Forthcoming Days Research Articles

Digital Inclusive Finance for Rural Revitalization: A Literature Review

The evolving landscape of rural revitalization has presented novel demands for the advancement of rural finance. Facilitating the expansion of digital inclusive finance in rural areas is instrumental in fortifying the progress of rural finance and realizing top-notch development in rural areas. This paper delves into the elucidation of the concept of inclusive finance and its digital evolution, elucidates the strategies and mechanisms of digital inclusive finance in propelling rural revitalization, and delineates the prevalent challenges and corresponding remedies encountered by digital inclusive finance in driving rural revitalization. By comprehensively encapsulating the contemporary research findings in the scholastic domain, this discourse aims to furnish a blueprint for forthcoming scholars' investigations and governmental agencies' governance initiatives. In the forthcoming days, it is imperative to actively harness the advantageous facets of pertinent mechanisms to alleviate the adverse repercussions of capacity exclusion and the digital divide, and ensure the sustainability of rural revitalization.

Effects of Syzygium aromaticum (clove) Extract on the Acquisition and Expression of Morphine-Induced Conditioned Place Preference in Swiss Albino Mice: an In vivo, In Silico Approach

Empirical findings have demonstrated that the application of an N-Methyl-D-aspartate (NMDA) receptor antagonist effectively prevents the formation of morphine-induced location preference. Eugenol, the primary constituent found in the extract of S. aromaticum (SA), exhibits notable efficacy in traversing the blood-brain barrier and possesses inhibitory properties against the metabotropic NMDA receptor. This study aimed to evaluate the actions of the hydro-alcoholic extract of S. aromaticum on the acquisition and expression of morphine-induced conditioned place preference (CPP) in mice, followed by in silico studies. The conditioned place preference (CPP) test was used for investigating addictive-seeking behavior. Morphine (5 mg/kg) was used to produce CPP, and saline was used as a control. Morphine significantly increased the preference scores on the drug-paired side (p<0.001), whereas both doses of S. aromaticum extract (200 and 400 mg/kg) did not reveal any liking compared to the control group. Higher doses of S. aromaticum extract (400 mg/kg) reduced the acquisition of MOR-induced CPP (p<0.001) but had no significant effect on the expression of morphine CPP. Molecular docking analysis showed that the binding affinities of eugenol to NMDA receptor are -5.1 kcal/mol, comparable to the reference NMDA antagonist memantine which has -5.6 kcal/mol binding affinity to NMDA receptor. Eugenol formed hydrophobic bonds with NMDA receptors at VAL644, PHE554, TRP563 and TYR647 residues, comparable to the binding affinity of NMDA antagonist memantine. ADMET analysis showed that eugenol has high intestinal absorption and good bioavailability (>90%) and can cross the BBB easily (logBB> 0.3). On the basis of our in vivo trials and in silico report, we settled that the acquisition effect of S. aromaticum might be due to the antioxidant and antagonistic properties of eugenol to NMDA receptor. It would be reasonable to conduct mechanistic research in the forthcoming days to elucidate the underlying mechanisms utilizing various methodologies. Dhaka Univ. J. Pharm. Sci. 23(1): 77-92, 2024 (June)

Laser-assisted synthesis of MnOx and 3D graphene composites for wide-voltage flexible planar microcapacitors

Herein, we propose a simple and eco-friendly strategy based on laser direct writing technology to regulate the valence state of Mn-based oxides nanoparticles anchored on three-dimensional(3D) framework of laser-induced graphene (LIG) for the application of high-performance flexible planar interdigital microsupercapacitors (MSCs). Under the laser radiation, a polyimide (PI) and MnO2 composite film was directly converted into 3D graphene and MnOx (MnO, MnO2 and Mn3O4 mixture). Furthermore, the results reveal that LIG/MnOx MSCs device exhibits a high specific capacitance of 23.3 mF cm−2, which is about 29 times higher than that of LIG MSCs (0.8 mF cm−2). Moreover, LIG/MnOx MSCs possess a high energy density of 7.28 μWh cm−2, outstanding cycle stability with the capacitance retention rate of 97.4 % after 10,000 cycles, and excellent mechanical flexibility. Therefore, this preparation strategy of LIG/MnOx provides a reference for the performance regulation of Mn-based oxides and the low-cost construction of MSCs devices with high energy density, which carries significant research implications for diverse flexible wearable electronic applications in the forthcoming days.

Progression of COVID-19 Cases in Telangana State by using ARIMA, MLP, ELM and LSTM Prediction Models by Retrospective Confirmation

Objective: The importance of this research article is to evaluate efficient model for diagnosing pandemic COVID-19 positive cases in Telangana State, India. Method: Neural Network models (Extreme Learning Machine and Multi-Layer Perception), Deep Learning Neural Network model (Long Short Term Memory-LSTM) and traditional Auto Regressive Integrated Moving Average (ARIMA) models were applied and the data was converted from non-linear to linear (stationarity) for forecasting Covid-19 positive cases. The study of the data covered from 1st. Dec 2020 to 30th May 2021. 80% of train data was taken to fit the models and then 20% of the test data was used to predict the values. The deviation between original test data and predicted data led to an error. Among these error values, the model which had minimum errors was considered as the best of the four models. Findings: LSTM model was proved to be the most efficient model, as a result of the least Root mean square error (RMSE = 71.12) compared to ARIMA (258.20), ELM (553.67) and MLP (641.86) values. Novelty: These forecasting methods succour to predict the Covid-19 positive cases in the forthcoming days. This has been suggested for taking the better preventive steps to control the Covid-19 positive cases. Keywords: COVID­19, ARIMA, LSTM, MLP, ELM Forecasting

An Autoregressive Graph Convolutional Long Short-Term Memory Hybrid Neural Network for Accurate Prediction of COVID-19 Cases

Efficient prediction of COVID-19 cases could prepare the healthcare system to accommodate the COVID-19 cases in the forthcoming days and improve the overall resource management. A hybrid model comprised of an autoregressive filter, a graph convolutional neural network (GCN), and a long short-term memory neural network is proposed for COVID-19 cases prediction in USA. It captures accurately both linearities and nonlinearities present in the time series. An adjacency matrix is exploited in GCN that relies on Granger causality tests applied to historical COVID-19 cases for each state in USA. By doing so, the latent information about the spread of the virus is captured efficiently and the prediction performance of the hybrid model is improved, revealing which state truly affects the other ones. The proposed method outperforms the state-of-the-art techniques.

Revisiting the stress recovery hypothesis: Differential associations of cortisol stress reactivity and recovery after acute psychosocial stress with markers of long-term stress and health

Exposure to excessive and long-term stress may result in dysregulation of the stress system, including the acute stress response. In particular, failure to downregulate stress-related reactivity may lead to prolonged stress responses and the accumulation of allostatic load. However, the contribution of altered acute cortisol recovery to chronic stress and associated health impairments has often been neglected. Addressing this lack of research, we explored whether recovery from – more so than reactivity to – acute stress captures the basal stress load of an individual. Using Piecewise Growth Curve Models with Landmark Registration, we analyzed cortisol reactivity and recovery slopes of 130 healthy participants exposed to a standardized psychosocial laboratory stressor. Reactivity and recovery were predicted by measures indicative of long-term stress and its downstream effects, including self-report questionnaires, diurnal cortisol indices [cortisol awakening response (CAR); diurnal cortisol slope], markers of pro-inflammatory activity (interleukin-6; high-sensitive C-reactive protein), and hippocampal volume (HCV). Among these measures, only an increased CAR was specifically and consistently associated with relatively impaired recovery. Since the CAR represents the physiological enhancement needed to meet the anticipated demands of the forthcoming day, this finding may highlight the contribution of cognitive processes in determining both CAR and acute stress recovery. Furthermore, greater cortisol reactivity covaried with smaller HCV, showing that increased acute reactivity translates to health-relevant downstream effects. The lack of further associations between long-term and acute stress measures may arise from biases in self-reported chronic stress and the rigorously health-screened study sample. Overall, our findings suggest that while cortisol stress recovery might not supersede reactivity as an indicator of the long-term stress load or associated health effects, recovery and reactivity have differential utility in describing individuals’ allostatic states.

Smart Air Pollution Monitoring System

<p>Air is one of the most basic and important elements for human beings to survive. Clean and Sound air is the key to a good and healthy life. But nowadays in city life it has become the most threatening factor. Pollution of air has become the most concerning and affected issue now for us. As time goes, people are finding it very difficult to cope with the growing levels of air pollution. This proposed model aims to present a smart air pollution monitoring system that calculates and predicts the air quality in real time in the environment. The proposed system measures the concentration of air pollution causing gases in the environment using sensors. The acquired data is processed using Raspberry Pi and the collected information is stored in Firebase. With the help of the training data set, the air quality in forthcoming days can be predicted using the android application and a notification feature is included to indicate very severe pollution levels. The forecasting is done using the machine learning algorithms such as Linear Regression and Random Forest Regression with accuracy of 96.52% and 99.2% respectively.</p>

On-chip supply noise in multiprocessors: impact and clock gating inspired mitigation strategies

ABSTRACT In several instances, the electronic appliances used in the Internet-on-Things (IoT) systems fail to perform according to their full capability. One of the main reasons pertaining to this issue is the IoT devices are usually controlled by clock-driven application-specific processors enabling them to communicate over the common network and accomplish given tasks. But these processor chips are mostly prone to supply noise and adversely affect the performance of the IoT systems. In this article, the problem of on-chip power supply noise (PSN) is discussed and how clock gating (CG) is a potential candidate to mitigate i ( t ) and d i ( t ) d t which are the dominant factors of instigating the overall on-chip PSN. Majority of the CG schemes are examined and it is seen that LCT-CG implementation can potentially commit 84.44% less PSN compared to the non-gated peer as well as 55.21%, 54.45% and 74.47% lesser than the other CG schemes like LB-CG, NC2MOS-CG and DG-CG, respectively. In addition, it is also observed that with the inclusion of LCT-CG in IBM® i650 A-Z80 processor chip, the system lifetime is improved by 17.70% compared to the non-gated peer. In addition, the industry standard of implementing CG schemes in integrated circuit (IC) chips is reviewed and it is found quite different from the traditional methods. Therefore, an extensive study is performed to portray the possibility of LCT-CG can become an industry standard in the forthcoming days.

Enabling Unmanned Aerial Vehicle Borne Secure Communication With Classification Framework for Industry 5.0

The fifth industrial revolution (Industry 5.0) integrates humans and machines to satisfy the increasing customization demands of the manufacturing complexity using an optimized robotized manufacturing process. Industry 5.0 make use of collaborative robots (cobots) for optimizing productivity and ensuring safety. At the same time, unmanned aerial vehicles (UAVs) are predicted to be the main part of industry 5.0 in the forthcoming days. Regardless of high mobility and energy-limited UAVs for wireless communication as significant advantages, different issues are also existing in the UAV networks, such as security, reliability, etc. Several research works have focused on resolving security issues in UAV communication to support safety-critical applications. With this motivation, this article presents an artificial intelligence-based UAV-borne secure communication with classification (AIUAV-SCC) framework for industry 5.0 environment. The proposed AIUAV-SCC model involves two major phases namely image steganography-based secure communication and deep learning (DL)-based classification. At the initial stage, a new image steganography technique with multilevel discrete wavelet transformation, quantum bacterial colony optimization based optimal pixel selection, and encryption processes take place. Next, in the second stage, the Bayesian optimization (BO)-based SqueezeNet model is applied for the classification of securely received UAV images where the parameters in the SqueezeNet method are optimally tuned by the utilize of the BO technique. To validate the performance of the presented model, extensive simulations are applied using the UC Merced dataset (UCM) aerial dataset and the outcomes are investigated under several dimensions. The outcomes make sure the goodness of the presented model on test UCM aerial dataset over the compared methods.

Energy Efficient Data Transmission Scheme for Internet of Things Applications

Recently, there is a rapid increase in the use of Internet of Things (IoT) technology, and it is envisaged that in the forthcoming days, billions of devices and things are going to be interconnected among themselves with the help of Internet. To make this technology self-sustainable, there is a need for an incessant energy supply that can be achieved through green energy harvesting. IoT has attracted the attention of researchers as well as practitioners all over the globe by serving as an important architecture for communication systems, but the terminal devices used in IoT are resource-constrained, which results in low energy storage capacity and low computing power. Among several tasks that need to be performed at the level of IoT node, the data transmission is the most energy intensive phase. To provide continuous power to nodes used in IoT systems, it is imperative that the available energy source should be used judiciously and in an optimized manner. In this paper, an energy efficient data transmission scheme for IoT devices has been proposed. The result obtained through extensive experiments depicts that there is a high potential for saving energy during the process of data transmission.

Machine Learning-Driven High-Throughput Screening of Alloy-Based Catalysts for Selective CO2 Hydrogenation to Methanol

The revolutionary development of machine learning and data science and exploration of its application in material science are huge achievements of the scientific community in the past decade. In this work, we have reported an efficient approach of machine learning-aided high-throughput screening for finding selective earth-abundant high-entropy alloy-based catalysts for CO2 to methanol formation using a machine learning algorithm and microstructure model. For this, we have chosen earth-abundant Cu, Co, Ni, Zn, and Mg metals to form various alloy-based compositions (bimetallic, trimetallic, tetrametallic, and high-entropy alloys) for selective CO2 reduction reaction toward CH3OH. Since there are several possible surface microstructures for different alloys, we have used machine learning along with DFT calculations for high-throughput screening of the catalysts. In this study, the stability of various 8-atom fcc periodic (111) surface unit cells has been calculated using the atomic-size difference factor (δ) as well as the ratio taken from Gibbs free energy of mixing (Ω). Thinking about the simplicity and accuracy, microstructure models by considering the neighboring atoms of the adsorption sites and others as Cu atoms have been considered for different adsorption sites (on-top, bridge, and hollow-hcp). Moreover, the adsorption energies of the *H, *O, *CO, *HCO, *H2CO, and *H3CO intermediates have been predicted using the best fitted algorithm of the training set. The predicted adsorption energies have been screened based on the pure Cu adsorption energy. Furthermore, the screened catalysts have been correlated among different adsorption site microstructures. At the end, we were able to find seven active catalysts, among which two catalysts are CuCoNiZn-based tetrametallic, three catalysts are CuNiZn-based trimetallic, and two catalysts are CuCoZn-based trimetallic alloys. Hence, this work demonstrates not an ultimate but an efficient approach for finding new product-selective catalysts, and we expect that it can be convenient for other similar types of reactions in forthcoming days.

Epidemic Modelling of COVID-19

Abstract: COVID-19 has had a disastrous impact on millions of lives all over the world.199,466,211 confirmed cases of COVID19 and 4,244,541 deaths have been reported to WHO till 4th august. Analyzing the available data and predicting the pandemic trend is important since the situation can be controlled only when there is adequate preparation. Research using epidemiological models helps in analyzing different facets of COVID including infection, recovery and death rate. Predicting the daily increase of cases can help reduce the burden on health care workers and government by aiding them in planning the required resources in advance. Thus, in this project data driven epidemic modelling approach is used. COVID cases of 10 forthcoming days using three modelling techniques namely Polynomial Regression, Bayesian Ridge Regression and Support Vector Machine are predicted. The performance metric used to identify the best model are MSE and MAE. Polynomial Regression is found to have best performance followed by Bayesian ridge regression. Support Vector Machine has a poor performance. Keywords: Epidemic Modelling, COVID-19, Machine Learning, Polynomial Regression, Bayesian Ridge Regression, Support Vector Machine

Economic Analysis on Covid-19 in Rural Economy

IntroductionThe government of India under Prime Minister Narendra Modi ordered a nationwide lockdown to contain the spread of coronavirus outbreak in four phases as Phase 1: 25 March 2020-14 April 2020 (21 days), Phase 2: 15 April 2020-3 May 2020 (19 days), Phase 3: 4 May 2020-17 May 2020 (14 days), Phase 4: 18 May 2020-31 May 2020 (14 days) and ban the people from stepping out of their homes and all human economic activities for their breadwinning are paralyzing all over India. The government of Tamil Nadu followed the central government order. It implemented the same, which leads a great loss of economic activities of every individual, specifically in the rural agricultural economy.Objectives• The general objective is to identify the Economic effect of Covid-19 in the study area, specifically.• To examine the socio-economic status of the respondents in Musuvanoothu Panchayat.• To find out the awareness, perception and adapted preventive measures apart from social distance in the study area and• To identify the challenges and problems due to lockdown in the study area.Methods and MaterialsIt is a cross-sectional study with primary data collected from a panchayat. A panchayat named Musuvanoothu was selected from Nilakkottai Taluk, Dindigul District. The panchayat is consists of six hamlets with 1078 ration cardholders receiving their ration items from two ration shops one in head village Musuvanoothu and another one in Kallukottai. The respondents were selected from the head village ration shop accessing people alone and the interviewed number is 615 out of 670. The remaining persons are reluctant to answer the questions raised by the interviewer. A Purposive nonrandom sampling method was used in this paper with simple percentage analysis.Results and DiscussionThree fourth of the respondents are belong to the high potential age group of the labor force and doing farming activities, specifically floriculture and intercropping with vegetables. The yields obtain during the lockdown period leftover on the farm and losing earnings in front of their eyesight. Advances of money taken from the flower and vegetable merchants for farming activities cannot repay/settle by the farmers in the study area. Also, respondents borrowed money from money lenders for their lockdown food consumption expenses.ConclusionHealthy innocent farming communities are suffering from stress and mental health issue due to lockdown. A good amount of economic loss incurred which can be mitigated or avoided if public cold storage is availed in the study area. Small and marginal farmer’s agricultural loans & credit may be waived for their betterment in forthcoming days.

COVID-19 pandemic in India: Modelling, forecasting and risk assessment

Amid the relentless spread of COVID-19 pandemic, India spiked to the fifth rank among the nations on the daily increase of death toll during August 2020. In this scenario, the study mainly focuses on appalling and unprecedented COVID-19 mortality in India. The most adaptable epidemiological index for measuring the severity of the disease is Case Fatality Rate (CFR) and is estimated using the Yoshikura method. The estimated CFR of 10 states in India is compared along with the general formula of CFR and Kerala is found to be having the least CFR of 0.40% indicating the least severity of disease. The steadily increasing deaths in India are modelled using probability distributions such as Weibull, Gamma, and Lognormal in order to obtain the best fitted model with the data. The study demonstrated that the Gamma distribution is the best fitting probability model. Time-series modelling is used to analyse the trend and forecasting pattern of mortality. The ARIMA model indicates an ascending trend of death in upcoming days and this prescient model gives help to the administrative authorities and medical personnel in health care service and infrastructure arrangements in forthcoming days. The major mitigation step to resolve and restrain the pandemic is vaccination.

Resource Utilization Prediction in Cloud Computing using Hybrid Model

In cloud environment, maximum utilization of resource is possible with good resource management strategies. Workload prediction plays a vital role in estimating the actual resource required for successful execution of an application on cloud. Most of the existing works concentrated on predicting workloads which either showed clear seasonality/trend or for irregular workload patterns. This paper presents a new perspective in forecasting both seasonal and non-seasonal workloads. To accomplish this, a hybrid prediction model which is a combination of statistical and machine learning technique is proposed. Suppose the seasonality exists in the workload pattern, Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is applied for prediction. For non-seasonal workloads Long Short-Term Memory networks (LSTM) or AutoRegressive Integrated Moving Average (ARIMA) model is used based on the results of normality test. This paper presents a prediction model which forecasts the actual resource required for diverse time intervals of daily, hourly and minutes utilization. The experimental results confirm that accuracy of the prediction of LSTM model outperformed ARIMA for irregular workload patterns. The SARIMA model accurately forecasts the resource usage for forthcoming days. This work actually helps the cloud service provider (CSP) to analyze the workload and predict accordingly to avoid over or under provisioning of the cloud resources.

Business Impact of COVID-19 Pandemic on Pharmacy Shops of Dharan-18: A Sample Survey

The present study aims to describe the effect of the COVID-19 pandemic and the government imposed lockdown on the business activities of pharmacy shop owners (as small business operators) in the locality of Dharan-18. A sample survey was carried out in 32 participants using a self-designed and self-administered questionnaire comprising participants ‘demographic details and 14 question items concerning the impact of the pandemic and lockdown on their businesses. All the participants responded. The results show that all the pharmacy operators were adversely affected by the pandemic and the imposed lockdown. Most of the businesses temporarily suspended their operations while some stopped operating. The essential supplies and access to goods and services as well as financial access have been severely hindered; however, no remarkable price hike has been observed. The expectations of the participants about changes in near future lack consistencies. The participants have expressed lack of confidence in carrying out business in the forthcoming days. In conclusion, the COVID-19 pandemic and the government-imposed lockdown has affected almost all of the pharmacy operators in the locality of Dharan-18.

The Unexpected Setback to Bengaluru - COVID-19 Disaster Management Review

Negative impact of COVID-19 exist in every place of the Indian1 subcontinent. Chennai, Mumbai and Delhi were affected so badly in Quarter 2 with a huge number of cases; however, Kolkata and Bangalore were doing good with a smaller number of cases. At Bangalore, as on 15 June 2020, the situation was good2 and by end of June 2020, the COVID19 situation of Bangalore started towards the negative side as the number of cases is increasing more. By the last week of October, the number of confirmed cases came down. Aim: The primary objective of the research paper is to explore the current state of COVID-19 pandemic of Bangalore by comparing the previous months. We attempt to explain how the condition was good till June and how badly it was affected by the huge number of cases (4-5 times) from June end onwards. Further, we predict the confirmed cases and death rates for the forthcoming days for Bangalore. Technique, Sample and method: For this research work, we have used the data from 15 June 2020 to 26 October 2020 from the government of Karnataka and Bruhat Bengaluru Mahanagara Palike3 (BBMP). To forecast the COVID-19 cumulative confirmed and death cases for Bangalore and Other major cities (for comparison), we have used FORECAST.ETS function in Microsoft Excel. The forecasting is estimated till 31 December 2020. Results: By 15 Nov 2020, the forecasted cumulative cases would be 368264 in the Bangalore city. By 30 Nov 2020, the forecasted cumulative cases would be 398929. By 15 Dec 2020, the forecasted cumulative cases would be 429594 and by 31 Dec 2020, the forecasted cumulative cases would be 462303. If the situation goes bad due to various reasons, the number of cumulative cases may go up to 634462. The average COVID-19 case growth of Bangalore is 141% (Average from June to Oct 2020). Conclusion: When Major cities of India were doing bad in terms of COVID-19, Bangalore was doing well for the first few months. From June end onwards, the covid-19 cases increased in Bangalore by 4-5 times which is unexpected. By the end of October, the rate of COVID-19 increase is comparatively less than the previous months. For now, COVID-19 situation may be under control to some extent, however, that does not mean that this scenario will continue. At any point in time, the situation may go back to the worst condition as similar to the second wave of COVID-19 is being encountered by the European countries. Proactive approaches and pandemic management strategies should be in place to manage the worst situation. People and the economy affected badly due to COVID-19 already and it may not be feasible to suffer once again.

Current scenario of wind power in India, government policies, initiatives, status and challenges

PurposeThis paper aims to review the role of government initiatives for the development of wind power industries in India, to provide better and benevolent policies in the production of wind energy density and to maximize the use of the renewable source of energy which permits to reduce carbon emission from the coal-based power plant and to curtail tackle need of society and mitigate poverty.Design/methodology/approachThe present study is carried out on the current position of wind power generation in India. Government policies for promoting clean energy and associated problems are also analysed herein detail. However, secondary approaches are opted in terms of alertness of caring for the environment hazardous and reduced the major economies aspects by fulfilling the schema of Kyoto Protocol and Paris Agreement, United Nations Framework Convention on Climate Change.FindingsThe prospective of wind energy generation is huge, as an ancient source of energy, wind can be used both as a source of electricity and for agricultural, irrigation uses. The study of wind turbine blades and its features showed how it can be properly fabricated and used to extract the maximum power, even at variable and low wind speeds.Research limitations/implicationsAlthough India has achieved a remarkable advancement in wind power sectors, it needs to eradicate all the loopholes to evolve as super power in wind energy sector leaving behind its rivalry China. To do this, it is required to develop in many fields such as skilled manpower, advancement in research and development, grid and turbine installation, proper distribution, smooth land acquisition, modern infrastructure, high investment and above all industry friendly government policy.Practical implicationsThe present study finds out effects of wind power energy as a source of renewable energy to mitigate energy crisis.Social implicationsAs a source of renewable energy and cost effectiveness, wind power can be evolved as a potential means enhance social life.Originality/valueThe present paper caries out critical analysis for the active use of renewable energy in the present and forthcoming days. Such unique analysis must help India as a developing nation to balance its energy crisis.

Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States

An accurate forecast of fine particulate matter (PM2.5) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM2.5 pollution events. Though the European Copernicus Atmosphere Monitoring Service (CAMS) provides global PM2.5 forecasts up to the next 120 h at a 3 h time interval, the data accuracy of this product had not been well evaluated. By using hourly PM2.5 concentration data that were sampled in China and United States (US) between 2017 and 2018, the data accuracy and bias levels of CAMS PM2.5 concentration forecast over these two countries were examined. Ground-based validation results indicate a relatively low accuracy of raw PM2.5 forecasts given the presence of large and spatially varied modeling biases, especially in northwest China and the western United States. Specifically, the PM2.5 forecasts in China showed a mean correlation value ranging 0.31–0.45 (0.24–0.42 in US) and RMSE of 38–83 (8.30–16.76 in US) μg/m3, as the forecasting time horizons increased from 3 h to 120 h. Additionally, the data accuracy was found to not only decrease with the increase of forecasting time horizons but also exhibit an evident diurnal cycle. This implies the current CAMS forecasting model failed to resolve the local processes that modulate the diurnal variability of PM2.5. Moreover, the data accuracy varied between seasons, as accurate PM2.5 forecasts were more likely to be derived in the autumn in China, whereas these were more likely in spring in the US. To improve the data accuracy of the raw PM2.5 forecasts, a statistical bias correction model was then established using the random forest method to account for large modeling biases. The cross-validation results clearly demonstrated the effectiveness and benefits of the proposed bias correction model, as the diurnal varied and temporally increasing modeling biases were substantially reduced after the calibration. Overall, the calibrated CAMS PM2.5 forecasts could be used as a promising data source to prevent general public from severe PM2.5 pollution events given the improved data accuracy.

An Overview on Pulp and Paper Manufacturing Process and its Waste Treatment

The pulp and paper industry has a great impact in the world. It has been manufactured for ages and ages, it is still booming up as industry in small scale and large scale levels. Despite the fact that they produce printing and writing papers, paper rolls, packaging material etc. they also produce large amount of waste materials from their Industry by Manufacturing. This when left untreated leads to environmental pollution, harsh contamination and so on. As every industry has its own waste water treatment methods to recycle, reuse and reduce pollution. This Industry also has its own criteria to carry out the process of waste water treatment, yet it lags in being efficient in it. By the sources collected and compared with other kinds of industries it is listed that pulp and paper manufacturing industry is one of the top most industries to produce large volumes of waste water which has degradable organics at higher level. Hence this paper aims to study the manufacturing process, waste water components released and its effect on environment, the treatment methods carried out and how it can be improvised efficiently in forthcoming days