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Carbon dioxide enrichment inside a greenhouse is a sustainable approach to increasing crop production worldwide. Recently, the F1 hybrid strawberry became an alternative to runner-propagated cultivation as an innovative method to shorten the production period and increase strawberry production. This work aims to present CO2 enrichment as a sustainable tool that improves the yield in a controlled greenhouse and addresses the efficiency of three F1 hybrid strawberry varieties grown under Saudi Arabian conditions. A greenhouse experiment was conducted at the National Research and Development Center for Sustainable Agriculture (Estidamah), KSA, to study the impact of two CO2 levels (400 ppm (“ambient”) and 600 ppm (“enrichment”)) on the growth, photosynthesis traits, fruit yield and fruit quality of three F1 hybrid strawberry varieties grown under soilless culture conditions. The results show that CO2 enrichment significantly improved the phenotyping of strawberry growth traits at 60 days post-transplanting. The physiological response of the varieties to CO2 enrichment reveals a significant increase in the photosynthetic rate (129.7%) and intercellular CO2 (43.7%) in the leaves of strawberry exposed to CO2 enrichment rather than in ambient conditions, combined with a significant increase in the number of fruits per plant (27.5%) and total fruit yield (42.2%). A similar pattern was observed with varieties D and S in terms of fruit number, length and diameter. However, CO2 at 600 ppm promoted total soluble solid accumulation and vitamin C for the tested varieties. In contrast, CO2 enrichment significantly decreased nitrogen, phosphorus, potassium and magnesium accumulation in the leaves of the exposed plants in comparison to 400 ppm of CO2. These results suggest that increasing CO2 enrichment could contribute to an increase in strawberry yield and nutritional value and demonstrate that understanding the response of each variety to CO2 enrichment is important to support selecting suitable greenhouse strawberry varieties to improve crop yield.

This study presents an efficient vibration-based fault detection method for rotating machines utilising the poly-coherent composite spectrum (pCCS) and machine learning techniques. pCCS combines vibration measurements from multiple bearing locations into a single spectrum, retaining amplitude and phase information while reducing background noise. The use of pCCS significantly reduces the number of extracted parameters in the frequency domain compared to using individual spectra at each measurement location. This reduction in parameters is crucial, especially for large industrial rotating machines, as processing and analysing extensive datasets demand significant computational resources, increasing the time and cost of fault detection. An artificial neural network (ANN)-based machine learning model is then employed for fault detection using these reduced extracted parameters. The methodology is developed and validated on an experimental rotating machine at three different speeds: below the first critical speed, between the first and second critical speeds, and above the second critical speed. This range of speeds represents the diverse dynamic conditions commonly encountered in industrial settings. This study examines both healthy machine conditions and various simulated fault conditions, including misalignment, rotor-to-stator rub, shaft cracks, and bearing faults. By combining the pCCS technique with machine learning, this study enhances the reliability, efficiency, and practical applicability of fault detection in rotating machines under varying dynamic conditions and different machine conditions.

Abstract Aim The aim of the study was to examine the relationship between stress, psychological symptoms and job satisfaction among frontline nursing staff at a military hospital in Saudi Arabia during the COVID‐19 pandemic. Design Descriptive cross‐sectional study. Methods Data were collected using an online survey. All Registered Nurses (N = 1,225) working at a military hospital between February to April 2021 were contacted, 625 responded (51%). Data were analysed using descriptive and multivariate analysis, Student's t‐test for independent samples and one‐way analysis of variance followed by Tukey's multiple comparison tests. Results Stress was experienced more significantly than depression or anxiety. Approximately 29% of the change in scores for psychological symptoms was explained by age group, being a Saudi national and working in emergency departments (F[3,620] = 19.063, p

Key points The study estimated a minimum prevalence of Rheumatoid Arthritis of 0.34% in Western Australia based on hospital diagnosis records over 20 years and 0.36% based on biological therapy dispensing over a decade. The study found that older females with Rheumatoid Arthritis who live in rural areas are more likely to experience frequent hospitalisations suggesting unmet needs in their primary care access.

One of the main approaches for performance improvement of thermal power plants is employing preheating units. Preheating the air or stream in Brayton or Rankine cycles can significantly improve the overall efficiency output power. Solar energy, as an available renewable energy, is an attractive option to be employed as the source of preheating. In the present article, applications of solar energy for preheating air and steam in thermal power plants are reviewed. According to reviews, enhancement in the performance of the systems is under influence of several factors such as the configuration of reference system, operating condition, applied technology etc. Besides performance enhancement of the power plant, integration of solar preheating system can remarkably reduce the fuel consumption and consequently the emission of carbon dioxide. Furthermore, due to the unavailability of the solar energy in nigh and cloudy hours, applying thermal storage units can enhance the reliability of the system and increase contribution of solar energy in the output of the system.

IntroductionThe burgeoning population of the world is causing food insecurity not only by less food availability but also by the malnutrition of essential nutrients and vitamins. Malnutrition is mostly linked with food having micronutrients lower than the optimal concentration of that specific food commodity and becoming an emerging challenge over the globe. Microbial biofortification in agriculture ensures nutritional security through microbial nitrogen fixation, and improved phosphate and zinc solubilization, which increase the uptake of these nutrients. The present study evaluates the novel plant growth-promoting rhizobacteria (PGPR) to biofortify maize gain.MethodsFor this purpose, a pot and two field experiments for maize were conducted. PGPRs were applied alone and in combination for a better understanding of the biofortification potential of these strains. At physiological maturity, the growth parameters, and at harvest, the yield, microbial population, and nutritional status of maize were determined.Results and discussionResults revealed that the consortium (ZM27+ZM63+S10) has caused the maximum increase in growth under pot studies like plant height (31%), shoot fresh weight (28%), shoot dry weight (27%), root fresh (33%) and dry weights (29%), and microbial count (21%) in the maize rhizosphere. The mineral analysis of the pot trial also revealed that consortium of ZM27+ZM63+S10 has caused 28, 16, 20, 11 and 11% increases in P, N, K, Fe, and Zn contents in maize, respectively, as compared to un-inoculated treatment in pot studies. A similar trend of results was also observed in both field trials as the consortium of ZM27+ZM63+S10 caused the maximum increase in not only growth and biological properties but also caused maximum biofortification of mineral nutrients in maize grains. The grain yield and 1000-grain weight were also found significantly higher 17 and 12%, respectively, under consortium application as compared to control. So, it can be concluded from these significant results obtained from the PGPR consortium application that microbial inoculants play a significant role in enhancing the growth, yield, and quality of the maize. However, the extensive evaluation of the consortium may help in the formulation of a biofertilizer for sustainable production and biofortification of maize to cope with nutritional security.

To decrease indoor relative humidity and have relaxing environments, small dehumidifiers are widely used in tropical climatic. Due to the benefits of eco-friendly, small size and silence operation, the thermoelectric dehumidifier has gained interest but has limited practical application due to poor efficiency. Therefore, this study investigates the dehumidification characteristics of the thermoelectric module powered by a photovoltaic system for the production of fresh water under real climatic conditions. The performance of a novel prototype named as the Photo-Thermoelectric Dehumidifier (PVTE-D) was investigated both numerically and experimentally in different combinations of airflow rate and input power. The results obtained from the experiment suggested that the water condensate collection was increased by increasing the input power from a PV panel to the TE-D. In the month of May, the maximum water condensate collection of 1,852.3 mL/hr was attained at the input supply of 6 A and 5 V to the PVTE-D system. In the majority of cases, when the airflow rate is below 0.013 kg/s, maximum collections of water condensate have been achieved. This study provides a detailed understanding of the optimally suitable structural parameters of the PVTE-D under different operating conditions and reveals a novel configuration for higher water condensation capacity. HIGHLIGHTS Photovoltaic powered thermoelectric dehumidification system.; Simulation and experimental study of PVTED system.; Optimum performance parameter evaluation by regulating input power and air flow rate.;

Abstract Natural air ventilation in the hot‐dry regions plays a key role to decrease indoor air temperature in hot season, also to improve thermal comfort during the cold season. One of the most common ways to take advantage of natural ventilation is using wind catcher with an underground tunnel. In this method, the tower catches the airflow and directs it to the underground tunnel to decrease the air temperature by transferring heat to the ground, which is cooler in the summer and warmer in the winter. Earth‐to‐air heat exchanger (EAHE) is a modern form of wind catcher with underground tunnel. In this method, air after passing through buried pipes exchanges heat with the ground, and its temperature increases in the winter and decreases during the summer. This study analyzes the energy performance and cost‐effectiveness of earth‐to‐air heat exchanger to be utilized in a residential building in climate condition of the province of Kermanin Iran. In this regard, 9 different configurations of the EAHE are investigated to find the optimized EAHE. The system performance and cost‐effectiveness are studied in 3 different depths including 1, 2, and 3 m with 3 different pipe lengths including 25, 50, and 75 m. The results show that the EAHE is capable of reducing the inlet air temperature by 0.5‐9.9℃ in the summer and increasing it by 0.9‐11.2℃ in the winter. Furthermore, by integrating the EAHEs in a building design, one can reduce the total annual cooling load by 1.25%‐3.97% and for heating by 1.34%‐3.96%. The payback period of the system with a pipe length of 25 m is 16 years, but for the systems with pipe lengths of 50 and 75 m, this period reduces to almost 3 years.

Researchers’ concentration has been on hybrid systems that can fulfill economic and environmental goals in recent years. In this study, first, the prediction of CO2 emission and electricity consumption of Saudi Arabia by 2040 is made by employing multi-layer perceptron (MLP) and support vector regression (SVR) methods to see the rate of CO2 emission and electricity consumption. In this regard, the most important parameters such as gross domestic product (GDP), population, oil consumption, natural gas consumption, and renewable consumption are considered. Estimating CO2 emission by MLP and electricity consumption by SVR showed 815 Mt/year and 475 TWh/year, respectively, where R2 for MLP and SVR was 0.99. Prediction results showed a 31% and 39% increase in CO2 emission and electricity consumption by 2040 compared to 2020. Second, the optimum combination of components for supplying demand load and desalination load in residential usages are found where 0% capacity shortage, 20–60$/t penalty for CO2 emission, sell back to the grid, and both fixed and random grid outages are considered. Load demands were considered under two winter and non-winter times so that 4,266, 2,346, and 3,300 kWh/day for Aseer, Tabuk, and the Eastern Region were shown, respectively. Results show that 0.12, 0.11, and 0.12 (kW (PV))/(kWh/day(load)) and 0.1, 0.08, and 0.08 (kW(Bat))/(kWh/day(load)) are required under the assumption of this study for Aseer, Tabuk, and the Eastern Region, respectively. Also, COEs for the proposed systems are 0.0934, 0.0915, and 0.0910 $/kWh for Aseer, Tabuk, and the Eastern Region, respectively. Also, it was found that renewable fractions (RFs) between 46% and 48% for all of the case studies could have rational COE and NPCs and fulfill the increasing rate of CO2 emission and electricity consumption. Finally, sensitivity analysis on grid CO2 emission and its penalty, load and solar Global Horizontal Irradiance (GHI), PV, and battery prices showed 45%–55%, 42%–52%, and 43%–49% RFs for Aseer, Tabuk, and the Eastern Region, respectively.

A decrease in the utilization of fossil energies, mainly by replacing them with renewable energy sources (RESs), is regarded as a potential energy source in today’s applications. RESs are broadly utilized for heating purposes and particularly with applications in solar water heater (SWH). Despite the accessibility of SWH technologies and their affordable prices in Iran, there is no comprehensive study to explain the potential of Iranian regions to supply hot water for household applications. This one-year work, hence, attempts the first dynamical simulation of a solar heating system to provide sanitary hot water (SHW) as well as hot water demanded to heat 47 stations in Iran. Weather data were extracted from METEONORM and environmental-technical analyses performed by thermal solar (TSOL) software. Stations were ranked based on CCR and BCC models in data envelopment analysis (DEA) method using GAMS V 24.1. As with results, a total of 223.1 MWh solar heat is generated annually from all stations that prevent the emission of 64.5 t CO2 every year. According to CCR and BCC models, Bandar Abbas, Chabahar, Fasa, Iranshahr, Kermanshah, Khoramabad, Sarab, Shahr-e-kord, Yasuj, Zanjan, and Zahedan are the best in this regard. Also according to the economic analysis, the average price of home solar heating in Iran is 0.160 $/kWh.

The smart platform of generating, collecting, managing and processing dynamic data from different sources in the Internet of Vehicles (IoV) pave the way for a large-scale dataset to be accumulated. The dataset can contain records running into hundreds of thousands and even millions of relevant, irrelevant and redundant features. Therefore, feature selection to select only the significant features for developing vehicle collision detection alarm systems for deployment in the IoV edge is critical. However, previous studies on vehicle collision detection in the IoV have not conducted rigorous feature selection. Limited studies have mainly applied Pearson correlation coefficient (PCC) to select subset features influencing vehicle collision in the domain of IoV. However, PCC can cause relevant features to be discarded if the correlation of the non-linear association is too small, thereby providing incorrect feature ranking, which, in turn, increases the chances of developing a model that will give a poor outcome. To close this gap, this paper proposed a multi-objective, filter-based hybrid non-dominated sorted genetic algorithm III with a gain ratio and bi-directional wrapper for the selection of subset features influencing vehicle collision in the IoV. The proposed approach selected the minimal most significant subset features for developing a vehicle collision detection classifier with maximum accuracy for deployment in the IoV. A comparative study proves that the proposed approach performs better than the compared algorithms across hybrid-, wrapper- and filter-based feature selection methods within the family of the NSGA. Further, a comparative analysis with other evolutionary algorithms proves the superiority of the proposal. This study can help researchers in the future by avoiding the use of large-scale computing resources in acquiring data to develop vehicle collision alert systems in the IoV. This can be achieved since only the subset features discovered in this study are collected, as opposed to collecting large features, thus saving time and resources in the subsequent vehicle collision detection data collection in the IoV.

A significant growth in solar photovoltaic (PV) installation has observed during the last decade in standalone and grid-connected power generation systems. The solar PV system has a non-linear output characteristic because of weather intermittency, which tends to have a substantial effect on overall PV system output. Hence, to optimize the output of a PV system, different maximum power point tracking (MPPT) techniques have been used. But, the confusion lies while selecting an appropriate MPPT, as every method has its own merits and demerits. Therefore, a proper review of these techniques is essential. A “Google Scholar” survey of the last five years (2015-2020) was conducted. It has found that overall seventy-one review articles are published on different MPPT techniques; out of those seventy-one, only four are on uniform solar irradiance, seven on non-uniform and none on hybrid optimization MPPT techniques. Most of them have discussed the limited number of MPPT techniques, and none of them has discussed the online and offline under uniform and hybrid MPPT techniques under non-uniform solar irradiance conditions all together in one. Unfortunately, very few attempts have made in this regard. Therefore, a comprehensive review paper on this topic is need of time, in which almost all the well-known MPPT techniques should be encapsulated in one paper. This article focuses on classifications of online, offline, and hybrid optimization MPPT algorithms, under the uniform and non-uniform irradiance conditions. It summarizes various MPPT methods along with their mathematical expression, operating principle, and block diagram/flow charts. This research will provide a valuable pathway to researchers, energy engineers, and strategists for future research and implementation in the field of maximum power point tracking optimization.

While Afghanistan's power sector is almost completely dependent on fossil fuels, it still cannot meet the rising power demand of this country. Deploying a combination of renewable energy systems with hydrogen production as the excess energy storage mechanism could be a sustainable long-term approach for addressing some of the energy problems of Afghanistan. Since Badakhshan is known to have a higher average wind speed than any other Afghan province, in this study, a technical, economic, and carbon footprint assessment was performed to investigate the potential for wind power and hydrogen production in this province. Wind data of four stations in Badakhshan were used for technical assessment for three heights of 10, 30, and 40 m using the Weibull probability distribution function. This technical assessment was expanded by estimating the energy pattern factor, probability of wind speeds greater than 5 m/s, wind power density, annual power output, and annual hydrogen output. This was followed by an economic assessment, which involved computing the Leveled Cost Of Energy (LCOE), the Leveled Cost Of Hydrogen (LCOH), and the payback period, and finally an carbon footprint assessment, which involved estimating the consequent CO2 reduction in two scenarios. The assessments were performed for 22 turbines manufactured by reputable companies with capacities ranging from 600 kW to 2.3 MW. The results showed that the entire Badakhshan province, and especially Qal'eh-ye Panjeh and Fayazabad, have excellent potentials in terms of wind energy that can be harvested for wind power and hydrogen production. Also, wind power generation in this province will be highly cost-effective, as the produced electricity will cost about one-third of the price of electricity supplied by the government. For better evaluation, the GIS maps of wind power and hydrogen outputs were prepared using the IDW method. These maps showed that the eastern and northeastern parts of Badakhshan province have higher wind power-hydrogen production potentials. The results of ranking the stations with SWARA-EDAS hybrid MCDM methods showed that Qal'eh-ye Panjeh station was the best location to produce hydrogen from wind energy.

Solar energy is a free and environmentally friendly supply of power that has negligible impact on the environment, and it has long been used by humans through different methods. However, solar energy technology would use for heating water for buildings as an alternative resource, which would help reduce CO2 reduction and safe environment. A data analysis was performed using the RETScreen software, and the financial outcomes that were calculated using two methods. Clearly, the software is operational with two different methods. Both methods indicate that this project is economically feasible. The aim of this study is to evaluate the utilization of solar energy for household and commercial purposes. The first goal is to explore the technical, economic, and environmental aspects of using evacuated glass tube solar collectors to heat water in an indoor pool in the building, having an area of 50 m2, in the city of Yazd, Iran. The second goal is to evaluate the amount of hydrogen that can be obtained from the installation photovoltaic systems in the province. The results also show that this project will decrease greenhouse gas emissions by 142 ton-CO2 over 20 years of the useful life of a collector, thereby indicating the possible significant role of such collectors in the reduction of greenhouse gas emissions. Furthermore, the findings indicate that installing a single X21-345 photovoltaic system with a performance rate of 20% can result in the production of 2.1 kg of hydrogen annually.

An API standard drilling fluid was investigated from laminar to turbulent flow conditions using an in-house-built viscometer at speeds from 200 to 1600 RPM. A power-based method was applied to obtain the apparent viscosity and the shear stress of the water-based drilling mud (WBM) in the annulus of the viscometer. Then, a MATLAB optimization program was developed to obtain model parameters for five rheology models integrated in a generalized Herschel-Bulkley-Extended (HBE) model and two widely used 4-parameter models in drilling industry. It is found that the Bingham, Cross, and HBE rheology models have precisely matched the WBM measurements in the viscometer. A generalized Reynolds number was applied to determine the Darcy friction factor although the PL (power law model) and the HB (Herschel-Bulkley model) exhibited a nonrealistic negative shift from the laminar friction factor.

The world is moving towards the generation of electricity with renewable energy sources (RES) due to the deterioration of the green environment and trying to replace non-renewable energy resources. The real-time results are achieved with the help of an arm controller, having good controller efficiency with the Waijung toolbox, compatible with MATLAB using STM32ST-link utility. In this paper, the authors are focused on areas such as easy to implement controller efficiency, and real-time solutions for modified direct-control perturbation & observation (DC-P&O) technique based on 32- bit ARM Cortex microcontroller (STM32F407VGT6) with embedded programming using Waijung blocksets, which offers very expected outcomes of the problem to make the stand-alone system efficient with fast-tracking. The observation setup is tested with a 40-watt photovoltaic (PV) panel with resistive load for achieving its stability. The designed algorithm enhances the efficiency of the controller by 84.48% for the real-time parameters of the PV panel at maximum power point (MPP) for a 57% duty ratio.

The effect of foliar spray with algae extract and/or potassium nitrate on the quantity and quality of Barhee date palm yields was investigated during the 2017 and 2018 seasons. Inflorescences of Barhee date palms were sprayed twice, after pollination (1st April of each season) and one month later, with algae extract at concentrations of 0.5% and 1.0% and with potassium nitrate at concentrations of 1% and 2% applied individually or in combinations. The obtained results showed that all of the sprayed treatments enhanced the yield and fruit properties when compared to the controls. Spraying inflorescences of Barhee date palms with algae extract and/or potassium nitrate had a significant effect on the yield and on the physical and chemical properties of the fruit when compared with the untreated palms. The increase in yield and qualitative properties was associated with increasing concentrations of both materials (algae extract and potassium nitrate). The best results were detected with spray containing high rates of combined treatment (1% algae extract + 2% potassium nitrate) applied twice in both seasons studied because this treatment resulted in the highest value of fruit and bunch weight, increased the yield per palm compared to the control by about 60.2%, and improved the fruit physical properties (fruit length, fruit diameter, fruit shape index, flesh weight, and seed weight) and fruit chemical properties (total soluble solids (TSS); acidity; TSS/acid ratio; tannins; and reducing, nonreducing, and total sugars) of the Barhee date palm given the experimental conditions.

The objective of this work was to investigate the advantages of using dichloro bisphenol A-glycidyl methacrylate (dCl-BisGMA) as a potential matrix for dental resin composites. A series of model composites containing 65 wt% resin (urethane dimethacrylate/triethylene glycol dimethacrylate/BisGMA as 1:3:1) and 35 wt% silanated silica were prepared. Thus, BisGMA was replaced by dCl-BisGMA as 0, 25, 50, and 100 wt% to obtain UTBC0, UTBC25, UTBC50, and UTBC100, respectively. The composites’ rheological properties, degree of double-bond conversion (DC), water sorption (WSP), and water solubility (WSL) were examined. The data revealed a statistically significant reduction in the complex viscosity of composites containing dCl-BisGMA, compared with UTBC0. No significant differences between DCs were detected (p < 0.05). A significant enhancement in the reduction of the dCl-BisGMA composite WSP was also detected, and conversely, WSL was increased. Although the viscosity, DC, and WSP characters were enhanced, a WSL increase is an undesirable development. However, WSL is supposedly caused by cyclization of small flexible chains, which is more likely to occur in the presence of hydrophobic monomers such as dCl-BisGMA and more prone to leaching than are crosslinked networks. We concluded that dCl-BisGMA is a monomer that could potentially be used as an alternative or in combination with traditional monomers, including BisGMA, in resin-based dental composites, and it deserves further investigation.

The influence of salicylic acid (SA) on growth, yield, fruits’ quality and enzymes’ activities was monitored in strawberry plants cv. Camarosa grown under salinity stress via two pot experiments in two successive years of 2018 and 2019. The examined concentrations of SA were 30, 60, and 90 ppm, which foliary applied in addition to control (sprayed with water), while the used levels of salinity were 20, and 40 mM as NaCl as irrigation application in addition to control (without salinity). The results showed a significant effect of salinity at 40 mM where the mean values of shoot fresh and dry weights, chlorophyll, leaves’ NPK, yield plant−1, yield ha−1, and fruits’ ascorbic acid were significantly decreased. However, the 40 mM salinity resulted in a significant increase in leaves’ content of Na and proline as well as catalase (CAT) and peroxidase (POD) enzymes’ activity and the fruits’ TSS and acidity. The application with 90 ppm SA was found to be the most significant positive treatment for all of the studied characters except the Na leaves’ content. Regarding tolerance index percentages (STI%), the high values of CAT, POD, and proline referred to the ability to use them as indicators for strawberry salinity response in other physiological and plant breeding studies. The findings of this study suggest that the 90 ppm SA foliar application can ameliorate the negative effect of salinity on the growth of strawberry cv. Camarosa.