Search Results (3,616)

Waste from zinc−carbon batteries poses a serious environmental protection problem. One of the main problems is also the reliable and rapid determination of some compounds that may be present in food and beverages consumed worldwide. This study addresses these problems and presents a possible solution for the electrochemical detection of xanthine using carbon from spent batteries. Cyclic voltammetry and differential pulse voltammetry are electrochemical methods used for the detection of xanthine. The techniques used demonstrate the mechanism of xanthine oxidation in the tested environment. A linear correlation was found between the oxidation current peaks and the xanthine concentration in the range of 5·10⁻⁷ to 1·10⁻⁴ M, as well as the values for the limit of detection and the limit of quantification, 7.86·10⁻⁸ M and 2.62·10⁻⁷ M, respectively. The interference test shows that the electrode obtained from waste Zn-C batteries has good selectivity, which means that the electrode can be used for xanthine determination in the presence of various ions. The data obtained show that carbon sensors from used zinc−carbon batteries can be used to detect xanthine in real samples. Full article

Introduction/Objective: Post-bariatric surgery patients require long-term, coordinated care to address complex nutritional, physiological, and behavioral challenges. Personalized smart nutrition, combining individualized dietary strategies with targeted monitoring, has emerged as a valuable direction for optimizing recovery and long-term outcomes. This article examines how traditional medical protocols can be enhanced by digital solutions in a multidisciplinary framework. Methods: The study analyzes current clinical practices, including personalized meal planning, physical rehabilitation, biochemical marker monitoring, and psychological counseling, as applied in post-bariatric care. These established approaches are then analyzed in relation to the NutriMonitCare system, a digital health system developed and tested in a laboratory environment. Used here as an illustrative example, the NutriMonitCare system demonstrates the potential of digital tools to support clinicians through real-time monitoring of dietary intake, activity levels, and physiological parameters. Results: Findings emphasize that medical protocols remain the cornerstone of post-surgical management, while digital tools may provide added value by enhancing data availability, supporting individualized decision making, and reinforcing patient adherence. Systems like the NutriMonitCare system could be integrated into interdisciplinary care models to refine nutrition-focused interventions and improve communication across care teams. However, their clinical utility remains theoretical at this stage and requires further validation. Conclusions: In conclusion, the integration of digital health tools with conventional post-operative care has the potential to advance personalized smart nutrition. Future research should focus on clinical evaluation, real-world testing, and ethical implementation of such technologies into established medical workflows to ensure both efficacy and patient safety. Full article

The majority of the challenges faced in power system engineering are presented as constrained optimization functions, which are frequently characterized by their complicated architectures. Metaheuristics are mathematical techniques used to solve complicated optimization problems. One such technique, Artificial Rabbits Optimization (ARO), has been designed to address global optimization challenges. However, ARO has limitations in terms of search functionality, restricting its efficiency in dealing with constrained optimization environments. To improve ARO’s compatibility with a variety of challenging problems, this work proposes implementing the Cauchy mutation operator into the position-updating procedure during the exploration stage. Furthermore, a novel multi-mode control parameter is developed to facilitate a smooth transition between exploration and exploitation phases. The enhancements may boost the performance and serve as an effective optimization tool for tackling complex engineering tasks. The improved version is known as Cauchy Artificial Rabbits Optimization (CARO). The proposed CARO’s performance is evaluated using eleven power system challenges as part of the CEC2020 competition’s test set of real-world constrained problems. The experimental results demonstrate the practical applicability of the proposed CARO in engineering applications and provide areas for future investigation. Full article

The rising complexity of industrial systems following the Industry 4.0 era involves new challenges and the need for innovative solutions. In the context of arising digital technologies, Digital Twins represent a holistic solution to overcome heterogeneity and to achieve remote and dynamic control of cyber–physical systems. In common reference architectures, decision-making modules are usually integrated for system and process optimization. This work aims at introducing the adoption of a multi-purpose simulation module in a Digital Twin environment, with the objective of proving its versatility for different scopes. This is implemented in a relevant laboratory environment, strongly employed for the test and validation of mechatronic solutions. The paper starts from revising the common techniques adopted for decision-making modules in Digital Twin frameworks, proposing then a multi-purpose approach based on physics simulation. Performance profiling of the simulation environment demonstrates the potential of real-time-capable simulation while also revealing challenges related to computational load and communication latency. The outcome of this work is to provide the reader with an exemplary modular arrangement for the integration of such module in Digital Twin applications, highlighting challenges and limitations related to computational effort and communication. Full article

In this article, thermal investigation methods for electrically insulating and thermally conductive substrate materials will be presented. The investigations were performed in their real-world application environment, i.e., in the form of IGBT (insulated gate bipolar transistor) module substrate plates. First, the overall thermal resistance and thermal structure function of the system in a multivariable parameter space were revealed using CFD (computational fluid dynamics) simulations. Afterwards, thermal transient testing was performed on real samples, with the help of which the thermal resistance values of the modules were determined using the thermal dual interface test method. The presented tests are not intended to determine material parameters, but to rank different substrate materials based on their thermal performance. Full article

High demand for sustainable solutions in the construction industry determines the significant relevance of developing new eco-friendly composites with a reduced carbon impact on the environment. The main aim of this study is to investigate the possibility and efficiency of using technical sulfur (TS) as a modifying additive for geopolymer composites and to select the optimal content of polypropylene fiber (PF). To assess the potential of TS, experimental samples of geopolymer solutions based on metakaolin and fly ash were prepared. The TS content varied from 0% to 9% by weight of binder in 3% increments. In the first stage, the density, compressive and flexural strength, capillary water absorption and microstructure of hardened geopolymer composites were tested. The TS additive in an amount of 3% was the most effective and provided an increase in compressive strength by 12.6%, flexural strength by 12.8% and a decrease in capillary water absorption by 18.2%. At the second stage, the optimal PF content was selected, which was 0.75%. The maximum increases in strength properties were recorded for the composition with 3% TS and 0.75% PF: 8% for compression and 32.6% for bending. Capillary water absorption decreased by 12.9%. The geopolymer composition developed in this work, modified with TP and PF, has sufficient mechanical and physical properties and can be considered for further study in order to determine its competitiveness with cement composites in real construction practice. Full article

Wheat is one of the important grain crops, and spike counting is crucial for predicting spike yield. However, in complex farmland environments, the wheat body scale has huge differences, its color is highly similar to the background, and wheat ears often overlap with each other, which makes wheat ear detection work face a lot of challenges. At the same time, the increasing demand for high accuracy and fast response in wheat spike detection has led to the need for models to be lightweight function with reduced the hardware costs. Therefore, this study proposes a lightweight wheat ear detection model, CML-RTDETR, for efficient and accurate detection of wheat ears in real complex farmland environments. In the model construction, the lightweight network CSPDarknet is firstly introduced as the backbone network of CML-RTDETR to enhance the feature extraction efficiency. In addition, the FM module is cleverly introduced to modify the bottleneck layer in the C2f component, and hybrid feature extraction is realized by spatial and frequency domain splicing to enhance the feature extraction capability of wheat to be tested in complex scenes. Secondly, to improve the model’s detection capability for targets of different scales, a multi-scale feature enhancement pyramid (MFEP) is designed, consisting of GHSDConv, for efficiently obtaining low-level detail information and CSPDWOK for constructing a multi-scale semantic fusion structure. Finally, channel pruning based on Layer-Adaptive Magnitude Pruning (LAMP) scoring is performed to reduce model parameters and runtime memory. The experimental results on the GWHD2021 dataset show that the ${\mathrm{AP}}_{50}$ of CML-RTDETR reaches 90.5%, which is an improvement of 1.2% compared to the baseline RTDETR-R18 model. Meanwhile, the parameters and GFLOPs have been decreased to 11.03 M and 37.8 G, respectively, resulting in a reduction of 42% and 34%, respectively. Finally, the real-time frame rate reaches 73 fps, significantly achieving parameter simplification and speed improvement. Full article

The acoustic performance of windows significantly influences evaluations of building quality, particularly in urban environments. This study presents the results of laboratory tests on the airborne sound insulation of windows with dimensions greater than those specified in ISO 10140-5:2021-10. The aim was to determine the impact of construction details and installation techniques on sound insulation, specifically R_w and R_w + C_tr values. The experimental variables included mounting methods (expansion tape versus low-pressure polyurethane foam), the presence or absence of a threshold in the lower frame, and the type of mullion (fixed versus movable). The tests involved two types of IGUs characterized by different acoustic properties. The findings indicate that the frame configuration, including threshold and mullion type, has a negligible influence on sound insulation. However, the standard method for estimating acoustic performance (EN 14351-1:2006 + A2:2017), which relies on IGU-based data, proved unreliable for modern window assemblies. The estimated values of R_w and R_w + C_tr were consistently lower than those obtained from direct laboratory measurements. These results highlight the need for verification through full-size window testing and suggest that reliance on simplified estimation procedures may lead to underperformance in real-world acoustic applications. Full article

Accurate methane emission estimates are essential for climate policy, yet current field methods often struggle with spatial constraints and source complexity. Ground-based mobile approaches frequently miss key plume features, introducing bias and uncertainty in emission rate estimates. This study addresses these limitations by using small unmanned aerial systems equipped with precision gas sensors to measure methane alongside co-released tracers. We tested whether arc-shaped flight paths and alternative ratio estimation methods could improve the accuracy of tracer-based emission quantification under real-world constraints. Controlled releases using ethane and nitrous oxide tracers showed that (1) arc flights provided stronger plume capture and higher correlation between methane and tracer concentrations than traditional flight paths; (2) the cumulative sum method yielded the lowest relative error (as low as 3.3%) under ideal mixing conditions; and (3) the arc flight pattern yielded the lowest relative error and uncertainty across all experimental configurations, demonstrating its robustness for quantifying methane emissions from downwind plume measurements. These findings demonstrate a practical and scalable approach to reducing uncertainty in methane quantification. The method is well-suited for challenging environments and lays the groundwork for future applications at the facility scale. Full article

Intelligent driving is a key technology in the field of automotive manufacturing due to its advantages in environmental protection, energy efficiency, and economy. However, since the intelligent driving model is an uncertain multi-input multi-output dynamic system, especially in an interactive environment, it faces uncertainties such as non-uniform trial lengths, unknown nonlinear parameters, and unknown control direction. In this paper, an adaptive iterative learning control method is proposed for vehicle systems with non-uniform trial lengths and asymmetric control gain matrices. Unlike the existing research on adaptive iterative learning for non-uniform test lengths, this paper assumes that the elements of the system’s control gain matrix are asymmetric. Therefore, the assumption made in traditional adaptive iterative learning methods that the control gain matrix of the system is known or real, symmetric, and positive definite (or negative definite) is relaxed. Finally, to prove the convergence of the system, a composite energy function is designed, and the effectiveness of the adaptive iterative learning method is verified using vehicle systems. This paper aims to address the challenges in intelligent driving control and decision-making caused by environmental and system uncertainties and provides a theoretical basis and technical support for intelligent driving, promoting the high-quality development of intelligent transportation. Full article

The electrification of road vehicles is a relatively mature sector, while other areas of mobility, such as construction machinery, are just beginning their transition to electric solutions. This work presents the design and realization of an integrated drive system specifically developed for retrofitting fan drives in heavy machinery, like bulldozers and tractors, utilizing existing 48 VDC batteries. By replacing or complementing internal combustion and hydraulic technologies with electric solutions, significant advantages in efficiency, reduced environmental impact, and versatility can be achieved. Focusing on the fan drive system addresses the critical challenge of thermal management in high ambient temperatures and harsh environments, particularly given the high current requirements for $3\mathrm{k}\mathrm{W}$-class applications. A sensorless architecture has been selected to enhance reliability by eliminating mechanical position sensors. The developed fan drive has been extensively tested both on a braking bench and in real-world applications, demonstrating its effectiveness and robustness. Future work will extend this prototype to electrify additional onboard hydraulic motors in these machines, further advancing the electrification of heavy-duty equipment and improving overall efficiency and environmental impact. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

To address the issues of low permeability, clogging susceptibility, and insufficient circumferential bearing capacity of traditional drainage blind pipes behind tunnel linings in water-rich areas, this study proposes a novel curved-mesh circumferential drainage blind pipe specifically designed for such environments. First, through engineering surveys and comparative analysis, the limitations and application demands of conventional circumferential annular drainage blind pipes in highway tunnels were identified. Based on this, the key parameters of the new blind pipe—including material, wall thickness, and aperture size—were determined. Laboratory tests were then conducted to evaluate the performance of the newly developed pipe. Subsequently, the pipe was applied in a real-world tunnel project, where a construction process and an in-service blockage inspection method for circumferential drainage pipes were proposed. Field application results indicate that, compared to commonly used FH50 soft permeable pipes and F100 semi-split spring pipes, the novel curved-mesh drainage blind pipe exhibits superior circumferential load-bearing capacity, anti-clogging performance, and deformation resistance. The proposed structure provides a total permeable area exceeding 17,500 mm², three to four times larger than that of conventional drainage pipes, effectively meeting the drainage requirements behind tunnel linings in high-water-content zones. The use of four-way connectors enhanced integration with other drainage systems, and inspection of the internal conditions confirmed that the pipe remained free of clogging and deformation. Furthermore, the curved-mesh design offers better conformity with the primary support and demonstrates stronger adaptability to complex installation conditions. Full article

This study examines the effects of gamified and non-gamified digital board games on students’ digital literacy and engagement. A total of 98 undergraduate students (n = 98) were randomly assigned to one of two conditions: gamified or non-gamified. The digital board game, designed to simulate real-world digital literacy scenarios, was implemented in a classroom setting. Students’ digital literacy performance was assessed through pre- and post-tests, and their engagement was measured using the Game Engagement Questionnaire. The results revealed that students in the gamified condition significantly outperformed those in the non-gamified condition in digital literacy post-test scores (p = 0.039). Additionally, the gamified group showed significantly higher engagement scores in flow (p = 0.039), enjoyment (p = 0.033), immersion (p = 0.042), and social interaction (p = 0.030). These findings highlight the effectiveness of gamified learning environments in enhancing digital literacy skills and multidimensional engagement. Full article

Superb Fairy-wren Optimization Algorithm (SFOA) is an animal-based meta-heuristic algorithm derived from Fairy-wren’s behavior of growing, feeding, and avoiding natural enemies. The SFOA has some shortcomings when facing complex environments. Its switching mechanism is not enough to adapt to complex optimization problems, and it faces a weakening of population diversity in the late stage of optimization, leading to a higher possibility of falling into local optima. In addition, its global search ability needs to be improved. To address the above deficiencies, this paper proposes an Adaptive Superb Fairy-wren Optimization Algorithm (ASFOA). To assess the ability of the proposed ASFOA, three groups of experiments are conducted in this paper. Firstly, the effectiveness of the proposed improved strategies is checked on the CEC2018 test set. Second, the ASFOA is compared with eight classical/highly cited/newly proposed metaheuristics on the CEC2018 test set, in which the ASFOA performed the best overall, with average rankings of 1.621, 1.138, 1.483, and 1.966 in the four-dimensional cases, respectively. Then the convergence and robustness of ASFOA is verified on the CEC2022 test set. The experimental results indicate that the proposed ASFOA is a competitive metaheuristic algorithm variant with excellent performance in terms of convergence and distribution of solutions. In addition, we further validate the ability of ASFOA to solve real optimization problems. The average ranking of the proposed ASFOA on 10 engineering constrained optimization problems is 1.500. In summary, ASFOA is a promising variant of metaheuristic algorithms. Full article