Search Results (44)

To improve the utilization of port resources and reduce the consumption of resources due to vessel waiting and delays, this paper investigates the Berth Allocation and Quay Crane Assignment Problem (BACAP) in container ports, focusing on the Quay Crane (QC) profile. The objective is to assign berths, berthing times, and QC profiles to vessels arriving at the port within a given planning horizon, thereby extending the traditional BACAP framework. To minimize the sum of idle time costs caused by vessel waiting and delay time costs due to late vessel departures, a mixed-integer linear programming (MILP) model is proposed. Additionally, a variable neighborhood search (VNS) algorithm is designed to solve the model, tailored to the specific characteristics of the problem. The proposed MILP model and VNS algorithm are evaluated using two sets of BACAP instances. The numerical results demonstrate the effectiveness of both the model and the algorithm, showing that VNS efficiently and reliably solves instances of various sizes. Furthermore, each neighborhood structure contributes uniquely to the iterative process. This study also analyzes the impact of different idle and delay costs on BACAP, providing valuable managerial insights. The proposed framework contributes to enhancing operational efficiency and supports sustainable port management. Full article

The efficient migration of virtual machines (VMs) is critical for optimizing resource management, ensuring service continuity, and enhancing resiliency in cloud and edge computing environments, particularly as 6G networks demand higher reliability and lower latency. This study addresses the challenges of dynamically balancing server loads while minimizing downtime and migration costs under stochastic task arrivals and variable processing times. We propose a queuing theory-based model employing continuous-time Markov chains (CTMCs) to capture the interplay between VM migration decisions, server resource constraints, and task processing dynamics. The model incorporates two migration policies—one minimizing projected post-migration server utilization and another prioritizing current utilization—to evaluate their impact on system performance. The numerical results show that the blocking probability for the first VM for Policy 1 is 2.1% times lower than for Policy 2 and the same metric for the second VM is 4.7%. The average server’s resource utilization increased up to 11.96%. The framework’s adaptability to diverse server–VM configurations and stochastic demands demonstrates its applicability to real-world cloud systems. These results highlight predictive resource allocation’s role in dynamic environments. Furthermore, the study lays the groundwork for extending this framework to multi-access edge computing (MEC) environments, which are integral to 6G networks. Full article

To enhance the accuracy and real-time capability of estimated in-block time (EIBT) predictions at airports, this study proposes a two-stage integrated prediction method. By extending the prediction time window for arrival times, this method systematically models and analyzes the integrated arrival time, thereby achieving precise EIBT predictions. This study divides the arrival process into the approach flight stage and the taxi-in stage, constructing predictive models for each and identifying key influencing factors. Additionally, copula entropy is employed to optimize feature selection. Based on operational data from Shanghai Pudong International Airport, a LightGBM-based prediction model was developed and validated across multiple datasets. The results demonstrate that the two-stage integrated forecasting method significantly outperforms single-stage modeling, with the best model achieving a prediction accuracy of 87.11% within a ±5 min error margin. Furthermore, this study validates the effectiveness of copula entropy in enhancing model prediction performance. This research provides theoretical support and practical references for improving the real-time predictive capabilities of airport collaborative decision-making systems, as well as a technical pathway for integrated air-surface management research at multi-runway airports. Full article

When inter-arrival times between events follow an exponential distribution, this implies a Poisson frequency of events, as both models assume events occur independently and at a constant average rate. However, these assumptions are often violated in real-insurance applications. When the rate at which events occur changes over time, the exponential distribution becomes unsuitable. In this paper, we study the distribution of inter-arrival times of severe storms, which exhibit substantial variability, violating the assumption of a constant average rate. A new approach is proposed for modeling severe storm recurrence patterns using a finite mixture of log-normal distributions. This approach effectively captures both frequent, closely spaced storm events and extended quiet periods, addressing the inherent variability in inter-event durations. Parameter estimation is performed using the Expectation–Maximization algorithm, with model selection validated via the Bayesian information criterion (BIC). To complement the parametric approach, Kaplan–Meier survival analysis was employed to provide non-parametric insights into storm-free intervals. Additionally, a simulation-based framework estimates storm recurrence probabilities and assesses financial risks through probable maximum loss (PML) calculations. The proposed methodology is applied to the Billion-Dollar Weather and Climate Disasters dataset, compiled by the U.S. National Oceanic and Atmospheric Administration (NOAA). The results demonstrate the model’s effectiveness in predicting severe storm recurrence intervals, offering valuable tools for managing risk in the property and casualty insurance industry. Full article

Electric vehicles (EVs) help reduce transportation emissions. A user-friendly charging infrastructure and efficient charging processes can promote their wider adoption. Low-power charging is effective for short-distance travel, especially when vehicles are parked for extended periods, like during daily commutes. These idle times present opportunities to improve coordination between EVs and service providers to meet charging needs. The present study examines strategies for coordinated charging in workplace parking lots to minimize the impact on the power grid while maximizing the satisfaction of charging demand. Our method utilizes a heuristic approach for EV charging, focusing on event logic that considers arrival and departure times and energy requirements. We compare various charging management methods in a workplace parking lot against a first-in-first-out (FIFO) strategy. Using real data on workplace parking lot usage, the study found that efficient electric vehicle charging in a parking lot can be achieved either through optimized scheduling with a single high-power charger, requiring user cooperation, or by installing multiple chargers with alternating sockets. Compared to FIFO charging, the implemented strategies allow for a reduction in the maximum charging power between 30 and 40%, a charging demand satisfaction rate of 99%, and a minimum SOC amount of 83%. Full article

The rapid growth in air traffic has led to increasing congestion at airports, creating bottlenecks that disrupt ground operations and compromise the efficiency of air traffic management (ATM). Ensuring the predictability of ground operations is vital for maintaining the sustainability of the ATM sector. Flight efficiency is closely tied to adherence to assigned airport arrival and departure slots, which helps minimize primary delays and prevents cascading reactionary delays. Significant deviations from scheduled arrival times—whether early or late—negatively impact airport operations and air traffic flow, often requiring the imposition of Air Traffic Flow Management (ATFM) regulations to accommodate demand fluctuations. This study leverages a data-driven machine learning approach to enhance the predictability of in-block and landing times. A Bidirectional Long Short-Term Memory (BiLSTM) neural network was trained using a dataset that integrates flight trajectories, meteorological conditions, and airport operations data. The model demonstrated high accuracy in predicting landing time deviations, achieving a Root-Mean-Square Error (RMSE) of 8.71 min and showing consistent performance across various long-haul flight profiles. In contrast, in-block time predictions exhibited greater variability, influenced by limited data on ground-level factors such as taxi-in delays and gate availability. The results highlight the potential of deep learning models to optimize airport resource allocation and improve operational planning. By accurately predicting landing times, this approach supports enhanced runway management and the better alignment of ground handling resources, reducing delays and increasing efficiency in high-traffic airport environments. These findings provide a foundation for developing predictive systems that improve airport operations and air traffic management, with benefits extending to both short- and long-haul flight operations. Full article

Time-resolved spectroscopic and electron–ion coincidence techniques are essential to study dynamic processes in materials or chemical compounds. For this type of analysis, it is necessary to have detectors capable of providing, in addition to image-related information, the time of arrival for each individual detected particle (“x, y, time”). The electronics capable of handling such sensors must meet requirements achievable only with time-to-digital converters (TDC) with a resolution on the order of tens of picoseconds and the use of a field-programmable gate array (FPGA) to manage data acquisition and transmission. This study introduces the design and implementation of an innovative TDC based on two FPGAs working symbiotically with different tasks: the first (AMD/Xilinx Artix^® 7) directly implements a TDC, aiming for a temporal precision of 12 picoseconds, while the second (Intel Cyclone^® 10) manages the acquisition and connectivity with the external world. The TDC has been optimized to operate on eight channels (+ sync) simultaneously but is potentially extendable to a greater number of channels, making it particularly suitable for coincidence measurements where it is necessary to temporally correlate multiple pieces of information from various measurement systems. Full article

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft. Full article

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system. Full article

Ransomware attacks have emerged as a significant threat to critical data and systems, extending beyond traditional computers to mobile and IoT/Cyber–Physical Systems. This study addresses the need to detect early ransomware behavior when only limited data are available. A major step for training such a detection model is choosing a set of relevant and non-redundant features, which is challenging when data are scarce. Therefore, this paper proposes an incremental mutual information-selection technique as a method for selecting the relevant features at the early stages of ransomware attacks. It introduces an adaptive feature-selection technique that processes data in smaller, manageable batches. This approach lessens the computational load and enhances the system’s ability to quickly adapt to new data arrival, making it particularly suitable for ongoing attacks during the initial phases of the attack. The experimental results emphasize the importance of the proposed technique in estimating feature significance in limited data scenarios. Such results underscore the significance of the incremental approach as a proactive measure in addressing the escalating challenges posed by ransomware. Full article

Background: Asthma is the most prevalent chronic respiratory condition in children. An asthma exacerbation (AE) is a frequent reason for emergency department (ED) visits. An important step in the management of a moderate to severe AE is the administration of systemic corticosteroids (SCS) within 1 h after ED presentation. This study aimed to determine the timing of SCS administration and correlate this with the length of stay and oxygen therapy duration and to explore factors predicting timely administration. Methods: This study used a retrospective multicenter observational design based on electronic medical records review. Children aged < 18 years, presenting to the ED with a moderate to severe AE were included. Results: 205 patients were included. Only 28 patients received SCS within 60 min after ED arrival. The median time to SCS administration was 169 min (Q₁ 92–Q₃ 380). A correlation was found between timing and oxygen treatment duration (r = 0.363, p < 0.001) and length of stay (r = 0.368, p < 0.001). No patient characteristics predicted timely SCS administration. Conclusions: Three in four children who presented with a moderate to severe AE at the ED did not receive SCS within the first hour. A prolonged timing of SCS administration correlated with a prolonged length of stay and extended need for oxygen support. Full article

COVID-19 has affected travel and will undoubtedly impact how people view travel and future intentions to travel as we adjust to life moving forward. Understanding how people arrive at these travel intentions will be paramount for managers and planners in determining how best to reactively and proactively plan for tourism, especially considering perceived risk and uncertainty related to COVID-19. By extending the theory of planned behavior, this study aims to examine the relationship between perceived risk, perceived uncertainty, subjective norms, attitudes about future travel, and perceived behavioral control in explaining individuals’ intentions to travel in the near future. This study employed a quantitative research method, and data were gathered using an online questionnaire distributed through Qualtrics from a sample of 541 potential travelers (representing residents of 46 US states) from 23 June 2020 to 1 July 2020. Of the eight hypotheses tested, four were supported. Surprisingly, neither perceived risk nor uncertainty were significant within the model. Subjective norms significantly predicted both attitudes about traveling and perceived behavioral control. Subjective norms and perceived behavioral control, in turn, explained a moderate degree of variation in individuals’ intentions to travel. Study implications, limitations, and future research suggestions are offered. One of the main managerial implications includes the need for destinations to be proactive and focus on intentional planning for sustainable tourism. Full article

Introduction: The Buschke–Löwenstein tumor (BLT) is an uncommon sexually transmitted ailment attributed to the human papillomavirus (HPV)—usually the 6 or 11 type (90%)—with male predominance and an overall infection rate of 0.1%. BLT or giant condyloma acuminatum is recognized as a tumor with localized aggressiveness, displaying distinctive features: the potential for destructive growth, benign histology, a rate of 56% malignant transformation, and a high rate of recurrence after surgical excision. There are several treatment choices which have been tried, including laser, cryotherapy, radiotherapy, electrocoagulation, immunotherapy, imiquimode, sincatechins, intralesional injection of 5-fluoruracil (5-FU), isolated perfusion, and local or systemic chemotherapy. In the case of an extensive tumor, preoperative chemotherapy or radiotherapy is used for tumor shrinkage, making the debulking procedure safer. HPV vaccines significantly decrease the incidence of genital warts, also decreasing the risk of BLT; HPV-6 and HPV-11 are included in these vaccines. Materials and methods: We present a 53-year-old heterosexual man, hospitalized in our department in June 2021 with a typical cauliflower-like tumor mass involving the perianal region, which progressively increased in size for almost 7 years. The perianal mass was completely removed, ensuring negative surgical margins. The large perianal skin defect which occurred was reconstructed with fascio-cutaneous V-Y advancement flap. There was no need for protective stoma. The literature review extended from January 1980 and December 2022, utilizing Pubmed and Google Scholar as search platforms. Results: Due to the disease’s proximity to the anal verge and the limited number of reported cases, arriving at a definitive and satisfactory treatment strategy becomes challenging. The optimal approach entails thorough surgical removal of the lesion, ensuring well-defined surgical margins and performing a wide excision to minimize the likelihood of recurrence. In order to repair the large wound defects, various rotation or advancement flaps can be used, resulting in reduced recovery time and a diminished likelihood of anal stricture or other complications. Our objective is to emphasize the significance of surgical excision in addressing BLT through the presentation of a case involving a substantial perianal condyloma acuminatum, managed successfully with complete surgical removal and the utilization of a V-Y advancement flap technique. In the present case, after 5 months post operation, the patient came back with a buttock abscess, which was incised and drained. After another 5 months, the patient returned for difficult defecation, with an anal stenosis being diagnosed. An anal dilatation and sphincterotomy were carried out, with good postoperative results. Conclusions: The surgical management of Buschke–Löwenstein tumors needs a multidisciplinary team with specialized expertise. The reconstruction techniques involved can be challenging and may introduce additional complications. We consider aggressive surgery, which incorporates reconstructive procedures, as the standard treatment for Buschke–Löwenstein tumors. This approach aims to achieve optimal surgical outcomes and prevent any recurrence. Full article

This nationwide, population-based observational study investigated the association between the floor level of out-of-hospital cardiac arrest (OHCA) incidence and survival outcomes in South Korea, notable for its significant high-rise apartment living. Data were collected retrospectively from OHCA patients through the South Korean Out-of-Hospital Cardiac Arrest Surveillance database. The study incorporated cases that included the OHCA’s building floor information. The primary outcome assessed was survival to discharge, analyzed using multivariate logistic regression, and the secondary outcome was favorable neurological outcome. Among 36,977 patients, a total of 29,729 patients were included, and 1680 patients were survivors. A weak yet significant correlation between floor level and hospital arrival time was observed. Interestingly, elevated survival rates were noted among patients from higher floors despite extended emergency medical service response times. Multivariate analysis identified age, witnessed OHCA, shockable rhythm, and prehospital return of spontaneous circulation (ROSC) as primary determinants of survival to discharge. The floor level’s impact on survival was less substantial than anticipated, suggesting residential emergency response enhancements should prioritize witness interventions, shockable rhythm management, and prehospital ROSC rates. The study underscores the importance of bespoke emergency response strategies in high-rise buildings, particularly in urban areas, and the potential of digital technologies to optimize response times and survival outcomes. Full article

Given the trend of digitization, it is imperative to ascertain the role of the digital supply chain on sustainable trade credit provision. Based on data from Chinese listed firms from 2008 to 2020, we utilized the TF-IDF algorithm to measure the digital supply chain and ascertained its impact on trade credit. We found that the digital supply chain was positively associated with trade credit provision. Specifically, we arrived at the following conclusions: (1) the digital supply chain strengthens trade credit provision, including to customers and suppliers; (2) top management team power positively and significantly moderates the effect of digital supply chain; (3) among the sub-indicators of the digital supply chain, the dimensions of logistics, products and information have significant and positive impacts, while cash is insignificant; (4) curbing financialization and enhancing asset specialization are the mechanisms of the effect of the digital supply chain; and (5) the effect is more pronounced in firms with higher agency costs and lower supply chain collaboration and non-state ownership, and it is more salient in industries with higher competition and non-national support. We extend the theory of trade credit and enrich the literature on the digital supply chain. Our study offers managerial insights into the digital supply chain for emerging countries and enterprises. Full article