Search Results (425,131)

As hybrid work arrangements become more established in organisations, the need for effective design and implementation strategies has grown significantly. Evidence indicates that employee wellbeing and engagement in hybrid work environments are declining and this presents a critical challenge for human resource management (HRM) professionals. This presents HRM professionals with a critical imperative of improving wellbeing, while maintaining engagement and productivity at work. This aligns closely with the United Nations’ 17 Sustainable Development Goals, particularly those that promote wellbeing and decent work. Through a systematic synthesis of 78 studies, this research investigates the key determinants of employee engagement and wellbeing in hybrid work contexts. The conceptual framework for this study is grounded in existing theoretical perspectives from the Job Demands–Resources model, Saks Frameworks and wellbeing perspective presented by Guest. The analysis identifies five critical factors that influence engagement and wellbeing outcomes in hybrid work, accompanied by evidence-based propositions for practice. These recommendations encompass: establishing well-equipped workspaces with appropriate flexibility in both location and time; developing organisational culture and leadership through enhanced communication and collaboration mechanisms; strategically allocating jobs and tasks whilst fostering effective networks and collaboration tools and implementing targeted training interventions to mitigate technostress and burnout associated with digital workloads. We advocate for future research to develop comprehensive models, frameworks and wellbeing interventions to guide HRM professionals in addressing these challenges at both the local and global levels. Full article

The performance of FastEddy, a GPU-based large eddy simulation model, in simulating building-induced turbulent flow in an operating airport is studied for the first time through four examples, including a super typhoon case at Hong Kong International Airport (HKIA) and a real case of low-level wind effect. The simulation results are quantitatively compared with wind observations from Light Detection and Ranging (LIDAR) systems for selected cases, and with aircraft data and pilot reports in one example of low-level wind effect. The FastEddy model is found to perform reasonably well through these case studies, even for the radial component of the winds exceeding 20 m/s in a highly turbulent airflow simulation of a typhoon, as well as turbulent airflow features in a building complex at and around HKIA. The building-induced turbulent flow as observed by the LIDARs and the aircraft are largely reproduced. The scatter plots of the model-simulated and the observed Doppler velocities have good correlation in terms of the slope of the best-fit linear equation, correlation coefficient and root-mean-square difference. Moreover, for the case of low-level wind effect, FastEddy simulation is found to provide useful insight into the turbulent flow arising from the new terminal building over the northeastern part of HKIA (near 22.325° N 113.918° E) under construction. Further research directions for studying the performance of FastEddy are also discussed, such as considering more complex urban environments, comparison with in situ measurements of anemometers, and direct output of the eddy dissipation rate (EDR) from the model for comparing with LIDAR and anemometer-based measurements. Full article

Autism Spectrum Disorder is a long-term neurodevelopmental disorder. Early diagnosis is crucial for timely rehabilitation and intervention. Recently, machine learning and deep learning techniques have been widely explored and have produced encouraging results using eye-tracking scanpath images for the early detection of ASD. However, these approaches exhibit inconsistent performance and classification error rates, as well as limited generalization, due to differences in learning approaches and architectural designs across individual models. To address these problems, we employed a weighted-average ensemble of deep learning models using eye-tracking scanpath images. In this work, two different pretrained convolutional neural networks were selected, including Xception and VGG16, based on their proven efficacy and performance. Moreover, we fine-tuned each model individually and evaluated them on a dataset containing eye-tracking scanpath images. We implemented a weighted-average ensemble technique to combine the diverse predictions of the two models. It reduces classification errors and improves the model’s generalization and overall performance. The adopted weighted-average ensemble technique achieved an accuracy of 98.18%, with a perfect recall, and a competitive Area Under the Curve (AUC) of 99.59%. These findings highlight that applying a weighted average to integrate multiple models’ predictions strengthens the generalization and reliability of ASD detection. Full article

Cybersecurity insider threats remain a significant challenge for modern organisations due to their potential to cause substantial financial and reputational damage. This paper presents a systematic review of insider-threat research (2019–2026) using the PRISMA methodology and introduces an empirically validated ensemble framework for insider-threat detection. The proposed approach combines User-Based Sequences (UBS), a self-supervised Transformer trained on next-token prediction and time-gap modelling, and an unsupervised anomaly detection ensemble operating on model-derived behavioural features. An answers directory is incorporated to provide grounded truth for insider entities and episodes within the CERT r6.2 dataset, enabling direct validation of detection outcomes. The framework integrates behavioural theory with machine-learning techniques to improve understanding of insider-threat precursors. Evaluation was performed using a seven-stage Isolation Forest ensemble incorporating multimodal behavioural and technical data streams. The approach successfully identified all insider users, achieving 100% recall and an AUROC of 0.93. Comparative analysis against a previously reported model showed comparable AUROC and perfect recall despite differences in evaluation methodology. While precision remained low (0.004) due to the extreme class imbalance in the full CERT r6.2 population (5 insiders among 4000 users), the results highlight the operational challenges of insider-threat detection in realistic enterprise environments. This research contributes a novel, reproducible framework that combines behavioural theory and advanced machine learning to support the detection and analysis of insider threats. Full article

High-temperature metallic surfaces in aero-engine hot sections and related thermal systems are subjected to MW/m²-level heat-flux loads and transient thermal conditions, creating a need for sensors capable of quantifying heat flux under high-temperature conditions. This study aims to develop a screen-printed Ag–AgPd thick-film thermopile heat-flux sensor (HFS) for MW/m²-level heat-flux measurement on high-temperature metallic surfaces. Its main feature is the integration of an Ag–AgPd thermopile sensing layer, an insulating layer, and a thermal-resistance layer on a SUS430 stainless-steel substrate through a screen-printing-based multilayer fabrication route. Microstructural characterization, annealing condition comparison, laser comparison calibration, repeated loading, dynamic-response testing, and flame-heating testing were conducted to evaluate the sensor structure and performance. Under laser comparison calibration, the sensor achieved a MW/m²-level calibrated heat-flux response over 0.32–1.37 MW/m², with a near-linear output relationship of $R^2>0.998$, a sensitivity of 2.67 μV/(kW/m²), a nonlinearity of 1.83%, a hysteresis error below 0.29%, a repeatability error below 0.43%, a sample-to-sample consistency error of 1.06%, a maximum accuracy-test deviation of 1.84%, and a maximum repeated-loading stability error of 1.33%. The sensor also exhibited a time constant of 0.806 s under laser step excitation, and the baseline-corrected equivalent heat-flux response remained stable during approximately 120 s of flame heating at about 800 °C. These results indicate that the proposed HFS provides a feasible thick-film thermopile sensing approach for MW/m²-level heat-flux measurement on high-temperature metallic surfaces. Full article

Objective: Perform endoscopic surgery for radiculopathy caused by compression fractures and evaluate the results. Methods: A total of 20 patients who underwent biportal endoscopic foraminotomy and unilateral screw fixation using a dynamic rod for radiculopathy secondary to osteoporotic compression fractures were included in this study. All surgeries were performed between July 2021 and January 2025. Patient demographic data, operated level, length of hospital stay, intraoperative blood loss, and operative time were reviewed. Radiological follow-up included assessment of segmental kyphosis, scoliosis, subsidence, and adjacent-level fractures. Complications and pain patterns—separately evaluated for back pain and radiculopathy—were assessed using the visual analog scale (VAS) preoperatively and during follow-up. Only single-level cases were included. Patients with infections, significant stenosis, instability, tumors, prior revision surgery, multilevel pathology, or ambiguous symptoms were excluded. Results: The mean age of the patients was 78.8 years (range, 69–89 years), reflecting an elderly cohort. The mean follow-up period was 13.0 ± 11.9 months (range, 1–41 months). The mean operative time was 164.8 ± 25.7 min, and the mean hospital stay was 10.2 ± 4.6 days (range, 4–25 days). The mean intraoperative blood loss was 126.5 ± 77.6 mL (range, 50–400 mL). One female patient developed postoperative pneumonia, which resolved after appropriate treatment; no other medical complications were observed. Radiculopathy improved significantly immediately after surgery and continued to improve during follow-up. Back pain also improved, but tended to persist to a mild degree. Radiologic evaluation revealed no significant changes in segmental lordosis, and there were no cases of subsidence, scoliosis, or symptomatic screw loosening during the available follow-up period. Conclusions: Biportal endoscopic foraminotomy with unilateral screw fixation may be an effective solution for radiculopathy caused by compression fractures. Full article

In casting processes, mechanical properties such as hardness are highly sensitive to both chemical composition and process parameters, making parameter design a complex and uncertain task during the embodiment stage of engineering design. Conventional trial-and-error-based approaches are often costly, time-consuming, and impractical in industrial environments. To address these challenges, this study proposes an optimized fuzzy artificial neural network (FANN)-based decision-support approach for hardness-oriented parameter design in a casting process. The developed model uses chemical composition variables, including carbon, silicon, manganese, phosphorus, sulfur, chromium, copper, and tin, together with process parameters such as casting temperature and casting time as inputs, while Brinell hardness is considered as the output. A dataset consisting of 170 experimental casting samples was employed; 128 samples were used for model development and hyperparameter selection, and 42 samples were reserved as an independent final test set. The proposed model was implemented as a scaled direct FANN weighted ensemble, in which fuzzified input variables were used to predict standardized continuous hardness values. A total of 300 FANN configurations were evaluated using five-fold cross-validation, and the five best-performing configurations were combined through RMSE-based weighted ensemble averaging. The final model was compared with Random Forest, Linear Regression, Ridge Regression, and SVR-RBF models using MSE, RMSE, MAE, R², MAPE, normalized RMSE, and ±5% prediction success rate. The results showed that the optimized FANN ensemble achieved the lowest mean RMSE in the full-data five-fold cross-validation analysis, slightly outperforming the Random Forest benchmark. In the independent final test set, Random Forest produced the lowest prediction error, whereas the proposed FANN ensemble remained competitive and achieved the same ±5% prediction success rate as Random Forest, Linear Regression, and Ridge Regression. Furthermore, a target-hardness case study demonstrated that the proposed approach could identify candidate casting conditions very close to a desired hardness level, with the nearest prediction reaching 202.985 HB for a target value of 203 HB. These findings indicate that the proposed FANN-based framework can serve not only as a hardness prediction model but also as a practical fuzzy decision-support tool for target-hardness-oriented parameter design in casting processes. Full article

Business influence increasingly unfolds in platform environments where visibility is ranked, repeated, measured, and monetized. At the same time, many platform interactions have shifted from reciprocal sociality toward creator-centered attention, mediated familiarity, and one-sided attachment. This perspective article develops the concept of parasocial business to explain how business actors convert platform visibility into credibility, trust, advocacy, and commercial value. Drawing on an integrative reading of research on parasociality, platformization, influencer labor, organizational reputation, marketing communication, and creator economies, this paper identifies three linked mechanisms: algorithmic visibility, persona design, and parasocial conversion. The conceptual analysis shows that parasocial business is not simply influencer marketing, personal branding, brand community, or consumer–brand relationship management. Its distinctive feature is the platformed conversion of repeated, persona-based familiarity into business-relevant authority and value. This paper develops the related concept of platformed authority and illustrates it through LinkedIn thought leadership, podcast-based business influence, and B2B expert visibility. This paper contributes to business administration and management research by showing how leadership communication, reputation governance, expert visibility, and organizational influence are increasingly shaped by platform infrastructures and public-facing personae. Full article

Background: Rapid palatal expansion (RPE) is the gold standard for maxillary deficiency in growing patients. Conventional soldered expanders often present challenges in adaptation, chairside procedures and bond stability. This study compares traditional RPEs with customized CAD (Computer Aided Design)/CAM (Computer Aided Manufacturing) expanders regarding clinical efficiency and patient experience. Methods: Thirty growing patients (mean age: 9.2 ± 1.1 years) were allocated to two groups: traditional RPEs (n = 15) and customized CAD/CAM RPEs (n = 15). Outcomes included bond failures, chairside time (from initial try into cementation) and short-term patient-reported discomfort via a 10-point Visual Analogue Scale (VAS) 24 h after appliance placement. Significance was set at p < 0.05. Results: The CAD/CAM group showed significantly fewer bond failures (0.2 ± 0.4 vs. 1.1 ± 0.8; p < 0.05) and shorter chairside time (12.4 ± 2.1 min vs. 24.6 ± 3.8 min; p < 0.001). Patient discomfort was also significantly lower in the CAD/CAM group (VAS: 4.1 ± 1.0 vs. 6.3 ± 1.2; p < 0.05). Conclusions: Within the limitations of this pilot exploratory non-randomized study, customized CAD/CAM RPEs were associated with fewer bond failures, shorter chairside application time, and lower short-term discomfort at 24 h compared with traditional appliances. These preliminary findings should be interpreted with caution and confirmed by larger randomized controlled studies. Full article

Background/Objectives: Myocardial blood flow (MBF) and myocardial flow reserve (MFR) are key measurements in myocardial perfusion imaging (MPI), with [¹⁵O]H₂O-PET considered the reference standard. To further establish clinical and research utility, we investigated intra-individual variability of MBF and MFR over time in patients with angina, but no obstructive coronary disease. Methods: In a routine clinical setting, we prospectively studied 21 patients, >50 years with normal left ventricular function and no known coronary stenosis, undergoing clinically indicated PET MPI. Scan and re-scan were conducted within 30 days. Analyses were conducted by general clinical staff and re-evaluated by an expert reader. Reproducibility was assessed using paired t-tests, Bland–Altman analysis, repeatability coefficient (RC), within-subject coefficient of variation (CV) and intraclass correlation coefficient (ICC). Results: Twenty-one patients (mean age 70 ± 8.2 years; 48% female) underwent repeated scans with a median interval of 21 days. Resting MBF was 0.91 ± 0.24 vs. 0.92 ± 0.22 mL/min/g (r = 0.87; RC 0.23 mL/min/g; CV 9%; ICC 0.87). Hyperaemic MBF averaged 3.06 ± 0.9 vs. 2.97 ± 0.78 mL/min/g (r = 0.83; RC 0.98 mL/min/g; CV 11.6%; ICC 0.81). MFR showed only moderate reproducibility (3.47 ± 1.23 vs. 3.23 ± 0.92; RC 1.90; CV 21%; ICC 0.60). Neither expert re-evaluation nor rate–pressure product correction of the resting MBF improved the reproducibility. Variability was largely unaffected by atrial fibrillation and diurnal variation. Conclusions: Resting and hyperaemic MBF are reproducible, whereas MFR shows moderate variability, which should be considered when interpreting changes in individual patients. Full article

The global agricultural sector faces the dual challenge of increasing productivity while mitigating environmental impacts caused by synthetic agrochemicals and massive agro-industrial waste. This review examines the transition to “Biostimulants 4.0,” a circular economy paradigm driven by the valorization of biomass residues into high-value biological inputs through nanotechnology and Artificial Intelligence (AI). Our analysis highlights that green extraction methods, specifically enzymatic hydrolysis, preserve bioactive integrity and reduce carbon emissions by up to 23.2 times compared to synthetic nitrogen production. Furthermore, waste-derived formulations and nanoscale smart-delivery systems dramatically enhance crop performance; for instance, chitosan nanoparticles can achieve up to a 471% increase in specific growth metrics through sustained-release pathways. To move the industry beyond empirical trial-and-error, the integration of AI-driven predictive models now achieves up to 87% accuracy in forecasting biostimulant efficacy. Finally, we contrast global regulatory frameworks and evaluate the monetization of biostimulant-driven carbon sequestration, capable of generating high-integrity credits priced up to $35 per tonne, as a critical economic pathway to accelerate commercial adoption and incentivize a resilient, decarbonized agricultural system. Full article

Importance: Most couples who turn to assisted reproductive technology (ART) treatment do so, usually, after giving up emotionally on the chances of conceiving naturally. Others undergo ovulation induction with intrauterine insemination (IUI) and turn to ART after the latter has failed. Spontaneous conception after having experienced the exhausting process of ART, whether it was successful or not, could be very surprising and confusing for many couples. Objective: Review all the scenarios within which an unexpected spontaneous conception can occur and the likelihood of its occurrence. These are four such scenarios: (1) after being referred to ART but before the actual initiation of ART; (2) between ART cycles; (3) after a successful ART pregnancy; (4) after giving up on treatment. We have found only a systematic review on #3, but not the other three. Evidence Review: We collected all PubMed citations for the terms “spontaneous conception” and ART or IVF. Thereafter, we realized that no AI tool can filter only the relevant literature. Hence, we exhausted all possible cross-references by manual search to ensure the completeness of the search. Findings: In each of the four scenarios, spontaneous conceptions occur. Before treatment, a critical element is the length of the waiting time, as is the gap between treatments when already treated, with the cost of treatment being a critical determinant. After the conclusion of treatment, whether successful or failed, the main determinants of the chance for spontaneous conception are age, length of infertility, and the leading etiology for infertility. Overall, the chances range from as little as 2% and up to 25%, with severe male factor and a woman’s age being the most notable for low rates. Conclusion and Relevance: Each couple entering ART treatment should be informed of the chances for spontaneous conception, whether as an aid to the decision to enter or the decision to leave after a failure, and on the more cheerful side, to be aware of the chances for unplanned pregnancy after a successful treatment. Full article

Hands-free command interfaces are essential for users who cannot reliably operate joysticks or upper-limb myoelectric control. Neck–shoulder surface electromyography (sEMG) is a promising alternative; however, performance is often reported using window-level validation which can overestimate accuracy due to overlap and trial leakage, and false-trigger behavior is not always quantified when an idle REST state is included. This pilot study presents a motion-capture (MoCap)-referenced decoding framework that uses four bilateral upper trapezius (UT) and sternocleidomastoid (SCM) sEMG channels with integrated inertial measurement units (IMUs). Optical MoCap was used as an external kinematic reference to support baseline-posture assessment and movement-execution quality control. Seven commands were decoded (shrug L/R, double shrug, rotation L/R, rotation + shrug L/R). To enable an eight-class formulation, a REST class was defined using low-activity segments extracted from baseline recordings and included in the evaluation. Computationally efficient time-domain sEMG features, pattern/symmetry descriptors, and baseline-referenced IMU kinematics (including an SCM yaw-range indicator) were classified using linear discriminant analysis (LDA), k-nearest neighbors (kNN), and linear support vector machine (SVM), evaluated using within-subject testing, trial-wise grouped cross-validation, and leave-one-subject-out (LOSO) testing. Across six participants, within-subject mean best-per-subject accuracy was 96.02% (seven-class) and 96.35% (eight-class); and pooled trial-wise accuracy reached 92.1% and 90.5%, respectively. Under LOSO, best-configuration accuracy decreased to 60.4% and 63.8% for the seven-class and eight-class formulations, respectively. Across the top LOSO configurations, REST FAR ranged from approximately 9.8% to 25.6%. These findings demonstrate controlled offline pilot feasibility and quantify key generalization and REST false-activation trade-offs, providing a foundation for future validation in larger, more diverse, and clinically relevant populations. Full article