A deep reinforcement learning algorithm for the rectangular strip packing problem.

As a branch of the two-dimensional (2D) optimal blanking problem, rectangular strip packing is a typical non-deterministic polynomial (NP-hard) problem. The classical packing solution method relies on heuristic and metaheuristic algorithms. Usually, it needs to be designed with manual decisions to guide the solution, resulting in a small solution scale, weak generalization, and low solution efficiency. Inspired by deep learning and reinforcement learning, combined with the characteristics of rectangular piece packing, a novel algorithm based on deep reinforcement learning is proposed in this work to solve the rectangular strip packing problem. The pointer network with an encoder and decoder structure is taken as the basic network for the deep reinforcement learning algorithm. A model-free reinforcement learning algorithm is designed to train network parameters to optimize the packing sequence. This design can not only avoid designing heuristic rules separately for different problems but also use the deep networks with self-learning characteristics to solve different instances more widely. At the same time, a piece positioning algorithm based on the maximum rectangles bottom-left (Maxrects-BL) is designed to determine the placement position of pieces on the plate and calculate model rewards and packing parameters. Finally, instances are used to analyze the optimization effect of the algorithm. The experimental results show that the proposed algorithm can produce three better and five comparable results compared with some classical heuristic algorithms. In addition, the calculation time of the proposed algorithm is less than 1 second in all test instances, which shows a good generalization, solution efficiency, and practical application potential.

Heuristic Greedy Scheduling of Electric Vehicles in Vehicle-to-Grid Microgrid Owned Aggregators.

In on-grid microgrids, electric vehicles (EVs) have to be efficiently scheduled for cost-effective electricity consumption and network operation. The stochastic nature of the involved parameters along with their large number and correlations make such scheduling a challenging task. This paper aims at identifying pertinent innovative solutions for reducing the relevant total costs of the on-grid EVs within hybrid microgrids. To optimally scale the EVs, a heuristic greedy approach is considered. Unlike most existing scheduling methodologies in the literature, the proposed greedy scheduler is model-free, training-free, and yet efficient. The proposed approach considers different factors such as the electricity price, on-grid EVs state of arrival and departure, and the total revenue to meet the load demands. The greedy-based approach behaves satisfactorily in terms of fulfilling its objective for the hybrid microgrid system, which is established of photovoltaic, wind turbine, and a local utility grid. Meanwhile, the on-grid EVs are being utilized as an energy storage exchange location. A real time hardware-in-the-loop experimentation is comprehensively conducted to maximize the earned profit. Through different uncertainty scenarios, the ability of the proposed greedy approach to obtain a global optimal solution is assessed. A data simulator was developed for the purposes of generating evaluation datasets, which captures uncertainties in the behaviors of the system's parameters. The greedy-based strategy is considered applicable, scalable, and efficient in terms of total operating expenditures. Furthermore, as EVs penetration became more versatile, total expenses decreased significantly. Using simulated data of an effective operational duration of 500 years, the proposed approach succeeded in cutting down the energy consumption costs by about 50-85%, beating existing state-of-the-arts results. The proposed approach is proved to be tolerant to the large amounts of uncertainties that are involved in the system's operational data.

Smartphone-Based Healthcare Apps for Older Adults in the COVID-19 Era: Heuristic Evaluation.

Many national governments have attempted to prevent and combat COVID-19 using mobile health (mHealth) technologies during the epidemic. During this time, governments began developing smartphone-based apps for prevention, call tracking, and monitoring people with COVID-19. An important question is, does everyone benefit from these technologies equally and fairly? To answer this question, we evaluated the user interface of smartphone-based apps developed during the COVID-19 era by considering their design for older adults, in order to determine whether social justice has been considered in the development of these apps.

Elementary Schools' Response to Student Wellness Needs during the COVID-19 Shutdown: A Qualitative Exploration Using the R = MC2 Readiness Heuristic.

During spring of 2020, the COVID-19 pandemic and accompanying public health advisories forced K-12 schools throughout the United States to suspend in-person instruction. School personnel rapidly transitioned to remote provision of academic instruction and wellness services such as school meals and counseling services. The aim of this study was to investigate how schools responded to the transition to remote supports, including assessment of what readiness characteristics schools leveraged or developed to facilitate those transitions. Semi-structured interviews informed by school wellness implementation literature were conducted in the spring of 2020. Personnel (n = 50) from 39 urban and rural elementary schools nationwide participated. The readiness = motivation capacity2 (R = MC2) heuristic, developed by Scaccia and colleagues, guided coding to determine themes related to schools' readiness to support student wellness in innovative ways during the pandemic closure. Two distinct code sets emerged, defined according to the R = MC2 heuristic (1) Innovations: roles that schools took on during the pandemic response, and (2) Readiness: factors influencing schools' motivation and capacity to carry out those roles. Schools demonstrated unprecedented capacity and motivation to provide crucial wellness support to students and families early in the COVID-19 pandemic. These efforts can inform future resource allocation and new strategies to implement school wellness practices when schools resume normal operations.

MRFGRO: a hybrid meta-heuristic feature selection method for screening COVID-19 using deep features.

COVID-19 is a respiratory disease that causes infection in both lungs and the upper respiratory tract. The World Health Organization (WHO) has declared it a global pandemic because of its rapid spread across the globe. The most common way for COVID-19 diagnosis is real-time reverse transcription-polymerase chain reaction (RT-PCR) which takes a significant amount of time to get the result. Computer based medical image analysis is more beneficial for the diagnosis of such disease as it can give better results in less time. Computed Tomography (CT) scans are used to monitor lung diseases including COVID-19. In this work, a hybrid model for COVID-19 detection has developed which has two key stages. In the first stage, we have fine-tuned the parameters of the pre-trained convolutional neural networks (CNNs) to extract some features from the COVID-19 affected lungs. As pre-trained CNNs, we have used two standard CNNs namely, GoogleNet and ResNet18. Then, we have proposed a hybrid meta-heuristic feature selection (FS) algorithm, named as Manta Ray Foraging based Golden Ratio Optimizer (MRFGRO) to select the most significant feature subset. The proposed model is implemented over three publicly available datasets, namely, COVID-CT dataset, SARS-COV-2 dataset, and MOSMED dataset, and attains state-of-the-art classification accuracies of 99.15%, 99.42% and 95.57% respectively. Obtained results confirm that the proposed approach is quite efficient when compared to the local texture descriptors used for COVID-19 detection from chest CT-scan images.

Parameter setting of meta-heuristic algorithms: a new hybrid method based on DEA and RSM.

The parameter setting of meta-heuristic algorithms is one of the most effective issues in the performance of meta-heuristic algorithms and is usually done experimentally which is very time-consuming. In this research, a new hybrid method for selecting the optimal parameters of meta-heuristic algorithms is presented. The proposed method is a combination of data envelopment analysis method and response surface methodology, called DSM. In addition to optimizing parameters, it also simultaneously maximizes efficiency. In this research, the hybrid DSM method has been used to set the parameters of the cuckoo optimization algorithm to optimize the standard and experimental functions of Ackley and Rastrigin. In addition to standard functions, in order to evaluate the performance of the proposed method in real problems, the parameter of reverse logistics problem for COVID-19 waste management has been adjusted using the DSM method, and the results show better performance of the DSM method in terms of solution time, number of iterations, efficiency, and accuracy of the objective function compared to other.

Opportunities to Improve COVID-19 Dashboard Designs for the Public.

Many organizations created COVID-19 dashboards to communicate epidemiologic statistics or community health capabilities with the public. In this paper we used dashboard heuristics to identify common violations observed in COVID-19 dashboards targeted to citizens. Many of the faults we identified likely stem from failing to include users in the design of these dashboards. We urge health information dashboard designers to implement design principles and test dashboards with representative users to ensure that their tools are satisfying user needs.

The Omic Insights on Unfolding Saga of COVID-19.

The year 2019 has seen an emergence of the novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease of 2019 (COVID-19). Since the onset of the pandemic, biological and interdisciplinary research is being carried out across the world at a rapid pace to beat the pandemic. There is an increased need to comprehensively understand various aspects of the virus from detection to treatment options including drugs and vaccines for effective global management of the disease. In this review, we summarize the salient findings pertaining to SARS-CoV-2 biology, including symptoms, hosts, epidemiology, SARS-CoV-2 genome, and its emerging variants, viral diagnostics, host-pathogen interactions, alternative antiviral strategies and application of machine learning heuristics and artificial intelligence for effective management of COVID-19 and future pandemics.

Evaluation of a Parsimonious COVID-19 Outbreak Prediction Model: Heuristic Modeling Approach Using Publicly Available Data Sets.

BACKGROUND: The COVID-19 pandemic has changed public health policies and human and community behaviors through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment and other equipment to mitigate disease spread in affected regions. Current models that predict COVID-19 case counts and spread are complex by nature and offer limited explainability and generalizability. This has highlighted the need for accurate and robust outbreak prediction models that balance model parsimony and performance. OBJECTIVE: We sought to leverage readily accessible data sets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis. METHODS: Our modeling approach leveraged the following data inputs: COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model utilized a per capita running 7-day sum of the case counts per county per day and the mean cumulative case count to develop baseline values. The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020. RESULTS: The model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%. CONCLUSIONS: Our parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.

Policy responses to COVID-19 present a window of opportunity for a paradigm shift in global health policy: An application of the Multiple Streams Framework as a heuristic.

Drawing on Kingdon's Multiple Streams Framework as a heuristic, this article reviews the three streams - problems, policies, and politics - as applied to the adoption of economic policies in response to the socioeconomic impacts of COVID-19. In doing so, we argue that we are currently presented with a window of opportunity to better address the social determinants of health. First, through assessing the problem stream, an understanding of inequity as a problem gained wider recognition through the disproportionate impacts of COVID-19. Second, in the policy stream, we demonstrate that appropriate and unprecedented policies can be enacted even in the face of changing evidence or evidentiary uncertainty, which are needed to address upstream factors that influence health. Lastly, in the politics stream, we demonstrate that addressing a public health 'problem' can be well-received by the public, making it politically viable. However, it is important to ensure the 'problem' is clearly relayed to the public and that this information is not perceived to change, as this can undermine trust. The social, political, and behavioural lessons presented by the COVID-19 pandemic should be drawn on in this pivotal moment for global public health.

Phases of psychological response in COVID-19: A preliminary heuristic.

OBJECTIVE: To explore the putative phases of the psychological response to disaster: preimpact, impact, heroic, honeymoon, disillusionment, and recovery, and make recommendations for corresponding interventions. CONCLUSIONS: Disasters such as the COVID-19 pandemic are often characterized by chaos and uncertainty. As a result, public health disaster planning and response represent formidable challenges. Although disasters can result from a wide array of hazards, regardless of the agent at work, they may follow a rather predictable trajectory of psychological phases. A heuristic of those phases can provide an opportunity for a more organized disaster mental health response and more efficient utilization of scarce resources.

Ethical heuristics for pandemic allocation of ventilators across hospitals.

In response to the COVID-19 pandemic philosophers and governments have proposed scarce resource allocation guidelines. Their purpose is to advise healthcare professionals on how to ethically allocate scarce medical resources. One challenging feature of the pandemic has been the large numbers of patients needing mechanical ventilatory support. Guidelines have paradigmatically focused on the question of what doctors should do if they have fewer ventilators than patients who need respiratory support: which patient should get the ventilator? There is, however, an important higher level allocation problem. Namely, how are we to ethically distribute newly obtained ventilators across hospitals: which hospital should get the ventilator(s)? In this paper, we identify a set of principles for allocating newly obtained ventilators across hospitals. We focus particularly on low and middle income countries, who frequently have limited pre-existing intensive care capacity, and have needed to source additional ventilators. We first provide some background. Second, we argue that the main population healthcare aim during the COVID-19 pandemic should be to save the most lives. Next, we assess a series of potential heuristics or principles that could be used to guide allocation: allocation to the most densely populated cities, random allocation, allocation based on the ratio of patients to ICU personnel, prioritisation in terms of intrahospital mortality, prioritisation of younger populations, and prioritisation in terms of population mortality. We conclude by providing a plausible ranking of the principles, while noting a number of epistemological challenges, in terms of how they best further the aim of increasing the probability of saving the most lives.

The Influence of Cognitive Heuristics and Biases on Palliative Social Workers' Support of Patient and Caregiver Decision Making: The Pulse Oximeter Buying Trend during the COVID-19 Pandemic.

A phenomenon occurring early in the pandemic involving media-based recommendations of pulse oximeters, devices purported to detect a dangerous Covid-19 symptom, invites attention to effects of decision-making shortcuts, or cognitive heuristics, and associated cognitive biases or errors, on patient/caregiver healthcare decisions. Heuristics also affect palliative/medical social workers' recommendations to patients/caregivers. This article looks at availability, confirmation, affect, false consensus and framing biases, and suggestions for debiasing decision making. Implications for other healthcare decisions are considered.

Heuristic assessment of choices for risk network control.

Data-driven risk networks describe many complex system dynamics arising in fields such as epidemiology and ecology. They lack explicit dynamics and have multiple sources of cost, both of which are beyond the current scope of traditional control theory. We construct the global economy risk network by combining the consensus of experts from the World Economic Forum with risk activation data to define its topology and interactions. Many of these risks, including extreme weather and drastic inflation, pose significant economic costs when active. We introduce a method for converting network interaction data into continuous dynamics to which we apply optimal control. We contribute the first method for constructing and controlling risk network dynamics based on empirically collected data. We simulate applying this method to control the spread of COVID-19 and show that the choice of risks through which the network is controlled has significant influence on both the cost of control and the total cost of keeping network stable. We additionally describe a heuristic for choosing the risks trough which the network is controlled, given a general risk network.

Why are self-medication opportunities limited in Austria? An interview study and comparison with other countries.

BACKGROUND: Austria has high health resource use compared to similar countries. Reclassifying (switching) medicines from prescription to non-prescription can reduce pressure on health resources and aid timely access to medicines. Since Austria is less progressive in this area than many other countries, this research aimed to elucidate enablers and barriers to it reclassifying medicines and make recommendations for change in the context of similar research conducted elsewhere. METHODS: Qualitative research using a heuristic approach was conducted in Austria in 2018. Informed by their own "insider" and "outsider" knowledge, the authors identified themes from personal interviews with 24 participants, including reclassification committee members, government officials and stakeholders, before comparing these themes with earlier research findings. RESULTS: Significant barriers to reclassification included committee conservatism, minimal political support, medical negativity and few company applications. Insufficient transparency about committee decisions, expectations of adverse committee decisions and a limited market discouraged company applications. Austria's 'social partnership' arrangement and consensus decision making aided a conservative approach, but the regulator and an alternative non-committee switch process were enabling. Pharmacy showed mixed interest in reclassification. Suggested improvements include increasing transparency, committee composition changes, encouraging a more evidence-based approach by the committee, more pharmacy undergraduate clinical training, and companies using scientific advisory meetings and submitting high quality applications. CONCLUSION: Removing barriers to reclassification would facilitate non-prescription availability of medicines and encourage self-care, and could reduce pressure on healthcare resources.

Novel quantitative structure-activity relationship model to predict activities of natural products against COVID-19.

Currently, COVID-19 is spreading in a large scale while no efficient vaccine has been produced. A high-effective drug for COVID-19 is very necessary now. We established a satisfied quantitative structure-activity relationship model by gene expression programming to predict the IC50 value of natural compounds. A total of 27 natural products were optimized by heuristic method in CODESSA program to build a liner model. Based on it, only two descriptors were selected and utilized to build a nonlinear model in gene expression programming. The square of correlation coefficient and s2 of heuristic method were 0.80 and 0.10, respectively. In gene expression programming, the square of correlation coefficient and mean square error for training set were 0.91 and 0.04. The square of correlation coefficient and mean square error for test set are 0.86 and 0.1. This nonlinear model has stronger predictive ability to develop the targeted drugs of COVID-19.

Elective cardiovascular care in the era of the COVID-19 pandemic: managing tragic choices.

The COVID-19 pandemic has led to significant morbidity and mortality globally. As health systems grapple with caring for patients affected with COVID-19, cardiovascular procedures that are deemed 'elective' have been postponed. Guidelines concerning which cardiac procedures should be performed during the pandemic vary by specialty and geography in the USA. We propose a clinical heuristic to guide individual physicians and governing bodies in their decision making regarding which cardiac procedures should be performed during the COVID-19 pandemic using the behavioural economics concept of heuristics and ecological rationality.