Found 369 results
Open Access
Review
07 November 2025Value Engineering in the Era of Industry 4.0: From Gap Analysis to Research Methodologies and Strategic Framework
Traditional Value Engineering (VE) has long focused on optimizing the function-to-cost ratio but faces limitations in digitalized industrial contexts. Conventional VE lacks integration with advanced technologies, empirical validation in smart environments, and alignment with sustainability and circular economy objectives. The emergence of Industry 4.0—driven by cyber-physical systems, IoT, big data analytics, digital twins, and artificial intelligence—has transformed industrial ecosystems, necessitating a redefinition of VE practices. This study employs a systematic literature review and structured gap analysis to examine the evolution, applications, and challenges of VE across manufacturing, construction, supply chain, and service sectors. The analysis identifies three key deficiencies in conventional VE: (i) absence of integrated digital frameworks, (ii) limited empirical validation in smart environments, and (iii) weak incorporation of sustainability and circular economy principles. To address these gaps, Value Engineering 4.0 (VE 4.0) is proposed as a function-driven, data-intelligent, and human-centric methodology. It is structured around a six-component strategic framework: (1) digital foundations for technological readiness and organizational alignment; (2) smart VE processes leveraging AI, IoT, and advanced analytics for predictive, connected decision-making; (3) an enhanced Job Plan integrating AR/VR, NLP, and blockchain for improved speed, accuracy, and lifecycle alignment; (4) a phased implementation roadmap; (5) real-time DMAIC integration for continuous optimization; and (6) enablers covering leadership, skills, infrastructure, and cybersecurity. VE 4.0 provides both a research agenda and a practical roadmap, enabling organizations to innovate, enhance resilience, and achieve sustainable competitiveness in Industry 4.0 ecosystems.
Open Access
Article
07 November 2025Parking Space Detection Using a Machine Learning-Enhanced Unmanned Aerial Vehicle in a Virtual Environment
Unmanned aerial vehicles (UAVs) have increased in popularity for several diverse applications over the past few years. Parking, especially in crowded parking lots, can be very time-consuming, as a driver must manually search for vacant spaces among many occupied ones. In this work, reinforcement learning—a category of machine learning in which an agent receives inputs from the environment while outputting actions in order to maximize reward—was utilized in tandem with AirSim, a drone simulator developed by Microsoft, to automate a virtual UAV’s movement. A convolutional neural network (CNN) was then utilized to detect both vacant and filled parking spots, which achieved 98% recall and 93% accuracy. Unreal Engine was used to create a custom environment that resembled a parking lot, and the virtual drone was trained using a Deep Q-Network (DQN). The DQN achieved a mean reward of 394.5 in training and 460.4 in evaluation. A pre-trained CNN integrated with the DQN enables the real-time classification of vacant/occupied parking spaces from drone imagery. Results validate the effectiveness of combining reinforcement learning navigation with CNN image classification, demonstrating deployment-ready performance for real-world congested parking applications.
Open Access
Article
06 November 2025Sustainable Recycling Mechanisms for Waste Cooking Oil in China’s Third-Tier Cities: Evidence from Restaurant Practices
The conversion of waste cooking oil (WCO) into biodiesel is a key strategy for advancing energy sustainability, particularly within China’s rapidly expanding restaurant industry. In third-tier cities such as Shantou, Guangdong Province, WCO collection faces unique challenges. Through in-depth interviews with 20 restaurant operators, this study identifies multiple barriers to effective WCO management, including an aging population, underdeveloped local economies, limited technological infrastructure, and unequal access to educational opportunities, all of which hinder the adoption of advanced filtration systems and broader environmental sustainability initiatives. Moreover, the non-standardized operations of third-party WCO collection services, coupled with space constraints in small restaurant kitchens, further exacerbate inefficiencies in recovery processes. To address these challenges, this study develops a comprehensive framework for WCO collection that is adaptable to regions with similar socio-economic conditions. Integrating grounded theory, Interpretive Structural Modeling (ISM), and Latent Dirichlet Allocation, the framework fills critical gaps in existing research. The analysis reveals that government financial incentives occupy the foundational layer of the ISM hierarchy and serve as a key driver of recycling behavior among restaurant operators; educational attainment enhances awareness and compliance but is moderated by structural constraints; and trust in third-party recyclers exerts a relatively limited influence. Correspondingly, H1 receives qualitative support, H2 is partially supported, and H3 gains only limited support. Building on these findings, the study proposes a multi-stakeholder governance framework that includes a “community-school-family” education system, an intelligent third-party management platform, and a government-led industrial chain to promote the formation of a closed-loop circular economy. The results demonstrate that the proposed framework not only offers actionable policy recommendations but also facilitates the adoption of sustainable practices and deepens the understanding of socio-economic and operational factors affecting WCO management, thereby providing strong support for energy and environmental sustainability.
Open Access
Communication
05 November 2025Computer Simulation of the Heat Treatment of Knitting Needles
The article discusses the main steels that are used to make needles for knitting machines. Based on an analysis of literature data, needles for knitting machines are primarily made of high-carbon steel, the main alloying elements of which are carbon in an amount of about 1.0 wt. %, silicon (0.3–0.5 wt. %), manganese (0.55–0.75% by weight), and chromium (about 0.4% by weight). In addition, these steels may contain microalloying additives, such as niobium in an amount of about 0.010% by weight. The publicly available computer model has been expanded to simulate the heat treatment of new materials for knitting machine needles. Using the developed computer model, the optimal structural and phase composition of the knitting needle material is established, which confirms its performance characteristics. It is shown that computer simulation of heat treatment modes makes it possible to conduct computer simulations of heat treatment modes with good accuracy and evaluate the effect of optimizing heat treatment parameters to obtain the best properties. Based on the results of computer modeling, one or more promising heat treatment modes can be selected, which can ultimately have a positive effect on the quality and service life of knitting needles.
Open Access
Review
04 November 2025Tools and Strategies for Engineering Bacillus methanolicus: A Versatile Thermophilic Platform for Sustainable Bioproduction from Methanol and Alternative Feedstocks
Bacillus methanolicus MGA3 is a methylotrophic bacterium with a high potential as a production host in the bioeconomy, particularly with methanol as a feedstock. This review presents the recent acceleration in strain engineering technologies through advances in transformation efficiency, the development of CRISPR/Cas9-based genome editing, and the application of genome-scale models (GSMs) for strain design. The generation of novel genetic tools broadens the biotechnological potential of this thermophilic methylotroph. B. methanolicus is a facultative methylotroph and apart from methanol it can grow on mannitol, arabitol and glucose, and was engineered for starch and xylose utilisation. Here, the central carbon metabolism of B. methanolicus for various native and non-native carbon sources is described, with an emphasis on methanol metabolism. With its expanding product portfolio, B. methanolicus demonstrates its potential as a microbial cell factory for the production of tricarboxylic acid(TCA) cycle and ribulose monophosphate (RuMP) cycle intermediates and their derivatives. Beyond small chemicals, B. methanolicus is both a valuable source of novel thermostable proteins and a host for the production of heterologous proteins, enabled by advances in genetic tools and cultivation methods. Continued progress in understanding its physiology and refining its genetic toolbox will be decisive in transforming B. methanolicus from a promising candidate into a fully established industrial workhorse for sustainable methanol-based biomanufacturing.
Open Access
Article
03 November 2025Sequential Thermal and Optical Upgrades for Passive Solar Stills: Toward Sustainable Desalination in Arid Climates
This study investigates the thermal performance and freshwater productivity of a passive single-slope solar still under four distinct configurations, aimed at enhancing distillation efficiency using low-cost modifications. The experiments were conducted in Tabuk, Saudi Arabia (28°23′50″ N, 36°34′44″ E), a region characterized by high solar irradiance ranging from 847 to 943 W/m2. The baseline system, constructed with a stainless-steel basin and inclined transparent glass cover, served as the control, achieving a cumulative distillate yield of 3.237 kg/m2/day and a thermal efficiency of 36.27%. Subsequent modifications included the addition of external reflective mirrors (Experiment 2), aluminum foil foam insulation (Experiment 3), and internal enhancements with side glass panels and internal aluminum mirrors (Experiment 4). Results demonstrated that the external mirror modification improved the distillate yield by 16% to 3.757 kg/m2/day, with a corresponding efficiency of 41.66%. However, insulation under dusty conditions led to a reduced yield of 2.000 kg/m2 and an efficiency of 25.18%, highlighting the critical influence of solar transmittance. The most notable improvement was recorded in the fourth configuration, which combined internal reflective elements and transparent side panels, resulting in a maximum yield of 4.979 kg/m2/day and thermal efficiency of 56.45%. These findings confirm that optical and thermal design enhancements can significantly augment the performance of passive solar stills, especially under high-irradiance, clear-sky conditions. The proposed modifications are low-cost, scalable, and suitable for implementation in remote and arid regions facing freshwater scarcity. This study offers valuable insights into the systematic optimization of solar distillation systems to improve sustainable water production.
Open Access
Communication
31 October 2025Impact of SGLT2 Inhibitors on PA Pressures in D-TGA after Atrial Switch Operations
Heart failure (HF) is the leading cause of mortality in adults with congenital heart disease (ACHD), including patients with systemic right ventricles, such as those with dextro-transposition of the great arteries with an atrial switch (DTGA-AS). With more ACHD patients surviving well into adulthood, there is an increase in advanced heart failure (HF) and pulmonary hypertension (PH), many of whom are being treated with SGLT2-inhibitors (SGLT2-i). However, there is a paucity of data supporting SGLT2-i inhibitor use in the ACHD population and on how they may impact pulmonary artery pressures (PAP). This single center retrospective study aimed to evaluate the impact of SGLT2-i on (PAP) in patients with DTGA-AS. Six patients were studied, all male (mean age 41 [range 38–52] years), with a mean systemic right ventricular ejection fraction of 27% (range 22–32%), with an implanted hemodynamic CardioMEMs monitor data were recorded one month prior to medication start and six months afterwards. Half of the patients had normal PAP, and the addition of SGLT2i did not result in a significant change in PAP in all patients. However, half of the patients demonstrated a trend towards improvement. In conclusion, in this study with a small sample size of DTGA-AS patients, there was no significant reduction in PAP.
Open Access
Article
31 October 2025Performance Evaluation of Machine Learning Algorithms for Predicting Organic Photovoltaic Efficiency
This study forecasts the power conversion efficiency (PCE) of organic solar cells using data from experiments with donors and non-fullerene acceptor materials. We built a dataset that includes both numerical and categorical features by using standard scaling and one-hot encoding. We developed and compared several machine learning (ML) models, including multilayer perceptron, random forest, XGBoost, multiple linear regression, and partial least squares. The modified XGBoost model performed best, achieving a root mean squared error (RMSE) of 0.564, a mean absolute error (MAE) of 0.446, and a coefficient of determination (R2) of 0.980 on the test set. We also assessed the model’s ability to generalize and its reliability by examining learning curve trends, calibration curve analysis, and residual distribution. Plots of feature correlation and permutation importance showed that ionization potential and electron affinity were key predictors. The results demonstrate that with proper tuning, gradient boosting methods can provide highly accurate and easy-to-understand predictions of organic solar cell efficiency. This work establishes a repeatable machine learning process to quickly screen and thoughtfully design high-efficiency photovoltaic materials.
Open Access
Article
30 October 2025Smart Drone Neutralization: AI Driven RF Jamming and Modulation Detection with Software Defined Radio
The increasing use of wireless technologies in many aspects of people’s lives has led to a congested electromagnetic spectrum, making it critical to manage the limited available spectrum as efficiently as possible. This is particularly important for military activities such as electronic warfare, where jamming is used to disrupt enemy communication, self-attacking drones, and surveillance drones. However, current detection methods used by armed personnel, such as optical sensors and Radio Detection and Ranging (RADAR), do not include Radio Frequency (RF) analysis, which is crucial for identifying the signals used to operate drones. To combat security vulnerabilities posed by the rogue or unidentified transmitters, RF transmitters should be detected not only by the available data content of broadcasts but also by the physical properties of the transmitters. This requires faster fingerprinting and identifying procedures that extend beyond the traditional hand-engineered methods. In this paper, RF data from the drones’ remote controller is identified and collected using Software Defined Radio (SDR), a radio that employs software to perform signal-processing tasks that were previously accomplished by hardware. A deep learning model is then provided to train and detect modulation strategies utilized in drone communication and a suitable jamming strategy. This paper overviews Unmanned Aerial Vehicles (UAV) neutralization, communication signals, and Deep Learning (DL) applications. It introduces an intelligent system for modulation detection and drone jamming using Software Defined Radio (SDR). DL approaches in these areas, alongside advancements in UAV neutralization techniques, present promising research opportunities. The primary objective is to integrate recent research themes in UAV neutralization, communication signals, and Machine Learning (ML) and DL applications, delivering a more efficient and effective solution for identifying and neutralizing drones. The proposed intelligent system for modulation detection and jamming of drones based on SDR, along with deep learning approaches, holds great potential for future research in this field.
Open Access
Article
29 October 2025Solutions of Minimized Agrochemicals Input in the Post Zero-Growth Era: A State-of-the-Art Analysis of the Hengduan Mountains, China
How to further reduce the input of agrochemicals after zero-growth is an important challenge faced by mountainous areas. Up to now, the combined solution for minimized agrochemicals intervention in the post zero-growth era has not been systematically analyzed globally. Here, the Hengduan Mountain regions (HMR) in China, as a case, we estimated the turning points of agrochemicals input intensities using a quadratic equation, as well as integrating policy document analysis and literature review. Results show that the occurred timeline of fertilizer and pesticide use zero-growth in 10 municipalities (prefectures) in the HMR is relatively wide, with a distribution from 2009 to 2019, illustrating that all municipalities (prefectures) have been achieved national goals ahead of 2020 deadline. Thus, the incentive of a series of national-level policies focusing on chemical fertilizers and pesticides has proven effective in achieving the zero-growth target of agrochemicals input in the HMR. However, comparison with major mountainous countries like Germany, Italy, Portugal, Romania, Austria, and Spain etc., there are clearly many opportunities for enhancement in reducing fertilizer and pesticide uses. We present a practical route to minimize agrochemicals application in the HMR through crop rotation-based agro-biodiversity solutions, organic alternative-based soil health solutions, professionalization-based precision farming solutions, smallholder farmers’ awareness-based behavior intervention solutions, conservation reserve-based zoning solutions, etc.
