Agricultural tourism (agritourism) has become an emerging development pathway in the Mekong Delta, where agriculture, culture, and water-based ecosystems intersect to form distinctive rural landscapes. Within Vietnam’s broader agenda of rural restructuring and sustainable development, agritourism in the region has gradually evolved from small-scale household initiatives into a more organized sector shaped by the interaction of state policies, enterprise strategies, and community participation. This study examines how these three actors collectively influence agritourism development in the Mekong Delta. Using a qualitative research design and an embedded case study approach, the analysis draws on national and provincial policy documents, industry reports, and representative agritourism models such as My Khanh Tourist Village, Con Son Community Cooperative, Con Chim Ecotourism Site, Con Ong experiential farm, and the Dinh Yen Mat weaving craft village. The findings indicate that since 2010, the Vietnamese government has established a policy framework promoting experiential agriculture-based tourism, regional connectivity, climate-resilient development, and cultural preservation, thereby enabling enterprises to upgrade facilities, diversify tourism services, and expand regional tour circuits. At the same time, local communities have reorganized tourism activities through cooperative and community-based models that integrate agricultural practices, culinary heritage, and craft traditions into visitor experiences. Successful initiatives demonstrate improvements in income generation, employment opportunities, cultural continuity, and environmental awareness. However, agritourism in the Mekong Delta still faces challenges, including fragmented policy implementation, limited destination management capacity, weak interprovincial coordination, repetitive tourism products, and environmental pressures associated with climate change. The study argues that the sustainability of agritourism in the region depends on strengthening the alignment between policy frameworks, enterprise innovation, and communitybased stewardship.

Silicene nanoribbons (SiNRs), as one-dimensional derivatives of silicene, exhibit highly anisotropic charge transport and hold significant promise for future nanoelectronics applications. In this work, we systematically investigate the structural stability and electronic properties of hydrogen-passivated SiNRs doped with aluminum using first-principles density functional theory calculations performed with the VASP package. Several possible Al substitutional doping configurations are examined, among which three representative geometries-top, valley, and 1-1 arrangements-are identified as energetically stable, while other configurations undergo severe structural distortions or bond breaking during structural relaxation. Formation energy analysis indicates that the 1-1 alloy configuration is the most thermodynamically favorable due to the symmetric distribution of Al dopants and a balanced local bonding environment. Electronic structure calculations reveal that pristine hydrogenated SiNRs are narrow-gap semiconductors with a band gap of approximately 0.325 eV, whereas all stable Al-doped systems undergo a transition to semi-metallic behavior. This electronic transformation originates from the incorporation of group-III aluminum atoms, which introduce hole carriers and shift the Fermi level, leading to enhanced electrical conductivity. In addition, the tunability of the electronic properties is further explored under a constant external electric field of 0.15 eV/Å, demonstrating additional control over the electronic response of the doped nanoribbons. These results highlight aluminum doping, in combination with external electric-field modulation, as an effective strategy for tailoring the electronic characteristics of silicene nanoribbons and suggest promising opportunities for the design of low-dimensional materials with controllable transport properties for advanced nanoelectronics and optoelectronic applications.

Currently, chemical dishwashing liquids are among the most commonly used cleaning products in households due to their convenience, rapid effectiveness, and low cost. Although chemical dishwashing liquids provide significant cleaning efficiency, they pose many potential risks to human health and the environment, particularly aquatic environments. This is because industrial dishwashing liquids are mostly formulated from water combined with various chemical components such as LAS, SLS, NaOH, SLES, MgSO₄, NH₄Cl, acids, alkalis, fragrances, formaldehyde, and the antibacterial agent triclosan (Adelliya, 2021). These substances can cause numerous health problems with frequent exposure, including the risk of irritant dermatitis. Moreover, if not thoroughly rinsed off, residues may remain on dishes and enter the body, leading to serious health impacts on users, especially pregnant homemakers. In addition, when discharged into the environment, industrial dishwashing liquids contribute to environmental pollution and harm aquatic organisms (Hong-Yan et al., 2009). Given these concerns, the replacement of industrial dishwashing liquids with environmentally friendly alternatives has become increasingly necessary. The fermentation of coconut is a complex biological process in which microorganisms convert sugars in coconut water into products such as alcohols, organic acids, and flavor compounds. Coconut enzyme is fermented coconut water produced by a microbial system. Due to its organic acid content and synergistic combination with natural ingredients—including coconut ash water (for odor removal), coconut essential oil extract (cocamidopropyl betaine source), coco glucoside (foaming agent), guar gum (thickener), baking soda (NaHCO₃), and table salt (NaCl)—the formulation offers effective cleaning, skin moisturization, and safety for children and individuals with sensitive skin.

This paper presents the design and implementation of an Internet of Things (IoT)-based smart home model that integrates voice control and environment-based automation. The proposed system uses an ESP32 microcontroller as the main IoT communication module and an Arduino Mega 2560 for local hardware control. Several sensors and modules, including a DHT22 temperature-humidity sensor, MQ-4 gas sensor, rain sensor, and RFID authentication module, are integrated to support environmental monitoring, safety detection, and automated device operation. The system communicates with the E-Ra IoT platform to provide real-time monitoring and remote control through a web-based interface, while voice commands are implemented using Google Assistant. A physical prototype was developed and tested under normal operating conditions. Experimental results show that the system operates reliably and responds quickly to control commands, with an average response time of less than 1 second for basic device operations. The proposed model demonstrates the feasibility of building a low-cost and flexible smart home system suitable for research, educational applications, and small-scale residential environments.

This paper develops a systematic framework for polynomials over division algebras, focusing on degree, the Euclidean algorithm, left and right divisibility, greatest common divisors, and minimal polynomials. The relations among these notions are clarified, and conditions ensuring agreement between the left and right constructions are identified. The results extend key features of the commutative theory to the noncommutative setting.

Proton exchange membrane fuel cells (PEMFCs) have attracted significant attention due to their high efficiency and low emission characteristics. However, the cell performance is strongly influenced by operating conditions and membrane properties, which are difficult to investigate comprehensively by experimental approaches alone. This study develops a complete electrochemical model of a single PEM fuel cell in the MATLAB – Simulink environment based on the voltage loss mechanisms including the Nernst potential, activation overpotential, ohmic losses, and concentration losses. The model is employed to quantitatively investigate the effects of operating temperature, hydrogen partial pressure, oxygen partial pressure, and membrane thickness on the polarization characteristics (I – V curves) of the PEMFC. Simulation results indicate that increasing temperature significantly enhances activation kinetics and improves cell voltage, while elevated oxygen partial pressure yields the most pronounced performance improvement among gas parameters. Conversely, increasing membrane thickness leads to higher ohmic losses and voltage degradation, especially in the high –current – density regime. The proposed model provides an effective numerical tool for teaching, system analysis, and preliminary optimization of PEMFC operating conditions.

This paper presents the design, implementation, and empirical evaluation of a sophisticated automated alcohol distillation system. The system integrates modern control theory with Internet of Things (IoT) technology to overcome the limitations of traditional manual distillation, which often suffers from inconsistent product quality, high labor dependency, and significant safety risks. The core of the system employs a REX-C100 PID temperature controller for precise thermal regulation, an ESP8266 microcontroller for IoT connectivity, and an array of sensors including a K-type thermocouple and an MQ-3 alcohol concentration sensor for comprehensive process monitoring and safety. A detailed mathematical model of the distillation process and an enhanced PID control algorithm with feedforward compensation are provided. Experimental results demonstrate a 50% reduction in processing time, an increase in process efficiency from 60% to 90%, and a remarkable improvement in product quality consistency from 70% to 95%, all while maintaining a temperature control accuracy of ±1°C. The system successfully enables remote monitoring and control via the Blynk IoT platform, establishing a robust framework for intelligent, safe, and efficient distillation applicable to both small-scale and industrial production

In recent years, water hyacinth (Eichhornia crassipes) has been widely recognized as an invasive aquatic plant that proliferates rapidly on rivers, canals, ponds, and lakes, obstructing waterway transportation, impeding water flow, and contributing to environmental degradation. Despite its abundance in large river systems such as the Bach Dang River in Thu Dau Mot City, Binh Duong Province, this biomass resource remains largely underutilized, leading to significant waste of natural materials and ongoing ecological challenges. This study proposes an eco-friendly alternative by transforming water hyacinth into handmade paper sheets with natural coloration, rustic aesthetic, and complete absence of harmful chemicals. The resulting products exhibit acceptable strength and surface quality, making them suitable for practical and decorative applications including coasters, shoe insoles, greeting cards, notebooks, biodegradable packaging, paper bags, and eco-handicraft items. Raw materials were collected directly from the Bach Dang River by a student research group. The research employed a combination of primary and secondary data collection methods, along with experimental, analytical, and synthesis approaches to develop and evaluate the manual paper-making process. The developed chemical-free production method successfully yielded durable paper sheets that are environmentally safe and biodegradable. The findings demonstrate the feasibility of converting an invasive plant into value-added sustainable products, thereby contributing to waste reduction, biomass reuse, and the promotion of green production practices. Although the study is preliminary and limited by manual processing, lack of mechanization, and absence of standardized quantitative testing (e.g., tensile strength, water absorption, and biodegradability under controlled conditions), it provides a promising foundation for further optimization and scale-up. Future research should focus on improving uniformity, enhancing mechanical properties through natural additives, and conducting comprehensive performance and life-cycle assessments to support practical commercialization and broader environmental impact

High penetration of photovoltaic (PV) sources causes volatility in distribution networks, challenging conventional operational strategies. This study introduces a multi-objective optimization framework using a Stabilized Genetic Algorithm (SGA) that co-optimizes daily energy losses and switching asset depreciation over typical and extreme loading scenarios. Contradicting common assumptions, results show that zero switching operations, i.e., maintaining a robust static configuration - yield optimal economic outcomes for the IEEE 33-bus test system, regardless of switching cost magnitude. The work formalizes an economic viability threshold for DDNR, providing network operators with a quantitative tool to assess when dynamic reconfiguration is truly justified. Results reveal that for the IEEE 33-bus system with PV integration, a robust static configuration remains economically optimal regardless of switching cost magnitude. The primary contribution is the formalization of an "Economic Viability Threshold" framework, providing DNOs a quantitative tool to determine when DDNR is truly justified. This framework provides a crucial, data-driven tool for network operators to prevent unnecessary investment in complex control schemes, ensuring that grid modernization efforts are both technically sound and economically viable

The administrative merger in Southeast Vietnam has fundamentally reshaped regional governance, spatial configurations, and development priorities, creating urgent requirements for a more integrated approach to science and technology (S&T) human resource development. To assess the implications of this restructuring, the study employs a mixed-methods design that combines institutional diagnostics, comparative policy analysis, and quantitative evaluation of workforce indicators. Empirical data are sourced from national statistical agencies, ministerial datasets, provincial development reports, and international benchmarking studies. The analysis focuses on the S&T workforce within the newly configured administrative units of expanded Ho Chi Minh City, Dong Nai, and Tay Ninh, examining competency structures, spatial distribution, coordination mechanisms, and post-merger system dynamics. The findings reveal significant disparities in qualification profiles, weak cross-provincial linkages in training and research, and limited alignment between workforce planning and emergent regional development trajectories. Despite these constraints, the merger presents opportunities to consolidate training capacity, strengthen innovation networks, and enhance talent mobility. The study argues for a coordinated regional S&T human resource strategy supported by institutional harmonization, a functionally differentiated training system, AI-enabled workforce planning tools, and expanded regional–national–international cooperation to advance a knowledge-based, climate-adaptive development pathway for Southeast Vietnam.

This study forecasts electricity demand for Vietnam’s data center sector through 2030 in the context of rapid digitalization and the accelerating adoption of Artificial Intelligence (AI), both of which are expected to exert significant pressure on national power infrastructure. Using a baseline IT load of 524.7 MW in 2025 derived from industry market reports, the analysis employs a scenario-based approach with two growth trajectories: a high-growth case using a 16% CAGR and a market-aligned case using a 12.61% CAGR. Applying a Power Usage Effectiveness (PUE) value of 1.4, consistent with Vietnam’s green data center standards, projected electricity demand increases from 734.6 MW in 2025 to 1,542.8 MW under the high-growth scenario and 1,330.6 MW under the moderate-growth scenario by 2030, corresponding to increases of 110% and 81%, respectively. These findings indicate that the expansion of digital infrastructure will require proactive power system planning. The study highlights the importance of integrating renewable energy through Direct Power Purchase Agreements (DPPAs) and implementing stringent energy-efficiency standards to ensure the sustainable development of Vietnam’s data center ecosystem.

Submerged cultivation of medicinal mushrooms is receiving increasing attention and is considered an effective alternative to traditional substrate cultivation methods for producing fungal mycelial biomass and bioactive metabolites with diverse applications. This method allows for the control of culture environment conditions, enabling more efficient synthesis of bioactive compounds such as polysaccharides, triterpenoids, cordycepin, polyphenols, etc. Furthermore, the bioactivity of these compounds, including antioxidant, anticancer, antibacterial, and immunomodulatory effects, further emphasizes the potential of producing medicinal mushroom biomass by submerged cultivation in the pharmaceutical and functional food industries. Submerged cultivation is considered a promising alternative to traditional mushroom fruiting body cultivation because it offers better control over culture conditions and product quality, as well as shorter cultivation times. Submerged fungal cultivation has significant industrial potential; however, there are still challenges in optimizing production yield and scaling up the process for industrial application. The successful application of this method on a commercial scale depends on increasing product yield and developing new production systems to address the issues related to submerged mushroom cultivation techniques. Although many researchers are making efforts to produce bioactive metabolites from fungi, the physiological and technical aspects of submerged cultivation still require extensive and long-term research.

Xylaria nigripes is a rare medicinal mushroom in the Xylariaceae family, which has long been used in traditional medicine to aid in treating conditions such as insomnia, neurasthenia, and inflammation. This fungus usually grows in an environment characterized by termite nests. Recent studies have shown that X. nigripes contains many valuable biological compounds such as polysaccharides, nucleosides, and sterols, which provide important biological effects, such as antioxidants, liver protection, immune system regulation, and diabetes treatment. In addition to pharmacological potential, many research works have focused on developing X. nigripes biomass kernel techniques under artificial culture conditions, in order to optimize growth and accumulation of active ingredients. These results not only contribute to clarifying the application potential of this mushroom in the pharmaceutical field but also create a scientific foundation for the sustainable exploitation of this rare medicinal resource.

AI is transforming English as a Foreign Language (EFL) education by facilitating personalized learning and intelligent tutoring globally. This study examines the readiness and intentions of educators in Thu Dau Mot City, Vietnam, to adopt AI in their EFL classrooms. Through surveys and interviews with 102 teachers and lecturers, results show a high perceived usefulness and intention to integrate AI (M = 4.10). However, challenges remain with moderate ease of use (M = 3.92), low confidence in AI tools (M = 3.68), and limited institutional support (M = 3.45). Qualitative insights indicate a need for systematic training and collaborative environments. The findings emphasize that successful AI adoption relies on institutional investment in training and infrastructure. Without this support, the gap between enthusiasm and actual implementation may hinder AI's transformative potential. Policymakers and educational leaders need to create structured frameworks for effective AI integration in EFL classrooms.

In this study, molecular dynamics simulations were employed to investigate the influence of pressure on the structural properties of silver (Ag) at 300K. The results reveal that an increase in pressure leads to a reduction in nearest-neighbor distance, a promotion of local ordering, and a transition from a largely disordered state to a predominantly face-centered cubic FCC crystalline structure. At intermediate pressures, both hexagonal close-packed HCP and body-centered cubic BCC phases are observed; however, these phases diminish as pressure rises, with FCC becoming the prevailing phase at higher pressures. These findings demonstrate that pressure is a key factor in driving phase transitions and improving crystallinity in metallic systems.

The application of Artificial Intelligence (AI) in education is rapidly transforming the teaching and learning landscape in Vietnam. AI technology is being integrated into various educational platforms to provide personalized learning experiences, support educators, and enhance the overall efficiency of the education system. In Vietnam, AI is utilized to develop adaptive learning programs, intelligent tutoring systems, and automated administrative processes. AI-powered tools such as virtual teaching assistants and chatbots are also being employed to offer real-time support and feedback to students. Furthermore, AI-driven data analytics is used to monitor and improve student performance and engagement levels. AI assists teachers in automating grading, reducing assessment time, and enabling speech recognition systems to evaluate students’ English-speaking skills. These innovations contribute to a more dynamic, interactive, and inclusive educational environment. However, challenges such as data privacy concerns, the digital divide, and the demand for a skilled workforce remain significant. Addressing these issues is crucial for the sustainable integration of AI into Vietnam’s education sector.

Conductivity is a crucial and widely recognized concept in material science, particularly significant in the study of low-dimensional systems. This research extends the analysis of the conductivity tensor within a quantum well with infinite potential, focusing on electron-acoustic phonon scattering. The system is subjected to two external fields: an electromagnetic wave and a laser field. The study explores the detailed effects of these external fields, noting that significant impacts occur only at high frequencies. Among the factors affecting conductivity, the amplitude of the laser field is the most influential. Additionally, when the electromagnetic wave frequency exceeds 1012 s-1, its impact on conductivity becomes considerable.

Eosinophilic gastroenteritis (EGE) is a rare condition characterized by eosinophilic infiltration into the gastrointestinal tract. Symptoms are often non-specific and gastrointestinal in nature, such as nausea, vomiting, abdominal pain, diarrhea, weight loss, and malabsorption. Diagnosis is primarily based on clinical findings, laboratory tests, imaging, and histopathological examination through endoscopic biopsy. Treatment involves corticosteroids, proton pump inhibitors (PPIs), and dietary modifications. This article presents the case of a 15-year-old male diagnosed with eosinophilic gastroenteritis with a history of cow’s milk allergy. The patient was treated with systematic corticosteroids (1mg/kg/24h), PPIs (esomeprazole 40mg/day) and elimination dietary therapy (peptide-based formula), resulting in positive clinical outcome. Through this case, we emphasize the importance of early diagnosis and appropriate management in handling this rare disorder.

Tourist motivation is a critical aspect of tourism research, providing valuable insights into why people travel and what influences their destination choices. This article aims to provide a systematic review of the leading models used to understand tourist motivation, examining both psychological and external factors that drive tourist behaviours. Key frameworks such as Maslow’s Hierarchy of Needs, Push-Pull Theory, and Iso-Ahola’s Motivation-Relaxation Model are explored to highlight the various intrinsic and extrinsic forces that motivate tourists. Additionally, models like Plog’s Psychographic Typology and the Travel Career Ladder offer valuable perspectives on how travel motivations evolve with experience and personality traits. By synthesizing these diverse models, the review not only provides a broad understanding of tourist motivation but also underscores the complexity and multidimensional nature of travel behaviour. Understanding these models is essential for tourism practitioners, as it enables more targeted marketing strategies, improved destination management, and enhanced visitor satisfaction. This article aims to contribute to a deeper understanding of the motivations that shape tourism trends and to offer a framework for future research in the field.

The paper analyzes the radial distribution power system with the series connection of FACTS devices, which is easily implemented using a formula from the power flow equation (PFE - power flow equation) with the voltage magnitude and power flow on the lines are treated as independent variables. When control variables such as the form of reactive power at nodes and lines are directly manipulated in the formulation, the application of FACTS device control operations in the power system is carried out quickly and directly. Using the ratio matrix at the nodes of a radial distribution system is primarily represented on the main diagonal to reduce computational procedures. All FACTS device models are unified under static stability conditions and can be easily integrated within the new framework through the process of "variable exchange". Using the IEEE standard system, the formulation of the formula is based on the trend on the line - Line Flow Based (LFB) by the author to provide easy implementation with multiple FACTS devices connected in the system and its efficiency.

The rising demand for automation in agriculture and manufacturing necessitates efficient, cost-effective sorting systems to replace labor-intensive manual processes. This paper introduces an innovative system integrating a Siemens S7-1200 Programmable Logic Controller (PLC), LabVIEW-based image processing, and OPC (OLE for Process Control) communication for automated tomato sorting. Utilizing real-time vision analysis, the system classifies tomatoes by color and size, offering a low-cost, scalable solution tailored for small-scale industries. A high-resolution camera captures images, processed in LabVIEW using HSV color space and size thresholds, with results relayed via OPC to the PLC, which actuates a stepper motor-driven sorting mechanism. Experimental validation in a controlled setting achieved 92% sorting accuracy and a throughput of 60 tomatoes per minute, surpassing manual sorting in speed and consistency. The modular design supports scalability to other agricultural products, enhancing its practical utility.

Asia’s financial ecosystems, while distinct from Western paradigms, remain underexplored. This study integrates cultural finance, regime-switching machine learning, and ESG asymmetries into a novel analytical framework tailored to Asia’s unique financial architecture. We develop three models: a Hybrid LSTM-GARCH for crisis forecasting, a Bayesian Structural Equation Model capturing informal institutional dynamics, and a machine learning-enhanced Difference-in-Differences model to assess ESG impacts. Theoretically, we propose the Cultural-Statistical Nexus Framework, embedding sociocultural variables into predictive finance, the concept of institutional plasticity to explain regulatory divergence, and ESG Arbitrage Theory to highlight sustainability’s dual role as risk mitigator and speculative signal. Empirically, Confucian Risk Aversion reduces corporate leverage by 15 percent, ESG adoption lowers systemic risk but increases greenwashing, and hybrid models outperform conventional tools in FX crisis prediction. Practical implications include cultural-risk-adjusted capital buffers, AI-based liquidity tools, and region-specific ESG strategies, advancing a globally inclusive paradigm of financial science.

This research investigates the effectiveness of three Maximum Power Point Tracking (MPPT) algorithms—Incremental Conductance (IC), Perturb and Observe (P&O), and Fuzzy Logic Controller (FLC)—in optimizing power output in grid-tied photovoltaic (PV) systems. Each algorithm was tested under varying environmental conditions, focusing on performance in terms of energy extraction, stability, and adaptability to fluctuating irradiance and temperature. Results indicate that FLC offers superior performance, exhibiting reduced power fluctuations and faster responsiveness to environmental changes compared to IC and P&O. These insights contribute to enhancing PV system efficiency and reliability in modern power grids.

This paper presents a method for liquid level stabilization using a fuzzy logic algorithm implemented on the PLC S7-1200. Maintaining liquid levels accurately is a critical requirement in various industrial processes to ensure safety, efficiency, and consistent product quality. The proposed approach employs fuzzy logic to manage the inherent nonlinearities and uncertainties in the system, providing robust control performance under varying operating conditions. The fuzzy controller is designed with rules and membership functions tailored to the dynamic characteristics of the liquid level system. The control logic is programmed and deployed on the Siemens PLC S7-1200, a widely used industrial automation device. Experimental results demonstrate that the fuzzy logic controller effectively stabilizes the liquid level, achieving better performance compared to traditional PID controllers in terms of response time, overshoot, and steady-state error. This study highlights the potential of integrating fuzzy logic with PLCs for advanced industrial automation applications.