Search

This paper designs, simulates, and analyzes the S-band microstrip patch antenna (MPA) for wireless applications. FR-4 (lossy) and Rogger RT/duroid, whose dielectric permittivity is 4.3 and 2.2, respectively, have been used as substrate materials. Simulation is done by computer simulation technology (CST) suite studio 2019 software. Simulations with FR-4 material showed that the return loss was -20.405 dB, the gain was 2.592 dB, the directivity was 7.47 dBi, the voltage standing wave ratio (VSWR) was 1.221, the bandwidth (BW) was 0.0746 GHz, and the efficiency was 34.69%. Also, Rogers RT/duroid material gives results of a return loss of -12.542 dB, a bandwidth (BW) of 0.0349 GHz, a gain of 8.092 dB, a directivity of 8.587 dBi, and an efficiency of 94.24%. The main goal of this antenna is to have a low return loss while getting as close as possible to a VSWR of 1. This will improve the antenna's gain, directivity, and efficiency compared to other antennas. Copper was used to make the patch and the ground, which were 0.35 mm and 0.0077 mm thick, respectively. The results obtained from the proposed antenna were better than those previously published in various in modern scientific journal and conference papers.

Metronidazole-induced encephalopathy (MIE) is a rare disorder that can be reversible but fatal if not recognized early. Here, the author presents a case of encephalopathy in a 36-year-old patient who took 500 mg of metronidazole three times daily for three weeks. The patient was classified as a probable MIE case on the Naranjo causality assessment scale. An interesting neuroimaging finding in this patient was an isolated dentate nucleus signal abnormality. Author aims to report reversible clinical and radiological features of MIE in this patient and compare them with documented cases in recent and old literature. Data from this case report may guide clinicians to make judicious considerations in the early detection of MIE and may influence future researchers to focus on identifying critical mechanisms of devastating adverse effects, predictability of neurotoxicity.

This paper designs, simulates, and analyzes the S-band microstrip patch antenna (MPA) for wireless applications. FR-4 (lossy) and Rogger RT/duroid, whose dielectric permittivity is 4.3 and 2.2, respectively, have been used as substrate materials. Simulation is done by computer simulation technology (CST) suite studio 2019 software. Simulations with FR-4 material showed that the return loss was -20.405 dB, the gain was 2.592 dB, the directivity was 7.47 dBi, the voltage standing wave ratio (VSWR) was 1.221, the bandwidth (BW) was 0.0746 GHz, and the efficiency was 34.69%. Also, Rogers RT/duroid material gives results of a return loss of -12.542 dB, a bandwidth (BW) of 0.0349 GHz, a gain of 8.092 dB, a directivity of 8.587 dBi, and an efficiency of 94.24%. The main goal of this antenna is to have a low return loss while getting as close as possible to a VSWR of 1. This will improve the antenna's gain, directivity, and efficiency compared to other antennas. Copper was used to make the patch and the ground, which were 0.35 mm and 0.0077 mm thick, respectively. The results obtained from the proposed antenna were better than those previously published in various in modern scientific journal and conference papers.

Background: Besides maintaining health precautions, vaccination has been the only prevention from SARS-CoV-2, though no clinically proved 100% effective vaccine has been developed till date. At this stage, to withhold the debris of this pandemic-experts need to know the impact of the vaccine efficacy rates, the threshold level of vaccine effectiveness and how long this pandemic may extent with vaccines that have different efficacy rates. In this article, a mathematical model study has been done on the importance of vaccination and vaccine efficiency rate during an ongoing pandemic. Methods: We simulated a five compartment mathematical model to analyze the pandemic scenario in both California, and whole U.S. We considered four vaccines, Pfizer (95%), Moderna (94%), AstraZeneca (79%), and Johnson & Johnson (72%), which are being used rigorously to control the SARS-CoV-2 pandemic, in addition with two special cases: a vaccine with 100% efficacy rate and no vaccine under use. SARS-CoV-2 related data of California, and U.S. were used in this study. Findings: Both the infection and death rates are very high in California. Our model suggests that the pandemic situation in California will be under control in the last quartile of the year 2023 if vaccination program is continued with the Pfizer vaccine. During this time, six waves may happen from the beginning of the immunization where the case fatality and recovery rates will be 1.697% and 98.30%, respectively. However, according to the considered model, this period might be extended to the mid of 2024 when vaccines with lower efficacy rates are used. On the other hand, the daily cases and deaths in the U.S. will be under control at the end of 2026 with multiple waves. Although the number of susceptible people will fall down to none in the beginning of 2027, there is less chance to stop the vaccination program if vaccinated with a vaccine other than a 100% effective vaccine or Pfizer, and at that case vaccination program must run till the mid of 2028. According to this study, the unconfirmed-infectious and infected cases will be under control at the end of 2027 and at the mid of 2028, respectively. Interpretation: The more effective a vaccine is, the less people suffer from this malign infection. Vaccines which are less than 90% effective do not have notable contribution to control the pandemic besides hard immunity. Furthermore, specific groups of people are getting prioritized initially, mass vaccination and quick responses are required to control the spread of this disease.

During the past decade, the increased use of Managed Pressure Drilling (MPD) equipment has significantly improved the safety and efficiency of gas influx management. However, it is still not clear to the industry what should be the safest and most effective pressure control method for removing gas influxes out of a riser. The objective of this study is to perform a systematic evaluation of different pressure control methods for riser gas handling, including the constant surface backpressure method, the constant riser bottom pressure method, and the fixed choke and constant outflow method. A transient multiphase flow simulator based on a Drift Flux Model was developed to simulate riser gas handling events in a Water Based Mud (WBM) system. Multiple sets of full-scale experimental data were used for the calibration and validation of the simulator. In the full-scale experiments, riser gas events were simulated by injecting gas into the bottom of an experimental well, followed by applying different pressure control methods. Besides conventional downhole and surface pressure and flow measurement instrumentations, a Distributed Fiber Optic Sensing (DFOS) system was used for the high-resolution monitoring of gas influxes in the annulus. The performance of different pressure control methods was evaluated based on the simulation results, including the behaviors of surface and riser bottom pressures, peak surface outflow rates, and the time required for riser gas handling. The numerical simulations carried out in this study can help better understand the different pressure control methods and improve the design of riser gas handling strategies.

Determination of breast tumors from B-Mode Ultrasound (US) image is a perplexing one. Researches employing statistical modeling such as Nakagami, Normal Inverse Gaussian (NIG) distributed parametric images in this classification task have already explored but experimentation of those statistical models on contourlet transformed coefficient image in breast tumor classification task has not reported yet. The proposed method is established by considering 250 clinical cases from a publicly available database. In this database each clinical case exists as *.bmp format. In the preprocessing step firstly, the ultrasound B-Mode image is binarized to detect the lesion contour. Then contourlet transformation is employed. These contourlet sub band coefficients are shown to be modeled effectively by Nakagami and NIG distributions. These Nakagami and NIG parametric images are obtained by estimating the parameters of those prior statistical distributions locally. Few shape and statistical features are chosen according to their effectiveness on those parametric images. The benign and malignant breast tumors are classified utilizing these features with different classifiers such as the support vector machine, k-nearest neighbors, fitted binary classification decision tree, binary Gaussian kernel classification model, linear classification models for binary learning with high-dimensional etc. It is observed that classification performance of NIG statistical model based parametric version of contourlet coefficient images gained better accuracy than those of Nakagami statistical model. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 21-26

Chloroflexus aurantiacus is a thermophilic bacterium that produces a multitude of proteinswithin its genome. Bioinformatics strategies can facilitate comprehending this organism throughfunctional and structural interpretation assessments.This study aimed to allocate the structure andfunction through an in-silico approach required for bacterial protein biosynthesis. This in-silicoviewpoint provides copious properties, including the physicochemical properties, subcellular location,three-dimensional structure, protein-protein interactions, and functional elucidation of the protein(WP_012256288.1). The STRING program is utilized for the explication of protein-proteininteractions. The in-silico investigation documented the protein's hydrophilic nature withpredominantly alpha (α) helices in its secondary structure.The tertiary-structure model of the proteinhas been shown to exhibit reasonably high consistency based on various quality assessmentmethods.The functional interpretation suggested that the protein can act as a translation initiationfactor, a protein required for translation and protein biosynthesis. Protein-protein interactions alsodemonstrated high credence that the protein interconnected with 30S ribosomal subunit involved inprotein synthesis. This study is bioinformatically examined that the protein (WP_012256288.1) isaffiliated in protein biosynthesis as a translation initiation factor IF-3 of C. aurantiacus.

Halal food and food products consumption is a major part of living in Muslim community. Pork meat or meat items are not considered halal in Muslim countries and consumers. Ensuring pork-free food items is a challenge for the food industry and exporters to Muslim nations. This study aims to detect porcine DNA in imported chocolate products in Bangladesh for halal safety assurance. The imported chocolate samples were collected from various multi shops in Dhaka. Polymerase chain reaction (PCR) method is used in our research to detect the porcine DNA. Two primer sets are used for the detection of porcine mitochondrial cyt-b (cytochrome-b) gene fragments in chocolate samples. To visualize the amplified DNA, agarose gel (1%) was used. After electrophoresis, DNA band in agarose gel indicated that the gene fragments are amplified properly. In our research, out of 42 chocolate samples, only 2 samples were found positive. The chocolate samples were branded as Wild Berry Flavor Chocolate and Cadbury Milk Tray Chocolate. In comparison with the positive pork sample, these two samples also containing the 165bp and 359bp fragment of the porcine cytochrome b gene. We reported that chocolate products contain the pork contamination were not labeled as halal. While other samples that did not have any halal logo originated from outside Bangladesh and imported also showed negative result. The present study established the DNA-based porcine detection system based on mitochondrial cyt-b that is viable in highly processed products. It can be used in the halal certification process to determine the pork items presence in food and halal safety. Our research also reported that imported chocolates should halal certify before release into the market.

During oil and gas drilling operations, formation gas (mostly methane) may enter the wellbore annulus or the drilling riser. This gas influx may get absorbed into the surrounding mud causing complexity for kick detection. Absorption mass transfer experiments of methane into four different fluid systems were performed. The continuous phase for all these fluids was Internal olefins (IO) which is widely used within the drilling industry as a base fluid. Two of the fluids were emulsions (Water as the second phase) and the other fluid comprised of Internal Olefins with Suspentone. Ultrahigh purity methane was used as the absorbing gas phase. The system pressure was kept at 100psi (0.689MPa) with a superficial velocity of 0.009 m/s. Volumetric mass transfer coefficients (kLa) from these experiments were individually determined using a bubble column with an internal diameter of 0.0381m. The volume of the liquid was kept constant during each of the tests. It was observed that methane in pure IO has the highest kLa value while its value is the lowest when it was flown through the IO/Water/suspentone fluid system. The reduction of kLa values was attributed to the increase of viscosity of the fluid and reduction of interfacial area of the IO due to the addition of water.

There is a long ritual of acquired immunity using physical exercise, a balanced diet, and pharmaceutical medication to generate immunity against a particular disease insight into the human body. This paper has extensively reviewed the impact of exercise, daily life practice, food selection, and several other issues to improve the immune system that combat infection. Studying the effect of exercise in varying degrees on the immunity system of humans is well developed and exhibit in this study. It investigates the prevention of pandemics due to herd immunity and finds the perfect amount of exercise to boost immunity to its maximum. Besides the life practice, it is also explored that vaccination can improve and optimize herd immunity.

Numerical analysis is the area of mathematics that creates, analyzes, and implements algorithms for solving numerically the problems from real-world applications of algebra, geometry, and calculus, and they involve variables which vary continuously. Till now, numerous numerical methods have been introduced. Spectral method is one of those techniques used in applied mathematics and scientific computing to numerically solve certain differential equations, potentially involving the use of the Fast Fourier Transform (FFT). This study presents some of the fundamental ideas of spectral method. Orthogonal basis are used to establish computational algorithm. The accuracy and efficiency of proposed model are discussed. This manuscript estimates for the error between the exact and approximated discrete solutions. This paper shows that, grid points for polynomial spectral methods should lie approximately in a minimal energy configuration associated with inverse linear repulsion between points. The wave equation, linear and non-linear boundary value problems are solved using spectral method on the platform of MATLAB language. GANIT J. Bangladesh Math. Soc.Vol. 40 (2020) 28-42

Background: The world is now in an emergency of preventing the drastic spread of COVID-19. After the infection was first reported in December 2019, almost every country did not pay attention to this highly contaminated disease and failed to react swiftly. Now the whole planet is in an vulnerable state, resulting to increase the mortality rate and facing difficulties in all socio-economic aspects. That is why we have the urge to develop an efficient mathematical model (quarantine) based on social consciousness to control the epidemic. Methods: This is a quarantine mathematical model. The outcome of the system is dependent on social consciousness. We have calculated the awareness level by considering various socio-economic factor of each country. In our model, the parameters are Education Index, Gross Domestic Product (GDP) per capita, population density, high literacy and stable economy. To maximize the efficiency of the model, it has to be implemented in initial stage. However, strictapplication of the method in vigorous stage of epidemic will also bring a satisfactory outcome. Results: In Spain, quarantine was effected on March 14, 2020. Spain experienced an increase in reported cases for 13 days of quarantine enforcement and from the 14th day, daily reported cases started to decrease with small fluctuation. Government ensured the social isolation through quarantine. After imposition of a quarantine on March 9, 2020 in Italy, within 13 days of lock-down, the maximum number of infection started to decrease. Similar results observed in France. Higher social consciousness would decrease the number of infected population dramatically while minimal or lower awareness will do a outburst. Conclusion: Outbreak will be in control of health care system which yields to reduce the death rate and will ensure social and economic stability.

In the last two decades the world had faced three respiratory syndrome outbreaks incurred by Coronavirus. Though the wild animals are the primary carriers of the virus, the human population managed to survive sacrificing more than 1,600 lives from 2002 to 2012. But the current virus outbreak has already taken more than 2,462 lives since 22 February 2020. In the first few days, when the cases were being introduced under light, there were no treatment for the infection and the unleashed spread demands to be analyzed to see the pattern of the outbreak. This manuscript aims to look into the growth map of the COVID-19 outbreak under mathematical growth functions and tries to understand which growth pattern assembles the scenario for the cases.

Absorption mass transfer coefficients (k_l a) of methane into two widely used drilling fluids base oils-internal olefins and diesel were determined. A laboratory-scale bubble column (0.0254m diameter and 0.71m static liquid height) was used to perform the experiments. Ranges of superficial gas velocity (0.0045-0.0491 m/s) and system pressure (100-300 psig) were covered. Based on the results, the effects of the varying parameters on k_l a have been discussed. Based on the observations, an empirical model for volumetric mass transfer coefficient as a function of superficial gas velocity and gas density was developed. This correlation fitted well (R² >0.98) with the measured k_L a values for the investigated range of operating conditions. This study gives new quantitative insights into the absorption kinetics of methane into two drilling fluid base oils, which are important for improving gas influx management with non-aqueous drilling fluids.

Despite the advances in diagnosis and management of breast cancer, metastasis has been responsible for the staggering percentage of breast cancer-related death. Mortality threat can be explained mostly by the lack of proper understanding of the diversity of pathological features and underlying mechanism of breast cancer metastasis and effective targeted therapy. Breast cancer stem cells (BCSCs) are the potential source of tumor cells spread to distant organs. BCSCs targeted therapy can suppress the breast cancer progression to metastasis. Spreading of tumor cells to the bone, lung, liver, and brain occurs through a distinct non-random process; called metastasis organotropism. Recently, brain metastasis in breast cancer patients has been detected more frequently, causing a significant clinical burden. BRCA1 and BRCA2 associated breast cancers carry a remarkably higher propensity of CNS metastasis. BRCA1 and BRCA2 associated breast cancers commonly have the propensity to be the triple-negative (TN) and hormone receptors (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative molecular subtypes, respectively. Regardless of molecular subtypes, metastasis is most commonly evident at the bone. Heterogeneity is a critical pathological feature, leads to therapeutic resistance. BCSCs, biomarkers expression patterns, and mutations contribute to heterogeneity. In this paper, we discuss crucial pathological features of breast cancer metastasis, emphasizing metastasis organotropism and heterogeneity; and mechanisms of breast cancer metastasis, highlighting the pathways of metastasis to the brain. We consider that this paper reinforces future research areas and benefits the general readers, physicians, and researchers to identify potential areas to develop targeted therapies.

Chloroflexus aurantiacus is a thermophilic bacterium that produces a multitude of proteins within its genome. Bioinformatics strategies can facilitate comprehending this organism through functional and structural interpretation assessments. This study is aimed at allocating the structure and function through an in silico approach required for bacterial protein biosynthesis. This in silico viewpoint provides copious properties, including the physicochemical properties, subcellular location, three-dimensional structure, protein-protein interactions, and functional elucidation of the protein (WP_012256288.1). The STRING program is utilized for the explication of protein-protein interactions. The in silico investigation documented the protein's hydrophilic nature with predominantly alpha (α) helices in its secondary structure. The tertiary-structure model of the protein has been shown to exhibit reasonably high consistency based on various quality assessment methods. The functional interpretation suggested that the protein can act as a translation initiation factor, a protein required for translation and protein biosynthesis. Protein-protein interactions also demonstrated high credence that the protein interconnected with 30S ribosomal subunit involved in protein synthesis. This study bioinformatically examined that the protein (WP_012256288.1) is affiliated in protein biosynthesis as a translation initiation factor IF-3 of C. aurantiacus.

Automated detection of breast tumor in early stages using B-Mode Ultrasound image is crucial for preventing widespread breast cancer specially among women. This paper is primarily focusing on the classification of breast tumors through statistical modelling such as Rician inverse Gaussian (RiIG) pdf of contourlet transformed B-Mode image of breast tumors which is not reported yet in other earlier works. The suitability of RiIG distribution in modeling the contourlet coefficients is illustrated and compared with that of Nakagami distribution. The proposed method consists of pre-processing to remove the speckle noise, segmentation of the lesion region, contourlet transform on the B-Mode Ultrasound image and using the corresponding contourlet sub-band coefficients and the RiIG parameters, production of contourlet parametric (CP) images and weighted contourlet parametric (WCP) images. A number of geometrical, statistical, and texture features are calculated from B-Mode and the contourlet parametric images. In order to classify the features, seven different classifiers are employed. The proposed approach is applied to two different datasets (Mendeley Data and Dataset B) those are available publicly. It is shown that with parametric images, accuracies in the range of 94-97% are achieved for different classifiers. Specifically, with the support vector machine and k-nearest-neighbor classifier, very high accuracies of 97.2% and 97.55% can be obtained for the Mendeley Data and Dataset B,respectively, using the weighted contourlet parametric images.The reported classification performance is also compared with that of other works using the datasets employed in this paper. It is seen that the proposed approach using weighted contourlet parametric images can provide a superior performance.

This study presents a spatially dependent vaccination model considering therapeutic impact with non-linear incidence rate where the spatial habitat is a subset of \( \mathbb {R}^{n} \) with smooth boundary. Auxiliary results such as disease-free and disease equilibrium states’ basic reproduction number are calculated. This study also includes both local and global stability constraints, uniform persistence condition and existence of the unique solution of the model. Our study showed that the global stability of the model depends on the threshold level \( \mathcal {R}_{0} \) in the way that \( \mathcal {R}_{0} \le 1 \) refers to disease-free equilibrium \( E_{0} \) where \( \mathcal {R}_{0} > 1 \) indicates unique disease equilibrium \( E^{*} \).

The human pathogen Mycobacterium tuberculosis (MTB) is indeed one of the renowned, important, longtime infectious diseases, tuberculosis (TB). Interestingly, MTB infection has become one of the world’s leading causes of human death. In trehalose synthase, the protein NCGM 946K2 146 found in MTB has an important role. For carbohydrate transport and metabolism, trehalose synthase is required. The protein is not clarified yet, though. In this research, an in silico approach was, therefore, formulated for functional and structural documentation of the uncharacterized protein NCGM946K2_146.Three distinct servers, including Modeller, Phyre2, and Swiss Model, were used to evaluate the predicted tertiary structure. The top materials are selected using structural evaluations conducted with the analysis of Ramachandran Plot, Swiss-Model Interactive Workplace, ProSA-web, Verify 3D, and Z scores. This analysis aimed to uncover the value of the NCGM946K2_146 protein of MTB. This research will, therefore, improve our pathogenesis awareness and give us a chance to target the protein compound.

Automated visual identification of Benign and Malignant breast tumors even with ultrasound (US) B-Mode image is still an open area of research. This paper presents parametric image-based approach to classify and detect benign and malignant breast tumors from ultrasound images using a custom-made convolutional neural network architecture. The Rician Inverse Gaussian (RiIG) distribution is presented here as a suitable model for describing the statistics of the ultrasound images in the Contourlet Transform domain. Locally computed values of the dispersion parameters of the RiIG distribution in various contourlet sub-bands yield parametric images those are classified using the proposed convolutional neural network. Experiments are conducted on a publicly available dataset of 250 images, of 100 belong to the benign Fibroadenoma and 150 in the malignant category. The proposed method provides accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV) of 96%, 97.3%, 94.12%, 96% and 96%, respectively. It is also shown that the accuracy obtained by the Proposed Method is higher than several recently reported results.

The movement of a projectile, which can be a mass or a charge, through a static field, can be important in various fields of physics ranging from particle physics to rocket science. In many cases, the movement of the projectile can be solely dependent on the initial velocity provided to the projectile. The determination of this initial velocity can be very complicated if the existing static field is too complex. In such cases, neural networks can be the key to modelling the environment and accurately estimate the initial velocity. This AI-driven velocity estimation can be extremely difficult using supervised learning due to the lack of seen examples of the state (initial position) to action (velocity) relation, which is also known as the policy. In this work, this problem is treated as a continuous-action-space contextual bandit problem and a neural network-based actor critic method is used to model the environment and determine the action policy. This method is tested on two different static field configurations using a Python-based GUI named Pygame. After training, the success rates of the projectile hitting the targeted destinations are 94.8% and 77.9% respectively, which implies that the proposed method can successfully model the action policy with high accuracy.

Segmentation or lesion boundary detection in classification of Benign and Malignant breast tumors from B-Mode ultrasound image analysis is a challenging one. In this study, first a suitable frame is chosen by strain and velocity imaging from a raw radio frequency (RF) echo of clinical cases. The consequent B-Mode ultrasound (US) image is calculated and binarized. Finally, the lesion boundary is well-defined by binary dilation technique using MATLAB. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 5(1), Dec 2018 P 75-78

Water-lubricated flow technology is an environmentally friendly and economically beneficial means of transporting unconventional viscous crudes. The current research was initiated to investigate an engineering model suitable to estimate the frictional pressure losses in water-lubricated pipelines as a function of design/operating parameters such as flow rates, water content, pipe size, and liquid properties. The available models were reviewed and critically assessed for this purpose. As the reliability of the existing models was not found to be satisfactory, a new two-parameter model was developed based on a phenomenological analysis of the dataset available in the open literature. The experimental conditions for these data included pipe sizes and oil viscosities in the ranges of 25–260 mm and 1220–26,500 mPa·s, respectively. A similar range of water equivalent Reynolds numbers corresponding to the investigated flow conditions was 103–106. The predictions of the new model agreed well with the experimental results. The respective values of the coefficient of determination (R2) and the root mean square error (RMSE) were 0.90 and 0.46. The current model is more refined, easy-to-use, and adaptable compared to other existing models.

Classification of Benign and Malignant breast cancer tumours by B-Mode ultrasound image analysis is a challenging problem. In this study, first suitable raw radio frequency (RF) frame is selected based on strain and velocity imaging. The consequent B-Mode ultrasound (US) image is binarized and subsequently subjected to contourlet transform. In the proposed method only six contourlet sub-band coefficients are considered from the binary image to reduce the computation time duration and then texture and statistical features are extracted from the contourlet coefficient image. Finally, the lesion is classified as benign or malignant using three classifiers such as artificial neural network (ANN), K-Nearest Neighbours (KNN) and Support Vector Machine (SVM). It is shown that the proposed method yields classification accuracies of 95%, 92.5% and 90% over a competent computational time by using ANN, KNN and SVM classifiers respectively.

Hoolock gibbons (Hoolock hoolock) are endangered small apes occurring in Bangladesh, India, Myanmar, and south China. All known populations have undergone declines primarily due to habitat destruction or alteration. We examined the influence of area of natural forests, area of plantations, area of agricultural land and edible plant species richness on the hoolock gibbon numbers from 22 sites in Bangladesh using multiple regression analysis. Area of natural forests and plantations were not linked to hoolock gibbon numbers, whereas area of agricultural land and edible plant species richness were both significantly linked. Higher edible plant species richness was directly related to hoolock gibbon numbers, while the area of agricultural land was inversely related. This is the first quantitative study illustrating the importance of these two variables with hoolock gibbon numbers. We also analyzed the existing habitat characteristics in two of the largest hoolock gibbon populations in Bangladesh: Lawachara (42 individuals) and Kaptai (84 individuals). Trees in Lawachara were generally smaller compared to those in Kaptai. Artocarpus, Tectona, and Dipterocarpus were the three most important genera in both sites, although Artocarpus was more important in Lawachara. Each site also had an abundant variety of tree species utilized by hoolock gibbons for food. We discuss the current context of primate conservation in Bangladesh and suggest that the current conservation priority for gibbons in the country should be protection and improvement of habitat.

Speckle noise is an inherent phenomenon in medical ultrasound (US) images. Since it degrades an ultrasound image quality and reduces its diagnostic value, reduction of speckle noise is a very important pre-processing step in ultrasound image processing. For this purpose, the knowledge of the statistics of speckle noise is necessary; especially in the multi-resolution transform domain due to their sparse and efficient representation of images. In this paper a Bessel K-Form (BKF) probability density function (pdf) is proposed as a highly suitable prior for modeling the log-transformed speckle noise in the well-known contourlet transform domain. A maximum likelihood based method is presented for estimating the parameters of the BKF pdf. The appropriateness of the BKF pdf in modeling the speckle is studied for different noise levels in the contourlet transform domain, in addition the suitability of BKF model is investigated for the case of real US images. It is shown that, in general the BKF can model the statistics of the contourlet transform coefficients corresponding to log-transformed speckle better than the traditional Gaussian and normal inverse Gaussian pdfs.

Medical ultrasound images are inherently corrupted with speckle noise in a multiplicative manner. The most popular approach of despeckling is homomorphic filtering, in which the multiplicative speckle noise converted to an additive one by log-transformation. The knowledge of the statistics of the log-transformed speckle is necessary for developing effective methods for speckle reduction. The Bessel K-Form (BKF) probability density function (pdf) is proposed to model the logarithmically transformed speckle noise in the dual-tree complex wavelet transform (DT-CWT) domain. A maximum likelihood based method is developed for estimating the parameters of the BKF pdf. The suitability of the BKF pdf in modeling the speckle is studied for different noise levels in the 2D DT-CWT domain. It is shown that the BKF can capture the statistics of the DT-CWT coefficients corresponding to log-transformed speckle better than the Gaussian and normal inverse Gaussian pdfs.

Medical ultrasound images are inherently corrupted with speckle noise. The knowledge of the statistics of the speckle is necessary for developing effective methods to reduce speckle noise. In this paper, the Bessel K-Form (BKF) probability density function (PDF) is proposed to model the logarithmically transformed speckle noise. A maximum likelihood -based method is developed for estimating the BKF parameters. The appropriateness of the BKF distribution in modeling the speckle is studied for different noise levels in the wavelet and curvelet transform domains. It is shown that the BKF can capture the noise statistics better than the Gaussian and normal inverse Gaussian PDFs.

Speckle noise in medical ultrasound (US) degrades the image quality and reduces its diagnostic value. Reduction of speckle noise is an important pre-processing step for the analysis and processing of medical ultrasound images. Knowledge of the statistics of the log-transformed speckle especially in the multi-resolution transform domain is important for developing effective homomorphic despeckling techniques, the most popular approach of speckle reduction from ultrasound images. In this paper, the bessel K-form (BKF) probability density function (pdf) is proposed as a highly suitable prior for modeling the log-transformed speckle noise in the well-known contourlet transform domain. A maximum likelihood based method is introduced for estimating the parameters of the BKF pdf. The effectiveness of the proposed estimation method is demonstrated using Monte Carlo simulations. The appropriateness of the BKF pdf in modeling the speckle is first studied extensively for simulated noise of different levels in the contourlet transform domain. Next, the suitability of BKF model is investigated for the case of real US images that include neonatal brain and breast tumors. It is shown that, in general the BKF prior can model the statistics of the contourlet transform coefficients corresponding to the log-transformed speckle better than the traditionally used Gaussian, normal inverse Gaussian and generalized Nakagami pdfs.

% . ' ( $ / 0 1 ) *+ , / 2 **!, ** , !"" # 3 4 5 * !, * 6 ' % 70 5 0 ( ( % ' % 8 . ( !""6 ( ' % 0 0 / 1 . ' # $ ' % 9 !""! : % . ) & 0 # ; !"" % ( : % . ) & ! ? # < ( 1 ! # $ ' % ( ( % ' ' % # @ ' % ''# %# . '' ) 9 % !"" # @ %& A % ; % : % B ( # $ ' % % % % ' ' % # !""6 # $ . $ ' % 8 1C/. 3 : / / D # 1C/. 3 : 3 A D #" %% / ( 8 ) E & ' !""6 # $ ( & %% % B %% % %% ' % 0 9 % 0 # ( ' % 0 D 0 F! . $ % ' 1 % ' % #