Search Results (193)

To study the effect of cold plasma (CP) on the refrigerator shelf life of coconut water, microorganism growth and diversity and physicochemical properties were investigated. Results indicated that CP treatment did not cause significant color changes in coconut water, with turbidity remaining lower than the control even after 6 days of storage. Enzymatic activity analysis revealed reduced polyphenol oxidase (PPO) and peroxidase (POD) levels in treated samples. Specifically, the 12 s CP treatment resulted in the lowest antioxidant capacity values: 15.77 Fe²⁺/g for ferric reducing antioxidant power (FRAP), 37.15% for DPPH radical scavenging, and 39.51% for ABTS⁺ radical scavenging. Microbial enumeration showed that extended CP treatment effectively inhibited the growth of total viable counts, psychrophilic bacteria, lactic acid bacteria, and yeast. High-throughput sequencing identified Leuconostoc, Carnobacterium, and Lactobacillus as the dominant bacterial genera. During storage, Carnobacterium was the primary genus in the early stage, while Leuconostoc emerged as the dominant genus by the end of the storage period. In summary, CP as an effective non-thermal technology was able to maintain quality and antioxidant capacity, inhibit microbial growth, and delay the spoilage in coconut water to help extend the refrigerated shelf life of the product. Full article

Reproductive efficiency is a critical determinant of productivity and profitability in sheep farming. Traditional selection methods have largely relied on phenotypic traits and historical reproductive records, which are often limited by subjectivity and delayed feedback. Recent advancements in artificial intelligence (AI), including video tracking, wearable sensors, and machine learning (ML) algorithms, offer new opportunities to identify behavior-based indicators linked to key reproductive traits such as estrus, lambing, and maternal behavior. This review synthesizes the current research on AI-powered behavioral monitoring tools and proposes a conceptual model, ReproBehaviorNet, that maps age- and sex-specific behaviors to biological processes and AI applications, supporting real-time decision-making in both intensive and semi-intensive systems. The integration of accelerometers, GPS systems, and computer vision models enables continuous, non-invasive monitoring, leading to earlier detection of reproductive events and greater breeding precision. However, the implementation of such technologies also presents challenges, including the need for high-quality data, a costly infrastructure, and technical expertise that may limit access for small-scale producers. Despite these barriers, AI-assisted behavioral phenotyping has the potential to improve genetic progress, animal welfare, and sustainability. Interdisciplinary collaboration and responsible innovation are essential to ensure the equitable and effective adoption of these technologies in diverse farming contexts. Full article

This paper examines the relationship between investment in water education and economic performance, focusing on the context of widening countries (EU Member States and Associated Countries with lower research and innovation performance). Through time-series data and panel regression analysis, the study investigates whether increased spending on education correlates with Gross Domestic Product (GDP) growth. While the initial static model indicates a positive but statistically insignificant association, a dynamic model with lagged GDP significantly improves explanatory power, suggesting that educational investments may influence growth with a temporal delay. Complementing the macroeconomic data, the paper analyses how targeted investments in educational innovation, especially in digital technologies such as virtual reality (VR) applications, enhance teaching quality and student engagement. Examples from partner universities involved in the WATERLINE project (Horizon Europe, 101071306) show how custom-built VR modules, aligned with existing hydraulic labs, contribute to advanced water-related skills. The paper also presents a cost-benefit analysis of VR applications in water education, highlighting their economic efficiency compared to traditional laboratory equipment. Additionally, it explores how micro-level innovations in education can generate macroeconomic benefits through widespread adoption and systemic impact. Ultimately, the research highlights the long-term value of education and innovation in strengthening both economic and human capital across diverse regions. Full article

5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article

In the present study, the primary by-products of the hemp-seed oil process—hemp seed cake flour and hemp seed protein concentrate—underwent enzymatic hydrolysis using proteases and carbohydrases, either individually or in combination. The effectiveness of these enzymatic treatments in releasing bioactive compounds was evaluated by assessing the antioxidant and anti-inflammatory properties of the aqueous extracts of both hydrolysed and untreated hemp by-products. The aim was to explore their potential senotherapeutic properties and promote their application as dietary supplements. Secondary metabolites such as flavonoids, phenolic acids, and catechins were analysed using high-performance liquid chromatography. Total phenolic, flavonoid, and protein contents were determined using spectrophotometric methods. Scavenging activity (2,2-Diphenyl-1-picrylhydrazyl scavenging assay (DPPH assay)), antioxidant power (Ferric reducing antioxidant power assay (FRAP assay)), and lipid peroxidation-reducing activity (thiobarbituric acid-reactive substance analysis) were assessed through in vitro assays. Possible anti-inflammatory effects were evaluated by assessing haemolysis inhibition. The impact of extracts on albumin glycation induced by exposure to fructose was also determined. To assess the toxicity of extracts, a zebrafish larvae model was employed. All extracts contained significant amounts of phenolic compounds, flavonoids, and proteins, and they exhibited notable activities in reducing lipid peroxidation and stabilising erythrocyte cell membranes. However, they did not significantly influence protein glycation (the glycation inhibition was only in the range of 15–40%). Our research demonstrates the substantial health-promoting potential, including senescence delay, of aqueous extracts from by-products of the hemp-seed oil process, which are available in large quantities and can serve as valuable supplements to support the health of animals, including humans, rather than being discarded as waste from oil production. Full article

Dynamic comparators play an important role in electronic systems, requiring high accuracy, low power consumption, and minimal offset voltage. This work proposes an accurate and low-complexity offset calibration design based on a capacitive load approach. It was designed using a 65 nm CMOS technology and comprehensively evaluated under Monte Carlo simulations and PVT variations. The proposed scheme was built using MIM capacitors and transistor-based capacitors, and it includes Verilog-based calibration algorithms. The proposed offset calibration is benchmarked, in terms of precision, calibration time, energy consumption, delay, and area, against prior calibration techniques: current injection via gate biasing by a charge pump circuit and current injection via parallel transistors. The evaluation of the offset calibration schemes relies on Analog/Mixed-Signal (AMS) simulations, ensuring accurate evaluation of digital and analog domains. The charge pump method achieved the best Energy-Delay Product (EDP) at the cost of lower long-term accuracy, mainly because of its capacitor leakage. The proposed scheme demonstrated superior performance in offset reduction, achieving a one-sigma offset of 0.223 mV while maintaining precise calibration. Among the calibration algorithms, the window algorithm performs better than the accelerated calibration. This is mainly because the window algorithm considers noise-induced output oscillations, ensuring consistent calibration across all designs. This work provides insights into the trade-offs between energy, precision, and area in dynamic comparator designs, offering strategies to enhance offset calibration. Full article

Ammonia is gaining increasing attention as a zero-carbon fuel and hydrogen carrier, offering high energy density, mature liquefaction infrastructure, and strong compatibility with existing energy systems. This review presents a comprehensive summary of the recent advances in ammonia-based clean energy systems. It covers the fuel’s physicochemical properties, green synthesis pathways, storage and transport technologies, combustion behavior, NO_X formation mechanisms, emission control strategies, and safety considerations. Co-firing approaches with hydrogen, methane, coal, and DME are evaluated to address ammonia’s low reactivity and narrow flammability limits. This paper further reviews engineering applications across power generation, maritime propulsion, and long-duration energy storage, drawing insights from current demonstration projects. Key technical barriers—including ignition delay, NO_X emissions, ammonia slip, and economic feasibility—are critically examined. Finally, future development trends are discussed, highlighting the importance of integrated system design, low-NO_X combustor development, solid-state storage materials, and supportive policy frameworks. Ammonia is expected to serve as a strategic energy vector bridging green hydrogen production with zero-carbon end-use, facilitating the transition to a sustainable, secure, and flexible energy future. Full article

A PCB stator axial flux permanent magnet (AFPM) motor is presented that overcomes the manufacturing challenges associated with the complex geometry of conventional stators by employing a PCB substrate. Traditionally, AFPM motors are produced by winding coils around the stator teeth, a process that requires specialized winding machinery and is both labor intensive and time consuming, ultimately incurring considerable manufacturing costs and delays. In contrast, PCB substrates offer significant advantages in manufacturability and mass production, effectively resolving these issues. Furthermore, the primary material used in PCB substrates, FR-4, exhibits a permeability similar to that of air, resulting in negligible electromagnetic cogging torque. Cogging torque arises from the attraction between permanent magnets and stator teeth, creating forces that interfere with motor rotation and generate unwanted vibration, noise, and potential mechanical collisions between the rotor and stator. In the PCB stator design, the conventional PCB circuit pattern is replaced by the motor’s coil configuration, and the absence of stator teeth eliminates these interference issues. Consequently, a slotless motor configuration with minimal vibration and noise is achieved. The PCB AFPM motor has been applied to a vehicle-mounted electric water pump (EWP), where mass production and space efficiency are critical. In an EWP, which integrates the impeller with the motor, it is essential that vibrations are minimized since excessive vibration could compromise impeller operation and, due to fluid resistance, require high power input. Moreover, the AFPM configuration facilitates higher torque generation compared to a conventional radial flux permanent magnet synchronous motor (RFPM). In a slotless AFPM motor, the absence of stator teeth prevents core flux saturation, thereby further enhancing torque performance. AC losses occur in the conductors as a result of the magnetic flux produced by the permanent magnets, and similar losses arise within the PCB circuits. Therefore, an optimized PCB circuit design is essential to reduce these losses. The Constant Trace Conductor (CTC) PCB circuit design process is proposed as a viable solution to mitigate AC losses. A 3D finite element analysis (3D FEA) model was developed, analyzed, fabricated, and validated to verify the proposed solution. Full article

Embryo rice, as a product of processing rice, improves palatability and retains the nutritional characteristics of brown rice. However, the storage period of embryo rice is only 30 d at room temperature. To delay the deterioration in the quality of embryo rice during storage, this study used polyethylene terephthalate/aluminum foil/polyethylene (PET/AL/PE) to vacuum-package embryo rice, and analyzed the quality changes under 25 or 4 °C storage conditions. At the same time, volatile compound analysis and transcriptomic analysis were integrated to explore the quality deterioration mechanism of embryo rice during storage. The electronic nose results showed that the odor of embryo rice changed significantly during different storage periods (p < 0.05). A total of 72 volatile compounds were identified by Headspace–Solid-Phase Micro-Extraction–Gas Chromatography–Mass Spectrometry (HS-SPME-GC-MS), with 2-pentylfuran, naphthalene, and styrene contributing the most in the early stage, and 2-hexenal, nonanal, trans-2-nonenal, and ethanol contributing more in the later stage. Correlation analysis showed that fatty acids, malondialdehyde (MDA), lipase, and ferric-reducing antioxidant power (FRAP) were positively correlated with aldehydes and acids (p < 0.05), while catalase (CAT) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) were negatively correlated (p < 0.05). This was mainly because the oxidative decomposition of lipids and the weakening of antioxidant capacity would lead to the accumulation of aldehydes. In the Mantel test analysis, color had the strongest correlation with volatile compounds, followed by taste value, and finally texture. In transcriptomic analysis, lipid synthesis and metabolism were key pathways for the storage deterioration of embryo rice, and the LOX gene played an important regulatory role. These results can provide a theoretical basis for the evaluation of quality and selection of storage method of embryo rice. Full article

Data accuracy is critical for sensor systems. As essential components of digital circuits within sensor systems, nanoscale CMOS latches are particularly susceptible to single-node upsets (SNUs) and double-node upsets (DNUs), which can lead to data errors. In this paper, a highly robust Double-Node-Upset-Tolerant Latch-Based on Input Splitting C-Elements (DNUISC) is proposed. The DNUISC latch is designed by interconnecting three sets of input-splitting C-elements to form a feedback loop, and it incorporates clock gating and fast-path techniques to minimize power consumption and delay. Simulations are conducted using the 28 nm process in HSPICE. The simulation results show that the DNUISC can self-recover from any single-node upset and is tolerant of any double-node upset. Compared with existing hardened latches, the DNUISC achieves a 55.21% reduction in area-power-delay product (APDP). Furthermore, the proposed DNUIS demonstrates high reliability and low sensitivity under varying process, voltage, and temperature conditions. Full article

Approximate computation has emerged as a promising alternative to accurate computation, particularly for applications that can tolerate some degree of error without significant degradation of the output quality. This work analyzes the application of approximate computing for machine learning, specifically focusing on k-means clustering, one of the more widely used unsupervised machine learning algorithms. The k-means algorithm partitions data into k clusters, where k also denotes the number of centroids, with each centroid representing the center of a cluster. The clustering process involves assigning each data point to the nearest centroid by minimizing the within-cluster sum of squares (WCSS), a key metric used to evaluate clustering quality. A lower WCSS value signifies better clustering. Conventionally, WCSS is computed with high precision using an accurate adder. In this paper, we investigate the impact of employing various approximate adders for WCSS computation and compare their results against those obtained with an accurate adder. Further, we propose a new approximate adder (NAA) in this paper. To assess its effectiveness, we utilize it for the k-means clustering of some publicly available artificial datasets with varying levels of complexity, and compare its performance with the accurate adder and many other approximate adders. The experimental results confirm the efficacy of NAA in clustering, as NAA yields WCSS values that closely match or are identical to those obtained using the accurate adder. We also implemented hardware designs of accurate and approximate adders using a 28 nm CMOS standard cell library. The design metrics estimated show that NAA achieves a 37% reduction in delay, a 22% reduction in area, and a 31% reduction in power compared to the accurate adder. In terms of the power-delay product that serves as a representative metric for energy efficiency, NAA reports a 57% reduction compared to the accurate adder. In terms of the area-delay product that serves as a representative metric for design efficiency, NAA reports a 51% reduction compared to the accurate adder. NAA also outperforms several existing approximate adders in terms of design metrics while preserving clustering effectiveness. Full article

Radio Frequency Identification (RFID) assistive systems, which integrate RFID devices with IoT technologies, are vital for enhancing the independence, mobility, and safety of individuals with disabilities. These systems enable applications such as RFID navigation for blind users and RFID-enabled canes that provide real-time location data. Central to these systems are resource-constrained RFID devices that rely on RFID tags to collect and transmit data, but their limited computational capabilities make them vulnerable to cyberattacks, jeopardizing user safety and privacy. Implementing the Elliptic Curve Cryptography (ECC) algorithm is essential to mitigate these risks; however, its high computational complexity exceeds the capabilities of these devices. The fundamental operation of ECC is finite field multiplication, which is crucial for securing data. Optimizing this operation allows ECC computations to be executed without overloading the devices’ limited resources. Traditional multiplication designs are often unsuitable for such devices due to their excessive area and energy requirements. Therefore, this work tackles these challenges by proposing an efficient and compact field multiplier design optimized for the Montgomery multiplication algorithm, a widely used method in cryptographic applications. The proposed design significantly reduces both space and energy consumption while maintaining computational performance, making it well-suited for resource-constrained environments. ASIC synthesis results demonstrate substantial improvements in key metrics, including area, power consumption, Power-Delay Product (PDP), and Area-Delay Product (ADP), highlighting the multiplier’s efficiency and practicality. This innovation enables the implementation of ECC on RFID assistive devices, enhancing their security and reliability, thereby allowing individuals with disabilities to engage with assistive technologies more safely and confidently. Full article

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility. Full article

The results of numerous studies show that the control of power grid modes is carried out mainly using a technical criterion. The economic criterion is taken into account through the use of complex and inaccurate models that do not accurately predict the result. The emergence of market relations in the energy sector makes power systems economic entities in terms of production and satisfaction of demand for electricity by various economic entities (industry, households, businesses, etc.). Under these conditions, electricity is a commodity with a corresponding price and quality indicators. This requires the application of the risk assessment methodology as an economic category in the activities of power systems as a business entity. The methodology of risk assessment in market conditions requires business entities to search for methods to minimize risk as a possibility of adverse events. Under these conditions, it becomes possible to make the best management decisions regarding the most important criterion that reflects the interests of business entities at a given time. However, the imperfection of the relevant methodology for risk assessment in the energy sector delays their application in the industry. At the same time, when making management decisions, three possible levels can be distinguished: decision-making in conditions of certainty, when the result is presented in a deterministic form and can be determined in advance; decision-making under conditions of risk, when the outcome cannot be determined in advance, but there is information on the probability of distribution of possible consequences; decision-making in conditions where the outcome is random and there is no information about the consequences of the decision. An analysis of scientific publications shows that some authors’ works are devoted to solving the issues of applying the theory and principles of risks in the energy sector, in which the problem is solved only at the first two levels. At the same time, the operation of energy facilities is characterized by a high level of uncertainty and incomplete information about the consequences of such decisions. Therefore, the development of a methodology for making management decisions in the energy sector based on the theory and practice of risks, taking into account the high level of uncertainty and incomplete information, is an urgent scientific task. Implementation of algorithms and programs for controlling the modes of power grids based on them can meet the requirements for reliable and high-quality energy supply to the most demanding consumers and create favorable conditions for their business. This work is devoted to the development of scientific and methodological foundations for determining the voltage risk in power system networks, taking into account the uncertain nature of the loads and its impact on consumers. Based on the results of the study, a mathematical model of the risk of voltage collapses in networks, an algorithm and a methodology for its calculation were proposed. Full article

In this paper, a NOR2 standard-cell-based dynamic comparator providing rail-to-rail input common mode range (ICMR) is presented, together with a novel standard-cell oriented design methodology. The proposed topology provides better speed performance and lower power-delay-product than the previously presented standard-cell-based dynamic comparators with rail-to-rail ICMR features. The NOR2 topology, which is also better than the complementary NAND2-based topology previously presented by the authors, is even able to guarantee improvements in the order of 8× –16× higher speed and 7× lower PDP, with respect to the other rail-to-rail ICMR standard-cell-based topologies in the literature. Concerning the standard-cell oriented design methodology, it is focused on the impact of the cell’s strength, which is the only free parameter, on delay, power consumption, ICMR and offset. The circuit performances are demonstrated for supply voltages equal to 600 mV, 300 mV and 150 mV, considering a 45 nm CMOS technology. Full article