DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

Beyond the Biophysical: Contribution of Community Forestry in Building Social-Ecological Resilience

People's engagement in forestry has been a key strategy to sustainably manage forests. While such approaches have proven effective in protecting and restoring forests, emerging evidence shows that they go much further in enhancing the resilience of local communities against shocks and stresses, ultimately sustaining forest resources. Sporadic evidence reports community forestry's (CF) support to local communities in dealing with climate impacts as well as disasters. This article reviews the multiple roles of CF in different contexts, through case studies done in Cambodia, Myanmar, Nepal, and Viet Nam in tackling climate impacts and COVID-19 restrictions. The article provides an illustration of key contributions from CF in addressing sources of vulnerability of forest-dependent communities and points out potential improvements in the CF programs. In doing so, it adapts Ostrom's Social-Ecological Sustainability framework. The article presents conclusive evidence that CF has the potential to help improve the resilience of social-ecological systems. However, the results are not optimal, and performance varies greatly across social, ecological, political, institutional, and livelihood contexts. We argue that with stronger integration to larger systems of multi-level and nested governance, the CF has potential to deliver more benefits to resilience building, climate adaptation, and disaster preparedness. This is complemented by integrating CF programs in climate adaptation and disaster-related policies and plans and providing capacity and resource support for on-the-ground actions under the leadership of local communities. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

A Classification Approach to Detect Public Sentiments towards COVID-19 Vaccines

In this paper, team University of Botswana Computer Science (UBCS) investigate the opinions of Twitter users towards vaccine uptake. In particular, we build three different text classifiers to detect people's opinions and classify them as provax-for opinions that are for vaccination, antivax for opinions against vaccination and neutral-for opinions that are neither for or against vaccination. Two different datasets obtained from Twitter, 1 by Cotfas and the other by Fire2022 Organizing team were merged to and used for this study. The dataset contained 4392 tweets. Our first classifier was based on the basic BERT model and the other 2 were machine learning models, Random Forest and Multinomial Naive Bayes models. Naive Bayes classifier outperformed other classifiers with a macro-F1 score of 0.319. © 2022 Copyright for this paper by its authors.

Development and Current Situation of Forest Use by Camping Sites in Japan

After the Second World War, camping and camping sites in forests have developed and increased significantly from the 1980 s to 1990 s in Japan, relying on the laws and institutions established from the 1950 s to 1970 s across multiple administrative sectors, obtaining social approval as a legitimatized outdoor activity and forest use. Since the 2000s, the management of these camping sites has deteriorated mainly owing to economic recession, which caused the movement of camping site renewal by the private sector. This movement directed the diversification of forest use by camping sites in recent years. Camping facilities have been developed in many ways to meet the needs of campers, including organized group camps that promote education and experience in forests, solo camps, glamping, and workcations under the spread of the COVID-19 that demand relaxing or productive environment, and leisure camps that require enrichment of outdoor activities. As a result of this diversification, possibilities for effective utilization of forests and regional revitalization through the management of camping sites have been observed. Many camping sites have utilized forest lands, standing trees, and forest spaces to develop facilities and services, and there are cases where firewood production for campers has promoted the reorganization and development of local forestry and securing of personnel for forest management. In addition to securing local employment brought by reorganization, local revitalization in rural and mountainous areas has been promoted through the linkage of the needs of campers to positive economic effects, increase of the visitors who deeply connected to local people, and comprehensive and sustainable use of resources in local societies. © 2023 Nihon Ringakkai. All rights reserved.

Impact of COVID-19 (2020-2022) on cotton and garments market of Bangladesh: a small country case. (Special Issue: COVID-19 impacts on selected commodities.)

Bangladesh imports roughly 98% of cotton from abroad to produce fabric or yarn (USDA 2020. Cotton and Products Update. Bangladesh. Also available at https://apps.fas.usda.gov/newgainapi/api/Report/DownloadReportByFileName?fileName=Cotton%20and%20Products%20Update_Dhaka_Bangladesh_11-30-2020). The production of textiles in Bangladesh depends on the price of raw material, the demand for garment products in the importing countries, smooth supply chain management, and the domestic supply of cheap garment laborers. The global pandemic of COVID-19 disrupted the supply chain of almost all physical goods and services, including textiles. It caused the price of textiles to fall due to a drop in worldwide demand, and increased the marginal cost of textile production due to supply chain interruptions. This paper shows how the decline in the demand for garments, coupled with an increase in cost, shrinks the producer welfare of textile manufacturing and garment exports of the small producing country, Bangladesh.

Exploring Health Outcomes By Occupation: Using the National Health and Wellness Survey to Describe Differences in Self-Reported Occupation Profiles

Objectives: Health is distributed unequally by occupation (Ravesteijn,2013). This research aims to explore patient-reported outcomes by occupation profiles using the National Health and Wellness Survey (NHWS). Method(s): Data from the 2022 US NHWS included employed respondents at least 18 years of age with information on occupation profile, defined as 22 categories from the Bureau of Labor Statistics. Descriptive statistics were used to analyze respondent characteristics and outcomes such as COVID-19 diagnoses, healthcare resource use over the past six months, and work impairment as measured by the Work Productivity and Activity Impairment Questionnaire (WPAI). Result(s): A total of 35,789 respondents were employed and had occupation information. Respondents were predominantly white (62.0%) and male (53.9%). Sales and Related occupations had the greatest proportion of respondents reporting a COVID-19 diagnosis (16.1%) while Building and Grounds Cleaning and Maintenance had the lowest proportion (3.8%). Educational Instruction and Library had the most respondents reporting that they had received at least one dose of the COVID-19 vaccine (79.2%) while Farming, Fishing, and Forestry had the least respondents (52.9%). Life, Physical, and Social Science had the greatest COVID-19 vaccination rate over the past year (66.5%) while Farming, Fishing, and Forestry had the lowest (45.0%). Office and Administrative Support had the greatest proportion of respondents with a traditional healthcare provider visit (79.8%), but the lowest proportion with an emergency room (ER) visit (12.7%) or a hospitalization (8.1%). Farming, Fishing, and Forestry had the greatest proportion of respondents with an ER visit (41.6%) or hospitalization (41.6%). The greatest proportion of respondents with any overall work impairment or activity impairment was in Farming, Fishing, and Forestry (work: 91.1%, activity: 87.4%) while the lowest proportion was in Office and Administrative Support (work: 50.0%, activity: 53.3%). Conclusion(s): Certain occupation profiles consistently show higher impairment while others consistently show lower impairment.Copyright © 2023

Coupling Machine Learning and Agent-Based Modeling to Characterize Contamination Sources in Water Distribution Systems for Changing Demand Regimes

Water distribution systems (WDSs) deliver clean, safe drinking water to consumers, providing an essential service to constituents. WDSs are increasingly at risk of contamination due to aging infrastructure and intentional acts that are possible through cyber-physical vulnerabilities. Identifying the source of a contamination event is challenging due to limited system-wide water quality monitoring and non-uniqueness present in solving inverse problems to identify source characteristics. In addition, changes in the expected demand patterns that are caused by, for example, social distancing during a pandemic, adoption of water conservation behaviors, or use of decentralized water sources can change the anticipated propagation of contaminant plumes in a network. This research develops a computational framework to characterize contamination sources using machine learning (ML) techniques and simulate water demands and human exposure to a contaminant using agent-based modeling (ABM). An ABM framework is developed to simulate demand changes during the COVID-19 pandemic. The ABM simulates population movement dynamics, transmission of COVID-19 within a community, decisions to social distance, and changes in demands that occur due to social distancing decisions. The ABM is coupled with a hydraulic simulation model, which calculates flows in the network to simulate the movement of a contaminant plume in the network for several contamination event scenarios. ML algorithms are applied to determine the location of source nodes. Research results demonstrate that ML using random forests can identify source nodes based on inline and mobile sensor data. Sensitivity analysis is conducted to explore the number of mobile sensors that are needed to accurately identify the source node. Rapidly identifying contamination source nodes can increase the speed of response to a contamination event, reducing the impact to the community and increasing the resiliency of WDSs during periods of changing demands. © World Environmental and Water Resources Congress 2023.All rights reserved

Determinants of Softwood Lumber Prices in the US Northwest

Lumber prices can be volatile and hard to predict from month to month yet are important for many sectors of the economy, ranging from forestry and construction. An economic model of lumber prices was developed and applied to data representing multiple supply and demand determinants of lumber. Using a suite of econometric models, monthly lumber prices were related back to variables including construction permits, US reserve bank credit, tariffs with Canada, exchange rates with Canada, and variables representing shocks associated with the COVID-19 pandemic. Preferred models use relatively small amounts of publicly available information, making them more accessible to industry participants who want to make their own price predictions. Such information can help guide decisions about whether to expand or scale back an operation in preparation for likely future price movements. Study Implications: This study shows that Douglas-fir lumber prices in the US Northwest can be predicted quite accurately with selected macro-economic variables that are commonly reported in the public domain. Using statistical techniques, monthly lumber prices in the United States were related back to variables including new home construction permits, US reserve bank credit, tariffs, and exchange rates. With suitable assumptions about future economic conditions, the models could be used by researchers as well as professionals at lumber mills, wholesales, and retailers to make near term predictions.

Exploring Language-Interfaced Fine-Tuning for COVID-19 Patient Survival Classification

We present Language-Interfaced Fine-Tuning (LIFT) in application to COVID-19 patient survival classification. LIFT describes translating tabular Electronic Health Records (EHRs) into text inputs for transformer neural networks. We study LIFT with a dataset of 5,371 COVID-19 patients. We focus on the predictive task of survival classification utilizing demographic and medical history features. We begin by presenting information about our dataset. We preface our investigation in text-based transformers by reporting the performances of conventional machine learning models such as Logistic Regression and Random Forest classifiers. We also present the results of a few configurations of tabular input-based Deep Multilayer Perceptron (MLP) networks. 86% of the patients in our database survived in the measured time window. Thus, predictive models are heavily biased to predict that a patient will survive. We emphasize that this problem of Class Imbalance was a major challenge in developing these models. Our balanced sampling strategy from examples in the majority and minority classes is crucial to achieving even reasonable predictive performance. For this reason, we also report performance based on Precision, Recall, and F-score metrics, in addition to Accuracy. Having established baselines with tabular inputs, we then shift our focus to the prompts for translating from tabular to text inputs. We report the performance of 5 prompts. The LIFT model achieves an F-score on the held-out test set of 0.21, slightly behind the Deep MLP with Tabular Features score of 0.23. Both models outperform the Random Forest with Tabular Features at 0.15. We believe that LIFT is a very exciting direction for machine learning in healthcare applications because text-based inputs enables us to take advantage of recent advances in Transfer Learning and Retrieval-Augmented Learning. This study illustrates the effectiveness of converting tabular EHRs to text inputs and utilizing transformer neural networks for prediction. © 2022 IEEE.

Impacts of rising COVID-19 incidence and changed working conditions on forest visits in early 2020 of the pandemic: Evidence from Switzerland.

The COVID-19 pandemic forced many nations to implement a certain degree of lockdown measures to contain the spread of the virus. It has been reported that recreational visits to forests and green spaces increased in response to the lockdown. In this study, we investigated the effect of the policy-induced changes in working conditions during the lockdown period, as well as the effect of COVID-19 infection rates, on forest visits throughout Switzerland early in the COVID-19 pandemic. We analyzed data from an online panel survey first conducted one week before the government imposed the lockdown in Switzerland and repeated two weeks after the lockdown began. We use a modeling approach to assess the impact of the home-office and short-time working situation on forest visitation frequency, as well as their effects on the length of visits to the forest. For those who visited the forest both before and during the lockdown, the frequency of forest visits increased during the early lockdown phase considered here, while the duration of visits decreased. According to our model, the opportunity to work from home was a significant driver of the increased frequency of forest visits by this visitor group, while COVID-19 infection rates had no effect on their forest visits.

The Ogres Zilie kalni park urban forest management

The impact of the Covid-19 pandemic demonstrated the importance of urban forests for human well-being at a time of tight constraints, when large forests close to urban areas were in high demand. Increased use affects the management of territories. Urban forests play an important role in providing ecosystem services. Urban forests show a close link between ecosystem services and forest functions. A literature review was carried out, exploring the ecosystem services and specific urban forest services provided by such territories. This article examines the experience of the Ogres Zilie kalni during the Covid-19 pandemic, taking into account the peculiar functions of urban forests. Different types of recreation that take place in the Ogres Zilie kalni, and their impact on park management are discussed. The aim of the article is to analyse and present the challenges of urban forest governance and management under the influence of Covid-19, looking through the functions of urban forests. Taking into account the classifications of ecosystem services available in Zilie kalni, zoning and assessment of the territory have been carried out. Cartographic material has been created based on practical experience and employee interviews.The practical experience of territory management gained during Covid-19 is important and should be taken into account in the future development of green spaces, respecting the new habits of visitors potentially affected by the pandemic, where one of the most important proposals is to develop more small localised recreation areas on smaller

Study on the Spatial Evolution of China's Pulp and Paper Product Import Trade and Its Influencing Factors

The purpose of this research is to study the changes in the market structure of China's pulp and paper product imports. In particular, the import trade environment and market layout of Chinese pulp and paper products have changed under the international context of the coronavirus pandemic and the Sino-US trade dispute and the domestic policy context of zero imports of Chinese waste paper. This study attempts to fill the gap regarding the influencing factors of market structure, while contributing new ideas on China's trade in pulp and paper products. Based on pulp and paper product import and export trade data from 2005 to 2021, a trade gravity model was used to explore the changes in the share of China's trade partners for pulp and paper product imports and their influencing factors. The results indicated that the outbreak of COVID-19 led to a significant increase in China's imports of packaging paper products, bringing about an increase in Indonesia's status as a partner in China's pulp and paper product trade. The US-China trade dispute had an impact on pulp and paper product trade between the two countries, with China's tax countermeasures causing the US to lose its status as a trading partner in China for pulp and paper product imports. The center of gravity for paper product imports has moved from the US and Japan to Indonesia and Russia. The restrictions on waste paper imports have shifted the focus of China's paper raw material imports, with the US no longer being the main importer of China's paper raw materials. Specifically, the main importers of wood pulp are Brazil and Chile, while the main importers of waste paper pulp are Thailand and Malaysia. In the future, China needs to continuously strengthen dialogue with the United States to resolve trade disputes and create a favorable environment for trade in pulp and paper products. At the same time, China's paper enterprises should strengthen the expansion of the Southeast Asian market and reduce dependence on the US market, and China should continue to improve the waste paper recycling system and improve the utilization rate of domestic waste paper.

A Prediction Model for Screening Covid-19 Patients

This study aims to compare the performance of data classifying for COVID-19 patients. In this study, the patients' data acquired from the department of disease control (1,608,923 patients) are collected. They are patients records from January 2020 to October 2021. The study focus on three main data classification techniques: Random forest;Neural Network;and Naïve Bayes. The authors study the comparative performance of the techniques. We apply the split test method to evaluate the performance of data prediction. The data are divided into two parts: training data. The results show that Random Forest has an accuracy of 93.51%. Neural network has an accuracy of 93.02%. Naive Bayes has an accuracy of 27.54%. This presents the Random Forest with the highest accuracy Figure for screening of COVID-19 patients © 2022 IEEE.

A New Machine Learning Framework for Detecting COVID-19 From Clinical Data on Lung And Heart Function

Currently, the need for real-time COVID-19 detection methods with minimal tools and cost is an important challenge. The available methods are still difficult to apply, slow, costly, and their accuracy is low. In this work, a novel machine learning-based framework to predict COVID-19 is proposed, which is based on rapid inpatient clinical tests of lung and heart function. Compared with current cognition therapy techniques, the proposed framework can significantly improve the accuracy and time performance of COVID-19 diagnosis without any lab or equipment requirements. In this work, five parameters of clinical testing were adopted;Respiration rate, Heart rate, systolic blood pressure, diastolic blood pressure, and mean arterial blood pressure. After obtaining results for these tests, a pre-trained intelligent model based on Random Forest Tree (RFT) machine learning algorithm is used for detection. This model was trained by about 13,558 records of the COVID19 testing dataset collected from King Faisal Specialist Hospital and Research Centre (KFSH&RC) in Saudi Arabia. Experiments have shown that the proposed framework performs highly in detecting COVID infections by 96.9%. Its results can be output in minutes, which supports clinical staff in screening COVID-19 patients from their inpatient clinical data. © 2022 IEEE.

Utilizing Sentiment Analysis to Enhance the Quality of Online Learning

The COVID-19 pandemic spread worldwide in the year 2020 and became a global health emergency. This pandemic has brought awareness that social distancing and quarantine are ideal ways to protect people in the community from infection. Therefore, Saudi Arabia used online learning instead of stopping it completely to continue the education process. This paper proposes to use machine-learning algorithms for Arabic sentiment analysis to find out what students and teaching staff thought about online learning during the COVID-19 outbreak. During the pandemic, a real-world data set was gathered that included about 100,000 Arabic tweets related to online learning. The overall goal is to use sentiment analysis of tweets to find patterns that help improve the quality of online learning. The data set that was collected has three classes: 'Positive,' 'Negative,' and 'Neutral.' Crossvalidation is used to run the experiments ten times. Precision, recall, and F-measure was used to measure how well the algorithms worked. Classifiers, such as Support Vector Machines, K nearest neighbors, and Random Forest, were used to classify the dataset. Moreover, a detailed analysis and comparison of the results are made in this research. Finally, a visual examination of the data is made using the word cloud technique. © 2022 IEEE.

Scenic Beauty Evaluation of Forests with Autumn-Colored Leaves from Aerial and Ground Perspectives: A Case Study in Qixia Mountain in Nanjing, China

In recent years, on-site visitation has been strictly restricted in many scenic areas due to the global spread of the COVID-19 pandemic. "Cloud tourism”, also called online travel, uses high-resolution photographs taken by unmanned aerial vehicles (UAVs) as the dominant data source and has attracted much attention. Due to the differences between ground and aerial observation perspectives, the landscape elements that affect the beauty of colored-leaved forests are quite different. In this paper, Qixia National Forest Park in Nanjing, China, was chosen as the case study area, and the best viewpoints were selected by combining tourists' preferred viewing routes with a field survey, followed by a scenic beauty evaluation (SBE) of the forests with autumn-colored leaves in 2021 from the aerial and ground perspectives. The results show that (1) the best viewpoints can be obtained through the spatial overlay of five landscape factors: elevation, surface runoff, slope, aspect, and distance from the road;(2) the dominant factors influencing the beauty of colored-leaved forests from the aerial perspective are terrain changes, forest coverage, landscape composition, landscape contrast, the condition of the human landscape, and recreation frequency;and (3) the beauty of the ground perspective of the colored-leaved forests is strongly influenced by the average diameter at breast height (DBH), the dominant color of the leaves, the ratio of the colored-leaved tree species, the canopy width, and the fallen leaf coverage. The research results can provide scientific reference for the creation of management measures for forests with autumn-colored leaves. © 2023 by the authors.

Research on fatigue identification methods based on low-load wearable ECG monitoring devices

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments. © 2023 Author(s).

Measuring the performance of SMEs during the pandemic situation using system dynamic

Purpose: This study aims to create a system dynamics simulation model to forecast the performance of small and medium-sized enterprises (SMEs) if some decision-making is executed to reduce the negative of the coronavirus disease 2019 (COVID-19) pandemic. In particular, this study will focus on SMEs that belong to the furniture industry because the furniture industry is one of the leading industries in Indonesia. Design/methodology/approach: The study develops a system dynamics-based model by using three subsystems, i.e. the "production subsystem,” "demand and revenue subsystem” and "raw material (or wood supply) subsystem.” Findings: The best scenario is the third scenario which increases the capacity to the normal situation and government subsidy during and after the pandemic. This scenario gives the best performance for industry revenue and gross domestic product (GDP). However, for the government, the most significant expenditure occurs in the third scenario. This seems a trade-off for the government whether to save the wooden-based furniture industry by encouraging the industry to continue operating during the pandemic accompanied by high subsidies or limiting the activities of the wooden-based furniture industry to prevent the spread of COVID-19 by providing low subsidies. Research limitations/implications: First, this study does not try to combine the system dynamics (SD) methodology with the other method or use a multi-methodology since SD has several limitations and the other method may have several advantages compared to SD. Second, the models used in this study do not consider the decline in forest area and quality. Third, the demand for wooden-based furniture is obtained from historical data on domestic and foreign sales and fourth, the model does not include the government budget as a constraint to make any subsidy to help the SMEs. Practical implications: This study provides essential insights into implementing the policies in the world pandemic situation when SMEs face lockdown policy. Social implications: The study revealed that relevant policy scenarios could be built after simulating and analyzing each scenario's effect on SMEs' performance during the pandemic. Originality/value: This study will enrich the previous study on the impact of the pandemic on SMEs and the dynamic system modeling on SMEs. The previous study discussed the pandemic's impact on SME performance and the impact's analysis in isolation from the dynamic nature of SME owners' decisions or government policy. In this study, the impact generated from the pandemic situation could be different depending on the decision and policies taken by managers from SMEs and the government. © 2023, Emerald Publishing Limited.