Search Results (878)

This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution—the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures—not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%). Full article

Background/Objectives: Food-related memory influences appetite regulation, with memory inhibition potentially reducing cravings. While obesity is linked to inhibitory deficits, how restrained eating affects memory suppression in healthy-weight individuals remains unclear. This study examined the cognitive and neural mechanisms of food-memory suppression in young women. Methods: Forty-two female participants completed a think/no-think task with high-/low-calorie food cues while an EEG was recorded. Event-related potentials (ERPs) were assessed and time–frequency analyses (theta/beta oscillations) were performed. Results: Restrained eaters showed reduced memory control for both food types. The ERP analysis revealed significant N200 amplitude differences between think/no-think conditions (p = 0.03) and a significant interaction between food calories and think/no-think conditions (p = 0.032). Theta oscillations differed by group, food calories, and conditions (p = 0.038), while beta oscillations reflected food-cue processing variations. Conclusions: In conclusion, restrained eaters exhibit distinct neural processing and attenuated food-memory suppression. These results elucidate the neurocognitive mechanisms underlying dietary behavior, suggesting that targeted interventions for maladaptive eating could strengthen memory inhibition. Full article

Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article

The early detection of Alzheimer’s disease (AD) is important for effective therapeutic interventions and optimized enrollment for clinical trials. Recent studies have shown high accuracy in identifying mild AD by applying visibility graph and machine learning methods to electroencephalographic (EEG) data. We present a novel analytical framework combining Weighted Visibility Graphs (WVG) and ensemble learning to detect individuals in the “preclinical” stage of AD (preAD) using a word repetition EEG paradigm, where WVG is an advanced variant of natural Visibility Graph (VG), incorporating weighted edges based on the visibility degree between corresponding data points. The EEG signals were recorded from 40 cognitively unimpaired elderly participants (20 preclinical AD and 20 normal old) during a word repetition task. Event-related potential (ERP) and oscillatory signals were extracted from each EEG channel and transformed into a WVG network, from which relevant topological features were extracted. The features were selected using t-tests to reduce noise. Subsequent statistical analysis reveals significant disparities in the structure of WVG networks between preAD and normal subjects. Furthermore, Principal Component Analysis (PCA) was applied to condense the input data into its principal features. Leveraging these PCA components as input features, several machine learning algorithms are used to classify preAD vs. normal subjects. To enhance classification accuracy and robustness, an ensemble method is employed alongside the classifiers. Our framework achieved an accuracy of up to 92% discriminating preAD from normal old using both linear and non-linear classifiers, signifying the efficacy of combining WVG and ensemble learning in identifying very early AD from EEG signals. The framework can also improve clinical efficiency by reducing the amount of data required for effective classification and thus saving valuable clinical time. Full article

Background: This study aims to evaluate the efficacy of a combined transcranial magnetic stimulation (TMS) protocol incorporating intermittent theta burst stimulation (iTBS) and low-frequency TMS in adults diagnosed with first-episode and recurrent depressive disorders. Methods: A prospective, double-blind, parallel-group trial was conducted involving 42 participants (21 with first-episode depressive disorder and 21 with recurrent depressive disorder) recruited from Chengdu, China. All subjects received 10 sessions of TMS over two weeks. The primary outcome measure was suicidal ideation, assessed using the Beck scale for suicide ideation. Secondary outcomes included sleep quality, depressive symptoms, anhedonia, and cognitive function. Event-related potentials (ERPs) were also recorded. Data were analyzed using SPSS V.21.0, with statistical significance defined as p < 0.05. Results: Both patient groups exhibited significant reductions in suicidal ideation following the composite TMS intervention. Secondary outcomes showed significant improvements in sleep quality, overall depressive symptoms, anhedonia, and cognitive function. Notably, a significant association was found between improvements in sleep quality and depressive symptoms in the first-episode group, suggesting differential underlying mechanisms compared to recurrent depression. Limitations: The relatively short intervention and follow-up period limits the ability to assess the long-term sustainability of the observed benefits. Future studies with extended follow-up periods are warranted to evaluate the persistence of TMS effects and the potential need for maintenance sessions. Conclusions: The combined protocol of iTBS and low-frequency TMS effectively reduces suicidal ideation and improves various clinical outcomes in both first-episode and recurrent depressive disorders, indicating the effectiveness of the physical intervention, especially for the first-episode patients. These findings underscore the importance of personalized treatment strategies based on the clinical history of depressive episodes. Further research with longer follow-up periods is warranted to assess the long-term sustainability of TMS effects. Full article

In this study, the ultrasonic-assisted extraction of deep eutectic solvents (UADES) for tea polysaccharides was optimized, and their physicochemical properties and antioxidant activities were analyzed. The optimal DES comprised choline chloride (CC) and ethylene glycol (EG) in a molar ratio of 1:3, with a water content of 40%. The optimized condition was an extraction temperature of 61 °C, an ultrasonic power of 480 W, and an extraction time of 60 min. The UADES extraction rate of polysaccharides (ERP) was 15.89 ± 0.13%, significantly exceeding that of hot water (HW) extraction. The polysaccharide content in the UADES-extracted tea polysaccharides (UADESTPs) was comparable to that of hot-water-extracted tea polysaccharides (HWTPs) (75.47 ± 1.35% vs. 74.08 ± 2.51%); the UADESTPs contained more uronic acid (8.35 ± 0.26%) and less protein (12.91%) than HWTP. Most of the UADESTPs (88.87%) had molecular weights (Mw) below 1.80 × 10³ Da. The UADESTPs contained trehalose, glucuronic acid, galactose, xylose, and glucose, with molar ratios of 8:16:1:10. The free radical scavenging rate and total reducing power of the UADESTPs were markedly superior to those of the HWTPs. Moreover, the UADESTPs had a better alleviating effect on H₂O₂-induced oxidative injury in HepG2 cells. This study develops an eco-friendly and efficient extraction method for tea polysaccharides, offering new insights for the development of tea polysaccharides. Full article

Background: Selecting enterprise resource planning (ERP) systems for complex port environments is a significant challenge. This study addresses a key research gap by identifying and prioritizing the critical factors for ERP selection within the strategic context of United Arab Emirates (UAE) ports, which function as vital hubs in global trade. Methods: A hybrid methodology was employed, first using the Fuzzy Delphi Method (FDM) to validate thirteen challenges with five industry experts. Subsequently, the Relative Importance Index (RII) was used to rank these challenges based on survey data from 48 UAE port professionals. Results: The analysis revealed “Cybersecurity concerns” as the highest-ranked challenge (RII = 0.896), followed by “Engagement with external stakeholders” (RII = 0.842), and both “Process optimization” and “Technical capabilities” (RII = 0.808). Notably, factors traditionally seen as critical in other sectors, such as “Organizational readiness” (RII = 0.746), were ranked significantly lower. Conclusions: The findings indicate a strategic shift in ERP selection priorities toward digital resilience and external integration rather than internal organizational factors. This research provides a sector-specific decision-support framework and offers actionable insights for port authorities, vendors, and policymakers to enhance ERP implementation in the maritime industry. Full article

Background/Objectives: Concussions have been associated with deficits in attentional control. The current work examined whether attentional correlates could be enhanced following acute aerobic exercise in those with a history of concussion (CH). Methods: EEG was collected as participants completed a flanker task to evoke stimulus-locked (N2, P3) and response-locked error-related (ERN, Pe) ERPs, before and after participants completed a bout of acute aerobic exercise at moderate intensity. Conflict was modulated with distance (close/far) and congruency (incongruent/congruent) of the distractors relative to the targets. Results: CH individuals had reduced accuracy in high-conflict conditions, with improvements following exercise. No differences were observed in attentional cognitive control across the four conditions (close/far congruent, close/far incongruent); however, reduced interference control was shown in far conditions, when compared to close conditions. When compared to non-concussed controls, increased accuracy with increased response time in individuals with a concussion history was likely attributed to the speed–accuracy trade-off. Close conditions highlighted a decreased Pe amplitude in CH individuals (as opposed to the active controls), suggesting CH individuals may present with challenges when evaluating an error with working memory. Conclusions: The findings demonstrated acute exercise improved accuracy among CH individuals, and performance monitoring is impacted negatively long term following a concussion. Full article

The present study aimed to investigate the electrophysiological correlates of face-identity recognition in newborn infants immediately after birth. Electroencephalographic acquisition was continuously recorded in 23 newborn infants (3 < age < 24 h of life) during the following visual task: presentation of a woman’s face for 60 s (“known face”); random presentation of 50 known faces, 50 novel women’s faces, and 50 chessboards (for 2 s each). The final sample included in ERP analyses was composed of 11 newborn infants (male/female: 6/5; age: 5 h 16′ ± 3 h 51′). A greater negative amplitude of the N290 and smaller P400 and LC2 were found in response to the known face compared with the novel one in the left hemisphere. A shorter N290 latency was detected during the known face presentation compared with the novel one, and a longer latency of the same component was observed during novel face presentation compared with the chessboard. These findings suggest that newborns process a face differently from an object at birth and that they can discriminate a new face from a familiar one previously viewed for one minute. Full article

Introduction: Huntington’s disease (HD) disrupts cortico-striato-thalamocortical circuits decades before clinical onset. Electroencephalography (EEG) offers millisecond temporal resolution, low cost, and broad accessibility, yet its mechanistic and biomarker potential in HD remains underexplored. We conducted a mechanistic review to synthesize half a century of EEG findings, identify reproducible electrophysiological signatures, and outline translational next steps. Methods: Two independent reviewers searched PubMed, Scopus, Google Scholar, ResearchGate, and the Cochrane Library (January 1970–April 2025) using the terms “EEG” OR “electroencephalography” AND “Huntington’s disease”. Clinical trials published in English that reported raw EEG (not ERP-only) in human HD gene carriers were eligible. Abstract/title screening, full-text appraisal, and cross-reference mining yielded 22 studies (~700 HD recordings, ~600 controls). We extracted sample characteristics, acquisition protocols, spectral/connectivity metrics, and neuroclinical correlations. Results: Across diverse platforms, a consistent spectral trajectory emerged: (i) presymptomatic carriers show a focal 7–9 Hz (low-alpha) power loss that scales with CAG repeat length; (ii) early-manifest patients exhibit widespread alpha attenuation, delta–theta excess, and a flattened anterior-posterior gradient; (iii) advanced disease is characterized by global slow-wave dominance and low-voltage tracings. Source-resolved studies reveal early alpha hypocoherence and progressive delta/high-beta hypersynchrony, microstate shifts (A/B ↑, C/D ↓), and rising omega complexity. These electrophysiological changes correlate with motor burden, cognitive slowing, sleep fragmentation, and neurovascular uncoupling, and achieve 80–90% diagnostic accuracy in shallow machine-learning pipelines. Conclusions: EEG offers a coherent, stage-sensitive window on HD pathophysiology—from early thalamocortical disinhibition to late network fragmentation—and fulfills key biomarker criteria. Translation now depends on large, longitudinal, multi-center cohorts with harmonized high-density protocols, rigorous artifact control, and linkage to clinical milestones. Such infrastructure will enable the qualification of alpha-band restoration, delta-band hypersynchrony, and neurovascular coupling as pharmacodynamic readouts, fostering precision monitoring and network-targeted therapy in Huntington’s disease. Full article

Background: The early repolarization pattern (ERP) on electrocardiography (ECG) has been associated with an increased risk of ventricular arrhythmias in susceptible individuals. This study aimed to evaluate the impact of exercise on echocardiographic parameters to explore the potential influence of ERP on hemodynamic response. Methods: Twenty-five healthy, young males with ERP (ERP+ group) and 25 age-matched healthy males without ERP (ERP− group) were enrolled. Comprehensive transthoracic echocardiography was performed at rest and during the early recovery phase following a treadmill exercise test. Baseline values and exercise-induced changes in both conventional and strain-derived echocardiographic parameters were analyzed and compared between groups. Results: Anthropometric measures and resting vital signs were similar in both groups. At baseline, the ERP+ group had a shorter QRS duration. Both groups demonstrated excellent cardiovascular fitness, with comparable chronotropic and pressor responses to exercise. Resting and early recovery-phase echocardiographic parameters were largely similar between ERP+ and ERP− individuals, with no overt structural or functional abnormalities observed in either group. However, ERP+ individuals showed significantly greater reductions in left ventricular end-diastolic volume and stroke volume following exercise, suggesting a distinct volumetric response to physical stress. Conclusions: ERP in healthy young males is not associated with structural cardiac abnormalities or overt myocardial dysfunction. The observed exercise-induced volumetric changes may indicate subtle differences in hemodynamic adaptation, warranting further investigation. Full article

Rare earth elements (REEs) have garnered significant global attention due to their essential role in advanced technologies. Sri Lanka is endowed with various REE-bearing minerals, including the apatite-rich deposit in the Eppawala area, commonly known as Eppawala rock phosphate (ERP). However, direct extraction of REEs from ERP is technically challenging and economically unfeasible. This study introduces a novel, integrated approach for recovering REEs from ERP as a by-product of nitrophosphate fertilizer production. The process involves nitric acid-based acidolysis of apatite, optimized at 10 M nitric acid for 2 h at 70 °C with a pulp density of 2.4 mL/g. During cooling crystallization, 42 wt% of calcium was removed as Ca(NO₃)₂.4H₂O while REEs remained in the solution. REEs were then selectively precipitated as REE phosphates via pH-controlled addition of ammonium hydroxide, minimizing the co-precipitation with calcium. Further separation was achieved through selective dissolution in a sulfuric–phosphoric acid mixture, followed by precipitation as sodium rare earth double sulfates. The process achieved over 90% total REE recovery with extraction efficiencies in the order of Pr > Nd > Ce > Gd > Sm > Y > Dy. Samples were characterized for their phase composition, elemental content, and morphology. The fertilizer results confirmed the successful production of a nutrient-rich nitrophosphate (NP) with 18.2% nitrogen and 13.9% phosphorus (as P₂O₅) with a low moisture content (0.6%) and minimal free acid (0.1%), indicating strong agronomic value and storage stability. This study represents one of the pioneering efforts to valorize Sri Lanka’s apatite through a novel, dual-purpose, and circular approach, recovering REEs while simultaneously producing high-quality fertilizer. Full article

Research suggests that monosodium glutamate (MSG), known for its association with umami taste, may influence appetite. This event-related potential (ERP) study with source localization examined the effects of umami taste on visual food cue reactivity. A total of 88 females (mean age: 26 years) were randomly assigned to one of two groups: one tasted an MSG solution, while the other tasted water, before viewing images from four food categories with varying umami concentrations (meat dishes, vegetables, fruits, sweets). The participants rated their desire to eat the depicted foods, and ERP late positivity (P300; late positive potential: LPP) was analyzed. The results showed that MSG did not influence reported appetite. However, the MSG group exhibited reduced parietal P300/LPP amplitudes across all food categories. These group differences in food cue processing could be traced back to sources in the left occipital gyrus and fusiform gyrus indicating an alteration in motivated attention due to umami taste. Full article

This study examines how employees perceive technology-based demands during the digital transformation process and how these perceptions affect job performance. The research utilized data obtained from 388 experts in the accounting and financial reporting profession, a knowledge-intensive field that heavily employs new technologies (e.g., ERP systems, digital audit tools). The data collected through a convenience sampling method was analyzed using SPSS 27 and SmartPLS 4 software. The findings reveal that the direct effect of technostress on job performance is not significant; however, this stress indirectly contributes to performance through techno-eustress. In this study, techno-eustress refers to the cognitive appraisal of technology-related demands as development-enhancing challenges rather than threats. This concept is theoretically grounded in the broader eustress framework, which views stressors as potentially motivating and growth-promoting when positively interpreted. The model is based on Cognitive Evaluation Theory, the Job Demands–Resources Model, and Self-Determination Theory. This study demonstrates that digital transformation can promote not only operational improvements but also organizational resilience by enhancing employees’ psychological resources and adaptive capacities. By highlighting the mediating role of techno-eustress, this research offers a nuanced perspective on how human-centered cognitive mechanisms can strategically support performance and sustainability in the face of technological disruption—an increasingly relevant area for organizations striving to thrive amid uncertainty. Full article

Cultural landscapes in World Heritage cities are attracting a growing global tourist population. Given the limitations of self-report methods in capturing tourists’ immediate and deep perceptions, and the lack of comprehensive investigation into the cultural types and naturalness of landscapes, this study aims to investigate how cultural landscape types influence tourists’ recommendation intention through the mediating roles of place attachment and perceived restorativeness while examining the moderating effect of landscape naturalness. Integrating Place Attachment Theory (PAT), Attention Restoration Theory (ART), and the Associative–Propositional Evaluation Model (APE), three studies were conducted using behavioral and neurophysiological approaches. Study 1, a scenario-based experiment, revealed that high-culture landscapes enhance recommendation intention via place attachment, with the effect of perceived restorativeness being stronger under low naturalness conditions. Study 2, an event-related potential (ERP) experiment, showed that landscapes with low culture and low naturalness elicit stronger emotional responses, as indicated by heightened P2 and LPP amplitudes. Study 3 demonstrated the efficacy of a Decision Tree model in classifying landscape naturalness based on EEG features. This study deepens the understanding of the complexity of tourist experiences in cultural heritage sites, provides new evidence for the application of Place Attachment Theory in tourism contexts, and offers scientific foundations and practical implications for optimizing landscape design in heritage sites, enhancing tourist experiences, and exploring brain–computer interface applications in the tourism field. Full article