Minia University

The present study was conducted to determine the adverse effects of high temperature and humidity not only on live performance and egg quality but also on immune function in commercial laying hens. One hundred eighty 31-wk-old laying hens at peak production were used in this study. Hens were housed in cages (15 cages of 4 birds/cage) in each of 3 environmental chambers and received 1 of 3 treatments. The 3 treatments were control (average temperature and relative humidity), cyclic (daily cyclic temperature and humidity), and heat stress (constant heat and humidity) for 5 wk. Different production and immune parameters were measured. Body weight and feed consumption were significantly reduced in hens in the heat stress group. Egg production, egg weight, shell weight, shell thickness, and specific gravity were significantly inhibited among hens in the heat stress group. Likewise, total white blood cell (WBC) counts and antibody production were significantly inhibited in hens in the heat stress group. In addition, mortality was higher in the heat stress group compared to the cyclic and control groups. Even though T- and B-lymphocyte activities were not significantly affected by any of the treatments, lymphocytes from hens in the heat stress group had the least activity at 1 wk following treatment. These results indicate that heat stress not only adversely affects production performance but also inhibits immune function.

The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity. Although many approaches for data extraction from Web pages have been developed, there has been limited effort to compare such tools. Unfortunately, in only a few cases can the results generated by distinct tools be directly compared since the addressed extraction tasks are different. This paper surveys the major Web data extraction approaches and compares them in three dimensions: the task domain, the automation degree, and the techniques used. The criteria of the first dimension explain why an IE system fails to handle some Web sites of particular structures. The criteria of the second dimension classify IE systems based on the techniques used. The criteria of the third dimension measure the degree of automation for IE systems. We believe these criteria provide qualitatively measures to evaluate various IE approaches.

Non-alcoholic fatty liver disease (NAFLD) is a potentially serious liver disease that affects approximately one-quarter of the global adult population, causing a substantial burden of ill health with wide-ranging social and economic implications. It is a multisystem disease and is considered the hepatic component of metabolic syndrome. Unlike other highly prevalent conditions, NAFLD has received little attention from the global public health community. Health system and public health responses to NAFLD have been weak and fragmented, and, despite its pervasiveness, NAFLD is largely unknown outside hepatology and gastroenterology. There is only a nascent global public health movement addressing NAFLD, and the disease is absent from nearly all national and international strategies and policies for non-communicable diseases, including obesity. In this global Delphi study, a multidisciplinary group of experts developed consensus statements and recommendations, which a larger group of collaborators reviewed over three rounds until consensus was achieved. The resulting consensus statements and recommendations address a broad range of topics - from epidemiology, awareness, care and treatment to public health policies and leadership - that have general relevance for policy-makers, health-care practitioners, civil society groups, research institutions and affected populations. These recommendations should provide a strong foundation for a comprehensive public health response to NAFLD.

The world is currently striving to achieve the globally adopted sustainable development goals (SDGs). Exploring the role of technology in achieving the SDGs is critical for the decision-makers and will allow them to overcome any possible trade-off. In this work, the role of wastewater management in achieving the SDGs has been indicated. The analysis shows that wastewater treatment could contribute to achieving 11 out of 17 SDGs. The major contribution came from its ability to increase water availability (SDG 2: zero hunger and SDG 6: clean water and sanitation), enhance human health worldwide (SDG 3: Good health and wellbeing), providing a new source of income for smallholder (SDG 1: no poverty and SDG 8: decent work and economic growth), converting waste to energy (SDG 7: affordable and clean energy, and SDG 9: industry, innovation and infrastructure) and reducing the environmental impact of wastewater (SDG 11: sustainable cities and communities, SDG 12: responsible consumption and production, SDG 13: climate action, and SDG 14: life below water). The challenges related to implementing and assessing these targets were discussed as well. A set of indicators (guideline) were proposed to improve the contribution of the wastewater treatment facility to the SDGs. This study emphasizes on the significant influence of wastewater treatment on the United Nations' SDGs and targets worldwide.

BACKGROUND: Post-COVID-19 symptoms and diseases appeared on many survivors from COVID-19 which are similar to that of the post-severe acute respiratory syndrome (SARS) fatigue. Hence, the study aims to investigate and characterise the manifestations which appear after eradication of the coronavirus infection and its relation to disease severity. METHOD: About 287 survivors from COVID-19 were included in the study, each received a questionnaire divided into three main parts starting from subjects' demographic data, data about the COVID-19 status and other comorbidities of the subject, and finally data about post-COVID-19 manifestations. Response surface plots were produced to visualise the link between several factors. RESULTS: Only 10.8% of all subjects have no manifestation after recovery from the disease while a large percentage of subjects suffered from several symptoms and diseases. The most common symptom reported was fatigue (72.8%), more critical manifestations like stroke, renal failure, myocarditis and pulmonary fibrosis were reported by a few percent of the subjects. There was a relationship between the presence of other comorbidities and severity of the disease. Also, the severity of COVID-19 was related to the severity of post-COVID-19 manifestations. CONCLUSION: The post-COVID-19 manifestation is largely similar to the post-SARS syndrome. All subjects recovered from COVID-19 should undergo long-term monitoring for evaluation and treatment of symptoms and conditions that might be precipitated with the new coronavirus infection.

The use of fossil fuels has contributed to climate change and global warming, which has led to a growing need for renewable and ecologically friendly alternatives to these. It is accepted that renewable energy sources are the ideal option to substitute fossil fuels in the near future. Significant progress has been made to produce renewable energy sources with acceptable prices at a commercial scale, such as solar, wind, and biomass energies. This success has been due to technological advances that can use renewable energy sources effectively at lower prices. More work is needed to maximize the capacity of renewable energy sources with a focus on their dispatchability, where the function of storage is considered crucial. Furthermore, hybrid renewable energy systems are needed with good energy management to balance the various renewable energy sources’ production/consumption/storage. This work covers the progress done in the main renewable energy sources at a commercial scale, including solar, wind, biomass, and hybrid renewable energy sources. Moreover, energy management between the various renewable energy sources and storage systems is discussed. Finally, this work discusses the recent progress in green hydrogen production and fuel cells that could pave the way for commercial usage of renewable energy in a wide range of applications.

A commonly held view is that nanocarriers conjugated to polyethylene glycol (PEG) are non-immunogenic. However, many studies have reported that unexpected immune responses have occurred against PEG-conjugated nanocarriers. One unanticipated response is the rapid clearance of PEGylated nanocarriers upon repeat administration, called the accelerated blood clearance (ABC) phenomenon. ABC involves the production of antibodies toward nanocarrier components, including PEG, which reduces the safety and effectiveness of encapsulated therapeutic agents. Another immune response is the hypersensitivity or infusion reaction referred to as complement (C) activation-related pseudoallergy (CARPA). Such immunogenicity and adverse reactivities of PEGylated nanocarriers may be of potential concern for the clinical use of PEGylated therapeutics. Accordingly, screening of the immunogenicity and CARPA reactogenicity of nanocarrier-based therapeutics should be a prerequisite before they can proceed into clinical studies. This review presents PEGylated liposomes, immunogenicity of PEG, the ABC phenomenon, C activation and lipid-induced CARPA from a toxicological point of view, and also addresses the factors that influence these adverse interactions with the immune system.

One of the goals of smart environments is to improve the quality of human life in terms of comfort and efficiency. The Internet of Things (IoT) paradigm has recently evolved into a technology for building smart environments. Security and privacy are considered key issues in any real-world smart environment based on the IoT model. The security vulnerabilities in IoT-based systems create security threats that affect smart environment applications. Thus, there is a crucial need for intrusion detection systems (IDSs) designed for IoT environments to mitigate IoT-related security attacks that exploit some of these security vulnerabilities. Due to the limited computing and storage capabilities of IoT devices and the specific protocols used, conventional IDSs may not be an option for IoT environments. This article presents a comprehensive survey of the latest IDSs designed for the IoT model, with a focus on the corresponding methods, features, and mechanisms. This article also provides deep insight into the IoT architecture, emerging security vulnerabilities, and their relation to the layers of the IoT architecture. This work demonstrates that despite previous studies regarding the design and implementation of IDSs for the IoT paradigm, developing efficient, reliable and robust IDSs for IoT-based smart environments is still a crucial task. Key considerations for the development of such IDSs are introduced as a future outlook at the end of this survey.

Preclinical Research & Development Appropriate translation and determination of the maximum recommended starting dose in human is a vital task in new drug development and research. Allometric scaling is the most frequently used approach for dose extrapolation based on normalization of dose-to-body surface area. Misinterpretation of allometric dose conversion and safety factor application can lead to major problems in calculating maximum recommended safe starting dose in first-in-human clinical trials. Therefore, dose translation always necessitates careful consideration of body surface area, pharmacological, physiological and anatomical factors, pharmacokinetic parameters, metabolic function, receptor, and life span. The concept of estimating the first-in-human dose, interspecies scaling between species and the factors influencing the dose escalation were reviewed. The pros and cons of various approaches to determine first-in-human dose including allometric scaling, pharmacokinetically guided approach, minimal anticipated biological effect level, pharmacokinetic-pharmacodynamic modeling, similar drug approach, and microdosing were explained. The five steps to estimate maximum recommended starting dose for human studies by scaling factor were elaborated. Few examples, illustrating the application of different approaches were also demonstrated along with concerns that may be considered while applying such methods. Furthermore, typical considerations in dose administration, dosing through diet, maximum absorbable dose, blood sampling, and anesthesia in animal species were discussed. In summary, this review may serve as a concise guide for predicting human equivalent dose from animal species for researchers involved in various phases of preclinical and early clinical drug development.

Abstract Affective computing, a subcategory of artificial intelligence, detects, processes, interprets, and mimics human emotions. Thanks to the continued advancement of portable non-invasive human sensor technologies, like brain–computer interfaces (BCI), emotion recognition has piqued the interest of academics from a variety of domains. Facial expressions, speech, behavior (gesture/posture), and physiological signals can all be used to identify human emotions. However, the first three may be ineffectual because people may hide their true emotions consciously or unconsciously (so-called social masking). Physiological signals can provide more accurate and objective emotion recognition. Electroencephalogram (EEG) signals respond in real time and are more sensitive to changes in affective states than peripheral neurophysiological signals. Thus, EEG signals can reveal important features of emotional states. Recently, several EEG-based BCI emotion recognition techniques have been developed. In addition, rapid advances in machine and deep learning have enabled machines or computers to understand, recognize, and analyze emotions. This study reviews emotion recognition methods that rely on multi-channel EEG signal-based BCIs and provides an overview of what has been accomplished in this area. It also provides an overview of the datasets and methods used to elicit emotional states. According to the usual emotional recognition pathway, we review various EEG feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods (e.g., convolutional and recurrent neural networks with long short term memory). In addition, EEG rhythms that are strongly linked to emotions as well as the relationship between distinct brain areas and emotions are discussed. We also discuss several human emotion recognition studies, published between 2015 and 2021, that use EEG data and compare different machine and deep learning algorithms. Finally, this review suggests several challenges and future research directions in the recognition and classification of human emotional states using EEG.

Machine learning models are used today to solve problems within a broad span of disciplines. If the proper hyperparameter tuning of a machine learning classifier is performed, significantly higher accuracy can be obtained. In this paper, a comprehensive comparative analysis of various hyperparameter tuning techniques is performed; these are Grid Search, Random Search, Bayesian Optimization, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). They are used to optimize the accuracy of six machine learning algorithms, namely, Logistic Regression (LR), Ridge Classifier (RC), Support Vector Machine Classifier (SVC), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) classifiers. To test the performance of each hyperparameter tuning technique, the machine learning models are used to solve an Arabic sentiment classification problem. Sentiment analysis is the process of detecting whether a text carries a positive, negative, or neutral sentiment. However, extracting such sentiment from a complex derivational morphology language such as Arabic has been always very challenging. The performance of all classifiers is tested using our constructed dataset both before and after the hyperparameter tuning process. A detailed analysis is described, along with the strengths and limitations of each hyperparameter tuning technique. The results show that the highest accuracy was given by SVC both before and after the hyperparameter tuning process, with a score of 95.6208 obtained when using Bayesian Optimization.

Recently, using artificial intelligence (AI) in drug discovery has received much attention since it significantly shortens the time and cost of developing new drugs. Deep learning (DL)-based approaches are increasingly being used in all stages of drug development as DL technology advances, and drug-related data grows. Therefore, this paper presents a systematic Literature review (SLR) that integrates the recent DL technologies and applications in drug discovery Including, drug-target interactions (DTIs), drug-drug similarity interactions (DDIs), drug sensitivity and responsiveness, and drug-side effect predictions. We present a review of more than 300 articles between 2000 and 2022. The benchmark data sets, the databases, and the evaluation measures are also presented. In addition, this paper provides an overview of how explainable AI (XAI) supports drug discovery problems. The drug dosing optimization and success stories are discussed as well. Finally, digital twining (DT) and open issues are suggested as future research challenges for drug discovery problems. Challenges to be addressed, future research directions are identified, and an extensive bibliography is also included.

Human existence and societal growth are both dependent on the availability of clean and fresh water. Photocatalysis is a type of artificial photosynthesis that uses environmentally friendly, long-lasting materials to address energy and environmental issues. There is currently a considerable demand for low-cost, high-performance wastewater treatment equipment. By changing the structure, size, and characteristics of nanomaterials, the use of nanotechnology in the field of water filtration has evolved dramatically. Semiconductor-assisted photocatalysis has recently advanced to become among the most promising techniques in the fields of sustainable energy generation and ecological cleanup. It is environmentally beneficial, cost-effective, and strictly linked to the zero waste discharge principle used in industrial effluent treatment. Owing to the reduction or removal of created unwanted byproducts, the green synthesis of photoactive nanomaterial is more beneficial than chemical synthesis approaches. Furthermore, unlike chemical synthesis methods, the green synthesis method does not require the use of expensive, dangerous, or poisonous ingredients, making it a less costly, easy, and environmental method for photocatalyst synthesis. This work focuses on distinct greener synthesis techniques utilized for the production of new photocatalysts, including metals, metal doped-metal oxides, metal oxides, and plasmonic nanostructures, including the application of artificial intelligence and machine learning to the design and selection of an innovative photocatalyst in the context of energy and environmental challenges. A brief overview of the industrial and environmental applications of photocatalysts is also presented. Finally, an overview and recommendations for future research are given to create photocatalytic systems with greatly improved stability and efficiency.

Non-ionic surfactant vesicles, simply known as niosomes are synthetic vesicles with potential technological applications. Niosomes have the same potential advantages of phospholipid vesicles (liposomes) of being able to accommodate both water soluble and lipid soluble drug molecules control their release and as such serve as versatile drug delivery devices of numerous applications. Additionally, niosomes can be considered as more economically, chemically, and occasionally physically stable alternatives to liposomes. Niosomes can be fabricated using simple methods of preparations and from widely used surfactants in pharmaceutical technology. Many reports have discussed niosomes in terms of physicochemical properties and their applications as drug delivery systems. In this report, a brief and simplified summary of different theories of self-assembly will be given. Furthermore manufacturing methods, physical characterization techniques, bilayer membrane additives, unconventional niosomes (discomes, proniosomes, elastic and polyhedral niosomes), their recent applications as drug delivery systems, limitations and directions for future research will be discussed.

This study scrutinizes the reliability and validity of existing analyses that focus on the impact of various environmental factors on a photovoltaic (PV) system’s performance. For the first time, four environmental factors (the accumulation of dust, water droplets, birds’ droppings, and partial shading conditions) affecting system performance are investigated, simultaneously, in one study. The results obtained from this investigation demonstrate that the accumulation of dust, shading, and bird fouling has a significant effect on PV current and voltage, and consequently, the harvested PV energy. ‘Shading’ had the strongest influence on the efficiency of the PV modules. It was found that increasing the area of shading on a PV module surface by a quarter, half, and three quarters resulted in a power reduction of 33.7%, 45.1%, and 92.6%, respectively. However, results pertaining to the impact of water droplets on the PV panel had an inverse effect, decreasing the temperature of the PV panel, which led to an increase in the potential difference and improved the power output by at least 5.6%. Moreover, dust accumulation reduced the power output by 8.80% and the efficiency by 11.86%, while birds fouling the PV module surface was found to reduce the PV system performance by about 7.4%.

Additive Manufacturing (AM) is the fastest growing industrial technique, harboring innovative, cost effective and environmentally friendly solutions. Over the years, AM technologies have been utilized in the aerospace and automotive industries mainly for prototyping purposes. However, 3D printing of aircraft and automobile components and parts has recently proven its efficiency. In this paper, a comprehensive review on the utilization of AM technologies in the aerospace and automotive industries is presented. The various AM techniques are compared based on their process, materials, and applications. In addition, the opportunities and limitations of AM in aerospace and automotive industries are highlighted. Finally, the contribution of AM and 3D printing in achieving the Sustainable Development Goals (SDGs) is examined and evaluated.

In recent years, attention has been drawn to battery thermal safety issues due to the importance of personal safety and vehicle service security. The latest advancements in battery thermal management (BTM) are conducted to face the expected challenges to ensure battery safety. The BTM technology enhances battery safety with a heat transfer intensifying method, which guarantees the battery operation performance based on the battery's thermokinetic, electrochemical, and mechanical characteristics at normal and abnormal operating conditions. Preventing overheating and providing an ideal working temperature for safe operation are also important. Therefore, developing a BTM system that is both safe and reliable has a vital research goal. A comprehensive review of BTM with enhanced safety is presented in this article. The present study introduces the advances in the applications of BTM with cyclic stability served, high energy density, and electrification of automobiles. A summary of relevant research is also provided to improve thermo-safe design innovation and cooperative optimization to meet the needs of green-energy vehicle commercialization. The current work discusses the applications of air, liquid, nanofluids, phase change material, heat pipe, and combinations of these technics for BTM. Finally, the current study describes the challenges and prospects for utilizing different types of BTM to distribute its technology for diverse applications. The present study shows that proper thermal management system (TMS) is required to increase the batteries' efficiency and lifetime. However, each TMS has its characteristics that differ from one to one. Therefore, the proposed TMS's configuration and optimum performance must be examined before real application.

The purpose of this study was to identify risk factors for hepatitis C virus (HCV) infection in a rural village in the Nile Delta with a high prevalence of antibodies to HCV (anti-HCV). One half of the village households were systematically selected, tested for anti-HCV, and interviewed: 973 of 3,999 (24.3%) subjects were anti-HCV-positive (reflecting prior HCV infection but not necessarily current liver disease), with nearly equal prevalence among males and females. Anti-HCV prevalence increased sharply with age among both males and females, from 9.3% in those 20 years of age and younger to >50% in those older than 35, suggesting a cohort effect with reduced transmission in recent years. Multivariate regression was used to estimate independent effects of risk factors on seropositivity. Among those over 20 years of age, the following risk factors were significantly associated with seropositivity: age (P <.001); male gender (odds ratio [OR] = 2.5, 95% CI = 1.3-4.7); marriage (OR = 4.1, 2.4-6.9); anti-schistosomiasis injection treatment (OR = 2.0, 1.3-2.9); blood transfusion (OR = 1.8, 1.1-2.9), invasive medical procedure (surgery, catheterization, endoscopy, and/or dialysis) (OR = 1.5, 1.1-1.9); receipt of injections from "informal" health care provider (OR = 1.3, 1.0-1.6); and cesarean section or abortion (OR = 1.4, 1.0-1.9). Exposures not significantly related to anti-HCV positivity in adults included: history of, or active infection with, Schistosoma mansoni, sutures or abscess drainage, goza smoking in a group, and shaving by community barbers. Among those 20 years old or younger, no risk factors were clearly associated with anti-HCV positivity; however, circumcision for boys by informal health care providers was marginally associated with anti-HCV (OR = 1.7, 1.0-3.0). Prevention programs focused primarily on culturally influenced risks in rural Egyptian communities are being implemented and evaluated.

Chlorophyll (Chl) fluorescence is a subtle reflection of primary reactions of photosynthesis. Intricate relationships between fluorescence kinetics and photosynthesis help our understanding of photosynthetic biophysical processes. Chl fluorescence technique is useful as a non-invasive tool in eco-physiological studies, and has extensively been used in assessing plant responses to environmental stress. The review gives a summary of some Chl fluorescence parameters currently used in studies of stress physiology of selected cereal crops, namely water stress, heat stress, salt stress, and chilling stress.

Abstract Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts of precision agriculture and crop protection. Modernizing agriculture and improving production efficiency are significantly affected by using computer vision technology for crop disease diagnosis. This technology is notable for its non-destructive nature, speed, real-time responsiveness, and precision. Deep learning (DL), a recent breakthrough in computer vision, has become a focal point in agricultural plant protection that can minimize the biases of manually selecting disease spot features. This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, and reference datasets of more than 278 research articles were analyzed and subsequently highlighted in accordance with the architecture of computer vision and deep learning models. Key findings include the effectiveness of imaging techniques and sensors like RGB, multispectral, and hyperspectral cameras for early disease detection. Researchers also evaluated various DL architectures, such as convolutional neural networks, vision transformers, generative adversarial networks, vision language models, and foundation models. Moreover, the study connects academic research with practical agricultural applications, providing guidance on the suitability of these models for production environments. This comprehensive review offers valuable insights into the current state and future directions of deep learning in plant disease detection, making it a significant resource for researchers, academicians, and practitioners in precision agriculture.