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Abstract In the current study, a modified solar dryer (SD) integrated automatic solar collector tracker (ASCT) was used for drying tomato fruit (TF) at three slice thicknesses of 4, 6, and 8 mm on both drying systems at three air speeds of 1, 1.5, and 2 m/s until reaching the equilibrium moisture content. Where the comparison study was conducted between the ASCT, and another SD integrated with a fixed solar collector (FSC). The obtained results of the current study showed that the maximum solar intensity and ambient air temperature during the test period were 900 W/m2, and 43.6 °C, respectively. As well as the highest efficiency of the PV system was 16.69% at the same time. On the other hand, the height, greatest diameter and smallest diameter of the TF used in the current study ranged between (4.6 and 5.2 cm), (3.5 and 4.2 cm), and (3.4 and 4.1 cm), respectively. As well as both the athematic and geometric diameters ranging between 3.87 and 4.47 cm and 2.26 and 2.38 cm, the sphericity values of tomatoes tend to have a round shape. In addition, the obtained results that there was no significant effect of the hot air velocities on the drying time, but the final moisture content (MC) decreased with increasing the hot air velocities. The lowest final MC was 6%, and it was recorded with a slice thickness of 4.0 mm that dried on SD integrated with ASCT. Additionally, color analysis showed that, the darkest tomato slices were dried on the SC integrated with FSC at a hot air velocity of 1.0 m/s. Meanwhile, the intensity of red and yellow colors significantly increased after drying with the SD integrated with ASDT. Furthermore, the chemical analysis of the dried tomato slices showed that the highest rehydration ratio of 4.43 kg water/kg dry matter was obtained for the dried tomato slices dried on SD integrated with ASCT at a hot air velocity of 1.5 m/s and a slice thickness of 8.0 mm. As well as the highest values of pH were monitored on the dried tomato slices on ASCT in comparison to the other tomato slices dried on FSC. Also, the highest ascorbic acid content recorded was 141 mg/100 g (d.b.) in tomato slices with an 8.0 mm thickness, dried using SD combined with FSC at an air velocity of 2 m/s. After drying, the total phenols content increased in all dried tomato samples but decreased with lower hot air velocities.

Abstract The fluctuation of solar radiation throughout the day presents a significant obstacle to the widespread adoption of solar dryers for the dehydration of agricultural products, particularly those that are sensitive to high temperatures, such as basil leaf drying during the winter season. Consequently, this recent study sought to address the limitations of solar-powered dryers by implementing a hybrid drying system that harnesses both solar energy and liquid petroleum gas (LPG). Furthermore, an innovative automatic electronic unit was integrated to facilitate the circulation of air between the drying chamber and the ambient environment. Considering the solar radiation status in Egypt, an LPG hybrid solar dryer has been developed to be suitable for both sunny and cloudy weather conditions. This hybrid solar dryer (HSD) uses indirect forced convection and a controlled auxiliary heating system (LPG) to regulate both temperature and relative humidity, resulting in increased drying rates, reduced energy consumption, and the production of high-quality dried products. The HSD was tested and evaluated for drying basil leaves at three different temperatures of50, 55, and 60 °C and three air changing rates of 70, 80, and 90%, during both summer and winter sessions. The obtained results showed that drying basil at a temperature of 60 °C and an air changing rate of 90% led to a decrease in the drying time by about 35.71% and 35.56% in summer and winter, respectively, where summer drying took 135–210 min and winter drying took 145–225 min to reach equilibrium moisture content (MC). Additionally, the effective moisture diffusivity ranged from 5.25 to 9.06 × 10− 9 m2/s, where higher values of effective moisture diffusivity (EMD) were increased with increasing both drying temperatures and air change rates. Furthermore, the activation energy decreased from 16.557 to 25.182 kJ/mol to 1.945–15.366 kJ/mol for the winter and summer sessions, respectively. On the other hand, the analysis of thin-layer kinetic showed that the Modified Midilli II model has a higher coefficient of determination R2, the lowest χ2, and the lowest root mean square error (RMSE) compared to the other models of both winter and summer sessions. Finally, the LPG hybrid solar dryer can be used for drying a wide range of agricultural products, and it is more efficient for drying medicinal plants. This innovative dryer utilizes a combination of LPG and solar energy, making it efficient and environmentally friendly.

Broiler weighing is essential in poultry production for growth monitoring, feed management, health detection, and meeting market requirements. Traditional weighing methods, which use electronic platform weighers, can stress broilers and may not capture accurate weight data, particularly heavy broilers. To overcome these limitations, this study proposes a camera-based weighing approach that relies on morphological changes in different growth stages of broilers rather than body dimensions. The study utilizes YOLO version 8, a deep learning-based network segmentation technique, for precise broiler segmentation, significantly improving weight accuracy in complex environments. The YOLOv8 architecture builds a model that demonstrates improved and trustworthy results in broiler weight prediction, achieving a mean average precision across a range of intersection over union thresholds from 50 % to 95 % of 0.829. By accurately estimating broiler weights based on their morphological features, the developed trained YOLOv8 model eliminates the need for measuring their dimensions or sizes, making the process efficient and convenient.

In contemporary business organizations, the pivotal role of automation in control processes is evident through Inventory Management Systems (IMS), which leverage advanced techniques and data analytics algorithms to optimize inventory levels, enhance accuracy, and minimize costs. However, existing security techniques for IMS, including access controls, firewalls, and audits, face challenges in effectively addressing the evolving threat landscape. These limitations, including struggles with dynamic user roles, susceptibility to data manipulation, and challenges in thwarting various cyber threats, necessitate innovative solutions for robust real-time management and security. Consequently, this work proposes a novel hybrid approach that integrates blockchain with RFID data, Generative Adversarial Networks, and Elliptic Curve Diffie-Hellman cryptographic techniques. In the developed hybrid approach, RFID readers are leveraged to collect inventory data, while the Generative Adversarial Network is specifically designed for processing the raw dataset, encompasses data filtering, normalization, and error correction tasks. The utilization of the Elliptic Curve Diffie-Hellman technique is integral for generating both private and shared keys, facilitating secure transmission between the IMS client and cloud-based servers. The blockchain module is engineered to enhance data security and protect shared secret keys, which is achieved through a two-layer mechanism involving encryption via the Advanced Encryption Standard algorithm and SHA-256 hashing function. Additionally, it incorporates the Artificial Algae Algorithm and an Elman Neural Network to ensure robust data access and integrity. To assess the effectiveness of the proposed hybrid approach, it is implemented on a publicly available dataset. The performance assessment involves a comparison with state-of-the-art security methods, considering key metrics such as encryption time, decryption time, key generation time, throughput, latency, and data confidentiality rate. Simulation results conclusively demonstrate that the proposed hybrid approach significantly reduces encryption time, decryption time, key generation time, and latency. Furthermore, it notably improves throughput and data confidentiality rates while aligning with stringent IMS security requirements.

The Internet of Things (IoT) edge is an emerging technology of sensors and devices that communicate real-time data to a network. IoT edge computing was introduced to handle the latency concerns related to cloud computing data management, as the data are processed closer to their point of origin. Clustering and scheduling tasks on IoT edge computing are considered a challenging problem due to the diverse nature of task and resource characteristics. Metaheuristics and optimization methods are widely used in IoT edge task clustering and scheduling. This paper introduced a new task clustering and scheduling mechanism using differential evolution optimization on IoT edge computing. The proposed mechanism aims to optimize task clustering and scheduling to find optimal execution times for submitted tasks. The proposed mechanism for task clustering is based on the degree of similarity of task characteristics. The proposed mechanisms use an evolutionary mechanism to distribute system tasks across suitable IoT edge resources. The clustering tasks process categorizes tasks with similar requirements and then maps them to appropriate resources. To evaluate the proposed differential evolution mechanism for IoT edge task clustering and scheduling, this study conducted several simulation experiments against two established mechanisms: the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). The simulation configuration was carefully created to mimic real-world IoT edge computing settings to ensure the proposed mechanism’s applicability and the simulation results’ relevance. In the heavyweight workload scenario, the proposed DE mechanism started with an execution time of 916.61 milliseconds, compared to FA’s 1092 milliseconds and PSO’s 1026.09 milliseconds. By the 50th iteration, the proposed DE mechanism had reduced its execution time significantly to around 821.27 milliseconds, whereas FA and PSO showed lesser improvements, with FA at approximately 1053.06 milliseconds and PSO stabilizing at 956.12 milliseconds. The simulation results revealed that the proposed differential evolution mechanism for edge task clustering and scheduling outperforms FA and PSO regarding system efficiency and stability, significantly reducing execution time and having minimal variation across simulation iterations.

Mobile devices have become very important for our daily needs. The user authentication protocols with the key agreement are required to deal with the security issues that arise from the use of mobile devices through Internet applications. However, existing user authentication protocols are only suitable if the client and the server use a similar cryptographic approach. Therefore, it is important to develop an authentication protocol for mobile environments with heterogeneous cryptographic approaches. In this paper, an efficient user authentication and key agreement protocol is proposed for a heterogeneous client-server mobile environment. The security of the proposed scheme is formally proved under the ${q}$ -strong Diffie-Hellman problem ( ${q}$ -SDH), the ${q}$ -bilinear Diffie-Hellman inversion problem ( ${q}$ -BDHI), and the modified bilinear Diffie-Hellman inversion problem (mBDHI), respectively. Our scheme has reasonable processing costs and communication costs on the client and server sides. Moreover, our scheme is suitable for applications that use different cryptographic approaches. In particular, the proposed protocol can work when the client applies the identity-based cryptosystem and the server applies the certificateless cryptosystem.

The computational cloud aims to move traditional computing from personal computers to cloud providers on the internet. Cloud security represents an important research area. Confidentiality, integrity, and availability are the main cloud security characteristics addressed. Cloud providers apply dynamic load balancing and reactive fault tolerance techniques to build secure cloud services to achieve high service availability. Dynamic cloud load balancing approaches distribute submitted tasks to virtual machines during tasks execution and the load of virtual machines is updated based on the system’s state. Reactive cloud fault tolerance is activated for system process failures after failure effectively happens. Reactive cloud fault tolerance handles failure after the fault has occurred. Despite the significance of dynamic load balancing and reactive fault tolerance techniques and mechanisms, few reviews focus on these approaches in a systematic, unbiased method focusing on integrating cloud dynamic load balancing and reactive fault tolerance techniques. This paper conducts a systematic literature review of the existing literature concerning reactive fault tolerance, dynamic load balancing, and their integration in their basic approaches, types, frameworks, and future directions.

Cloud computing is considered a key Internet technology. Cloud providers offer services through the Internet, such as infrastructure, platforms, and software. The scheduling process of cloud providers’ tasks concerns allocating clients’ tasks to providers’ resources. Several mechanisms have been developed for task scheduling in cloud computing. Still, these mechanisms need to be optimized for execution time and makespan. This paper presents a new task-scheduling mechanism based on Discrete Prey–Predator to optimize the task-scheduling process in the cloud environment. The proposed Discrete Prey–Predator mechanism assigns each scheduling solution survival values. The proposed mechanism denotes the prey’s maximum surviving value and the predator’s minimum surviving value. The proposed Discrete Prey–Predator mechanism aims to minimize the execution time of tasks in cloud computing. This paper makes a significant contribution to the field of cloud task scheduling by introducing a new mechanism based on the Discrete Prey–Predator algorithm. The Discrete Prey–Predator mechanism presents distinct advantages, including optimized task execution, as the mechanism is purpose-built to optimize task execution times in cloud computing, improving overall system efficiency and resource utilization. Moreover, the proposed mechanism introduces a survival-value-based approach, as the mechanism introduces a unique approach for assigning survival values to scheduling solutions, differentiating between the prey’s maximum surviving value and the predator’s minimum surviving value. This improvement enhances decision-making precision in task allocation. To evaluate the proposed mechanism, simulations using the CloudSim simulator were conducted. The experiment phase considered different scenarios for testing the proposed mechanism in different states. The simulation results revealed that the proposed Discrete Prey–Predator mechanism has shorter execution times than the firefly algorithm. The average of the five execution times of the Discrete Prey–Predator mechanism was 270.97 s, while the average of the five execution times of the firefly algorithm was 315.10 s.

Administrative contracts are conducted to meet government agencies' requirements in terms of procurement, which is done by tendering. The government infrastructure demand and day-to-day other needs are contracted with vendors, who render their services by applying the announced tender. Hence, the legal framework related to tendering is very important, which protects the legal rights of tender authority and vendors. This present research aims to explore the tendering law in Saudi Arabia and Egypt, having a close legal framework regarding tendering. Egypt is carrying the tender law of 1998, which is updated in 2018 recently. Moreover, tendering in Saudi Arabia depends on the Government Tenders and Procurement Law (GTPL) of 2019, which is updated from the previous version of GTPL 2006. The research explores the basic structure of the procurement system and tendering in both systems, including the discussions on procurement agency, tendering methods, basic tendering regulations, public-private partnership, transparency issue, and tendering with foreigners' bidders. Also, the recent improvements in both tendering systems are discussed. The research traces many similarities and differences in both tendering systems and suggests taking help from each other's experiences.

On 11 March 2020, the World Health Organization announced the novel coronavirus COVID-19 outbreak as a pandemic due to the rapid growth in the number of cases worldwide. The ability of countries to contain and mitigate interventions is crucial in controlling the exponential spread of the novel virus. Several social distancing and control measurements have been applied in Saudi Arabia to mitigate COVID-19 epidemic such as quarantine, schools closure, suspending travels, reducing crowds, people movement restrictions, self-isolation and contacts tracing. This research aims to study the country interventions in Saudi Arabia and their impact on decreasing the spread of COVID-19. This paper examined different control measurements scenarios produced by a modified SEIR mathematical model with an emphasis on testing capacity expansion and number of critical cases. The modified SEIR mathematical model is solved numerically using Rung-Kutta analysis method for solving the modified SEIR system of ordinary differential equations. The simulation results revealed that the interventions are vital to flatten the virus spread curve. Early implementation of country interventions can delay the peak and decrease the population fatality rate.

Software reliability is prioritised as the most critical quality attribute. Reliability prediction models participate in the prevention of software failures which can cause vital events and disastrous consequences in safety-critical applications or even in businesses. Predicting reliability during design allows software developers to avoid potential design problems, which can otherwise result in reconstructing an entire system when discovered at later stages of the software development life-cycle. Several reliability models have been built to predict reliability during software development. However, several issues still exist in these models. Current models suffer from a scalability issue referred to as the modeling of large systems. The scalability solutions usually come at a high computational cost, requiring solutions. Secondly, consideration of the nature of concurrent applications in reliability prediction is another issue. We propose a reliability prediction model that enhances scalability by introducing a system-level scenario synthesis mechanism that mitigates complexity. Additionally, the proposed model supports modeling of the nature of concurrent applications through adaption of formal statistical distribution toward scenario combination. The proposed model was evaluated using sensors-based case studies. The experimental results show the effectiveness of the proposed model from the view of computational cost reduction compared to similar models. This reduction is the main parameter for scalability enhancement. In addition, the presented work can enable system developers to know up to which load their system will be reliable via observation of the reliability value in several running scenarios.

Privacy-preserving techniques allow private information to be used without compromising privacy. Most encryption algorithms, such as the Advanced Encryption Standard (AES) algorithm, cannot perform computational operations on encrypted data without first applying the decryption process. Homomorphic encryption algorithms provide innovative solutions to support computations on encrypted data while preserving the content of private information. However, these algorithms have some limitations, such as computational cost as well as the need for modifications for each case study. In this paper, we present a comprehensive overview of various homomorphic encryption tools for Big Data analysis and their applications. We also discuss a security framework for Big Data analysis while preserving privacy using homomorphic encryption algorithms. We highlight the fundamental features and tradeoffs that should be considered when choosing the right approach for Big Data applications in practice. We then present a comparison of popular current homomorphic encryption tools with respect to these identified characteristics. We examine the implementation results of various homomorphic encryption toolkits and compare their performances. Finally, we highlight some important issues and research opportunities. We aim to anticipate how homomorphic encryption technology will be useful for secure Big Data processing, especially to improve the utility and performance of privacy-preserving machine learning.

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

This research investigated the Saudi procurement system and regulations in the context of local and international administrative contracts. Mainly, Saudi Government Tenders and Procurement Law was investigated in more detail to understand basic rules and regulations of the bidding, selection process, and penalties in case of delay in the administrative contract process. Moreover, a matter of direct purchase was also investigated to understand the circumstances and conditions of a direct purchase. In addition, the international administrative contract was discussed to comprehend the nature and regulations of such contracts. A matter of arbitration was also investigated to know the arbitrator’s role and powers in case of a dispute in contracting and performing international administrative contracts outside the country. The arguments against arbitration were also deliberated to recognize the limitations of arbitration in the presence of local and foreign legislations. Overall, Saudi Government Tenders and Procurement Law is well-versed and organized in displaying all-important jurisdictions and matters regarding administrative contracts and the procurement system.

Spontaneous subcapsular hematoma of the liver is an extremely rare but potentially life-threatening condition. We report a case of subcapsular hematoma of the liver without any apparent lesion and in the absence of coagulopathy or trauma. A CT scan of the abdomen demonstrated a huge subcapsular hematoma around the liver. The patient was treated conservatively and was discharged home after one week. Up to our knowledge, this is one of the very few reported cases of a spontaneous subcapsular hepatic hematoma.

[This corrects the article DOI: 10.1371/journal.pone.0163346.].

Behaviour models are the most commonly used input for predicting the reliability of a software system at the early design stage. A component behaviour model reveals the structure and behaviour of the component during the execution of system-level functionalities. There are various challenges related to component reliability prediction at the early design stage based on behaviour models. For example, most of the current reliability techniques do not provide fine-grained sequential behaviour models of individual components and fail to consider the loop entry and exit points in the reliability computation. Moreover, some of the current techniques do not tackle the problem of operational data unavailability and the lack of analysis results that can be valuable for software architects at the early design stage. This paper proposes a reliability prediction technique that, pragmatically, synthesizes system behaviour in the form of a state machine, given a set of scenarios and corresponding constraints as input. The state machine is utilized as a base for generating the component-relevant operational data. The state machine is also used as a source for identifying the nodes and edges of a component probabilistic dependency graph (CPDG). Based on the CPDG, a stack-based algorithm is used to compute the reliability. The proposed technique is evaluated by a comparison with existing techniques and the application of sensitivity analysis to a robotic wheelchair system as a case study. The results indicate that the proposed technique is more relevant at the early design stage compared to existing works, and can provide a more realistic and meaningful prediction.

INTRODUCTION: medical students are the source of a country's physicians. Determining how medical students select their areas of specialization is the key to achieve a balanced distribution of doctors among all specialties. The objective is to identify the number of medical students who have decided their postgraduate specialty career, their career specialties preference, and factors that may influence their decision to select a particular specialty. METHODS: a facility based cross-sectional study was conducted in September 2013 at Faculty of Medicine, University of Medical Sciences and Technology, Khartoum, Sudan. A self-administered semi-structured questionnaire comprising demographic data and questions about future specialties preferences and factors influencing those preferences was distributed to 887 male and female students, (from first to fifth academic years) recruited in the study. RESULTS: response rate was 73% with 647 questionnaires collected, out of 887 eligible medical students. Of the returned questionnaires, 604 were valid. The majority of students (541, 89.6%) have chosen a specialty. Surgery, medicine, paediatrics and obstetrics and gynecology were the most selected specialties. The least selected specialty was anaesthesiology. A significant association was found between gender and specialty choice using Chi-square test (p=0.00). There was no association between undergraduate level and specialty choice (p=0.633). The most common reason for choosing a specific specialty was "Personal Interest" (215, 39.7 %) followed by being "Helpful to the community" (144, 26.6%). CONCLUSION: surgery, medicine, paediatrics and obstetrics and gynecology were the most selected specialties.

This research motivates to identify the legal education model in the Arab region and Saudi Arabia in particular. It is found that Arab legal system is derived from the Roman law and French models. The French teaching model is spread in the Kingdom through the professors educated from the French law schools. The french model of legal education follows the lecture approach. The formal legal education in the Kingdom is initiated by Institute of Public Administration (IPA) during 1970s. IPA offered two-year displomas after Sha'ria graduation. Afterwards, King Saudi University started the systematic legal education since 1980s. Law graduates are required at least 4-years law degree and 3-years apprenticeship in a law firm as a trainee lawyer. This research also differentiates the legal education system of France, Saudi Arabia and US. We find that that Saudi Arabian system is more similar to French system and lesser similar to the US system.

The model of any type of education is very important for learning and every type of learning requires a different approach. This research explores the Socratic method, the Case Method, the Problem-Based Method, Clinical Legal Education and Lecture Approach for legal education. It also discusses the merits and demerits of each educational model in the legal education context. It was observed that all teaching methods were complementary to each another and that legal education requires a mix of all as per the subject matter and resource-availability of education institutions. This research also identified the legal education model in Saudi Arabia. Saudi Arabia predominately adopted the lecture approach due to historical dependence on French-educated Egyptian professors. The lecture approach is very traditional and has more demerits as compared to merits with rote learning. Therefore, we suggest the Kingdom use a mixed approach for legal education for maximum advantage.