Search Results (74)

Guaranteeing food safety in the European Union (EU) is a continuing issue affected by diverse national traditions, regulatory power, and consumer culture. Despite the presence of a harmonized regulatory context, there continues to be variability in performance among the 27 member states. This study gives an extensive comparative evaluation of EU food safety based on three indicators: Rapid Alert System for Food and Feed (RASFF) alerts, pesticide maximum-residue-limit (MRL) violation, and per capita food loss. Fuzzy TOPSIS, K-means clustering, and scenario-based sensitivity tests are used to give an extensive appraisal of the performance of member states. Alarming differences are quoted as findings of significance. The highest number of RASFF notifications (212) and percentage of pesticide MRL non-compliance (1.5%) were reported in 2022 by Bulgaria, whereas the lowest values were reported by Estonia and Lithuania—15–20 RASFF notifications and less than 0.6% MRL violation rates. A statistically significant correlation (r = 0.72, p < 0.001) between pesticide MRL violation and food safety warnings was confirmed in favor of pesticide regulation as the optimal predictor of food safety warnings. On the other hand, food loss did not significantly affect safety measures but indicated very high variation (from 76 kg/capita per year in Croatia to 142 kg/capita per year in Greece). These findings suggest that while food loss remains an environmental problem, pesticide control is more central to the protection of food safety. Targeted policy is what the research necessitates: intervention and stricter enforcement in low-income countries, and diffusion of best practice from successful states. The composite approach adds to EU food safety policy discourse through the combination of performance indicators and targeted regulatory emphasis. Full article

Nodal tides, which follow an 18.6-year cycle, influence tidal variations at any given location in the ocean. Conventional nodal tide theory neglects land effects and topological change. Due to the complex seabed topography around Taiwan waters, the purpose of this paper is to use the long-term tidal data of six stations to discuss the effects of perigean and nodal tides on 20 constituents and to compare the results with previous theories. A modulation method is employed to fit the annual amplitude estimated by harmonic analysis (HA). The top four constituents of the fitted and theoretical values of nodal amplitude factor (AF) and phase factor (PF) are O₁, K₁, K₂, and Q₁. We find that perigean tides or second-order nodal tides considered in the fitting contribute to almost identical performance. The linear time change considered in the AF fitting has better fitting than the mean water level involved. Full article

Under the backdrop of urban renewal, harmonizing transit-oriented development (TOD) with urban renewal to maximize rail value has emerged as a critical focus in contemporary planning. Based on this, this paper proposes the node–place–revenue (NPR) model, which constructs evaluation indexes from the three dimensions of the node, place, and revenue. It determines the weights of each index by using expert scoring and the Analytic Hierarchy Process (AHP). Taking Shenyang Metro Line 1 as an example, the study first used the model to measure the node value, place value, and revenue value of each sample TOD station area. Secondly, K-means clustering analysis was used to form a spatial classification of five station areas. Finally, this paper proposes one differentiated urban renewal strategy for each type of station area. It is found that (1) the NPR model classifies stations into five categories: stress and high revenue, balanced, unbalanced node, unbalanced place, and dependence and low revenue and (2) the differentiated urban renewal strategies for each type of station area can be explored in terms of precise decongestion, node upgrading, function expansion, endogenous optimization, and infill quality improvement. This paper examines the economic driving effect of Shenyang Metro Line 1 stations on the renewal of the surrounding areas from the perspective of the economic balance of payments, providing a new reference for Shenyang-rail-transit-guided urban renewal work. Full article

In today’s data-driven business landscape, effective customer segmentation is crucial for enhancing engagement, loyalty, and profitability. Traditional clustering methods often struggle with datasets containing both numerical and categorical variables, leading to suboptimal segmentation. This study addresses this limitation by introducing a novel application of Factor Analysis of Mixed Data (FAMD) for dimensionality reduction, integrated with K-means and Agglomerative Clustering for robust customer segmentation. While FAMD is not new in data analytics, its potential in customer segmentation has been underexplored. This research bridges that gap by demonstrating how FAMD can harmonize mixed data types, preserving structural relationships that conventional methods overlook. The proposed methodology was tested on a Kaggle-sourced retail dataset comprising 3900 customers, with preprocessing steps including correlation ratio filtering (η ≥ 0.03), standardization, and encoding. FAMD reduced the feature space to three principal components, capturing 81.46% of the variance, which facilitated clearer segmentation. Comparative clustering analysis showed that Agglomerative Clustering (Silhouette Score: 0.52) outperformed K-means (0.51) at k = 4, revealing distinct customer segments such as seasonal shoppers and high spenders. Practical implications include the development of targeted marketing strategies, validated through heatmap visualizations and cluster profiling. This study not only underscores the suitability of FAMD for customer segmentation but also sets the stage for more nuanced marketing analytics driven by mixed-data methodologies. Full article

This paper analyzes the power quality in a 400 kWp grid-connected solar photovoltaic system with storage (BESS), considering standards IEEE Std 519TM, IEEE Std 1159TM, and IEC 61000-4-30. For system analysis, a photovoltaic array model is developed. Neplan-Smarter Tools software is used for model validation, and experimental measurements are performed on the actual photovoltaic system, recording total harmonic distortion (THDi/THDv). A class B power quality monitor was used to measure three-phase electrical variables: current, voltage, power, power factor, and THD. The THD level was generated at an energy level below 20% of the rated power, resulting in high THDi. The recorded THDv remained below 2.5%, which means that its value is limited by the IEEE 519 standard. When the BESS was connected to the PCC grid, the voltage level remained regulated, and the electrical system appeared to be stable. This paper contributes a methodology and procedure for measurement and power quality assessment, allowing for THD identification and enabling designers to configure better designs and energy system protections when integrating solar photovoltaic energy into an electrical distribution network. Full article

The gradual deterioration of underground water infrastructure requires constant condition monitoring to prevent catastrophic failures, reduce leaks, and avoid costly unexpected repairs. However, given the large scale and tight budgets of water utilities, it is essential to implement strategies for optimal selection and deployment of monitoring technologies. This article introduces a unified framework and methods for optimally selecting condition monitoring technologies while locating their deployment at the most vulnerable pipe segments. The approach is underpinned by an R-E-R-A-V (Redundant, Established, Reliable, Accurate, and Viable) principle and asset management concepts. The proposed method is supported by a thorough review of assessment and monitoring technologies, as well as common sensor placement approaches. The approach selects optimal technology using a combination of technology readiness levels and SFAHP (Spherical Fuzzy Analytic Hierarchy Process). Optimal placement is achieved with a k-Nearest Neighbors (kNN) model tuned with minimal topological and physical pipeline system features. Feature engineering is performed with OPTICS (Ordering Points to Identify the Clustering Structure) by evaluating the pipe segment vulnerability to failure-prone areas. Both the optimal technology selection and placement methods are integrated through a proposed algorithm. The optimal placement of monitoring technology is demonstrated through a modified benchmark network (Net3). The results reveal an accurate model with robust performance and a harmonic mean of precision and recall of approximately 65%. The model effectively identifies pipe segments requiring monitoring to prevent failures over a period of 11 years. The benefits and areas of future exploratory research are explained to encourage improvements and additional applications. Full article

A grid-tied inverter needs excellent maximum power point tracking (MPPT) topology to extract the maximum energy from PV panels regarding energy creation. An efficient MPPT ensures that grid codes are met, maintains power quality and system reliability, minimizes power losses, and suppresses rapid response to power fluctuations due to solar irradiance. Moreover, appropriate MPPT enhances economic returns by increasing energy royalties and ensures high power quality with reduced harmonic distortion. For these reasons, an improved hybrid MPPT technique for a grid-tied solar system is presented based on particle swarm optimization (PSO) and grey wolf optimizer (GWO-PSO) to achieve these objectives. The proposed method is tested under MATLAB/Simulink 2024a for a 100 kW PV array connected with a boost converter to link with a voltage source converter (VSC). The simulation results show that the proposed GWO-PSO can reduce the overshoot on rise time along with settling time, meaning less time is wasted within the grid power system. Moreover, the suggested method is compared with PSO, GWO, and horse herd optimization (HHO) under different weather conditions. The results show that the other algorithms respond more slowly and exhibit higher overshoot, which can be counterproductive. These comparisons validate the proposed method as more accurate, demonstrating that it can enhance the real power quality that is transferred to the grid. Full article

This paper presents a comprehensive assessment of the accuracy of high-frequency (HF) earth meters in measuring the tower-footing ground resistance of transmission line structures, combining simulation and experimental results. The findings demonstrate that HF earth meters reliably estimate the harmonic grounding impedance ($R_{25\mathrm{k}\mathrm{H}\mathrm{z}}$) at their operating frequency, typically 25 kHz, for a wide range of soil resistivities and typical span lengths. For the analyzed tower geometries, the simulations indicate that accurate measurements are obtained for adjacent span lengths of approximately 300 m and 400 m, corresponding to configurations with one and two shield wires, respectively. Acceptable errors below 10% are observed for span lengths exceeding 200 m and 300 m under the same conditions. While the measured $R_{25\mathrm{k}\mathrm{H}\mathrm{z}}$ does not directly represent the resistance at the industrial frequency, it provides a meaningful measure of the grounding system’s impedance, enabling condition monitoring and the evaluation of seasonal or event-related impacts, such as damage after outages. Furthermore, the industrial frequency resistance can be estimated through an inversion process using an electromagnetic model and knowing the geometry of the grounding electrodes. Overall, the results suggest that HF earth meters, when correctly applied with the fall-of-potential method, offer a reliable means to assess the grounding response of high-voltage transmission line structures in most practical scenarios. Full article

The circular economy (CE) framework is increasingly recognized as essential for achieving sustainable development by addressing the challenges of resource depletion, waste generation, and environmental degradation. This study examines the relationship between resource consumption, waste management procedures, and energy efficiency within European Union (EU) member states, leveraging data from 2004 to 2023. Using Pearson correlation analysis, Principal Component Analysis (PCA), and K-means clustering, this study identifies key sustainability performance indicators and classifies EU nations into four distinct clusters based on CE adoption. These findings reveal a strong positive correlation between resource productivity and circular material use, indicating that efficient resource management significantly enhances sustainability performance. Similarly, energy productivity exhibits a moderate correlation with resource efficiency, suggesting that economies optimizing energy consumption also enhance material use efficiency. This study also assesses the role of policy instruments, such as environmental taxation, which show a weak negative correlation with resource productivity. These insights provide actionable recommendations for policymakers to tailor interventions, harmonize sustainability strategies, and address regional disparities to accelerate the transition to a resilient and efficient circular economy model. Full article

Wireless sensor networks (WSNs) are vulnerable to several challenges. Congestion control, the utilization of trust to ensure security, and the incorporation of clustering schemes demand much attention. Algorithms designed to deal with congestion control fail to ensure security and address challenges faced due to congestion in the network. To resolve this issue, a Hybrid Trust-based Congestion-aware Cluster Routing (HTCCR) protocol is proposed to effectively detect attacker nodes and reduce congestion via optimal routing through clustering. In the proposed HTCCR protocol, node probability is determined based on the trust factor, queue congestion status, residual energy (RE), and distance from the mobile base station (BS) by using hybrid K-Harmonic Means (KHM) and the Enhanced Gravitational Search Algorithm (EGSA). Sensor nodes select cluster heads (CHs) with better fitness values and transmit data through them. The CH forwards data to a mobile sink once the sink comes into the range of CH. Priority-based data delivery is incorporated to effectively control packet forwarding based on priority level, thus decreasing congestion. It is evident that the propounded HTCCR protocol offers better performance in contrast to the benchmarked TBSEER, CTRF, and TAGA based on the average delay, packet delivery ratio (PDR), throughput, detection ratio, packet loss ratio (PLR), overheads, and energy through simulations. The proposed HTCCR protocol involves 2.5, 2.3, and 1.7 times less delay; an 18.1%, 12.5%, and 5.5% better detection ratio; 2.9, 2.6, and 1.8 times less energy; a 2.2, 1.9, and 1.5 times lower PLR; a 14.5%, 10.5%, and 5.2% better PDR; a 30.7%, 28.5%, and 18.4% better throughput; and 2.27, 1.91, and 1.66 times lower routing overheads in contrast to the TBSEER, CTRF, and TAGA protocols, respectively. The HTCCR protocol involves 4.1% less delay for the ‘$C_1$’ and ‘$C_2$’ RT packets, and the average throughput of RT is 10.4% better when compared with NRT. Full article

Clustering algorithms have the characteristics of being simple and efficient and can complete calculations without a large number of datasets, making them suitable for application in noise reduction processing for audio module mass production testing. In order to solve the problems of the NMF algorithm easily getting stuck in local optimal solutions and difficult feature signal extraction, an improved NMF audio denoising algorithm combined with K-means initialization was designed. Firstly, the Euclidean distance formula of K-means has been improved to extract audio signal features from multiple dimensions. Combined with the initialization strategy of K-means decomposition, the initialization dictionary matrix of the NMF algorithm has been optimized to avoid getting stuck in local optimal solutions and effectively improve the robustness of the algorithm. Secondly, in the sparse coding part of the NMF algorithm, feature extraction expressions are added to solve the problem of noise residue and partial spectral signal loss in audio signals during the operation process. At the same time, the size of the coefficient matrix is limited to reduce operation time and improve the accuracy of feature extraction in high-precision audio signals. Then, comparative experiments were conducted using the NOIZEUS and NOISEX-92 datasets, as well as random noise audio signals. This algorithm improved the signal-to-noise ratio by 10–20 dB and reduced harmonic distortion by approximately −10 dB. Finally, a high-precision audio acquisition unit based on FPGA was designed, and practical applications have shown that it can effectively improve the signal-to-noise ratio of audio signals and reduce harmonic distortion. Full article

With the increasing demand for power supply reliability, online monitoring techniques for cable health condition assessments are gaining more attention. Most prevailing techniques lack the sensitivity needed to detect minor insulation defects. A new monitoring technique based on the harmonic anomaly feature analysis of the shield-to-ground current is introduced in this paper. The sensor installation and data acquisition are convenient and intrinsically safe, which makes it a preferred online monitoring technique. This study focuses on the single-core 10 kV distribution cable type. The research work includes the theoretical analysis of the cable defect’s impact on the current harmonic features, which are then demonstrated by simulation and lab experiments. It has been found that cable insulation defects cause magnetic field distortion, which introduces various harmonic current components, principally, the third-, fifth-, and seventh-order harmonic. The harmonic anomaly features are load current-, defect type-, and aging time-dependent. The K-means algorithm was selected as the data analysis algorithm and was used to achieve insulation defect prediction. The research outcome establishes a solid basis for the field application of the shield-to-ground harmonic current monitoring technique. Full article

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed by batch normalization and one-dimensional max-pooling (MaxPooling1D) layers, culminating in dense layers for regression-based SoC prediction. To improve training effectiveness, we introduce an advanced dynamic k-decay learning rate scheduling method. This technique dynamically adjusts the learning rate during training, responding to changes in validation loss to fine-tune the training process. Experimental validation was conducted on various drive cycles, including the dynamic stress test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), United States 2006 Supplemental Federal Test Procedure (US06), and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), spanning four temperature conditions (−5 °C, 5 °C, 25 °C, 45 °C). Notably, the test error of DST and US06 drive cycles, the CNN with optimization achieved a mean absolute error (MAE) of 0.0091 and 0.0080, respectively at 25 °C, and a root mean square error (RMSE) of 0.013 and 0.0095, respectively. In contrast, the baseline CNN without optimization yielded higher MAE and RMSE values of 0.011 and 0.014, respectively, on the same drive cycles. Additionally, training time with the optimization technique was significantly reduced, with a recorded time of 324.14 s compared to 648.59 s for the CNN without optimization at room temperature. These results demonstrate the effectiveness of the proposed CNN architecture combined with advanced dynamic learning rate scheduling in accurately predicting SoC across various battery types and drive cycles. The optimization technique not only improves prediction accuracy but also substantially reduces training time, highlighting its potential for enhancing battery management systems in electric vehicle applications. Full article

Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag. Full article

Quality prediction in additive manufacturing (AM) processes is crucial, particularly in high-risk manufacturing sectors like aerospace, biomedicals, and automotive. Acoustic sensors have emerged as valuable tools for detecting variations in print patterns by analyzing signatures and extracting distinctive features. This study focuses on the collection, preprocessing, and analysis of acoustic data streams from a Fused Deposition Modeling (FDM) 3D-printed sample cube (10 mm × 10 mm × 5 mm). Time and frequency-domain features were extracted at 10-s intervals at varying layer thicknesses. The audio samples were preprocessed using the Harmonic–Percussive Source Separation (HPSS) method, and the analysis of time and frequency features was performed using the Librosa module. Feature importance analysis was conducted, and machine learning (ML) prediction was implemented using eight different classifier algorithms (K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Decision Trees (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM)) for the classification of print quality based on the labeled datasets. Three-dimensional-printed samples with varying layer thicknesses, representing two print quality levels, were used to generate audio samples. The extracted spectral features from these audio samples served as input variables for the supervised ML algorithms to predict print quality. The investigation revealed that the mean of the spectral flatness, spectral centroid, power spectral density, and RMS energy were the most critical acoustic features. Prediction metrics, including accuracy scores, F-1 scores, recall, precision, and ROC/AUC, were utilized to evaluate the models. The extreme gradient boosting algorithm stood out as the top model, attaining a prediction accuracy of 91.3%, precision of 88.8%, recall of 92.9%, F-1 score of 90.8%, and AUC of 96.3%. This research lays the foundation for acoustic based quality prediction and control of 3D printed parts using Fused Deposition Modeling and can be extended to other additive manufacturing techniques. Full article