Search Results (68)

The increasing digitalization of industrial ecosystems under the Industrial Revolution 4.0 has intensified the demand for fast, reliable, and inclusive broadband connectivity. The expansion of 5G technology led by data-driven services addresses the growing demand for high-capacity, low-latency communication through Fixed Wireless Access (FWA) as a cost-effective broadband solution. FWA is a wireless broadband access technology that provides high-speed connectivity to fixed locations using 5G New Radio (NR) infrastructure instead of physical fiber networks, while reducing deployment time and infrastructure investment. This review examines the technical challenges, economic business implications, and comparative performance of 5G FWA relative to other broadband technologies. It also examines the implementation of Enhanced Telecom Operations Map (eTOM) in several telecommunication network functions. The analysis indicates that successful 5G FWA implementation requires not only technical optimization, but also the adaption of standardized, scalable, and AI-driven network management practices. Emphasis is placed on the role of the eTOM as a structured framework for aligning technical, operational, and organizational processes in FWA deployment. This review highlights how eTOM can support readiness assessment, process harmonization, and lifecycle management to ensure consistent and efficient service delivery. This study provides a comprehensive reference for researchers and industry stakeholders in developing sustainable and future-ready 5G FWA networks. Full article

This study presents a novel neural parameter diffusion approach (FWA-PDiff) designed to enhance data quality. To address the limitations of conventional diffusion models—such as inefficient sampling and insufficient feature sensitivity, which may compromise output fidelity—this study introduces four key innovations. First, the proposed model introduces an adaptive recalibration of the sampling frequency in the Fourier domain to optimize feature extraction for image data. Second, a dual-channel autoencoder architecture is employed, featuring a multi-scale, fine-grained encoder (MFE) that enables the simultaneous capture of features at multiple resolutions. Third, a wavelet-attention mechanism (WA) is incorporated into the decoder to highlight subtle high-frequency details. Fourth, the proposed model introduces a hybrid loss function that combines Mean Squared Error (MSE) and Kullback–Leibler (KL) divergence to improve data reconstruction. Collectively, these improvements enable the generation of high-fidelity parameters, thereby contributing to enhanced data quality. Extensive experiments conducted on benchmark datasets—including MNIST, CIFAR-10, CIFAR-100, and STL-10—demonstrate the effectiveness of the proposed approach, which consistently achieves superior performance in improving data quality. Full article

Industrial equipment data often exhibit high dimensionality and class imbalance, which make it difficult to achieve both accurate failure detection and identification of the factors contributing to failures. To address this issue, this study proposes an explainable failure classification framework, Feature-Wise Attention Graph Convolutional Network (FWA-GCN), which combines Feature-Wise Attention (FWA) with a Graph Convolutional Network (GCN) to provide both high classification performance and variable-level interpretability. In the proposed model, tabular sensor records are treated as nodes, and a similarity-based graph is constructed to capture relationships among samples. Feature-Wise Attention learns the importance of each feature and reweights node features accordingly, and the reweighted features are then used as input to the GCN to classify failure occurrences. To alleviate the class imbalance problem, a weighted loss function is applied during training by assigning a higher weight to the failure class. Experiments conducted on the Air Pressure System (APS) dataset demonstrate that the proposed FWA-GCN achieves Precision of 79.95%, Recall of 85.07%, and F1-score of 82.43%, outperforming conventional machine learning models including Random Forest, XGBoost, CatBoost, and Multi-Layer Perceptron, as well as a standard GCN model. Furthermore, an ablation study was conducted by removing the top features selected by the attention mechanism. The results show a significant decrease in recall, confirming the effectiveness of the attention-based feature importance and supporting the interpretability of the proposed framework. Full article

When extracting entities, relations, and their associated words from scientific literature, it is imperative to consider the supporting role of feature words on the extraction results. These feature words can provide local semantic information and be combined with the global feature representation of the sentence, improving the accuracy of information extraction. However, existing methods, when fusing local semantic feature words with global features, due to ineffective distinction between the influence of feature words and non-feature words, result in limited enhancement on model performance. To solve this problem, we propose a feature words adversarial scheme (FWAS) with dual pointer method. This method implements a dynamic filtering mechanism for feature words through feature pointers, in order to semantically enhance the encoding of the original text. Simultaneously, an inverse feature pointer is designed to establish a negative weight decay mechanism, weakening interference of non-key vocabulary. During joint training, annotation information for entity relations is introduced to supervise the dual feature selection mechanism. Experimental results on three public scientific information extraction datasets demonstrate that our method consistently outperforms strong baselines, achieving up to 4.9% improvement in F1-score. This method offers a new perspective for information extraction tasks in scientific and technical literature and provides scalable optimization directions for subsequent research. Full article

Predicting tractor performance factors accurately is crucial for enhancing energy efficiency and assisting with the choice of machinery in agricultural operations. Using the Nebraska Tractor Test Laboratory (NTTL) identical data, this study uses artificial neural network (ANN) modeling to forecast important performance metrics of a front wheel assist (FWA) Massey Ferguson tractor. A feed-forward ANN model was developed and validated using reported data from official tractor tests. Performance indicators, such as drawbar pull (kN), drawbar power (kW), hourly fuel consumption rate (kg/h), drawbar specific fuel consumption (kg/kW·h), and drawbar specific volumetric fuel efficiency (kW/kg·h), were utilized as outputs and certain operational factors, the tractor characteristics variables as well as other variables were used as inputs. Statistical measures, including the coefficient of determination and error metrics from training and testing datasets, were used to assess the model’s performance. The results showed that the ANN model produced excellent generalization capabilities and good prediction performance by correctly capturing the nonlinear correlations between inputs and tractor performance indicators. The suggested strategy performed better than traditional regression-based techniques documented in the literature, especially when operation variables and tractor characteristics varied. The results show that combining NTTL data with ANN techniques offers a dependable and affordable method for predicting tractor performance indicators and evaluating energy efficiency. This eliminates the need for extensive experimental procedures and promotes data-driven decision-making in agricultural machinery management. Full article

Residues in the grain tank of seed-production wheat harvesters often cause varietal admixture, challenging seed purity maintenance above 99%. To address this, an intelligent cleaning system was developed for automatic residue recognition and removal. The system utilizes an RGB-D camera and an embedded AI unit paired with an improved lightweight object detection model. This model, enhanced for feature extraction and compressed via LAMP, was successfully deployed on a Jetson Nano, achieving 92.5% detection accuracy and 13.37 FPS for real-time 3D localization of impurities. A D–H kinematic model was established for the 4-DOF cleaning manipulator. By integrating the PSO and FWA models, the motion trajectory was optimized for time-optimality, reducing movement time from 9 s to 5.96 s. Furthermore, a gas–solid coupled simulation verified the separation capability of the cyclone-type dust extraction unit, which prevents motor damage and centralizes residue collection. Field tests confirmed the system’s comprehensive functionality, achieving an average cleaning rate of 92.6%. The proposed system successfully enables autonomous residue cleanup, effectively minimizing the risk of variety mixing and significantly improving the harvest purity and operational reliability of seed-production wheat. It presents a novel technological path for efficient seed production under the paradigm of smart agriculture. Full article

Aviation permanent magnet synchronous motors (PMSMs) operate with high power density under high-altitude conditions, where the thermal sensitivity of permanent magnet materials and reduced air density make them prone to demagnetization faults. Even small performance degradation can therefore pose a risk to operational safety, and reliable demagnetization diagnosis is required. This paper analyzes the operating characteristics of an aviation interior PMSM under demagnetization faults and develops a dedicated diagnostic approach. A coupled electromagnetic–thermal finite element model is established to evaluate no-load and rated performance, compute losses under rated conditions, and obtain temperature distributions; the electromagnetic model is further corroborated using an RT-LAB semi-physical real-time simulation of the motor body. Altitude-dependent ambient air properties corresponding to 5000 m are then incorporated to assess the magneto–thermal field distribution and reveal the impact of high-altitude operation on temperature rise and demagnetization risk. Based on the thermal analysis, overall demagnetization faults are classified into several temperature-based levels, and representative local demagnetization cases are constructed; for each fault case, time-domain torque and phase-voltage signals and their frequency-domain components are extracted to form a fault dataset. Building on these features, an intelligent diagnostic method integrating a deep belief network (DBN) and an extreme learning machine (ELM) optimized by an enhanced fireworks algorithm (EnFWA) is proposed. Comparative results show that the proposed DBN–ELM–EnFWA framework achieves a favorable trade-off between diagnostic accuracy and training time compared with several benchmark deep learning models, providing a practical and effective tool for demagnetization fault diagnosis in aviation interior PMSMs. Full article

Maximum power point tracking (MPPT) control is a key technology for increasing the power generation of photovoltaic arrays under varying light and temperature conditions. Traditional perturb and observe methods and incremental conductance methods can achieve good tracking performance for single-peak characteristics. However, under complex conditions such as partial shading or dust accumulation, the power-voltage curve of a photovoltaic array exhibits multi-peak characteristics. In such cases, traditional methods may get trapped in local optima, preventing the photovoltaic array from operating at the maximum power point. Swarm intelligence algorithms perform well when solving multi-extremum functions and can be used for MPPT control of photovoltaic arrays in complex environments. Therefore, this paper focuses on the fireworks algorithm (FWA). To improve the computational speed and global optimization capability of the FWA, the characteristics of each stage of the algorithm are analyzed, a comprehensive improved fireworks algorithm (CIFWA) is proposed, and it is applied to the MPPT control of photovoltaic systems. The improved algorithm introduces an adaptive resource allocation and selection strategy with community inheritance features and applies tent chaos mapping to the algorithm’s explosion behavior. Multiple sets of test functions are used to compare the performance metrics of the optimization algorithm, demonstrating improvements in computational speed and global search capability of CIFWA. Finally, a control strategy for the MPPT of photovoltaic arrays based on CIFWA is presented, and a simulation experimental platform is built to analyze and verify the control performance. Full article

This study aims to explore how organizations plan and implement flexible work arrangements (FWAs) to support managers in fostering work–family balance. In doing so, we examine the sequential mediating roles of work–family conflict, CEO gender equality perceptions, and organizational commitment to elucidate the consequences of FWAs. Our study draws upon the Korean Women Manager Panel (KWMP), a three-year initiative that includes 2345 mother and father managers working in 469 Korean companies. We utilized the longitudinal multilevel macro process model 8 to examine the mediating effects of work–family conflict and CEO gender equality perceptions on the relationship between FWAs and organizational commitment. The findings show that both work–family conflict and CEO gender equality perceptions mediate the relationship between FWAs and organizational commitment. Notably, father managers perceive less work–family conflict than mother managers, which indicates that as FWAs increase, CEO gender equality perceptions and organizational commitment rise as well. The use of FWAs is more beneficial for father managers as it alleviates work–family conflict and fosters positive perceptions and attitudes about CEOs and organizations. Thus, to increase the effectiveness of FWAs, it is pivotal to consider managers’ gender. Additionally, the CEO must be actively involved in shaping and promoting gender equality in the workplace. Full article

The television white space (TVWS) spectrum presents a promising opportunity to extend wireless broadband access, particularly in rural, underserved, and hard-to-reach communities. To leverage this potential, low-power radio communication equipment must efficiently utilise the TVWS spectrum on a secondary basis while ensuring strict compliance with regulatory requirements to prevent harmful interference to primary services. This paper presents a comparative performance analysis of TVWS radio equipment from three original equipment manufacturers (OEMs). The equipment under test was identified to reflect each OEM, as follows: OEM 1 and OEM 2 from South Korea and OEM 3 from the USA. We evaluated their performance in two real-world field scenarios, namely outdoor short-distance and outdoor long-distance. The evaluation was based on the following key metrics: (i) spectrum utilisation efficiency (SUE), (ii) received signal strength (RSS), (iii) downlink throughput, and (iv) connectivity to the Geo-Location Spectrum Database (GLSD) in compliance with the South African TVWS regulatory framework. The overall preliminary experimental results indicate that in both scenarios, white space devices (WSDs) based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11af Standard demonstrated better performance than those based on the 3rd Generation Partnership Project Long-Term Evolution-Advanced (3GPP LTE-A) Standard in terms of the SUE, downlink throughput, and RSS metrics. All WSDs under test demonstrated sufficient compliance with the regulatory requirement metric. Full article

Purpose: This study investigates the relationship between flexible working arrangements (FWAs), employee experiences (EEs), and perceived productivity (PP) in the context of New Zealand employees. The study aims to understand how opportunities and challenges within FWAs impact employee productivity, utilising the Job Demands–Resources (JD-R) model as a theoretical framework. Design/methodology/approach: A survey was conducted with 176 employees who transitioned from traditional office settings to FWAs. Data were collected using a structured questionnaire measuring work demand, autonomy, employee experiences, and perceived productivity. The analysis involved correlational and moderated regression techniques to assess the relationships between the variables. Findings: The study found that positive employee experiences (expressed as opportunities) are significantly associated with higher perceived productivity (r = 0.610, p < 0.001), while negative experiences (expressed as challenges) are associated with lower perceived productivity (r = 0.515, p < 0.001). Moreover, management strategies were found to moderate these relationships, further influencing perceived productivity. Originality: This research contributes to the understanding of how FWAs, when effectively managed, can enhance employee productivity by fostering positive experiences. It also highlights the importance of addressing challenges to mitigate negative impacts on productivity. The use of the JD-R model offers a novel approach to exploring these dynamics in the context of FWAs. Practical and social implications: Organisations can enhance productivity by focusing on management strategies that amplify positive employee experiences and reduce challenges within FWAs. Effective FWAs can improve work–life balance, employee wellbeing, and organisational commitment, contributing to a more satisfied and productive workforce. Full article

This paper presents a problem definition and feasibility check for an algorithm to select a connection point in an existing fiber-optical access network topology that can be used to connect a new site, the planned location, via an E-band millimeter-wave radio link. The newly added fixed wireless access connections must meet end-to-end network requirements for availability, latency, and bandwidth. To accommodate highly dynamic service traffic patterns, requirements are defined with a suitable time granularity. Similarly, dynamic changes in available network capacity affect end-to-end availability, latency, and bandwidth. The proposed algorithm is designed to handle these dynamic changes both in the service requirements and in the available resources. Full article

Aiming at the problem that it is difficult to consider the prediction of foundation settlement in the case of multi-parameter coupling effect by theoretical formulas and numerical analysis, the fireworks algorithm with gravitational search operator (FAGSO) is introduced into the BP neural network model, and the FAGSO algorithm aims to enhance the neural network’s weight and threshold adjustment process; so, a new soft ground settlement prediction model was developed which uses a fireworks algorithm integrated with a gravitational search operator to optimize a BP neural network (referred to as FAGSO-BP). The FAGSO-BP neural network forecasting model is used to predict the soft foundation settlement of Hunan Wuyi Expressway Project. In the soft foundation settlement prediction analysis of Hunan Wuyi Expressway Project, the average relative error of the FAGSO-BP neural network test set was 6.06%, with an RMSE of 1.6, an MAE of 1.2, a MAPE of 0.12% and an MSE of 2.56, which compared to the traditional BP, GA-BP and FWA-BP neural models, had smaller error and higher model stability. Full article

To address the challenges of assessing information security risks in smart energy systems, this study proposes a multi-attribute decision support method based on interval type-2 fuzzy numbers (IT2TrFN). First, expert questionnaires were designed to gather insights from eight specialists in the fields of smart energy and safety engineering. Linguistic terms associated with IT2TrFN were employed to evaluate indicators, converting expert judgments into fuzzy numerical values while ensuring data reliability through consistency measurements. Subsequently, a decision hierarchy structure and an expert weight allocation model were developed. By utilizing the score and accuracy functions of IT2TrFN, the study determined positive and negative ideal solutions to rank and prioritize the evaluation criteria. Key influencing factors identified include the rate of excessive initial investment, regulatory stringency, information security standards, environmental pollution pressure, and incident response timeliness. The overall risk index was calculated as 0.5839, indicating a moderate level of information security risk in the evaluated region. To validate the robustness of the model, sensitivity analyses were conducted by varying IT2FWA (Weighted aggregated operator) and IT2FGA (Weighted geometric operator) operator selections and adjusting weight coefficients. The results reveal that key indicators exhibit high risk under different scenarios. This method provides an innovative tool for the scientific evaluation of information security risks in smart energy systems, laying a solid theoretical foundation for broader regional applications and the expansion of assessment criteria. Full article

The research’s main objective is to examine the mediating role of Employee Commitment (EC) in the relationship between Flexible Work Arrangements (FWAs) and employee behavior (Innovative Work Behavior (IWB) and Employee Performance (EP)) among employees in the Republic of Serbia. The research consists of a theoretical part (review of the literature on previous theoretical and empirical findings) and an empirical part (Partial least squares structural equation modeling (PLS-SEM) analysis conducted on a sample of 582 employees in Serbia). The main findings have determined that there is full mediation, as the indirect effect of FWAs on Innovative Work Behavior through employee commitment is significant, and partial mediation, as the indirect effect of FWAs on Employee Performance through Employee commitment. The flexibility provided by FWAs not only increases employee satisfaction and loyalty but also motivates them to reciprocate through improved behavior and employee performance. In this way, employee commitment becomes a key factor that links organizational flexibility policies with positive outcomes in employee behavior. Flexible work arrangements are key to HR sustainability by enabling a better work-life balance, reducing stress, increasing employee commitment, and fostering long-term innovation and productivity. The mediating role of employee commitment in the relationship between flexible work arrangements and employee behaviors, such as innovative work behavior and employee performance, is particularly important. A high level of commitment, which stems from flexible work conditions, significantly contributes to innovative practices and improved performance, further strengthening the sustainability of organizations. Full article