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Hypertension is a leading cardiovascular risk factor with significant morbidity and mortality. Sometimes it remains difficult to find out the actual cause and to manage in a large segment of population. So, the ability to identify a contributing or secondary cause that is potentially curable for all patient with hypertension is of great importance and challenging. Endocrine hypertension is one of the common forms of secondary hypertension. Primary aldosteronism, pheochromocytoma, and other causes of mineralocorticoid excess are among the common causes of endocrine hypertension. Published literature were reviewed about their prevalence, clinical presentation, screening methods and specific management. Now day by day it’s become more challenging for the clinicians to understand the pathophysiology, clinical spectrum of these diseases and improvement of the management protocol because of newer clinical, biochemical, and modern radiological approach. Cardiovasc j 2023; 15(2): 159-174

Recent COVID-19 has revealed that we are still unable to find a sustainable approach to combat such infections in order to maintain a stable economy and minimize death rates. COVID-19 vaccine is available but when COVID-19 first arrived, restricting movement was the only option to control the spread, yet, one of its consequences is an economic crisis. This paper proposes predicting optimum lockdown patterns of epidemic spread using AI techniques (MESA ABM) which will help to balance economic fall and death rates while conducting vaccine research. We created a computergenerated environment to replicate a pandemic, such as the initial wave of COVID-19. The model was created using the SIR (Susceptible, Infected, Recovered) hypothesis for disease propagation and implemented using the Mesa, which is an agent-based modeling framework. Using this model, we develop an optimum lockdown pattern using agents (entities that act like humans). Our model produces optimal movement restrictions by randomly putting lockdowns ranging from one to a hundred days using given conditions. Additionally, we carry out several assessments and offer the justification for the action.

Leaf disease in tomatoes is one of the most common and treacherous diseases. It directly affects the production of tomatoes, resulting in enormous economic loss each year. As a result, studying the detection of tomato leaf diseases is essential. To that aim, this work introduces a novel mechanism for selecting the most effective hyperparameters for improving the detection accuracy of deep CNN. Several cutting-edge CNN algorithms were examined in this study to diagnose tomato leaf diseases. The experiment is divided into three stages to find a full proof technique. A few pre-trained deep convolutional neural networks were first employed to diagnose tomato leaf diseases. The superlative combined model has then experimented with changes in the learning rate, optimizer, and classifier to discover the optimal parameters and minimize overfitting in data training. In this case, 99.31% accuracy was reached in DenseNet 121 using AdaBound Optimizer, 0.01 learning rate, and Softmax classifier. The achieved detection accuracy levels (above 99%) using various learning rates, optimizers, and classifiers were eventually tested using K-fold cross-validation to get a better and dependable detection accuracy. The results indicate that the proposed parameters and technique are efficacious in recognizing tomato leaf disease and can be used fruitfully in identifying other leaf diseases.

DBSCAN algorithm is a location-based clustering approach; it is used to find relationships and patterns in geographical data. Because of its widespread application, several data science-based programming languages include the DBSCAN method as a built-in function. Researchers and data scientists have been clustering and analyzing their study data using the built-in DBSCAN functions. All implementations of the DBSCAN functions require user input for radius distance (i.e., $\epsilon$) and a minimum number of samples for a cluster (i.e., min\_sample). As a result, the result of all built-in DBSCAN functions is believed to be the same. However, the DBSCAN Python built-in function yields different results than the other programming languages those are analyzed in this study. We propose a scientific way to assess the results of DBSCAN built-in function, as well as output inconsistencies. This study's research reveals various differences and advises caution when working with built-in functionality.

VisualCommunity is a platform designed to support community or neighborhood scale research. The platform integrates mobile, AI, visualization techniques, along with tools to help domain researchers, practitioners, and students collecting and working with spatialized video and geo-narratives. These data, which provide granular spatialized imagery and associated context gained through expert commentary have previously provided value in understanding various community-scale challenges. This paper further enhances this work AI-based image processing and speech transcription tools available in VisualCommunity, allowing for the easy exploration of the acquired semantic and visual information about the area under investigation. In this paper we describe the specific advances through use case examples including COVID-19 related scenarios.

This study presents a framework to study quantitatively geographical visual diversities of urban neighborhood from a large collection of street-view images using an Artificial Intelligence (AI)-based image segmentation technique. A variety of diversity indices are computed from the extracted visual semantics. They are utilized to discover the relationships between urban visual appearance and socio-demographic variables. This study also validates the reliability of the method with human evaluators. The methodology and results obtained from this study can potentially be used to study urban features, locate houses, establish services, and better operate municipalities.

Purpose of Review: The management of eye injuries is both difficult and argumentative. This study attempts to highlight the management of ocular trauma using currently available information in the literature and author experience. This review presents a workable framework from the first presentation, epidemiology, classification, investigations, management principles, complications, prognostic factors, final visual outcome and management debates. Review Findings: Mechanical ocular trauma is a leading cause of monocular blindness and possible handicap worldwide. Among several classification systems, the most widely accepted is Birmingham Eye Trauma Terminology (BETT). Mechanical ocular trauma is a topic of unsolved controversy. Patching for corneal abrasion, paracentesis for hyphema, the timing of cataract surgery and intraocular lens implantation are all issues in anterior segment injuries. Regarding posterior segment controversies, the timing of vitrectomy, use of prophylactic cryotherapy, the necessity of intravitreal antibiotics in the absence of infection, the use of vitrectomy vs vitreous tap in traumatic endophthalmitis is the issues. The pediatric age group needs to be approached by a different protocol due to the risk of amblyopia, intraocular inflammation, and significant vitreoretinal adhesions. The various prognostic factors have a role in the final visual outcome. B scan is used to exclude R.D, Intraocular foreign body (IOFB), and vitreous haemorrhage in hazy media. Individual surgical strategies are used for every patient according to the classification and extent of the injuries. Conclusion: This article examines relevant evidence on the management challenges and controversies of mechanical trauma of the eye and offers treatment recommendations based on published research and the authors’ own experience.

In Bangladesh, maize stands second place after rice; since it faces diverse natural calamities during its highest growing season (rabi/winter), particularly strong storm during the reproductive stage. Sometimes in some regions, this crop is completely damaged by natural disasters. Considering the burning issue, thirteen hybrids, including 10 previously selected short stature hybrids were evaluated against three local and standard checks: ‘BHM-9’, ‘981’ and ‘Sunshine’ in two consecutive years in seven locations of Bangladesh. Combined analysis over locations and seasons instigated that genotypes ‘Sunshine’, ‘981’ and ‘G10’ were the top-high yielders, while genotypes ‘G1’, ‘G2’, ‘BHM-9’ and ‘Sunshine’ were found the most stable. On the other hand, five genotypes such as ‘G3’, ‘G4’, ‘G6’, ‘G8’ and ‘G9’ had the below-average mean yield and the genotypes ‘G6’ and ‘G9’ were the most unstable. Among the seven environments, Jamalpur, Joydebpur and Dinajpur were most discriminating and Ishwardi was the least discriminating; whereas Joydebpur was more representative and Borishal was the least representative of other test environments. In the case of plant and ear height, most of the genotypes showed a lower value than all the checks, which was desirable. But among the top three high yielders, local cross-genotype, the ‘G10’ had the lowest and more stable value for both plant height and ear height. Therefore, considering the plant and ear height, grain yield, and yield stability, the genotype ‘G10’ has been recommended for release as commercial variety and has been released as new maize variety in Bangladesh with the local name of ‘BARI Hybrid Maize-16’ (BHM-16).

Selecting an appropriate clustering method as well as an optimal number of clusters in road accident data is at times confusing and difficult. This paper analyzes shortcomings of different existing techniques applied to cluster accident-prone areas and recommends using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS) to overcome them. Comparative performance analysis based on real-life data on the recorded cases of road accidents in North Carolina also show more effectiveness and efficiency achieved by these algorithms., 6 pages, 10 figures, 2 tables

This paper presents the prediction of traffic-violations using data mining techniques, more specifically, when most likely a traffic-violation may happen. Also, what are the contributing factors that may cause more damages (e.g., personal injury, property damage, etc.) are discussed in this paper. The national database for traffic-violation was considered for the mining and analyzed results indicated that a few specific times are probable for traffic-violations. Moreover, most accidents happened on specific days and times. The findings of this work could help prevent some traffic-violations or reduce the chance of occurrence. These results can be used to increase cautions and traffic-safety tips.