Search Results (3,263)

The instantaneous contact zone in robotic abrasive belt grinding involves highly coupled thermo-mechanical interactions between abrasive grains and the workpiece material. Acoustic Emission (AE) signals generated during this process are inherently nonlinear and nonstationary, posing challenges for accurate process monitoring and mechanistic understanding. To address this, this study introduces an innovative AE signal processing framework designed to elucidate the robotic grinding mechanism for Ti-6Al-4V (TC4) titanium alloy. An improved Completely Complementary Ensemble Empirical Mode Decomposition (CCEEMD) algorithm, building upon Empirical Mode Decomposition (EMD), is developed to precisely extract intrinsic mode functions (IMFs) from raw AE data. Subsequently, a novel denoising algorithm utilizing noise statistical characteristics effectively removes invalid noise from the robotic machining system. Validation through robotic grinding experiments on TC4 workpieces successfully established quantifiable relationships between extracted AE features and the underlying grinding mechanism. Significantly, implementing this methodology contributed to extending the effective service life of a structured abrasive belt by approximately 20% while increasing machining efficiency by approximately 12%. This work presents a novel methodology combining improved CCEEMD and statistical denoising for AE analysis in robotic grinding, providing a robust link between AE signatures and material removal mechanisms, ultimately enabling quantitative process optimization. Full article

Mealworm (Tenebrio molitor) is considered a sustainable protein source and classified as a non-novel food by Health Canada. However, data on safe consumption levels based on bioaccessible metal(loid) concentrations are limited. In this study, a modified continuous on-line leaching method (COLM) coupled with inductively coupled plasma mass spectrometry (ICPMS) was developed to quantify bioaccessible Cr, As, Se, and Cd in mealworm powder. Samples were packed into a transparent polypropylene flash column and sequentially leached with artificial saliva and gastric juice at 37 °C to simulate gastrointestinal digestion, with continuous monitoring of released elements by ICPMS. The proposed method required approximately 70 min per sample as opposed to over 2 h with conventional batch methods. Whereas the bioaccessible concentration of Se was negligible, 13 ± 5 µg/kg Cr, 18 ± 5 µg/kg As and 18 ± 6 µg/kg Cd were released, representing 14%, 15%, and 26% of their total concentration, respectively. Mass balance was verified for Cr, As, Se, and Cd, demonstrating the reliability of the method. Additionally, different sources of elements were revealed by plotting the temporal profile of one element versus that of another element for each gastro-intestinal fluid. A preliminary quantitative risk assessment indicated that adults can safely consume 105 g mealworm per day. Although no significant noncarcinogenic risk was identified, the incremental lifetime cancer risk of 5.2 × 10⁻⁶ for As and 1 × 10⁻⁶ for Cr exceeds or equals the Ontario threshold, indicating potential concern. This study is the first to apply the COLM to mealworm while integrating bioaccessibility data for a more realistic risk assessment. However, back-pressure issues result in a relative standard deviation up to 39%. Full article

Glamping tourism has expanded rapidly as travelers increasingly seek nature-based experiences combined with comfort and privacy, particularly in the post-COVID-19 period. Online reviews provide a valuable source of insight into how guests perceive such experiential accommodation, yet large-scale, data-driven analyses of glamping sentiment remain limited. This study applies machine-learning techniques to classify customer sentiment expressed in online reviews of glamping sites in South Korea. A total of 3233 reviews were collected from ten leading glamping locations on Naver Map, cleaned, and translated from Korean to English. Sentiment labels (negative, neutral, and positive) were generated using VADER (Valence Aware Dictionary and sEntiment Reasoner), a lexicon-based sentiment scoring tool validated for short informal texts and the labeled corpus was subsequently used to train and evaluate six supervised classifiers. Six supervised classifiers—Naïve Bayes, k-Nearest Neighbors, Random Forest, Logistic Regression, Gradient Boosting, and Support Vector Machine (SVM)—were trained and evaluated through stratified ten-fold cross-validation using accuracy, AUC, F1-score, and Matthews Correlation Coefficient (MCC). Results indicate that SVM achieved the strongest overall discriminatory performance, particularly in identifying minority sentiment classes under substantial class imbalance. These findings suggest that automated sentiment classification holds practical potential for supporting evidence-based service monitoring and reputation management in glamping tourism, although further validation in operational settings is needed before deployment can be recommended. Full article

To investigate the battery impedance variation after the occurrence of nail penetration, this paper adopts Dynamic Electrochemical Impedance Spectroscopy (DEIS) for real-time monitoring of the impedance changes of lithium-ion batteries during the nail penetration process. A piecewise multi-frequency superimposed sinusoidal excitation is designed, which not only complies with the stability principle of battery testing but also ensures the signal-to-noise ratio of the excitation signal. By injecting the designed excitation signal into the operating battery and combining it with the rapid DEIS generation technology, the acquisition of DEIS data within the target frequency band in a short time is realized. Based on the obtained DEIS data, a fractional-order model is established and fitted for analysis before and after nail penetration. The results show that the steel nail introduces inductive reactance and impedance to the battery. Due to the parallel connection between the steel nail and the internal resistance of the battery, the overall impedance decreases, exhibiting a short-circuit state, and both the real and imaginary parts of the impedance experience an abrupt change at the moment of nail penetration. Considering the characteristic of abrupt impedance change of the battery after nail penetration, a battery nail penetration detection method based on DEIS is proposed. Considering the abrupt change characteristics of battery impedance after nail penetration, this paper proposes a battery nail penetration detection method based on DEIS. This method can effectively solve the problem of low sensitivity of traditional voltage monitoring methods in detecting nail penetration during battery operation. It has higher sensitivity and faster response speed compared with traditional methods, enabling online monitoring of battery states. Additionally, this paper also explores its potential application in real-world vehicles. Full article

The construction industry remains among the most hazardous, with workers in horizontal transportation infrastructure facing additional risks from dynamic work zones, live traffic exposure, and variable environmental conditions. Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) offer new approaches to accident analysis and prevention, yet their applications toward improving occupational safety in transportation construction have not been comprehensively reviewed. This paper presents a systematic review of 54 studies published between 2016 and 2025 collected from two online databases (Transportation Research International Documentation and Web of Science). This review synthesizes how immersive technologies contribute to occupational risk assessment, safety training, and real-time hazard monitoring in the construction of roads, bridges, tunnels, and work zones. Each study is classified across two dimensions: the immersive medium (VR, AR, etc.) and the operational context within the construction lifecycle (onsite tools, offsite monitoring and planning, simulation-based analysis, and workforce education). This dual classification is the first to systematically map immersive technology applications for occupational safety, specifically within horizontal transportation infrastructure. The findings of this review demonstrate the unique use cases of each immersive medium, revealing that VR is primarily used for controlled experimentation and full-immersion remote analysis, whereas AR and handheld devices are preferred for field-deployed applications. Despite these promising capabilities, widespread adoption remains limited by hardware constraints, challenging field conditions, and organizational resistance. This suggests that future work should focus on safety systems tested in real-world settings and rigorously evaluated by domain experts to enable their integration into standard workplace risk management practices. Full article

Massively Multiplayer Online Role-Playing Games (MMORPGs) increasingly rely on player-developed third-party tools to extend functionality and personalise gameplay, creating a complex software ecosystem that introduces both usability benefits and security risks. This study investigates whether such tools can be exploited as an attack vector for cybercrime by designing and implementing a proof-of-concept add-on within a widely deployed commercial MMORPG using its native scripting and application programming interface. The developed tool supports automated player discovery, chat capture, target inspection, and local data persistence, enabling a systematic evaluation of how cyber-assisted and cyber-dependent crimes could be facilitated within the game client. Empirical testing demonstrates that while the platform’s protected execution model and interface restrictions prevent direct credential theft and remote code execution, the add-on architecture allows extensive behavioural data collection and social-engineering-relevant monitoring, making several forms of cyber-enabled crime technically feasible. These findings show that MMORPG add-on frameworks represent a non-trivial socio-technical attack vector in next-generation online platforms, where security depends not only on code isolation, but also on how user-generated extensions interact with human behaviour. The results highlight the need for architecture-aware security controls and governance mechanisms to mitigate emerging threats in large-scale, extensible virtual environments. Full article

Background and aims: Lactiplantibacillus plantarum is a widely studied probiotic species with well-documented benefits for gastrointestinal function and immune modulation. However, probiotic effects are strain-specific, and the safety of newly identified strains must be clinically established. L. plantarum K014, isolated from traditionally fermented vegetables, has not previously been evaluated in human subjects. This study aimed to evaluate the safety and tolerability of L. plantarum K014 in healthy Malaysian adults by assessing its effects on anthropometric measures, hematological indices, liver and renal function, gastrointestinal health, and selected immune-related outcomes, including the incidence and severity of common cold symptoms. Methods: This single-center, randomized, double-blind, placebo-controlled trial was conducted over a 6-month period. Of 304 healthy adults screened, 152 were enrolled and randomized in a 1:1 ratio to receive either L. plantarum K014 (≥1 × 10⁹ CFU/day) or placebo (maltodextrin), administered daily in sachet form; 125 participants completed the study. Clinical assessments, including physical examination, anthropometric measurements, and blood analyses, were performed at baseline, Month 4, and Month 6. Gastrointestinal symptoms, stool characteristics, and immune-related outcomes were monitored weekly using structured online questionnaires. Results: L. plantarum K014 was well tolerated, with no probiotic-related adverse events reported. No clinically significant changes were observed in body weight, BMI, hematological indices, or renal function in either group. Exploratory analyses indicated that participants receiving L. plantarum K014 exhibited statistically significant differences in several liver function markers, as well as lower severity of diarrhea and abdominal pain compared with placebo, though these findings were not prespecified efficacy endpoints and should be interpreted cautiously. Similarly, lower weekly ratings of common cold symptoms interfering with work or study were observed in the probiotic group as an exploratory observation. Conclusions: Daily consumption of L. plantarum K014 at a dose of ≥1 × 10⁹ CFU for six months was safe and well tolerated in healthy adults. The absence of adverse effects, together with observed trends toward lower gastrointestinal discomfort and immune-related symptoms, supports the suitability of L. plantarum K014 for further investigation in efficacy-driven clinical studies. Full article

Motivation: In modern organizations where remote and hybrid work has become normalized, fostering trust without frequent face-to-face interaction is a critical management challenge. This study aims to explore how non-verbal digital dynamics associate with trust formation within hybrid product development teams from a teamwork science perspective, integrating Big Five traits and established trust scales. Methods: The empirical study observed twelve product development teams (N = 40) participating in a major innovation competition over an eight-month period. Dynamic behavioral data, including speaking time, nodding, smiling, and silence, were extracted from online workshop recordings using synchronized behavioral coding validated by high inter-rater reliability (Cohen’s Kappa k ≥ 0.78). These were integrated with Big Five personality traits, mutual trust scales, and idea value metrics into a Bayesian Network (BN) to model probabilistic dependencies. The structural model was validated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to ensure predictive robustness. Furthermore, we performed sensitivity analysis on the BN to quantify how specific shifts in non-verbal cues—particularly nodding and the functional categories of silence—disproportionately affect the “Mutual Trust” node. While this exploratory study utilizes a sample of “digital native” student teams, it provides a critical baseline for “high digital fluency” collaboration, which we contextualize against the “asymmetric cues” found in multi-generational corporate environments. Results: Sensitivity analysis identified specific probabilistic associations suggesting that effective role fulfillment is the strongest predictor of idea originality. Crucially, nodding was identified as a behavioral ‘digital reward’ that enhances psychological safety, facilitating divergent thinking. Smiling showed a strong association with feasibility and consensus-building during convergent phases. The model further identifies distinct behavioral ‘fingerprints’: high-trust sequences are characterized by frequent non-verbal backchanneling and deliberate “thinking silences,” whereas low-trust sequences exhibit a disproportionate increase in unproductive lapses (e.g., a 10% increase in lapses correlating with an 18% decrease in trust probability). Furthermore, a probabilistic pathway was identified where teams with highly open members and frequent non-verbal validation exhibit higher mutual support behaviors. Conclusions: This research offers empirical insights into how trust can be modeled in hybrid environments through specific combinations of behavioral and personality traits. Practically, this study proposes “Hybrid Team Protocols”—such as intentional backchanneling and the normalization of deliberative silence—as actionable Organizational Development (OD) interventions. These provide managers with data-driven guidelines to visualize and monitor the quality of digital collaboration while emphasizing the ethical necessity of transparent implementation to prevent “digital performance” and ensure psychological safety across diverse organizational structures. Full article

Despite the growing importance of Environmental, Social, and Governance (ESG) performance in shaping brand perception and consumer trust, limited empirical evidence exists on how ESG indicators translate into measurable digital consumer engagement outcomes, particularly in ethically driven markets such as the vegan food sector. This study addresses this gap by examining how ESG performance translates into digitally observable consumer engagement and frames this relationship as a strategic performance measurement and decision-support problem. Building on the sector’s reliance on ethical positioning, trust, and online visibility, we integrate ESG indicators with digital marketing and web analytics metrics (e.g., traffic and engagement proxies) for a panel of five leading vegan food firms [Nestlé SA (Vevey, Switzerland), Kellanova (Chicago, IL, USA), Beyond Meat Inc. (El Segundo, CA, USA), Danone SA (Paris, France), and Conagra Brands Inc. (Chicago, IL, USA)], using data from the Semrush web analytics platform and the Eikon Refinitiv ESG database for the period January–December 2024. We employ a mixed-method design combining descriptive analytics with correlation analysis and simple linear regression to estimate the direction and strength of ESG–digital performance links, and we extend inference through Fuzzy Cognitive Mapping (FCM) using the MentalModeler platform to simulate “what-if” scenarios that support managerial foresight under digital uncertainty. Results indicate that stronger ESG profiles are associated with more favorable digital outcomes, with specific ESG mechanisms (e.g., human-capital and environmental initiatives) aligning with deeper engagement signals. The FCM scenarios further suggest that coordinated ESG improvements can amplify digital traction and reinforce sustainable brand growth. The proposed framework contributes to strategic management by operationalizing an ESG-enabled digital performance measurement system and a lightweight Decision Support System (DSS) that can guide resource allocation, KPI monitoring, and risk-aware positioning in sustainability-oriented markets. Full article

Continuous customer activity generated by modern digital platforms drives the evolution of behavioral segments over time. Traditional customer segmentation methods typically rely on periodic batch analysis of historical data, producing static snapshots that may quickly become outdated and fail to capture emerging behavioral patterns. This paper presents a monitoring-oriented framework for detecting customer segment evolution and generating timely notifications about meaningful structural changes in the customer population. The proposed system continuously ingests user activity events, incrementally updates customer profiles, and periodically recomputes behavioral segments using fixed-k KMeans clustering over standardized recency, frequency, and monetary (RFM) features. To improve robustness and interpretability, the framework incorporates adaptive event scoring, stability-aware segment validation, drift-aware centroid matching, and persistence-based filtering of transient changes. These mechanisms reduce noisy alerts caused by repeated clustering updates while preserving meaningful signals about evolving customer behavior. The framework is evaluated on the Online Retail II and Instacart datasets under streaming simulation conditions. Experimental results show that the proposed approach maintains stable clustering structures, identifies persistent segment changes, and uncovers economically meaningful customer groups. Compared with static segmentation and periodic clustering baselines, the framework improves clustering quality while enabling continuous monitoring of segment evolution. Overall, the results suggest that adaptive monitoring can extend traditional customer segmentation into a practical continuous analytics process for moderate-scale dynamic environments. Full article

In response to the limitations of traditional pipeline wax deposition monitoring, we propose a quantitative evaluation method based on the inversion of distributed optical fiber sensing signals. By establishing an experimental system and adopting a “noise suppression–restoration–enhancement” preprocessing method, the signal quality was significantly improved. The IBES-TPGM(1,1) model had the best nonlinear fitting ability, with a Root Mean Square Error of only 0.069 mm and a Mean Relative Error of 1.53%. Indoor and field experiments verified that this method has high accuracy and good stability, providing an effective technical means for the online quantitative monitoring of pipeline wax deposition, and thus, it has significant engineering value. Full article

Postpartum depression (PPD) affects a substantial proportion of women globally and is often underdiagnosed due to barriers in screening, stigma, and limited access to care. This study presents the design and pilot evaluation of an mHealth application (“HeartHabit”) intended to support user awareness, self-monitoring, and potential identification of symptoms of PPD among Greek-speaking mothers. An alpha version of the application was evaluated through an online survey with 30 women within the first postpartum year, using a walkthrough video. The evaluation focused on perceived usability and acceptability rather than clinical outcomes or real-world use. Usability and app quality were assessed via the System Usability Scale (SUS) and a qualitative version of the user Mobile Application Rating Scale (uMARS), respectively, adopting a mixed-methods approach. Demographics, and mood and stress screening data were also captured. Quantitative data were analysed via descriptive statistics and qualitative responses via Framework Analysis. The results indicated high perceived usability (mean SUS = 83.7/100). Qualitative findings highlighted the importance of practical usability, self-regulation tools, personalisation, and connectivity with healthcare professionals. Privacy, data transparency, and user control over personal data were perceived as critical for trust. The application was perceived as a potentially useful adjunct to formal care or as at-home support when access to services is limited. Larger, controlled trials, clinical implementation protocols and clinician training are needed to promote the app’s safe integration into formal care. This mixed-methods evaluation, incorporating usability assessment and patient involvement, may offer a useful paradigm for early-stage digital mental health intervention development. Full article

In high-value precision machining, existing tool wear monitoring models often suffer from two major limitations: poor generalization under varying cutting conditions and heavy reliance on large amounts of labeled data for new operating scenarios. These limitations hinder the practical deployment of intelligent monitoring systems. To address these challenges, this paper proposes a few-shot warm-start framework based on model-agnostic meta-learning. The method consists of two stages. First, meta-training is performed on historical machining data to learn a task-sensitive parameter initialization that enables rapid adaptation. Second, under a new operating condition, the few-shot warm-start mechanism collects a minimal number (1 to 5) of samples through a targeted physical trial-cutting process for online fine-tuning, aligning the model with the current physical environment. Experiments on the PHM2010 dataset fully simulate varying cutting scenarios. The experimental results demonstrate that the proposed framework consistently outperforms traditional transfer learning, deep learning models, and existing meta-learning approaches, offering an effective solution for fast and accurate tool wear prediction under few-shot and varying cutting conditions. Full article

Against the backdrop of multi-source water transfers and increasingly frequent extreme rainfall, short-term deterioration of reservoir inflow water quality has become a key risk to intake safety, treatment operations, and urban water-supply security. Traditional assessments based on static thresholds and annual or seasonal averages often fail to identify high-risk periods at the event scale. Using continuous online monitoring data from 2021 to 2024 for the inflow of Yuqiao Reservoir, Tianjin, China, this study developed a month-specific dynamic-threshold framework and green/yellow/red risk windows and integrated a reach-wise river–reservoir routing scheme; a two-box decay model; and a three-class risk trigger into a unified analytical framework for long-term background characterization, event propagation analysis, source-contribution interpretation, and early-warning evaluation. Results show that the permanganate index (COD_Mn) exhibits an overall stable-to-declining background with pronounced wet-season pulses, whereas total nitrogen (TN) and total phosphorus (TP) remain at moderate-to-high levels, with yellow/red risk windows clustering markedly in the wet season. In typical red and yellow events, nitrogen contributions from upstream control sections progressively accumulate toward the reservoir inlet along the river–reservoir cascade system, whereas in some events the residual contribution from unmonitored near-inlet inflows becomes dominant. The COD_Mn-based three-class trigger achieves an overall accuracy of approximately 71.5% and shows comparatively strong identification of yellow-level risk, while remaining conservative for red-level alarms. These findings indicate that coupling month-specific dynamic thresholds with event-scale routing-decay analysis and trigger-based classification can support inflow monitoring, intake-risk early warning, and coordinated operation of key upstream reaches and near-reservoir control zones in water-transfer–reservoir integrated systems. Full article

Robust numerical frameworks are essential for the simulation, design, monitoring, and control of membrane-based separation units, particularly under highly nonlinear and industrially relevant operating conditions. In this context, a comprehensive phenomenological and numerical framework is proposed for the simulation of hollow-fiber membrane modules, incorporating coupled mass, momentum (through pressure drop), and energy transport equations. The governing equations are discretized using a rigorous orthogonal collocation formulation, and the performances of two numerical solution strategies are systematically investigated for the first time to allow the in-line and real-time implementation of the model: a steady-state approach based on the Newton–Raphson method with careful treatment of initial estimates, and a pseudotransient formulation. Particularly, an original and consistent numerical treatment is introduced for the energy balance at boundaries where the permeate flow vanishes, enabling the stable incorporation of thermal effects and Joule–Thomson phenomena. The results clearly show that the steady-state Newton–Raphson approach provides the best overall performance in terms of computational efficiency, numerical robustness, and accuracy when physically consistent initial profiles are employed. In particular, the combination of a linear initial guess and a numerical mesh constituted of four collocation points yielded the most favorable balance between convergence speed, numerical robustness, and accuracy for the base-case sensitivity analysis. For monitoring-oriented applications, the numerical choice should be weighted primarily toward computational performance once physical consistency and convergence criteria are satisfied, rather than toward maximum mesh-refinement accuracy. In this context, small differences in internal-fiber profiles can be compensated through real-time permeance estimation and are negligible when compared with measurement uncertainty in real industrial processes. Under extreme operating conditions involving low concentrations, low flow rates, and highly permeable species, the pseudotransient formulation proved to be a reliable auxiliary strategy, enabling robust convergence when suitable initial guesses were not readily available. The proposed framework is validated against experimental data from the literature and subjected to extensive convergence and sensitivity analyses, providing a reliable basis for simulation and for assessing computational feasibility in in-line and real-time monitoring-oriented applications. A full demonstration of digital-twin integration, online parameter updating, reduced-order coupling, and closed-loop control is beyond the scope of the present study and will be addressed in future work. Full article