Search Results (21,082)

Hash maps are a widely used and efficient data structure for storing and accessing data organized as key-value pairs. Multithreading with hash maps refers to the ability to concurrently execute multiple lookup, insert, and delete operations, such that each operation runs independently while sharing the underlying data structure. One of the main challenges in hash map implementation is the management of collisions. Arguably, separate chaining is among the most well-known strategies for collision resolution. In this paper, we present a comprehensive study comparing two common approaches to implementing separate chaining—linked lists and dynamic arrays—in a multithreaded environment using a lock-based concurrent hash map design. Our study includes a performance evaluation covering parameters such as cache behavior, energy consumption, contention under concurrent access, and resizing overhead. Experimental results show that dynamic arrays maintain more predictable memory access and lower energy consumption in multithreaded environments. Full article

Introduction: The management of peri-operative pain significantly impacts the post-operative recovery following kidney transplant. For decades, regional blocks have been utilized for post-operative pain management following abdominal surgery. The data on the routine use of regional blocks peri-operatively during kidney transplants are limited. We aim to review our current clinical practice of peri-operative use of regional blocks during kidney transplants and management of peri-operative pain up to 24 h. Methods: A consecutive series of 100 patients who underwent kidney transplant was reviewed. All demographic data including patient’s age, gender, race, and body mass index were collected. Pre-transplant co-morbidities were summarized for all patients and included the American Society of Anesthesiologists (ASA) score. Patients were divided into two groups based on whether they received a transversus abdominis plane (TAP) block. Group A consisted of patients who received an ultrasound-guided TAP block, while Group B included patients who did not receive any form of TAP block. The intra-operative and post-operative use of analgesia was recorded for up to 24 h post kidney transplant. All peri-operative complications were reviewed. The chi-square test and Fisher’s exact test was used to compare symptoms (nausea, vomiting, and pruritus) between the two groups. Similarly, the use of analgesia was also compared. Results: A total of 100 patients were identified and equally distributed between the two groups [Group A = 50 (TAP block), Group B = 50 (non-TAP block)]. There was a statistically significant reduction in the use of intraoperative fentanyl (p = 0.04) in Group A. There was no difference in the post-operative use of hydromorphone (p = 0.665), oxycodone (p = 0.75), and acetaminophen (p = 0.64) up to 24 h after the kidney transplant procedure. There was no difference between post-operative nausea (p = 0.766), vomiting (p = 0.436), and pruritus. There were no complications recorded secondary to the use of regional blocks in Group A. Conclusions: The use of regional anesthesia in kidney transplant recipients is a safe approach without complications. The study concluded that regional blocks decrease the use of intra-operative opioids. However, there was no difference in the use of post-operative requirements for analgesia or side effects up to 24 h after kidney transplant. Full article

Mosses are key players in semi-arid ecosystems; however, the functional roles of mosses on hydrologic buffering and water quality have hardly been assessed. In the present study, the water storage, saturation dynamics, and ion release experiment of a set of four moss species (Hypnum lacunosum, Homalothecium lutescens, Dicranum scoparium, and Tortella tortuosa) was performed by a more simplified immersion and drainage procedure with water chemistry analyses. All species reached a sorption equilibrium between 10 and 20 min, with pleurocarpous taxa retaining 20–35% more water than acrocarpous species and possessing water-holding capacities (WHCs) between 300% and 700% of dry weight. Species-specific differences in water chemistry (pH, EC, and TDS) were observed: Tortella tortuosa presented the greatest ionic flux, and Hypnum lacunosum presented little variation in pH and electrical conductivity. These findings imply that the mosses operate as micro-scale buffers regulating both water quantity and water quality, and thereby the soil stability, infiltration, and drought resilience. The combined hydrological and biogeochemical view offers a novel understanding of bryophyte ecohydrology and highlights the significance of mosses in the practice of watershed management and climate-change mitigation. Full article

Background: Conservative mastectomy with prepectoral breast reconstruction is becoming increasingly widespread and validated in recent years. Today, while aesthetic advantages and improvement in quality-of-life outcomes are widely acknowledged, oncological safety remains subject of debate. There is limited evidence on residual breast tissue after conservative mastectomy, and it still represents an unknown risk for local recurrence. The recent spread of this surgical technique precludes a standardized surgical approach in case of local recurrence of ipsilateral breast cancer, and the lack of evidence in the literature complicates the decision-making process. The objective of this study is to describe the surgical treatment of local relapses for breast cancer patients following conservative mastectomy and prepectoral implant-based reconstruction. Methods: Between January 2018 and May 2024 at a single institution, 648 consecutive patients underwent conservative mastectomy and prepectoral reconstruction as their primary treatment. We identified 12 patients with T1-2 breast cancer who subsequently had histologically confirmed ipsilateral breast cancer recurrence and a local wide excision or radical mastectomy were performed. Each clinical case was discussed in a multidisciplinary meeting to define the most appropriate surgical treatment. At time of diagnosis of recurrence, patients with lymph node metastasis or systemic involvement were excluded from the study. Results: Among 648 consecutive patients who underwent conservative mastectomy, 12 with histologically confirmed ipsilateral breast cancer recurrence were included. The mean interval to recurrence was 43 months (range 10–76 months) from the primary operation. Recurrence sites were as follows: upper outer quadrant in four patients (33.4%), upper inner quadrant in three (25.0%), lower inner quadrant in two (16.6%), lower outer quadrant in one (8.4%), and central quadrant with nipple involvement in two (16.6%). Of the 12 patients, 9 (75%) underwent wide local excision, including 2 who also received partial capsulectomy, while 3 (25%) required radical mastectomy with implant removal. Adjuvant radiation therapy was administered to 6 patients (50%)—5/6 (83.3%) in the excision group and 1/6 (16.7%) in the mastectomy group. No significant differences were observed in distant disease–free survival or overall survival between the two groups. Conclusions: Currently, surgical treatment of ipsilateral breast tumor recurrence following conservative mastectomy and prepectoral breast reconstruction is not reported in the literature, and this study represents the first instance where wide local excision is described. The management of ipsilateral recurrence should be discussed in multidisciplinary meetings and could be performed safely in selected cases, sparing the prosthesis and avoiding radical mastectomy. Full article

The exponential increase in global solid waste generation poses significant environmental, economic, and social challenges, particularly in rapidly urbanizing regions. Traditional waste management methods that focus on handling and disposal have proven unsustainable because of their negative impacts on air, soil, and water quality, and their contribution to greenhouse gas emissions. In response, the concept of zero-waste cities, rooted in circular economy principles, has gained increasing attention in recent years. This study proposes a comprehensive and integrated waste management system designed to optimize resource recovery across four distinct waste streams: household, healthcare, green/organic, and inert. The system integrates four specialized facilities: a Secondary Sorting Facility, Energy Recovery Facility, Composting Facility, and Inert Processing Facility, coordinated through a central Primary Sorting Hub. By enabling interconnectivity between these processing units, the system facilitates material cascading, maximizes the reuse and recycling of secondary raw materials, and supports energy recovery and circular nutrient flow. The anticipated benefits include enhanced operational efficiency, reduced environmental degradation, and generation of multiple revenue streams. However, the implementation of such a system faces challenges related to high capital investment, technological complexity, regulatory fragmentation, and low public acceptance. Overcoming these limitations will require strategic planning, stakeholder engagement, and adaptive governance. Full article

With the development of dynamic wireless power transfer (DWPT) technology, the introduction of wireless charging lanes (WCLs) in traffic systems is seen as a promising trend for electrified transportation. Though there has been extensive discussion about the planning and allocation of WCLs in different situations, studies on traffic control models for WCLs are relatively lacking. Thus, this paper aims to design a coordinated optimization strategy for managing electric vehicle (EV) traffic on an expressway network, which integrates a corridor traffic flow model with a wireless power transmission model. Two components are considered in the control objective: the total energy increased for the EVs and the total number of EVs served by the expressway, over the problem horizon. By setting the trade-off coefficients for these two objectives, our model can be used to achieve mixed optimization of WCL traffic management. The decisions include metering of different on-ramps as well as routing plans for different groups of EVs defined by origin/destination pairs and initial SOC levels. The control problem is formulated as a novel linear programming model, rendering an efficient solution. Numerical examples are used to verify the effectiveness of the proposed traffic control model. The results show that with the properly designed traffic management strategy, a notable increase in charging performance can be achieved by compromising slightly the traffic performance while maintaining overall smooth operation throughout the expressway system. Full article

Background: This study explores the relationship among various supply chain management practices, including customer relationship management, lean practices, use of information technology, and quality of information sharing with operational performance in the readymade garments industry of Bangladesh. It also examines the mediating role of quality of information sharing in these relationships. Methods: Data were collected from 80 readymade garment companies across five different geographical locations, with companies of varying sizes (large, medium, and small), involving 365 respondents with a response rate of 65%. A self-administered questionnaire survey was conducted, and Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied for the analysis. Results: The results indicate that all four practices significantly enhance operational performance, while customer relationship management and use of information technology also improve performance indirectly through quality of information sharing, unlike lean practices. Conclusions: The findings suggest that supply chain managers and stakeholders can improve operational performance by implementing supply chain management practices and understanding the complexities of their interrelationships. Full article

Raman thermometry is a powerful technique for sub-microscale thermal measurements on semiconductor-based devices, provided that the active region remains accessible and is not obscured by metallization. Since pure metals do not exhibit Raman scattering, traditional Raman thermometry becomes ineffective in such cases. To overcome this limitation, we propose the use of atomically thin Two-Dimensional materials as local temperature sensors. These materials generate Raman spectra at the nanoscale, enabling highly precise absolute surface temperature measurements. In this study, we investigate the feasibility and effectiveness of this approach by applying it to power devices, including a calibrated gold resistor and an SiC Junction Barrier Schottky (JBS) diode. We assess the processing challenges and measurement reliability of 2D materials for thermal characterization. To validate our findings, we complement Raman thermometry with thermoreflectance measurements, which are well suited for metallized surfaces. For example, on the serpentine resistor, Raman thermometry applied to the 2D material yielded a thermal resistance of 22.099 °C/W, while thermoreflectance on the metallic surface measured 21.898 °C/W. This close agreement suggests good thermal conductance at the metal/2D material interface. The results demonstrate the potential of integrating 2D materials as effective nanoscale temperature probes, offering new insights into thermal management strategies for advanced electronic components. Additionally, thermal simulations are conducted to further analyze the thermal response of these devices under operational conditions. Furthermore, we investigate two 2D material integration methods, transfer and direct growth, and evaluate them through measured thermal resistances for the SiC JBS diode, highlighting the influence of the deposition technique on thermal performance. Full article

Remaining useful life (RUL) prediction is a core technology in prognostics and health management (PHM), crucial for ensuring the safe and efficient operation of modern industrial systems. Although deep learning methods have shown potential in RUL prediction, they often face two major challenges: an insufficient generalization ability when distribution gaps exist between training data and real-world application scenarios, and the difficulty of comprehensively capturing complex equipment degradation processes with single-modal data. A key challenge in current research is how to effectively fuse multimodal data and leverage transfer learning to address RUL prediction in small-sample and cross-condition scenarios. This paper proposes an innovative deep multimodal fine-tuning regression (DMFR) framework to address these issues. First, the DMFR framework utilizes a Convolutional Neural Network (CNN) and a Transformer Network to extract distinct modal features, thereby achieving a more comprehensive understanding of data degradation patterns. Second, a fusion layer is employed to seamlessly integrate these multimodal features, extracting fused information to identify latent features, which are subsequently utilized in the predictor. Third, a two-stage training algorithm combining supervised pre-training and fine-tuning is proposed to accomplish transfer alignment from the source domain to the target domain. This paper utilized the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbine engine dataset publicly released by NASA to conduct comparative transfer experiments on various RUL prediction methods. The experimental results demonstrate significant performance improvements across all tasks. Full article

Intravenous push (IVP) administration of controlled substances in hospital settings presents operational challenges related to medication waste, documentation, and diversion risk. This multi-site observational study aimed to quantify the pharmacy workforce time and associated costs linked to IVP waste management across a 16-hospital health system in Southwest Florida. Data were collected from over 4400 controlled substance transactions involving fentanyl, midazolam, hydromorphone, morphine, ketamine, and lorazepam. Methods included automated transaction analysis, manual chart reviews, and software-based compliance case evaluations. Results indicated patterns of partial dose waste, particularly for midazolam (85.2%) and hydromorphone (78.8%), and identified opportunities where documentation efforts could be further optimized through automation. Manual review of 333 incidents required an average of 6 min and 43 s per case, extrapolating to over 496 h of quarterly pharmacy labor or nearly 1985 h annually. Software-based case reviews added another 32 h per quarter or 130 h annually. Additionally, waste receptacle systems incurred over USD 1.1 million in capital costs and USD 322,500 in annual maintenance, with technician labor contributing further operational burden. These findings underscore the resource demands of IVP waste management and support the need for standardized dosing, enhanced documentation workflows, and pharmacy-led interventions to improve efficiency and reduce diversion risk. Full article

This work presents the design and evaluation of a distributed IoT protocol for intelligent parking management. It exploits the communication protocol LoRa and is designed to operate fully autonomously, without requiring Internet connectivity, enabling real-time parking slot detection and allocation through long-range wireless communication. The protocol also includes an optional MQTT-based synchronisation layer to support data exchange between gateways and with a central collector, allowing for telemetry, system monitoring, and analytics. We performed a set of experiments, proving that the protocol holds system resilience and scalability, which are key aspects for deployment in urban environments with unreliable or limited network access. We also observed a significant reduction in parking search time while preserving acceptable levels of system latency. To complete our evaluation, we deployed, in our laboratory, a test-bed made by ESP32-based nodes and simulated gateway breakage and replacement, in order to prove the fault recovery capabilities of the entire network. Finally, we conducted a few empirical stress tests simulating high communication traffic and interactions, confirming acceptable effectiveness and stability for real urban contexts. Full article

With the steady global progress in integrating technology into healthcare delivery, doctors’ behavioral patterns on online healthcare platforms have increasingly become a focal point in the fields of digital health and healthcare service management. Grounded in Job Crafting Theory, this study constructs a proactive crafting index, which captures doctors’ proactive behaviors on the platform across three dimensions: consultation rate, number of consultations, and response speed. We systematically examine the multidimensional impacts of such behaviors on performance outcomes, including online consultation volume, offline service volume, and user evaluation performance. This study collects publicly available records from a major online healthcare platform in China and conducts empirical analysis using the entropy weight method and econometric techniques. The results reveal that there is an optimal level of proactive engagement: moderate proactivity maximizes online consultation volume, while both insufficient and excessive proactivity reduce it. Offline service volume, in contrast, follows a U-shaped relationship, where moderate proactive engagement minimizes offline visits, while too little or too much engagement leads to more offline service needs. These nonlinear patterns highlight the importance of framing doctors’ proactive behavior to optimize both online engagement and offline service. The findings enrich Job Crafting Theory by identifying boundaries in platform-based service environments and provide actionable insights for platform operators to design behavior management and incentive systems tailored to doctors’ professional rank, patient condition, and regional context. Full article

With the rapid development of urban underground rail transit, its enclosed and densely populated environment significantly increases fire risks, posing serious threats to personnel safety and operational stability. Based on the WSR methodology and 4M theory, this study identifies fire-related factors from the physical, operational, and human dimensions. And refine indicators at the four levels of personnel, equipment and facilities, environment, and management to establish a resilience assessment system for urban underground rail transit fires. The results detailed display the application of Cross-Influence Analysis (CIA) and analytic network process (ANP) methods in fire resilience evaluation, including theoretical framework construction, computational procedures, and result analysis. A comprehensive assessment system is developed, comprising 14 secondary indicators under four primary criteria: resistance capacity, adaptation capacity, absorption capacity, and resilience capacity. And then, the CIA and ANP methods were employed to quantify inter-indicator relationships and weights through 15 expert evaluations and 52 judgment matrices, facilitating disaster-adaptive strategy formulation. Finally, an empirical analysis of Xi’an Metro Line 1 reveals that resistance capacity and resilience capacity are critical to fire resilience, with fire cause investigation and post-incident review exhibiting the highest weights. Meanwhile, resilience enhancement strategies are proposed, including optimized monitoring equipment deployment, strengthened emergency drills, and improved personnel training. The paper innovatively integrates WSR methodology and 4M theory to establish a comprehensive, representative metro fire resilience assessment system with CIA-ANP quantification. This study provides novel methodological support for fire safety assessment in urban underground rail transit systems, offering significant theoretical and practical value. Full article

This study addresses the critical need for accurate tourism demand (TD) forecasting in Bulgaria using economic indicators, developing robust predictive models to navigate post-pandemic market volatility. The COVID-19 pandemic exposed tourism’s vulnerability to systemic shocks, highlighting deficiencies in traditional forecasting approaches. Bulgaria’s tourism industry, characterized by strong seasonal variations and economic sensitivity, requires enhanced methodologies for strategic planning in uncertain environments. The research employs comprehensive comparative analysis of machine learning (ML) and deep machine learning (DML) methodologies. Monthly overnight stay data from Bulgaria’s National Statistical Institute (2005–2024) were integrated with COVID-19 case data, Consumer Price Index (CPI) and Bulgarian Gross Domestic Product (GDP) variables for the same period. Multiple approaches were implemented including Prophet with external regressors, Ridge regression, LightGBM, and gradient boosting models using inverse MAE weighting optimization, alongside deep learning architectures such as Bidirectional LSTM with attention mechanisms and XGBoost configurations, as each model statistical significance was estimated. Contrary to prevailing assumptions about deep learning superiority, traditional machine learning ensemble approaches demonstrated superior performance. The ensemble model combining Prophet, LightGBM, and Ridge regression achieved optimal results with MAE of 156,847 and MAPE of 14.23%, outperforming individual models by 10.2%. Deep learning alternatives, particularly Bi-LSTM architectures, exhibited significant deficiencies with negative R² scores, indicating fundamental limitations in capturing seasonal tourism patterns, probable data dependence and overfitting. The findings, provide tourism stakeholders and policymakers with empirically validated forecasting tools for enhanced decision-making. The ensemble approach combined with statistical significance testing offers improved accuracy for investment planning, marketing budget allocation, and operational capacity management during economic volatility. Economic indicator integration enables proactive responses to market disruptions, supporting resilient tourism planning strategies and crisis management protocols. Full article

As the core component of aircraft systems, aeroengines require accurate Remaining Useful Life (RUL) prediction to ensure flight safety, which serves as a key part of Prognostics and Health Management (PHM). Traditional RUL prediction methods primarily fall into two main categories: physics-based and data-driven approaches. Physics-based methods mainly rely on extensive prior knowledge, limiting their scalability, while data-driven methods (including statistical analysis and machine learning) struggle with handling high-dimensional data and suboptimal modeling of multi-scale temporal dependencies. To address these challenges and enhance prediction accuracy and robustness, we propose a novel hybrid deep learning framework (CLSTM-TCN) integrating 2D Convolutional Neural Network (2D-CNN), Long Short-Term Memory (LSTM) network, and Temporal Convolutional Network (TCN) modules. The CLSTM-TCN framework follows a progressive feature refinement logic: 2D-CNN first extracts short-term local features and inter-feature interactions from input data; the LSTM network then models long-term temporal dependencies in time series to strengthen global temporal dynamics representation; and TCN ultimately captures multi-scale temporal features via dilated convolutions, overcoming the limitations of the LSTM network in long-range dependency modeling while enabling parallel computing. Validated on the NASA C-MAPSS data set (focusing on FD001), the CLSTM-TCN model achieves a root mean square error (RMSE) of 13.35 and a score function (score) of 219. Compared to the CNN-LSTM, CNN-TCN, and LSTM-TCN models, it reduces the RMSE by 27.94%, 30.79%, and 30.88%, respectively, and significantly outperforms the traditional single-model methods (e.g., standalone CNN or LSTM network). Notably, the model maintains stability across diverse operational conditions, with RMSE fluctuations capped within 15% for all test cases. Ablation studies confirm the synergistic effect of each module: removing 2D-CNN, LSTM, or TCN leads to an increase in the RMSE and score. This framework effectively handles high-dimensional data and multi-scale temporal dependencies, providing an accurate and robust solution for aeroengine RUL prediction. While current performance is validated under single operating conditions, ongoing efforts to optimize hyperparameter tuning, enhance adaptability to complex operating scenarios, and integrate uncertainty analysis will further strengthen its practical value in aircraft health management. Full article