VISITORS’ WILLINGNESS TO PAY FOR VISITING THE PATENGA BEACH, BANGLADESH

Tourism has not yet been recognized as an industry in Bangladesh. There is a great dearth of studies to understand the significance of this sector to the nation. This study is a pioneer effort of its kind to evaluate the services of the Patenga Beach offered to its visitors. A total of 400 visitors were interviewed using a predesigned questionnaire. The study reveals that the maximum willingness to pay for entering into the beach without any reduction in the number of trips was BDT 475.69 inclusive of a beach entrance fee of BDT 100 (USD 1.25) per visitor. The study suggests that if the beach had well-planned facilities for the beach goers, they are willing to pay more for the development of the beach

Investigating the effect of processing parameters in the electrospinning of nanofibres

Nylon 6, Nylon 6.6, PEO/water, PEO/water/Ethanol, PVA/FeCl3 and PEO/wood pulp have been successfully electrospun into nanofibres in the range of 200 nm to 1500 nm. A comprehensive understanding of the effect of processing parameters on the morphological structure of these nanofibres has been established. Parameters such as concentration of polymer solution, applied voltage, electrospinning collection distance and flow rate have been found to affect fibre morphology. For Nylon 6 and Nylon 6.6 uniform nanofibres were produced using polymer solution concentrations, 20 wt.% and 25 wt.%, applied voltage of 15 kV, spinning distance of 8 cm and volume feed rate of 0.20 ml/hr. The produced nanofibres average diameter was 924 nm and 827 nm. For PEO/water, PEO/water/Ethanol and PEO/wood pulp optimum conditions of polymer solution concentration of 14 wt.% and 10 wt.%, an applied voltage of 15 kV, spinning collection distance of 11cm and volume feed rate of 0.20 ml/hr and 0.25 ml/hr, produced uniform nanofibres. The produced nanofibres average diameter was 452 nm, 544 nm and 494 nm. As for PVA/FeCl3, optimum conditions of polymer solution concentration of 8 wt.%, an applied voltage 15 kV, spinning collection distance 11 cm and flow rate 0.25 ml/hr produced uniform magnetic nanofibres. The produced nanofibres average diameters were 789 nm. These parameters have consequently been optimized to obtain uniform quality at different ranges of nanofibres. In this context it was established that the processing parameters vary significantly from one polymer to another but in general, the concentration the most important role in obtaining uniform fibre diameter without thin and thick places and beads. Applied voltage also played a significant role in determining the diameter of nanofibres and little significant effect on the fibre morphology was observed with the variation of spinning collection distance and noticeable structural change with a change in the solution flow rate. A selective range of microscopic techniques such as, Scanning Electron Microscopy, Atomic Force Microscopy and Transmission Electron Microscopy were used to characterise and evaluate the nanofibres produced during this study. DSC, X-ray diffraction and FTIR was used to identify the thermal properties of Nylon 6.6, PEO and PEO/wood pulp nanofibres produced. Nanofibres produced in this study have a wide range of potential applications in different fields, including, biomedical, magnetic sensor, mats for composite protective clothing, filtration, aerospace and others

流域スケール水文気候学的データによる土壌水分メモリ及び新安江モデルスピンアップ時間の特徴に関する研究

国立大学法人長岡技術科学大

A Non-Invasive Interpretable Diagnosis of Melanoma Skin Cancer Using Deep Learning and Ensemble Stacking of Machine Learning Models

A skin lesion is a portion of skin that observes abnormal growth compared to other areas of the skin. The ISIC 2018 lesion dataset has seven classes. A miniature dataset version of it is also available with only two classes: malignant and benign. Malignant tumors are tumors that are cancerous, and benign tumors are non-cancerous. Malignant tumors have the ability to multiply and spread throughout the body at a much faster rate. The early detection of the cancerous skin lesion is crucial for the survival of the patient. Deep learning models and machine learning models play an essential role in the detection of skin lesions. Still, due to image occlusions and imbalanced datasets, the accuracies have been compromised so far. In this paper, we introduce an interpretable method for the non-invasive diagnosis of melanoma skin cancer using deep learning and ensemble stacking of machine learning models. The dataset used to train the classifier models contains balanced images of benign and malignant skin moles. Hand-crafted features are used to train the base models (logistic regression, SVM, random forest, KNN, and gradient boosting machine) of machine learning. The prediction of these base models was used to train level one model stacking using cross-validation on the training set. Deep learning models (MobileNet, Xception, ResNet50, ResNet50V2, and DenseNet121) were used for transfer learning, and were already pre-trained on ImageNet data. The classifier was evaluated for each model. The deep learning models were then ensembled with different combinations of models and assessed. Furthermore, shapely adaptive explanations are used to construct an interpretability approach that generates heatmaps to identify the parts of an image that are most suggestive of the illness. This allows dermatologists to understand the results of our model in a way that makes sense to them. For evaluation, we calculated the accuracy, F1-score, Cohen\u27s kappa, confusion matrix, and ROC curves and identified the best model for classifying skin lesions

Inheritance studies of SSR and ISSR molecular markers and phylogenetic relationship of rice genotypes resistant to tungro virus

Multivariate analyses were performed using 13 morphological traits and 13 molecular markers (10 SSRs and three ISSRs) to assess the phylogenetic relationship among tungro resistant genotypes. For morphological traits, the genotypes were grouped into six clusters, according to D2 statistic and Canonical vector analysis. Plant height, days to flowering, days to maturity, panicle length, number of spikelet per panicle, number of unfilled grain per panicle and yield were important contributors to genetic divergence in 14 rice genotypes. Based on Nei's genetic distance for molecular studies, seven clusters were formed among the tungro resistant and susceptible genotypes. Mantel's test revealed a significant correlation (r = 0.834*) between the morphological and molecular data. To develop high yielding tungro resistant varieties based on both morphological and molecular analyses, crosses could be made with susceptible (BR10 and BR11) genotypes with low yielding but highly resistant genotypes, Sonahidemota, Kumragoir, Nakuchimota, Khaiyamota, Khairymota and Kachamota. The chi-square analysis for seven alleles (RM11, RM17, RM20, RM23, RM80, RM108 and RM531) of SSR and five loci (RY1, MR1, MR2, MR4 and GF5) of three ISSR markers in F2 population of cross, BR11 × Sonahidemota, showed a good fit to the expected segregation ratio (1:2:1) for a single gene model

Human Resource Practices and Job Satisfaction: Underlying Mechanism of Self Perceived English Ability, Motivation and Chinese Traditionality

Objective: Although international management literature has emphasized the value of human resource (HR) practices into the internationalization process, the mechanism in whereby HR practices impact towards employee’s cognitive abilities and motivation is relatively unknown from emerging economies multinationals. This paper examines the association between HR practices that promote learning a foreign language and employees’ self-perceived language ability and motivation to use English in the workplace and how these affects towards overall job satisfaction with Chinese Multinational Enterprises (CMNEs) employees. Methodology: We tested our conceptual model using survey data from 180 CMNEs employees. Results: The results show: (1) HR practices which support developing skills such as English will enhance selfperceived English ability and employee’s motivation to use English in the work place (2) which subsequently has positive behavioural outcomes such as job satisfaction, and (3) Chinese traditionality is a strong moderator between the relationship of HR practices and job satisfaction. Implication: This paper offers new perspectives by advancing the discourse within the field of international management, in the Chinese context.It also presents the managerial implications and recommendations for future research

A Vector Error Correction Model Approach to Investigate the Causal Relationship among Energy Consumption, Real GDP, and Industry Value Added of Bangladesh

The paper focuses on investigating the casual relationship among Energy Consumption, Real GDP, and Industry Value Added of Bangladesh using the World Bank Development Indicators data set. The Granger causality approach has been applied to identify the short-run causality direction for all possible pairs of dynamic variables of the study. Results from the approach indicate the unidirectional short run causal relationship from Real GDP to Energy Consumption, while another unidirectional short-run causality has been found from Industry Value Added to Real GDP. The concept of cointegration and Vector Error Correction Model (VECM) are employed to find the long-run relationships among the variables. Our results show the existence of long run relationship between each pair of variables. Furthermore, the Variance Decomposition (VDC) techniques and Impulse Response Function (IRF) was also used to measure the extent/degree of dynamic properties of the variables. Bangladesh has an emerging economy with limited energy resources. Here, the evidence from our study would help policymakers in setting the appropriate energy consumption policies that will enhance and sustain economic growth for the welfare/development of this country

Peer-to-Peer Power Energy Trading in Blockchain Using Efficient Machine Learning Model

The advancement of mircogrids and the adoption of blockchain technology in the energy-trading sector can build a robust and sustainable energy infrastructure. The decentralization and transparency of blockchain technology have several advantages for data management, security, and trust. In particular, the uses of smart contracts can provide automated transaction in energy trading. Individual entities (household, industries, institutes, etc.) have shown increasing interest in producing power from potential renewable energy sources for their own usage and also in distributing this power to the energy market if possible. The key success in energy trading significantly depends on understanding one’s own energy demand and production capability. For example, the production from a solar panel is highly correlated with the weather condition, and an efficient machine learning model can characterize the relationship to estimate the production at any time. In this article, we propose an architecture for energy trading that uses smart contracts in conjunction with an efficient machine learning algorithm to determine participants’ appropriate energy productions and streamline the auction process. We conducted an analysis on various machine learning models to identify the best suited model to be used with the smart contract in energy trading

EFFECTS OF SOLVENT POLARITY ON SOLVATION FREE ENERGY, DIPOLE MOMENT, POLARIZABILITY, HYPERPOLARIZABILITY AND MOLECULAR REACTIVITY OF ASPIRIN

Objective: The aim of the study is to explore the effects of solvent polarity on solvation free energy, dipole moment, polarizability, first order hyperpolarizability and different molecular properties like chemical hardness and softness, chemical potential, electronegativity, electrophilicity index of aspirin which may lead to better understand the reactivity and stability of aspirin in different solvent systems.Methods: Becke, 3-parameter, Lee-Yang-Parr (B3LYP) level of theory with 6-31G(d,p) basis set was employed to conduct all type of calculations for both in the gas phase and in solution. The solvation free energy, dipole moment and molecular properties were calculated by applying the Solvation Model on Density (SMD) in four solvent systems namely water, methanol, ethanol and n-octanol.Results: The solvation energies steadily increased as the dielectric constant was decreased i.e. free energy increases with decreasing polarity of the solvent. The dipole moment of aspirin was found to be increased when going from non-polar to polar solvents. The dipole moment of aspirin was higher in different solvents than that of the gas phase. The polarizability and first order hyperpolarizability were also increased with the increasing dielectric constant of the solvent. Moreover, ongoing from non-polar to polar solvent the chemical potential, electronegativity and electrophilicity index were increased except in n-octanol. The chemical potential, electronegativity and electrophilicity index of aspirin in n-octanol was higher than that of ethanol. On the other hand, chemical hardness was increased with decreasing polarity of the solvent and the inverse relation was found in the case of softness.Conclusion: The calculated solvation free energy, dipole moment, polarizability, first order hyperpolarizability and molecular properties found in this study may lead to the understanding of stability and reactivity of aspirin in different solvent systems

Spatial and temporal variations of PM10 in Chittagong City

Healthy environment is precondition of good health. Fresh air, pure water and soil are the main component of a healthy environment. Contaminants from different anthropogenic activities and natural sources such as, particulate matter (PM), Oxides of Sulphur (SOx), oxides of Nitrogen (NOx), carbon dioxide (CO2) etc. contaminate and pollute this natural fresh air and threaten human life as well as environment.Every year the morbidity due to Particulate matter (PM) is about 50% of the total premature morbidity because of air pollution. World Health Organization's (WHO) International Agency for Research on Cancer classified it as group 1 human carcinogen. Considering its importance, in most of the country, monitoring of particulate matter has been given priority in assessing air quality monitoring. Analyzing the concentrations of particulate matter measured by high volume air sampler from six sampling sites of the city, differences in the concentrations of PM10 were observed in this study. The monthly mean concentration of PM10 hardly remain within the standards of Bangladesh while, the mean annual average of PM10 at all sampling sites was many times higher than the standard of Bangladesh and WHO. As, the concentration of PM10in Chittagong is not praiseworthy enough and indicate a serious threat to human health, strategic air quality management and monitoring system are recommended in this study