Is pancreatectomy with resection of the retro-pancreatic vessels for cancer justified?

Collagenase-1 (C1) is the predominant matrix metalloproteinase present in newly formed microvessels and serves as a marker of neovascularization. The expression of the oncofetal fragment of fibronectin (Fn-f) was found to be increased during angiogenesis. In the present study, we investigated the relationship between the expression of collagenase-1 and the oncofetal fragment of fibronectin in newly formed microvessels as markers of tumor angiogenesis. In aggressive skin tumors (i.e., morpheaform and recurrent basal cell carcinomas) and squamous cell carcinomas, neovascularization was associated with a marked increase in the number of C1-positive and Fn-f-positive microvessels. At the beginning of elongation, microvessels begin to produce C1 but lose their ability to express type IV collagen and FVIII-related antigen. Later, this endothelium produces both Fn-f and C1. As maturation of microvessels occurs, C1-containing endothelium fails to express Fn-f but begins to produce a type IV collagen-containing basement membrane and FVIII-related antigen. These studies show that there is a selective expression of both Fn-f and collagenase by immature endothelial cells. C1 production begins at early stages of blood vessel formation and continues throughout angiogenesis. In contrast, Fn-f expression is limited to later stages of vasculogenesis, indicating that these proteins are reliable markers of angiogenesis.     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

Intelligent Machine Learning with Metaheuristics Based Sentiment Analysis and Classification

Sentiment Analysis (SA) is one of the subfields in Natural Language Processing (NLP) which focuses on identification and extraction of opinions that exist in the text provided across reviews, social media, blogs, news, and so on. SA has the ability to handle the drastically-increasing unstructured text by transforming them into structured data with the help of NLP and open source tools. The current research work designs a novel Modified Red Deer Algorithm (MRDA) Extreme Learning Machine Sparse Autoencoder (ELMSAE) model for SA and classification. The proposed MRDA-ELMSAE technique initially performs preprocessing to transform the data into a compatible format. Moreover, TF-IDF vectorizer is employed in the extraction of features while ELMSAE model is applied in the classification of sentiments. Furthermore, optimal parameter tuning is done for ELMSAE model using MRDA technique. A wide range of simulation analyses was carried out and results from comparative analysis establish the enhanced efficiency of MRDA-ELMSAE technique against other recent techniques.     

Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

With the rapid development of emerging 5G and beyond (B5G), Unmanned Aerial Vehicles (UAVs) are increasingly important to improve the performance of dense cellular networks. As a conventional metric, coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous environment. In recent years, stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network systems. In this paper, an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been proposed. An assumption was considered that all users are distributed according to Poisson Cluster Process (PCP) around base stations, in particular, Thomas Cluster Process (TCP). Using this model, the impact of beam alignment errors on the coverage probability was investigated. Initially, the Probability Density Function (PDF) of directional antenna gain between the user and its serving base station was obtained. Then, association probability with each tier was achieved. A tractable expression was derived for coverage probability in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) condition links. Numerical results demonstrated that at low UAVs altitude, beam alignment errors significantly degrade coverage performance. Moreover, for a small cluster size, alignment errors do not necessarily affect the coverage performance.     

Occurrence and removal of microbial indicators from municipal wastewaters by nine different MBR systems

Nine different membrane bioreactor (MBR) systems with different process configurations (submerged and external), membrane geometries (hollow-fiber, flat-sheet, and tubular), membrane materials (polyethersulfone (PES), polyvinylidene fluoride (PVDF), and polytetrafluoroethylene (PTFE)) and membrane nominal pore sizes (0.03-0.2 μm) were evaluated to assess the impact of influent microbial concentration, membrane pore size and membrane material and geometries on removal of microbial indicators by MBR technology. The log removal values (LRVs) for microbial indicators increased as the influent concentrations increased. Among the wide range of MBR systems evaluated, the total and fecal coliform bacteria and indigenous MS-2 coliphage were detected in 32, 9 and 15% of the samples, respectively; the 50th percentile LRVs were measured at 6.6, 5.9 and 4.5 logs, respectively. The nominal pore sizes of the membranes, membrane materials and geometries did not show a strong correlation with the LRVs.     

Model-based test case generation and prioritization: a systematic literature review

Software and Systems Modeling - Model-based test case generation (MB-TCG) and prioritization (MB-TCP) utilize models that represent the system under test (SUT) for test generation and...     

The Avenue of Sphinxes： Restoration of a Legend

The redevelopment of historic cities is often challenged by intricate--and in many cases contradictory--missions. From one side, there is the urge to comprehensively preserve cultural resources. At the same time, opportunities of economic growth should be made available and needs of contemporary living maintained and nourished. The main aim of this paper is to reconcile probable incompatibilities between such missions through promoting ＂sensitive＂ redevelopment approaches in historic cities. The paper focuses on the city of Luxor, Egypt with its immensely capturing yet quite undermined legend： the Avenue of Sphinxes. In Luxor, the injection of nonintrusive interventions presents itself as a highly potential candidate in protecting and enhancing the experience of the avenue while meeting contemporary needs of living. Adopting less sensitive development approaches can lead the quality of experiencing the whole city to be worsened for the increasing numbers of tourists and locals.     

Approaches to atomistic triple-line properties from first-principles

Different approaches to study wetting and adhesion by applying density-functional theory (DFT) methods are highlighted. The ab initio thermodynamics method is used to demonstrate the link between the calculated work of separation and the work of adhesion and wetting angles from sessile-drop measurements. An approach to extend DFT calculations to the case of large-scale interfaces relevant for wetting systems is also discussed.