In this paper, we consider the jet engine vibration system. With the help of the homotopy perturbation method (HPM), we solve the jet engine vibration (JEV) equation, which is a second-order nonlinear differential equation in the presence of damping and external forces. We compare the results with the corresponding numerical method to show the efficiency and reliability of this method. The findings demonstrate how well HPM works as a solution to these concerns, and it is anticipated that HPM will be used in a variety of new problems.
The necessity to achieve global sustainability and renewable energy resources to overcome the threat of climate change has led to unparalleled social and economic alterations ubiquitous throughout the world. The environment surrounding pollution correlated with global warming is essential to technological advances for developing energy resources. Renewable and sustainable energy resources are eco-friendly energy sources that transform heat directly into electrical energy. The transformation of the global energy system underway in various countries can play a predominant role in power sectors. It helps in establishing a low-power solution with high performance in advance. This review paper highlights technological feasibility within the economic activity of sustainable resources in various sectors. The sustainable resource would represent the world's most advanced technology and cost-effective global power transition pathway. The present status, prospects, and updated information about these energy resources have been discussed in detail. This review provides a recommendation on which energy would be suitable for electrical energy generation and would establish a green environment for the next generation to survive in the world.
Biomedical waste management is a serious issue to health and environment that must be addressed at primary health care level, especially in Bauchi local government area of Bauchi state. The objectives of this study were to assess on the knowledge, methods and problems of biomedical waste management among the primary health care workers in some selected Health Care Centers of Bauchi L.G.A. of Bauchi state. A cross sectional study was employed and sampling techniques were used to distribute a questionnaire and interview among the health workers at twenty (20) selected primary health centers with (200) sample respondents from public and private health care centers. The result from the current study shows that the knowledge of biomedical waste management awareness among the workers was represented (94%), and only (6%) were not aware. The majority of PHC workers practiced open dumping and burning method (74%), incineration (7%), chemical treatment and only 1% for autoclave methods. while, microwaving (0%), encapsulation (0%) and sanitary landfill methods (0%) were not practiced. The problems of biomedical waste management included improper planning (33%), insufficient funds (25%), lack of material (19%) and (15%) lack of staffs training. There was improper segregation, lack of planning, lack of funds and practiced open dumping and burning, which is against the biomedical Waste (Management and Handling) Rules of 2016. There was improper biomedical waste management at primary health care level in Bauchi local government of Bauchi state. Thus, the Bauchi government should give more consideration towards good plan, allocation resources, set a committee of adequate supervision, monitoring and evaluation for the sustainable biomedical waste management at Bauchi local government of Bauchi state. .
The sustainability of the recent economic progress of Bangladesh is critically dependent on how it faces environmental challenges, as the country is one of the primary victims of climate alteration. Taking into account the crucial roles of energy sources in this scenario, we analyze the impacts of non-renewable and renewable energy consumption (NREC and REC) on the growth-environment nexus in Bangladesh from 1980 to 2018. Based on the Auto-Regressive Distributed Lag (ARDL) model with and without structural breaks and policy dummies, our findings show that REC significantly upsurges economic growth, whereas NREC diminishes it. However, NREC leads to environmental deterioration, while REC enhances environmental quality. Besides, our results fail to support the Environmental Kuznets Curve hypothesis for Bangladesh. Interestingly, the policy dummy upsurges CO 2 discharges while lessening economic growth, implying that the Bangladesh government's policies do not adequately cut pollution. Our Toda-Yamamoto non-causality test indicates a unidirectional causality running from GDP and its square term and NREC to CO 2 emissions. Our findings suggest that policymakers in Bangladesh should adopt and implement strategies like enhancing renewable energy production, investment subsidies, tax credits, quota policies, and technological advancements to boost REC while plunging NREC to achieve economic sustainability.
The modified Zakharov–Kuznetsov (mZK) and the (2 + 1)-dimensional Calogero -Bogoyavlenskii-Schif (CBS) models convey a significant role to instruct the internal structure of tangible composite phenomena in the domain of two-dimensional discrete electrical lattice, plasma physics, wave behaviors of deep oceans, nonlinear optics, etc. In this article, the dynamic, companionable and further broad-spectrum exact solitary solitons are extracted to the formerly stated nonlinear models by the aid of the recently enhanced auxiliary equation method through the traveling wave transformation. )e implication of the soliton solutions attained with arbitrary constants can be substantial to interpret the involuted phenomena. The established soliton solutions show that the approach is broad-spectrum, efficient, and algebraic computing friendly and it may be used to classify a variety of wave shapes. We analyze the achieved solitons by sketching figures for distinct values of the associated parameters by the aid of the Wolfram Mathematica program.
Many Japanese literary texts have been translated recently into Bangla. However, nobody has yet identified the first translation of Japanese literary text in the periodicals until today. The purpose of this paper is first to map the early phase of Japanese literature in Bangla periodicals; second, it attempts to distinguish the first piece of Japanese literature in Bangla; and third, it strives to ascertain the trends in interpreting Japanese literature in the second half of the 19th century. At least four pieces of literature are unearthed for the first time in this article. Despite not embracing the original Japanese or English name in the title in Bangla, Gonpachikomurasakihiyokufun (The Loves of Gompachi and Komurasaki) and Hone Kawa (Bones and Ribs) became the earliest Japanese literary specimens in Bangla. Colonial influence and preference for works of religious significance pertinent to Bengal society – are pinned down as the trends in interpreting Japanese literature in periodicals in the 19th century.
The spatial characteristics of the propagation channel have a considerable impact on the applicability of multi‐antenna systems. In this paper, a non‐stationary 3‐D GBSM vehicle‐to‐vehicle channel model is proposed in the tunnel environment based on massive multiple‐input multiple‐output antenna arrays. Instead of the plane wavefront assumptions utilized in traditional multiple‐input multiple‐output systems, the proposed channel model for vehicle‐to‐vehicle communications uses spherical wavefront assumptions. Initially, the channel impulse response and closed‐form expression for the probability density function of angle‐of‐departure and angle‐of‐arrival are derived in the elevation and azimuth planes. Following that, due to the mobility of transmitting and receiving antenna arrays, expressions for the delay spread (DS), Doppler power spectrum density, temporal cross‐correlation function, and channel capacity are extracted by examining line of sight and the non line of sight propagation paths. The influence of numerous model parameters on the temporal cross‐correlation function is also investigated, including antenna array spacing, K‐factor, movement velocity, and time separation. The proposed 3‐D model's statistical characteristics are verified through measurements, simulations, and analytical results, revealing its adaptability and effectiveness in the high‐speed‐train environment.
Current literature conveys that in spite of multiple studies being conducted to explore the influences of various macroeconomic factors both geographical and non-geographical on the CO2 emissions in different parts of the world, there is a scarcity of the same analyses from oil-producing countries. In this study, we reveal a new dimension by investigating the dynamic linkage of climate change, economic growth, energy use, and agricultural and rural development to the CO2 emissions of oil-producing countries around the world. In doing so, we apply Pedroni and Kao panel cointegration test, vector error correction model (VECM), pairwise Granger causality test, impulse response function (IRF), and some supportive models such as-generalized method of moments (GMM), and fixed-effect models. Our primary VAR-based models’ evidence that energy use (EUE), foreign direct investment (FDI), and trade to GDP (TPR) rate have both short-run and long-run casual consequences in CO2 emissions, while only long-run Granger causality is running from agricultural land ratio (ALR), forest area ratio (FAR), gross domestic product (GDP), population growth rate (PGR), renewable energy consumption (REC), and rural population rate (RPR) to CO2 emissions. However, bidirectional associations are observed between CO2 to foreign direct investment and trade percentage rate; EUE to renewable energy consumption and TPR; and TPR to FDI and gross domestic product. To demonstrate the significant impact, our secondary analysis tools GMM and fixed-effect regressions’ results disclose that high energy use and more domestic products significantly contaminate the environmental condition by increasing CO2 emissions in the atmosphere. Hence, our research provides great implications for the authorities of government, producers, businessmen, and general public in the oil-producing countries to ensure a sustainable environment by reducing energy use or alternating with renewable energies and emphasizing environmentally friendly products production over the long-run rather than conventional products production in the short-run.
Purchase intention has become a critical issue to the marketers of smartphones as the market has become very competitive, volatile, uncertain and dynamic during Covid-19 than ever before. For sustaining in the competitive market, every marketer is trying to upgrade its product appearance, product quality, service quality, attractive features, and latest version of software as a whole. This study has investigated the effects of product features, brand image, product price, and social influences on young customers’ purchase intention of smartphone during this Covid-19 pandemic time. Survey was conducted using structured questionnaire by collecting data from 305 respondents by using convenience sampling technique. Statistical Package for the Social Sciences (SPSS) integrated with AMOS was employed for data analysis. Cronbach’s alpha, composite reliability and average variance extracted (AVE) were used to test the reliability and validity of the collected data while hypotheses were tested by using Structural equation modeling (SEM). The findings of the study shows that, there is a significant effect of product features, brand image, and product price on purchase intention of a smartphone but social influences has no significant impact on young customers’ purchase intention. The study results will help the smartphone marketers to redesign their pandemic and post pandemic segmenting, targeting, differentiation and positioning strategies. Practical and managerial implications along with the future research directions have been discussed at the end of this paper also.
This study analyses the impact of different uncertainties on commodity markets to assess commodity markets’ hedging or safe-haven properties. Using time-varying dynamic conditional correlation and wavelet-based Quantile-on-Quantile regression models, our findings show that, both before and during the COVID-19 crisis, soybeans and clean energy stocks offer strong safe-haven opportunities against cryptocurrency price uncertainty and geopolitical risks (GPR). Soybean markets weakly hedge cryptocurrency policy uncertainty, US economic policy uncertainty, and crude oil volatility. In addition, GSCI commodity and crude oil also offer a weak safe-haven property against cryptocurrency uncertainties and GPR. Consistent with earlier studies, our findings indicate that safe-haven traits can alter across frequencies and quantiles. Our findings have significant implications for investors and regulators in hedging and making proper decisions, respectively, under diverse uncertain circumstances.
By integrating speech act and conservation of resources (COR) theories, the link between motivating language (ML) and commitment to quality customer service (CQCS) was tested. Furthermore, work engagement was introduced as a mediator and employee resilience as a moderator. Partial least squares-structural equation modelling (PLS-SEM) was applied to analyze the data collected from 424 employees in the hotel industry in Thailand. ML has direct and indirect effects on CQCS via employee work engagement. Employee resilience moderates the relationship between ML, work engagement, and CQCS. Overall, the findings indicate the use of ML, employee resilience, and engaged employees to generate CQCS in the hotel industry in Thailand. The study's novelty is that it provides greater insight into how ML, employee resilience, and engaged employees affect quality customer service in the hotel industry in Thailand. The findings contribute to COR and speech act theories by examining the direct outcomes of ML, i.e., CQCS, and how ML is more effective when employee resilience is a boundary condition. Practical and theoretical implications are described.
The Internet of Things (IoT) concept increases the spectrum demands of mobile users in wireless communications because of the intensive and heterogeneous structure of IoT. Various devices are joining IoT networks every day, and spectrum scarcity may be a crucial issue for IoT environments in the near future. Cognitive Radio (CR) is capable of sensing and detecting spectrum holes. With the aim of CR, more powerful IoT devices will be constructed in such crowded wireless environments. Also, dynamic and ad-hoc CR networks have not a fixed base station. Therefore, CR capable IoT (CR-based IoT) device approach with routing capabilities will be a solution for future IoT environments. In this study, spectrum aware Ad hoc On-Demand Distance Vector (AODV) routing protocol is proposed for CR-based IoT devices in IoT environments. For the performance analysis of the proposed method, various network scenarios with different idle probability have been performed and throughput and delay results for different offered loads have been analyzed.
Metabolic syndrome (MetS) is a common feature in obesity, comprising a cluster of abnormalities including abdominal fat accumulation, hyperglycemia, hyperinsulinemia, dyslipidemia, and hypertension, leading to diabetes and cardiovascular diseases (CVD). Intake of carbohydrates (CHO), particularly a sugary diet that rapidly increases blood glucose, triglycerides, and blood pressure levels is the predominant determining factor of MetS. Complex CHO, on the other hand, are a stable source of energy taking a longer time to digest. In particular, resistant starch (RS) or soluble fiber is an excellent source of prebiotics, which alter the gut microbial composition, which in turn improves metabolic control. Altering maternal CHO intake during pregnancy may result in the child developing MetS. Furthermore, lifestyle factors such as physical inactivity in combination with dietary habits may synergistically influence gene expression by modulating genetic and epigenetic regulators transforming childhood obesity into adolescent metabolic disorders. This review summarizes the common pathophysiology of MetS in connection with the nature of CHO, intrauterine nutrition, genetic predisposition, lifestyle factors, and advanced treatment approaches; it also emphasizes how dietary CHO may act as a key element in the pathogenesis and future therapeutic targets of obesity and MetS.
The present study was conducted with a view to examining the impact of occupational stress on employees' health risks. A total number of 350 garment employees (114 supervisors and 236 workers) were selected from 25 readymade garment factories in Dhaka, Narayanganj, and Gazipur industrial areas of Bangladesh on a random sampling basis. Occupational stress was estimated using an ERIs modified questionnaire; when self-reported health problems, work-related information, and socio-demographic information were obtained using face-to-face interviews using a pre-formed questionnaire. The survey was conducted for 2 years from January 2020 to December 2021 in Dhaka, Narayanganj, and Gazipur districts where most of the garment industries in Bangladesh are located. All data were processed by using Statistical Package for Social Sciences (SPSS) and Decision Analyst Stats, Version 2.0. For analyzing data, suitable statistical tools such as two-way ANOVA, z-test, chi-square test, Pearson's product-moment correlation, stepwise multiple regressions, and descriptive statistics were used. The results of the present study reveal that occupational stress had a significant positive influence on health risk. The findings also reveal that both the male and female employees perceived garment jobs as highly stressful and risky for their health causing many dies and sicknesses, but it was higher among the female employees than their counterparts. The study suggests that due to major illnesses and diseases garments employees lack sound health that has to consider remedying for reducing occupational stress and health risk.
The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.
The COVID-19 pandemic has posed a massive disruption to the finance sector. Islamic financial markets are no exception. We explore the resilience of Islamic financial markets to the COVID-19 pandemic vis-à-vis conventional markets. A comparative analysis of the impact of the first and second waves of COVID-19 is also conducted. We use five Dow Jones Islamic stock indices and two bond indices and their conventional counterparts as proxies of Islamic and conventional financial markets. Using wavelet, wavelet-based Granger causality, hedge ratio, optimal weights, and hedging effectiveness methods from January 1, 2019, to February 26, 2021, our empirical estimates indicate that both Islamic and conventional stock indices are almost similarly affected by the extreme market turbulence triggered by COVID-19. Hence, Islamic stock markets fail to provide diversification benefits. We also unveil no significant differences between the first and second waves of COVID-19 in the case of dependency. Conversely, Islamic bonds exhibit low dependence on their conventional counterparts, indicating their diversification benefits. We further demonstrate that Islamic and conventional bond pairs could be utilized as a strong portfolio mix because the least hedging cost and highest hedging effectiveness are observed in those portfolios, especially during COVID-19. Overall, our results suggest that global Sukuk offers more resilience in times of extreme market turmoil than other instruments considered in this study. Our findings present global investors and regulators with new insights on diversification and hedging strategy with Islamic finance during a worldwide, severe economic crisis. We present some policy recommendations in creating a more sustainable financial system post-COVID-19.
Pansharpening produces a high spatial‐spectral resolution pansharpened image by combining multispectral (MS) and panchromatic (PAN) images. In the traditional multi‐resolution analysis (MRA) method, detailed PAN images are extracted by transformation methods that are injected into MS images. This gives spatial and spectral distortions in the pansharpened image. These distortions can be reduced in the pansharpened image by the correct matching of the PAN detail image component. This correct matching is possible by the convolutional neural network (CNN)–based models. This paper obtains the detailed image component using the CNN models. This CNN model extracts the PAN detail image that is suitable for the MRA‐based pansharpening scheme which significantly reduces the spatial and spectral distortions. It is demonstrated by qualitative and quantitative analysis applied on GeoEye‐1 and IKONOS satellite images and shows the effectiveness of the proposed scheme.
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