Operational Capabilities Research Articles

Analysis and evaluation of characteristics of the battery systems in electric cars

The paper focuses on analyzing and evaluating the characteristics of the electric battery system to optimize performance, extend lifespan, and ensure safety for electric vehicles. To achieve this goal, a simulation model based on the specifications of the BYD ATTO 3 was developed to study and assess the vehicle’s operational capability. The simulation results show the impact of temperature, environmental conditions, and operating modes on the overall system performance. The study also identifies limitations in the vehicle’s operational capability, providing assessments and improvement solutions. This paper contributes to enhancing the reliability and efficiency of the battery system while optimizing energy management in electric vehicles. The findings help guide future research and the development of battery technology applications in electric vehicles.

Achieving competitive advantage through business innovation and digitalization by South African small and medium-sized enterprises: A systematic literature review

Innovation and small and medium enterprises (SMEs) are associated with economic development and industrial revitalization. Innovation and digitalization have long been established as the primary contributors to improved business performance in SMEs, and digitalization is becoming increasingly regarded as enabling the transformation of business model and operational capabilities. However, South African SMEs face many challenges in adopting digitization and fostering innovation. This paper critically examines how South African SMEs can systematically leverage business innovation and digitalization to acquire sustainable competitive advantage. The study used a systematic literature review approach, focusing on literature published from 2019 through 2024. Through database analysis of academic literature and industry trends, the study aimed to discover the best practices, barriers, and the role of external partnerships in fostering innovation and digital transformation. The findings indicate that SMEs that emphasize innovation and digital technology improve their productivity and competitiveness. However, obstacles including a lack of funding, expertise, and restricted access to technology prevent many SMEs from reaching their full potential. To overcome these obstacles and promote a culture of ongoing innovation, cooperation with outside partners and emphasis on consumer interactions is crucial. This article contributes to innovation in the digital transformation landscape of SMEs by offering approaches and frameworks for South African SMEs to facilitate the adoption of innovation and digitization for sustainable competitive advantage. This addresses a core gap by guiding how to overcome barriers such as constraints of resources and focus on collaborative networks to drive digital and innovative growth.

Study of payload calculation and motion prediction for unpowered diving and floating of deep-sea manned submersible

The preferred method of diving and floating for deep-sea manned submersibles is unpowered. This method has the potential to significantly reduce the energy expenditure of the submersible, extend the operational time of the submersible underwater, and is a fundamental aspect of the submersible’s overall underwater operational capability. Environmental parameters, including seawater density and pressure, as well as the displacement volume of the submersible, fluctuate with depth. This results in a discrepancy between the calculated weights of the submersible diving ballast and floating ballast and the actual requirements. The weight of the diving ballast and floating ballast has a certain effect on the speed of the manned submersible during the processes of diving and floating. Therefore, conducting research into the matching calculation and motion prediction methods for unpowered diving ballast and floating ballast is highly practical for engineering. This paper presents a mathematical model of unpowered diving and floating motion for a manned submersible. It analyzes the forces acting on the submersible and establishes a method for matching ballast and predicting motion during unpowered diving and floating motions in deep water. The feasibility and effectiveness of the method described in this paper are verified and its suitability for engineering applications is demonstrated by comparing and analyzing the sea trial data with those of the “Jiao Long” manned submersible and the “Shen Hai Yong Shi” manned submersible.

The role of UPI (Unified Payments Interface) in Financial Interoperability in India

Unified Payments Interface (UPI) is a communication protocol interface that connects multiple banks accounts to one mobile payment gateway. Interoperability of this interface –a feature which was enabled recently has made these connection even more flexible, as this interface enabled with interoperability feature can connect to any bank to a mobile payment gateway making payment seamless through a single unified QR code which is unique to each retail merchant. Impacting the payment time and access to payment gateways. The National Payments Corporation of India (NPCI) introduced UPI as a payment interface for the Indian market during 2016 to enable instant digital fund transfers among customers banking systems and secured payment. A total of 350 million active UPI users have been accounted who have generated payments for a value of 245 billion dollars as per the NPCI report of 2025. In this study the role of UPI's in financial interoperability and its advantages are explored by explaining the essential capabilities and operational capabilities of UPI in India. NPCI UPI usage data shows that the retailmerchant users and end-users usage rate is at 81.8% of the all modes of payment. This study uses quantitative research approach to study the usage rate of UPI. The study results shows that UPI is one of the popularly used mode of digital payment proving that the digital empowerment alongside trustworthy infrastructure has increased trust in the system. Studying the NPCI transaction data and transaction data collected from 79 retail merchants shows that insights of merchants getting the advantage of interoperability due which they can have a unified QR code and link of any nationalised or private bank accounts. Factors that influences more merchants to use the UPI mode of payment indicates interoperability as an advantage.

Impact of Firm Dynamic Capabilities on Fresh Agri-Supply Chain Performance

This study investigates how dynamic capabilities—operational, collaboration, and learning capabilities—affect fresh agricultural supply chain performance (FASCP) in Vietnamese firms, focusing on the moderating role of supply chain uncertainty (FASCU). In this volatile, perishable sector, these capabilities are essential. Using a mixed-method approach, it combines quantitative analysis via PLS-SEM with qualitative insights to provide a comprehensive view. Results show operational and collaboration capabilities enhance FASCP, while learning capability has limited short-term effects. FASCU moderates these impacts, emphasizing the need for adaptive strategies. This study offers insights into optimizing fresh agri-supply chains in developing countries.

Autonomous inventory Intelligence: ML-driven predictive and prescriptive analytics for supply chain optimization

Artificial intelligence and machine learning technologies have transformed supply chain management through the integration of predictive demand forecasting with prescriptive inventory optimization. Modern ML algorithms process diverse data streams—from historical sales and promotions to external factors like weather patterns and market trends—to generate significantly more accurate demand predictions than conventional methods. Building on these forecasts, prescriptive analytics dynamically optimize inventory parameters across multi-echelon supply chains, simulating scenarios to balance service levels against holding costs. These integrated systems enable real-time automation of procurement decisions with continuous model refinement through feedback loops. Implementations across retail, manufacturing, and logistics sectors demonstrate substantial improvements in operational metrics, with various platforms offering distinctive capabilities for specific industry contexts. The evaluation of performance outcomes identifies key integration challenges with existing ERP ecosystems while highlighting operational resilience benefits in dynamic global markets. The transition toward autonomous supply chain management represents a fundamental advancement in operational capability that addresses contemporary volatility in global supply networks.

Cooperative Drone and Water Supply Truck Scheduling for Wildfire Fighting Using Deep Reinforcement Learning

Wildfires often spread rapidly and cause significant casualties and economic losses. Firefighting drones carrying water capsules provide an efficient way for wildfire extinguishing, but their operational capabilities are limited by their payloads. This weakness can be compensated by using ground vehicles to provide mobile water supply. To this end, this paper presents an optimization problem of scheduling multiple drones and water supply trucks for wildfire fighting, which allocates burning subareas to drones, routes drones to perform fire-extinguishing operations in burning subareas and reload water between every two consecutive operations, and routes trucks to provide timely water supply for drones. To solve the problem within the limited emergency response time, we propose a deep reinforcement learning method, which consists of an encoder for embedding the input instance features and a decoder for generating a solution by iteratively predicting the subarea selection decision through attention. Computational results on test instances constructed upon real-world wilderness areas demonstrate the performance advantages of the proposed method over a collection of heuristic and metaheuristic optimization methods.

AI in Private TV Broadcasting: Opportunities and challenges in Pakistani Context

The study aims to explore the opportunities and challenges associated with the integration of Artificial Intelligence in the Private broadcasting industry of Pakistan. This qualitative research utilized semi-structured interviews with ten industry experts, selected through purposive sampling including senior and executive producers with ample experience in the field. Manual thematic analysis was performed to identify key themes related to AI's impact on the industry. The findings suggest several opportunities such as catalyzing enhanced content creation, improved audience engagement, and operational efficiency. However, significant challenges were also noted, including financial constraints, technical complexity, language barriers, ethical considerations, and lack of regulatory frameworks. This study underscores the need for strategic approaches to navigate the complexities of AI adoption in broadcasting, ultimately aiming to enhance both operational capabilities and audience experiences. Future research should consider broader geographical contexts and incorporate quantitative analyses to validate these insights further.

CHALLENGES OF INNOVATIVE ENTREPRENEURSHIP IN A TECHNOLOGICAL ERA: AN INTEGRATIVE REVIEW

The COVID-19 pandemic significantly affected entrepreneurs, compelling them to close, downsize, diversify, or adopt innovative approaches to stay afloat. This global challenge, coupled with technological disruptions, resulted in many losing their jobs and entering the competitive entrepreneurship space that requires them to be innovative to succeed. However, without the right resources, context, and environment, entrepreneurs may struggle to innovate. This paper aims to investigate the challenges to innovative entrepreneurship in a technological era. We used an integrative review to synthesize challenges to innovative entrepreneurship from 42 empirical studies in different countries worldwide. A thematic approach was used to analyse the content of the articles. Results reveal that factors related to the entrepreneur’s skills and traits impede innovative entrepreneurship. Also, challenges related to finance, human resources, operational capabilities, and marketing obstruct innovation in entrepreneurship. In addition, environmental factors related to the country, social and cultural norms, and the entrepreneurship ecosystem pose challenges to innovative entrepreneurship. Innovation has become the order of the day, and knowledge of the obstacles to innovative entrepreneurship is vital. Findings can help in designing all-around support to promote innovative entrepreneurship.

Resource acquisition, entrepreneurial competence, and entrepreneurial growth performance of new farmers

IntroductionEntrepreneurial resources and abilities are pivotal factors influencing the success of new farmers’ ventures. Understanding how these elements drive entrepreneurial growth is of significant practical importance.MethodsGrounded in a framework system for entrepreneurial competence (comprising opportunity recognition/exploitation capabilities and operational/strategic management proficiencies), this study uses field data from 512 new farmers in Jiangxi, Henan, and Anhui provinces. Mediation and structural equation models are applied to assess the impact of resource acquisition on entrepreneurial growth and explore the mediating role of entrepreneurial ability.ResultsResource acquisition significantly bolsters entrepreneurial growth performance. Within entrepreneurial skills, both opportunity recognition and operational management capabilities mediate the relationship between human, social, material resources and growth performance (effect sizes: 0.134, 0.286, 0.257, 0.105, 0.279, and 0.240). No mediation is observed for policy resources. The study identifies eight pathways: human, social, and material resources influence performance both directly and indirectly, while policy resources exert only a direct effect.DiscussionThese findings underscore the imperative to enhance new farmers’ accessibility to entrepreneurial resources, augment their capabilities (particularly opportunity recognition and operational management), reinforce policy support, and cultivate sustainable development of their entrepreneurial endeavors.

Then What? The Need for Iterative Assessments to Achieve Successful Operational Capabilities

Then What? The Need for Iterative Assessments to Achieve Successful Operational Capabilities

O Brasil e operações humanitárias da ONU

The Brazilian Air Force has participated in United Nations (UN) Peacekeeping Missions, deploying troops since 2011 in the United Nations Stabilization Mission in Haiti (MINUSTAH). This study analyzed Brazil's participation in MINUSTAH with the aim of identifying the key lessons learned by the country regarding UN humanitarian operations and how these lessons influenced the development of doctrine and national strategies within the Armed Forces. Methodology: Review study. Results: Advances were observed in troop training, interoperability, and revision of standards. Additionally, participation expanded the country’s influence in its foreign policy and increased the use of Law and Order Operations. These lessons not only strengthened operational capabilities but also shaped future policies. Conclusion: This study highlights the importance of international experience in the evolution of the Armed Forces and in projecting national interests in global scenarios.

The transformative impact of AI on future supply chain operations

This article examines the revolutionary impact of artificial intelligence technologies on supply chain management, exploring how deep learning, reinforcement learning, and other AI approaches are fundamentally reshaping operational capabilities and strategic frameworks. The COVID-19 pandemic accelerated digital transformation initiatives while exposing vulnerabilities in traditional supply chain models, catalyzing a shift from efficiency-focused approaches toward resilience and adaptability. The article explores multiple dimensions of AI implementation across forecasting and inventory management, autonomous operations in warehousing and transportation, edge computing for real-time processing, and digital twin technologies for scenario planning and risk management. Despite transformative potential, organizations face substantial implementation challenges including data quality issues, cybersecurity vulnerabilities, and ethical considerations. The article identifies critical research priorities including explainable AI models that provide transparency in decision-making processes, self-learning algorithms capable of adapting to dynamic conditions without manual intervention, and human-AI collaborative platforms that leverage complementary strengths of machine intelligence and human judgment. As these technologies mature, supply chains will increasingly demonstrate intelligence and self-optimization capabilities, fundamentally redefining operational possibilities in terms of efficiency, responsiveness, and resilience within increasingly complex global business environments.

Autonomous IoT Agents Powered by Generative Reasoning

The emergence of Generative Artificial Intelligence (GenAI) has unleashed its operational capabilities to bring about a revolution for many autonomous systems, especially those in the domain of the Internet of Things (IoT). This paper explores a new mechanism for promoting generative reasoning in autonomous IoT agents for dynamic, context-situated planning and decision-making. The agents use generative models to simulate highly intricate emerging scenarios of the environment and system and can react to them in real time. The demonstration of the framework on smart agricultural systems, where agents manage irrigation and pest control tasks autonomously on a preliminary basis, was highly encouraging in significant improvements of resource efficiency and yield productivity. The approach proposed here marries reinforcement learning, scenario simulation, and adaptive proactive mechanisms to rid most of the challenges facing the lately built reactive IoT framework. Hence, the agents imbued with generative reasoning can decide based not only on sensor data but rather also on predicted-and-anticipated outcomes, thus dealing with the changing scenarios with the appropriate strategy-making. The generative cognitive architecture shows utmost potential for transforming autonomous systems in agriculture, transportation, and energy sectors. Specific areas around multi-agent collaboration, secure deployment, and ethical issues regarding autonomous decisions in the future are elaborated in the presented study

Capacity‐Learning Paradox: How Hong Kong and Singapore's Crisis Responses Shape and Are Shaped by Policy Capacities

ABSTRACTThis study examines the paradoxical relationship between policy learning and capacity: governments need certain capacities to learn effectively, yet these same capacities often emerge from previous learning experiences. Through a comparative analysis of Hong Kong and Singapore's responses to SARS and COVID‐19, we demonstrate how policy learning requires and manifests as enhanced analytical, operational, and political capacities. Our research reveals three key findings. First, learning outcomes materialize as enhanced capacities rather than just cognitive shifts and accumulated knowledge, as evidenced by both cities' institutional developments following SARS. Second, the effectiveness of learning processes depends heavily on existing capacities, particularly political capacity, which enables or constrains the deployment of analytical capacities. Third, capacity development is not linear—while both cities addressed many capacity gaps identified during SARS, COVID‐19 exposed new vulnerabilities in areas like cross‐border coordination and inclusive crisis management. These findings advance theoretical understanding of policy learning by showing how it manifests through changes in capacities. They also highlight the interdependence of different capacity types, particularly how political capacity enables or constrains the effectiveness of analytical and operational capabilities. For practitioners, our analysis emphasizes the importance of balanced capacity development and maintaining strong political trust alongside technical capabilities for effective crisis management.

Evaluation of unmanned combat aerial vehicles using q-rung orthopair fuzzy entropy based multi-attribute border approximation area comparison method

Unmanned combat aerial vehicles (UCAVs) have become an indispensable part of modern military operations. As they can be used for both defensive and offensive purposes, they play a crucial role in shaping military strategies and improving the operational capabilities of security forces. Many countries are investing in UCAV technology and placing these vehicles at the centre of their defence strategies. Choosing the right UCAV enables a country to strengthen its national security and its position in international relations by enhancing its defence capabilities. This study considers the evaluation of UCAVs as a multi-criteria decision making (MCDM) problem. In the study, the q-ROF (q-rung orthopair fuzzy) entropy-based MABAC (Multi-Attribute Border Approximation Area Comparison) method is proposed as a new integrated MCDM technique to solve the problem. The theoretical framework of the proposed method is explained in detail and applied to an UCAV selection problem. In practise, fourteen different UCAV alternatives were evaluated based on nine criteria (length, wingspan, height, empty weight, maximum takeoff weight, payload capacity, maximum cruising speed, maximum altitude, duration in the air). As a result of the application, the best-performing alternative was identified as UCAV-9 (A9). In addition, the results of the proposed method were compared with the results of the classical q-ROF MABAC, q-ROF MAIRCA (Multi Attributive Ideal-Real Comparative Analysis), q-ROF TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), q-ROF CRITIC-EDAS (Criteria Importance through Inter-criteria Correlation-Evaluation based on Distance from Average Solution), and q-ROF BWM-MARCOS (Best–Worst-Method-Measurement of Alternatives and Ranking according to COmpromise Solution) methods. UCAV-9 (A9) emerged as the strongest alternative based on the comparison analysis. In addition, two different sensitivity analyses were also carried out. The sensitivity analysis on the criteria weights revealed that the alternatives were highly influenced by these weights. Based on these results, it can be concluded that this study offers a practical framework for countries to select the appropriate UCAV and makes a significant contribution to the literature in this field.

Minimum Critical Test Scenario Set Selection for Autonomous Vehicles Prior to First Deployment and Public Road Testing

The growing complexity of autonomous vehicle functionalities poses significant challenges for vehicle testing, validation, and regulatory approval. Despite the availability of various testing protocols and standards, a harmonized and widely accepted method specifically targeting the selection of critical test scenarios—especially for safety assessments prior to public road testing—has not yet been developed. This study introduces a systematic methodology for selecting a minimum critical set of test scenarios tailored to an autonomous vehicle’s Operational Design Domain (ODD) and capabilities. Building on existing testing frameworks (e.g., EuroNCAP protocols, ISO standards, UNECE and EU regulations), the proposed method combines a structured questionnaire with a weighted cosine similarity based filtering mechanism to identify relevant scenarios from a robust database of over 1000 test cases. Further refinement using similarity metrics such as Euclidean and Manhattan distances ensures the elimination of redundant test scenarios. Application of the framework to real-world projects demonstrates significant alignment with expert-identified cases, while also identifying overlooked but relevant scenarios. By addressing the need for a structured and efficient scenario selection method, this work supports the advancement of systematic safety assurance for autonomous vehicles and provides a scalable solution for authorities and vehicle testing companies.

Enhanced Control System for Thrust Vectoring: Design, Verification, and Validation

This paper presents the integration of Thrust Vector Control (TVC) as an effective approach to enhancing the maneuverability of UAVs beyond classical aerodynamic control methods. Unlike classical aerodynamic controls, which primarily depend on using control surfaces to influence flight dynamics, TVC utilizes thrust vectoring to optimize vehicle performance, addressing limitations such as reduced effectiveness at low speeds or in complex flight regimes. TVC involves manipulating the direction of thrust produced by the UAV’s propulsion system, thereby providing a more versatile and responsive means of controlling flight dynamics compared to classical aerodynamic methods. This capability is particularly advantageous in scenarios where traditional control surfaces may be less effective, such as during high angles of attack or in turbulent environments. This paper uses a 6-DOF mathematical model that describes the dynamics of the under-test body. This model will be linearized to get simplicity, allowing easier analysis and design. This work proposes a control system that employs both PID and Fuzzy PID controllers for the presented TVC technique. This hybrid control strategy is designed to optimize performance by combining the stability of PID control with the adaptability of Fuzzy logic to enhance the robustness, enabling the system to adjust the variation flight conditions in real time. The proposed system aims to achieve precise control over the pitch and yaw axes through TVC, while roll control is managed via canard surfaces. The verification process involves simulations that replicate various flight scenarios to assess the performance of the TVC system under different conditions. By demonstrating the efficacy of TVC in addressing the limitations of aerodynamic control, this research contributes valuable insights into the design of advanced TVC control systems that promise enhanced maneuverability and operational capabilities. The results demonstrate the efficacy of the proposed control design in achieving desired flight behaviors, thus validating the model and control techniques.

Advancements, Challenges, and Future Trends of Vehicle‐to‐Grid Technologies: A Review

Within the evolving energy framework, vehicle‐to‐grid (V2G) technologies are demonstrating operational integration capabilities in modern power systems, blending sustainability with technological innovation and economic opportunity. This article presents a comprehensive investigation into V2G technologies, offering insights into bidirectional power flow, seamless integration of renewable sources, and intelligent charging strategies. It critically examines the challenges associated with V2G implementation, including technical complexities, infrastructure demands, battery management, and the need for standardized protocols. Furthermore, the article explores emerging trends such as vehicle‐to‐home (V2H) and vehicle‐to‐building (V2B) concepts, the role of artificial intelligence and predictive analytics in optimizing V2G systems, and the symbiotic relationship between V2G and the electrification of public transportation. By analyzing these multifaceted dimensions, this article provides a roadmap to harnessing V2G's potential, reshaping energy distribution dynamics, fostering sustainability across sectors, and redefining the future of transportation and energy management.

Interfacial Electrochemical Self-Assembly Enables Mechanically Robust Infrared Stealth Coatings on Complex-Shaped Metallic Substrates.

Transition metal carbides/carbonitrides (MXene) have emerged as highly promising infrared stealth coating materials due to their exceptional low infrared emissivity and high visible light absorption. Conventional coating techniques─such as blade coating, spraying, and spin-coating, the primary methods for existing MXene coatings─require specific substrate properties and face significant challenges in conforming to geometrically complex surfaces. To address these limitations, we developed an electrochemical ion-diffusion-induced gelation approach for fabricating MXene-based composite coatings (Fe2+ M/G). This method enables uniform deposition on substrates of arbitrary geometry while achieving remarkable mechanical strength (198.31 MPa) and infrared stealth capability (infrared emissivity: 0.19). Furthermore, the coating exhibits exceptional electrical conductivity (3571.4 S cm-1), enabling dual functionality: (1) an average electromagnetic shielding effectiveness of 49.35 dB in the X-band and (2) rapid Joule heating (reaching 84 °C at 1.5 V in 120 s), suitable for low-temperature deicing applications. Beyond its core infrared stealth performance, this multifunctional coating system integrates superior physical properties, offering both fundamental insights and practical solutions for developing advanced stealth materials with extended operational capabilities.