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For optimal equipment procurement in Taiwan, first, the specifications and prices of a product are evaluated. Based on these evaluations, procurement decisions are made. Ensuring accurate evaluation criteria for procurement is a multicriteria decision making (MCDM) problem. Printed circuit board (PCB) manufacturers have not grasped the decision-making factors of the new equipment specification, and they continue to use the previous generation specifications as the decision-making criteria for new equipment; this results in inappropriate evaluation criteria. When new equipment with different specifications is launched, purchasers may still use the specifications of older equipment as a basis for their purchase decisions. For example, thermal power generation specifications are often used to evaluate equipment for nuclear power generation. The difference in criteria renders it difficult to make decisions for new equipment. We proposed the use of a scientific method to establish a set of criteria decision-making models for equipment procurement to help PCB factories accurately evaluate contemporary specifications. This research established criteria for new technologies to help decision makers select the most appropriate method. We used the Delphi method and the analytic hierarchy process (AHP) to provide insights into technological and commercial requirements, and a model was further developed for optimal equipment procurement.

This study presents the design and implementation of a Fractional Order Proportional, Integral, and Derivative (FOPID) controller intended for the regulation of blood glucose levels in individuals with type 1 diabetes mellitus (T1DM). The efficacy of this controller is evaluated through its application to a nonlinear Stolwijk–Hardy model simulating T1DM patients, accounting for a range of physiological conditions. Utilizing a genetic algorithm, the parameters of the FOPID controller are fine-tuned. By conducting a comparative analysis with previously established control algorithms, the performance of the proposed controller is validated, demonstrating its superior performance. The results underscore the significant improvements achieved by the proposed controller in maintaining blood glucose concentrations within safe thresholds, particularly in scenarios involving disruptions due to meals.

AimsMiR-181c-5p overexpression associates with heart failure (HF) and cardiac damage, but the underlying pathophysiology remains unclear. This study investigated the effect of miR-181c-5p inhibition on cardiac function and fibrosis in a rodent model of diastolic dysfunction, and evaluated additional effects on kidney as relevant comorbid organ.Methods and resultsDiastolic dysfunction was induced in male C57/BL6J mice (n = 20) by combining high-fat diet, L-NG-nitroarginine methyl ester, and angiotensin II administration, and was compared to sham controls (n = 18). Mice were randomized to subcutaneous miR-181c-5p antagomiR (INH) or scrambled antagomiR injections (40 mg/kg/week). HF mice demonstrated diastolic dysfunction and increased fibrosis, which was attenuated by INH treatment. Remarkably, HF + INH animals had a threefold higher mortality rate (60%) compared to HF controls (20%). Histological examination revealed increased glomerular damage in all INH treated mice, and signs of thrombotic microangiopathy (TMA) in mice who died prematurely. Quantitative polymerase chain reaction demonstrated a miR-181c-5p-related downregulation of cardiac but not renal Tgfbr1 in HF + INH mice, while INH treatment reduced renal but not cardiac Vegfa expression in all mice.ConclusionThis study demonstrates cardiac anti-fibrotic effects of miR-181c-5p inhibition in a rodent HF model through targeting of Tgfbr1 in the heart. Despite improved diastolic function, HF + INH mice had higher mortality due to increased predisposition for TMA, increased renal fibrosis and glomerular damage, associated with Vegfa downregulation in kidneys.

Abstract Background The tissue response viewer (TRV) is a novel marker for ablation lesion quality that aims to classify lesions into transmural or nontransmural lesions (high or low dielectric response, HDR or LDR) using dielectric‐based tissue assessment. The objective of this study was to gain insight in the TRV by relating its outcomes to conventional ablation parameters. Methods Patients that had repeat ablation for atrial fibrillation with a dielectric imaging‐based mapping system were enrolled. All ablation data were downloaded from the mapping system and analyzed to explore associations between TRV outcomes and other ablation parameters. Results The cohort included 24 patients, in which 58 pulmonary veins and 8 superior vena cavas were targeted. A total of 388 energy applications were applied, resulting in 639 ablation points. The system classified 36% of ablation points as HDR and 44% as LDR. The system did not provide a dielectric response in 20%. The system's ability to provide a dielectric response was related to longer ablation duration and absence of dragging ablation. HDR (versus LDR) was multivariably associated with longer energy applications, higher mean ablation power, and lower wall thickness. Greater impedance drop was univariably associated with HDR. Conclusion Outcomes of the TRV are associated with conventional ablation parameters (e.g., duration and power) but also local wall thickness. Catheter stability seems important for successful lesion assessment with the TRV. Further reduction of missing outcomes and validation of the tool are warranted before widespread use.

BackgroundThere is a gross shortage of sleep specialist providers within the military health system. Telehealth and mobile health represent promising approaches to increase access to high quality, cost-effective care in the U.S military.ObjectivesThis paper reports findings from a mixed-methods clinical implementation study of a novel sleep telehealth platform at two military treatment facilities in the National Capitol Region. The platform includes a mobile app and integrated wearable sensors (i.e., a commercial off-the-shelf sleep tracker [Fitbit]). The primary purpose was to evaluate the implementation of a 10-day remote monitoring assessment and provision of evidence-based sleep treatment recommendations to patients and providers. In addition, we sought to observe, in an exploratory manner, subsequent engagement with the app during 5 days of personalized sleep education and training.MethodsPatients with sleep problems completed an intensive 10-day remote monitoring assessment that included a baseline intake questionnaire, daily sleep diaries, twice daily symptom surveys, and Fitbit. Based on this assessment, patients received personalized assessment results. Concurrently, a provider report was generated that included provisional diagnoses and evidence-based treatment recommendations. Next, participants gained access to personalized sleep education and trainings within the mobile app. Within an established implementation science framework, outcomes were assessed via behavioral adherence (engagement with the app) and separate questionnaires for patients and providers. Last, we conducted four focus groups with patients and 12 key informant interviews with primary care managers (PCMs) and economic stakeholders to seek feedback and recommendations for future directions.ResultsTwo hundred and seventy patients participated in the study. Using validated research questionnaires, participants reported high-risk for obstructive sleep apnea (65.6%), moderate to severe insomnia (38.2%), and moderate to severe daytime sleepiness (38.5%), and moderate to severe anxiety (14.1%) and depressive (20.4%) symptoms. Total sleep time was 6.6 (SD = 1.8) h based on sleep diaries and 6.1 (SD = 1.8) h based on Fitbit. Regarding implementation, reach, effectiveness, adoption, implementation, and maintenance were all notably high, based on quantitative and qualitative data from participants and PCMs.ConclusionsSleep telehealth and mobile health represent promising approaches to increase access to cost-effective, evidence-based care for sleep disorders in the U.S. military.

Psychostimulants alter cellular morphology and activate neuroimmune signaling in a number of brain regions, yet few prior studies have investigated their persistence beyond acute abstinence or following high levels of voluntary drug intake. In this study, we examined the effects of the repeated binge-like self-administration (96 h/week for 3 weeks) of methamphetamine (METH) and 21 days of abstinence in female and male rats on changes in cell density, morphology, and cytokine levels in two addiction-related brain regions—the prefrontal cortex (PFC) and dorsal striatum (DStr). We also examined the effects of similar patterns of intake of the cocaine-like synthetic cathinone derivative 3,4-methylenedioxypyrovalerone (MDPV) or saline as a control. Robust levels of METH and MDPV intake (~500–1000 infusions per 96 h period) were observed in both sexes. We observed no changes in astrocyte or neuron density in either region, but decreases in dendritic spine densities were observed in PFC pyramidal and DStr medium spiny neurons. The microglial cell density was decreased in the PFC of METH self-administering animals, accompanied by evidence of microglial apoptosis. Changes in microglial morphology (e.g., decreased territorial volume and ramification and increased cell soma volume) were also observed, indicative of an inflammatory-like state. Multiplex analyses of PFC and DStr cytokine content revealed elevated levels of various interleukins and chemokines only in METH self-administering animals, with region- and sex-dependent effects. Our findings suggest that voluntary binge-like METH or MDPV intake induces similar cellular perturbations in the brain, but they are divergent neuroimmune responses that persist beyond the initial abstinence phase.

BackgroundSleep problems are common and costly in the US military. Yet, within the military health system, there is a gross shortage of trained specialist providers to address sleep problems. As a result, demand for sleep medicine care far exceeds the available supply. Telehealth including telemedicine, mobile health, and wearables represents promising approaches to increase access to high-quality and cost-effective care. ObjectiveThe purpose of this study was to evaluate patient engagement and provider perceived effectiveness of a novel sleep telehealth platform and remote monitoring assessment in the US military. The platform includes a desktop web portal, native mobile app, and integrated wearable sensors (ie, a commercial off-the-shelf sleep tracker [Fitbit]). The goal of the remote monitoring assessment was to provide evidence-based sleep treatment recommendations to patients and providers. MethodsPatients with sleep problems were recruited from the Internal Medicine clinic at Walter Reed National Military Medical Center. Patients completed intensive remote monitoring assessments over 10 days (including a baseline intake questionnaire, daily sleep diaries, and 2 daily symptom surveys), and wore a Fitbit sleep tracker. Following the remote monitoring period, patients received assessment results and personalized sleep education in the mobile app. In parallel, providers received a provisional patient assessment report in an editable electronic document format. Patient engagement was assessed via behavioral adherence metrics that were determined a priori. Patients also completed a brief survey regarding ease of completion. Provider effectiveness was assessed via an anonymous survey. ResultsIn total, 35 patients with sleep problems participated in the study. There were no dropouts. Results indicated a high level of engagement with the sleep telehealth platform, with all participants having completed the baseline remote assessment, reviewed their personalized sleep assessment report, and completed the satisfaction survey. Patients completed 95.1% of sleep diaries and 95.3% of symptom surveys over 10 days. Patients reported high levels of satisfaction with most aspects of the remote monitoring assessment. In total, 24 primary care providers also participated and completed the anonymous survey. The results indicate high levels of perceived effectiveness and identified important potential benefits from adopting a sleep telehealth approach throughout the US military health care system. ConclusionsMilitary patients with sleep problems and military primary care providers demonstrated high levels of engagement and satisfaction with a novel sleep telehealth platform and remote monitoring assessment. Sleep telehealth approaches represent a potential pathway to increase access to evidence-based sleep medicine care in the US military. Further evaluation is warranted.

The impact of traumatic brain injury (TBI) severity and loss of consciousness (LOC) on the development of neuropsychiatric symptoms was studied in injured service members (SMs; n?=?1278) evacuated from combat settings between 2003 and 2012. TBI diagnoses of mild TBI (mTBI) or moderate-to-severe TBI (MS-TBI) along with LOC status were identified using International Classification of Diseases, Ninth Revision (ICD-9) codes and the Defense and Veterans Brain Injury Center Standard Surveillance Case Definition for TBI. Self-reported psychiatric symptoms were evaluated for post-traumatic stress disorder (PTSD) with the PTSD Checklist, Civilian Version for PTSD, the Patient Health Questionnaire-9 for major depressive disorder (MDD), and the Patient Health Questionnaire-15 for somatic symptom disorder (SSD) in two time periods post-injury: Assessment Period 1 (AP1, 0.0?2.5 months) and Assessment Period 2 (AP2, 3?12 months). mTBI, but not MS-TBI, was associated with increased neuropsychiatric symptoms: PTSD in AP1 and AP2; MDD in AP1; and SSD in AP2. A subgroup analysis of mTBI with and without LOC revealed that mTBI with LOC, but not mTBI without LOC, was associated with increased symptoms as compared to non-TBI: PTSD in AP1 and AP2; MDD in AP1; and SSD in AP1 and AP2. Moreover, mTBI with LOC was associated with increased MDD symptoms in AP2, and SSD symptoms in AP1 and AP2, compared to mTBI without LOC. These findings reinforce the need for the accurate characterization of TBI severity and a multi-disciplinary approach to address the devastating impacts of TBI in injured SMs.

Background: Image guidance to assist left ventricular (LV) lead placement may improve outcome after cardiac resynchronization therapy (CRT), but previous approaches and results varied greatly, and multicenter feasibility is lacking altogether. Objective: We sought to investigate the multicenter feasibility of image guidance for periprocedural assistance of LV lead placement for CRT. Methods: In 30 patients from 3 hospitals, cardiac magnetic resonance imaging was performed within 3 months prior to CRT to identify myocardial scar and late mechanical activation (LMA). LMA was determined using radial strain, plotted over time. Segments without scar but clear LMA were classified as optimal for LV lead placement, according to an accurate 36-segment model of the whole heart. LV leads were navigated using image overlay with periprocedural fluoroscopy. After 6 months, volumetric response and super-response were defined as ≥15% or ≥30% reduction in LV end-systolic volume, respectively. Results: Periprocedural image guidance was successfully performed in all CRT patients (age 66 ± 10 years; 59% men, 62% with nonischemic cardiomyopathy, 69% with left bundle branch block). LV leads were placed as follows: within (14%), adjacent (62%), or remote (24%) from the predefined target. According to the conventional 18-segment model, a remote position occurred only once (3%). On average, 86% of patients demonstrated a volumetric response (mean LV end-systolic volume reduction 36 ± 29%), and 66% of all patients were super-responders. Conclusion: On-screen image guidance for LV lead placement in CRT was feasible in a multicenter setting. Efficacy will be further investigated in the randomized controlled ADVISE (Advanced Image Supported Lead Placement in Cardiac Resynchronization Therapy) trial (NCT05053568).

Abstract This article demonstrates how to create Chemical Space Networks (CSNs) using a Python RDKit and NetworkX workflow. CSNs are a type of network visualization that depict compounds as nodes connected by edges, defined as a pairwise relationship such as a 2D fingerprint similarity value. A step by step approach is presented for creating two different CSNs in this manuscript, one based on RDKit 2D fingerprint Tanimoto similarity values, and another based on maximum common substructure similarity values. Several different CSN visualization features are included in the tutorial including methods to represent nodes with color based on bioactivity attribute value, edges with different line styles based on similarity value, as well as replacing the circle nodes with 2D structure depictions. Finally, some common network property and analysis calculations are presented including the clustering coefficient, degree assortativity, and modularity. All code is provided in the form of Jupyter Notebooks and is available on GitHub with a permissive BSD-3 open-source license: https://github.com/vfscalfani/CSN_tutorial Graphical Abstract

This research introduces the Multi-Depot Waste Collection Vehicle Routing Problem with Time Windows and Self-Delivery Option (MDWCVRPTW-SDO). The problem comes from the waste bank operation implemented in Yogyakarta City, Indonesia. A set of vehicles is dispatched from the waste banks to pick up waste from residents’ locations within the time windows specified by the residents. Residents may be compensated for delivering their waste to a waste bank by themselves. The objective of MDWCVRPTW-SDO is minimizing the sum of investment costs, routing costs, and total compensation paid to the residents. We model this problem as a mixed integer linear programming model and propose Simulated Annealing (SA) as an effective solution approach. Extensive computational experiments confirm that SA is effective to solve MDWCVRPTW-SDO. Moreover, the number of waste banks, compensation paid to residents, and the distribution of residents of each type are crucial for the success of the implementation.

Abstract This paper presents a framework for achieving machine learning (ML)‐assisted direction‐of‐arrival (DoA) accuracy enhancement using a millimetre‐wave (mmWave) dynamic aperture. The technique used for the enhanced DoA estimation accuracy leverages an over‐sized lens‐loaded cavity antenna connected to a single RF chain in the physical layer and a computational method in the computational layer of the framework. It is shown for the first time that by introducing a reconfigurable mode‐mixing mechanism inside the over‐sized lens‐loaded cavity hardware, a greater number of spatially orthogonal radiation modes can be achieved giving rise to many cavity states. If the best cavity state is determined and selected by means of design exploration using a contemporary ML‐assisted antenna optimisation method, the computational DoA estimation accuracy can be improved. The mode‐mixing mechanism in this work is a randomly oriented metallic scatterer located inside an over‐sized constant−ϵr lens‐loaded cavity, connected to a stepper motor that is electronically controlled by inputs from the computational layer of the presented framework. Measurement results in terms of near‐field radiation mode scans are included in this study to verify and validate that the proposed ML‐assisted framework enhances the DoA estimation accuracy. Moreover, this investigation simultaneously provides a simplification in the physical layer implementation of mmWave radio hardware, and DoA accuracy enhancement, which in turn lends itself favourably to the adoption of the proposed framework for channel sounding in mmWave communication systems.

Abstract This paper presents a millimeter-wave direction of arrival estimation (DoA) technique powered by dynamic aperture optimization. The frequency-diverse medium in this work is a lens-loaded oversized mmWave cavity that hosts quasi-random wave-chaotic radiation modes. The presence of the lens is shown to confine the radiation within the field of view and improve the gain of each radiation mode; hence, enhancing the accuracy of the DoA estimation. It is also shown, for the first time, that a lens loaded-cavity can be transformed into a lens-loaded dynamic aperture by introducing a mechanically controlled mode-mixing mechanism inside the cavity. This work also proposes a way of optimizing this lens-loaded dynamic aperture by exploiting the mode mixing mechanism governed by a machine learning-assisted evolutionary algorithm. The concept is verified by a series of extensive simulations of the dynamic aperture states obtained via the machine learning-assisted evolutionary optimization technique. The simulation results show a 25 $$\%$$ % improvement in the conditioning for the DoA estimation using the proposed technique.

This research studies the Field Technician Scheduling Problem with Experience-Dependent Service Times (FTSP–EDST), involving three main features: matching maintenance tasks with available technicians, sequencing the tasks, and considering the experience-dependent service times. Given a limited number of technicians, the objective is to maximize the collected profit for servicing tasks. This study formulates the problem as a mixed-integer linear programming model and proposes a Modified Iterated Local Search (MILS) to solve the benchmark problem instances of various sizes. A set of FTSP–EDST instances is generated based on existing publicly accessible data, and MILS is utilized to solve these newly generated instances. Computational results confirm the effectiveness of MILS in solving FTSP–EDST.

This study investigates the flying sidekick traveling salesman problem (FSTSP), in which a truck and an unmanned aerial vehicle work together to make deliveries. This study develops a revised mixed-integer linear programming (MILP) model for the FSTSP. The revised MILP model performs better than the existing model. Due to the FSTSP’s high complexity, we propose an effective heuristic based on simulated annealing (SA) to solve the problem. The novelty of the proposed SA heuristic lies in the new solution representation, which not only determines the visiting sequence of customers but also the service type of customers and rendezvous positions. Another feature of the proposed SA is a new operator specifically designed for the FSTSP. To evaluate the performance of the proposed SA heuristic, we conduct a comprehensive computational study where we fine-tune the parameters of the SA heuristic and compare the performance of the SA heuristic with several state-of-the-art algorithms including hybrid genetic algorithm (HGA) and iterated local search (ILS) in solving existing FSTSP benchmark instances. The results indicate that the proposed SA heuristic outperforms ILS and is statistically competitive with HGA. It obtains best-known solutions for all small FSTSP instances and 29 best-known solutions for the 60 large FSTSP instances, including 20 new best-known solutions.

Abstract Mathematical models in oncology aid in the design of drugs and understanding of their mechanisms of action by simulation of drug biodistribution, drug effects, and interaction between tumor and healthy cells. The traditional approach in pharmacometrics is to develop and validate ordinary differential equation models to quantify trends at the population level. In this approach, time‐course of biological measurements is modeled continuously, assuming a homogenous population. Another approach, agent‐based models, focuses on the behavior and fate of biological entities at the individual level, which subsequently could be summarized to reflect the population level. Heterogeneous cell populations and discrete events are simulated, and spatial distribution can be incorporated. In this tutorial, an agent‐based model is presented and compared to an ordinary differential equation model for a tumor efficacy model inhibiting the pERK pathway. We highlight strengths, weaknesses, and opportunities of each approach.

An efficient maintenance schedule for gas turbines of power plants ensures a reliable electricity supply. This study addresses a generator maintenance scheduling problem arising from Taiwan’s combined cycle power plant with two notable characteristics, i.e., a specific sequence of various maintenance types and the concept of performing maintenance according to operational hours spent. The objective is to minimize the total maintenance cost. The problem is formulated as a Mixed Integer Linear Program which is solvable by an off-the-shelf exact solver, i.e., CPLEX. Moreover, a set of newly generated instances is proposed as benchmark instances for the problem. The instances generated are based on the realistic conditions obtained from the historical record of Taiwan’s combined cycle power plant. Computational studies are presented as interesting insights regarding the complexity of the problem and the factors driving the total maintenance costs.

Due to the growth in e-commerce, city logistics needs to cope with the rise of customers’ demand. Consequently, it adds to the complexity of the last-mile delivery process. Moreover, this process often contributes significantly to the distribution cost of companies. An alternative to alleviate the problem is the utilization of parcel lockers. It allows the delivery to be extended not only for home delivery but also for locker delivery, which brings several advantages in terms of cost-saving. However, considering multiple delivery options means adding new complexity to the delivery system. In the previous works, vehicle routing problem with time windows (VRPTW) has been widely dealt by only considering home delivery. This research proposes a new VRPTW variant by adding locker delivery as one of the delivery options. The developed problem is called the vehicle routing problem with parcel lockers (VRPPL). The goal of VRPPL is to minimize the total traveling cost. In this research, we formulate a new mathematical programming model and develop a simulated annealing (SA) algorithm to deal with VRPPL. A newly generated set of instances was developed from the well-known Solomon’s VRPTW instances. The performance of the proposed algorithm is also presented.

Background Blockade of the lectin‐like oxidized low‐density lipoprotein receptor‐1 (LOX‐1) is a potentially attractive mechanism for lowering inflammatory and lipid risk in patients with atherosclerosis. This study aims to assess the safety, tolerability, and target engagement of MEDI6570, a high‐affinity monoclonal blocking antibody to LOX‐1. Methods and Results This phase 1, first‐in‐human, placebo‐controlled study (NCT03654313) randomized 88 patients with type 2 diabetes to receive single ascending doses (10, 30, 90, 250, or 500 mg) or multiple ascending doses (90, 150, or 250 mg once monthly for 3 months) of MEDI6570 or placebo. Primary end point was safety; secondary and exploratory end points included pharmacokinetics, immunogenicity, free soluble LOX‐1 levels, and change in coronary plaque volume. Mean age was 57.6/58.1 years in the single ascending doses/multiple ascending doses groups, 31.3%/62.5% were female, and mean type 2 diabetes duration was 9.7/8.7 years. Incidence of adverse events was similar among cohorts. MEDI6570 exhibited nonlinear pharmacokinetics, with terminal half‐life increasing from 4.6 days (30 mg) to 11.2 days (500 mg), consistent with target‐mediated drug disposition. Dose‐dependent reductions in mean soluble LOX‐1 levels from baseline were observed (>66% at 4 weeks and 71.61–82.96% at 10 weeks in the single ascending doses and multiple ascending doses groups, respectively). After 3 doses, MEDI6570 was associated with nonsignificant regression of noncalcified plaque volume versus placebo (−13.45 mm3 versus −8.25 mm3). Conclusions MEDI6570 was well tolerated and demonstrated dose‐dependent soluble LOX‐1 suppression and a pharmacokinetic profile consistent with once‐monthly dosing. Registration URL: https://clinicaltrials.gov/; Unique identifier: NCT03654313.

Abstract Antenna arrays and multi-antenna systems are essential in beyond 5G wireless networks for providing wireless connectivity, especially in the context of Internet-of-Everything. To facilitate this requirement, beamforming technology is emerging as a key enabling solution for adaptive on-demand wireless coverage. Despite digital beamforming being the primary choice for adaptive wireless coverage, a set of applications rely on pure analogue beamforming approaches, e.g., in point-to-multi point and physical-layer secure communication links. In this work, we present a novel scalable analogue beamforming hardware architecture that is capable of adaptive 2.5-dimensional beam steering and beam shaping to fulfil the coverage requirements. Beamformer hardware comprises of a finite size Maxwell fisheye lens used as a scalable feed network solution for a semi-circular array of monopole antennas. This unique hardware architecture enables a flexibility of using 2 to 8 antenna elements. Beamformer development stages are presented while experimental beam steering and beam shaping results show good agreement with the estimated performance.

Background: It is known that sleep disturbance is associated with increased suicidal thinking. Moreover, completed suicides, when adjusted for the proportion of the populace that is awake at a given time, are more probable during the late night/early morning hours. Despite these concerns, no studies have examined the role of trait-like individual differences in vulnerability to suicidal ideation during sleep deprivation or insomnia. In two separate studies, we examined whether the trait of extraversion is predictive of changes in suicidal thinking following two nights of sleep deprivation and among individuals meeting the criteria for insomnia.Methods:Study 1: Twenty-five healthy military personnel (20 males), ages 20–35 completed the NEO-PI-R Extraversion scale and the Suicidal Ideation (SUI) scale of the Personality Assessment Inventory (PAI). Participants completed 77 h of continuous sleep deprivation. After 56 h of sleep deprivation, participants completed the SUI scale a second time. We predicted a change in SUI scores from baseline extraversion. Study 2: 2,061 adults aged 18–79 (900 males) were divided into two groups based on the clinical threshold (≥ 10) on the Insomnia Severity Index (ISI) and completed measures of extraversion and depression, including the suicide item of the Patient Health Questionnaire-9 (PHQ9).Results:Study 1: After controlling for the caffeine group and changes in PAI Depression, Extraversion scores were used to predict changes in SUI scores using stepwise multiple linear regression. Higher Extraversion was significantly associated with increased non-clinical suicidal ideation following sleep loss, β = 0.463, partial r = 0.512, p = 0.013. Study 2: After controlling for depression, the effect of insomnia on suicidal ideation was moderated by trait extraversion (p < 0.0001). Overall, the presence or absence of insomnia had little effect on individuals low in trait extraversion (i.e., introverts), but insomnia was associated with significantly higher suicidal ideation among high trait extraverted individuals.Conclusions: Higher trait extraversion was associated with increased vulnerability to suicidal ideation between rested baseline and total sleep deprivation and was associated with greater suicidal ideation among those meeting criteria for clinically severe insomnia. These findings point to a potential trait-like vulnerability factor that may further our understanding of sleep disruption in the phenomenology of suicide.

This paper presents an extension of the Electric Vehicle Routing Problem with Time Windows and Partial Recharges (EVRPTW-PR), which incorporates the use of parcel lockers as a delivery method (i.e., self-pickup method). This variant, named the electric vehicle routing problem with time windows, partial recharges, and parcel lockers (EVRPTW-PR-PL), focuses on minimizing delivery costs by employing a homogeneous fleet of electric vehicles (EVs) and providing two delivery methods for serving customers: home delivery and self-pickup methods. We derive a mathematical formulation and propose an adaptive large neighborhood search (ALNS) algorithm to address EVRPTW-PR-PL. Moreover, in ALNS, the solution representation is constructed to handle the assignment of delivery methods. The performance of our proposed ALNS algorithm is evaluated by solving EVRPTW-PR benchmark instances. Finally, the results of EVRPTW-PR-PL obtained by using the GUROBI solver and our ALNS algorithm are provided, accompanied by managerial insights on the implementation of parcel lockers.

Abstract Erectile dysfunction (ED) is mostly due to the lack of blood flow into the penis. In the past 20 years, near-infrared spectroscopy (NIRS) was used in measuring the concentrations and temporal dynamics of different hemoglobin types. However, the dynamics of the light absorption (photoplethysmography; PPG) have not been applied to survey penile hemodynamics and erection quality. This paper compared the use of photoplethysmography (PPG) to study vascular ED with standard penile Doppler ultrasonography. Men diagnosed with vascular ED for at least 6 months and nominated for penile ultrasonography were included. PPG signals were collected during the ultrasound examination. All beat-to-beat PPG waveforms were aligned with the peak and averaged to one representative template waveform for feature analysis, including amplitude differences (APD) index, reflection time index (RTI), augmentation index (AI), and perfusion index (PI). An inverse correlation was found between end-erection amplitude and both erection hardness score (EHS) and resistive index (RI). APD index and EHS as well as the international index of erectile function-5 (IIEF) and RI were positively correlated. RTI and AI were inversely correlated to IIEF and RI. PI was positively correlated to RI. PPG may therefore be useful as a noninvasive, convenient, technique for sexual function evaluation.

In robot-assisted manufacturing or assembly, following a predefined path became a critical aspect. In general, inverse kinematics offers the solution to control the movement of manipulator while following the trajectory. The main problem with the inverse kinematics approach is that inverse kinematics are computationally complex. For a redundant manipulator, this complexity is further increased. Instead of employing inverse kinematics, the complexity can be reduced by using a heuristic algorithm. Therefore, a heuristic-based approach can be used to solve the inverse kinematics of the robot manipulator end effector, guaranteeing that the desired paths are accurately followed. This paper compares the performance of four such heuristic-based approaches to solving the inverse kinematics problem. They are Bat Algorithm (BAT), Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and Whale Optimization Algorithm (WOA). The performance of these algorithms is evaluated based on their ability to accurately follow a predefined trajectory. Extensive simulations show that BAT and GSA outperform PSO and WOA in all aspects considered in this work related to inverse kinematic problems.

Cells encountering stressful situations activate the integrated stress response (ISR) pathway to limit protein synthesis and redirect translation to better cope. The ISR has also been implicated in cancers, but redundancies in the stress-sensing kinases that trigger the ISR have posed hurdles to dissecting physiological relevance. To overcome this challenge, we targeted the regulatory node of these kinases, namely, the S51 phosphorylation site of eukaryotic translation initiation factor eIF2α and genetically replaced eIF2α with eIF2α-S51A in mouse squamous cell carcinoma (SCC) stem cells of skin. While inconsequential under normal growth conditions, the vulnerability of this ISR-null state was unveiled when SCC stem cells experienced proteotoxic stress. Seeking mechanistic insights into the protective roles of the ISR, we combined ribosome profiling and functional approaches to identify and probe the functional importance of translational differences between ISR-competent and ISR-null SCC stem cells when exposed to proteotoxic stress. In doing so, we learned that the ISR redirects translation to centrosomal proteins that orchestrate the microtubule dynamics needed to efficiently concentrate unfolded proteins at the microtubule-organizing center so that they can be cleared by the perinuclear degradation machinery. Thus, rather than merely maintaining survival during proteotoxic stress, the ISR also functions in promoting cellular recovery once the stress has subsided. Remarkably, this molecular program is unique to transformed skin stem cells, hence exposing a vulnerability in cancer that could be exploited therapeutically.

Abstract We developed and evaluated a method for making early predictions of best overall response (BOR) and overall survival at 6 months (OS6) in patients with cancer treated with immunotherapy. This method combines machine learning with modeling of longitudinal tumor size data. We applied our method to data from durvalumab‐exposed patients with recurrent/metastatic head and neck cancer. A fivefold cross‐validation was used for model selection. Independent trial data, with various degrees of data truncation, were used for model validation. Mean classification error rates (90% confidence intervals [CIs]) from cross‐validation were 5.99% (90% CI 2.98%–7.50%) for BOR and 19.8% (90% CI 15.8%–39.3%) for OS6. During model validation, the area under the receiver operating characteristic curves was preserved for BOR (0.97, 0.97, and 0.94) and OS6 (0.85, 0.84, and 0.82) at 24, 18, and 12 weeks, respectively. These results suggest our method predicts trial outcomes accurately from early data and could be used to aid drug development.

Abstract N-Acetylneuraminic acid is the most abundant sialic acid (SA) in humans and is expressed as the terminal sugar on intestinal mucus glycans. Several pathogenic bacteria harvest and display host SA on their own surfaces to evade Siglec-mediated host immunity. While previous studies have identified bacterial enzymes associated with SA catabolism, no reported methods permit the selective labeling, tracking, and quantitation of SA-presenting microbes within complex multi-microbial systems. We combined metabolic labeling, click chemistry, 16S rRNA gene, and whole-genome sequencing to track and identify SA-presenting microbes from a cultured human fecal microbiome. We isolated a new strain of Escherichia coli that incorporates SA onto its own surface and encodes for the nanT, neuA, and neuS genes necessary for harvesting and presenting SA. Our method is applicable to the identification of SA-presenting bacteria from human, animal, and environmental microbiomes, as well as providing an entry point for the investigation of surface-expressed SA-associated structures.

This research discusses the sequence of inbound and outbound aircrafts on a mixed operation single runway at an airport. Influenced by the climate, the target airport cannot meet landing conditions. As a result, aircrafts have to land at other nearby airports. Since the sequence for considering both normal and alternate aircrafts arriving in a fixed time window becomes difficult, few studies have considered this situation. Therefore, in this article, we propose a mixed integer programming model for formulating and possibly solving this problem to minimize total delay time. Since the problem is NP-hard, we propose an Improved Immunoglobulin-based Artificial Immune System (IIAIS) to find an optimal or near optimal solution to the considered aircraft scheduling problem. Using the problem instances from the OR library and a real-life practical case, we empirically evaluate the performance of the proposed algorithm. The results show that the proposed algorithm performs better than other existing algorithms.

Triple-negative breast cancer (TNBC) afflicts women at a younger age than other breast cancers and is associated with a worse clinical outcome. This poor clinical outcome is attributed to a lack of defined targets and patient-to-patient heterogeneity in target antigens and immune responses. To address such heterogeneity, we tested the efficacy of a personalized vaccination approach for the treatment of TNBC using the 4T1 murine TNBC model. We isolated tumor membrane vesicles (TMVs) from homogenized 4T1 tumor tissue and incorporated glycosyl phosphatidylinositol (GPI)-anchored forms of the immunostimulatory B7-1 (CD80) and IL-12 molecules onto these TMVs to make a TMV vaccine. Tumor-bearing mice were then administered with the TMV vaccine either alone or in combination with immune checkpoint inhibitors. We show that TMV-based vaccine immunotherapy in combination with anti-CTLA-4 mAb treatment upregulated immunomodulatory cytokines in the plasma, significantly improved survival, and reduced pulmonary metastasis in mice compared to either therapy alone. The depletion of CD8+ T cells, but not CD4+ T cells, resulted in the loss of efficacy. This suggests that the vaccine acts via tumor-specific CD8+ T cell immunity. These results suggest TMV vaccine immunotherapy as a potential enhancer of immune checkpoint inhibitor therapies for metastatic triple-negative breast cancer.

Abstract Background Laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) with thyroid cartilage invasion are considered T4 and need total laryngectomy. However, the accuracy of preoperative diagnosis of thyroid cartilage invasion remains lower. Therefore, the purpose of this study was to assess the potential of computed tomography (CT)-based radiomics features in the prediction of thyroid cartilage invasion from LHSCC. Methods A total of 265 patients with pathologically proven LHSCC were enrolled in this retrospective study (86 with thyroid cartilage invasion and 179 without invasion). Two head and neck radiologists evaluated the thyroid cartilage invasion on CT images. Radiomics features were extracted from venous phase contrast-enhanced CT images. The least absolute shrinkage and selection operator (LASSO) and logistic regression (LR) method were used for dimension reduction and model construction. In addition, the support vector machine-based synthetic minority oversampling (SVMSMOTE) algorithm was adopted to balance the dataset and a new LR-SVMSMOTE model was constructed. The performance of the radiologist and the two models were evaluated with receiver operating characteristic (ROC) curves and compared using the DeLong test. Results The areas under the ROC curves (AUCs) in the prediction of thyroid cartilage invasion from LHSCC for the LR-SVMSMOTE model, LR model, and radiologist were 0.905 [95% confidence interval (CI): 0.863 to 0.937)], 0.876 (95%CI: 0.830 to 0.913), and 0.721 (95%CI: 0.663–0.774), respectively. The AUCs of both models were higher than that of the radiologist assessment (all P

Abstract Background To evaluate the efficacy and safety of Holmium YAG laser circumcision in adolescents. Methods Eighty-one patients underwent circumcision for medical reasons, and patients’ requests were collected retrospectively during February 2017 to February 2019. They were divided into two groups: Holmium YAG laser (group 1, n = 41) and conventional group (group 2, n = 40). The guillotine method with a Holmium YAG laser was applied for circumcisions, and all the procedures were performed by a single urologist who was well-experienced with circumcision practices. Results The average age of group 1 was 15.53 ± 7.35 years old, and the average age was 16.34 ± 9.22 years old in group 2. There was no significant difference in age and indications. The average operative time was significantly shorter in group 1 than in group 2 (24.40 ± 3.94 vs. 27.25 ± 4.35 min, p

Abstract Background The Walter Reed Army Institute of Research (WRAIR) Operational Research Kit-Actigraphy (WORK-A) is a set of unique practice parameters and actigraphy-derived measures for the analysis of operational military sleep patterns. The WORK-A draws on best practices from the literature and comprises 15 additional descriptive variables. Here, we demonstrate the WORK-A with a sample of United States Army Reserve Officers’ Training Corps (ROTC) cadets (n = 286) during a month-long capstone pre-commissioning training exercise. Methods The sleep of ROTC cadets (n = 286) was measured by Philips Actiwatch devices during the 31-day training exercise. The preliminary effectiveness of the WORK-A was tested by comparing differences in sleep measures collected by Actiwatches as calculated by Philips Actiware software against WORK-A-determined sleep measures and self-report sleep collected from a subset of ROTC cadets (n = 140). Results Actiware sleep summary statistics were significantly different from WORK-A measures and self-report sleep (all P ≤ 0.001). Bedtimes and waketimes as determined by WORK-A major sleep intervals showed the best agreement with self-report bedtime (22:21 ± 1:30 vs. 22:13 ± 0:40, P = 0.21) and waketime (04:30 ± 2:17 vs. 04:31 ± 0:47, P = 0.68). Though still significantly different, the discrepancy was smaller between the WORK-A measure of time in bed (TIB) for major sleep intervals (352 ± 29 min) and self-report nightly sleep duration (337 ± 57 min, P = 0.006) than that between the WORK-A major TIB and Actiware TIB (177 ± 42, P ≤ 0.001). Conclusions Default actigraphy methods are not the most accurate methods for characterizing soldier sleep, but reliable methods for characterizing operational sleep patterns is a necessary first step in developing strategies to improve soldier readiness. The WORK-A addresses this knowledge gap by providing practice parameters and a robust variety of measures with which to profile sleep behavior in service members.

LaNiO3 is a strange metal, for reasons that are not well understood. Here, Liu et al. report evidence for scattering of charge carriers by antiferromagnetic quantum fluctuations in high-purity epitaxial thin films of LaNiO3, suggesting it is close to an antiferromagnetic quantum critical point.

Background: Tumors are highly plastic metabolic entities composed of cancer and host cells that can adopt different metabolic phenotypes. For energy production, cancer cells may use 4 main fuels that are shuttled in 5 different metabolic pathways. Glucose fuels glycolysis that can be coupled to the tricarboxylic acid (TCA) cycle and oxidative phosphorylation (OXPHOS) in oxidative cancer cells or to lactic fermentation in proliferating and in hypoxic cancer cells. Lipids fuel lipolysis, glutamine fuels glutaminolysis, and lactate fuels the oxidative pathway of lactate, all of which are coupled to the TCA cycle and OXPHOS for energy production. This review focuses on the latter metabolic pathway. Scope of review: Lactate, which is prominently produced by glycolytic cells in tumors, was only recently recognized as a major fuel for oxidative cancer cells and as a signaling agent. Its exchanges across membranes are gated by monocarboxylate transporters MCT1-4. This review summarizes the current knowledge about MCT structure, regulation and functions in cancer, with a specific focus on lactate metabolism, lactate-induced angiogenesis and MCT-dependent cancer metastasis. It also describes lactate signaling via cell surface lactate receptor GPR81. Major conclusions: Lactate and MCTs, especially MCT1 and MCT4, are important contributors to tumor aggressiveness. Analyses of MCT-deficient (MCT+/- and MCT−/-) animals and (MCT-mutated) humans indicate that they are druggable, with MCT1 inhibitors being in advanced development phase and MCT4 inhibitors still in the discovery phase. Imaging lactate fluxes non-invasively using a lactate tracer for positron emission tomography would further help to identify responders to the treatments. Keywords: Cancer metabolism, Monocarboxylate transporters (MCTs), GPR81, Metabolic symbiosis, Angiogenesis, Metastasis

Objective: We constructed research camps at single-effort ultramarathons (50 and 100 miles) in order to study human endurance capabilities under extreme sleep loss and stress. It takes > 24h, on average, to run 100 miles on minimal sleep, allowing us to construct 24h human performance profiles (HPP). Methods: We collected performance data plotted across time (race splits) and distance (dropout rates; n=257), self-reported sleep and training patterns (n=83), and endpoint data on cardiovascular fitness/adaptation to total sleep deprivation and extreme exercise/stress (n=127). Results: In general, we found that self-reported napping was higher for 100-miler versus 50-miler runners and that ultra-endurance racing may possibly pre-select for early morning risers. We also compared HPPs between the first 50 miles completed by all runners in order to examine amplitude and acrophase differences in performance using a cosinor model. We showed that even though all runners slowed down over time, runners who completed a 100-miler ultramarathon had an earlier acrophase shift in race pace compared to non-finishers. Discussion: We were able to identify timedependent predictions on overall performance under minimal sleep, warranting the ultramarathon athlete as a unique demographic for future study of sleep and chronobiological relationships in the real world.

This research introduces a new variant of the two-echelon vehicle routing problem (2EVRP) called the two-echelon vehicle routing problem with transshipment nodes and occasional drivers (2EVRP-TN-OD). In addition to city freighters in the second-echelon network, a set of occasional drivers (ODs) is available to serve customers. ODs are the basis of a crowd-shipping system in which crowds with planned trips are willing to take detours to deliver packages in exchange for some compensation. To serve customers, ODs collect the assigned packages at either satellite served by first-echelon trucks or transshipment nodes served by city freighters. We formulate this problem as a mixed-integer nonlinear programming model and develop an adaptive large neighborhood search (ALNS) to solve it. New problem-specific destroy and repair operators and a tailored local search procedure are embedded into ALNS to deal with the problem’s unique characteristics. The experiments show that the proposed ALNS effectively solves 2EVRP-TN-OD by outperforming Gurobi in terms of both solution quality and computational time. Moreover, the experiments confirm that employing occasional drivers leads to lower operational costs. Sensitivity analyses on the characteristics of occasional drivers and the impact of transshipment nodes are presented as interesting managerial insights from 2EVRP-TN-OD.

Multimodal transport refers to the transportation of goods under a single contract but performed with at least two different modes of transport. This research designs a new method for solving the Transportation Problem (TP) by introducing multimodal transport systems under fuzzy-stochastic environment, which we refer to as Fuzzy-Stochastic Multimodal Transportation Problem (FSMMTP). An algorithm is developed to reduce FSMMTP to a deterministic TP, which is mainly based on α-cut of fuzzy numbers, and uses a signed distance function based on the mean expectation of the fuzzy-stochastic cost parameters. We derive the optimal solution as well as optimal selection of mode for transporting the goods in our proposed model. A numerical example justifies the effectiveness of our proposed study. The paper ends with a conclusion and an outlook to future studies.

A substantial distribution system is achievable to attain through the simultaneous design of the location facility and route generation. This problem is generally called a location routing problem (LRP), where both strategic decision and operational decisions are considered during the decision initial phase. Nevertheless, for some real applications, a consideration of time restriction due to several reasons should be included in the model. Some examples include the distribution system of perishable goods, a company’s objective towards a high level of customer service, or emergency/disaster situations. Consequently, this research incorporates the location routing problem (LRP) with time window constraints, including the working hour and also time restriction in each customer. The location routing problem with time windows (LRPTW) could not be solved easily, especially for a large-sized problem and or one which is considered an NP-hard problem. In consideration with its large solution pools, this paper suggests an integration of variable neighborhood search (VNS) with path relinking (PR) to solve LRPTW. The method is first applied in the LRP benchmark to validate the effectiveness and the robustness of the proposed method. Computational results clarify that VNSPR can achieve some of the best and most competitive solutions for the LRP when compared to existing approaches. Further, we developed LRPTW instances from the best solution found in LRP datasets. The proposed VNSPR method is also able to yield nearly optimal solutions for LRPTW instances.

Referring to the widespread problem of diploma forgery in Indonesian educational institutions as the impetus for UNTIRTA’s latest vision as an “Integrated, Smart, and Green University,” UNTIRTA intends to use blockchain technology to prevent diploma forgery and overcome issues related to existing platforms at UNTIRTA, such as frequent connection interruptions when accessed by a large number of users simultaneously. Before using blockchain technology, UNTIRTA must evaluate several readiness issues. This study presented the interpretative structural modeling (ISM) method to assess the primary preparedness elements for adopting blockchain technology in universities and sought to provide pertinent strategy ideas for UNTIRTA’s blockchain technology application. The results reveal sixteen major parameters that influence the adoption readiness of blockchain technology at UNTIRTA. The primary variables impacting the adoption and deployment of blockchain technology at UNTIRTA are management and employee support and a grasp of the technology. To realize UNTIRTA’s mission as an “Integrated, Smart, and Green University”, the proposed method entails determining an initial agreement in which all stakeholders have a shared understanding and commitment to Blockchain technology implementation at UNTIRTA. The objective of the tactical proposal is to establish each unit’s mission in the blockchain implementation program. The objective of the technical proposal is to construct a planning document that will serve as a coordination tool between the chairman and members, as well as all parties interested in the adoption of Blockchain technology at UNTIRTA.

Introduction Achieving optimal placement of the left ventricular (LV) lead in cardiac resynchronisation therapy (CRT) is a prerequisite in order to achieve maximum clinical benefit, and is likely to help avoid non-response. Pacing outside scar tissue and targeting late activated segments may improve outcome. The present study will be the first randomised controlled trial to compare the efficacy of real-time image-guided LV lead delivery to conventional CRT implantation. In addition, to estimate the cost-effectiveness of targeted lead implantation, an early decision analytic model was developed, and described here.Methods and analysis A multicentre, interventional, randomised, controlled trial will be conducted in a total of 130 patients with a class I or IIa indication for CRT implantation. Patients will be stratified to ischaemic heart failure aetiology and 1:1 randomised to either empirical lead placement or live image-guided lead placement. Ultimate lead location and echocardiographic assessment will be performed by core laboratories, blinded to treatment allocation and patient information. Late gadolinium enhancement cardiac magnetic resonance imaging (CMR) and CINE-CMR with feature-tracking postprocessing software will be used to semi-automatically determine myocardial scar and late mechanical activation. The subsequent treatment file with optimal LV-lead positions will be fused with the fluoroscopy, resulting in live target-visualisation during the procedure. The primary endpoint is the difference in percentage of successfully targeted LV-lead location. Secondary endpoints are relative percentage reduction in indexed LV end-systolic volume, a hierarchical clinical endpoint, and quality of life. The early analytic model was developed using a Markov-model, consisting of seven mutually exclusive health states.Ethics and dissemination The protocol was approved by the Medical Research Ethics Committee Utrecht (NL73416.041.20). All participants are required to provide written informed consent. Results will be submitted to peer-reviewed journals.Trial registration number NCT05053568; Trial NL8666.

Oxidative stress is an important pathophysiological mechanism in the development of numerous cardiovascular disorders, but few studies have examined the levels of oxidative stress in adults with congenital heart disease (CHD). The objective of this study was to investigate oxidative stress levels in adults with CHD and the association with inflammation, exercise capacity and endothelial function. To this end, 36 adults with different types of CHD and 36 age- and gender-matched healthy controls were enrolled. Blood cell counts, hs-CRP, NT-proBNP, fasting glucose, cholesterol levels, iron saturation and folic acid concentrations were determined in venous blood samples. Levels of superoxide anion radical in whole blood were determined using electron paramagnetic resonance spectroscopy in combination with the spin probe CMH. Physical activity was assessed with the IPAQ-SF questionnaire. Vascular function assessment (EndoPAT) and cardiopulmonary exercise testing were performed in the patient group. Superoxide anion radical levels were not statistically significantly different between adults with CHD and the matched controls. Moreover, oxidative stress did not correlate with inflammation, or with endothelial function or cardiorespiratory fitness in CHD; however, a significant negative correlation with iron saturation was observed. Overall, whole blood superoxide anion radical levels in adults with CHD were not elevated, but iron levels seem to play a more important role in oxidative stress mechanisms in CHD than in healthy controls. More research will be needed to improve our understanding of the underlying pathophysiology of CHD.

Objective: To assess the effects of one week of sleep extension on mood, fatigue and subjective sleepiness in normal-sleeping young adults. Methods: Twenty-seven adults (age 24.4±5.4 years, 11 female) participated. At-home baseline sleep/wake patterns were recorded with wrist actigraphy for 14 days. This was followed by two nights of in-lab baseline sleep with 8 hours time in bed (TIB), then 7 nights with TIB extended to 10 hours (2100-0700 hours). Fatigue, mood, and sleepiness were assessed following the 2nd and 9th nights of in-laboratory sleep (i.e., 2 nights with 8hTIB and 7 nights with 10 hours TIB, respectively) using the Automated Neuropsychological Assessment Metric and Karolinska Sleepiness Scale. Paired t-tests were used to compare mood, fatigue, and sleepiness ratings between conditions. Results: At-home wrist actigraphy revealed a mean nightly total sleep time (TST) of 7.53 +/- 0.88 hours of sleep per night. Mean in-lab baseline sleep duration (7.76 +/- 0.59) did not differ from at-home sleep. However, during sleep extension, mean TST was 9.36 +/- 0.37 hours per night, significantly more than during the in-lab baseline (p < .001). Following sleep extension, fatigue ratings were significantly reduced, relative to baseline (p = .03). However, sleep extension had no other significant effects on subjective ratings of mood or sleepiness. Conclusions: Sleep extension resulted in reduced fatigue in healthy, normal-sleeping young adults, although subjective sleepiness and mood were not improved. Implications include the possibility that (a) the effects of sleep extension on various aspects of mood depend upon the extent to which those aspects of mood are made salient by the study design and methodology; and (b) sleep extension may prove beneficial to fatigue-related conditions such as “burnout.”

This research proposes a new variant of the location-routing problem (LRP) called LRP with Demand Range (LRPDR) by allowing flexibility in the delivery quantity. The goal of the LRPDR is to minimize the objective value calculated by the total cost minus the extra revenue. The total cost consists of the travelling cost of vehicles, the opening cost of the depots, and the activation cost of vehicles. This study proposes a new hybrid algorithm, SAPSO, that combines simulated annealing (SA) and particle swarm algorithm (PSO) for solving the LRPDR. Since this problem has not yet been studied in the literature, a mathematical model is proposed and solved by the Gurobi solver. The results obtained by Gurobi are then compared with those obtained by the proposed SAPSO algorithm. In addition, the performance of the proposed SAPSO algorithm is assessed by solving the LRP benchmark instances, and comparing the results with those of existing state-of-the-art algorithms for LRP. Based on the experimental results, the proposed SAPSO algorithm improves the performance of the basic SA algorithm and outperforms Gurobi. Moreover, the benefits of the LRPDR over LRP are presented in terms of total cost reduction.

Aggregate production planning (APP) was developed for solving the problem of determining production, inventory, and workforce levels to meet fluctuating demand requirements over a planning horizon. In this work, multiple objectives were considered to determine the most effective means of satisfying forecasted demand by adjusting production rates, hiring and layoffs, inventory levels, overtime work, back orders, and other controllable variables. An extended technique for order preference via the similarity ideal solution (TOPSIS) approach was developed. It was formulated to solve this complicated, multi-objective APP decision problem. Compromise (ideal solution) control minimized the measure of distance, providing which of the closest solutions has the shortest distance from a positive ideal solution (PIS) and the longest distance from a negative ideal solution (NIS). The proposed method can transform multiple objectives into two objectives. The bi-objective problem can then be solved by balancing satisfaction using a max–min operator for resolving the conflict between the new criteria based on PIS and NIS. Finally, an application example demonstrated the proposed model’s applicability to practical APP decision problems.

Municipal waste management has become a challenging issue with the rise in urban populations and changes in people’s habits, particularly in developing countries. Moreover, government policy plays an important role associated with municipal waste management. Thus, this research proposes the regional location routing problem (RLRP) model and multi-depot regional location routing problem (MRLRP) model, which are extensions of the location routing problem (LRP), to provide a better municipal waste collection process. The model is constructed to cover the minimum number of depot facilities’ policy requirements for each region due to government policy, i.e., the large-scale social restrictions in each region. The goal is to determine the depot locations in each region and the vehicles’ routes for collecting waste to fulfill inter-regional independent needs at a minimum total cost. This research conducts numerical examples with actual data to illustrate the model and implements a hybrid genetic algorithm and simulated annealing optimization to solve the problem. The results show that the proposed method efficiently solves the RLRP and MRLRP.