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The prediction of solar power generation is a challenging task due to its dependence on climatic characteristics that exhibit spatial and temporal variability. The performance of a prediction model may vary across different places due to changes in data distribution, resulting in a model that works well in one region but not in others. Furthermore, as a consequence of global warming, there is a notable acceleration in the alteration of weather patterns on an annual basis. This phenomenon introduces the potential for diminished efficacy of existing models, even within the same geographical region, as time progresses. In this paper, a domain adaptive deep learning-based framework is proposed to estimate solar power generation using weather features that can solve the aforementioned challenges. A feed-forward deep convolutional network model is trained for a known location dataset in a supervised manner and utilized to predict the solar power of an unknown location later. This adaptive data-driven approach exhibits notable advantages in terms of computing speed, storage efficiency, and its ability to improve outcomes in scenarios where state-of-the-art non-adaptive methods fail. Our method has shown an improvement of $10.47 \%$, $7.44 \%$, $5.11\%$ in solar power prediction accuracy compared to best performing non-adaptive method for California (CA), Florida (FL) and New York (NY), respectively.

Accurate prediction of wind power is essential for the grid integration of this intermittent renewable source and aiding grid planners in forecasting available wind capacity. Spatial differences lead to discrepancies in climatological data distributions between two geographically dispersed regions, consequently making the prediction task more difficult. Thus, a prediction model that learns from the data of a particular climatic region can suffer from being less robust. A deep neural network (DNN) based domain adaptive approach is proposed to counter this drawback. Effective weather features from a large set of weather parameters are selected using a random forest approach. A pre-trained model from the source domain is utilized to perform the prediction task, assuming no source data is available during target domain prediction. The weights of only the last few layers of the DNN model are updated throughout the task, keeping the rest of the network unchanged, making the model faster compared to the traditional approaches. The proposed approach demonstrates higher accuracy ranging from 6.14% to even 28.44% compared to the traditional non-adaptive method.

The objective of this study to identify the problem areas and developmental issues attached to entrepreneurship related to small medium enterprises (SMEs) particularly in an underdeveloped country like Bangladesh. Small and medium-sized enterprises (SMEs) are the backbone of the national economy in Bangladesh. This sector is playing an important role to develop the economy of our country. The role SMEs sector is immense to alleviate the poverty from the country as well. Small and Medium enterprises are particularly suitable for the densely populated countries like Bangladesh where SME sector can provide huge employment opportunity with much lower investment. They are expected to create jobs, reduce poverty, and drive a resilient national economy. The International Monetary Fund (IMF) Country Report (2015)1indicated that SMEs in Bangladesh accounted for more than 99% of private sector industrial establishments and created job opportunities for 75%–85% of the nonagricultural labor force. Findings of this study are useful for entrepreneurs and policy makers of the country. In the paper a questionnaire has been constructed and used SPSS 20 version software to analyze the data. This paper is, therefore, an attempt to analyze various issues, prospects and challenges of financing this particular sector and find out the ways to overcome these challenges. It will also survey various literatures and reports on the concerned field and recommend supporting actions to help this highly prospective industrial sector to operate in a fully yielding manner.

The objective of this study to identify the problem areas and developmental issues attached to entrepreneurship related to small medium enterprises (SMEs) particularly in an underdeveloped country like Bangladesh. Small and medium-sized enterprises (SMEs) are the backbone of the national economy in Bangladesh. This sector is playing an important role to develop the economy of our country. The role SMEs sector is immense to alleviate the poverty from the country as well. Small and Medium enterprises are particularly suitable for the densely populated countries like Bangladesh where SME sector can provide huge employment opportunity with much lower investment. They are expected to create jobs, reduce poverty, and drive a resilient national economy. The International Monetary Fund (IMF) Country Report (2015)1indicated that SMEs in Bangladesh accounted for more than 99% of private sector industrial establishments and created job opportunities for 75%–85% of the nonagricultural labor force. Findings of this study are useful for entrepreneurs and policy makers of the country. In the paper a questionnaire has been constructed and used SPSS 20 version software to analyze the data. This paper is, therefore, an attempt to analyze various issues, prospects and challenges of financing this particular sector and find out the ways to overcome these challenges. It will also survey various literatures and reports on the concerned field and recommend supporting actions to help this highly prospective industrial sector to operate in a fully yielding manner.

The objective of this study to identify the problem areas and developmental issues attached to entrepreneurship related to small medium enterprises (SMEs) particularly in an underdeveloped country like Bangladesh. Small and medium-sized enterprises (SMEs) are the backbone of the national economy in Bangladesh. This sector is playing an important role to develop the economy of our country. The role SMEs sector is immense to alleviate the poverty from the country as well. Small and Medium enterprises are particularly suitable for the densely populated countries like Bangladesh where SME sector can provide huge employment opportunity with much lower investment. They are expected to create jobs, reduce poverty, and drive a resilient national economy. The International Monetary Fund (IMF) Country Report (2015)1indicated that SMEs in Bangladesh accounted for more than 99% of private sector industrial establishments and created job opportunities for 75%–85% of the nonagricultural labor force. Findings of this study are useful for entrepreneurs and policy makers of the country. In the paper a questionnaire has been constructed and used SPSS 20 version software to analyze the data. This paper is, therefore, an attempt to analyze various issues, prospects and challenges of financing this particular sector and find out the ways to overcome these challenges. It will also survey various literatures and reports on the concerned field and recommend supporting actions to help this highly prospective industrial sector to operate in a fully yielding manner.

The structure and material properties of the silicone rubber keys have great effects on the assembly reliability of the pins of the rubber keys’ in the holes of the printed circuit board (PCB). Too soft pins cause the insertion problem but too stiff pins cause shock. The interference between insert pins and the holes in PCB also has effects on the insertion. The spreading of surface mount technology (SMT) has generated a need for flexible placement machines that are capable of placing components very fast in a large variety of component types. PCB components that are not placed correctly in the layout cause problems when the board is being assembled by the author. Incorrectly placed components do not solder correctly, which results in a host of reliability problems in this project. The author has made a tape design for packing the rubber key. Tape shape and keys base shape relation have effects on the assembly process.

A brain tumor is a cancerous disease that can be fatal. The classification of brain tumors in clinical methods is time-consuming and error-prone. This paper proposes three deep convolutional neural network (CNN)-based architectures, ConVGXNet, ConResXNet, and ConIncXNet, by utilizing the concept of transfer learning. The proposed models extricate diversified features from magnetic resonance images (MRI). Many multi-class brain MRIs corresponding to glioma, pituitary, and no tumor patients are used to train the proposed models. This study also carries out data pre-processing and data augmentation for the models' effectiveness. A proper training configuration monitors the performance of the models. ConIncXNet is the most accurate of the architectures proposed. The accuracy of the ConIncXNet architecture is 97 percent. The models are also evaluated based on weighted F1 score, precision, and recall. This proposed CNN architecture can improve the classification of brain tumors in medical image diagnosis systems.

A brain tumor is a cancerous disease that can be fatal. The classification of brain tumors in clinical methods is time-consuming and error-prone. This paper proposes three deep convolutional neural network (CNN)-based architectures, ConVGXNet, ConResXNet, and ConIncXNet, by utilizing the concept of transfer learning. The proposed models extricate diversified features from magnetic resonance images (MRI). Many multi-class brain MRIs corresponding to glioma, pituitary, and no tumor patients are used to train the proposed models. This study also carries out data pre-processing and data augmentation for the models' effectiveness. A proper training configuration monitors the performance of the models. ConIncXNet is the most accurate of the architectures proposed. The accuracy of the ConIncXNet architecture is 97 percent. The models are also evaluated based on weighted F1 score, precision, and recall. This proposed CNN architecture can improve the classification of brain tumors in medical image diagnosis systems.

The structure and material properties of the silicone rubber keys have great effects on the assembly reliability of the pins of the rubber keys’ in the holes of the printed circuit board (PCB). Too soft pins cause the insertion problem but too stiff pins cause shock. The interference between insert pins and the holes in PCB also has effects on the insertion. The spreading of surface mount technology (SMT) has generated a need for flexible placement machines that are capable of placing components very fast in a large variety of component types. PCB components that are not placed correctly in the layout cause problems when the board is being assembled by the author. Incorrectly placed components do not solder correctly, which results in a host of reliability problems in this project. The author has made a tape design for packing the rubber key. Tape shape and keys base shape relation have effects on the assembly process.

Detecting out-of-distribution (OOD) data at inference time is crucial for many applications of machine learning. We present XOOD: a novel extreme value-based OOD detection framework for image classification that consists of two algorithms. The first, XOOD-M, is completely unsupervised, while the second XOOD-L is self-supervised. Both algorithms rely on the signals captured by the extreme values of the data in the activation layers of the neural network in order to distinguish between in-distribution and OOD instances. We show experimentally that both XOOD-M and XOOD-L outperform state-of-the-art OOD detection methods on many benchmark data sets in both efficiency and accuracy, reducing false-positive rate (FPR95) by 50%, while improving the inferencing time by an order of magnitude.

Recovering waste heat is one of the solutions found to lessen the emission and fuel consumption. Waste heat is heat energy produced by internal combustion wasted to the atmosphere. However, these low grade waste heats are not sufficient enough in generating power due to insufficient low temperature. Thus, to recover these waste heat, Organic Rankine Cycle (ORC) system is one of the beneficial exhaust heat recovery technologies which is widely used for the applications of low grade heat recovery rather than conventional Rankine cycle. This paper provides analytical study of the performance of supercritical ORC using exhaust aircraft engine as waste heat in order to find the best design configuration for the ORC system. The results show that supercritical ORC with superheater achieved higher net power output and thermal efficiency compared to the ORC system with preheater.

Recovering waste heat is one of the solutions found to lessen the emission and fuel consumption. Waste heat is heat energy produced by internal combustion wasted to the atmosphere. However, these low grade waste heats are not sufficient enough in generating power due to insufficient low temperature. Thus, to recover these waste heat, Organic Rankine Cycle (ORC) system is one of the beneficial exhaust heat recovery technologies which is widely used for the applications of low grade heat recovery rather than conventional Rankine cycle. This paper provides analytical study of the performance of supercritical ORC using exhaust aircraft engine as waste heat in order to find the best design configuration for the ORC system. The results show that supercritical ORC with superheater achieved higher net power output and thermal efficiency compared to the ORC system with preheater.

A Chatbot is a popular platform to enable users to interact with a software or website to gather information or execute actions in an automated fashion. In recent years, chatbots are being used for executing financial transactions, however, there are a number of security issues, such as secure authentication, data integrity, system availability and transparency, that must be carefully handled for their wide-scale adoption. Recently, the blockchain technology, with a number of security advantages, has emerged as one of the foundational technologies with the potential to disrupt a number of application domains, particularly in the financial sector. In this paper, we forward the idea of integrating a chatbot with blockchain technology in the view to improve the security issues in financial chatbots. More specifically, we present BONIK, a blockchain empowered chatbot for financial transactions, and discuss its architecture and design choices. Furthermore, we explore the developed Proof-of-Concept (PoC), evaluate its performance, analyse how different security and privacy issues are mitigated using BONIK., Comment: Accepted at the 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020)

Six heavy metals (chromium [Cr], nickel [Ni], copper [Cu], arsenic [As], cadmium [Cd], and lead [Pb]) were measured in sediments and soft tissues of eleven commonly consumed fish species collected from an urban river in the northern part of Bangladesh. The abundance of heavy metals in sediments varied in the decreasing order of Cr > Ni > Cu > Pb > As > Cd. The ranges of mean metal concentrations in fish species, in mg/kg wet weight (ww), were as follows: Cr, 0.11–0.46; Ni, 0.77–2.6; Cu, 0.57–2.1; As, 0.43–1.7; Cd, 0.020–0.23; and Pb, 0.15–1.1. Target hazard quotients (THQs) and target carcinogenic risk (TR) showed the intake of As and Pb through fish consumption were higher than the recommended values, indicating the consumption of these fish species is associated with noncarcinogenic and carcinogenic health risks.

Primary infections caused by rubella and Cytomegalovirus (CMV) can front-runner to thoughtful complications in pregnancy. Rubella and CMV screening of adult women is not consistently conceded out in Bangladesh. The aim of this study was to determine the prevalence of rubella infection among CMV IgG sero-positive adult women. Throughout this study we reviewed the testing records of CMV IgG sero-positive adult women aged of 17-36 years who presented by random at immunology laboratory records at Dhaka Hospital Ltd., Bangladesh. Between January, 2014 to July, 2014, 51 anti-CMV IgG positive adult women were included and retrospectively evaluated in this study. The mean age of the selected CMV IgG sero-positive adult women in this study was 24.67±5.46 years. The highest sero-positivity for the anti-CMV IgG (15, 41.2%) and anti-Rubella IgG (14, 37.25%) were documented in 22-26 age group and followed by 29.4% for CMV IgG and 27.45% for Rubella IgG in 17-21 age group. The mean CMV IgG titer was 289.49 IU mL-1 for 17-21 age group while 99.03 IU mL-1 in Rubella IgG titer, 248.65 IU mL-1 CMV IgG titer for 22-26 age group whereas 103.21 IU mL-1 Rubella IgG, 275.06 IU mL-1 CMV IgG titer and 116.36 IU mL-1 Rubella IgG titer for 27-31 age group and 301.60 IU mL-1 CMV IgG titer although 109.76 IU mL-1 Rubella IgG titer for 32-36 age group. The median of CMV IgG titer and Rubella IgG titer were 233.80 IU mL-1 and 68.20 IU mL-1 with 298.00 and 126.90 IU mL-1 of 75% percentile respectively. Although high prevalence of CMV IgG and Rubella IgG were found (41.2 and 37.25%) in 22-26 age group, but the highest mean CMV IgG titer (301.60 IU mL-1) found in 32-36 age group while highest mean Rubella IgG titer (116.36 IU mL-1) was found in 27-31 age group. So, Deterrent actions must be taken to lessening the mortality and morbidity related to cong

Primary infections caused by rubella and Cytomegalovirus (CMV) can front-runner to thoughtful complications in pregnancy. Rubella and CMV screening of adult women is not consistently conceded out in Bangladesh. The aim of this study was to determine the prevalence of rubella infection among CMV IgG sero-positive adult women. Throughout this study we reviewed the testing records of CMV IgG sero-positive adult women aged of 17-36 years who presented by random at immunology laboratory records at Dhaka Hospital Ltd., Bangladesh. Between January, 2014 to July, 2014, 51 anti-CMV IgG positive adult women were included and retrospectively evaluated in this study. The mean age of the selected CMV IgG sero-positive adult women in this study was 24.67±5.46 years. The highest sero-positivity for the anti-CMV IgG (15, 41.2%) and anti-Rubella IgG (14, 37.25%) were documented in 22-26 age group and followed by 29.4% for CMV IgG and 27.45% for Rubella IgG in 17-21 age group. The mean CMV IgG titer was 289.49 IU mL-1 for 17-21 age group while 99.03 IU mL-1 in Rubella IgG titer, 248.65 IU mL-1 CMV IgG titer for 22-26 age group whereas 103.21 IU mL-1 Rubella IgG, 275.06 IU mL-1 CMV IgG titer and 116.36 IU mL-1 Rubella IgG titer for 27-31 age group and 301.60 IU mL-1 CMV IgG titer although 109.76 IU mL-1 Rubella IgG titer for 32-36 age group. The median of CMV IgG titer and Rubella IgG titer were 233.80 IU mL-1 and 68.20 IU mL-1 with 298.00 and 126.90 IU mL-1 of 75% percentile respectively. Although high prevalence of CMV IgG and Rubella IgG were found (41.2 and 37.25%) in 22-26 age group, but the highest mean CMV IgG titer (301.60 IU mL-1) found in 32-36 age group while highest mean Rubella IgG titer (116.36 IU mL-1) was found in 27-31 age group. So, Deterrent actions must be taken to lessening the mortality and morbidity related to cong

This paper presents the result of investigations carried out in studying the emission and performance of diesel engine using the castor biodiesel and its blend with diesel from 0% to 40% by volume. The acid-based catalyzed transesterification system was used to produce castor biodiesel and the highest yield of 82.5% was obtained under the optimized condition. The FTIR spectrum of castor biodiesel indicates the presence of C=O and C–O functional groups, which is due to the ester compound in biodiesel. The smoke emission test revealed that B40 (biodiesel blend with 40% biodiesel and 60% diesel) had the least black smoke compared to the conventional diesel. Diesel engine performance test indicated that the specific fuel consumption of biodiesel blend was increased sufficiently when the blending ratio was optimized. Thus, the reduction in exhaust emissions and reduction in brake-specific fuel consumption made the blends of caster seed oil (B20) a suitable alternative fuel for diesel and could help in controlling air pollution.

This paper presents the result of investigations carried out in studying the emission and performance of diesel engine using the castor biodiesel and its blend with diesel from 0% to 40% by volume. The acid-based catalyzed transesterification system was used to produce castor biodiesel and the highest yield of 82.5% was obtained under the optimized condition. The FTIR spectrum of castor biodiesel indicates the presence of C=O and C–O functional groups, which is due to the ester compound in biodiesel. The smoke emission test revealed that B40 (biodiesel blend with 40% biodiesel and 60% diesel) had the least black smoke compared to the conventional diesel. Diesel engine performance test indicated that the specific fuel consumption of biodiesel blend was increased sufficiently when the blending ratio was optimized. Thus, the reduction in exhaust emissions and reduction in brake-specific fuel consumption made the blends of caster seed oil (B20) a suitable alternative fuel for diesel and could help in controlling air pollution.