Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivates the need for an adaptive application framework where users provide execution budgets and a customized quality notion. This article presents a novel adaptive program graph representation that enables user-level, customizable quality based on basic quality aspects defined by application developers. Developers also define application configuration spaces, with possible customization to eliminate undesirable configurations. At runtime, the graph enables the dynamic selection of the configuration with maximal customized quality within the user-provided resource budget.
An adaptive application framework based on our novel graph representation has been implemented on Android and Linux platforms and evaluated on eight benchmark programs, four with fully customizable quality. Using custom quality instead of the default quality, users may improve their subjective quality experience value by up to 3.59×, with 1.76× on average under different resource constraints. Developers are able to exploit their application structure knowledge to define configuration spaces that are on average 68.7% smaller as compared to existing, structure-oblivious approaches. The overhead of dynamic reconfiguration averages less than 1.84% of the overall application execution time.
Compression is a standard procedure for making convolutional neural networks (CNNs) adhere to some specific computing resource constraints. However, searching for a compressed architecture typically involves a series of time-consuming training/validation experiments to determine a good compromise between network size and performance accuracy. To address this, we propose an image complexity-guided network compression technique for biomedical image segmentation. Given any resource constraints, our framework utilizes data complexity and network architecture to quickly estimate a compressed model which does not require network training. Specifically, we
the dataset complexity to the target network accuracy degradation caused by compression. Such mapping enables us to predict the final accuracy for different network sizes, based on the computed dataset complexity. Thus, one may choose a solution that meets both the network size and segmentation accuracy requirements. Finally, the mapping is used to determine the convolutional layer-wise multiplicative factor for generating a compressed network. We conduct experiments using 5 datasets, employing 3 commonly-used CNN architectures for biomedical image segmentation as representative networks. Our proposed framework is shown to be effective for generating compressed segmentation networks, retaining up to ≈95% of the full-sized network segmentation accuracy, and at the same time, utilizing ≈32x fewer network trainable weights (average reduction) of the full-sized networks.
AbstractThe car of the future will be driven by software and offer a variety of customisation options. Enabling these customisation options forces modern automotive manufacturers to update their standardised scheduling concepts for testing and commissioning cars. A flexible scheduling concept means that every chosen customer configuration code must have its own testing procedure. This concept is essential to provide individual testing workflows where the time and resources are optimised for every car. Manual scheduling is complicated due to constraints on time, predecessor-successor relationships, mutual exclusion criteria, resources and status conditions on the car engineering and assembly line. Applied methods to handle the mathematical formulation for the corresponding industrial optimisation problem and its implementation are not yet available. This paper presents a procedure for automated and non-preemptive scheduling in the testing and commissioning of cars, which is built on a Boolean satisfiability problem on parallel and identical machines with temporal and resource constraints. The presented method is successfully implemented and evaluated on a variant assembly line of an automotive Original Equipment Manufacturer. This paper is the starting point for an automated workflow planning and scheduling process in automotive manufacturing.
Open pit mine production scheduling is a computationally expensive large-scale mixed-integer linear programming problem. This research develops a computationally efficient algorithm to solve open pit production scheduling problems under uncertain geological parameters. The proposed solution approach for production scheduling is a two-stage process. The stochastic production scheduling problem is iteratively solved in the first stage after relaxing resource constraints using a parametric graph closure algorithm. Finally, the branch-and-cut algorithm is applied to respect the resource constraints, which might be violated during the first stage of the algorithm. Six small-scale production scheduling problems from iron and copper mines were used to validate the proposed stochastic production scheduling model. The results demonstrated that the proposed method could significantly improve the computational time with a reasonable optimality gap (the maximum gap is 4%). In addition, the proposed stochastic method is tested using industrial-scale copper data and compared with its deterministic model. The results show that the net present value for the stochastic model improved by 6% compared to the deterministic model.
Introduction: Pediatric septic shock and acute respiratory distress syndrome (pARDS) are major causes of morbidity and mortality in pediatric intensive care units (PICUs). While standardized guidelines for sepsis and pARDS are published regularly, their implementation and adherence to guidelines are different in resource-rich and resource-limited countries. The purpose of this study was to conduct a survey to ascertain variation in current clinician-reported practice in pediatric septic shock and acute respiratory distress syndrome, and the clinician skills in a variety of hospital settings throughout Thailand.Methods: We conducted an electronic survey in pediatricians throughout the country between August 2020 and February 2021 using multiple choice questions and clinical case scenarios based on the 2017 American College of Critical Care Medicine's Consensus guideline for pediatric and neonatal septic shock and the 2015 Pediatric Acute Lung Injury Consensus Conference.Results: The survey elicited responses from 255 pediatricians (125 general pediatricians, 38 pulmonologists, 27 cardiologists, 32 intensivists, and 33 other subspecialists), with 54.5% of the respondents having <5 years of PICU experience. Among the six sepsis scenarios, 72.5 and 78.4% of the respondents had good adherence to the guidelines for managing fluid refractory shock and sedation for intubation, respectively. The ICU physicians reported greater adherence during more complex shock. In ARDS scenarios, 80.8% of the respondents reported having difficulty diagnosing ARDS mimic conditions and used lesser PEEP than the recommendation. Acceptance of permissive hypercapnia and mild hypoxemia was accepted by 62.4 and 49.4% of respondents, respectively. The ICU physicians preferred decremental PEEP titration, whereas general pediatricians preferred incremental PEEP titration.Conclusion: This survey variation could be the result of resource constraints, knowledge gaps, or ambiguous guidelines. Understanding the perspective and rationale for variation in pediatricians' practices is critical for successful guideline implementation.
Background: Current Coronavirus pandemic causing millions of deaths and unfathomable damage of nations worldwide, especially in health sector. Bangladesh is dealing with the biggest catastrophic public health event of the history in a courageous and effective way. An evidence based narrative review has been undergone to scientifically describe Bangladesh government’s measures to encounter the Corona pandemic, so far. The aim of this study is to document the collaborative action of different ministries of Bangladesh government during this pandemic to understand the in-depth steps of the healthcare provision and disaster preparedness of the public-private-international association in a low-resource setting.
Methods: A literature review over five months has been conducted to write down the evidential narration of the activities against the pandemic damage in Bangladesh. Keyword and result based literatures and current media reports searched has been done.
Selection criteria: Both online and offline reports, descriptive articles, governmental portal and ministerial websites were reviewed. The description is reported specifically based on the documents directed by government to fight against COVID-19 from the beginning of the pandemic till the writing period.
Findings and discussion: In spite of the resource constraints, government of Bangladesh has been able to limit the damage in an optimal level. The inter- and- inter ministerial functional proposition and collaboration in national and international stakeholders initiated and sustained by the government strengthen the shield against the Coronavirus invasion.
Conclusion: The sufferings brought by the pandemic knows no bound. The pandemic damage and ruin are unspeakable and undeniable at the same time. It is time to observe the positivity and critically appreciate the efforts taken by the current governmental authority to make a constructive remark for present situation, and be prepare for future building of the nation.
JOPSOM 2021; 40(1): 66-71
Closed-loop supply chains have attracted more attention by researchers and practitioners due to strong government regulations, environmental issues, social responsibilities and natural resource constraints over past few years. This paper presents a mixed-integer linear programming model to design a closed-loop supply chain network and optimizing pricing policies under random disruption. Reusing the returned products is applied as a resilience strategy to cope with the waste of energy and improving supply efficiency. Moreover, it is necessary to find the optimal prices for both final and returned products. Therefore, the model is formulated based on demand function and it maximizes total supply chain’s profit. Finally, its application is explored through using the real data of an industrial company in glass industry.
The transplant community has faced unprecedented challenges balancing risks of performing living donor transplants during the COVID-19 pandemic with harms of temporarily suspending these procedures. Decisions regarding postponement of living donation stem from its designation as an elective procedure, this despite that the Centers for Medicare and Medicaid Services categorise transplant procedures as tier 3b (high medical urgency—do not postpone). In times of severe resource constraints, health systems may be operating under crisis or contingency standards of care. In this manuscript, the United Network for Organ Sharing Ethics Workgroup explores prioritisation of living donation where health systems operate under contingency standards of care and provide a framework with recommendations to the transplant community on how to approach living donation in these circumstances.To guide the transplant community in future decisions, this analysis suggests that: (1) living donor transplants represent an important option for individuals with end-stage liver and kidney disease and should not be suspended uniformly under contingency standards, (2) exposure risk to SARS-CoV-2 should be balanced with other risks, such as exposure risks at dialysis centres. Because many of these risks are not quantifiable, donors and recipients should be included in discussions on what constitutes acceptable risk, (3) transplant hospitals should strive to maintain a critical transplant workforce and avoid diverting expertise, which could negatively impact patient preparedness for transplant, (4) transplant hospitals should consider implementing protocols to ensure early detection of SARS-CoV-2 infections and discuss these measures with donors and recipients in a process of shared decision-making.
This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).