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In recent times, machine learning-based methods have gained popularity in various materials science applications including microstructure image classification. This paper explores the use of classifier combination approaches for classifying the microstructure images with an improved accuracy. Classifier combination methods have been recognized as a state-of-the-art approach to enhance the performance of many challenging image classification tasks. Ensemble methods are used to increase the predictive performance of a learning system by combining the predictive performances of several base learners. In our proposed model, the features of three-class microstructural images are extracted using the rotational local tetra pattern feature descriptor. These features are separately fed to three different classifiers, namely support vector machine, random forest, and K nearest neighbor. Then, a classifier combination approach based on the confidence scores provided by these classifiers using fuzzy measures and fuzzy integrals is applied for the image recognition purpose. Unlike other straightforward classical classifier combination methods, this method nonlinearly aggregates the objective evidences in terms of a fuzzy membership function, with the subjective assessments of the relative importance of different classifiers. The proposed method has also been compared with many standard classifier combination approaches commonly found in the literature. The experimental results support the effectiveness of fuzzy combination to produce higher classification accuracy than that of the best base classifiers and some popular classifier combination methods.

PurposeThis study intends to explore the impact of occupation and income on informal migrants in the face of COVID-19 induced lockdown in Bangladesh and their coping strategies to survive the pandemic situation.Design/methodology/approachThe study adopted a qualitative research design in which four urban areas were chosen purposively from various parts of Dhaka city. The authors conducted 21 semi-structured in-depth interviews, four FGDs and eight months over participant observation for achieving study objectives. The four stages of data analysis used a thematic approach in the interpretive phenomenological analysis.FindingsThe results showed that respondents were massively affected due to loss of income and occupation in the period of induced lockdown. Besides, most people lost their earning sources entirely in this amid pandemic which bound them starvation in the mealtime along with several dynamic complications. The findings also revealed that they followed some surviving strategies such as taking loans, reducing expenses, consuming less food, selling land, jewelry, and goods, relatives and neighbor support, and government relief. Although these strategies somewhat supported them to struggle with the situation, their livelihood features became fragile immensely.Research limitations/implicationsThe findings will be an important guiding principle for the policymakers, aid organizations and development practitioners to prepare development policies for vulnerable informal migrants in developing countries like Bangladesh.Originality/valueThis is the first study that explores the informal migrants’ occupation and income during COVID-19 induced lockdown in Bangladesh. This research also highlights coping strategies of the informal migrants to survive the pandemic situation.

Corduroy yarn is generally introduced in a fabric structure of garments due to developing different designs in terms of appearance of fabric which is called corduroy garment. In this study, both corduroy and denim garments have been made to develop a denim-like wash effect on corduroy garments that will create a new fashion, value addition and sustainability in washing technology. Both the garments are treated with the same washing process. Also, some physical properties have been investigated for both washed garments such as Washing Effect Test, Tear Strength Test, GSM Test, and Rubbing Test. Then a comparative study has been done on both washed garments. The appearance of washed corduroy garments is satisfactory. The physical properties also show a better result. Finally, a satisfactory result is achieved from this work.

The power performance of PV module with dc-dc buck converter for photovoltaic energy application has been analyzed in this paper. Since the solar module produces DC voltage at various levels depending on irradiance variation, it is needed to connect a DC-DC converter to adjust the level of DC voltage at a certain level. The power output has been investigated by MATLAB simulation. To improve the output power Proportional Integral Derivative (PID) control is used. From the simulation it has been observed that around 13.85 Watts more output power can be extracted if PID control is used.

It's been told from the beginning that the liver is one of the most important organs of our body function. Once upon a time, it couldn't see that a large number of people are suffering from liver diseases. But in recent years, the number of patients with liver diseases is increasing day by day. For this reason, affected people must go to a medical center for checking. In this paper, a model for the liver-affected people is implemented by which they do not need to go outside for checking the possibility of liver disease problems. To implement this model, datasets were collected based on some basic attributes which are related to liver diseases so that the possibility of liver problem can be detected. The data carries both liver-affected people and non-affected people. Those data were used to train our model so that the model can identify the affected people and non-affected people easily. Several machine learning algorithms were applied to generate results. The evaluation was done by following two approaches. Firstly, the complete result was generated using all of the attributes from both of the datasets where KNN provides the highest accuracy for both of the datasets which are 73% and 75.19% respectively. But after gone through an important attribute selection process, SVM gave the highest and increased accuracy which is about 82.68% and 81.15% respectively.

As per the report of the World Health Organization (WHO), diabetes has become one of the rapidly expanding chronic diseases that has affected the life of 422 million people all over the world. The number of deaths in Bangladesh due to diabetes has reached 28,065, which is 3.61% of the total deaths of Bangladesh, according to the latest data published by the WHO in 2018. So we need to be concerned about the risks of diabetes disease. If we cannot take proper steps to diagnose diabetes at an early stage, eventually we have to face serious health issues. In this paper, we have shown the relation of different symptoms and diseases that cause diabetes so that we can help a person to diagnose diabetes at an early stage. Nowadays, machine learning classification approaches are well accepted by researchers for developing disease risk prediction models. Therefore eleven machine learning classification algorithms such as Logistic Regression (LR), Gaussian Process (GP), Adaptive Boosting (AdaBoost), Decision Tree (DT), K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), Bernoulli Naive Bayes (BNB), Bagging Classifier (BC), Random Forest (RF), and Quadratic Discriminant Analysis (QDA) have been used in this study. Among all these machine learning classifiers, Random Forest (RF) classifier has showed the best accuracy of 98%. And its Area Under Curve(AUC) is also the highest.

Mechanical properties and corrosion resistance of metal largely depend on the solidification of microstructure. Microstructure of metallic alloy is described by the grain size, type of phases, structure, shape, etc. Material characterization is an important issue in material science. A large number of attempts was made for automatic characterization of microstructure using computer vision and machine learning methods. In this paper, an attempt is made to distinguish dendrite morphology among others by using transfer learning approach. A transfer learning approach takes advantage of fully connected layer of pre-trained Deep Convolutional Neural Network (DCNN) for weight initialization and feature extraction. In this work, three different pre-trained DCNN architectures, namely DenseNet121, MnasNet0_5, and MnasNet1_0 are used as a feature extractor. A LogSoftmax layer is used as a binary classifier to classify the microstructure images. A maximum classification accuracy 95% is achieved using customized framework of pre-trained DenseNet121 architecture.

Bangladesh is widely known as one of the most disaster-prone countries in the world in terms of number of people affected and the loss of properties. Flood is the most common natural disaster and has led to extensive morbidity and mortality in Bangladesh as well as throughout the world. In this case, institutional support is crucial for the health service of the people at risk. This study aims to assess the role of institution on health amenities program in the flood- prone char areas. This study employed a mixed-method approach, where quantitative data were collected through a questionnaire survey from flood-affected households living in the char areas of Gaibandha, Bangladesh. The results revealed that most of the respondents encountered many health-related issues where prevailing fever and cold, diarrhoea and skin disease were the leading ailments. Both the government organizations (GOs) and non-government organizations (NGOs) played an effective role in response to the illness of individuals. The majority of affected people (80.4%) received healthcare services in which emergency indispensable treatment and medicine were substantial amenities. The sufficiency of medical facilities afforded by GOs was significant. Conversely, the quality of medical facilities rendered by NGOs was appreciable. But the affected dwellers came across complications enormously while getting the health services like disrupted communication, mismanagement and overlapping, insufficient medicine, and so on. The outcomes of this study would be worthwhile for the policymakers, civil society, and all concerned persons interested in healthcare planning and implementing recovery measures.

Background: Breast cancer is a heterogeneous disease typically prevalent among women and is the second-largest cause of death worldwide. Early diagnosis is the key to minimize the cancer-induced complication, however, the conventional diagnostic strategies have been sluggish, complex, and, to some extent, non-specific. Therapeutic tools are not so convenient and side effects of current therapies offer the development of novel theranostic tool to combat this deadly disease. Objective: This article aims to summarize the advances in the diagnosis and treatment of breast cancer with gold nanoparticles (GNP or AuNP). Methods: A systematic search was conducted in the three popular electronic online databases including PubMed, Google Scholar, and Web of Science, regarding GNP as breast cancer theranostics. Results: Published literature demonstrated that GNPs tuned with photosensitive moieties, nanomaterials, drugs, peptides, nucleotide, peptides, antibodies, aptamer, and other biomolecules improve the conventional diagnostic and therapeutic strategies of breast cancer management with minimum cytotoxic effect. GNP derived diagnosis system assures reproducibility, reliability, and accuracy cost-effectively. Additionally, surface-modified GNP displayed theranostic potential even in the metastatic stage of breast cancer. Conclusion: Divergent strategies have shown the theranostic potential of surface tuned GNPs against breast cancer even in the metastatic stage with minimum cytotoxic effects both in vitro and in vivo.

A low loss and highly sensitive gold nano-film externally coated dual core refractive index (RI) sensor using photonic crystal fiber (PCF) platform is proposed in this work. The perfectly matched layer (PML) is circled around to the external side of analyte to avoid the radiation of reflected power. The numerical analysis of the recommended sensor has been done in the analyte range from 1.33 to 1.42 in the $x$ -polarization by means of the finite element method (FEM). The maximum amplitude sensitivity, maximum wavelength sensitivity, and sensor resolution are 1102.57 $RIU^{-1}$ , 19000 nm/RIU, and $5.26\times 10^{-6}$ RIU, respectively, with maximum confinement loss as low as 0.173 dB/cm. So, due to the low loss and high sensitive characteristics, the proposed sensor can be used in detecting unknown chemical and analyte.

Image inpainting is exploring the way of filling the missing or left out regions in an image by only having knowledge of surrounding pixel values. One of the block matching algorithms use for image inpainting is exemplar based technique. Exemplar based image inpainting method search the best matching patch for the target region in the image and when it is obtained, the pixel values are copied from the source region and pasted to the target region. The selections of different patch matching criteria, patch size and filling order have a great affect on the results. This paper describes some of the variations in traditional exemplar method which include change in patch selection, priority, patch size and multi-resolution inpainting. The advantages and drawbacks for the modified methods are also discussed here.

Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue damage or described in terms of such damage [1].

The pH level of Bryophillum Pinnatum leaves (BPL) is 4.25, which is a suitable amount of acid for generating electricity. When the mixture of water and the acidic juice of Bryophillum Pinnatum comes to contact with Zinc and Copper plates, forming an organic cell which generates a small amount of voltage. In this paper, improved efficiency can be observed by changing the gap between the electrodes and the mixture procedure. The surprising fact is that, after using the cell for a week, the output voltage level continues to decline. It can be improved again by reusing the previous BPL fiber and juice. This well-developed cell shows more efficiency than previous works. A cheap battery can be developed with a combination of this type of cells. Using this technology it is possible to provide electricity to the isolated community like Bandarban, Sandip of Bangladesh where free space is available for cultivating this plant. So, in this paper, electricity generation is not only the main concern but to develop a more efficient cell with maximum utilization of natural leaves. This environment-friendly, cheap and easy way will be a good solution for the isolated community.

Prostate cancer (PCa) is the most common malignancy in men and the leading cause of death for men all over the world. Early diagnosis is the key to start treatment at an early stage of PCa and to reduce the death toll. Generally, PCa expresses characteristic morphologic features and serum biomarkers; however, early diagnosis is challenging due to its heterogeneity and long-term indolent phase in the early stage. Following positive diagnosis, PCa patients receive conventional treatments including surgery, radiation therapy, androgen deprivation therapy, focal therapy, and chemotherapy to enhance survival time and alleviate PCa-related complications. However, these treatment strategies have both short and long-term side effects, notably impotence, urinary incontinence, erectile dysfunctions, and recurrence of cancer. These limitations warrant the quest for novel PCa theranostic agents with robust diagnostic and therapeutic potentials to lessen the burden of PCa-related suffering. Iron oxide nanoparticles (IONPs) have recently drawn attention for their symmetrical usage in the diagnosis and treatment of several cancer types. Here, we performed a systematic search in four popular online databases (PubMed, Google Scholar, Scopus, and Web of Science) for the articles regarding PCa and IONPs. Published literature confirmed that the surface modification of IONPs with biopolymers and diagnostic biomarkers improved the early diagnosis of PCa, even in the metastatic stage with reliable accuracy and sensitivity. Furthermore, fine-tuning of IONPs with biopolymers, nucleic acids, anticancer drugs, and bioactive compounds can improve the therapeutic efficacy of these anticancer agents against PCa. This review covers the symmetrical use of IONPs in the diagnosis and treatment of PCa, investigates their biocompatibility, and examines their potential as PCa theranostic agents.

Drug counterfeiting and production of substandard drug is a global problem. This study was aimed to assess the pharmaceutical quality of ranitidine hydrochloride tablets manufactured in Bangladesh. Tablets were collected from different parts of Bangladesh and quality parameters were evaluated according to the United States Pharmacopoeia and the British Pharmacopoeial methods. The potency of tablets was measured spectrophotometrically. Weight variation and disintegration time were performed according to pharmaceutical monographs. Among 43 brands tested, 8 failed to comply with the USP specification (active ingredient: 90±10%) due to containing of less amount of ranitidine of which 6 brands were spurious and 2 were substandard in nature. Two brands did not comply with the specification for weight variation of tablets whereas all brands passed disintegration time test. The findings clearly demonstrate the production of substandard ranitidine tablets in Bangladesh. The drug control authority of Bangladesh should take effective steps to prevent the production of substandard drugs to secure public health.

Now-a-days implantable device for biomedical applications are the interesting research area through all over the world due to the health care industry is highly announced to develop better efficient system which are more suitable and user friendly for patient. The main concern of the implantable device is to monitor various ailments within the body efficiently and then sending the corresponding monitoring signal to the server or base station. In that case, an efficient and miniaturized implantable antenna design is a great concern to ensure proper communication between implanted device and base station. In this paper, an implantable microstrip patch antenna (IMPA) has been proposed and characterized for implantable biomedical applications in the frequency of industrial scientific and medical (ISM, 2.4 GHz to 2.48 GHz) band. The total size of the proposed antenna is 12×12×2 mm3. Here patch of the antenna is designed on a dielectric substrate named Teflon having dielectric constant of 2.1 and it also have tangent loss of 0.00028. This proposed antenna is designed and simulated by using CST (Computer Simulation Technology) microwave studio 3D electromagnetic solver. The proposed IMPA shows good results such as lower return loss (−31.628 dB), better gain (−13.6 dB), wide bandwidth (727 MHz), VSWR (1.05) very close to unity, good impedance matching as well as miniature in size (volume of 288mm3).

Although the demand and generation of electric power have kept on increasing at comet's pace post-independence of our country, maintaining systems reliability has been an issue we are struggling with even today. With large grid blackouts taking place, there exists a possibility of saving at least the cities of economic and strategic importance through power system islanding and hence avoid facing huge intermittent economic losses during collapse of power systems. In this paper the analysis of the existing islands in our country and the practices followed in India and abroad during such a scenario to come up with a design and analysis of the possibility of islanding system installation for Bhopal, the capital city of Madhya Pradesh which is also under proposed consideration at PGCIL. The power flows and disturbance dynamics were done with real time simulation and an accepted stability was observed and results with plots were documented for the different cases consideration.

Image inpainting has been an area of interest for researchers in this digital image processing era. All the methods of image inpainting start with a selection of the area which is needed to be inpainted. However, this selection of area or mask is to be done manually as there is no way for a machine to identify it. The process of creation of mask is cumbersome and time consuming and requires human expertise. Using image segmentation, the image can be divided into probable areas of inpainting and then the user can select area/s to generate the mask accurately and easily. In this paper we present a method to do selection of segments to generate mask which is further used to inpaint the image using exemplar based inpainting algorithm. The proposed scheme greatly reduces the overall inpainting time by drastically reducing the manual work of drawing the mask. Comparative results of masking object manually and by using proposed method are shown. Subjective analysis of inpainting using proposed method of masking and manual masking shows the effectiveness of the same.

This demo paper describes the architecture and communication protocols — both single hop and multi-hop — for DSRC devices. The paper also describes an Android application that enables visualization of real-time vehicle movements on Google map using DSRC and Bluetooth communication. The application receives information about position, speed and direction of mobility that multiple vehicles obtain through the GPS Receiver attached to their DSRC OBU. The android application communicates with one of the DSRC units through Bluetooth to gather real-time traces collected from all DSRC-equipped vehicles. The application displays live movement of these vehicles on Google map with their path history, speed and direction. The source code and installation files of this application will be released through the Open Source Application Development Portal (OSADP) hosted by the U.S. Department of Transportation.

A hybrid method for robust and efficient optimization process is developed by integrating a new response surface method and pattern search algorithm. The method is based on: (1) multipoint approximations of the objective and constraint functions, (2) a multiquadric radial basis function (RBF) for the zeroth-order function approximation and a new RBF plus polynomial-based moving least-squares approximation for the first-order enhanced function approximation, and (3) a pattern search algorithm to impose a descent condition and applied adaptive subregion management strategy. Several numerical examples are presented to illustrate accuracy and computational efficiency of the proposed method for both function approximation and design optimization. To demonstrate the effectiveness of the proposed hybrid method, it is applied to obtain optimum designs of a microelectronic packaging system. A two-stage optimization approach is proposed for the design optimization. The material properties of microelectronic packaging system and the shape parameters of solder ball are selected as design variables. Through design optimization, significant improvements of durability performances are obtained using the proposed hybrid optimization method.

This paper presents a global–local methodology for predicting mechanical deformation and fatigue durability of solder joints in electronic packaging systems subject to cyclic thermal loading. It involves a global deformation analysis, a local critical solder–joint analysis, and a fatigue life analysis. The global deformation analysis includes a new optimization formulation for determining an equivalent model. The methodology developed was applied to fine pitch ball grid array (fpBGA) and super ball grid array (SBGA) packages. Selective experimental efforts were also undertaken to evaluate the predicted deformation characteristics of the fpBGA package. A good agreement was obtained between the simulated deformation results and experimental observations. For the durability analysis, the total fatigue life predicted using the energy-based method is larger than 2500 cycles—a trend observed experimentally for both packages entailing widely different designs. Based on proposed modeling and simulation results and package designs studied, the SBGA package is more durable than the fpBGA package.

Inpainting has been an attractive and useful art in this fast growing era of digital image processing. Its applications in numerous fields such as restoration, object removal, superimposed text removal, red-eye removal etc. has made an impression on researchers to evolve generation of inpainting methods. After introduction of exemplar-based algorithm, several variations have been made in order to improve quality of inpainting region containing structure and texture information. Since inpainting starts from the border of the structure and propagates inward, selection of appropriate points on the structure becomes a very important criterion. Data term is responsible for extracting the structures in an image. With overview of exemplar algorithm, few variations in gradient based data terms are presented. Moreover, a data term based on sparsity of the image structure can also be an area of research. Evaluation of all these techniques is done using common analytical measures such as Peak Signal to Noise Ratio (PSNR), Similarity Structure Index Measure (SSIM) and Feature Similarity Index Measure (FSIM) in this paper. Besides qualitative comparison, subjective evaluation using Mean Opinion Score (MOS) is also presented.

For many years network operators have struggled to maintain fragile, statically configured and extremely complex networks. The constant threat of viruses, malware, intruders and misconfigured devices has made the task even more difficult. The use of an Intrusion Detection System (IDS) has become a standard defense model in many networks, however they are expensive and difficult to maintain and further complicate a network. This paper introduces a novel approach that integrates a distributed Intrusion Detection System into a Software Defined Network (SDN) and in doing so provides a more scalable security and threat management solution. The core mechanisms that enable SDN to provide an IDS function have been implemented and their performance evaluated. The viability of this approach was evaluated and found to be an effective alternative to the current IDS deployment model.

One of the great attractions of Software Defined Networking (SDN) has been the promise of transforming the network by providing a degree of flexibility and automation that until now has never been available. Of interest however is that all of these exciting solutions are usually discussed in isolation and rarely do proponents allude to the fact that numerous applications can be deployed simultaneously. Unfortunately most SDN Controllers are incapable of managing multiple applications that require access to the same events notifications. In this paper, we introduce the concept of an Event Arbitration Manager (EAM), a modified event handler that provides mediation between applications on a single SDN controller. We show that the introduction of an event mediation layer between SDN applications would reduce conflicts between multiple applications on the same SDN controller and allows them to respond to controller events messages equally.