Formalizing a hierarchical file system

An abstract file system is defined here as a partial function from (absolute) paths to data. Such a file system determines the set of valid paths. It allows the file system to be read and written at a valid path, and it allows the system to be modified by the Unix operations for creation, removal, and moving of files and directories. We present abstract definitions (axioms) for these operations. This specification is refined towards a pointer implementation. The challenge is to have a natural abstraction function from the implementation to the specification, to define operations on the concrete store that behave exactly in the same way as the corresponding functions on the abstract store, and to prove these facts. To mitigate the problems attached to partial functions, we do this in two steps: first a refinement towards a pointer implementation with total functions, followed by one that allows partial functions. These two refinements are proved correct by means of a number of invariants. Indeed, the insights gained consist, on the one hand, of the invariants of the pointer implementation that are needed for the refinement functions, and on the other hand of the precise enabling conditions of the operations on the different levels of abstraction. Each of the three specification levels is enriched with a permission system for reading, writing, or executing, and the refinement relations between these permission systems are explored. Files and directories are distinguished from the outset, but this rarely affects our part of the specifications. All results have been verified with the proof assistant PVS, in particular, that the invariants are preserved by the operations, and that, where the invariants hold, the operations commute with the refinement functions

A review of different deep learning techniques for sperm fertility prediction

Sperm morphology analysis (SMA) is a significant factor in diagnosing male infertility. Therefore, healthy sperm detection is of great significance in this process. However, the traditional manual microscopic sperm detection methods have the disadvantages of a long detection cycle, low detection accuracy in large orders, and very complex fertility prediction. Therefore, it is meaningful to apply computer image analysis technology to the field of fertility prediction. Computer image analysis can give high precision and high efficiency in detecting sperm cells. In this article, first, we analyze the existing sperm detection techniques in chronological order, from traditional image processing and machine learning to deep learning methods in segmentation and classification. Then, we analyze and summarize these existing methods and introduce some potential methods, including visual transformers. Finally, the future development direction and challenges of sperm cell detection are discussed. We have summarized 44 related technical papers from 2012 to the present. This review will help researchers have a more comprehensive understanding of the development process, research status, and future trends in the field of fertility prediction and provide a reference for researchers in other fields

Formalizing a hierarchical file system

An abstract file system is defined here as a partial function from (absolute) paths to data. Such a file system determines the set of valid paths. It allows the file system to be read and written at a valid path, and it allows the system to be modified by the Unix operations for creation, removal, and moving of files and directories. We present abstract definitions (axioms) for these operations. This specification is refined towards a pointer implementation. The challenge is to have a natural abstraction function from the implementation to the specification, to define operations on the concrete store that behave exactly in the same way as the corresponding functions on the abstract store, and to prove these facts. To mitigate the problems attached to partial functions, we do this in two steps: first a refinement towards a pointer implementation with total functions, followed by one that allows partial functions. These two refinements are proved correct by means of a number of invariants. Indeed, the insights gained consist, on the one hand, of the invariants of the pointer implementation that are needed for the refinement functions, and on the other hand of the precise enabling conditions of the operations on the different levels of abstraction. Each of the three specification levels is enriched with a permission system for reading, writing, or executing, and the refinement relations between these permission systems are explored. Files and directories are distinguished from the outset, but this rarely affects our part of the specifications. All results have been verified with the proof assistant PVS, in particular, that the invariants are preserved by the operations, and that, where the invariants hold, the operations commute with the refinement functions

Mobile Application Testing in Pakistan: A Survey

Research regarding MAT (Mobile Application Testing) in Pakistan is hard to discover and to the best of our knowledge, no work has been done in surveying MAT in Pakistan. In this work, we have examined the current trend and status of MAT in Pakistan. Main objective was to investigate to what extent MAT is currently applied in Pakistan software companies and what experience the companies have with using MAT. Furthermore, efforts were made to find out what testers think about MAT, e.g. issue, advantages and disadvantages of MAT, what factors affects MAT and how they plan to improve MAT. In order to achieve our objectives, we used a comprehensive online survey so we converted our research questions into correspondence survey questions. We served a questionnaire of the survey to 66 testing relevant officials of leading software companies in different cities of Pakistan to develop a model study about general trend and status of MAT which can be generalized all over Pakistan. We received 56 replies in total after over 2 months. After that, we used SPSS tool to analyze the replies of this questionnaire. Cross-Tabulation Analysis and Pearson Chi-square tests have been computed to examine the results. We found some interesting results on current status and practice of MAT in Pakistan software companies

Predicting Thalassemia Using Feature Selection Techniques: A Comparative Analysis

Thalassemia represents one of the most common genetic disorders worldwide, characterized by defects in hemoglobin synthesis. The affected individuals suffer from malfunctioning of one or more of the four globin genes, leading to chronic hemolytic anemia, an imbalance in the hemoglobin chain ratio, iron overload, and ineffective erythropoiesis. Despite the challenges posed by this condition, recent years have witnessed significant advancements in diagnosis, therapy, and transfusion support, significantly improving the prognosis for thalassemia patients. This research empirically evaluates the efficacy of models constructed using classification methods and explores the effectiveness of relevant features that are derived using various machine-learning techniques. Five feature selection approaches, namely Chi-Square (χ2), Exploratory Factor Score (EFS), tree-based Recursive Feature Elimination (RFE), gradient-based RFE, and Linear Regression Coefficient, were employed to determine the optimal feature set. Nine classifiers, namely K-Nearest Neighbors (KNN), Decision Trees (DT), Gradient Boosting Classifier (GBC), Linear Regression (LR), AdaBoost, Extreme Gradient Boosting (XGB), Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Support Vector Machine (SVM), were utilized to evaluate the performance. The χ2 method achieved accuracy, registering 91.56% precision, 91.04% recall, and 92.65% f-score when aligned with the LR classifier. Moreover, the results underscore that amalgamating over-sampling with Synthetic Minority Over-sampling Technique (SMOTE), RFE, and 10-fold cross-validation markedly elevates the detection accuracy for αT patients. Notably, the Gradient Boosting Classifier (GBC) achieves 93.46% accuracy, 93.89% recall, and 92.72% F1 score

Sperm Abnormality Detection Using Sequential Deep Neural Network

Sperm morphological analysis (SMA) is an essential step in diagnosing male infertility. Using images of human sperm cells, this research proposes a unique sequential deep-learning method to detect abnormalities in semen samples. The proposed technique identifies and examines several components of human sperm. In order to conduct this study, we used the online Modified Human Sperm Morphology Analysis (MHSMA) dataset containing 1540 sperm images collected from 235 infertile individuals. For research purposes, this dataset is freely available online. To identify morphological abnormalities in different parts of human sperm, such as the head, vacuole, and acrosome, we proposed sequential deep neural network (SDNN) architecture. This technique is also particularly effective with low-resolution, unstained images. Sequential deep neural networks (SDNNs) demonstrate high accuracy in diagnosing morphological abnormalities based on the given dataset in our tests on the benchmark. Our proposed algorithm successfully detected abnormalities in the acrosome, head, and vacuole with an accuracy of 89%, 90%, and 92%, respectively. It is noteworthy that our system detects abnormalities of the acrosome and head with greater accuracy than current state-of-the-art approaches on the suggested benchmark. On a low-specification computer/laptop, our algorithm also requires less execution time. Additionally, it can classify photos in real time. Based on the results of our study, an embryologist can quickly decide whether to use the given sperm

Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos

In medical imaging, the detection and classification of stomach diseases are challenging due to the resemblance of different symptoms, image contrast, and complex background. Computer-aided diagnosis (CAD) plays a vital role in the medical imaging field, allowing accurate results to be obtained in minimal time. This article proposes a new hybrid method to detect and classify stomach diseases using endoscopy videos. The proposed methodology comprises seven significant steps: data acquisition, preprocessing of data, transfer learning of deep models, feature extraction, feature selection, hybridization, and classification. We selected two different CNN models (VGG19 and Alexnet) to extract features. We applied transfer learning techniques before using them as feature extractors. We used a genetic algorithm (GA) in feature selection, due to its adaptive nature. We fused selected features of both models using a serial-based approach. Finally, the best features were provided to multiple machine learning classifiers for detection and classification. The proposed approach was evaluated on a personally collected dataset of five classes, including gastritis, ulcer, esophagitis, bleeding, and healthy. We observed that the proposed technique performed superbly on Cubic SVM with 99.8% accuracy. For the authenticity of the proposed technique, we considered these statistical measures: classification accuracy, recall, precision, False Negative Rate (FNR), Area Under the Curve (AUC), and time. In addition, we provided a fair state-of-the-art comparison of our proposed technique with existing techniques that proves its worthiness

Development and Characterization of Eudragit® EPO-Based Solid Dispersion of Rosuvastatin Calcium to Foresee the Impact on Solubility, Dissolution and Antihyperlipidemic Activity

Poor solubility is the major challenge involved in the formulation development of new chemical entities (NCEs), as more than 40% of NCEs are practically insoluble in water. Solid dispersion (SD) is a promising technology for improving dissolution and, thereby, the bioavailability of poorly soluble drugs. This study investigates the influence of a pH-sensitive acrylate polymer, EPO, on the physicochemical properties of rosuvastatin calcium, an antihyperlipidemic drug. In silico docking was conducted with numerous polymers to predict drug polymer miscibility. The screened-out polymer was used to fabricate the binary SD of RoC in variable ratios using the co-grinding and solvent evaporation methods. The prepared formulations were assessed for physiochemical parameters such as saturation solubility, drug content and in vitro drug release. The optimized formulations were further ruled out using solid-state characterization (FTIR, DSC, XRD and SEM) and in vitro cytotoxicity. The results revealed that all SDs profoundly increased solubility as well as drug release. However, the formulation RSE-2, with a remarkable 71.88-fold increase in solubility, presented 92% of drug release in the initial 5 min. The molecular interaction studied using FTIR, XRD, DSC and SEM analysis evidenced the improvement of in vitro dissolution. The enhancement in solubility of RoC may be important for the modulation of the dyslipidemia response. Therefore, pharmacodynamic activity was conducted for optimized formulations. Our findings suggested an ameliorative effect of RSE-2 in dyslipidemia and its associated complications. Moreover, RSE-2 exhibited nonexistence of cytotoxicity against human liver cell lines. Convincingly, this study demonstrates that SD of RoC can be successfully fabricated by EPO, and have all the characteristics that are favourable for superior dissolution and better therapeutic response to the drug