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1.
Work, Aging and Retirement ; 2022.
Article in English | Web of Science | ID: covidwho-2107594

ABSTRACT

This research challenges the technology-related age stereotype that older employees might be disadvantaged in dealing with work-related information communication technology (ICT) demands. Rather, we hypothesize an age advantage in this regard. Based on theorizing on aging at work, we suggest that older employees are better at psychologically detaching from work under high availability expectations and that they show more adaptive responsiveness to response expectations. We examined a potential age-related mechanism underlying this effect, namely internal workplace telepressure. We pursued a two-study approach. Study 1 examined data from 5,938 individuals who participated in a large-scale survey of employees in Germany just before the COVID-19 pandemic, testing age as moderator of the relationship between availability expectations and psychological detachment from work. Results supported the hypothesized age advantage effect showing that for older employees, availability expectations were less strongly related to impaired psychological detachment. Study 2, a diary study with 106 participants answering more than 500 daily surveys during the pandemic, supported lower telepressure as explanation for this age advantage effect. Study 2 further extended this finding to the relationship of response expectations with responsiveness, identifying both age and telepressure as predicted by age to moderate this relationship. This research shows age advantage effects in dealing with ICT demands, enhancing understanding of the intersection between age and technology use at work.

2.
Acs Food Science & Technology ; 1(10):1776-1786, 2021.
Article in English | Web of Science | ID: covidwho-2106317

ABSTRACT

Mango processing waste (MPW) is an inexpensive and rich source of valuable substances. Hence, the mango kernel powder (MKP) from four cultivars (Chausa, Neelum, Barahmasi, and Dashehari) was characterized for the selection of the best cultivar. The MKP of the best cultivar (Dashehari) was analyzed for the profiling of polyphenols using LC-MS/MS in both modes of ionization (positive and negative) and indicated the presence of 50 compounds with specific retention times. After identification, gallic acid (GA), an important industrial compound, was targeted and purified followed by its confirmation using NMR (600 MHz) and HRMS. The antioxidant activity (IC50: 1.96 mu g/mL) of extracted GA proposes its use as a natural antioxidant in novel food formulations. Additionally, SARS-CoV-2 main protease (M-pro) was selected for molecular docking based virtual screening of seven major polyphenols (MKP), and the results were compared with hydroxychloroquine. The docking scores of targeted polyphenols revealed that three compounds (epicatechin, mangiferin, and quercetin) exhibited appreciable proteolytic activity against M-pro. In this way, it is a favorable approach toward environmental safety on the standpoint of green chemistry owing to the use of food processing waste and elimination of the waste dumping/composting problems.

3.
Fuel ; 333, 2023.
Article in English | Scopus | ID: covidwho-2104949

ABSTRACT

The hybrid renewable system's potential to create standard type E capsules for Covid-19 patients was explored in this study. In addition to delivering the requisite energy to the building, standard oxygen capsules were produced using the electrolysis of water using nanomaterial-supported electrolysis in the hydrogen storage system. In addition to the simulation, multi-objective optimization was done using a deep learning neural network and a genetic algorithm to maximize the number of oxygen capsules generated in a year and the system price, and the system's front beam was acquired. The system can produce 19530 units of type E oxygen capsules in a year, and the price of the electrolyzer and fuel cell is 120296 Euros at the best point of the front beam, considering both the objective variable of price and the number of produced oxygen capsules. In this scenario, the electrolyzer and fuel cell have rated powers of 61.9 kW and 15.3 kW, respectively. After determining the optimal point, researchers investigated the connection between meteorological data and other system characteristics including the amount of hydrogen in the tank, the number of oxygen capsules generated each hour, fuel cell power, and the electrolyzer. Lastly, the system's capacity to lower the amount of power required for the office building from the municipal network was investigated, indicating the system's excellent capability in this respect. © 2022 Elsevier Ltd

4.
Expert Systems with Applications ; : 119239, 2022.
Article in English | ScienceDirect | ID: covidwho-2104914

ABSTRACT

COVID-19 quickly swept across the world, causing the consequent infodemic represented by the rumors that have brought immeasurable losses to the world. It is imminent to achieve rumor detection as quickly and accurately as possible. However, the existing methods either focus on the accuracy of rumor detection or set a fixed threshold to attain early detection that unfortunately cannot adapt to various rumors. In this paper, we focus on textual rumors in online social networks and propose a novel rumor detection method. We treat the detection time, accuracy and stability as the three training objectives, and continuously adjust and optimize this objective instead of using a fixed value during the entire training process, thereby enhancing its adaptability and universality. To improve the efficiency, we design a sliding interval to intercept the required data rather than using the entire sequence data. To solve the problem of hyperparameter selection brought by integration of multiple optimization objectives, a convex optimization method is utilized to avoid the huge computational cost of enumerations. Extensive experimental results demonstrate the effectiveness of the proposed method. Compared with state-of-art counterparts in three different datasets, the recognition accuracy is increased by an average of 7%, and the stability is improved by an average of 50%.

5.
Expert Systems with Applications ; 213, 2023.
Article in English | Web of Science | ID: covidwho-2104912

ABSTRACT

SARS-CoV2 (COVID-19) is the virus that causes the pandemic that has severely impacted human society with a massive death toll worldwide. Hence, there is a persistent need for fast and reliable automatic tools to help health teams in making clinical decisions. Predictive models could potentially ease the strain on healthcare systems by early and reliable screening of COVID-19 patients which helps to combat the spread of the disease. Recent studies have reported some key advantages of employing routine blood tests for initial screening of COVID-19 patients. Thus, in this paper, we propose a novel COVID-19 prediction model based on routine blood tests. In this model, we depend on exploiting the real dependency among the employed feature pool by a sparsification procedure. In this sparse domain, a hybrid feature selection mechanism is proposed. This mechanism fuses the selected features from two perspectives, the first is Pearson correlation and the second is a new Minkowski-based equilibrium optimizer (MEO). Then, the selected features are fed into a new 1D Convolutional Neural Network (1DCNN) for a final diagnosis decision. The proposed prediction model is tested with a new public dataset from San Raphael Hospital, Milan, Italy, i.e., OSR dataset which has two sub-datasets. According to the experimental results, the proposed model outperforms the state-of-the-art techniques with an average testing accuracy of 98.5% while we employ only less than half the size of the feature pool, i.e., we need only less than half the given blood tests in the employed dataset to get a final diagnosis decision.

6.
J Clean Prod ; 376: 134192, 2022 Nov 20.
Article in English | MEDLINE | ID: covidwho-2105286

ABSTRACT

The process of collecting and transporting hazardous medical waste poses a potential threat to the environment and public safety. Furthermore, the waste management system faces higher transportation costs due to the increasing human activities related to rapid population growth. The absence of an efficient and safe logistics network for the timely collection and transportation of hazardous wastes may have negative effects on the environment and public health. Therefore, more sustainable transportation of hazardous waste services is a necessity This paper attempts to design a sustainable network for hazardous medical waste collection services during the COVID-19 pandemic. An electric medical waste collection vehicle routing problem is introduced to construct optimal routes and rosters for a fleet of electric vehicles as well as cover their choice of charging technologies, times and locations. This problem allows us to minimize the health risk of hazardous medical waste while providing cost-effective, zero-emission waste management logistics. Therefore, this problem covers environmental and economic objectives to achieve sustainable development. An effective heuristic that covers adaptive large neighbourhood search and a local search is designed to deal with the complex problem. A series of extensive computational experiments is carried out using real-life benchmark instances to assess the performance of the algorithm. A sensitivity analysis is also conducted to investigate the effect of multiple charger types on the cost and risk objectives. The experiment results indicate that mixed-use of different charger types can reduce the total energy cost and transport risk compared to the case of using only a single charger.

7.
Comput Electr Eng ; : 108479, 2022 Nov 14.
Article in English | MEDLINE | ID: covidwho-2104658

ABSTRACT

Recent studies have shown that computed tomography (CT) scan images can characterize COVID-19 disease in patients. Several deep learning (DL) methods have been proposed for diagnosis in the literature, including convolutional neural networks (CNN). But, with inefficient patient classification models, the number of 'False Negatives' can put lives at risk. The primary objective is to improve the model so that it does not reveal 'Covid' as 'Non-Covid'. This study uses Dense-CNN to categorize patients efficiently. A novel loss function based on cross-entropy has also been used to improve the CNN algorithm's convergence. The proposed model is built and tested on a recently published large dataset. Extensive study and comparison with well-known models reveal the effectiveness of the proposed method over known methods. The proposed model achieved a prediction accuracy of 93.78%, while false-negative is only 6.5%. This approach's significant advantage is accelerating the diagnosis and treatment of COVID-19.

8.
Comput Biol Med ; 150: 106181, 2022 Oct 05.
Article in English | MEDLINE | ID: covidwho-2104647

ABSTRACT

Aiming at the problem that the single CT image signal feature recognition method in the self-diagnosis of diseases cannot accurately and reliably classify COVID-19, and it is easily confused with suspected cases. The collected CT signals and experimental indexes are extracted to construct different feature vectors. The support vector machine is optimized by the improved whale algorithm for the preliminary diagnosis of COVID-19, and the basic probability distribution function of each evidence is calculated by the posterior probability modeling method. Then the similarity measure is introduced to optimize the basic probability distribution function. Finally, the multi-domain feature fusion prediction model is established by using the weighted D-S evidence theory. The experimental results show that the fusion of multi-domain feature information by whale optimized support vector machine and improved D-S evidence theory can effectively improve the accuracy and the precision of COVID-19 autonomous diagnosis. The method of replacing a single feature parameter with multi-modal indicators (CT, routine laboratory indexes, serum cytokines and chemokines) provides a more reliable signal source for the diagnosis model, which can effectively distinguish COVID-19 from the suspected cases.

9.
Comput Ind Eng ; 174: 108811, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2104549

ABSTRACT

The COVID-19 pandemic hit the medical supply chain, creating a serious shortage of medical equipment. To meet the urgent demand, one realistic way is to collect abandoned medical equipment and then remanufacture, where the disassembled modules are shared with all stock-keeping units (SKUs) to improve utilization. However, in an emergency, the equipment should be processed sequentially and immediately, which means the decision is short-sighted with limited information. We propose a hybrid combinatorial remanufacturing (HCR) strategy and develop two reinforcement learning frameworks based on Q-learning and double deep Q network to find the optimal recovery option. In the frameworks, we transform HCR problem into a maze exploration game and propose a rule of descending epsilon-greedy selection on reweighted valid actions (DeSoRVA) and Espertate knowledge dictionary to combine the cost-minimizing objective with human judgment and the global state of the problem. A real-time environment is further implemented where the quality status of the in-transit equipment is unknown. Numerical studies show that our algorithms can learn to save cost, and the larger scale of the problem is, the more cost-down can be achieved. Moreover, the sophisticated knowledge refined by Espertate is effective and robust, which can handle remanufacturing problems at different scales corresponding to the volatility of the pandemic.

10.
Comput Ind Eng ; 174: 108808, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2104548

ABSTRACT

The vast nationwide COVID-19 vaccination programs are implemented in many countries worldwide. Mass vaccination is causing a rapid increase in infectious and non-infectious vaccine wastes, potentially posing a severe threat if there is no well-organized management plan. This paper develops a mixed-integer mathematical programming model to design a COVID-19 vaccine waste reverse supply chain (CVWRSC) for the first time. The presented problem is based on minimizing the system's total cost and carbon emission. The uncertainty in the tendency rate of vaccination is considered, and a robust optimization approach is used to deal with it, where an interactive fuzzy approach converts the model into a single objective problem. Additionally, a Lagrangian relaxation (LR) algorithm is utilized to deal with the computational difficulty of the large-scale CVWRSC network. The model's practicality is investigated by solving a real-life case study. The results show the gain of the developed integrated network, where the presented framework performs better than the disintegrated vaccine and waste supply chain models. According to the results, vaccination operations and transportation of non-infectious wastes are responsible for a large portion of total cost and emission, respectively. Autoclaving technology plays a vital role in treating infectious wastes. Moreover, the sensitivity analyses demonstrate that the vaccination tendency rate significantly impacts both objective functions. The case study results prove the model's robustness under different realization scenarios, where the average objective function of the robust model is less than the deterministic model ones' in all scenarios. Finally, some insights are given based on the obtained results.

11.
Vaccines (Basel) ; 10(11)2022 Oct 31.
Article in English | MEDLINE | ID: covidwho-2099898

ABSTRACT

The coronavirus disease 2019 (COVID-19) has spread worldwide and imposed a substantial burden on human health, the environment, and socioeconomic development, which has also accelerated the process of nucleic acid vaccine development and licensure. Nucleic acid vaccines are viral genetic sequence-based vaccines and third-generation vaccines after whole virus vaccines and recombinant subunit vaccines, including DNA vaccines and RNA vaccines. They have many unique advantages, but there are many aspects that require optimization. Therefore, the purpose of this review is to discuss the research and development processes of nucleic acid vaccines, summarize the advantages and shortcomings, and propose further optimization strategies by taking COVID-19 vaccines as an example. Hopefully, this work can make a modest contribution in promoting the construction of emergency nucleic acid vaccine platforms and in avoiding the reemergence of similar public health emergencies.

12.
IEEE Journal on Selected Areas in Communications ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-2097636

ABSTRACT

Since the outbreak of COVID-19 pandemic in 2020, a dramatic loss of human life has occurred and this trend presents an unprecedented challenge to public health, economic systems and social operations. Hence, it is urgent for us to take some countermeasures to restrain and dispel epidemic diffusion to the uttermost. Data freshness plays an inevitable role in timely infestor determination during this process. However, existing works pay little attention to optimizing this indicator in health monitoring. To make up this research gap, in this paper, we propose a mixed game-based Age of Information (AoI) optimization scheme, where the edge-based wireless technologies and AI-empowered diagnostic bots are adopted. Firstly, we establish the system model for Epidemic Prevention and Control Center (EPCC)-based health state monitoring network, where ultimate biosensing data is transmitted from AI bots via edge servers. Then, upon deriving AoI expression with a closed form, the minimization goal between edge servers and bots is specified. Simultaneously, we reformulate the AoI optimization problem from the mixed game viewpoint (i.e., coalition formation game and ordinary potential game), and then propose two algorithms for cooperative order-based bot deployment and stochastic learning-based channel selection. Finally, compared with the typical baselines, the experiment result shows our scheme can reach the lower AoI value for biosensing data transmission under different parameter settings. IEEE

13.
Knowledge-Based Systems ; : 110086, 2022.
Article in English | ScienceDirect | ID: covidwho-2095727

ABSTRACT

Online learning is also referred to as E-learning which has gained huge attention and attracted most people during the COVID-19 lockdowns. Due to the excess of online information, users face severe challenges and difficulties realizing the best course that is being competitive in the global market. Therefore, it is necessary to develop an online recommendation system that supports the users in selecting the finest course with E-learning. Thus, the proposed work develops a robust RS model using different approaches. Initially, the pre-processing stage is performed to reduce the presented noise in the website data. Then, the feature extraction stage is done to extract the needed features using Improved TF-IDF, W2V (Word 2 Vector), and Hybrid N-gram. Finally, Elman Minimal Redundancy Maximum Relevance and Enhanced Aquila Optimization (EMRMR_EAO) model is proposed to provide Robust course recommendations. In this work, the ERNN method is used to classify the sentiments based on the similarity measure of the MRMR model. The top course recommendation is afforded depending on the similarity scores like Jaccard similarity, cosine similarity and euclidean similarity. Also, the loss function in the classifier is reduced by optimizing the weight parameters using the EAO approach. The performance analysis shows that the proposed recommendation model obtains improved results in terms of accuracy of 99.98%, recall of 99.81%, precision of 99.65%, and F-measure of 99.95%. The comparative analysis exhibit that the proposed EMRMR_EAO model attains better performance than the other existing works in the literature.

14.
Expert Systems with Applications ; : 119206, 2022.
Article in English | ScienceDirect | ID: covidwho-2095343

ABSTRACT

Applying Deep Learning (DL) in radiological images (i.e., chest X-Rays) is emerging because of the necessity of having accurate and fast COVID-19 detectors. Deep Convolutional Neural Networks (DCNN) have been typically used as robust COVID-19 positive case detectors in these approaches. Such DCCNs tend to utilize Gradient Descent-Based (GDB) algorithms as the last fully-connected layers’ trainers. Although GDB training algorithms have simple structures and fast convergence rates for cases with large training samples, they suffer from the manual tuning of numerous parameters, getting stuck in local minima, large training samples set requirements, and inherently sequential procedures. It is exceedingly challenging to parallelize them with Graphics Processing Units (GPU). Consequently, the Chimp Optimization Algorithm (ChOA) is presented for training the DCNN's fully connected layers in light of the scarcity of a big COVID-19 training dataset and for the purpose of developing a fast COVID-19 detector with the capability of parallel implementation. Following that, two publicly accessible datasets termed COVID-Xray-5k and COVIDetectioNet are used to benchmark the proposed detector known as DCCN-Chimp. In order to make a fair comparison, two structures are proposed: i-6c-2s-12c-2s and i-8c-2s-16c-2s, all of which have had their hyperparameters fine-tuned. The outcomes are evaluated in comparison to standard DCNN, Hybrid DCNN plus Genetic Algorithm (DCNN-GA), andMatched Subspace classifier with Adaptive Dictionaries (MSAD). Due to the large variation in results, we employ a weighted average of the ensemble of ten trained DCNN-ChOA, with the validation accuracy of the weights being used to determine the final weights. The validation accuracy for the mixed ensemble DCNN-ChOA is 99.11%. LeNet-5 DCNN's ensemble detection accuracy on COVID-19 is 84.58%. Comparatively, the suggested DCNN-ChOA yields over 99.11% accurate detection with a false alarmrate of less than 0.89 %. The outcomes show that the DCCN-Chimp can deliver noticeably superior results than the comparable detectors. The Class Activation Map (CAM) is another tool used in this study to identify probable COVID-19-infected areas. Results show that highlighted regions are completely connected with clinical outcomes, which has been verified by experts.

15.
Int J Environ Res Public Health ; 19(18)2022 Sep 07.
Article in English | MEDLINE | ID: covidwho-2093838

ABSTRACT

At the early stage of a major public health emergency outbreak, there exists an imbalance between supply and demand in the distribution of emergency supplies. To improve the efficiency of emergency medical service equipment and relieve the treatment pressure of each medical treatment point, one of the most important factors is the emergency medical equipment logistics distribution. Based on the actual data of medical equipment demand during the epidemic and the characteristics of emergencies, this study proposed an evaluation index system for emergency medical equipment demand point urgency, based on the number of patients, the number of available inpatient beds, and other influencing factors as the index. An urban emergency medical equipment distribution model considering the urgency of demand, the distribution time window, and vehicle load was constructed with the constraints. Wuhan, Hubei Province, China, at the beginning of the outbreak was selected as a validation example, and the Criteria Importance Though Intercriteria Correlation (CRITIC) method and the genetic algorithm were used to simulate and validate the model with and without considering the demand urgency. The results show that under the public health emergencies, the distribution path designed to respond to different levels of urgency demand for medical equipment is the most efficient path and reduces the total distribution cost by 5%.


Subject(s)
Emergency Medical Services , Epidemics , China/epidemiology , Emergencies , Humans , Public Health
16.
Traitement Du Signal ; 39(4):1407-1419, 2022.
Article in English | Web of Science | ID: covidwho-2091157

ABSTRACT

The World Health Organization (WHO) made the announcement that the SARS virusinduced Coronavirus contamination-2019 (COVID-19) has been raised to the status of an international pandemic in March of 2020. If this virus is discovered at a relatively early stage in its life cycle, then it will be possible to contain it and treat it in an effective manner. Because of this fact, real-time polymerase chain reaction (RT-PCR) has emerged as the screening method of first desire for the rapid detection of COVID-19 in blood samples. This is a direct result of the fact Large-scale research have shown that the outcomes of an RTPCR experiment can be misleadingly bad up to sixty two percent of the time. As a final result, the focus of these studies is on a thorough examination of COVID-19 detection and complexity through the utilisation of photos obtained from chest x-rays that are centred at the lung. In the beginning, the research looked into the many layers of computer-aided detection, such as deep learning and meta-heuristics techniques. After that, the research is concentrated on the processes of feature extraction, feature preference, and sophistication operations by utilising techniques such as gadget learning, deep learning, and biooptimization. The study highlights the contemporary difficulties presented by artificial intelligence structures for the detection of COVID-19, which can help to put into action a hybrid system.

17.
Ieee Access ; 10:107010-107021, 2022.
Article in English | Web of Science | ID: covidwho-2083045

ABSTRACT

A continuous increase in privacy attacks has caused the research and application of differential privacy (DP) to gradually increase. We can improve the efficiency of the DP model by Optimizing its parameters significantly. Inspired by the performance of various optimization methods for differential privacy, this paper proposes an improved RDP-AdaBound optimization method with bias correction, which is called "AdaBias", to increase the performance of Renyi differential privacy (RDP). The bias correction is used to realize the learning rate and speed up the convergence by upper and lower bound functions. We evaluate our method on the three datasets by training two different privacy model. We further compare three traditional optimization algorithms, namely, RDP-SGD, RDP-Adagrad, and RDP-Adam. And we use AdaBias to verify the performance of privacy protection on the COVID-19 dataset. Experimental results show that the new variant better implements learning rate adjustment to accommodate updates of noisy gradients. As a result, it can achieve higher accuracy and lower losses with a lower privacy budget, thereby better protecting data privacy.

18.
2021 Ieee International Conference on Communications Workshops (Icc Workshops) ; 2021.
Article in English | Web of Science | ID: covidwho-2082878

ABSTRACT

The recent worldwide sanitary pandemic has made it clear that changes in user traffic patterns can create load balancing issues in networks (e.g., new peak hours of usage have been observed, especially in suburban residential areas). Such patterns need to be accommodated, often with reliable service quality. Although several studies have examined the user association and resource allocation (UA-RA) issue, there is still no optimal strategy to address such a problem with low complexity while reducing the time overhead. To this end, we propose Performance-Improved Reduced Search Space Simulated Annealing (PIRS(3)A), an algorithm for solving UA-RA problems in Heterogeneous Networks (HetNets). First, the UA-RA problem is formulated as a multiple 0/1 knapsack problem (MKP) with constraints on the maximum capacity of the base stations (BS) along with the transport block size (TBS) index. Second, the proposed PIRS(3)A is used to solve the formulated MKP. Simulation results show that PIRS(3)A outperforms existing schemes in terms of variability and Quality of Service (QoS), including throughput, packet loss ratio (PLR), delay, and jitter. Simulation results also show that PIRS3 A generates solutions that are very close to the optimal solution compared to the default simulated annealing (DSA) algorithm.

19.
Operations Research Perspectives ; : 100257, 2022.
Article in English | ScienceDirect | ID: covidwho-2082743

ABSTRACT

In this paper, we develop a supply chain optimization model for the preparation, provision, transportation, and execution of swab tests during COVID-19 pandemic. The proposed approach is based on a multi-tiered network consisting of manufacturing companies of reagents, processing laboratories (where the swab kits are prepared and some swab tests are analyzed), landing stations for UAVs and test centers. As innovations in the supply chain, the sharing of reagents between processing laboratories and the use of UAVs, using 5G technology, are contemplated in the management of the COVID-19 Pandemic. To obtain the optimal solutions of the underlying optimization problem, we provide a variational formulation problem for which results of existence and uniqueness will be provided. Finally, some numerical simulations are examined to validate the effectiveness of our approach.

20.
Statistica Sinica ; 32:2023-2046, 2022.
Article in English | Web of Science | ID: covidwho-2082512

ABSTRACT

Features extracted from aggregated data are often contaminated with errors. Errors in these features are usually difficult to handle, especially when the feature dimension is high. We construct an estimator of the feature effects in the context of a Poisson regression with a high dimensional feature and additive measurement errors. The procedure penalizes a target function that is specially designed to handle measurement errors. We perform optimization within a bounded region. Benefiting from the convexity of the constructed target function in this region, we establish the theoretical properties of the new estimator in terms of algorithmic convergence and statistical consistency. The numerical performance is demonstrated using simulation studies. We apply the method to analyze the possible effect of weather on the number of COVID-19 cases.

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