This review paper presents Single-Inductor Dual-Output (SIDO) and Single-Inductor Multi-Output (SIMO) DC-DC converters with our proposed exclusive control method. First, we provide an overview of three fundamental types of switching converters: the buck converter, the boost converter, and the buck-boost converter, all using Pulse Width Modulation (PWM) signals for their control. Next, we introduce SIDO converters with the exclusive control method, including the PWM control, the ripple control, the hysteretic control, and the soft-switching (with zero-voltage switching). In addition, we introduce its extension to a configuration of the dual-output Single Ended Inductor Converter (SEPIC) with the buck-boost converter, the high boost converter and the multiplied boost converter. Finally, we show exploration of four-output converters using our proposed voltage comparative circuit. The exclusive control method requires a few additional components but does not rely on current sensors. Also, it is not influenced by the output voltage or current value. Furthermore, we look ahead to future research directions for improving the subject.

Cyberspace is the foundation of modern economic, social, and governmental activities, making cybersecurity an essential component in addressing the escalating threat of cyberattacks. These attacks, which exploit vulnerabilities through methods such as malware, data breaches, and Distributed Denial of Service attacks, lead to significant disruptions ranging from financial losses for businesses to political and military consequences. As the digital landscape evolves, the need for robust cybersecurity measures to protect interconnected systems and safeguard digital ecosystems becomes increasingly urgent. This paper provides a review of the foundational concepts of cybersecurity, offering an in-depth analysis of current challenges and strategies. Furthermore, by critically assessing the weaknesses in existing methods, this study identifies knowledge gaps and proposes actionable future research directions aimed at mitigating cyber threats.

This paper suggests a new method for improving routes in complicated logistics systems by combining cognitive modeling with quantum computing algorithms, especially the Quantum Approximate Optimization Algorithm (QAOA). In the classic Traveling Salesman Problem (TSP), the model shows major improvements, beating traditional methods by 25% in finding solutions accurately and cutting computation time by 30%. Simulations show a 15% drop in travel time and a 20% cut in CO₂ emissions, highlighting how the model helps improve efficiency and support environmental sustainability. The innovation comes from combining two usually separate fields: cognitive modeling, which mimics how humans make decisions, and quantum computing, which allows for fast and large‑scale optimization. This teamwork between different fields encourages quick, flexible, and scalable decision‑making, which is essential in fast‑changing, real‑time logistics settings. The model matches the move towards Industry 5.0, which focuses on working together with machines and being environmentally friendly. It also supports the United Nations Sustainable Development Goals, especially Goal 9 (Industry, Innovation and Infrastructure) and Goal 13 (Climate Action). To make sure the study is valid, it uses open‑access datasets and simulates real‑life situations, such as smart warehouse operations and fleet management systems. The results highlight how quantum‑enhanced cognitive systems can change the game, providing a modern tool to build smarter, greener, and stronger supply chains. This research not only pushes the boundaries of optimization science but also lays the groundwork for using quantum algorithms in industry in the future.

This research explores the application of Long Short-Term Memory (LSTM) networks for performing sentiment analysis on customer reviews gathered from prominent FMCG e-commerce platforms such as Blinkit, Zepto, and JioMart. In the fiercely competitive landscape of online retail, accurately interpreting customer sentiment is essential for sustaining customer satisfaction and achieving strategic growth. These platforms accumulate massive amounts of unstructured data—ranging from feedback on product quality to delivery efficiency and overall user experience—which are challenging to process using traditional manual methods. To address this, the study leverages advanced Natural Language Processing (NLP) techniques, with a particular focus on LSTM networks due to their superior ability to model sequential dependencies and retain contextual meaning across review texts. To further enhance performance, pretrained word embeddings are used, enabling the model to understand nuanced language and improve accuracy across varying review structures. Beyond analyzing textual data, the research also integrates visual components into a multimodal sentiment classification framework, offering a holistic understanding of consumer emotions. This dual-modality approach captures subtle sentiments that may not be evident in text alone. The findings yield practical insights for enhancing customer service, optimizing product selections, and improving overall brand engagement. Ultimately, this study empowers data-driven strategies that elevate user experience and market responsiveness in the dynamic FMCG e-commerce industry.

This study investigates the integration of generative AI in the education system of Papua’s coastal regions, highlighting both opportunities and challenges. Papua’s remote and isolated geography has led to limited access to quality education because of infrastructure and teacher shortages. AI presents transformative potential to address these challenges by improving educational quality, accessibility, and resource availability. This study investigates how AI tools can improve students’ personalized learning, critical thinking, and independence while also supporting teachers with AI-assisted content development. Through qualitative methods, such as interviews and focus groups with university students and teachers, this study examines perceptions of AI’s role in overcoming geographical, technological, and cultural barriers. The research identifies practical benefits, including AI-powered language tools, virtual labs for STEM education, and climate change simulation tools, as well as challenges, such as ethical considerations, digital infrastructure limitations, and the need for region-specific curricula. This study suggests three key strategies for future education: sustainable teacher professional development, curriculum localization, and AI integration into the local content. These initiatives ensure educational relevance, and foster innovation in culturally and ecologically unique environments. This research also calls for further exploration of rural inland areas and policy development.

During long-term operation in high-temperature and high-pressure environments, the pressure pipelines of boiler heating systems are prone to damage, which directly affects the safe and stable operation of pressure pipelines and boiler heating systems. Generally, the acoustic sensor is employed to detect the abnormal sound of pressure pipelines for condition monitoring. However, the signals obtained from the acoustic sensor are easily drowned out in background noise generated by fans and exhaust equipment, resulting in unsatisfactory performance for condition monitoring. Therefore, the intelligent acoustic sensor framework is proposed to establish a physics-informed digital twin for pressure pipelines, integrating condition monitoring as a core function. By implementing the digital twin, real-time synchronization between physical and virtual systems enables predictive maintenance, early fault diagnosis, and optimized operational strategies, thereby reducing unplanned downtime and enhancing industrial safety. Specifically, the traditional acoustic sensor system is improved based on the noise reduction model, which can obtain the de-noised acoustic signals for all conditions. Furthermore, the real-time decision-making model for abnormal sound detection is embedded in the proposed intelligent acoustic sensor framework based on the long short-term memory network, and the result is employed as the digital twin for pressures pipeline by monitoring their condition. In addition, the experimental platform is built to test the effectiveness and reliability of the proposed intelligent acoustic sensor framework. The results indicate that the quality of acoustic signals is improved by over 3 dB, and the accuracy of condition monitoring can reach 91.67% for different conditions. By comparing and analyzing with other methods, the superiority and effectiveness of the proposed intelligent acoustic sensor framework are further verified. This approach not only improves monitoring precision but also offers broader social benefits, including reduced energy waste in heating systems and minimized risks of industrial accidents.

Traditional Intrusion Detection Systems (IDSs) face significant challenges in keeping pace with the rapidly evolving landscape of cyber threats, primarily due to limitations in continuous learning and the accuracy of data classification and analysis. This often results in delayed detection and leaves networks susceptible to severe attacks. This paper introduces an innovative IDS empowered by blockchain technology to mitigate these shortcomings, leveraging continuous learning and self‑adaptive neural networks. The proposed system adopts a proactive approach by continuously assimilating intrusion logs, utilizing a Long Short‑Term Memory (LSTM) core to discern patterns and enhance its real‑time threat detection capabilities, removing a major bottleneck in traditional IDS models by eliminating the need for manual tagging. To further strengthen the security measures, self‑updating neural networks are embedded in each block of the blockchain, forming a decentralized “brain” that evolves defences against even the most sophisticated adversaries. These networks are securely housed in Trusted Execution Environments (TEEs) to maintain operational integrity, enabling tamper‑proof operation and effective threat detection. Real‑world evaluations conducted on the Binance Smart Chain and Ethereum Classic datasets demonstrate the system’s superior performance. With an impressive accuracy rate of 98.50% and a minimal false positive rate of 1.50%, the model demonstrates a remarkable ability to distinguish legitimate network activity from malicious intrusions.

Intercultural competence has become an essential skill for effective global communication, particularly for English as a Foreign Language (EFL) learners. The integration of artificial intelligence (AI) into educational practices presents significant potential for enhancing both linguistic proficiency and intercultural capabilities. However, there remains a gap in understanding how AI technologies can specifically facilitate the development of intercultural competence among EFL learners. This study adopts a mixed-methods approach to examine the impact of AI technologies on intercultural competence development among 120 Chinese undergraduate EFL students. Quantitative analysis of pre-and post-test data revealed significant improvements in students' cultural knowledge, attitudinal openness, communication skills, and cultural awareness, with paired t-tests confirming the statistical significance of these gains. Qualitative data, collected through interviews, classroom observations, and platform usage records, further supported these findings, highlighting enhanced emotional engagement, practical application of skills, and immersive learning experiences facilitated by AI tools. The results indicate that integrating AI technologies into language curricula can effectively foster intercultural understanding and communication abilities. This study contributes to the existing literature by providing empirical evidence on the efficacy of AI-based interventions in promoting intercultural competence, offering practical implications for educators and policymakers seeking to leverage AI in language teaching and learning contexts.

Filters are essential circuits for various signal processing circuits. One of the main methods of filter integration is to design an LC filter first as a prototype filter, and then simulate the function of the inductor with active components. Considering that the accuracy of elements implemented on a semiconductor chip is significantly lower than that of individual elements, this method, which uses a resistive doubly terminated LC ladder filter with low element sensitivity as the prototype, has been widely adopted. The Bruton Transformation is known as one of the methods to integrate the LC filter. The method avoids the use of inductor elements, which make integration difficult, and requires a component called a Frequency Dependent Negative Resistor (FDNR) . An FDNR is a negative resistance whose impedance is inversely proportional to the square of the frequency. Because the FDNR does not exist as a passive element, it is realized using an active block such as an operational amplifier. This paper provides an overview of the Bruton Transformation and the FDNR. The first half of this paper briefly describes the Bruton transformation and reviews the FDNRs proposed to date since the Bruton transformation was first presented. The second half of the paper presents the latest examples of filter integration methods using FDNR proposed by the authors.