems with torpedo capabilities using to support the indonesian navy's maritime operations
Unmanned Underwater Vehicles (UUVs) play a crucial role in modern naval operations, particularly in Intelligence, Surveillance, and Reconnaissance (ISR) and Anti-Submarine Warfare (ASW). Their stealth and long-range capabilities provide strategic advantages, yet extended missions pose significant challenges due to power limitations. This study proposes a novel approach to predicting and managing UUV battery capacity for missions lasting up to 30 days. Utilizing OpenModelica, we simulate various operational scenarios by modeling the Direct Current Permanent Magnet (DCPM) motor and its interaction with propulsion systems under different mission profiles including patrol, standby, and attack phases to estimate power consumption and optimize endurance. The results demonstrate key strategies for enhancing UUV autonomy and operational flexibility through advanced power management. These findings contribute to the development of more efficient UUV systems capable of prolonged underwater missions with minimal recharging.
Copyright (c) 2025 Gustav Irgi Aldhiantoro, Kuntjoro Pinardi, Adhi Kusumadjati, Annisa Mutiara Putri Abdul, Dinda Rahma Dewi, Moh. Hisni Alfan Baarik, Endah Kinarya Palupi https://creativecommons.org/licenses/by-nc-sa/4.0
Zero trust framework for protecting federal networks and cloud services
There has been a rapid uptake of cloud technologies in public sectors due to increased efficiency across operations while increasing the complexity of cyber threats. This paper analyses the original Zero Trust Architecture (ZTA) concept as a security concept applicable to federal networks and cloud services protection. It mainly involves linking ZTA principles with FedRAMP regulations and insists on constant validation, minimisation of rights, and breach presumption. The study outlines guidelines for ZTA implementations for compliance and readiness in the cloud environments.
Copyright (c) 2025 Sudheer Kotilingala https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing digital resilience through GEN-AI driven video content moderation and copyright protection
In the digital era, ensuring digital resilience in video content moderation and copyright enforcement is crucial due to the vast volume of uploads. Traditional manual review methods are inefficient, necessitating AI-driven automation. This paper presents an AI-powered system integrating computer vision, deep learning, and NLP for real-time video analysis. The system detects inappropriate content using CLIP for visual moderation and Whisper for speech analysis, ensuring high-precision filtering with human oversight. A copyright protection mechanism employs watermarking and fingerprinting to generate unique digital signatures, preventing unauthorized content usage. A React-based UI with Vite framework provides an interactive reviewer experience. By combining automation with human intervention, this approach enhances moderation accuracy, copyright enforcement, and compliance with global content standards, fostering a more secure and resilient digital ecosystem. This system enhances digital resilience and security, making it applicable for defense and national security in protecting sensitive content.
Copyright (c) 2025 Muthumanickam K, Kathirvel T, Harish Vishnu K, Mukesh Rajan N https://creativecommons.org/licenses/by-nc-sa/4.0
Bibliometrix research of noise removal techniques in digital images for defense
In modern defense applications, the accuracy and clarity of digital images are crucial, especially for tasks like surveillance, reconnaissance, and intelligence gathering. However, noise introduced during image acquisition or transmission significantly degrades image quality. This paper presents a comprehensive review of various noise removal techniques employed in digital image processing for defense systems. The review focuses on both linear and non-linear methods, including matrix decomposition, hybrid deep learning, Generative Adversarial Networks (GANs), and trimming filters. Emphasis is placed on the effectiveness of each technique in enhancing image quality while preserving critical details. The use of linear and non-linear methods such as deep learning-based approaches is shown to outperform traditional linear filters in handling complex noise patterns, particularly in scenarios requiring precise object detection and image restoration. The paper highlights a comprehensive overview of the researched literature and shows the latest trends and developments in the field. Finally, recommendations for future research and the development of more robust noise reduction methods are provided, aiming to improve operational effectiveness in defense applications.
Copyright (c) 2025 Fulkan Kafilah Al Husein, Muhammad Yusuf Al Habsy, Damaris Nugrahita Christi, Agnes Emanuela Hutagaol, Ahmad Kadri bin Junoh https://creativecommons.org/licenses/by-nc-sa/4.0
Rice distribution optimization using initial basic feasible and modified solution to increase food security in West Java
Rice is one of the main commodities needed by more than 95% of developing countries, including Indonesia. High population growth that is not accompanied by sufficient rice production will lead to many problems such as malnutrition, famine, and economic crisis, so the availability of rice in Indonesia is the main focus in food security. Farmer welfare is one of the factors in increasing rice production. But in reality, farmers are often constrained by the high cost of distributing rice. As one of the largest rice producing regions in Indonesia, optimisation of rice distribution costs in West Java needs to be done. the process of optimising rice distribution costs can be done using the transportation method. The transportation method is carried out in 2 stages, namely obtaining the initial solution and the optimal solution. Initial Basic Feasible Solution (IBFS) as one of the approaches in obtaining the initial solution works by considering the average value of new unit costs and columns. As for obtaining the optimal solution, MODI is used, which calculates the opportunity cost of cells that are not given a distribution allocation to assess the optimality of the table. Optimisation of rice distribution using IBFS and MODI resulted in an initial solution of Rp 27,259,921600 and an optimal solution of Rp 27,259,921600 with the Bandung area as the area that supplies the most rice in West Java.
Copyright (c) 2025 Fachri Al Aziz Putrandi, Divacha Vicha Mollie, Atmadi, Julio Rivaldo Sembiring, Abdul Fakhar https://creativecommons.org/licenses/by-nc-sa/4.0
Preliminary study of dynamic modeling based on quaternion analysis for tricopter drone
Recently drone was used in many aspects, especially on military operation. Drone type three rotor, namely tricopter, was used for surveillance with stability motion needed too well operating. This study examines the dynamical aspects of a tricopter. A quaternion-based transformation method is developed to transition between reference coordinate systems. It forms a mathematical foundation for modeling tricopter dynamics. The quaternion formulation used as a mathematical tool to obtain equation of motion in translational and rotational. The result show that the derived equations provide a quaternion-based framework for modeling the tricopter's motion, enabling singularity-free transformations and accurate translational and rotational dynamics for real-time flight control and stability. These models form the basis for advanced navigation systems, offering precise trajectory planning and attitude control. Further research should focus on advanced control strategies, aerodynamic effects, and experimental validation to optimize tricopter’s performance.
Copyright (c) 2024 Agya Sewara Alam, Adhi Kusumadjati, Aditya Tri Oktaviana https://creativecommons.org/licenses/by-nc-sa/4.0
The conceptual model to improve failure risk management water distribution system using ordinary differential equation model to support water resilience in military residential facilities
Water resilience is still big problem in Indonesia. In border and underdeveloped areas in Indonesia, the use of water sources is still considered not resilience. Especially in military context, where water needs are bigger and also more fundamental, this water resilience problem demanding a comprehensive solution. To address this issue, this research proposes the use of ordinary differential equations as a mathematical tool to model the dynamics of system damage over time, consumption, maintenance scheme, water crisis scheme, and other factors affecting water distribution resilience in military facilities. This journal presents a conceptual model of failure risk management water distribution system using a differential equation model approach to support water resilience. Specifically, the derivation of failure equation in the “reliability and maintenance system technical” textbook will be the basic reference for generating mathematical model. It is used because our model will be focused in improving failure risk management. By using the model, there are a lot of problem will be tackled such as Identify and manage failure risks in the water supply system, design an efficient water distribution maintenance scheme, and predict how strong the system to face water crisis. But before the model applied, the prediction of model will be tested by applying it in form of computer program. The case study of this research will be focused in testing the model in form of computer program with some simplicity and assumption. Through this approach, it is expected to find solutions that improve water usage efficiency, support the well-being of military personnel, and contribute to national water resilience to bolster national defense especially in case of water crisis happened. This research holds significant benefits for scientific advancement by providing a conceptual model that can serve as a reference for future research. It has the potential to make a tangible contribution but also still need so much development especially for application in real data, adding others variables that can be included for next research, conducting the interpretation, and better defining the measurement boundaries.
Copyright (c) 2024 Fulkan Kafilah Al Husein, Muhamad Syazali, Suhaila Saidat, Nursyiva Irsalinda, Fajri Farid https://creativecommons.org/licenses/by-nc-sa/4.0
Insights into latent tuberculosis biomarkers from differential gene expression analysis in CD8 memory cells using secondary data Insilico approach
Tuberculosis (TB) remains a global health challenge, significantly impacting infectious disease mortality and morbidity. In the quest for effective diagnostic tools and precise treatment strategies, differential gene expression (DEG)-based biomarkers offer a promising avenue. These biomarkers provide specific insights into disease states and treatment responses by deciphering gene alterations within body cells. In this study, we aimed to identify immunological signatures associated with latent Mycobacterium tuberculosis infection in memory T cells. Leveraging transcriptomic analysis, we examined memory CD8 T cells from individuals with latent TB (NCBI-GEO GSM2643205) and healthy controls (NCBI-GEO GSM2643198). Our findings highlight candidate biomarker genes—LDB1, ZNF121, and STAT6—whose differential expression could significantly enhance our understanding of CD8 T cell genetic regulation during latent TB infection. These results hold promise for the development of more accurate biomarkers for diagnosing latent tuberculosis.
Copyright (c) 2024 Atmadi, Tiara Rahayu, Indah Kusuma Wardani, Reihana Marsha Cahyani Elsadi, Azizah Eka Milasari, M. Ridwan Amarullah Witadi https://creativecommons.org/licenses/by-nc-sa/4.0
Anti-kyphosis smart bag detection of overweight bag load and improper spinal posture in army members when using work bags
Heavy field equipment owned by soldiers such as backpacks unknowingly makes soldiers move with the wrong body posture when in the field. Carrying a bag with a heavy load and the wrong posture (slouching), can cause abnormalities in the spine, such as slouching (kyphosis). Therefore, a posture therapy is needed with the method of habituation using a work bag with a load not exceeding 15 percent of the user's body weight and using it with the correct posture. This therapy / habituation is of course carried out outside of field training hours that do not ignore the use of heavy loads, for example in the office or anywhere else when carrying a bag. Seeing this, researchers are trying to create and develop an anti-kyphosis smart bag that can prevent and treat spinal deformities. After making the system, this smart bag was then tested quantitatively with the aim of seeing the qualification of the system's ability to detect excess bag weight, if the weight of the bag exceeds 15 percent of the user's body weight and the ability of the system to detect improper posture seen from the sensitivity level of the load cell sensor based on its placement position in the back area. The test was conducted on three respondents who were selected based on their height, namely 170 cm, 175 cm, and 182cm. The system calibration results show that the system works accurately, with a measuring error of 0% and effective sensor placement between the thoracic kyphosis and lumbar lordosis areas of the back, with the ability to detect a bend angle of at least 24.7°. Seeing the effectiveness of this anti-kyphosis smart bag, it is hoped that it can be a smart solution for TNI members in maintaining spinal posture to avoid kyphosis due to the habit of carrying excessive loads with the wrong body posture.
Copyright (c) 2024 Agung Hirawan, Muhammad Rey Renoult, Diyan Parwatiningtyas, Iqbal Ahmad Dahlan, Adhi Kusumadjati, Jofim Yordaniel Napitupulu https://creativecommons.org/licenses/by-nc-sa/4.0
Unmanned aerial vehicle classification and detection system based on deep learning, internet of military things, and PID control system
Indonesia is an archipelagic country situated between two continents and two oceans. With numerous islands, it is rich in natural resources but faces various military and non-military threats. One significant threat to maritime nations like Indonesia is from the air, which includes direct attacks from manned and unmanned aircraft and using aerial vehicles for intelligence and surveillance. The primary weapon system is crucial for national defense against such threats. Therefore, developing defense equipment in Indonesia must align with technological advancements to ensure quick and efficient operation. This research focuses on creating a classification and reconnaissance system for flying vehicles to enhance air defense capabilities. In the surveillance system, two servos are used for yaw and pitch axes, controlled by a Proportional, Integrative, and Derivative (PID) system. This PID control significantly improves servo movement both dynamically and statically. The system sends notifications via Telegram for monitoring, with an average FPS of 9.6. Flask is used for the website interface, averaging 6.8 FPS, and MIT App Inventor is used for the smartphone interface, averaging 7.6 FPS. This flying vehicle classification and reconnaissance system enhances Indonesia's air defense, utilizing YOLOv8 for classification, PID control for servo movements, and integrated notifications and interfaces for both web and smartphones.
Copyright (c) 2024 Azka Versantariqh Lesmana, Iqbal Ahmad Dahlan, Hendrana Tjahjadi, Setya Widyawan Prakosa, Muhammad Azka Firdaus https://creativecommons.org/licenses/by-nc-sa/4.0
There are two sources of vitamin D, namely from food and sun exposure. The content of vitamin D is given beneficial in bone and mineral metabolism. Vitamin D helps absorption of calcium in the intestines, thereby accelerating bone mineralization. Bone metabolism is a complex process that occurs throughout life. Very high doses of vitamin D can stimulate an increased rate of bone loss. Vitamin D deficiency causes bone demineralization. Which can cause a decrease in calcium absorption. The functions of these metabolites vary widely in vital metabolic pathways. This review aims to determine the specific function of vitamin D in bone. The search for the data base was carried out by the analytical method. Vitamin D which is absorbed from food or comes from the skin turns into an active form in the form of 1,25- (OH) 2-D3 which will enter the bloodstream, playing an important role in bone and extra-skeletal health.
Copyright (c) 2024 Dennisa Putri Ramadhani, Hammanda AD, Salsabila Azzahra, Akbar Rizkyadsyah, Anis Ulayya, Miftahul Huda Fendiyanto https://creativecommons.org/licenses/by-nc-sa/4.0
The effect of refugia on prey-predator model with parasite infection
This study examines the impact of prey-hiding behavior on predator-prey dynamics within theoretical and mathematical ecology. We conduct both analytical and numerical analyses to explore the effects of protection on these dynamics. Our model divides the prey population into two subclasses: susceptible prey (S(t)) and infected prey (I(t)), changing over time. Utilizing a Holling's model type-1, we find that the trivial equilibrium of the model is unstable, while both the disease-free and disease equilibrium points are stable. These simulations provide insights into the characteristics of animals in natural environments.
Copyright (c) 2024 Fitri Nur Azizah, Annisa Risda Alivia, Habban Riawan Syahrul Haz, Ubaidullah Yashkun https://creativecommons.org/licenses/by-nc-sa/4.0