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Floods are among the most devastating disasters in terms of socio-economics and casualties. However, these natural disasters can be managed and their effects can be minimized by flood modeling performed before the occurrence of a flood. In this study, flood modeling was developed for the Göksu River Basin, Mersin, Türkiye. Flood hazard and risk maps were prepared by using GIS, HEC-RAS, and HEC-HMS. In hydraulic modeling, Manning’s n values were obtained from 2018 CORINE data, return period flow rates (Q25, Q50, Q100, Q500) were obtained from HEC-HMS, and the application was carried out on a 5 m resolution digital surface model. In the study area, the water depths could reach up to 10 m, and water speeds were approximately 0.7 m/s. Considering these values and the fact that the study area is an urban area, hazard maps were obtained according to the UK Department for Environment, Food and Rural Affairs (DEFRA) method. The results indicated that possible flood flow rates from Q25 to Q500, from 1191.7 m3/s to 1888.3 m3/s, were detected in the study area with HEC-HMS. Flooding also occurred under conditions of the Q25 flow rate (from 4288 km2 to 5767 km2), and the impacted areas were classified as extremely risky by the DEFRA method.

East Lisbon is being exposed to large-scale urban regeneration processes, where luxury residential projects and mixed-use spatial developments are already underway. Thus, it is a living laboratory for “smart”, “creative” and “green” projects, as well as related urban public space interventions. Braço de Prata is an urban space overlooked by developers, being surrounded by obsolete industrial buildings. Concerning the recent interest in international investments in brownfield regeneration and greenfield developments, it represents an attractive urban terrain as a post-industrial working-class neighbourhood, where “smart” and “green” suggest transforming space so that both new and old residents can live and work together and share public space regardless of analysis on their environmental recognitions. The aim of this paper is to present an empirical evaluation model that examines the possible impacts of environmental negligence through the reorganisation of the physical and social fabric. The analyses focus on dwellers’ moral understanding of their changing environment as site-specific domains to address the unique conditions that affect transiently defined presumptions about the collective needs. Taking an evaluative approach in the Braço de Prata case, this paper demonstrates the specific socio-ecological implications of urban inequality in post-industrial neighbourhoods that could be threatened by new decisions, both through urban planning approaches and instruments.

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.

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.

The seeds of wild Crambe species have potential to be used as a source of industrial oil and animal feed. In this study, 48 genotypes of three Crambe species collected from the flora of Turkey were grown under field conditions in Ankara/Turkey in 2014–2016. The seed protein ratio, plant height, number of branches per plant, number of seeds per plant, seed yield per plant, thousand seed weight and hulless/hulled seed ratio (H/H) were determined. The highest protein ratio was determined as 26.02% in the t18 accession of Crambe tataria species. Variations in the characteristics were analyzed using principal component analysis. In the factor analysis of Crambe maritima, Crambe orientalis, Crambe tataria and the mean of these three species, the first two principal components accounted for 100%, 58.06%, 59.93% and 100% of the total variations, respectively. There were positive correlations between the plant height and number of seeds per plant, seed yield per plant for C. orientalis, and number of branches per plant for C. tataria. Although seed yield per plant was high in C. tataria and C. orientalis, they are not suitable for conventional agriculture due to shell thickness, inhomogeneous plant emergence and shooting. Conventional cultivation of wild Crambe species can be made possible by eliminating these negative features with breeding and agronomic studies.

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.