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Siderophores are low molecular weight secondary metabolites produced by microorganisms under low iron stress as a specific iron chelator. In the present study, a rhizospheric bacterium was isolated from the rhizosphere of sesame plants from Salem district, Tamil Nadu, India and later identified as Bacillus subtilis LSBS2. It exhibited multiple plant-growth-promoting (PGP) traits such as hydrogen cyanide (HCN), ammonia, and indole acetic acid (IAA), and solubilized phosphate. The chrome azurol sulphonate (CAS) agar plate assay was used to screen the siderophore production of LSBS2 and quantitatively the isolate produced 296 mg/L of siderophores in succinic acid medium. Further characterization of the siderophore revealed that the isolate produced catecholate siderophore bacillibactin. A pot culture experiment was used to explore the effect of LSBS2 and its siderophore in promoting iron absorption and plant growth of Sesamum indicum L. Data from the present study revealed that the multifarious Bacillus sp. LSBS2 could be exploited as a potential bioinoculant for growth and yield improvement in S. indicum.

Removal of Reactive Black 5 (RB5) dye from an aqueous solution was studied by its adsorption on banana peel biochars (BPBs). The factors affecting RB5 dye adsorption such as pH, exposure time, RB5 dye concentration, adsorbent dose, particle size and temperature were investigated. Maximum 97% RB5 dye removal was obtained at pH 3 with 75 mg/L adsorbate concentration by banana peel biochars. Fourier transform infrared (FTIR) and scanning electron microscopy (SEM) were used to characterize the adsorbent material. The data of equilibrium were analyzed by Langmuir and Freundlich isotherm models. The experimental results were best reflected by Langmuir isotherm with maximum 7.58 mg/g adsorption capacity. Kinetic parameters were explored and pseudo-second order was found suitable which reflected that rate of adsorption was controlled by physisorption. Thermodynamic variables exhibited that the sorption process was feasible, spontaneous, and exothermic in nature. Banana peel biochar showed excellent regeneration efficiency up to five cycles of successive adsorption-desorption. Banana peel biochar maintained >38% sorption potential of RB5 dye even after five cycles of adsorption-desorption. The phytotoxic study exhibited the benign nature of BPB-treated RB5 dye on tomato seeds.

This study explores the role of information technology (IT) in supporting sustainability efforts in Saudi Arabia. Through an analysis of four case studies, we examine how technology has been implemented by Saudi Arabian companies to advance sustainability goals. The findings demonstrate a notable shift towards green initiatives in the country, with the adoption of technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence. These technologies have been instrumental in enhancing energy efficiency and waste reduction. The study highlights the significance of policy interventions and public-private partnerships in achieving both environmental sustainability and economic growth. To ensure widespread adoption of sustainable practices, it is crucial for the government to focus on building digital capacity in companies and removing technological barriers. The case studies of four companies serve as illustrative examples of sustainable practices and the corresponding technological support in Saudi Arabia. The research underscores the importance of leveraging information technology and collaboration to accelerate progress towards the nation's sustainability goals.

Agriculture is essential to the existence of the human race, as well as the foundation of our civilization, because it provides food, fuel, fiber, and other resources necessary for survival; however, it is facing critical challenges due to anthropogenic climate change, which hampers food and nutritional security. Consequently, the agriculture industry must adjust to farming issues, such as the shift in global temperatures and environmental degradation, the scarcity of farm workers, population growth, and dietary changes. Several measures have been implemented to enhance agricultural productivity, including plant breeding, genetic engineering, and precision agriculture. In recent years, the world has witnessed the burgeoning development of novel scientific innovations and technological advancements enabled by drones, smart sensors, robotics, and remote sensing, resulting in a plethora of revolutionary methods that can be applied to real-time crop modeling, high-throughput phenotyping, weather forecasting, yield prediction, fertilizer application, disease detection, market trading, farming practices, and other environmental practices vital to crop growth, yield, and quality. Furthermore, the rise in big data, advanced analytics, falling technology costs, faster internet connections, increased connectivity, and increases in computational power are all part of the current digitalization wave that has the potential to support commercial agriculture in achieving its goals of smart farming, resilience, productivity, and sustainability. These technologies enable efficient monitoring of crops, soil, and environmental conditions over large areas, providing farmers with data to support precise management that optimizes productivity and minimizes environmental impacts. Though smart farming has significant potential, challenges like high implementation costs, data security concerns, and inadequate digital literacy among farmers remain. In summary, agriculture is rapidly transforming from conventional to digital farming, offering global solutions, efficient resource utilization, and minimized input costs while fostering farmer livelihoods and economic growth. Delivering a comprehensive view of how technology could help in tackling critical issues like environmental degradation and threatened world biodiversity, this perspective emphasizes the perks of digitalization. Future advancements may involve data encryption, digital literacy, and particular economic policies.

The development of a concrete mixture design process for high-quality concrete production with sustainable values is a complex process because of the multiple required properties at the green/hardened state of concrete and the interdependency of concrete mixture parameters. A new multicriteria decision making (MCDM) technique based on Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) methodology is applied to a fuzzy setting for the selection of concrete mix factors and concrete mixture design methods with the aim towards sustainable concrete quality management. Three objective properties for sustainable quality concrete are adopted as criteria in the proposed MCDM model. The seven most dominant concrete mixture parameters with consideration to sustainable concrete quality issues, i.e., environmental (density, durability) and socioeconomic criteria (cost, optimum mixture ingredients ratios), are proposed as sub-criteria. Three mixture design techniques that have potentiality to include sustainable aspects in their design procedure, two advanced and one conventional concrete mixture design method, are taken as alternatives in the MCDM model. The proposed selection support framework may be utilized in updating concrete design methods for sustainability and in deciding the most dominant concrete mix factors that can provide sustainable quality management in concrete production as well as in concrete construction. The concrete mix factors found to be most influential to produce sustainable concrete quality include the water/cement ratio and density. The outcomes of the proposed MCDM model of fuzzy TOPSIS are consistent with the published literature and theory. The DOE method was found to be more suitable in sustainable concrete quality management considering its applicable objective quality properties and concrete mix factors.

Room occupancy prediction based on indoor environmental quality may be the breakthrough to ensure energy efficiency and establish an interior ambience tailored to each user. Identifying whether temperature, humidity, lighting, and CO2 levels may be used as efficient predictors of room occupancy accuracy is needed to help designers better utilize the readings and data collected in order to improve interior design, in an effort to better suit users. It also aims to help in energy efficiency and saving in an ever-increasing energy crisis and dangerous levels of climate change. This paper evaluated the accuracy of room occupancy recognition using a dataset with diverse amounts of light, CO2, and humidity. As classification algorithms, K-nearest neighbors (KNN), hybrid Adam optimizer–artificial neural network–back-propagation network (AO–ANN (BP)), and decision trees (DT) were used. Furthermore, this research is based on machine learning interpretability methodologies. Shapley additive explanations (SHAP) improve interpretability by estimating the significance values for each feature for classifiers applied. The results indicate that the KNN performs better than the DT and AO-ANN (BP) classification models have 99.5%. Though the two classifiers are designed to evaluate variations in interpretations, we must ensure that they have accurate detection. The results show that SHAP provides successful implementation following these metrics, with differences detected amongst classifier models that support the assumption that model complexity plays a significant role when predictability is taken into account.

This study examines the online teaching abilities and competencies needed to teach online courses in Saudi Arabia’s higher education system. As a result, the goal of this study was to examine and expand the technology acceptance model (TAM) to assess online teaching abilities and competences utilizing digital technologies in higher education during the COVID-19 epidemic. Therefore, it aimed to develop a new model to measure and explore critical factors that influence online teaching skills, competencies, and actual use of digital tools in higher education. The participants in the study were 350 lecturers at King Faisal University. The research data were analyzed using structural equation modeling (AMOS-SEM). The findings revealed that: (a) perceived ease of use and perceived usefulness on using digital tools during the COVID-19 pandemic has a direct positive impact on perceived teaching self-efficacy, perceived enjoyment, online teaching skills, and digital tools access; (b) perceived ease of use and perceived usefulness of using digital tools have a direct positive impact on lecturers’ attitude toward use and lecturers’ behavioral intention to use digital tools during the COVID-19 pandemic; and (c) perceived ease of use. As a consequence of the findings, a validated instrument was designed to assess and investigate crucial elements that impact lecturers’ real usage of digital technologies for teaching and learning in Saudi Arabia’s higher education.

The study aimed to determine Aluminum sludge composition and structure for its valorisation as an alternative natural material for heavy metals removal from wastewater for further reuse as treated water in different applications. The study was conducted to investigate the introduction of Al-bearing sludge composition. The physical and chemical properties were examined using X-ray diffraction tests (XRD), scanning electron microscope tests (SEM), Fourier-transform infrared tests (FTIR), and Brunauer-Emmett-Teller tests (BET). Furthermore, the heavy metal concentrations of synthetic wastewater were measured using the spectrophotometry method. The experimental procedure is based on testing different pH limits and amounts of aluminum sludge to find the optimum conditions for copper (Cu) and zinc (Zn) removal. The results demonstrated a high removal efficiency where its value reached up to 97.4% and 96.6% for Zn and Cu, respectively, in an acidic medium (pH = 6) using a relatively high amount of sludge (1400 mg). Nevertheless, a low efficiency was obtained in the strongly acidic medium (pH = 4) and a smaller sludge amount of about 480 mg.

The main purpose of this study is to examine the impact of employer branding on employee retention and the mediating effect of the relational psychological contract between employer branding and employee retention among employees of the banking sector in Saudi Arabia. A cross-sectional survey is used to gather data from 459 employees working in the banking sector. The study findings reveal that employer branding is positively and significantly related to relational psychological contracts and employee retention. Furthermore, a relational psychological contract significantly mediates the relationship between employer branding and employee retention. The current study is considered the first to provide empirical evidence of the role of the relational psychological contract as a mediator between employer branding and employee retention. As a result, the current study will extend the employer branding literature by recognizing a new way through which employer branding impacts employee retention. Theoretical and practical implications are discussed.

The environmental control and management of municipal solid waste (MSW) dumping sites is considered one of the sensitive challenges faced by executive municipalities. This is especially true in Makkah due to the gradual increase in urban population and visitors, with an overall MSW generation of about one million tons per year. Consequently, the geo-environmental evaluation of the Kaakia dumping site shed light on the potential environmental threats, in terms of ambient air quality levels and meteorological parameters, in addition to geophysical inspection. An air quality survey discussed the major trends of ambient air pollutants (SO2, NO2, CO, O3, CH4, and PM10) downwind from the Kaakia dumping site. It indicated the presence of a significant increase in sporadic plumes of Methane concentration. The maximum hourly averages ranged between 22.9–26.6 µg/m3 for SO2, 44.4–64.0 µg/m3 for NO2, 0.86–1.38 mg/m3 for CO, 150.2–158.8 µg/m3 for O3, 5.09–5.9 ppm for CH4, and 955–994 µg/m3 for PM10. The ground penetrating radar (GPR) geophysical survey indicated the subsurface sequence of three geological layers, as confirmed by nearby bores of the investigated site: (1) a surface layer formed of alluvial sediments of sand, which were 2.5–3.1 m thick; (2) a second layer represented by sand and gravel, with a thickness of 4.6–6.5 m; and (3) a third layer equivalent to saturated alluvial sediments mixed with rock fragments that extended to a depth of 13 m. The signals of the GPR were attenuated at the base of the conducted profiles due to the percolation of generated leachate to the subsurface sequence and contaminated groundwater aquifer.