Decision tree is one of the best expressive classifiers in data mining. A decision tree is popular due to its simplicity and straightforward visualization capability for all types of datasets. Decision tree forest is an ensemble of decision trees. The prediction accuracy of the decision tree forest is more than a decision tree algorithm. Constant efforts are going on to create accurate and diverse trees in the decision tree forest. In this paper, we propose Tangent Weighted Decision Tree Forest (TWDForest), which is more accurate and diverse than random forest. The strength of this technique is that it uses a more accurate and uniform tangent weighting function to create a weighted decision tree forest. It also improves performance by taking opinions from previous trees to best fit the successor tree and avoids the toggling of the root node. Due to this novel approach, the decision trees from the forest are more accurate and diverse as compared to other decision forest algorithms. Experiments of this novel method are performed on 15 well known, publicly available UCI machine learning repository datasets of various sizes. The results of the TWDForest method demonstrate that the entire forest and decision trees produced in TWDForest have high prediction accuracy of 1-7% more than existing methods. TWDForest also creates more diverse trees than other forest algorithms.Imaging techniques are used to capture anomalies of the human body. The captured images must be understood for diagnosis, prognosis and treatment planning of the anomalies. Medical image understanding is generally performed by skilled medical professionals. However, the scarce availability of human experts and the fatigue and rough estimate procedures involved with them limit the effectiveness of image understanding performed by skilled medical professionals. Convolutional neural networks (CNNs) are effective tools for image understanding.  https://www.selleckchem.com/products/Streptozotocin.html  They have outperformed human experts in many image understanding tasks. This article aims to provide a comprehensive survey of applications of CNNs in medical image understanding. The underlying objective is to motivate medical image understanding researchers to extensively apply CNNs in their research and diagnosis. A brief introduction to CNNs has been presented. A discussion on CNN and its various award-winning frameworks have been presented. The major medical image understanding tasks, namely image classification, segmentation, localization and detection have been introduced. Applications of CNN in medical image understanding of the ailments of brain, breast, lung and other organs have been surveyed critically and comprehensively. A critical discussion on some of the challenges is also presented.During major infectious disease outbreak, such as COVID-19, the goods and parcels supply and distribution for the isolated personnel has become a key issue worthy of attention. In this study, we propose a delivery problem that arises in the last-mile delivery during major infectious disease outbreak. The problem is to construct a Hamiltonian tour over a subset of candidate parking nodes, and each customer is assigned to the nearest parking node on the tour to pick up goods or parcels. The aim is to minimize the total cost, including the routing, allocation, and parking costs. We propose three models to formulate the problem, which are node-based, flow-based and bilevel programing formulations. Moreover, we develop a variable neighborhood search algorithm based on the ideas from the bilevel programing formulations to solve the problem. Finally, the proposed algorithm is tested on a set of randomly generated instances, and the results indicate the effectiveness of the proposed approach.The Fixed-Tree BMEP (FT-BMEP) is a special case of the Balanced Minimum Evolution Problem (BMEP) that consists of finding the assignment of a set of n taxa to the n leaves of a given unrooted binary tree so as to minimize the BMEP objective function. Deciding the computational complexity of the FT-BMEP has been an open problem for almost a decade. Here, we show that a few modifications to Fiorini and Joret's proof of the NP -hardness of the BMEP suffice to prove the general NP -hardness of the FT-BMEP as well as its strong inapproximability.Access to resources that is equitable and sustainable provides a critical foundation for community harmony and development. Both natural and human-induced disasters present major risks to sustainable development trajectories and require strategic management within regional and local plans. Climate change and its impacts, including intensified storms, flash floods, and other water-based disasters (WD), also pose a serious and increasing threat. Small, remote communities prone to weather extremes are particularly vulnerable as they often lack effective early warning systems and experience energy insufficiency. Humanitarian engineering provides a transdisciplinary approach to these issues, supporting practical development resources such as renewable energy, which can also be adapted for disaster response. This study details an exploratory investigation of community vulnerability and capability mapping (VCM) that identifies communities with high WD risk and limited response capability which may benefit from risk reduction engagement and program co-development. By presenting criteria appropriate for VCM, we highlight the anthropocentric characteristics that could potentially be incorporated within community-led action as part of a comprehensive scheme that promotes sustainable development.The COVID-19 pandemic before mass vaccination can be restrained only by the limitation of contacts between people, which makes the digital economy a key condition for survival. More than half of the world's population lives in urban areas, and many cities have already transformed into "smart" digital/virtual hubs. Digital services ensure city life safe without an economy lockout and unemployment. Urban society strives to be safe, sustainable, well-being, and healthy. We set the task to construct a hybrid sociological and technological concept of a smart city with matched solutions, complementary to each other. Our modeling with the elaborated digital architectures and with the bionic solution for ensuring sufficient data governance showed that a smart city in comparison with the traditional city is tightly interconnected inside like a social "organism". Society has entered a decisive decade during which the world will change by moving closer towards SDGs targets 2030 as well as by the transformation of cities and their digital infrastructures.