Search Results (56,112)

Urban shared parking systems represent a complex socio-technical challenge. Despite vast potential, utilization remains persistently low (<15%), revealing a critical policy failure. To address this, this study develops a dynamic system framework based on Life-Cycle Cost (LCC) and Hamilton-Jacobi-Bellman (HJB) optimization to analyze and calibrate the key policy levers influencing owner participation timing (T*). The model, resolved using finite difference methods, captures the system’s non-linear threshold effects by simulating critical system parameters, including system instability (price volatility, ), internal friction (management fee, ), and demand signals (transaction ratio, Q). Simulations reveal extreme non-linear system responses: a 100% increase in system instability () delays participation by 325.5%. More critically, a 100% surge in internal friction (management fees) delays T* by 492% and triggers a 95% revenue collapse—demonstrating the risk of systemic collapse. Conversely, a 20% rise in the demand signal (Q) advances T* by 100% (immediate participation), indicating the system can be rapidly shifted to a new equilibrium by activating positive feedback loops. These findings support a sequenced calibration strategy: regulators must first manage instability via price stabilization, then counteract high friction with subsidies (e.g., 60%), and amplify demand loops. The LCC framework provides a novel dynamic decision support system for calibrating complex urban transportation systems, offering policymakers a tool for scenario testing to accelerate policy adoption and alleviate urban congestion. Full article

Exposure models are essential for seismic risk assessment to determine environmental vulnerabilities during earthquakes. However, developing these models at scale is challenging because it relies on manual inspection of buildings, which increases costs and introduces significant delays. Developing fast, consistent, and easy-to-deploy automated methods to support this process has become a priority. In this study, we investigate the use of deep learning to accelerate the classification of architectural and structural attributes from street-view imagery. Using the Alvalade dataset, which contains 4007 buildings annotated with 10 multi-class attributes, we evaluated the performance of multiple architecture types. Our analysis shows that deep learning models can successfully extract key structural features, achieving an average macro accuracy of 57%, and a Precision, Recall, and F1-score of 61%, 57%, and 56%, respectively. We also show that prediction quality is further improved by leveraging multi-view imagery of the target buildings. These results demonstrate that deep learning can be an effective solution to reduce the manual effort required for the development of reliable large-scale exposure models, offering a practical solution toward more efficient seismic risk assessment. Full article

Excavation of underground spaces often causes significant initial damage to surrounding rock, which can notably alter its mechanical properties. However, most studies on loading rate effects neglect the role of initial damage. This study investigates how initial damage and loading rate together affect granite’s mechanical behavior and fracturing characteristics. Granite specimens with different initial damage levels were subjected to uniaxial compression at varying loading rates to assess their mechanical parameters, stress thresholds, failure modes, energy evolution, and associated acoustic emission (AE) activity. Results indicate that granite’s mechanical behavior exhibits greater sensitivity to loading rate than to initial damage. As the loading rate increases, both strength and elastic modulus initially decrease and then rise, while the dissipated-to-input energy ratio reaches a maximum when the strength is at its lowest. This phenomenon occurs because, when cracks are allowed to fully develop, a relatively higher loading rate increases the likelihood of crack initiation and propagation, thereby reducing strength. The AE responses of initial damage granite samples (IDGSs), including counts, RA/AF value, b-value, and entropy, exhibit stage-dependent variations and contain precursory information before failure. Moreover, AE signals display multifractal characteristics across different loading rates. These findings reveal the mechanisms underlying granite’s mechanical response when both initial damage and loading rate act together: initial damage primarily affects the complexity and number of local microcracks, while loading rate determines the dominant crack initiation and propagation modes. Moreover, how the failure time of IDGSs varies with loading rate can be described by an inverse exponential function. These findings enhance insight into the coupling mechanism of initial damage and loading rate, with significant implications for failure warning and the cost-effectiveness of underground excavation. Full article

Background: Colorectal cancer (CRC) is currently the third most common cancer worldwide, with high heterogeneity and poor prognosis. Gene expression-based molecular subtypes can effectively dissect tumor heterogeneity, but their clinical translation remains challenging. This study aims to conduct radiogenomic analysis regarding molecular subtypes and establish prognostic signatures for survival prediction of colorectal cancer. Methods: In this retrospective study involving 2948 CRC patients from 8 cohorts, we utilized a supervised deep learning framework to extract quantitative feature representations of molecular subtypes. Through correlation analysis, we selected key gene expression features related to these subtypes to establish a prognostic signature. A similar pipeline was applied to derive a non-invasive radiomic prognostic signature. Finally, we validated the prognostic value of both signatures in multiple cohorts and explored their biological interpretation. Results: We successfully established a molecular subtype-associated gene signature and a non-invasive radiogenomic signature. The gene signature classified patients into high-risk and low-risk groups with significantly different prognoses. The low-risk group had a better prognosis and showed a greater potential benefit from immunotherapy. Similarly, the radiogenomic signature exhibited characteristics related to molecular subtypes and comparable performance in prognostic prediction. Multivariate analysis confirmed the independent prognostic value of both signatures. In summary, this retrospective study demonstrates that our framework translates molecular subtypes into cost-effective biomarkers for risk stratification and treatment guidance. Full article

Desalination is a vital solution to global water scarcity, yet its substantial energy demand persists as a major challenge. As the core energy-consuming components, pumps are fundamental to both membrane and thermal desalination processes. This review provides a comprehensive analysis of renewable energy source (RES)-driven pumping systems for desalination, focusing on the integration of solar photovoltaic and wind technologies. It examines the operational principles and efficiency of key pump types, such as high-pressure feed pumps for reverse osmosis, and underscores the critical role of energy recovery devices (ERDs) in minimizing net energy consumption. Furthermore, the paper highlights the importance of advanced control and energy management systems (EMS) in mitigating the intermittency of renewable sources. It details essential control strategies, including maximum power point tracking (MPPT), motor drive control, and supervisory EMS, that optimize the synergy between pumps, ERDs, and variable power inputs. By synthesizing current technologies and control methodologies, this review aims to identify pathways for designing more resilient, energy-efficient, and cost-effective desalination plants, supporting a sustainable water future. Full article

The economic importance of Carlin-type gold deposits is complicated by the concealed nature of stratiform gold-bearing zones and their occurrence at depths of several tens of meters or more below the present-day surface. This necessitates the use of a wide range of technologies and unconventional, including cost-effective and environmentally friendly, exploration methods to delineate potentially prospective areas. This study explores the possibilities of applying remote sensing methods to organize prospecting and exploration activities for targeting Carlin-type deposits in a more efficient and cost-effective way. The location of Carlin-type gold deposits within areas of orogenic and post-orogenic magmatism, mantle plumes, and linear crustal structures—as demonstrated by previous research in the Nevada and South China metallogenic provinces—may serve as a basis for developing a conceptual model of their distribution. To this end, we developed the GeoNEM (Geodynamic Numeric Environmental Modeling) software in Python, which enables the analysis of the formation of fold and fault structures, melt emplacement and contamination, as well as the duration and rate of geodynamic processes. GeoNEM is based on the computational geodynamics “marker-in-cell” (MIC) method, which treats geological media as extremely high-viscosity fluids. Locations of the brittle deformations of the crust, the formation of which was simulated numerically, can be detected through lineament analysis of remote sensing images. The spatial distribution of such structures—lineaments—serves as a predictive criterion for assessing the prospectivity of territories for Carlin-type gold deposits. It has been demonstrated that remote sensing provides a modern level of efficiency, cost-effectiveness, and comprehensiveness in approaching the exploration and assessment of new Carlin-type gold deposits. This is particularly important in the context of rational resource utilization and cost reduction. Full article

Background/Objectives: Chemical methods for quantifying resistant starch (RS) in rice are labor-intensive, costly, and lack high repeatability, creating a bottleneck in breeding. This study aimed to develop specific, codominant molecular markers for the Wx gene to enable rapid and accurate genotype screening for RS content, thereby accelerating the development of high-RS rice varieties. Methods: Based on sequence alignment of the Wx gene in rice varieties with divergent RS content, a key single-nucleotide polymorphism was targeted. Two sets of tetra-primer amplification refractory mutation system polymerase chain reaction (ARMS-PCR) markers, T-Wx9-RS1 and T-Wx9-RS2, were designed. These markers were used to genotype diverse rice varieties and F₄ segregating populations, with results validated against standard chemical assays. Results: Sequence analysis identified a critical T → C base mutation at position 202 of the ninth exon in high-RS varieties. The developed ARMS-PCR markers successfully and consistently distinguished all three possible genotypes (homozygous mutant, homozygous wild-type, and heterozygous). The genotyping results showed complete concordance with the phenotypes determined by chemical methods. Conclusions: The developed molecular markers, T-Wx9-RS1 and T-Wx9-RS2, provide a rapid, reliable, and cost-effective tool for marker-assisted selection of high resistant starch content in rice. Their implementation can significantly enhance screening efficiency and expedite the breeding pipeline for novel, nutritionally improved rice cultivars. Full article

Product quantization (PQ) is a widely adopted technique for efficient approximate nearest neighbor (ANN) search in high-dimensional spaces, offering a favorable balance between accuracy and memory efficiency. However, standard PQ suffers from high online computational cost when the number of subspaces is high. To address this dilemma, we propose Pyramid Product Quantization (PPQ), a novel adaptive quantization framework that dynamically selects the most suitable number of subspaces for different segments of each data vector. This leads to a significant reduction in the number of addition operations required during approximate distance computation, significantly accelerating online search. Experimental results demonstrate that the proposed PPQ method effectively lowers the computational complexity of product quantization and its variants, without compromising retrieval accuracy. Full article

Phosphorus is an essential resource for numerous industrial applications. However, its uneven global distribution makes Europe heavily dependent on imports. Recovering phosphorus from waste streams is therefore crucial for improving resource security. The FlashPhos project addresses this challenge by developing a process to recover phosphorus from sewage sludge, in which phosphorus-rich slag is produced in a flash reactor and subsequently reduced in a Submerged Arc Furnace (SAF). In this process, approximately 250 kg/h of sewage sludge is converted into slag, which is further processed in the SAF to recover about 8 kg/h of white phosphorus. This work focuses on the development of a computational model of the SAF, with particular emphasis on slag behaviour. Due to the extreme operating conditions, which severely limit experimental access, a numerically efficient three-dimensional CFD model was developed to investigate the internal flow of the three-phase, AC-powered SAF. The model accounts for multiphase interactions, dynamic bubble generation and energy sinks associated with the reduction reaction, and Joule heating. A temperature control loop adjusts electrode currents to reach and maintain a prescribed target temperature. To further reduce computational cost, a novel simulation approach is introduced, achieving a reduction in simulation time of up to 300%. This approach replaces the solution of the electric potential equation with time-averaged Joule-heating values obtained from a preceding simulation. The system requires transient simulation and reaches a pseudo-steady state after approximately 337 s. The results demonstrate effective slag mixing, with gas bubbles significantly enhancing flow velocities compared to natural convection alone, leading to maximum slag velocities of 0.9–1.0 m/s. The temperature field is largely uniform and closely matches the target temperature within ±2 K, indicating efficient mixing and control. A parameter study reveals a strong sensitivity of the flow behaviour to the slag viscosity, while electrode spacing shows no clear influence. Overall, the model provides a robust basis for further development and future coupling with the gas phase. Full article

In this paper, a series of Co-free FeCr_0.6Ni_0.6(AlSi)_x (x = 0, 0.1, 0.12, 0.14, 0.16) high-entropy alloys (HEAs) were designed and fabricated by suction casting, and the effects of equi-molar (Al, Si) co-addition in these Fe-rich Fe-Cr-Ni-based HEAs on microstructure, mechanical properties, and corrosion resistance were systematically investigated. It is found that equi-molar (Al, Si) co-addition could cause the phase formation from FCC to FCC + BCC, while the morphologies of the phases change from dendrite-type to sideplate-type. Moreover, trade-off between strength and plasticity occurs with the increase in (Al, Si) co-addition, and the production of ultimate tensile strength and plasticity reaches the highest value when x = 0.12, while there exists a narrow region for x values to realize excellent comprehensive mechanical properties. In addition, similar corrosion resistance in 3.5 wt.% NaCl solution higher than 316L stainless steel could be realized in the HEAs with x = 0.12 and 0.14, while the latter one is slightly lower in pitting corrosion and the width of passive region, which is possibly caused by the increase in the density of phase boundaries. This work provides a novel insight on designing high-performance cost-effective Fe-rich and (Al, Si)-containing (Fe-Cr-Ni)-based HEAs combining high mechanical properties and corrosion resistance. Full article

Background and Objectives: Hysteroscopy has become the “gold standard” in assessing uterine cavity abnormalities, and currently it can be performed in an “office setting”. Although office hysteroscopy has a better level of comfort than operative hysteroscopy, pain is a common concern. Nonsteroidal anti-inflammatory drugs (NSAIDs) are frequently used for pre-procedure analgesia, but they may cause gastrointestinal side effects. Paracetamol offers to be a safer alternative, but its efficacy in this setting is limited. This study aimed to compare the efficacy and safety of oral paracetamol with oral ketoprofen for pain management during office hysteroscopy. Materials and Methods: Double-blind, parallel-group, randomized controlled trial conducted at a single hysteroscopy center in Jakarta, Indonesia, over a 2-year period. Sixty women undergoing office hysteroscopy were randomized (1:1) to receive paracetamol 1000 mg orally or ketoprofen 100 mg orally 1 h before the procedure. Results: All participants completed the trial and were included in the analysis. The median visual analog score (VAS) during the procedure was 2 (range 0–8) in the paracetamol group versus 3 (range 0–6) in the ketoprofen group (p = 0.266). Median cramping scores 30 min post-procedure in the paracetamol group were 0 (range 0–5) vs. 0 (range 0–4) in the ketoprofen group, respectively (p = 0.499). Side effects occurred in 3 participants (10%) in the ketoprofen group and none of the paracetamol group. Comfort scores were high in both groups (median 9/10). No vagal reflexes were observed. Conclusions: Oral 1000 mg paracetamol was as effective as oral 100 mg ketoprofen for pain management during and after office hysteroscopy, with fewer side effects. Paracetamol may be a safe and cost-effective alternative for pre-procedure analgesia in office hysteroscopy. Full article

This study examines the recent diversification of the Korean golf market into traditional field, popular virtual reality (VR), and park golf, which is rapidly expanding among older adults. Comparing participants’ psychological characteristics and behavioral intentions across golf types is essential for sustainably developing the golf industry. Therefore, differences were investigated in participation satisfaction (physical, mental, and social), switching intention, loyalty, and continuous participation intention among regular participants in all three golf types in urban Korea. Data were analyzed from 327 adults aged 20 years or older (Field: 98, VR: 132, Park: 97) in Korea using on/offline surveys, and a multivariate analysis of variance with post hoc tests was implemented to compare psychological and behavioral differences across the three golf types. The findings showed that, first, physical and mental satisfaction were significantly higher in the park golf group than in the rest of the groups. Second, switching intention was higher in the field golf group than in the VR golf group. Third, loyalty and continuous participation intention were highest in the park golf group. Each golf type thus offers unique experiential value, with park golf particularly effective in fulfilling participants’ physical and psychological needs. Conversely, field golf faces potential risks of participant attrition because of cost and time burdens. The findings provide useful implications for predicting demand and developing differentiated marketing and management strategies tailored to generational needs. Full article

β-lactose is an anomer of interest for the pharmaceutical and food industries due to its techno-functional properties; however, its production is often costly and complex. In this study, the feasibility of using food-grade lactose (F-αL) to produce β-lactose was evaluated as an accessible and cost-effective alternative. For this purpose, the physicochemical characterization of this lactose was carried out through X-ray Diffraction (XRD), Thermogravimetric Analysis (TGA), Modulated Differential Scanning Calorimetry (MDSC), Fourier Transform Infrared Spectroscopy (FTIR), and Raman Spectroscopy. The mutarotation process was also performed using alcoholic KOH solutions. Physicochemical characterization confirmed that commercial lactose consists mainly of α-lactose monohydrate, which is an ideal precursor for β-lactose production. Likewise, the conversion process efficiently yielded β-lactose, validating the feasibility of using food-grade lactose in this process, with a residual α-lactose content below 10%, indicating a high conversion efficiency. Thus, food-grade lactose emerges as a viable alternative for producing high-purity β-lactose. This finding represents a 90% reduction in production costs of this anomer, promoting the development of high-quality products in the pharmaceutical and food sectors. Full article

Structural Health Monitoring (SHM) of large-scale civil infrastructure is essential to ensure safety, minimise maintenance costs, and support informed decision-making. Unsupervised anomaly detection has emerged as a powerful tool for identifying deviations in structural behaviour without requiring labelled damage data. The study initially reproduces and implements a state-of-the-art methodology that combines local density estimation through the Cumulative Distance Participation Factor (CDPF) with Semi-parametric Extreme Value Theory (SEVT) for thresholding, which serves as an essential baseline reference for establishing normal structural behaviour and for benchmarking the performance of the proposed anomaly detection framework. Using modal frequencies extracted via Stochastic Subspace Identification from the Z24 bridge dataset, the baseline method effectively identifies structural anomalies caused by progressive damage scenarios. However, its performance is constrained when dealing with subtle or non-linear deviations. To address this limitation, we introduce an innovative ensemble anomaly detection framework that integrates two complementary unsupervised methods: Principal Component Analysis (PCA) and Autoencoder (AE) are dimensionality reduction methods used for anomaly detection. PCA captures linear patterns using variance, while AE learns non-linear representations through data reconstruction. By leveraging the strengths of these techniques, the ensemble achieves improved sensitivity, reliability, and interpretability in anomaly detection. A comprehensive comparison with the baseline approach demonstrates that the proposed ensemble not only captures anomalies more reliably but also provides improved stability to environmental and operational variability. These findings highlight the potential of ensemble-based unsupervised methods for advancing SHM practices. Full article

Background/Objectives: The aim of this study was to assess the short-term and long-term effectiveness of group voice therapy in changing vocal behaviour and improving voice quality (VQ) among teachers with hyperfunctional voice disorders (HFVD), using both subjective and objective measures. Methods: Thirty-one teachers participated in a structured group voice therapy programme. Participants underwent videoendostroboscopic evaluation of laryngeal morphology and function, perceptual assessment of voice, acoustic analysis of voice samples, and aerodynamic measurements of phonation. Patients’ self-assessment of VQ and its impact on quality of life were measured using a Visual Analogue Scale (VAS) and the Voice Handicap Index-30 (VHI-30). Evaluations were conducted at four time points: pre-therapy (T0), immediately post-therapy (T1), and at 3-month (T3) and 12-month (T12) follow-up visits. Results: Significant improvement was observed between T0 and T1 in perceptual voice evaluations: grade, roughness, asthenia, strain, loudness, fast speaking rate, as well as in neck muscle tension, shimmer, patients’ most harmful vocal behaviours, VHI-30 scores, patients VQ evaluation, and its impact on quality of life (all p < 0.05). Almost all parameters of subjective and objective voice assessment improved over the 12-month observation period, with the greatest improvement between T0 and T12 (all p < 0.05), indicating lasting reduced laryngeal tension and improved phonatory efficiency. Conclusions: Group voice therapy has been shown to be an effective treatment for teachers with HFVD, leading to significant and long-lasting improvements in perceptual, acoustic, and self-assessment outcomes. Therapy also promoted healthier vocal and lifestyle behaviours, supporting its role as a successful and cost-effective rehabilitation and prevention method for occupational voice disorders. Full article