Implementation and Application of Large-Scale Drug Virtual Screening

The molecular docking-based virtual screening technique evaluates the binding abilities between multiple ligand compounds and receptors to screen for the active compounds. In the context of the global spread of the COVID-19 pandemic, large-scale and rapid drug virtual screening is crucial for identifying potential drug molecules from massive datasets of ligand structures. The powerful computing power of supercomputer provides hardware guarantee for drug virtual screening, but the super large-scale drug virtual screening still faces many challenges that affects the effective execution of the calculation. Based on the analysis of the challenges, this paper proposes a centralized task distribution scheme with a central database, and designs a multi-level task distribution framework. The challenges are effectively solved through multi-level intelligent scheduling, multi-level compression processing of massive small molecule files, dynamic load balancing and high error tolerance management technology. An easy-touse"tree”multi-level task distribution system is implemented. A fast, efficient and stable drug virtual screening task distribution, calculation and result analysis function is realized, and the computing efficiency is nearly linear. Then, heterogeneous computing technology is used to complete the drug virtual screening of more than 2 billion compounds, for two different active sites for COVID-19, on the domestic super computing system, which provides a powerful computing guarantee for the super large-scale rapid virtual screening of explosive malignant infectious diseases. © 2023, Journal of Computer Engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

Design for an Intelligent Waste Classifying System: A Case Study of Plastic Bottles

The use of plastic bottles has become a significant environmental concern, and recycling them has become a priority. Small and medium-sized recycling companies must collect and categorize large volumes of plastic bottles and sell them to larger recycling firms, a process that is time-consuming, costly, and labor-intensive. This manual sorting process can pose health risks, particularly during the COVID-19 pandemic, and can affect worker productivity. To address these issues, this study proposes the development of an automated conveyor belt system that can rapidly and accurately separate plastic bottles by type. The system utilizes an opaque and transparent plastic bottle separation platform, which saves time, cost, and manpower. This system design provides recycling SMEs with a competitive advantage by serving as a practical application model and a prototype with an easy-to-use concept. Key tools employed in this research include product design development (PDD), Kansei engineering, manufacturing process design, controlling system, and fault tree analysis (FTA). The light sensors are critical components in the separation process, detecting the opacity or transparency of the bottles' surfaces. The proposed prototype's reliability will be assessed by FTA, which considers all potential failures. This study contributes to the body of knowledge surrounding the integration of conveyor systems and provides valuable information for businesses seeking to optimize their sorting processes. The guidelines developed in this study can serve as a starting point for further research on the integration of conveyors in waste sorting plants. © 2013 IEEE.

Integration of neural network and distance relay to improve the fault localization on transmission lines

Power transmission lines are integral and very important components of power systems. Because of the length of these lines and the complexity of the power grids, the lines may encounter various incidents such as lightning strike, shortage, and breakage. When an incident or a fault occurs, a fast process of identification, localization, and isolation of the fault is desired. An accurate fault localization would have a great impact in reducing the restoration time of the system. One of the most popular solutions for fault detection and localization is the distance relays using the impedance-based algorithms. However, these relays are still not perfect with nonzero errors of the fault locations. This paper will present a new approach using the neural networks in addition to a distance relays to correct the fault location estimation of the relay. The solution will be based only on the voltage and current signals measured at the beginning of the lines. The training samples' signals of the transient states on the lines are generated using ATP/EMTP, and then regenerated into the relay tester Omicron CMC-356 to test with the real Siemens 7SA522 relay to improve its fault location results. The numerical results will show that the solution had helped to reduce the average fault location error from 0.92% to 0.42% for 4 types of shortage faults on the lines. [ FROM AUTHOR] Copyright of Turkish Journal of Electrical Engineering & Computer Sciences is the property of Scientific and Technical Research Council of Turkey and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Situational Based Reliability Indices Estimation of ULT Freezer using Preventive Maintenance under Fuzzy Environment

Reliability of high demand machines is quite necessary and it can be maintained through proper and timely maintenance, Ultra-low temperature (ULT) freezer is one of those kinds of machines which are in high demand during covid-19 pandemic for the storage of vaccine. The rapid production of vaccines for the prevention of coronavirus disease 2019 (COVID-19) is a worldwide requirement. Now the next challenge is to store the vaccine in a ULT freezer. It's become really a big problem to store the vaccine which creates the demand of ULT freezer. The present paper investigates a situational based performance of the ULT freezer with the aim to predict the impact of different component failures as well as human errors on the final performance of the same. For the study, it is not possible to extract the parameters (failure rate and repair time) of the components that never failed before. Thus, to overcome this difficulty, here authors use the possibility theory. Authors present the available data in Right triangular fuzzy number with some tolerance as suggested by system analyst. The lambda-tau methodology and arithmetic operations on right triangular generalized fuzzy numbers (RTrFN) are used to find the various performance parameters namely MTTF, MTTR, MTBF, reliability, availability, maintainability (RAM) and ENOF, under fuzzy environment. The proposed model has been studied using possibility theory under working conditions, preventive maintenance as well as under the rest of conditions. This study reveals the most and least critical component of the ULT freezer which helps maintenance department to plan the maintenance strategy accordingly.

THE RETURN TO FAULT AS A FOUNDATION OF CIVIL LIABILITY FOR THE SUPREME COURT AND THE CASE OF PRIVATE RESIDENCES FOR THE ELDERLY IN WHICH THERE WERE OUTBREAKS OF COVID Actualidad Jurídica Iberoamericana Nº18, febrero 2023, ISSN: 2386-4567, pp. 536-577

Based on the latest Supreme Court sentences, we cannot rely on the objectification of civil liability to demand that the burden of proof falls on nursing homes in all those cases in wich there were covid outbreaks causing deaths and injuries among their vulnerable residents. However, there are other legal reasons and jurisprudential arguments that do allow us to consider such a possibility, and all this with the indredible advantage that it would entail for the victims and their families. © 2023 Ibero-American Law Institute. All rights reserved.

Fault detection model for a variable speed heat pump

The impact of the COVID pandemic has resulted in many people cultivating a remote working culture and increasing building energy use. A reduction in the energy use of heating, ventilation, and air-conditioning (HVAC) systems is necessary for decreasing the energy use in buildings. The refrigerant charge of a heat pump greatly affects its energy use. However, refrigerant leakage causes a significant increase in the energy use of HVAC systems. The development of refrigerant charge fault detection models is, therefore, important to prevent unwarranted energy consumption and CO2 emissions in heat pumps. This paper examines refrigerant charge faults and their effect on a variable speed heat pump and the most accurate method between a multiple linear regression and multilayer perceptron model to use in detecting the refrigerant charge fault using the discharge temperature of the compressor, outdoor entering water temperature and compressor speed as inputs, and refrigerant charge as the output. The COP of the heat pump decreased when it was not operating at the optimum refrigerant charge, while an increase in compressor speed compensated for the degradation in the capacity during refrigerant leakage. Furthermore, the multilayer perception was found to have a higher prediction accuracy of the refrigerant charge fault with a mean square error of ± 3.7%, while the multiple linear regression model had a mean square error of ± 4.5%. The study also found that the multilayer perception model requires 7 neurons in the hidden layer to make viable predictions on any subsequent test sets fed into it under similar experimental conditions and parameters of the heat pump used in this study.

Blockchain-Based Trusted Federated Learning with Pre-Trained Models for COVID-19 Detection

COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.

Fast and Accurate Deep Learning Framework for Secure Fault Diagnosis in the Industrial Internet of Things

This article introduced a new deep learning framework for fault diagnosis in electrical power systems. The framework integrates the convolution neural network and different regression models to visually identify which faults have occurred in electric power systems. The approach includes three main steps: 1) data preparation;2) object detection;and 3) hyperparameter optimization. Inspired by deep learning and evolutionary computation (EC) techniques, different strategies have been proposed in each step of the process. In addition, we propose a new hyperparameters optimization model based on EC that can be used to tune parameters of our deep learning framework. In the validation of the framework's usefulness, experimental evaluation is executed using the well known and challenging VOC 2012, the COCO data sets, and the large NESTA 162-bus system. The results show that our proposed approach significantly outperforms most of the existing solutions in terms of runtime and accuracy.

PFL: A Probabilistic Logic for Fault Trees

Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Teaching Geology in Higher Education Institutions under COVID-19 Conditions

Teaching geology under COVID-19 pandemic conditions led to teaching limitations for educators and learning difficulties for students. The lockdown obstructed face-to-face teaching, laboratory work, and fieldtrips. To minimize the impact of this situation, new distance learning teaching methods and tools were developed. The current study presents the results of an empirical study, where distance learning teaching tools were constructed and used to teach geology to university students. A mineralogical mobile phone application was used to replace laboratory mineral identification and a flow chart to replace laboratory rock identification. Additionally, exercises on faults and maps were developed to fill the gap that was created as field work was impossible. A university course on geology was designed on the basis of the constructed distance learning teaching tools, and more than 100 students from the Department of Civil Engineering attended the course. The results show that the proposed tools helped the students to considerably understand scientific information on geology and supported the learning outcomes. Thus, it is suggested that the teaching tools, constructed for the purposes of the study, could be used in conditions when distance learning is required, or even under typical learning conditions after laboratories, as well as before or after fieldtrips, for better learning outcomes.

Attention-Based Deep Learning Approach for Semantic Analysis of Chest X-Ray Images Modality

The world today is suffering from a huge pandemic COVID-19 that has infected 106M people around the globe causing 2.33M deaths, as of February 9, 2021. To control the disease from spreading more and to provide accurate healthcare to existing patients, detection of COVID-19 at an early stage is important. As per the World Health Organization, diagnosing pneumonia is a common way of detecting COVID-19. In many situations, a chest X-ray is used to determine the type of pneumonia. However, writing a report for every chest X-ray becomes a tedious and time-taking task for physicians. We propose a novel method of creating reports from chest X-rays images automatically via a deep learning model using image captioning with an attention mechanism employed through CNN–LSTM architecture. On comparing the model that does not use an attention mechanism with our approach, we found that accuracy was increased from 80% to 87.5%. In conclusion, we found that results generated with attention mechanism are better, and the report thus produced can be utilized by doctors and researchers worldwide to analyze new X-rays in lesser time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19, Climate Change, and Conflict in Honduras: A food system disruption analysis.

In Honduras, as in many settings between 2020 and 2022, food security was affected by the COVID-19 pandemic, climate change, and conflicts-what some refer to as "The Three Cs." These challenges have had overlapping impacts on food supply chains, food assistance programs, food prices, household purchasing power, physical access to food, and food acceptability. This article applies a food system disruption analysis-adapted from a fault tree analysis originally developed for a municipal context in the United States-to the context of Honduras to systematically examine how the Three Cs affected food availability, accessibility, and acceptability. This article demonstrates the value of approaching food security through a disruption analysis, especially for settings impacted by multiple, interconnected, ongoing crises.

PFL: A Probabilistic Logic for Fault Trees

Safety-critical infrastructures must operate in a safe and reliable way. Fault tree analysis is a widespread method used for risk assessment of these systems: fault trees (FTs) are required by, e.g., the Federal Aviation Administration and the Nuclear Regulatory Commission. In spite of their popularity, little work has been done on formulating structural queries about and analyzing these, e.g., when evaluating potential scenarios, and to give practitioners instruments to formulate queries on in an understandable yet powerful way. In this paper, we aim to fill this gap by extending [37], a logic that reasons about Boolean. To do so, we introduce a Probabilistic Fault tree Logic is a simple, yet expressive logic that supports easier formulation of complex scenarios and specification of FT properties that comprise probabilities. Alongside, we present, a domain specific language to further ease property specification. We showcase and by applying them to a COVID-19 related FT and to a FT for an oil/gas pipeline. Finally, we present theory and model checking algorithms based on binary decision diagrams (BDDs). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Intelligent Image Processing for Monitoring Solar Photovoltaic Panels

Despite the COVID-19 pandemic, the global photovoltaic (PV) market grew significantly again in 2021, further enhancing the vital role of solar power in the battle against global climate change. One of the main reasons for the rapid growth of this market is that PV panels are almost maintenance-free after deployment, thereby low Levelized cost of solar power. However, this does not mean that PV panels will not fail in service. In fact, they may suffer from performance degradation, structural failure, or even complete loss of power generation capacity during operation. If these problems cannot be detected and solved in time, they may also bring significant economic losses to the operators. However, a large-scale solar power plant will contain hundreds of thousands of PV panels. How to quickly identify those defective ones from so many PV panels is a quite challenging issue. The research of this paper is to address this issue with the aid of intelligent image processing technology. In this study, an intelligent PV panel condition monitoring technique is developed using machine learning algorithms. It can rapidly process, analyze and classify the thermal images of PV panels collected from solar power plants. Therefore, it not only can quickly identify those defective PV panels but also can accurately diagnose the defect types of the PV panels. It is deemed that the successful development of such a technology will be of great significance to further strengthen the scientific management of solar power assets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Power Supply Reliability Optimization Strategy of Shipboard Integrated Power System Equipped with Medical Load and Energy Storage System

The continuing COVID-19 pandemic around the world has raised concerns about public health security and posed a serious threat to the international shipping industry. Considering the fact that current epidemic may exist for a long time, it is necessary to set up independent epidemic prevention function zones on ships to deal with possible outbreaks. Due to the characteristics of high precision and low fault tolerance of medical devices and related equipment in epidemic prevention functional zones, they require more strict power supply quality and reliability, and may cause the deterioration of the power quality of the system. To solve this problem, the paper introduced a novel power management strategy for the shipboard medical system by power generation equipment switching process optimization, partitioning and grading of different types of loads to solve the power supply and distribution problems of the introduced ship epidemic prevention zone, and ensure a stable and reliable power supply of high-power medical equipment on the ship. © 2022 IEEE.

Vertex metric resolvability of COVID antiviral drug structures

In November of 2019 year, there was the first case of COVID-19 (Coronavirus) recorded, and up to 3rd of April of 2020, 1,116,643 confirmed positive cases, and around 59,158 dying were recorded. Novel antiviral structures of the 2019 pandemic disease Coronavirus are discussed in terms of the metric basis of their molecular graph. These structures are named arbidol, chloroquine, hydroxy-chloroquine, thalidomide, and theaflavin. Metric dimension or metric basis is a concept in which the whole vertex set of a structure is uniquely identified with a chosen subset named as resolving set. Moreover, the fault-tolerant concept of those structures is also included in this study. By this concept of vertex-metric resolvability of COVID antiviral drug structures are uniquely identified and help to study the structural properties of the structure. © 2023 - IOS Press. All rights reserved.

Networking Data and Intelligent Management in the Post-COVID19 Era: A Report on APNOMS 2021

This article presents a report on APNOMS 2021, which was held on September 8–10, 2021 in Tainan, Taiwan. The theme of APNOMS 2021 was "Networking Data and Intelligent Management in the Post-COVID19 Era.”. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

The Distinctive Nature of Covid-19 Vaccines: Compensation for Potential Damages under the Legal Framework of Lithuanian State in the Context of Global Examples

The article analyses legal mechanisms of compensation for damages caused by side effects of COVID-19 vaccines in Lithuania. In particular, draft amendments to the Law on the Rights of Patients and Compensation of the Damage to their Health registered by the Parliament of the Republic of Lithuania in 2021 are evaluated and arguments for the need for further improvement are provided herein. In order to comprehensively assess the nature of the side effects that may be a substantiated cause for damages, pharmaceutical analysis and evaluation of COVID-19 vaccines eligible in Lithuania are analysed. Analysis of the legal framework and proposals are construed mainly in light of the assessment of global examples. Following thorough evaluation of the question at hand, it is the opinion of the authors that the product liability mechanism is not appropriate in the context of the vaccination program applied in Lithuania and "a no-fault compensation model" shall be adopted instead, which would be funded by a separate (non) State institute/fund in Lithuania.

Vertex metric resolvability of COVID antiviral drug structures

In November of 2019 year, there was the first case of COVID-19 (Coronavirus) recorded, and up to 3rd of April of 2020, 1,116,643 confirmed positive cases, and around 59,158 dying were recorded. Novel antiviral structures of the 2019 pandemic disease Coronavirus are discussed in terms of the metric basis of their molecular graph. These structures are named arbidol, chloroquine, hydroxy-chloroquine, thalidomide, and theaflavin. Metric dimension or metric basis is a concept in which the whole vertex set of a structure is uniquely identified with a chosen subset named as resolving set. Moreover, the fault-tolerant concept of those structures is also included in this study. By this concept of vertex-metric resolvability of COVID antiviral drug structures are uniquely identified and help to study the structural properties of the structure. [ FROM AUTHOR]