Time Cost Research Articles

An analysis of mental health service usage and injuries among NCAA Division I collegiate athletes

ABSTRACT Each year, significant resources and effort are devoted to protecting the health of the player and ameliorating the time and financial costs associated with injuries and rehabilitation. However, few studies have investigated the use of mental health services with injuries among the same collegiate athletes over time. This study aimed to investigate injury prevalence rates and injuries incidences in collegiate athletes as a function of mental health service utilization. Results suggest that psychological stress may be one of the key vulnerabilities for injury or reinjury in athlete populations. Athletes visiting a mental health professional were twice as likely to have also sustained a sport-related or non-sport-related injury and approximately 1.5 times as likely to sustain multiple injuries compared to their peers. Injury risk and recovery outcomes can likely be bolstered by making mental health professionals more available to student athletes and integrating psychological services into normal athletic training activities.

A locally calculable P3‐pressure in a decoupled method for incompressible Stokes equations

AbstractThis article will suggest a new finite element method to find a ‐velocity and a ‐pressure solving incompressible Stokes equations at low cost. The method solves first the decoupled equation for a ‐velocity. Then, using the calculated velocity, a locally calculable ‐pressure will be defined component‐wisely. The resulting ‐pressure is analyzed to have the optimal order of convergence. Since the pressure is calculated by local computation only, the chief time cost of the new method is on solving the decoupled equation for the ‐velocity. Besides, the method overcomes the problem of singular vertices or corners.

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

To optimize the evacuation process of rail transit passenger flows, the influence of the feeder bus network on bus demand is pivotal. This study first examines the transportation mode preferences of rail transit station passengers and addresses the feeder bus network’s optimization challenge within a three-dimensional framework, incorporating an elastic mechanism. Consequently, a strategic planning model is developed. Subsequently, a multi-objective optimization model is constructed to simultaneously increase passenger numbers and decrease both travel time costs and bus operational expenses. Due to the NP-hard nature of this optimization problem, we introduce an enhanced non-dominated sorting genetic algorithm, INSGA-II. This algorithm integrates innovative encoding and decoding rules, adaptive parameter adjustment strategies, and a combination of crowding distance and distribution entropy mechanisms alongside an external elite archive strategy to enhance population convergence and local search capabilities. The efficacy of the proposed model and algorithm is corroborated through simulations employing standard test functions and instances. The results demonstrate that the INSGA-II algorithm closely approximates the true Pareto front, attaining Pareto optimal solutions that are uniformly distributed. Additionally, an increase in the fleet size correlates with greater passenger volumes and higher operational costs, yet it substantially lowers the average travel cost per customer. An optimal fleet size of 11 vehicles is identified. Moreover, expanding feeder bus routes enhances passenger counts by 18.03%, raises operational costs by 32.33%, and cuts passenger travel time expenses by 21.23%. These findings necessitate revisions to the bus timetable. Therefore, for a bus network with elastic demand, it is essential to holistically optimize the actual passenger flow demand, fleet size, bus schedules, and departure frequencies.

Multi-objective optimization of machining parameters based on an improved Hopfield neural network for STEP-NC manufacturing

Aiming at solving the existing problems of machining parameters optimization for STEP-NC manufacturing, a method for multi-objective optimization of machining parameters based on an improved Hopfield neural network (IHNN) for STEP-NC manufacturing is proposed. In this method, a multi-objective optimization mathematical model of machining parameters compliant with STEP-NC is firstly established taking machining energy, machining time and machining cost as optimization objectives. Next, the IHNN for multi-objective optimization of STEP-NC machining parameters combining with Pareto theory, improved immune algorithm and non-monotone activation function is designed. Based on it, the optimal Pareto solutions of STEP-NC machining parameters are obtained, which intelligently realizes the multi-objective optimization of STEP-NC machining parameters and provides a decision support for the decision-maker. Finally, performance comparisons among the IHNN, classic non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO) are done by classic test functions with three objectives and multiple constraints which correspond to the mathematical model established in this paper, and its effectiveness and feasibility is verified by case study.

Intelligent optimal preventive replacement maintenance policy for non-repairable systems

This paper developed optimal maintenance policies for industrial machines to ensure uninterrupted production. Analytical and intelligent replacement models were constructed using real-time data from a commercial photocopier operator’s expenditure logbook between 2012 and 2023. A goodness-of-fit test affirmed a log-logistic failure distribution with shape and scale parameters of 1.7233 and 763.9220, respectively. Probability functions from this distribution established replacement models, determining optimal preventive maintenance conditions. Through feature engineering additional features crucial for operational and cost optimization were abstracted, with 4,881 unique data points simulated from boundary conditions defined by the original or raw data. Analytical results showed that the failure distribution-based replacement model outperformed the hazard function-based model in minimum replacement time and unit maintenance cost over time, while key parameters such as availability, reliability, and failure occurrence probability remained constant at 96 %, 94 %, and 0.07 %, respectively. Machine learning models (Extreme Gradient Boosting: XGBoost, Support Vector Machine: SVM, and Multilayer Perceptron: MLP) demonstrated very high accuracy in availability (XGBoost = 99.49 %, SVM = 99.18 %, RF = 100 %, MLP = 99.80 %), reliability (XGBoost = 99.49 %, SVM = 99.18 %, RF = 100 %, MLP = 99.80 %), and maintenance cost (XGBoost = 100 %, SVM = 100 %, RF = 100 %, MLP = 100 %), exceeding results of the analytical models.

Blockchain solutions with consensus algorithms and immediate finality: Toward Panopticon-style monitoring to enhance anti-money laundering

Banks can reduce resources spent on anti-money laundering (AML) compliance with blockchain-based transaction infrastructure. We consider AML compliance as a superset of know-your-customer (KYC) and transaction monitoring capabilities. We carried out this research with Danske Bank and Concordium, using internal documents and interviews that served as empirical data. We show how storing digital representations of verified IDs with a blockchain can automate tasks and reduce redundant verification in KYC onboarding. Blockchain transparency also improves identifying counterparties, determining funds sources, and creating alerts in transaction monitoring. These reduce time and labor costs for AML compliance, which may lead to smaller banks. When more banks commit to layer-1 blockchain technology, the benefits of blockchain-based AML will increase. We carried out this theory-based qualitative research and encourage ECRA readers to recognize that the emerging technology innovations we study in this article have not yet been widely adopted and implemented by financial services firms. We also include a theoretical model with study hypotheses to make the main constructs that we investigate easily understood by non-technical ECRA readers. The findings we have developed are consistent with early-stage exploration in our research context and are intended to encourage more well-developed empirical results as the passage of time permits such work to be undertaken.

Air–Ground Collaborative Multi-Target Detection Task Assignment and Path Planning Optimization

Collaborative exploration in environments involving multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) represents a crucial research direction in multi-agent systems. However, there is still a lack of research in the areas of multi-target detection task assignment and swarm path planning, both of which play a vital role in enhancing the efficiency of environment exploration and reducing energy consumption. In this paper, we propose an air–ground collaborative multi-target detection task model based on Mixed Integer Linear Programming (MILP). In order to make the model more suitable for real situations, kinematic constraints of the UAVs and UGVs, dynamic collision avoidance constraints, task allocation constraints, and obstacle avoidance constraints are added to the model. We also establish an objective function that comprehensively considers time consumption, energy consumption, and trajectory smoothness to improve the authenticity of the model and achieve a more realistic purpose. Meanwhile, a Branch-and-Bound method combined with the Improved Genetic Algorithm (IGA-B&B) is proposed to solve the objective function, and the optimal task assignment and optimal path of air–ground collaborative multi-target detection can be obtained. A simulation environment with multi-agents, multi-obstacles, and multi-task points is established. The simulation results show that the proposed IGA-B&B algorithm can reduce the computation time cost by 30% compared to the traditional Branch-and-Bound (B&B) method. In addition, an experiment is carried out in an outdoor environment, which further validates the effectiveness and feasibility of the proposed method.

Analysis of a time–cost trade-off in a resource-constrained GERT project scheduling problem using the Markov decision process

Analysis of a time–cost trade-off in a resource-constrained GERT project scheduling problem using the Markov decision process

4 Gbps serializer circuits in a 180 nm technology for monolithic pixel sensor prototypes developed for the CEPC vertex detector

Monolithic Active Pixel Sensor is one of the promising candidates for the Circular Electron Positron Collider Vertex detector, due to its good performance and trade-off of granularity, readout speed, material budgets and power consumption. A full-scale TaichuPix chip, including a matrix of 512 × 1024 pixels with a size of 25 × 25 μm2 is developed to provide a spatial resolution better than 5 μm. It requires a raw data rate up to 3.84 Gbps and power consumption less than 100 mA for the serializer circuit. Based on one of the small-scale prototypes, the highest serial data rate is tested to be 3.36 Gbps with a peak-to-peak jitter of about 150 ps and large current consumption. Moreover, the 32-bit parallel data width of the serializer isn't suitable for the 8B10B encoder. Therefore, two 4 Gbps serializers have been designed and optimized to meet these requirements based on the same process node of 180 nm as the TaichuPix, considering the funding and time costs. The serializer consists of a phase locked loop (PLL), several 5:1 sub-multiplexers based on a shift-register chain, a 4:1 or 8:1 sub-multiplexer based on the binary-tree structure, a clock distributor and a high-speed driver. Both ring-oscillating PLL and LC-tank PLL were integrated for performance evaluation. The preliminary tests showed low-jitter characteristics of the two PLLs despite this version's biasing problem, and both serializer circuits functioned correctly. A revision corrected the error and was submitted in October 2023.

Oh, the places you'll go! A qualitative study of resident career decisions in emergency medicine.

Postresidency career choices are complex decisions that involve personal, professional, and financial preferences and may be influenced by training programs. It is unknown how residents navigate these decisions during emergency medicine (EM) residency. We explored EM residents' perspectives on career decision making and how residency programs can support career planning. We conducted semistructured interviews at seven accredited EM residency programs from diverse locations and training formats. We used purposive sampling to reflect the diversity of trainees with regard to gender, level of training, and career plans. Two researchers independently coded the transcripts. We used a constructivist-interpretivist paradigm to guide our thematic analysis. We interviewed 11 residents and identified major themes in three categories. Residents described being exposed to career options through formalized curricula such as required rotations, career fairs, and subspeciality tracks, highlighting the importance of access to faculty with diverse areas of clinical and academic expertise. Many noted that exposure was often self-driven. We identified three major themes regarding career decisions: instrumental factors, people involved, and processes of decision making. Instrumental factors included personal interests, goals, and values as well as practice characteristics, financial considerations, timing, and opportunity costs. Mentors and family were highly involved in resident career decisions. Residents often utilized reflection and conversations with mentors and peers in their decision-making process. Participants recommended that programs provide exposure to diverse career options early in training, protect time for career education, and ensure adequate mentorship and a supportive community. Participants suggested specific curricular content and strategies to support career decisions. This study illuminates important factors involved in resident career decision making and how programs can support their trainees. Essential components include diverse experiences and building a reflective mentorship environment.

Chromatographic Profile of Polyphenols in the Agastache foeniculum (Pursh) Kuntze Herb: Evaluation of Optimal Extraction Efficiency

During its triple extraction, the HPLC analysis revealed polyphenols' contents in the A. foeniculum herb. Several hydroxycinnamic acids (rosmarinic, chlorogenic, ferulic and caffeic) and flavonoids (apigenin, apigenin-7-O-glucoside, hyperoside, quercitrin, rutin and quercetin) were identified in the A. foeniculum herb. It was established that rosmarinic acid followed by apigenin-7-O-glucoside and apigenin were the predominant compounds of the A. foeniculum raw material. The content of rosmarinic acid as the major compound during the primary, secondary and tertiary extraction decreased in the following order: 37.563>15.435>0.642 (mg/g); the content of apigenin-7-O-glucoside was 24.508>9.107>0.945 (mg/g) and apigenin was 19.547>9.676>1.816 (mg/g), respectively. Generally, the third extraction was determined to be inefficient in terms terms of low content of polyphenols as well as excessive analysis time and solvent costs.

Performance Prediction and Heating Parameter Optimization of Organic-Rich Shale In Situ Conversion Based on Numerical Simulation and Artificial Intelligence Algorithms.

In situ conversion technology is a green and effective way to realize the development of organic-rich shale. Supercritical CO2 can be used as a good heating medium for shale in situ conversion. Numerical simulation is an important means to explore the shale in situ conversion process, but it requires a lot of time and computational cost for in situ conversion simulation under different working conditions. Therefore, a computational framework for rapid prediction of shale in situ conversion development performance and heating parameter optimization is proposed by coupling artificial neural network (ANN) and particle swarm optimization (PSO). The results indicated that kerogen pyrolysis and hydrocarbon product release mainly occurred within 2 years of shale in situ conversion. The production curves of pyrolysis hydrocarbon obviously slowed after in situ conversion for 2 years. The database was constructed by a large number of in situ conversion simulations, and Pearson correlation analysis and the random forest method were adopted to obtain seven main controlling factors affecting reservoir temperature and hydrocarbon production. The determination coefficient of the obtained ANN-based prediction models is higher than 97%, and the mean square error (MSE) is lower than 0.3%. The basic reservoir case can choose to inject 350-450 °C supercritical CO2 (Sc-CO2) fluid with a rate of 600 m3/day to obtain a more promising development effect. The heating parameter optimization for three typical reservoir cases using PSO was performed, and reasonable injection temperature and injection rate were obtained. It realized accurate development prediction and rapid heating parameter optimization, which helps the effective application of shale in situ conversion development design.

Band Heater Heat Flux Characterization Using Inverse Heat Conduction Problem Models

Abstract An experimental apparatus was constructed to correlate water flowrate and temperature rise under an external band heater. Due to the physical characteristics of the band heater, its transient heating behavior is unknown. This paper investigates the application of inverse heat conduction problem (IHCP) methods to characterize the heat flux from the band heater. Three experiments with different heating times (5, 10, and 20 s) and no flowrate were conducted to measure the transient temperature under the 400 W band heater. Type-T thermocouples measure surface temperature at the centerline of the band heater. The experimental results are computed with five different heat conduction models. The models are chosen to identify how the heat flux response varies from a simplified to a realistic model. Additionally, the results of the experimental heat flux are compared to the manufacturer band heater data (58.9 kW/m2) for each model. The minimum time needed for the heater to fully energize the system is from 10 to 12 s. The residuals for each model are analyzed and used to evaluate the appropriateness of the five different models. The results show that the use of simpler models can achieve results similar to those of complex models, with less time and computational cost.

Comparison of endoscopic resection therapies for rectal neuroendocrine tumors

Aims This study was to evaluate and compare the efficacy and safety of endoscopic mucosal resection (EMR), clip-and-snare assisted endoscopic mucosal resection (CS-EMR), and endoscopic submucosal dissection (ESD) for the endoscopic resection of rectal NETs. Material and methods A retrospective analysis was performed on 47 patients with rectal NETs who underwent endoscopic treatment in The Second Affiliated Hospital of Soochow University. Manifestations of clinic pathological characteristics, complications, procedure time and hospitalization costs were studied. Results The complete resection rates with CS-EMR and ESD were significantly higher than those with EMR (CS-EMR vs. EMR, p = 0.038; ESD vs. EMR, p = 0.04), but no significant difference was found between the CS-EMR and ESD groups (p = 0.383). The lateral margin was less distant in the CS-EMR group than in the ESD group and there was no difference with regard to vertical margin (lateral margin distance, 1500 ± 3125 vs.3000 ± 3000 μm; vertical margin distance, 400 ± 275 vs.500 ± 500 μm). Compared to ESD, CS-EMR required less operation time (p < 0.01) and money (p < 0.01) and reduced the length of hospital stays (p < 0.01). Conclusions The CS-EMR technique is more effective and efficient than EMR for small rectal NETs. In addition, CS-EMR reduces procedure time, duration of post-procedure hospitalization and decreases patients’ cost compared to ESD while ensuring sufficient vertical margin distances.

Rhizarthrosis: Resection Arthroplasty versus Resection Suspension Arthroplasty - Still the Gold Standard?

Rhizarthrosis is one of the most common arthritic changes in the hand, which has led to the development of a large number of surgical methods in recent years. In addition to the traditional resection arthroplasty with tendon suspension or interposition, if needed, thumb saddle joint prostheses are being used more and more frequently. However, these are not an option for all patients such as those with severe osteoporosis, severe nickel allergy, insufficient height of the trapezium or STT osteoarthritis. This study examined whether the traditional surgical methods continue to have their "right to exist" or whether they will be replaced by prostheses. In a retrospective study with prospective data collection, 48 hands of 45 patients diagnosed with rhizarthrosis in stages II and III according to Eaton and Littler were clinically examined 2.5 years after resection arthroplasty/resection-suspension-interposition arthroplasty using flexor carpi radialis (FCR) or abductor pollicis longus (APL) tendon strips in the years 2015-2018. The postoperative outcome was assessed using mobility, grip strength, two-point discrimination, postoperative satisfaction with regard to pain and resilience, as well as the DASH and MHQ questionnaires. There were no significant differences in surgery duration, inability to work, time to full weight-bearing, pain, satisfaction, grip strength and mobility. Regardless of the surgical method used, patients achieved an almost free range of motion in the thumb and wrist as well as age-appropriate hand strength. Pain at rest was rare; 48% were pain-free during exercise. The DASH questionnaire scored 20.2, 13.9 and 22.1 points and the MHQ scored 76.3, 79.3 and 79.4 points. Hence, the traditional surgical techniques showed good postoperative results with high patient satisfaction. "Newer procedures" such as endoprosthesis offer promising results in terms of faster regeneration times. However, this is offset by a longer surgery time and higher material costs, which means that resection arthroplasty represents a long-established procedure with a low potential for complications and a lower revision rate and should continue to be used, especially if there are relative contraindications to an endoprosthesis.

An Equivalent-Perceptional Intertemporal Choice Heuristics Model for Electric Operation Vehicle Charging Behavior

The inherent stochasticity of electric operation vehicle (EOV) charging poses challenges to the stability and efficiency of regional power distribution networks. Existing charging behavior decision-making models often prioritize revenue considerations, neglecting the influence of multi-time-span characteristics and the potential irrationality of EOV owners. To address these limitations, this study proposes a comprehensive framework encompassing three aspects. First, operational data are statistically analyzed to reconstruct EOV operation scenarios, establishing a dynamic charging scheme tailored to multi-time-span characteristics. Second, an improved ITCH model is developed using operational equivalent change to incorporate both gains and losses. Third, a WFL framework is employed to integrate the perceptual attenuation of revenue into the ITCH model. Simulation results show that decision-makers (DMs) demonstrate a preference for charging schemes with high equivalent perceived revenues and low time costs. Moreover, when the charging price is doubled, revenue perception attenuation leads decision-makers to postpone their charging behavior. Compared to other models, the equivalent perception intertemporal choice heuristics (EP-ITCH) charging model results in reduced load peaks, valleys, and variances on the grid side. This study highlights the model’s effectiveness and accuracy in optimizing EOV charging infrastructure.

Оценка трудового потенциала: от количества сил до их возможностей

The article presents an approach to increasing labor productivity by evaluating the labor potential of an enterprise and subsequently developing a personnel incentive system. Labor potential is considered as the ability of human resources to increase their labor productivity by addressing hidden reserves, existence of which depends on internal and external factors. The authors present the results of a construction industry enterprise’s personnel survey. The employees assessed the organization of the labor process, the relationship between the team and departments, their own labor potential, as well as other aspects of their work. The results aim at determining the possible motives of employees to create a staff incentive system. Along with the motives of the staff, the incentive system should take into account the amount of possible labor productivity increase. Thus, it will correspond to the interests of both employees and the enterprise. The study proposes a methodology for assessing the personnel labor potential based on determining the strengths and weaknesses of the studied indicators. The methodology allows to identify promising areas for work with personnel and rationally allocate resources to stimulate labor potential, which, in turn, can reduce time costs and other expences.

An efficient and robust staggered scheme based on adaptive time field for phase field fracture model

The standard staggered scheme in phase field model is computationally slow, so that a large amount of iterations are required. In order to improve the computational efficiency, a novel efficient and robust solution scheme based on adaptive time field is proposed in this paper. This scheme is inspired by the adaptive mesh method for space field, and an error indicator based on phase field and history field variables is established, where the time field is coupled with the phase field and energy field in the space field, so that the time field changes adaptively with the computational iterative process, which improves the convergence rate and reduces the time cost. The numerical results show that the presented novel scheme not only significantly improves the computational efficiency, but also has accurate computational results. Compared with the standard staggered scheme, the time cost of the presented scheme is less than 4% of that of the standard scheme. At the same time, the computational efficiency of the presented solver is faster than the current monolithic BFGS scheme, and it has excellent convergence characteristics.

Bioconversion of pineapple wastes for production of Pleurotus pulmonarius (gray oyster mushroom) and Pleurotus ostreatus (white oyster mushroom)

AbstractMushroom cultivation is currently being widely ventured by farmers in Malaysia due to its high profits in a short time and low production cost. Mushrooms can be grown on various substrates such as rice husk and stalks, coconut fiber, and sawdust. This study was performed to find out the ability of using pineapple leaves waste as a substrate for the cultivation of Pleurotus pulmonarius (gray oyster mushroom) and Pleurotus ostreatus (white oyster mushroom) and compare the results obtained with mushrooms grown using traditional substrates (sawdust). The mushroom bags were prepared using a dry and wet medium of pineapple leaves waste. The results revealed that 60% of dry and wet pineapple leaves waste produced the highest output of P. pulmonarius and P. ostreatus in comparison to other percentages. The growing of P. pulmonarius on dry pineapple leaves waste substrate revealed the maximum average weight and number of fruiting bodies while P. ostreatus showed the best growth performance in wet pineapple leaves waste substrate. The nutritional content of 60% dry and wet pineapple leaves waste for both P. pulmonarius and P. ostreatus shown superior value in percentages of moisture, protein, fat, and carbohydrate compared to control. In the meantime, the mineral analysis revealed that 60% of the dry and wet pineapple leaves waste had greater levels of Cu, P, and Pb than the control. These findings suggested that the pineapple leaves waste can become a great alternative substrate for both P. pulmonarius and P. ostreatus cultivation for better economic and environmental benefits.

Multi-objective and multi constrained task scheduling framework for computational grids

Grid computing emerged as a powerful computing domain for running large-scale parallel applications. Scheduling computationally intensive parallel applications such as scientific, commercial etc., computational grids is a NP-complete problem. Many researchers have proposed several task scheduling algorithms on grids based on formulating and solving it as an optimization problem with different objective functions such as makespan, cost, energy etc. Further to address the requirements/demands/needs of the users (lesser cost, lower latency etc.) and grid service providers (high utilization and high profitability), a task scheduler needs to be designed based on solving a multi-objective optimization problem due to several trade-offs among the objective functions. In this direction, we propose an efficient multi-objective task scheduling framework to schedule computationally intensive tasks on heterogeneous grid networks. This framework minimizes turnaround time, communication, and execution costs while maximizing grid utilization. We evaluated the performance of our proposed algorithm through experiments conducted on standard, random, and scientific task graphs using the GridSim simulator.