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Sun Tzu was an ancient Chinese military strategist, general and philosopher thought to be the author of The Art of War. He is believed to have lived c. 544–496 bce. The Art of War contains a series of lessons on how best to prepare to win without, ideally, ever having to fight. The lessons provide some readily transferable (and for these particular purposes light-hearted) tactical reminders for any property professional assisting a client who may otherwise find themselves facing the battle of bi-party litigation. If a possible source of dispute can be headed off at the pass, or a settlement can be reached without darkening the door of the courtroom, that is a good win for all concerned. This paper is adapted from Janet and Adam's original 'Art of War' lecture delivered at the Royal Institute of Chartered Surveyors (RICS) Dilapidations Conference in London on 26th March, 2024. [ABSTRACT FROM AUTHOR]

Walking is a common and preferred form of exercise. Although there are current recommendations for walking volume (e.g., steps per day), recent research has begun to distinguish volume from intensity (e.g., "brisk" walking) as an important dimension of exercise. Increasing intensity may confer health advantages beyond volume measures because it shifts cardiovascular performance to more vigorous training zones. Reinforcement‐based approaches have been valuable in increasing volume measures of exercise, and the present study sought to develop a corresponding reinforcement approach to training walking intensity. For this study, we used a continuous reinforcement paradigm where music played only while walking met specified criteria; otherwise, music playback stopped. As a result, music was synchronized with walking performance. Seventeen participants walked on a nonmotorized treadmill at a self‐selected pace. Across the session, different conditions arranged for music to play independent of walking speed or contingent on speed increases or decreases. An extinction component assessed performance when music was withdrawn completely. Walking speed was selectively increased and decreased by adjusting the contingencies that were arranged for music, and variability in speed increased during extinction, with both findings indicating that music was a reinforcer. Heart rate was also increased to moderate–vigorous intensities during reinforcement. The findings provide a compelling case that walking intensity can be modified by music reinforcement. We suggest that synchronous schedules may be an important foundation for future exercise technologies that are based on reinforcement. [ABSTRACT FROM AUTHOR]

The English workday lunch receives heavy criticism. Given this, why do people eat the way that they do? Using in-depth interview data, findings represent an 'instruction manual' to the meal detailing (1) variation in the organisation of the workday lunch and standards for competent performance; (2) shared understandings of the meal; and (3) the guiding principle or meaning of the practice. The workday lunch takes much of its meaning from the practice of work. In conclusion, the workday lunch does not simply reflect weakening rules for a 'proper' meal, but contradictory orders sustaining the practices of eating and working. [ABSTRACT FROM AUTHOR]

The work discusses intermittent light scheduling for energy cost reduction in vertical farming, specifically for growing salad crops (Lactuca Sativa). The study explores how dynamically adjusting lighting schedules based on day-ahead energy prices can improve energy efficiency and reduce operational costs. The research uses a growth chamber equipped with LED lights, controlled environmental factors, and real-time data from the Nord Pool energy market to optimize energy use. The results show that intermittent lighting schedules, aligned with cheaper energy periods, lead to higher crop yields and significant energy cost savings compared to continuous lighting. The findings suggest that such adaptive lighting strategies can make vertical farming more economically viable and sustainable by optimizing resource use, aligning with global sustainability goals. The study also highlights the potential of vertical farms to act as flexible power consumers, adjusting their energy usage based on renewable energy availability. [ABSTRACT FROM AUTHOR]

Purpose: Currently, there are many schedules for exclusive vaginal cuff brachytherapy (VCB). In 3D treatment planning for VCB dosimetry, parameters have not been analyzed. The aim of this study was to compare the most common schedules using dose-volume histogram metrics. Material and methods: Three different computed tomography (CT) studies for vaginal cylinders of 3.5 cm, 3 cm, and 2.5 cm were performed. Clinical target volume (CTV) was delineated for 3 cm and 4 cm of vaginal length. Twelve schedules were analyzed obtaining overall vaginal surface dose (Gy) (EQD2 α/β = 10 and α/β = 3), overall D90 CTV (α/β = 10) (Gy), and overall D2cc (α/β = 3) for organs at risk (OARs), such as vagina, rectum, sigmoid, rectum, and bladder. Prescription at 5 mm from the applicator surface and at the surface were analyzed for each case. Results: The overall vaginal surface dose and dose to CTV varied widely among the different schedules, and CTV delineation was necessary in case of surface prescription. The applicator diameter of 3.5 cm showed the best dosimetry results for vaginal surface dose. The overall D2cc OARs' doses changed in the different CT studies. Conclusions: This dosimetry study allows for better selection of fractionation schedules, and helps to unify treatments among centers. Prospective studies are needed to establish the best schedule and CTV length in each patient using clinical data, such as late toxicity and relapses. [ABSTRACT FROM AUTHOR]

This article proposes a novel method for scheduling residential building loads, including thermal and EV (Electric Vehicle) loads, to maximize demand response potential in smart home environment. In this approach, a load scheduling solution based on deep reinforcement learning (DRL) has been proposed. A policy search method based on Home Energy Management with Noise-Adaptive Proximal Policy Optimization (HEN-PPO) has been used to train the network. HEN-PPO can mitigate the influence of noise on temperature and solar radiation sensor data. The modification of the reward function for the system has been taken into account to address the distinct human behavior of randomly distributed cases of EV anxiety about range and time in conjunction with the optimal utilization of photovoltaic (PV) generation. This approach effectively manages both discrete and continuous activities of electrical loads at the same time, categorized as shiftable and regulatable, throughout the entire building model. Additionally, to replicate human behavior, real switching data from different household appliances has been used to determine the parameters for the probability distribution of residential load usage. Dynamic and unpredictable factors such as user behavior, ambient conditions, and real-time electricity pricing challenge scheduling residential building loads which are efficiently managed in this approach. To validate the effectiveness of the proposed method, simulation tests were carried out using real-time data distribution of household loads and surrounding temperature. The simulation results demonstrate promising energy savings, achieving 24%–29% reductions with the proposed approach.

Time-sensitive networking (TSN) standards can provide high bandwidth and low latency for communication. Schedulability analysis under mechanism combinations needs to be studied. Under cyclic queuing and forwarding (CQF) with preemption, this letter analyzes the traffic transmission process and defines the traffic classes. Then, this letter provides a schedulability analysis on streams of all traffic classes. The results can provide the network designers with guidelines in the configuration under CQF with preemption and accelerate the usage of TSN for safety-critical communication scenarios. [ABSTRACT FROM AUTHOR]

With its position as the capital, Hanoi is the political center as well as the second largest economic center of the country. Therefore, the city is always allocated a large budget in construction investment to create material facilities for political tasks and economic and social development. During the implementation of construction projects, a number of difficulties and limitations have appeared. In which, projects are delayed in construction and disbursement, reducing investment efficiency and not meeting the expectations of the government and people. From this fact, the authors have conducted a study to evaluate the causes affecting the time schedule of construction projects in Hanoi. The method F-APH (Fuzzy Analytic Hierarchy Process) was used to analyze data objectively and accurately about the causes affecting the time schedule. From there, these causes are classified into groups of subjective causes (from within the project) and groups of objective causes (from outside the project). The results show that subjective causes, originating from project participants, have a stronger influence than objective causes. A number of specific proposals to the actors involved in construction projects are made to eliminate or limit the impact of the causes of construction progress.

Science in the elementary grades is often deprioritized in comparison to ELA and mathematics. We wondered, how comprehensively, frequently, and consistently is science included in elementary schools' schedules? We reviewed daily schedules for 14 schools in 9 districts across the U.S. to qualitatively examine how science is represented on the daily instructional schedule. These schools were selected as "best case scenarios" recommended by district or state science leaders as places where science is taken seriously. We complemented these schedules with data from 21 interviews with teachers, science specialists, and school leaders to better understand how science actually appears in children's daily instructional experiences. We found that, in these schools, science is taught comprehensively (though not as comprehensively as ELA or mathematics), has the potential for being taught frequently (even in the lower elementary grades), and is taught somewhat consistently (albeit usually in some kind of rotation with social studies). We present implications for how leaders can craft school schedules to make science comprehensive, frequent, and consistent in the elementary grades, to provide important opportunities to learn and thrive for all children. [ABSTRACT FROM AUTHOR]

A scheduling problem for parallel batch processing machines (BPMs) with jobs having unequal ready times in semiconductor wafer fabrication facilities (wafer fabs) is studied in this paper. A blended objective function combining the total weighted tardiness (TWT) and the total electricity cost (TEC) under a time-of-use (TOU) tariff is considered. A genetic programming (GP) procedure is designed to automatically discover priority indices for a heuristic scheduling framework. Results of computational experiments are reported that demonstrate that the learned priority indices lead to high-quality schedules in a short amount of computing time. [ABSTRACT FROM AUTHOR]

Motor imagery (MI) is widely employed in stroke rehabilitation due to the event-related desynchronization (ERD) phenomenon in sensorimotor cortex induced by MI is similar to actual movement. However, the traditional BCI paradigm, in which the patient imagines the movement of affected hand (AH-MI) with a weak ERD caused by the damaged brain regions, retards motor relearning process. In this work, we applied a novel MI paradigm based on the “sixth-finger” (SF-MI) in stroke patients and systematically uncovered the ERD pattern enhancement of novel MI paradigm compared to traditional MI paradigm. Twenty stroke patients were recruited for this experiment. Event-related spectral perturbation was adopted to supply details about ERD. Brain activation region, intensity and functional connectivity were compared between SF-MI and AH-MI to reveal the ERD enhancement performance of novel MI paradigm. A “wider range, stronger intensity, greater connection” ERD activation pattern was induced in stroke patients by novel SF-MI paradigm compared to traditional AH-MI paradigm. The bilateral sensorimotor and prefrontal modulation was found in SF-MI, which was different in AH-MI only weak sensorimotor modulation was exhibited. The ERD enhancement is mainly concentrated in mu rhythm. More synchronized and intimate neural activity between different brain regions was found during SF-MI tasks compared to AH-MI tasks. Classification results (>80% in SF-MI vs. REST) also indicated the feasibility of applying novel MI paradigm to clinical stroke rehabilitation. This work provides a novel MI paradigm and demonstrates its neural activation-enhancing performance, helping to develop more effective MI-based BCI system for stroke rehabilitation. [ABSTRACT FROM AUTHOR]

Home-based exercises are an important component of stroke rehabilitation but are seldom fully completed. Past studies of exercise perseverance in the general public have suggested the importance of early exercise frequency and schedule consistency (in terms of which days of the week exercises are performed) because they encourage habit formation. To test whether these observations apply after a stroke, we leveraged data from 2,583 users of a sensor-based system (FitMi) developed to motivate movement exercises at home. We grouped users based on their early exercise frequency (defined across the initial 6 weeks of use) and calculated the evolution of habit score (defined as exercise frequency multiplied by exercise duration) across 6 months. We found that habit score decayed exponentially over time but with a slower decay constant for individuals with higher early frequency. Only the group with an early exercise frequency of 4 days/week or more had non-zero habit score at six months. Within each frequency group, dividing individuals into higher and lower consistency subgroups revealed that the higher consistency subgroups had significantly higher habit scores. These results are consistent with previous studies on habit formation in exercise and may help in designing effective home rehabilitation programs after stroke. [ABSTRACT FROM AUTHOR]

The Exascale Computing Project (ECP) was one of the largest open source scientific software development projects ever. It supported approximately 1000 staff from the U.S. Department of Energy laboratories, and university and industry partners. About 250 staff contributed to 70 scientific libraries and tools to support applications on multiple exascale computing systems that were also under development. Funded as a formal construction project, ECP was required to use earned-value management, based on milestones, and a key performance parameter system based, in part, on integrations. With accelerated delivery schedules and significant project risk, we also emphasized software quality using community policies, automated testing, and continuous integration. Software development kit teams provided cross-team collaboration, and products were delivered via E4S, a curated portfolio of libraries and tools. In this article, we discuss the organizational and management elements of ECP that enabled the delivery of libraries and tools, our lessons learned, and our next steps. [ABSTRACT FROM AUTHOR]

This conceptual study aims to produce rough analysis methods and visualizations for production data (formatted in time, location, and work) that can be collected from construction sites that utilize takt production. The scope is on creating methods for evaluating the soundness of the takt plan and its execution. Relevant production literature regarding takt production management and data collection are utilized in the production of the methods and visualizations. However, only imaginary production data are utilized in this study to keep the indicators as simplified and clear as possible. A total of seven indicators with varying levels of novelty are provided in the study. The proposed indicators emphasize punctual adherence to the takt schedule, homogenous production pace, avoiding trade overlapping in locations, steady work in process, and coherent short and long-term production targets. Both as-planned and as-built perspectives are considered. The proposed indicators are argued to be valuable for production management and research and development processes since they provide status information and document the progression of the production for later indicators purposes. This study also acts as a foundation for further empirical studies regarding takt production data utilization. [ABSTRACT FROM AUTHOR]

The paper considers the convex hull of a set of schedules for servicing identical requests by parallel devices. Precedence conditions are given on the set of requests. All requests enter the service queue simultaneously and have the same service duration. Interruptions in request servicing are prohibited. Time is discrete. The polyhedral properties of some previously constructed classes of valid inequalities are studied. The "depth" cuts are compared, and the strongest subclasses of cuts are found. The relative position of the schedule polytope and hyperplanes generated by inequalities is also studied. [ABSTRACT FROM AUTHOR]

Several medical organisations have developed evidence‐based guidelines for the diagnosis, management, and follow‐up of testicular cancer. This article aimed to review, compare, and summarise the most updated international guidelines and surveillance protocols for clinical stage 1 (CS1) testicular cancer. We reviewed a total of 46 articles on proposed follow‐up strategies for testicular cancer, and six clinical practice guidelines including four guidelines published by urological scientific associations and two guidelines published by medical oncology associations. Most of these guidelines have been developed by panels of experts with different backgrounds in clinical training, and geographic practise patterns, which explains the considerable variability between published schedules, and recommended follow‐up intensity. We present you with a comprehensive review of the most important clinical practice guidelines and propose unifying recommendations based on the most up to date evidence to help standardise follow‐up schedules based on patterns and risk of disease relapse. [ABSTRACT FROM AUTHOR]

Patients have multiple outpatient appointments for various reasons. Analyzing patients' related appointments provides insight into referral patterns, leading to recommendations for ideal care and more efficient planning. We model these appointments with causal graphs via Judea Pearl's causal graph approach. Once we define the causal relationships in the appointment data, we leverage a graph database and visualization software to investigate valuable patterns and relationships in patient care over time. The Pathways tool allows yield management at specialty, provider, or appointment levels. Leaders use this tool to anticipate a patient's downstream appointments; the tool provides insights into staffing and the impact of growing demand. [ABSTRACT FROM AUTHOR]

The General Transit Feed Specification (GTFS) is an open source data format public transportation agency use to share information about routes and vehicle arrival and departure times. A variety of trip-planning applications, including Google Maps, rely on GTFS feeds to incorporate public transit information. In April 2020, the California Integrated Travel Project conducted a Feasibility Study that called for the widespread adoption of GTFS-static (GTFS-s) and GTFS-realtime (GTFS-r) to make transit simpler for California residents; however, there is little research on patterns of information sharing across transit agencies. This background paper highlights findings from an analysis of GTFS use among agencies that report to the National Transit Database (NTD) in California. The prevalence of transit service modifications in response to the COVID-19 pandemic were also studied.

Abstract Efficient utilization of available computing resources in Cloud computing is one of the most challenging problems for cloud providers. This requires the design of an efficient and optimal task-scheduling strategy that can play a vital role in the functioning and overall performance of the cloud computing system. Optimal Schedules are specifically needed for scheduling virtual machines in fluctuating & unpredictable dynamic cloud scenario. Although there exist numerous approaches for enhancing task scheduling in the cloud environment, it is still an open issue. The paper focuses on an improved & enhanced ordinal optimization technique to reduce the large search space for optimal scheduling in the minimum time to achieve the goal of minimum makespan. To meet the current requirement of optimal schedule for minimum makespan, ordinal optimization that uses horse race conditions for selection rules is applied in an enhanced reiterative manner to achieve low overhead by smartly allocating the load to the most promising schedule. This proposed ordinal optimization technique and linear regression generate optimal schedules that help achieve minimum makespan. Furthermore, the proposed mathematical equation, derived using linear regression, predicts any future dynamic workload for a minimum makespan period target.

Introduction: As per the World Health Organization's mental health report for 2022, nearly a billion people have mental health issues, and 82% of them are in low and middle-income countries where mental health services are largely absent. For the successful integration of mental health into primary health care, proper training and education of primary care professionals are mandatory. Primary care nurses are in an excellent position to screen, identify, dual collaboration for treatment planning/referral, and follow-up of persons with mental illness (PMI), but they often lack the confidence and competence to tackle mental health problems. The study aimed to develop and validate the clinical schedule for primary care psychiatric nursing (CSP-N). Materials and Methods: It is conducted in two phases: the development and validation phases. An extensive literature search has been conducted, and the ten themes derived from the two-focused group discussions and three-direct one-to-one interviews and input from mental health experts were used to design the CSP-N. The CSP-N was checked for content validity by a panel of 17 experts using the item-level content validity index (I-CVI) and the scale-level content validity index (S-CVI). Results: The draft version 1 of the CSP-N showed high content validity for individual items (I-CVI range: 0.82 to 1.00) and high overall content validity (S-CVI = 0.95), and suggestions from the experts were incorporated. The CSP-N was developed in four modules. The single-measure two-way mixed absolute agreement ICC value was calculated (for 32 subjects) for the reliability test, and the ICC value was 0.97 with a 95% CI (0.94, 0.99). Conclusions: Using an iterative approach, the development and validation of the CSP-N demonstrated high I-CVI and S-CVI for screening and identification, dual collaboration for the treatment plan, referral, and follow-up of a person with mental illness by the nurses in the community.

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Due to the mixed right-of-way and varying traffic conditions, urban bus operations are often subject to random delays and eventually bus bunching, undermining the schedule reliability. The existing studies have proposed different models to emulate bus bunching and control strategies to mitigate bus bunching, yet few of them considered the effect of the three important traffic factors: intersections with signal coordination, varying traffic volume, and passenger demand. To fill in the research gap, we first define the stop-level frequency of bus bunching events for a bus route in this study. We proceed to present a simulation-based approach to quantify the impact of the three traffic factors on bus bunching. Numerical experiments based on different scenarios are carried out to reveal the cause–effect relationship between these factors and bus bunching events. Contributors to bus bunching are evaluated, and the effect of control delays is examined through statistical measurements. Finally, a real-world case study based on bus route 51 in Singapore is performed, and some insights are provided to alleviate the bus bunching phenomenon. [ABSTRACT FROM AUTHOR]

Patient satisfaction is a critical indicator for assessing the quality of healthcare services, as it plays a crucial role in ensuring the effective, timely, and patient-centered delivery of high-quality healthcare. Additionally, patient satisfaction has a direct relationship with clinical outcomes. The objective of this study was to investigate the influence of clinic waiting time on patient satisfaction in an Ear Nose Throat (ENT) outpatient department. A total of 241 patients who visited hospitals and ENT outpatient departments in Jeddah were recruited for this cross-sectional study. Descriptive statistical analysis was performed using IBM SPSS version 25. The majority of patients expressed satisfaction with the waiting time at the clinic. Additionally, many patients reported being satisfied with the handling of appointments and the information they received from their friends or relatives. Significant statistical differences were observed between waiting time and demographic factors such as age, gender, employment status, and residence. Moreover, there was a statistically significant association between patient satisfaction with the appointment process and the information provided by the staff (P -value <.001). Notably, patients visiting the ENT outpatient department exhibited higher satisfaction scores. These findings hold the potential for informing quality improvement initiatives. Furthermore, it is recommended that future studies be conducted to assess patient satisfaction and provide valuable insights to policymakers and clinicians when making decisions related to healthcare delivery. [ABSTRACT FROM AUTHOR]

Synchronous schedules of reinforcement are those in which the onset and offset of a reinforcer are synchronized with the onset and offset of a target behavior. The current study replicated and extended Diaz de Villegas et al. (2020) by comparing synchronous reinforcement to noncontingent stimulus delivery while evaluating on‐task behavior of school‐age children. A concurrent‐chains preference assessment was then used to determine the preferred schedule. Results indicated that the synchronous schedule was more effective than the continuous, noncontingent delivery of the stimulus at increasing on‐task behavior but that the children preferred noncontingent delivery. Additionally, the use of synchronous and noncontingent delivery did not alter the children's preference for the task. [ABSTRACT FROM AUTHOR]

In January 2020 the UK changed from a 2 + 1 schedule for 13-valent pneumococcal conjugate vaccine (PCV13) to a 1 + 1 schedule (doses at 3 and 12 months) based on a randomized immunogenicity trial comparing the two schedules. Carriage prevalence measured at the time of booster and 6 months later in 191 of the 213 study infants was 57 % (109/191) and 60 % (114/190) respectively. There were eight episodes of vaccine-type (VT) or vaccine-related 6C carriage in the 2 + 1 and six in the 1 + 1 group; ≥4-fold rises in serotype-specific IgG in 71 children with paired post-booster and follow up blood samples at 21–33 months of age were found in 20 % (7/35) of the 2 + 1 and 15 % (6/41) of the 1 + 1 group. VTs identified in carriage and inferred from serology were similar comprising 3, 19A and 19F. Dropping a priming dose from the 2 + 1 PCV 13 schedule did not increase VT carriage in the study cohort. Ongoing population level carriage studies will be important to confirm this. [ABSTRACT FROM AUTHOR]

Optimal cyclic scheduling problems of wafer-residency-time-constrained dual-arm cluster tools in wafer fabrication are challenging and remain to be fully solved. Existing studies assume that all processing modules (PMs) of a required type are used to process the same type of wafers. This sometimes brings unneeded conservativeness to scheduling results, because we may be able to make a tool schedulable by reducing the number of PMs in some steps if the original one is not. In some cases, we may use fewer PMs to reach the same result if the original one is schedulable, thus saving energy and other production resources. This work selects a proper number of PMs of needed types to process wafers while ensuring the highest productivity of a wafer-residency-time-constrained dual-arm cluster tool. It proposes the necessary and sufficient conditions under which a tool is schedulable. It then develops a polynomial-complexity algorithm that finds an optimal cyclic schedule. Examples are given to show its superiority over existing ones, thus advancing this field of cluster tool scheduling greatly and helping semiconductor producers to realize the green manufacturing of wafers. [ABSTRACT FROM AUTHOR]

Introduction: As per the World Health Organization’s mental health report for 2022, nearly a billion people have mental health issues, and 82% of them are in low and middle‑income countries where mental health services are largely absent. For the successful integration of mental health into primary health care, proper training and education of primary care professionals are mandatory. Primary care nurses are in an excellent position to screen, identify, dual collaboration for treatment planning/referral, and follow‑up of persons with mental illness (PMI), but they often lack the confidence and competence to tackle mental health problems. The study aimed to develop and validate the clinical schedule for primary care psychiatric nursing (CSP‑N). Materials and Methods: It is conducted in two phases: the development and validation phases. An extensive literature search has been conducted, and the ten themes derived from the two‑focused group discussions and three‑direct one‑to‑one interviews and input from mental health experts were used to design the CSP‑N. The CSP‑N was checked for content validity by a panel of 17 experts using the item‑level content validity index (I‑CVI) and the scale‑level content validity index (S‑CVI). Results: The draft version 1 of the CSP‑N showed high content validity for individual items (I‑CVI range: 0.82 to 1.00) and high overall content validity (S‑CVI = 0.95), and suggestions from the experts were incorporated. The CSP‑N was developed in four modules. The single‑measure two‑way mixed absolute agreement ICC value was calculated (for 32 subjects) for the reliability test, and the ICC value was 0.97 with a 95% CI (0.94, 0.99). Conclusions: Using an iterative approach, the development and validation of the CSP‑N demonstrated high I‑CVI and S‑CVI for screening and identification, dual collaboration for the treatment plan, referral, and follow‑up of a person with mental illness by the nurses in the community. [ABSTRACT FROM AUTHOR]

Truck platooning is a well-studied technology that has the potential to reduce both the environmental impact and operational costs of trucks. The technology has matured over the last 20 years, and the commercial rollout of platooning is approaching. Cooperation across carriers is essential for the viability of platooning; otherwise, many platooning opportunities are lost. We first present a cross-carrier platooning system architecture in which many carriers cooperate in forming platoons through a platoon-hailing service. Then, we present a cross-carrier platoon coordination approach in which each carrier optimizes its platooning plans according to the predicted plans of other carriers. A profit-sharing mechanism to even out the platooning profit in each platoon is embedded in the platoon coordination approach. Finally, a simulation study over the Swedish road network is performed to evaluate the potential of platooning under realistic conditions. The simulation study shows that the energy consumption of trucks in Sweden can be reduced by 5.4% due to platooning and that cooperation across carriers is essential to achieve significant platooning benefits. [ABSTRACT FROM AUTHOR]

Efficient utilization of available computing resources in Cloud computing is one of the most challenging problems for cloud providers. This requires the design of an efficient and optimal task-scheduling strategy that can play a vital role in the functioning and overall performance of the cloud computing system. Optimal Schedules are specifically needed for scheduling virtual machines in fluctuating & unpredictable dynamic cloud scenario. Although there exist numerous approaches for enhancing task scheduling in the cloud environment, it is still an open issue. The paper focuses on an improved & enhanced ordinal optimization technique to reduce the large search space for optimal scheduling in the minimum time to achieve the goal of minimum makespan. To meet the current requirement of optimal schedule for minimum makespan, ordinal optimization that uses horse race conditions for selection rules is applied in an enhanced reiterative manner to achieve low overhead by smartly allocating the load to the most promising schedule. This proposed ordinal optimization technique and linear regression generate optimal schedules that help achieve minimum makespan. Furthermore, the proposed mathematical equation, derived using linear regression, predicts any future dynamic workload for a minimum makespan period target. [ABSTRACT FROM AUTHOR]

This document presents the modeling of load profile consumption for Low-and-Moderate-Income (LMI) communities in the Caribbean Islands, as well as an assessment of the solar-rooftop energy potential. In this work, real data, together with synthetic and electricity bill data, were collected to validate and improve the load profile models. The solar-rooftop energy potential was obtained through a National Renewable Energy Laboratory (NREL) software called the PVWatts calculator, and mathematical analysis. The analysis of rooftop solar energy potential was conducted to enable the minimum size of solar power systems to fit the energy demand in the community. The results obtained allow estimation of the capacity of the energy system for each house or an entire community. [ABSTRACT FROM AUTHOR]

This article presents a static deadlock analysis for single-path MPI programs. Deadlock is when processes are blocked indefinitely by a circular communication dependency. A single path program is one that does not decode messages for control flow. The analysis records a program execution in the form of a trace and then determines from that trace whether there exists any feasible deadlocking schedules. The primary contribution is the combining of identical consecutive sends or receives into single macro actions. This simplified trace is analyzed for potential deadlock cycles. An abstract machine identifies infeasible cycles, and those not identified by the machine are encoded as satisfiability problems for an SMT solver to resolve. The action combination reduces the complexity of identifying and filtering cycles before needing the costly SMT solver. This article shows the effectiveness of the action combination in experiments on a benchmark suite comparing to traces without action combination and other state-of-the-art deadlock analyses. [ABSTRACT FROM AUTHOR]

Time-varying electricity prices in demand response (DR) programs motivate users to change their consumption behavior with the aim of electricity bill cost minimization. Coordinated DR schemes aim to flatten the aggregate load profile and mitigate rebound peaks. However, a comprehensive model which integrates supply and demand sides has to be developed to consider system constraints and renewable energy resources. In this regard, this article proposes a bi-level framework for supply and demand side energy management in an islanded microgrid. In the first level, consumers schedule their appliances’ operation to minimize their expenses. In the second level, a dynamic economic dispatch problem is solved which finds the power generation schedule for the generators resulting in the minimum electricity production cost. A game-theoretic turn-based decentralized algorithm is suggested which manages the interaction between both levels. Simulation results show that the proposed algorithm minimizes users’ electricity bill costs while satisfying power system constraints. [ABSTRACT FROM AUTHOR]

This article studies multisensor scheduling for remote state estimation in cyber-physical systems. We consider that each sensor monitors a dynamic process and sends its data to the remote end. This article focuses on minimizing remote estimation errors over a temporally correlated communication channel. The problem is formulated as the Markov decision process (MDP) with finite-horizon cost criterion. The optimal structured policies are derived for both Markov packet dropout and finite-state Markov channel models, which can reduce computation overhead. For the infinite-horizon case, we design algorithms to address the issues of unknown channel statistics and the curse of dimensionality in the MDP, respectively. Particularly, a heuristic algorithm with linear complexity is proposed to schedule multisensor in a decentralized manner. Simulation examples are provided to verify the theoretical results. [ABSTRACT FROM AUTHOR]

In English, simple present (plain) and present progressive constructions can make reference to the future, in constructions known as futurates. In previous literature, these two types of futurate have often been discussed separately or treated as more or less equivalent. This squib argues that they convey different meanings: plain futurates presuppose the existence of a schedule, while progressive futurates do not. We propose a formal definition of a schedule and present novel empirical data based on a questionnaire study. We show that plain futurates are restricted to contexts providing a schedule, but progressive futurates are not. [ABSTRACT FROM AUTHOR]

This conceptual study aims to produce rough analysis methods and visualizations for production data (formatted in time, location, and work) that can be collected from construction sites that utilize takt production. The scope is on creating methods for evaluating the soundness of the takt plan and its execution. Relevant production literature regarding takt production management and data collection are utilized in the production of the methods and visualizations. However, only imaginary production data are utilized in this study to keep the indicators as simplified and clear as possible. A total of seven indicators with varying levels of novelty are provided in the study. The proposed indicators emphasize punctual adherence to the takt schedule, homogenous production pace, avoiding trade overlapping in locations, steady work in process, and coherent short and long-term production targets. Both as-planned and as-built perspectives are considered. The proposed indicators are argued to be valuable for production management and research and development processes since they provide status information and document the progression of the production for later indicators purposes. This study also acts as a foundation for further empirical studies regarding takt production data utilization.

Nowadays, high-throughput screening (HTS) systems are widely used in pharmaceutical industries and laboratories for the discovery of new drugs and biomedical substances. It is important to efficiently schedule them so as to reduce the cost. In the operation of an HTS system, a microplate may visit some resources more than once and complex time window constraints are imposed on some activities and activity sequences. Moreover, with the consistency requirement, a one-microplate cyclic schedule is necessary. Thus, its scheduling problem is very challenging. This article studies the scheduling problem of an HTS system for an enzymatic assay, a typical application of HTSs, from the perspective of control theory. The system is modeled by resource-oriented Petri nets (ROPNs). With the model, necessary and sufficient conditions under which a feasible cyclic schedule exists are established. Then, we determine how many microplates should be in the system for concurrent processing and the transition firing sequence to obtain the activity sequence for an optimal and feasible schedule. In this way, a feasible and optimal cyclic schedule can be found by very simple computations, which shows that polynomial algorithms for an optimal schedule exist, while the existing studies apply mathematical programming with exponential computation complexity. Also, its efficient implementation is given. [ABSTRACT FROM AUTHOR]

The KC-135R Stratotanker is a multifunction slim body aircraft that provides air refueling and airlift for the United States’ war and peacetime requirements, which demand a certain level of availability. As the KC-135R fleet ages, the aircraft availability (AA) rate degrades due to high demand use, stress, and the age of the equipment. Preventative and corrective maintenance is designed to return the aircraft to an available state to meet mission requirements, but the United States Air Force continues to fail at meeting every requirement communicated by commanders for KC-135R air refueling and airlift. Focusing on aircraft metrics can enable a prediction model of AA and provide the unit commanders the tools for data influenced decisions. The analysis of historical aircraft maintenance and flight metrics will show a correlation between tracked metrics and AA, thus, the ability to predict a future availability rate. Furthermore, analyzing the data with machine learning techniques will improve prediction accuracy by evaluating the variable importance and will make inferences learned from mining the data that are otherwise difficult to model in a complex system of systems. [ABSTRACT FROM AUTHOR]

Military technologies are typically the product of long-term development efforts. These technologies are often adapted late in the development process for use by other users, including other members of the defense enterprise, foreign militaries, or the commercial sector. The need to adapt a technology can arise from cost over-runs or changing operational requirements. This article describes a value-based methodology for developing a transition plan early in the development process, when the system is still in the conceptual phase. Performing this analysis early in the design phase allows for design choices that will support adapting the system for other users. Moreover, it provides a risk mitigation strategy against cost over-run and changes in operational requirements. The process begins by capturing the functional architecture of the technology and analyzing them against a set of tasks associated with different military positions. From there, the adapted technology is evaluated for its projected adequacy based on the value added and the effort required for adaptation. The end-state of this analysis is a value-based model that identifies a portfolio of possible alternative markets and/or uses, which can be comparatively analyzed. A case study is presented for adapting a military augmented reality system for a different market. [ABSTRACT FROM AUTHOR]

This article considers the problem of cyclic flow shop (CFS) scheduling problems in robotic cells deploying several single and dual gripper robots. In this problem, different part types are successively processed on multiple machines with different pickup criteria including free pickup, pickup within time windows, and no-waiting times. The parts are transported between the machines by the robots. We propose a novel integer programming approach that determines the optimal sequence of parts simultaneously for the sequencing of the robots’ movements and the cyclic schedule, which also results in maximizing the throughput rate. The proposed mathematical model is validated on a number of randomly generated test instances. These problem instances are constructed by varying the number of machines, part types, robots, and gripper options. This allows a detailed analysis of the model including how it scales with increasing numbers of machines, part types, robots, and grippers. An experimental evaluation shows that our proposed model is very effective for a range of small medium-sized problems. In particular, we find that differing number of robots and gripper options directly affects the cycle time and in turn, the throughput rate. Overall, our integer programming model finds good feasible solutions for up to 20 machines and 10 part types, which is comparable to real-world industrial problems. [ABSTRACT FROM AUTHOR]

Determining the proper sequence of interrelated activities in the design of a complex product is an irrefutable challenge for project management. The presence of components with different levels of technology in a complex product can disrupt project planning and cause cost overruns or schedule delays. This article presents a multiobjective model for organizing interrelated activities by taking into account the impact of technological capability and financial aspect of the project with a periodic perspective. First, a multidomain matrix is devised based on design structure matrix and technology readiness level to demonstrate the impact of components technology maturity level. Second, a mathematical model is developed according to rework caused by information dependence (minimizing total feedback value), project financial aspect (minimizing the difference between the budget and project costs), and technology maturity level of components (minimizing technology risk). Finally, a possibilistic programming approach is applied to cope with the uncertainty of input data; in addition, to deal with multiple objective functions, an interactive fuzzy solution approach is implemented. To validate the model and solution approach, a numerical experiment and a case study are conducted. Testing results illustrate the importance of technology maturity level in sequencing activities and indicate that the model can be introduced as a management tool for the financial feasibility study of the project due to the balanced use of budget. [ABSTRACT FROM AUTHOR]

Improving result accuracy in approximate computing (AC)-based real-time applications without violating deadlines has recently become an active research domain. Execution time of AC real-time tasks can individually be separated into: execution of the mandatory part to obtain a result of acceptable quality, followed by a partial/complete execution of the optional part to improve the result accuracy of the initial result within a given deadline. However, obtaining higher result accuracy at the cost of enhanced execution time may lead to deadline violation, along with higher energy usage. We present ACCURATE, a novel hybrid offline–online approximate real-time scheduling approach that first schedules AC-based tasks on multicore with an objective to maximize result accuracy and determines operational processing speeds for each task constrained by system-wide power limit, deadline, and task dependency. At runtime, by employing a way-sharing technique (WH_LLC) at the last level cache (LLC), ACCURATE improves performance, which is further leveraged, to enhance result accuracy by executing more from the optional part and to improve the energy efficiency of the cache by turning off a controlled number of cache ways. ACCURATE also exploits the slacks either to improve the result accuracy of the tasks or to enhance the energy efficiency of the underlying system, or both. ACCURATE achieves 85% QoS with 36% average reduction in cache leakage consumption with a 24% average gain in energy-delay product (EDP) for a 4-core-based chip multiprocessor (CMP) with 6.4% average improvement in performance. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) are developed as a promising alternative to artificial neural networks (ANNs) due to their more realistic brain-inspired computing models. SNNs have sparse neuron firing over time, i.e., spatio-temporal sparsity; thus, they are useful to enable energy-efficient hardware inference. However, exploiting spatio-temporal sparsity of SNNs in hardware leads to unpredictable and unbalanced workloads, degrading the energy efficiency. In this work, we propose an FPGA-based convolutional SNN accelerator called Skydiver that exploits spatio-temporal workload balance. We propose the approximate proportional relation construction (APRC) method that can predict the relative workload channel-wisely and a channel-balanced workload schedule (CBWS) method to increase the hardware workload balance ratio to over 90%. Skydiver was implemented on a Xilinx XC7Z045 FPGA and verified on image segmentation and MNIST classification tasks. Results show improved throughput by $1.4\times $ and $1.2\times $ for the two tasks. Skydiver achieved 22.6KFPS throughput, and $42.4~\mu \text{J}$ /image prediction energy on the classification task with 98.5% accuracy. [ABSTRACT FROM AUTHOR]