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The southwestern regions of the Kingdom of Saudi Arabia (KSA) are geographically and agriculturally diverse. Over millennia, crop production in these regions has evolved to use renewable water resources sustainably. This study analysed environmental and crop production data for 58 governorates in KSA’s four southwestern regions: Al Baha, Aseer, Jazan and Makkah. The biplot analysis generated six clusters of cropping systems: sorghum, cereals, dates, watermelon, vegetables and fruits, and mixed. The main biophysical factors shaping these systems are topography, altitude, rainfall, air temperature and soils. Sorghum, grown on more than half of the cultivated land, dominates the two main systems. Fruits (subtropical and temperate) and open field vegetables are important elements in most systems. The main challenge is combining a moderate use of water with greater crop diversity. Cereals consume relatively little water, but their dominance within the systems reduces overall diversity. Another important issue is the low-input technologies used to cultivate sorghum and other cereals. Landraces or older varieties are planted and little, if any, fertilizers and pesticides are applied. The introduction of sustainable intensification packages is needed, which can begin with making improved varieties accessible to farmers, promoting conservation agriculture, and integrated pest management practices.

The risk of vacuum loss in linear solar heat collection elements (HCEs) is one of the major practical challenges that are facing the parabolic trough solar concentrator (PTSC) systems in concentrating solar power (CSP) plants. Despite the long-life span of CSP systems, HCEs are frequently replaced due to permeated hydrogen (due to saturation of getters) or leaked air (due to failures in end metal-to-glass welding or cracked glass shells). This study proposes a low-tech solution for boosting the performance of PTSCs with partial or lost vacuum through rotating the HCEs. A carefully validated integrated 3D optical-thermal model is developed and used to quantify the energetic and exergetic performances of PTSCs with rotated and fixed HCEs for both cases of lost and maintained vacuum. Both steady and unsteady simulations are reported using high-precision ground measurements of direct irradiance and meteorological parameters. As the rotational speed increases from 0 to 20 rad/s, the energy efficiency of PTSCs with new and damaged HCEs is improved by ∼47 and 52%, respectively, at a Reynolds number of 4000 and an inlet temperature of 350 °C. By rotating a damaged HCE at only 10 rad/s, maximum improvements of ∼26 and 53% in the useful heat gain are obtained, compared to non-rotating evacuated and non-rotating damaged HCEs, respectively, at the same operating conditions. Overall, the advantage of the proposed concept is more pronounced when the PTSC is operating at relatively low flow rates, high temperatures, or under high irradiance levels.

The quality of decisions in the renewable energy sector is as good as the quality of available data. This makes data quality a cornerstone in renewable energy system planning, designing, operation, and assessment. Unfortunately, such data is not always available and usually is cost-prohibitive. One solution for this issue is using the data of few representative days (RDs) instead of the full year for reduced costs of the data itself and the system simulations. A new framework is proposed in this study to distinguish these RDs based on meteorological features. The new framework represents an end-to-end pipeline, starting with measurements, data curing, feature extraction, clustering, and representative year construction. The analysis showed that increasing the number of RDs indeed improves the representativeness of the reconstructed year with disagreement indices as low as 1.041. Including system-irrelevant meteorological parameters was found to increase the disagreement index between original data and reconstructed year from 0.206 to 0.989. The proposed autoencoder feature extraction approach outperformed the conventional statistical one, especially for shallow autoencoders, where the disagreement index was reduced from 1.564 to 1.001. Finally, a brief case study of a standard solar water heating system was performed using TRNSYS v18 software to verify the proposed approach, where the absolute percentage deviation in the annual solar fraction was found to be only 0.278%. This study takes the first steps towards offering decision-makers, designers, and modelers a framework that provides high-quality and high-resolution data compatible with the elevating measurements and simulation cost.

Molten salts are typically used as energy storage media in concentrating solar power systems for their lower costs and environmental impact. This study aims to map and optimize the performance of parabolic trough concentrators (PTCs) working with molten salt-based nanofluids (MSNFs) as heat transfer media at high temperatures. The thermal, hydraulic, energetic, and exergetic performances were analyzed and optimized using a unique framework of Monte Carlo optical simulations, computational fluid dynamics, data-drive support vector regression, particle swarm optimization, and decision-making techniques. Three molten salts (Solar Salt, Hitec, and Hitec XL) and three nanoparticle types (Al2O3, CuO, and SiO2) were investigated in a broad range of volumetric concentrations (0.0–4.0%), operating Reynolds numbers (4 × 103 to 40 × 103), and temperatures (535–805 K). The results showed a maximum energy efficiency of 69.1%, achieved when using SiO2-Hitec nanofluid (1.0%) at a Reynolds number of 40 × 104 and temperature of 535 K. The maximum achieved exergy efficiency was 70.48%, obtained using pure Hitec at a Reynolds number of 40 × 104 and temperature of 535 K. The maximum possible enhancements in energy and exergy efficiencies in the covered range are 17.0 and 42.0%, respectively. The optimal combination of energy and exergy efficiencies are ∼73.1 and 69.0%, obtained using CuO-Hitec nanofluid at temperature, Reynolds number, and concentration of 535 K, 39912.98, and 0.019%, respectively. The optimum combination of percentage enhancements in energy and exergy efficiencies are 0.465 and 7.182%, respectively, which corresponds to CuO-Hitec nanofluid operating at 805 K, 32025.4, and 0.092%, respectively.

Initially, proof assistants were targeted toward professional mathematicians, but interest has been rising regarding their potential use in undergraduate education. However, usability is still a major concern when it comes to introducing students to such systems. In this paper, we present Silkie, a system with an intuitive interface that would allow instructors to use proof assistants in undergraduate computer science education. It is built using SMLtoCoq, which translates SML code into Coq definitions, and an adapted version of JSCoq, a web-based implementation of the Coq theorem prover. Silkie is especially useful in courses that require students to write code and formally prove its correctness. Students can upload SML code into Silkie, and see the corresponding Coq definitions, which they can then use to prove properties about. The proof tactics were modified such that "simple'' steps that are typically expected from students need to be explicit, and the language resembles more what would be written on paper. Moreover, the interface will structure the proof script depending on the commands used, such that the hierarchy of each proof part is clear.

Accurate monitoring and operation of solar power systems require high-resolution solar radiation measurements and precise separation models. This study aims to improve the accuracy of classic diffuse fraction-clearness index piecewise separation models by applying data-driven classifications of sky conditions. This is achieved through a novel outlier-insensitive clustering algorithm and shape prescriptive modeling, applied to 1-, 10-, 30-, and 60-min ground measurements from 4 different locations in the MENA region. This study shows that classifications of sky conditions are not uniform among the selected locations even though all stations fall in the arid desert climate category. This highlights the importance of extracting the sky conditions from measurements rather than using available classifications in the literature. The selection of the number of clusters has to undergo optimization. The number of clusters is also a function of the time resolution. One of the selected locations shows four optimal clusters for 1-min data and six clusters for 60-min data. All developed piecewise separation models show high accuracy and stability with the mean bias errors approaching zero values and the mean absolute errors ranging between 8.7 and 11.8%. The models also outperform existing ones and have good generalization capabilities under the same climate classification.

Solar radiation is an essential input in the design and operation of many engineering systems. However, access to high-resolution data (hourly or sub-hourly) is usually limited, especially in developing countries, either due to its unavailability or expensive costs. A novel data-driven approach is proposed to predict the hourly global irradiation profiles from the cheaper and more likely available records of daily global irradiation. The proposed approach is based on a prior categorization of hourly observations using the K-means clustering algorithm, followed by non-parametric function approximation using the multi-layered perceptron artificial neural network. This approach is applied to measured data (130,000 data points) at six locations in the North African Sahara, and the developed models are benchmarked against all existing parametric models in the literature. The artificial neural network-based models outperformed all existing models, with maximum and minimum coefficients of determination of 0.960 and 0.930, respectively. The non-parametric models also captured the true asymmetric profiles of hourly irradiation with enhanced distributions of the residuals. Hence, the suggested models can be used to generate synthetic hourly data for multiple applications, most notably for building energy simulations and scheduling the operation of power generation systems.

Tool failure detection is a crucial task for continuous safe machining operations. In this study, a novel approach is proposed to develop an accurate and simple tool condition classification model (TCCM) for early failure detection during machining processes. Signals from current, vibration, and acoustic emission sensors were preprocessed and used for feature extraction in both time and frequency domains, leading to a total of 152 features. Next, a feature reduction was carried out based on relative importance, computed using a fully-grown random forest, which reduced the number of features to 15. To find out the best combination of relevant signal features, a total of 32,767 optimized support vector classifiers were developed. The comparison between different candidate models was based on both accuracy and complexity. The results showed that a classification accuracy up to 0.911 is attainable for a process-independent classification model using only current sensors. Besides, developing an ensemble of material-dependent models showed a good potential for improvement, recording a classification accuracy up to 0.958 while using features extracted only from the current sensors. The novelty in the present study is in its focus on developing a single sensor-based high-accuracy TCCM. This opens the door for wider utilization of such technology, especially that all existing studies focused on increasing the accuracy using multi-sensor TCCMs, which increases the cost of this technology and makes it inaccessible, especially for small and medium enterprises.

Stainless steel 316 has wide applications in different industries due to its distinct mechanical and corrosion resistance properties. In this paper, a new adaptive design approach is presented and discussed to optimize the machining process when face milling of Stainless steel 316 under different cooling and lubrication strategies. The proposed approach considers three process designs, namely: balanced, cost, and quality-oriented designs. Experimental runs were modeled using a support vector regression model, which was later used as objective functions for the particle swarm optimization algorithm. Finally, the technique of order preference similarity to the ideal solution (TOPSIS) was adopted with different weightings for different design criteria. In general, MQL and higher cutting speed are favorable in all designs. In contrast, lower feed rates are recommended in the quality-oriented design, compared to the cost-oriented one. This analysis does not only provide an adaptive design, but it also utilizes this approach to come up with different designs, each biased toward one of the responses. The approach and methodology are described in a straightforward, intuitive manner, which makes it replicable in different machining operations and even in other engineering optimization problems sharing the same nature of the present problem of interest.

Enhancing the thermal efficiency of parabolic trough collectors (PTCs) is essential for establishing CSP as a sustainable technology. This study proposes a simple procedure for evaluating, predicting, and optimizing the energetic and exergetic performances of PTCs operating with nanofluids. A coupled optical-thermal model is developed to simulate the turbulent flow of three common synthetic oils (Therminol VP-1, Syltherm 800, and Dowtherm Q) mixed with different nanoparticles (Al2O3, CuO, and SiO2) with different concentrations, under typical operating conditions of PTCs. The simulation results are fed to a soft-computing algorithm to develop prediction models that act as fitness functions in the multi-objective optimization process. For the considered range of input parameters and by assigning equal weights for the two optimization objectives (energy and exergy efficiencies), optimal design conditions corresponded to a PTC operating with CuO/Dowtherm Q nanofluid (volumetric concentration of 0.243%), at a direct irradiance level of 1000 W/m2, an inlet temperature of 240.793 °C, and a Reynolds number of 2.915E+05. These conditions led to energy and exergy efficiencies of 69.913 and 32.088%, respectively. The proposed procedure is described in detail to facilitate its adaptation and extension to other nanofluids, operating conditions, or other concentrating solar collectors.

Background: Youth represent 21% of the Egyptian population; such proportion can create a leading demographic power for economic development and transition. However, with the current COVID-19 pandemic, everyone is exposed to more than usual stressors, adding a burden to their mental health and well-being. Aim: This study aims to understand the pandemic’s effect on youth’s mental health in Egypt to strengthen the intervention areas needed to tackle such issues. Methods: This observational, analytical, cross-sectional study employed internet platforms of Facebook & WhatsApp groups for a web-based survey that included 412 respondents between 15 and 30 years old. RESULTS The median age of the respondents was 22 years. At least 30% reported increased violence in the street and/or household, and 27.4% of the respondents have considered visiting a psychiatrist during the last period. Conclusion: It is evident that the current situation is unprecedented and challenging for everyone; however, some populations are more vulnerable than others. Thus, it’s important to support young people to ensure that the whole community can withstand the pandemic. The governments should support and mitigate some of the stresses that can be directly amended, like the education and job security concerns.

Nanofluids are gaining attention as an attractive solution for the sustainable machining of difficult-to-cut materials. Despite the enormous recent work in the literature, there are still contradictions concerning the effect of different preparation factors on the characteristics of nanofluids and the underlying mechanisms governing them. In the present study, the effect of varying the preparation factors, namely, multi-walled carbon nanotube (MWCNT) concentration, sonication time, and surfactant amount on various nanofluid characteristics and the interactions among these characteristics were studied. The characteristics are divided into two categories: (a) dispersion/stability and (b) viscosity/wettability. The analysis showed strong interactions between these two categories which were mainly attributed to aggregates' formation and dynamics. For the stability/dispersion responses, the effect of aggregation and saturation phenomena is discussed in relation to the different preparation factors. Our analysis shows that the nanofluid viscosity is strongly dependent on aggregate morphology. As for wettability, a novel mechanism is proposed and used to explain the nanoparticles' influence on wettability based on the nanolayering theory. Finally, multi objective optimization (MOO) based on grey relational analysis (GRA) was performed. It was found that moderate MWCNT concentration, high sonication time, and low surfactant amount show the optimal characteristics within the current study design variables search domain. The novelty in the present study lies in its consideration of the simultaneous interaction between the nanofluids' properties and stability. Unlike the common practice in the literature, which focuses on one or two aspects of nanofluids, our approach broadens the analysis and provides in-depth insights into the nanofluid as a complete physical system.