Search Results (267)

This study investigates the stability risks associated with a substandard-thickness (42 m) backfill isolation layer in the open-underground coordinated mining system of the Yongping Copper Mine’s eastern panel at the −150 m level. A numerical simulation based on FLAC3D 3.00 was conducted to evaluate the impacts of four mining sequences (south-to-north, north-to-south, center-to-flank, and flank-to-center) on stress redistribution and displacement evolution. A three-dimensional geomechanical model incorporating lithological parameters was established, with 23 monitoring points tracking stress and displacement dynamics. Results indicate that the mining sequence significantly influences the stability of both the isolation layer and the slope. No abrupt displacement occurred during mining, with incremental isolation layer settlement controlled within 3 mm. Post-mining maximum displacement increased to 10–12 mm. The “north-to-south” sequence emerged as the theoretically optimal solution, reducing cumulative displacements in pillars and stopes by 9.1% and 7.8%, respectively, compared to the suboptimal scheme. However, considering the engineering continuity of the existing “south-to-north” sequence at the −100 m level, maintaining consistent directional mining at the −150 m level is recommended to ensure synergistic disturbance control, ventilation system stability, and operational management coherence. Full article

Eczema, or atopic dermatitis (AD), is a chronic inflammatory skin condition characterized by persistent itching and scratching, significantly impacting patients’ quality of life. Effective monitoring of scratching behaviour is crucial for assessing disease severity, treatment efficacy, and understanding the relationship between itch and sleep disturbances. This review explores current technological approaches for detecting and monitoring scratching and itching in AD patients, categorising them into contact-based and non-contact-based methods. Contact-based methods primarily involve wearable sensors, such as accelerometers, electromyography (EMG), and piezoelectric sensors, which track limb movements and muscle activity associated with scratching. Non-contact methods include video-based motion tracking, thermal imaging, and acoustic analysis, commonly employed in sleep clinics and controlled environments to assess nocturnal scratching. Furthermore, emerging artificial intelligence (AI)-driven approaches leveraging machine learning for automated scratch detection are discussed. The advantages, limitations, and validation challenges of these technologies, including accuracy, user comfort, data privacy, and real-world applicability, are critically analysed. Finally, we outline future research directions, emphasizing the integration of multimodal monitoring, real-time data analysis, and patient-centric wearable solutions to improve disease management. This review serves as a comprehensive resource for clinicians, researchers, and technology developers seeking to advance objective itch and scratch monitoring in AD patients. Full article

Fluids that exhibit self-rewetting properties, such as aqueous long-chain alcohol solutions, display a unique quadratic relationship between surface tension and temperature and are marked by a positive gradient. This characteristic leads to distinctive patterns of thermocapillary convection and associated interfacial dynamics, setting self-rewetting fluids apart from normal fluids (NFs). The potential to improve heat transfer using self-rewetting fluids (SRFs) is garnering interest for use in various technologies, including low-gravity conditions and microfluidic systems. Our research aims to shed light on the contrasting behaviors of SRFs in comparison to NFs regarding interfacial transport phenomena. This study focuses on the thermocapillary convection in SRF layers with a deformable interface enclosed inside a closed container modeled as a square cavity, which is subject to nonuniform heating, represented using a Gaussian profile for the heat flux variation on one of its sides, in the absence of gravity. To achieve this, we have enhanced a central-moment-based lattice Boltzmann method (LBM) utilizing three distribution functions for tracking interfaces, computing two-fluid motions with temperature-dependent surface tension and energy transport, respectively. Through numerical simulations, the impacts of several characteristic parameters, including the viscosity and thermal conductivity ratios, as well as the surface tension–temperature sensitivity parameters, on the distribution and magnitude of the thermocapillary-driven motion are examined. In contrast to that in NFs, the counter-rotating pair of vortices generated in the SRF layers, due to the surface tension gradient at the interface, is found to be directed toward the SRF layers’ hotter zones. Significant interfacial deformations are observed, especially when there are contrasts in the viscosities of the SRF layers. The thermocapillary convection is found to be enhanced if the bottom SRF layer has a higher thermal conductivity or viscosity than that of the top layer or when distributed, rather than localized, heating is applied. Furthermore, the higher the magnitude of the effect of the dimensionless quadratic surface tension sensitivity coefficient on the temperature, or of the effect of the imposed heat flux, the greater the peak interfacial velocity current generated due to the Marangoni stresses. In addition, an examination of the Nusselt number profiles reveals significant redistribution of the heat transfer rates in the SRF layers due to concomitant nonlinear thermocapillary effects. Full article

The rapid growth of Internet of Things (IoT) enabled devices in industrial environments and the associated increase in data generation are paving the way for the development of localized, distributed datacenters. In this paper, we have proposed a novel mini-datacenter in the form of wireless sensor networks to efficiently handle query-based data collection from Industrial IoT (IIoT) devices. The mini-datacenter comprises a command center, gateways, and IoT sensors, designed to manage stochastic query-response traffic flow. We have developed a duplication/aggregation query flow model, tailored to emphasize reliable transmission. We have developed a dataflow management framework that employs a multi-modal query forwarding approach to forward queries from the command center to gateways under varying environments. The query forwarding includes coarse-grain and fine-grain strategies, where the coarse-grain strategy uses a direct data flow using a single gateway at the expense of reliability, while the fine-grain approach uses redundant gateways to enhance reliability. A fuzzy-logic-based intelligence system is integrated into the framework to dynamically select the appropriate granularity of the forwarding strategy based on the resource availability and network conditions, aided by a buffer watching algorithm that tracks real-time buffer status. We carried out several experiments with gateway nodes varying from 10 to 100 to evaluate the framework’s scalability and robustness in handling the query flow under complex environments. The experimental results demonstrate that the framework provides a flexible and adaptive solution that balances buffer usage while maintaining over 95% reliability in most queries. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to movement impairment, cognitive decline, and psychiatric symptoms. Key manifestations of PD include bradykinesia (the slowness of movement), changes in voice or speech, and gait disturbances. The quantification of neurological disorders through voice analysis has emerged as a rapidly expanding research domain, offering the potential for non-invasive and large-scale monitoring. This review explores existing research on the application of machine learning (ML) in speech, voice, and language processing for the diagnosis of PD. It comprehensively analyzes current methodologies, highlights key findings and their associated limitations, and proposes strategies to address existing challenges. A systematic review was conducted following PRISMA guidelines. We searched four databases: PubMed, Web of Science, Scopus, and IEEE Xplore. The primary focus was on the diagnosis, detection, or identification of PD through voice, speech, and language characteristics. We included 34 studies that used ML techniques to detect or classify PD based on vocal features. The most used approaches involved free speech and reading-speech tasks. In addition to widely used feature extraction toolkits, several studies implemented custom-built feature sets. Although nearly all studies reported high classification performance, significant limitations were identified, including challenges in comparability and incomplete integration with clinical applications. Emerging trends in this field include the collection of real-world, everyday speech data to facilitate longitudinal tracking and capture participants’ natural behaviors. Another promising direction involves the incorporation of additional modalities alongside voice analysis, which may enhance both analytical performance and clinical applicability. Further research is required to determine optimal methodologies for leveraging speech and voice changes as early biomarkers of PD, thereby enhancing early detection and informing clinical intervention strategies. Full article

This article examines hydrogen production using Proton Exchange Membrane Electrolyzers (PEMELs) and photovoltaic (PV) panels using Maximum Power Point Tracking (MPPT). This method has great potential to maximize the production of pure hydrogen, making it possible to reduce and potentially eliminate the difficulties associated with sudden drops and interruptions in output power. The use of MPPT algorithms in conjunction with Direct Current to Direct Current (DC/DC) converters helps improve the energy efficiency of systems. This study aims to enhance green hydrogen production by optimizing PV-PEMEL performance using commercial power electronics, while improving energy storage and reducing costs for sustainable hydrogen generation. Full article

Background/Objectives: Fast-track (FT) protocols have been developed to reduce the surgical burden and enhance recovery, but they still need to be established for carotid endarterectomy (CEA). In this scenario, carotid stenting has gained momentum by answering the need for a less invasive treatment, despite a still debated clinical advantage. We aim to propose a FT protocol for CEA and to analyze its clinical outcomes. Methods: This retrospective, monocentric study enrolled consecutive patients who underwent CEA for asymptomatic carotid stenosis using an FT protocol between January 2016 and December 2024. Patients undergoing CEA for symptomatic carotid stenosis, carotid bypass procedures, and combined interventions were excluded. Our FT protocol comprises same-day hospital admission, exclusive use of local anesthesia, non-invasive assessment of cardiac and neurological status, and selective utilization of cervical drainage. Discharge criteria were goal-directed and included the absence of pain, electrocardiographic abnormalities, hemodynamic instability, neck hematoma, or cranial nerve injury, with a structured plan for rapid readmission if required. Postoperative pain was assessed using the numerical rating scale (NRS), administered to all patients. The perioperative clinical impact of the protocol was evaluated based on complication rates, pain control, length of hospital stay, and early readmission rates. Results: Among 1051 patients who underwent CEA, 853 met the inclusion criteria. General anesthesia was required in 17 cases (2%), while a cervical drain was placed in 83 patients (10%). The eversion technique was employed in 765 cases (90%). Postoperative intensive care unit (ICU) monitoring was necessary for 7 patients (1%). The mean length of hospital stay was 1.17 days. Postoperatively, 17 patients (2%) required surgical revision. Minor stroke occurred in three patients (0.4%), and acute myocardial infarction requiring angioplasty in two patients (0.2%). Inadequate postoperative pain control (NRS > 4) was reported by five patients (0.6%). Hospital readmission was required for one patient due to a neck hematoma. Conclusions: The reported fast-track protocol for elective carotid surgery was associated with a low rate of postoperative complications. These findings support its clinical value and highlight the need for further validation through controlled comparative studies. Furthermore, the implementation of fast-track protocols in carotid surgery should prompt comparative medico-economic research. Full article

Non-fungible tokens (NFTs) are an emerging application of blockchain technology, with the potential to transform various industries, including tourism. Despite conceptual discussions that have highlighted opportunities and challenges associated with using NFTs—such as in digital souvenirs, ticketing systems, loyalty programmes, and conservation initiatives—there is a critical gap in the literature consisting of the lack of a structured methodological framework to empirically evaluate the impact of real-world NFT implementations. This study addresses this gap by proposing a conceptual model and methodological framework designed to assess NFT projects in the tourism sector. The framework integrates diverse data collection methods, advanced analytical techniques (including econometric analysis, natural language processing, and machine learning), and a technological workbench for tracking key performance indicators (KPIs). To demonstrate its applicability, the framework is applied to the Dalmatia NFT project, an exploratory application in cultural tourism. The considered example highlights the potential of NFTs to enhance tourism experiences while addressing challenges such as scalability, sustainability, and user engagement. This study concludes with insights into the framework’s practical implications for stakeholders and outlines future research directions for empirical validation. By bridging the gap between theory and practice, this study aims to provide a robust foundation for effectively integrating NFTs into the tourism industry. Full article

Between 14 and 16 June 2023, an extratropical cyclone affected the south-southeastern coast of Brazil, causing significant damage and loss of life. In the state of Rio Grande do Sul, Civil Defense authorities reported at least 16 fatalities. Although numerical models can simulate the general characteristics of extratropical cyclones, they often struggle to accurately represent the intensity and timing of strong winds and heavy precipitation. One approach to improving such simulations is the use of convective-permitting models (CPMs), in which convection is explicitly resolved. In this context, the main objective of this study is to assess the performance of the Weather Research and Forecasting (WRF) model in CP mode, nested in the ERA5 reanalysis, in representing both the synoptic and mesoscale structures of the cyclone, as well as its associated strong winds and precipitation. The WRF-CP successfully simulated the cyclone’s track, though with some discrepancies in the cyclone location during the first 12 h. Comparisons with radar-based precipitation estimates indicated that the WRF-CP captured the location of the observed precipitation bands. During the cyclone’s occlusion phase—when precipitation was particularly intense—hourly simulated precipitation and 10 m wind (speed, zonal, and meridional components) were evaluated against observations from meteorological stations. WRF-CP demonstrated strong skill in simulating both the timing and intensity of precipitation, with correlation coefficients exceeding 0.4 and biases below 0.5 mm h⁻¹. Some limitations were observed in the simulation of 10 m wind speed, which tended to be overestimated. However, the model performed well in simulating the wind components, particularly the zonal component, as indicated by predominantly high correlation values (most above 0.4), suggesting a good representation of wind direction, which is a function of the zonal and meridional components. Overall, the simulation highlights the potential of WRF-CP for studying extreme weather events, including the small-scale structures embedded within synoptic-scale cyclones responsible for producing adverse weather. Full article

Natural language interfaces can transform the construction industry by enhancing accessibility and reducing administrative workload in the day-to-day operations of project teams. This paper introduces the Voice-Integrated Scheduling Assistant for 4D BIM (VISA4D) tool that integrates speech recognition and Natural Language Processing (NLP) capabilities with Building Information Modeling (BIM) to streamline construction schedule updating and maintenance processes. It accepts voice and text inputs for schedule updates, facilitating real-time integration with Autodesk Navisworks, and eliminates the need for direct access to or advanced knowledge of BIM tools. It also provides visual progress tracking abilities through colour-coded elements within the 4D BIM model for communicating task status updates within the project teams. To demonstrate its capability to enhance schedule updating and maintenance efficiency, the VISA4D tool is implemented in an office building project in Canada and user testing is performed. An overall accuracy of 89% was observed in successfully classifying 71 out of 80 tested construction-specific commands, while the user surveys indicated high usability, with 92% of participants finding VISA4D easy to use and reporting consistent command recognition accuracy. This study advances the existing work on AI-enhanced construction management tools by tackling the challenges associated with their practical implementation in field operations. Full article

Art exhibition spaces increasingly emphasize visitor experience, yet the relationships among spatial structure, visitor behavior, and emotional response remain unclear. Traditional space syntax analyses typically focus on physical spatial structures, insufficiently capturing visitors’ emotional and cognitive experiences. To address these gaps, this study presents an integrative evaluation framework that combines space syntax theory with multimodal physiological measurements to systematically assess spatial design performance in art exhibition environments. Eye-tracking and heart rate variability (HRV) experiments were conducted to investigate how spatial configuration affects visual attention and emotional responses. Visibility graph analysis, spatial integration metrics, and regression modeling were applied using the third-floor temporary exhibition hall of the Pudong Art Museum in Shanghai as a case study. The results revealed that HRV levels (β = −7.92) were significantly predicted via spatial integration, and the relationship between spatial integration and the number of fixations was partially mediated by HRV (indirect effect: β = −0.36; direct effect: β = 8.23). Additionally, zones with higher occlusivity were associated with more complex scanpaths (mean complexity: 0.14), whereas highly integrated regions triggered more fixations (mean = 10.54) and longer total fixation durations (mean = 2946.98 ms). Therefore, spatial syntax, when coupled with physiological indicators, provides a robust and actionable method for evaluating and optimizing exhibition space design. Full article

Background/Objectives: Transcranial Direct Current Stimulation (tDCS) has proven to be a promising intervention for major depressive disorder (MDD). Even so, the specific neurophysiological mechanisms underlying its therapeutic effects, particularly regarding frontal EEG markers, remain insufficiently understood. This pilot study investigated both the clinical efficacy and neurophysiological impact of frontal tDCS in individuals with mild to severe depression, with particular focus on mood changes and alterations in Frontal Alpha Asymmetry (FAA), Beta Symmetry, and Theta/Alpha Ratios at the F3 and F4 electrode sites. Methods: A total of thirty–one participants were enrolled and completed a standardized Flow Neuroscience tDCS protocol targeting the dorsolateral prefrontal cortex using a bilateral F3/F4 montage. The intervention included an active phase of five stimulations per week for three weeks, followed by a Strengthening Phase with two stimulations per week. Clinical outcomes were assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS), while neurophysiological changes were evaluated via standardized quantitative EEG (QEEG) recordings obtained before and after the treatment course. Among the participants, fourteen individuals had a baseline MADRS score of ≥20, indicating moderate to severe depressive symptoms. Results: Following tDCS treatment, significant reductions in MADRS scores were observed across the cohort, with clinical response rates notably higher in the moderate/severe group (71.4%) compared to the mild depression group (20.0%). Neurophysiological effects were modest: no significant changes were detected in FAA or Beta Symmetry measures. However, a substantial reduction in the Theta/Alpha Ratio at F4 was found in participants with moderate to severe depression (p = 0.018, Cohen’s d = −0.72), suggesting enhanced frontal cortical activation associated with clinical improvement. Conclusions: These findings indicate that frontal tDCS is effective in reducing depressive symptoms, particularly in cases of moderate to severe depression. While improvements in FAA and Beta Symmetry were not significant, changes in the Theta/Alpha Ratio at F4 point toward dynamic neurophysiological reorganization potentially linked to therapeutic outcomes. The Theta/Alpha Ratio may serve as a promising biomarker for tracking tDCS response, whereas other EEG metrics might represent more stable trait characteristics. Future research should prioritize individualized stimulation protocols and incorporate more sensitive neurophysiological assessments, including functional connectivity analyses and task-evoked EEG paradigms, to understand the mechanisms underlying clinical improvements. Full article

The therapeutic landscape for small cell lung cancer (SCLC) has remained stationary for decades, with chemotherapy representing the sole treatment strategy, with a modest survival benefit. The addition of immune checkpoint inhibitors (ICIs) to standard first-line chemotherapy for SCLC was a considerable milestone. However, despite high overall response rates, this strategy failed to deliver long-term benefits for most patients, who continue to face a poor prognosis. Over the last few years, a deeper knowledge of the molecular biology of SCLC and the impressive advancements in drug development, have led to the generation of novel classes of systemic therapies that promise to revolutionize the current therapeutic scenario. Among the various therapeutic approaches in development, T-cell Engagers (TCE) and antibody-drug conjugates (ADCs) stand out due to their unique structural characteristics and mechanisms of action. These therapies represent a paradigm shift from traditional monoclonal antibody (mAb) and chemotherapy regimens, allowing direct engagement of multiple targets associated with tumor progression. In this review, we provide an overview of current drug development in SCLC, specifically focusing on these new agents, summarizing available evidence, and tracking future directions. Full article

Most control system implementations rely on single structures optimized for specific performance criteria through rigorous derivation. While effective for their intended purpose, such controllers often underperform in areas outside their primary optimization focus and involve performance trade-offs. A notable example is the Internal Model Principle (IMP) controller, renowned for its robustness and precision in reference tracking under periodic disturbances. However, IMP controllers exhibit poor transient-state performance, characterized by significant overshoot and oscillatory responses, which remains a persistent challenge. To address this limitation, this paper proposes a reinforcement learning (RL)-based hybrid control scheme that overcomes the trade-off in IMP controllers between achieving zero steady-state tracking error and a fast transient response. The proposed method integrates a cascade control structure, optimized for transient-state performance, with an IMP controller, optimized for robust reference tracking under sinusoidal disturbances, through switching logic governed by a Deep Q-Network model. Smooth transitions between control modes are ensured using an internal state update mechanism. The proposed approach is validated through simulations and experimental tests on a direct current (DC) motor position control system. The results demonstrate that the hybrid structure effectively resolves the trade-off associated with IMP controllers, yielding improved performance metrics, such as rapid convergence to the reference, reduced transient overshoot, and enhanced nominal performance recovery against disturbances. Full article

Wastewater surveillance has emerged as a cost-effective and equitable approach for tracking the spread of SARS-CoV-2. In this study, we monitored the prevalence of SARS-CoV-2 on a university campus over three years (2021–2023) using wastewater-based epidemiology (WBE). Wastewater samples were collected from 11 manholes on campus, each draining wastewater from a corresponding dormitory building, and viral RNA concentrations were measured using reverse transcription-quantitative PCR (RT-qPCR). Weekly clinical case data were also obtained from the university health center. A strong positive and significant correlation was observed between Grab and Composite sampling methods, supporting their robustness as equally effective approaches for sample collection. Specifically, a strong correlation was observed between Aggie Village 4 Grab and Aggie Village 4 Composite samples (R² = 0.84, p = 0.00) and between Barbee Grab and Barbee Composite samples (R² = 0.80, p = 0.00). Additionally, higher viral RNA copies of SARS-CoV-2 (N1 gene) were detected during the Spring semester compared to the Fall and Summer semesters. Notably, elevations in raw N1 concentrations were observed shortly after the return of college students to campus, suggesting that these increases were predominantly associated with students returning at the beginning of the Fall and Spring semesters (January and August). To account for variations in fecal loading, SARS-CoV-2 RNA concentrations were normalized using Pepper Mild Mottle Virus (PMMoV), a widely used viral fecal biomarker. However, normalization using PMMoV did not improve correlations between SARS-CoV-2 RNA levels and clinical case data. Despite these findings, our study did not establish WBE as a consistently reliable complement to clinical testing in a university campus setting, contrary to many retrospective studies. One key limitation was that numerous off-campus students did not contribute to the campus wastewater system corresponding to the monitored dormitories. However, some off-campus students were still subjected to clinical testing at the university health center under mandated protocols. Moreover, the university health center discontinued reporting cases per dormitory after 2021, making direct comparisons more challenging. Nevertheless, this study highlights the continued value of WBE as a surveillance tool for monitoring infectious diseases and provides critical insights into its application in campus environments. Full article