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Sarcocystosis considerably occurs in a wide host range including animals, reptiles, humans, and birds. This study was conducted to determine the prevalence of Sarcocystis spp. using abattoir inspection, genetic characterization, as well as histopathology in water buffaloes in two provinces, Elbehera and Kafrelsheikh, Egypt. Specimens were collected from esophagus, tongue, and masseters of 400 slaughtered buffaloes in Elbehera (n= 215) and Kafrelsheikh (n= 185). Samples were examined macroscopically and histopathologically. Furthermore, genetic characterization of Sarcocystis spp. was performed using the 18SrRNA gene-based PCR. The total prevalence was 71.0% (75.3% and 65.9% in Elbehera and Kafrelsheikh, respectively). Aged buffaloes had a higher prevalence than young ones. Females had a higher prevalence than males. The esophagus was the most infected organ. Molecular analysis revealed that the recovered species was S. fusiformis. This is the first genetic characterization of S. fusiformis in water buffaloes from Elbehera and Kafrelsheikh, Egypt. Higher prevalence proposed the potential role of cats in the transmission of S. fusiformis, which, in turn, requires strict hygienic measures to protect animals and humans from infection.

A feature is a single measurable criterion to an observation of a process. While knowledge discovery techniques successfully contribute to many fields, however, the extensive required data processing could hinder the performance of these techniques. One of the main issues in processing data is the dimensionality of the data. Therefore, focusing on reducing the data dimensionality through eliminating the insignificant attributes could be considered one of the successful steps for raising the applied techniques’ performance. On the other hand, focusing on the applied field, ovarian cancer patients continuously suffer from the extensive analysis requirements for detecting the disease as well as monitoring the treatment progress. Therefore, identifying the most significant required analysis could be a positive step to reduce the emotional and financial suffering. This research aims to reduce the data dimensionality of the ovarian cancer disease and highlight the most significant analysis using the collaboration of clustering techniques and statistical techniques. The research succeeded to identify twelve significant analysis out of forty-four with a total of fourteen significant attributes for ovarian cancer data.

Calcium is an essential regulating factor in a variety of biological functions including reproduction. It is widely required for different physiological activities in spermatozoa including spermatogenesis, sperm motility, capacitation, acrosome response, and fertilization. Recent advancements in nanotechnology have broadened its potential applications in biomedicine, including improving animal reproductive aspects. Our research was planned fundamentally to examine the influence of calcium deficiency and calcium administration using Nano and commercial calcium in two doses for each type of calcium (1000 mg/Kg body weight (BW) and 500 mg/Kg BW once daily for 64 days orally) to reveal their effects on male reproductive function as well as regulatory mechanisms connected to male fertility. Semen examination, biochemical analyses, enzymatic antioxidant, lipid peroxidation, and testis histopathology were all evaluated after 64 days. Our results revealed that Serum calcium, testosterone, and ABP levels, sperm count, motility, and percentage of intact acrosomes, as well as testicular antioxidant enzymes, were all considerably lower in the calcium-free diet group, whereas sperm abnormalities and testicular Malondialdehyde were significantly higher. In calcium (Nano and commercial) administered male rats, serum calcium, testosterone, and ABP levels, sperm count, motility, percentage of intact acrosomes, and testicular antioxidant enzymes all increased significantly, whereas sperm abnormalities and testicular Malondialdehyde dropped dramatically. Overall, these results point to a strong link between Ca2+, sperm function, and fertility outcomes. what's more, Ca2+ supplementation, particularly nanoparticles could efficiently improve male reproductive function and fertility.

Ninety, one-day-old Cobb chicks were randomly divided into 3 groups, each with 3 replicates. A control group was fed a standard diet, whereas the other two treatment groups received a diet with 0.2 or 0.3 mL thyme essential oil (TEO) per kg of the diet for 21 days. The 0.3 group had more meat protein (P ≤ 0.048) than the control group, while the other meat constituents have not changed. Meat sensory parameters (external aspect, odour, colour, and elasticity) declined (P < 0.001), while meat quality parameters (pH, total volatile nitrogen TVN, thiobarbituric acid number TBA, peroxide value PV, and free fatty acids FFA) increased (P < 0.001), through storage of the groups. In TEO groups, improved (P < 0.001) meat overall scores, external aspect and muscular elasticity was observed compared to control. Quality parameters were improved (P < 0.001) including pH, TVN, TBA, PV, and FFA, compared to control during storage. TEO had no effect on heavy metal concentrations while reducing bacterial contamination in meat. TEO 0.2 samples were positive for Salmonella Kentuckey and Staphylococcus aureus, while TEO 0.3 group were free from pathogenic bacteria. Thyme essential oil can enhance quality, and safety of the chilled broiler’s breast meat.

Raw ground meat is known as a transmission vehicle for biological agents that may be harmful to human health. The objective of the present study was to assess microbiological quality of the ground meats. A total of 280 samples of local and imported chilled meats were randomly collected from retail shops in Buraydah City, Saudi Arabia. The meat samples were microbiologically analyzed using standard methods, peptide mass fingerprinting (PMF) technique, MicroScan Walkaway System (MicroScan) and qPCR System. The imported meat was more bacterially contaminated than local meat, with variable contamination degrees of

Recent breakthroughs in computer vision have led to the invention of several intelligent systems in different sectors. In transportation, this advancement led to the possibility of proposing autonomous vehicles. This recent technology relies heavily on wireless sensors and Deep learning. For an autonomous vehicle to navigate safely on highways, the vehicle needs equipment to aid with detecting road anomalies such as potholes ahead of time. The massive improvement in computer vision models such as Deep Convolutional Neural networks (DCNN) or vision transformers (ViT) resulted in many success stories and tremendous breakthroughs in object detection tasks; this enabled the use of such models in different application areas. But many of the reported results are theoretical and unrealistic in real-life. Usually, the nature of these models is extensive; they are trained on High-performance computers or cloud computing environments with GPUs, which challenge their usage on edge devices. However, to come up with a light model that can fit into embedded devices, the model size has to be reduced significantly so that the performance will not be affected. Therefore, this paper proposes a lightweight model of pothole detection for an embedded device. The model achieved a state-of-the-art accuracy of 98%, with the number of parameters reduced to more than 70% compared with a deep CNN model; the model can be trained and deployed on embedded devices such as smartphones efficiently.

The volume of image data produced today is increasing, which makes storing and transferring them a difficult task. There are some fields where loss-less image compression can be valuable because it allows the compression of images without compromising their quality. In this paper, we propose a lossless image compression technique based on linear prediction, integer wavelet transformation (IWT), and Arithmetic coding to improve the compression ratio of lossless images. As compared to state-of-the-art algorithms, the proposed algorithm increases compression ratios by at least 2.553% and up to 32.546%.

This study was implemented to assess the mechanism by which Moringa oleifera leaf extract (MOLE) improves the quality and prolongs shelf-life of the broilers’ breast meat. Ninety Cobb chicks were randomly allocated to 3 groups. A control group received the standard diet, whereas the other two groups received diets containing MOLE at the doses of 250 and 500 mg/kg for 21 days. Inclusion of MOLE in broilers diet significantly reduced the detrimental changes in the overall sensory attribute scores, characteristic color and odor, and the loss of breast muscle elasticity during storage. Furthermore, it significantly reduced concentrations of thiobarbituric acid, total volatile nitrogen, non-esterified fatty acids, and peroxide, during storage compared to the control samples. No effect on the concentrations of heavy metals, such as copper, cadmium, and lead, was observed. Decomposition of samples was delayed as indicated by lower pH values and higher sensory scores at 4 and 6 days of storage in the MOLE groups. Reduced contamination with E. coli and Salmonella species indicated an antibacterial effect of MOLE. Finally, the present study highlights that MOLE supplementation may play a role in improving quality and shelf-life of the chilled breast meat in broilers.

Fast torque control is crucial for various applications, including diesel-electric locomotives, electric automobiles, and steel plants. The purpose of this paper is to utilize a microcomputer to control the torque of a separately-excited DC motor fed from a single-phase semi-controlled converter. There are numerous strategies to determine torque using direct or indirect methods. The greatest issue of direct strategy using torque meters is that the measuring device must be installed between the motor and the load. In this paper, we avoid this challenge by employing the indirect approach, specifically by monitoring several DC machine-specific quantities such as voltage, current, and speed, which are easier to determine experimentally. This technology is unquestionably helpful in real-world applications since it eliminates any mechanical impact on the installation. This paper describes the system configuration and provides an explanation of the architecture and system features. The simulation of the proposed system using TUSTSIM dynamic simulation program which is capable of simulating the control system with a digital controller in the loop is presented. An implementation of microcomputer-assisted torque control of DC separately excited motor with proportional integral (PI) controllers is presented. A typical oscillography of the driving characteristics is provided along with the experimental results.

Background: Traumatic brain injury (TBI) is a critical public health and socioeconomic problem throughout the world, making epidemiological monitoring of incidence, prevalence and outcome of TBI necessary. In addition, TBI in elderly patients is a neglected global disease burden. The main cause is fall, followed by motor vehicle accidents. Severe trauma in elderly population imposes a significant health care burden and is associated with substantial morbidity and mortality. Objective: To determine the pattern and outcome of traumatic brain injury in geriatric population in order to improve the management of geriatric trauma patients at Emergency Hospital Mansoura University. Patients and methods: This was a prospective observational clinical study, which was conducted on 206 elderly trauma patients presented and admitted to Emergency Hospital Mansoura University (a level one trauma center with about 250,000 visit and 25,000 trauma cases per year) over a year from December 2019 to December 2020. Results: Polytrauma was the commonest type with only 35% of the studied cases were isolated head injury. Most cases had multiple lesions (63.6%) with only 36.4% of which had single lesion. The majority of cases had no associated injuries, with few cases had scalp injury (16.5%) and free abdominal fluid of various degrees (27%). The average period of hospital stay was 10.42 ± 3.110, while the average period of ICU stay was 7.75 ± 4.538. The average GOS among the studied cases was 4.83 ± 2.965 and the majority of which (359%) died (grade 5). Conclusion: The current study demonstrated that, TBI in elderly cases is a life threatening condition with a high mortality rate in which falling from height (FFH) was the predominant mode.

Resistin is a novel peptide which is known to be an adipocyte-derived cytokine. It belongs to resistin like molecule family. It was revealed that resistin is an important link between obesity, insulin resistance and type 2 Diabetes Mellitus. After that, several investigations showed the important physiological activities of resistin apart from its role in insulin resistance and diabetes in many pathological conditions as cardiovascular diseases (as coronary thrombosis), malignancy, endothelial dysfunction, hypertension, thrombosis, angiogenesis, inflammation, asthma, autoimmune disease, atherosclerosis and chronic kidney disease. Moreover, several studies have underlined it’s participation in reproductive functions. It is well known that resistin expression is recorded in murine white adipose tissue but in human its expression is noticed in monocytes and macrophages. The mechanism of action, receptors and signaling pathways are beginning to be distinguished. In the present review, the present data concerning the discovery, structure, tissue distribution and functional role of resistin is summarized.

Psychrotrophic Pseudomonas is one of the significant microbes that lead to putrefaction in chilled meat. One of the biggest problems in the detection of Pseudomonas is that several species are seemingly identical. Currently, antibiotic resistance is one of the most significant challenges facing the world's health and food security. Therefore, this study was designed to apply an accurate technique for eliminating the identification discrepancy of Pseudomonas species and to study their resistance against various antimicrobials. A total of 320 chicken meat specimens were cultivated, and the isolated bacteria’ were phenotypically recognized. Protein analysis was carried out for cultured isolates via Microflex LT. The resistance of Pseudomonas isolates was recorded through Vitek® 2 AST-GN83 cards. Overall, 69 samples were identified as Pseudomonas spp. and included 18 Pseudomonas lundensis (P. lundensis), 16 Pseudomonas fragi (P. fragi), 13 Pseudomonas oryzihabitans (P. oryzihabitans), 10 Pseudomonas stutzeri (P. stutzeri), 5 Pseudomonas fluorescens (P. fluorescens), 4 Pseudomonas putida (P. putida), and 3 Pseudomonas aeruginosa (P. aeruginosa) isolates. Microflex LT identified all Pseudomonas isolates (100%) correctly with a score value ≥ 2.00. PCA positively discriminated the identified isolates into various groups. The antimicrobial resistance levels against Pseudomonas isolates were 81.16% for nitrofurantoin, 71% for ampicillin and ampicillin/sulbactam, 65.22% for cefuroxime and ceftriaxone, 55% for aztreonam, and 49.28% for ciprofloxacin. The susceptibilities were 100% for cefotaxime, 98.55% for ceftazidime, 94.20% for each piperacillin/tazobactam and cefepime, 91.3% for cefazolin. In conclusion, chicken meat was found to be contaminated with different Pseudomonas spp., with high incidence rates of P. lundensis. Microflex LT is a potent tool for distinguishing Pseudomonads at the species level.

This article presents a parameter combination-based method selection (PCBMS) approach to select an optimal lossless data compression technique and provides an analysis based on experimental results to show its effectiveness. There are different types of data such as image, audio, video, and text. These data are classified based on the number of bits. Many algorithms have been developed to compress data over the past few decades, but no developed algorithm works well on all types of data. Lossless data compression techniques are mainly evaluated based on the compression ratio, encoding, and decoding time. While a higher compression ratio is more important for some applications, others may require faster encoding or decoding, or both. Alternatively, each of the three parameters can be equally significant. Choosing an optimal algorithm from many algorithms based on an application’s requirements is a significant challenge. By analyzing the data from each perspective, this model recommends an algorithm as the best for each type of data. Based on the proposed model, an analysis is provided. For some sets of data, it has been demonstrated that the proposed method gives a better prediction to select an algorithm according to the needs of an application.

The power system voltage controls are developed by generator excitation control, and power system components such as loads and transformers that generally absorb reactive power. This paper presents implementation of thyristor control series capacitor (TCSC) and auxiliary control that can be deployed in the network to improve the voltage profile of a system-based reactive control. Also, TCSCs can be used as controllable devices in power flow and voltage control devices. Since voltage magnitudes in a system are closely related to the ability of various components to absorb or supply reactive power, TCSC with auxiliary controls are used for transients corresponding to rotor relative motion. Two-area system is used to examine the feasibility of the proposed TCSC, with auxiliary control to improve the grid voltage profile, and network performance. The simulation results prove the effectiveness of the proposed method for voltage profile improvement and network performance.

Managing natural disasters is a social responsibility as they might cause a gloomy impact on human life. Efficient and timely alert systems for public and actionable recommendations for decision makers may well decrease the number of casualties. Web semantics strengthen the description of web resources for exploiting them better and making them more meaningful for both human and machine. In this work, the authors propose a semantic rule-based approach for disaster situation management (DSM) to reach the next level of decision-making power and its architecture for providing actionable intelligence in the domain of the earthquake. The system itself is based on a data pre-processing layer, a computation layer, and the middle layer relies on an extensive rule base of experts' advice stored over time and a disaster ontology along with its inherent semantics. The rule-based reasoning approach uses this knowledge base in combination with the expert rule base, written in SWRL rules, to infer recommendations for the response to an earthquake.

Several and complex factors cause accumulation of large amounts of body fat as well as a disparity between energy intake and output. Generally, any increase in bodyweight can be attributed to the elevation in daily food intake and the reduction in energy expenditure. Several functions could be achieved by moderate exercise including maintaining an optimum body weight and muscle mass, lowering abdominal fat and increasing lipid catabolism. However, the exact molecular mechanisms involved remain understudied. Our study was designed mainly to evaluate the influence of moderate treadmill exercise on body metabolism with special reference to hepatic lipid metabolism. Twenty adult male Sprague Dawley rats weighting 200±20 gm were randomly assigned to either sedentary or exercised groups. The exercise protocol lasted for 20 mins/ 5 times / week along 4 weeks. Although no significant changes in body weight, insulin and corticosterone levels, there was a significant decrease in fasting blood glucose and serum leptin levels (81.06%and 75.49% respectively). The current exercise regime significantly increased serum high density lipoprotein (HDL-c) level (127.56%) and significantly decreased serum triacylglycerol (TAG), low density lipoprotein (LDL-c) and very low-density lipoprotein (VLDL-c) levels (75.50%, 62.83% and 74.48% respectively). Also, treadmill exercise significantly upregulated the hepatic expression of acetyl-CoA oxidase (ACO) and carnitine palmitoyl CoA transferase 1 (CPT1) (173% and 148% respectively) as well as significantly downregulated the hepatic acetyl-CoA carboxylase (ACC) (52%) but not fatty acid synthase (FAS) mRNA expression. Overall, these results suggest a beneficial effect of moderate treadmill exercise hepatic lipid metabolism.

A new model of fractional telegraph point reactor kinetics FTPRK is introduced to approximate the time dependent Boltzmann transport equation considering new terms that contain time derivative of the reactivity and fractional integral of the neutron density. Caputo fractional derivatives and fractional Leibniz rule are used for such derivation. Cattaneoequation is applied to overcome the flaw of infinite neutron velocity and to describe the anomalous transport. Effect of the new term on the neutron behaviour is discussed. The new model is applied to both TRIGA reactor and to commercial pressured water reactor of a Three Mile Island type reactor, TMI-type PWR. Results for step, ramp and sinusoidal excess reactivities with thermal hydraulic feedback are presented and discussed for different values of anomalous sub-diffusion exponent, the fractional order, 0

Psychrotrophic Pseudomonas is one of the significant microbes that lead to putrefaction in chilled meat. One of the biggest problems in the detection of Pseudomonas is that several species are seemingly identical. Antibiotic resistance is an alternative considerable challenge worldwide. Therefore, this study was designed to apply an accurate technique for the discrepancy of Pseudomonas species and to study their resistance against various antimicrobials. A total of 320 chicken meat specimens were cultivated, and phenotypically recognized the isolated bacteria. Protein analysis was carried out for cultured isolates via Microflex LT. The resistance of Pseudomonas isolates was recorded through Vitek®ฏ 2 AST-GN83 cards. Overall, 69 samples were identified as Pseudomonas spp. and included 18 Pseudomonas lundensis (P. lundensis), 16 Pseudomonas fragi (P. fragi), 13 Pseudomonas oryzihabitans (P. oryzihabitans), 10 Pseudomonas stutzeri (P. stutzeri), 5 Pseudomonas fluorescens (P. fluorescens), 4 Pseudomonas putida (P. putida), and 3 Pseudomonas aeruginosa (P. aeruginosa) isolates. Microflex LT identified all Pseudomonas isolates (100%) correctly with a score value ≥2.00. PCA positively discriminated the identified isolates into various groups. The antimicrobial resistance levels against Pseudomonas isolates were 81.16% for nitrofurantoin, 71% for ampicillin and ampicillin/sulbactam, 65.22% for cefuroxime and ceftriaxone, 55% for aztreonam, and 49.28% for ciprofloxacin. The susceptibilities were 100% for cefotaxime, 98.55% for ceftazidime, 94.20% for each piperacillin/tazobactam and cefepime, 91.3% for cefazolin. In conclusion, chicken meat was found to be contaminated with different Pseudomonas spp., with high incidence rates of P. lundensis. Microflex LT is a potent tool for distinguishing Pseudomonads at the species level.

Recommender systems (RSs) are increasingly recognized as intelligent software for predicting users’ opinions on specific items. Various RSs have been developed in different domains, such as e-commerce, e-government, e-resource services, e-business, e-library, e-tourism, and e-learning, to make excellent user recommendations. In e-learning technology, RSs are designed to support and improve the learning practices of a student or an organization. This survey aims to examine the different works of literature on RSs that corroborate e-learning and classify and provide statistics of the reviewed articles based on their recommendation goals, recommendation techniques used, the target user, and the application platforms. The survey makes a prominent contribution to the e-learning RSs field by providing an overview of current research and traditional and nontraditional recommendation techniques to provide different recommendations for future e-learning. One of the most significant findings to emerge from this survey is that a substantial number of works followed either deep learning or context-aware recommendation techniques, which are considered more efficient than any traditional methods. Finally, we provided comprehensive observations from the quantitative assessment of publications, which can guide and support researchers in understanding the current development for potential future trends and the direction of deep learning-based RSs in e-learning.

Background: Tinnitus is a bothersome complain , some studies show that 13-18 percent of the people suffer from tinnitus, almost 5 percent of which, have chronic or bothersome tinnitus. Chronic tinnitus may be accompanied by anxiety, depression, insomnia, and impairment in cognitive functions such as memory and attention.The Aim of this Study: To compare the auditory memory and auditory attention between two groups with and without tinnitus all of them have normal hearing.Methods: This study were performed on 30 tinnitus patients (10 men and 20 women) aged from 20 to 50 years old and 30 healthy volunteers (12 men and 18 women) aged from 21 to 50 years old with normal hearing thresholds. Pure tone audiometry (PTA), auditory continuous performance test (ACPT), Memory for recognition (MR), memory for content (MC) and memory for sequence (MS) were employed for all participants. Tinnitus evaluation and tinnitus handicap inventory questionnaire (THI) was done for the study group only.Results: Comparing the results of ACPT between study and control groups show no statistically significant difference. While comparing the results of memory battery test between both groups were statistically highly significant.Conclusion: Tinnitus decreased the memory battery test scores at the study group in comparison with the control group. Auditory continuous performance test (ACPT) results show no difference between study and control group. Duration of tinnitus affect significantly ACPT results. Bilateral tinnitus affects memory for sequence results. While loudness decreased results of memory for recognition.

Accurate prediction of crude oil price can successfully be used to deviate from the negative impact of the crude oil price hike. Limitations of previous studies prompt the proposal of an alternative approach for the hybridization of Cuckoo Search Algorithm with lévy flight and Back-propagation neural network (CSBP) for the estimation of crude oil price to enhance convergence speed accuracy. To evaluate the effectiveness of the proposed CSBP, we used ABC to train BP neural network (ABCNN) and Levenberg-Marquardt neural network (ABCLM) to develop an approach for crude oil price prediction. The outcomes of simulated comparative indicate that the proposed CSBP outperforms the ABCLM and ABCNN in both accuracy and convergence speed. Analysis of variance including post hoc multiple comparative test shows that the performance of the proposed CSBP is statistically significant than the comparison algorithms. The negative side of the crude oil prices in the global market can be defeated with proper planning based on the crude oil prices predicted by the CSBP. The CSBP proposed in this paper could provide a new strategy for risk managers and analysts to create an efficient risk management framework to formulate policies related to risk issues in the global crude oil market.

This paper introduces the load flow on the Egypt Electricity Network system. The effect of capacitor compensation has been studied from three points. First, the problem of the series capacitor compensation was considered. The second type of compensation considered is shunt compensation. Finally, a mixed of shunt and series capacitors compensation was implemented. The analysis results were discussed based on the maximum reduction in the generated MVAR. The load variation is accounted for by considering three different load levels classified as light, medium and peak load with pre-specified durations. When solving the capacitor placement problem, the number, size, location and control settings of the capacitors at different load levels were determined. The load flow program is solved by the Power World Simulator (PWS) software. The results of series capacitor, shunt capacitor, and mixed compensation were studied. The investigation has been done for single capacitor and multi-capacitor compensation. Both 500kV and 220kV overhead lines have been considered.

Edge detection is one of the significant properties of many computer vision systems in image recognition, enhancement, compression, restoration, and so on. Though many research results were published, researchers still try to develop a more efficient edge detection algorithm that meets the current needs. We propose an image pre-processing based edge detection technique using canny edge detector. The main problem of canny edge detector is that it cannot identify the edges which are slightly vague due to the Gaussian smoothing. In this study, we solve the problem using the histogram processing of an image before using the canny edge detector. The result demonstrates that the proposed approach gives better outcomes compared to the state-of-the-art edge detectors.

It is necessary to have an energy management strategy (EMS) to control the operation of the different components inside renewable system. In renewable hybrid power system containing fuel cell (FC), battery storage (BS) and supercapacitor (SC) (FC/BS/SC), the main objective is to minimize the hydrogen consumption of fuel cell. In FC/BS/SC system, the battery is helping the FC when the load power is high while SC is working at the load transient or load fast change. The management strategy is based on fuzzy logic control (FLC). It is used to manage optimally the load demand among the different components of FC/BS/SC system. Modeling and simulation of the different system components have been performed using the Matlab/Simulink software. Simulation results reveal the ability of EMS based on FLC to optimize the system performance. In this paper, an energy management strategy was designed to achieve this objective.

Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%.

Background: Hearing loss affects not only the individual, the entire family and society generally. Infants and children require access to detailed sound for auditory learning. Aim: Recognize family's adjustment for their children with hearing impairment. Design: A descriptive analytical research was used. Setting: “Hearing and Speech Institute” at Embaba district . Sample: Convenient sample of (60) child with hearing impairment . Tools for data collection: Data were collected through the following tools “Family's adjustment questionnaire for children with hearing impairment ” it consists of fourth parts; Socio-demographic characteristics for family caregiver , Family caregivers' knowledge, reported practices, and An adjustment scale to assess family caregivers’ adjustment toward caring of the children with hearing impairment . Results: the study revealed that, most of family caregivers are female and mothers as a caregiver, and have acceptable score level of knowledge, and most of practices (98.3%) have done . Moreover, most of practices (98.3%) have done by caregivers. Besides, the majority of caregivers (98.3%) have high adjustment . Conclusion: there was negative significant correlation between knowledge and practice; there was positive non-significant correlation between knowledge and adjustment, and there was almost no correlation between practice and adjustment. There was non-significant correlation between education level for caregiver and practice, adjustment, and knowledge. Recommendation: Continuous supervision for maternal and neonatal care by the Ministry of Health and Population, and Periodic Hearing Screening for Early Identification should be done for children before 6 months of age.

One of the essential rights of the child is to obtain a high quality clothes that meets his needs and requirements according to his growth stage characteristics. Clothes play an important and effective role in the child's life as it gain confidence and improve his abilities. Scientists emphasize the importance of infancy stage (from birth: two years) in individual life; the experiences that pass through this stage affect his personality growth in the future, as well as it has a rapid and sequential growth in all aspects with an exceeding rate than the following growth stages; Therefore, an appropriate opportunity for growth in that period has the greatest importance, which should provide comfort and safety and provide the opportunity for growth at that crucial time.Low quality in designing and implementing some ready-made infants' garments has been observed. A survey had been made to investigate this problem, by interviewing a random sample of (38) mothers of infants, and asking them about the deficiencies they find in ready-made infants clothes, both in terms of design and implementation. It has been shown that they find difficulties related to infant growth requirements and caregivers usage requirements, whom infants depends completely at this stage. Their difficulties were related to wearing infants and taking off, short usage period that is not commensurate with the rapid physical growth of infant, the extent of availability and ease of infant movement while wearing, and the lack of safety factor in using some accessories that are easy for infant to tamper with and may make him at risk, the spread of outerwear made of synthetic fibers, exaggeration in decoration of infants' clothes by embroidering or printing, thus reducing clothing elegance. The research questions: What is the possibility of proposing clothing designs for infants according to special performance requirements?What is the evaluation of academics, producers and consumers to the proposed designs from the structural , functional , aesthetic , productive and marketing aspects? What is the possibility of implementing some of the proposed designs achieving the required performance quality? Research Objectives: The main objective of the research is to Design and implement clothes for infants achieving performance qualityrequired during infancy stage.Methodology: The analytical descriptive approach.Samples: Three random samples were used:Academics sample: (12) of teaching staff members in Egyptian universities specialized in clothing and textiles.Producers sample: (10) of workers in production of ready-made clothes for infants.Consumers sample: (50) of infants' mothers in Alexandria governorate. Results: Novel clothes for infants have been designed and implemented, achieving required performance quality during infancy stage according to growth requirements of infants and care requirements of their caregivers.

Lossless data reduction is essential for data transmission over the Internet and the storage of data in a digital device when data loss is not permitted. The application of image compression is essential for image storing, image classification, and image recognition, and image compression techniques compress an image by reducing redundancy in the image. Many image compression standards have already been developed. This article compares the most popular state-of-the-art lossless image compression techniques, and the methods are evaluated based on the bits per pixel or compression ratio. Finally, we recommend which of the algorithms is better for a few different datasets.

The ability to predict whether a specific section of a spreadsheet is faulty or not is frequently required for the development of spreadsheet functionality. Although errors in such spreadsheets are common and can have serious consequences, today’s spreadsheet creation and management tools offer weak capabilities for defect detection, localization, and fixing. In this thesis, we proposed a method for predicting faults in spreadsheet formulas that can detect faults in non-formula cells by combining a catalog of spreadsheet metrics with modern machine learning algorithms. An examination of the individual metrics in the catalog reveals that they are suited to detecting data where a formula is expected to have flaws. In this framework, Recall Score of 99% was achieved and performance was compared with that of Melford. The result of the experiment reveals that the proposed framework outperforms Melford framework.

Despite recent advances in deep learning, the rise of edge devices, and the exponential growth of Internet of Things (IoT) connected devices undermine the performance of deep learning models. It is clear that the future of computing is moving to edge devices. Autonomous vehicles and self-driving cars have leveraged the power of computer vision, especially object detection to navigate through traffic safely. Nevertheless, to be able to drive on all types of roads, these new vehicles have to be equipped with a road anomaly detection system, which strikes the need for small deep learning models of detecting road anomalies that can be deployed on these vehicles for safer driving experience. However, the current deep learning models are not practical on embedded devices due to the heavy resource requirements of the models, as such cannot be deployed on embedded devices. This paper proposes a theoretical approach to building a lightweight model from a cumbersome pothole detection model that is suitable on edge devices using knowledge distillation. It presents the theoretical approach of knowledge distillation, why it is a better technique of model compression compared to the rest. It shows that a cumbersome model can be made lightweight without sacrificing accuracy and with a reduced time complexity and faster training time.

The trigonometric Fourier-series method (TFS) is generalized to provide approximate solutions for non-linear point kinetics equations with feedback using varying step sizes. This method can provide a very stable solution against the size of the discrete time step allowing much larger step sizes to be used. Systems of the point kinetics equations are solved using Fourier-series expansion over a partition of the total time interval. The approximate solution requires determining the series coefficients over each time step in that partition. These coefficients are determined using the high-order derivatives of the solution vector at the beginning of the time step introducing a system of linear algebraic equations to be solved at each step. This system is similar to the Vandermonde system. Two successive orders of the partial sums are used to estimate the local truncation error. This error and some other constrains are used to produce the largest step size allowable at each step while keeping the error within a specific tolerance. The process of calculating suitable step sizes should be automatic and inexpensive. Convergence and stability of the proposed method are discussed and a new formula is introduced to maintain stability. The proposed method solves the general linear and non-linear kinetics problems. The method has been applied to five different types of reactivities including step/ramp insertions with temperature feedback. The method is seemed to be valid for larger time intervals than those used in the conventional numerical integration, and is thus useful in reducing computing time. Computational results are found to be consistent with the analysis, they demonstrate that the convergence of the iteration scheme can be accelerated and the resulting computing times can be greatly reduced while maintain computational accuracy.

Fractional order neutron point kinetics equations are generalization of the classical neutron point kinetics equations. The system of the fractional neutron point kinetics equations is treated in different context by many authors ignoring an important term related to the derivative of the reactivity. In this paper, we derive a time fractional neutron point kinetics equations (FNPK) model considering a new term for the time derivative of the reactivity. Fractional Leibenz rule is used for such derivation. The effect resulting from the additional term of the reactivity derivative is discussed. It has been found that without this additional term, the modified model will be compliant with the earlier published models for the FNPK. The proposed model of FNPK describes infinite and finite bar reactor cores according to the nonleakage probability and the geometric buckling values. We have applied an effective implicit difference approximation to solve the modified model for finite cylindrical reactor core for both average one group and six groups of delayed neutron precursors. The neutron density results with different values of fractional order for step, ramp and oscillatory reactivities are shown and compared with the classical neutron point kinetics equations.

During the early-1980s the term Human-centered design (HCD) evolved and since then it started to achieve a rapidly-growing attention. It is considered to be a user-incorporated design and development process. This is achieved by primarily understanding the users’ needs in buildings, communities, services, systems and products. HCD methods aim to achieve an ideal space emerging using the human centered interaction scheme. Interactive Architecture is thought to have a great potential in achieving HCD benefits. Therefore, this paper discusses the phenomenon of Interactive Architecture as well as defining the behavior of the human body in order to document and examine the buildings. The research‘s main aim is to incorporate the phases of the Human-centered design into the early design stage of architectural buildings using Interactive Architecture. The methodology is composed initially of a literature survey discussing the Human-centered design in general and then focusing on the methods of creating an Interactive building space. This is then followed by analyzing both aspects in relevant examples. Finally, a matrix is concluded reaching the optimum guideline methods of design in architecture, through integrating HCD methods with Interactive Architecture analyzed examples.

In this paper, we present a numerical solution for the time fractional neutron point kinetics equations (FNPK) model with Newtonian temperature feedback effects on finite medium. We consider reformulation of the FNPK model to include the time derivative of the reactivity. The model of FNPK describes a finite bare reactor core associated with values of the nuclear parameters which are adjusted to preserve the criticality of the reactor at the steady state. The modified model is compared with the earlier published models and we have found that they are consistent when the reactivity is constant. Implicit difference method is proposed to solve the model of the FNPK with six groups of delayed neutron precursors. The method is based on approximating the Caputo fractional derivative by shifted Grunwald-Letnikov fractional derivative. Stability of the method is discussed and it has proved that the method is unconditionally stable. The neutron density results with temperature reactivity feedback, for different values of fractional orders ( 0 α ≤ 1 ) , are shown and compared with the classical neutron point kinetics equations.

Stabilization of wind farm (WF) is considered one of the major serious issues for developing power systems based on renewable energy sources. A comparison of dynamic performances for FACTS devices like STATCOM and SVC is presented in this paper. Such devices are employed to stabilize WF integrated with multi-machine power system (WFMPS). Fuzzy logic controller (FLC) has been employed for controlling FACTS devices to enhance WFMPS performance under different abnormal conditions. Three-phase to ground short circuit fault has been taken into consideration in different locations of system. MATLAB/Simulink is used to perform the modeling and simulation of WFMPS. The obtained results demonstrate the effect of both SVC- and STATCOM-based FLC on damping the system oscillations and improving the system dynamic performance in the post-fault duration. The comparison confirms superior dynamic performance and fast fault recovery of STATCOM-based FLC through different fault conditions compared with SVC-based FLC.

In this paper, our purpose is to find approximate solutions of fractional neutron point kinetic equations by using non-standard finite difference method. The fractional neutron point kinetic equations are modelled with average one group of delayed neutron precursors and the fractional derivative is given in the form of Grunwald-Letnikov. The efficiency and reliability of the suggested approach are proved by some numerical experiments for critical reactivity, supercritical reactivity and subcritical reactivity for various values of fractional order. It is found that the nonstandard finite difference method (NSFDM) is preferable than the standard finite difference method (SFDM). Also, the stability of the numerical scheme is investigated. The stability range of the step size is introduced for different values of the anomalous diffusion order ( α ) and of the relaxation time ( τ ) . Numerical results and graphs for neutron flux for different values of the anomalous order and of the relaxation time are shown and compared with the classical solutions.

Ever since the inception of the industrial revolution, artificial acceleration of swift economic development has contributed to the continuous and ever-increasing demand for energy. Fuel consumption, especially conventional fossil fuels, is increasing continuously to meet the energy demand as a consequence. This has two consequences on living things—such as consumption of oxygen and emission of carbon dioxide (CO2) that causes global warming. Two approaches, namely active and passive, are being considered and implemented by different countries to decrease their CO2 emission. The passive approach involves in encouraging energy conservation and refining efficiency of the energy, utilization of fuels with low carbon, improve the consumption of renewable energy, and adopting a greener approach to increase the forest area, while the active approaches involve capturing carbon and storing them in other forms. Due to improvements in the field of nanotechnology, one-dimensional (1D) nanostructures are highly explored to effectively capture carbon. There are several 1D nanostructures namely nanorods, nanofibers, nanotubes, nanowires, nanocylinders, and nanowhiskers. Each morphology exhibits a unique carbon capturing property which makes them superior to other nanoparticles in reducing carbon emission. This chapter is an overview of various 1D nanostructures that are synthesized using several methods. In addition, information about different 1D nanostructures that are used for carbon capturing application and in maintaining the environmental sustainability via carbon storage, sustainable energy production, and pollution control are also discussed.

Recent developments in the field of recommender systems have led to a renewed interest in employing some of the sophisticated machine learning algorithms to combine multiple characteristics of items during the process of making recom-mendations. Considerable number of research papers have been published on multi-criteria recommendation techniques. Most of these studies have focused only on using some basic statistical methods or simply by extending the similarity computation of the traditional heuristic-based techniques to model the system. Researchers have not treated the uncertainty that exists about the relationship between multi-criteria modelling approaches and effectiveness of some of the complex and powerful machine learning techniques; in fact, no previous study has investigated the role of artificial neural networks to design and develop the system using aggregation function approach. This paper seeks to remedy these challenges by analysing the performance of multi-criteria recommender systems, modelled by integrating an adaptive linear neuron that was trained using delta rule, and asymmetric sin-gular value decomposition algorithms. The proposed model was implemented, trained and tested using a multi-criteria dataset for recommending movies to users based on action, story, direction, and visual effects of movies. Taken together, the empirical results of the study suggested that there is a strong association between artificial neural networks and the modelling approaches of multi-criteria recommendation technique.

Pseudomonas aeruginosa (P. aeruginosa) produces a suite of virulence factors that are coordinated by Quorum Sensing (QS) contributing to its disease-causing ability in aquaculture. The present study is first of its kind to obtain information regarding the presence and distribution of five QS genes, three virulence genes viz: lasI, lasR, rhlI, rhlR, rhlAB, toxA, aprA and plcH and seven of the Extended-spectrum βlactamases (blaVEB, blaPER, blaTEM,, blaSHV, blaCTX-M1, blaCTX-M2 and blaCTX-M3) of Pseudomonas species isolated from fish meat by direct PCR. Bacterial identification was based mainly on conventional biochemical techniques using the Vitek 2, automated system. Phenotypic sensitivity of antibiotics was established by the agar disc diffusion technique through 16 various antimicrobial drugs. Quantification of their in vitro production of numerous virulence genes outside the cell that are QS dependent namely, pyocyanin, elastase, alkaline protease, biofilm and cytotoxicity of Vero cell was as well executed. Fifteen genes demonstrated an enormous variety in their association. The total number of Pseudomonas species isolates were 30/100 to be identified by the API 20NE system as P. aeruginosa 12/30 (40%), P. fluorescens 8/30 (27%), P. putida 6/30 (20%) and P. alkylphenolia 4/30 (13%). The outcomes of this study have great significance for the strategic designation of QS quenching.