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The growing population and increasing urbanization have led to a surge in domestic wastewater generation, posing significant challenges for effective and sustainable treatment. The present study demonstrates a novel and sustainable approach for the onsite treatment of domestic wastewater using an integrated settler-based biofilm reactor (ISBR) with efficient biogas generation. The ISBR provides an optimized environment for the growth of biofilm, facilitating the removal of organic pollutants and pathogens. Moreover, the ISBR enables the recovery of a valuable resource in the form of biogas, thus enhancing the overall utility of the treatment process. The performance of the ISBR was comprehensively evaluated at laboratory scale through treating the actual domestic wastewater generated from the hostel of Manipal University Jaipur. The ISBR system was operated under an ambient environment at a hydraulic retention time (HRT) of 24 h. The results demonstrated remarkable efficiency in terms of chemical oxygen demand (COD), total suspended solids (TSS), and coliforms removal, with average removal efficiency being more than 90%. According to the COD mass balance analysis, 48.2% of the influent COD was recovered as bioenergy. The chromatogram revealed a high percentage of methane gas in the collected biogas sample. The field emission scanning electron microscope (FESEM) analysis of the accumulated sludge in the ISBR system depicted the morphology of methanogenic bacteria. Both the experimental and theoretical results confirmed the feasibility and sustainability of the ISBR system at the onsite level.

The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

Ubiquitous CRM (UCRM) enhances customer relationship management. It can sense customer needs and demands, to which firms can respond quickly. Therefore, UCRM helps to improve a firm’s agility. There is a growing interest among researchers and practitioners to understand how the adoption of UCRM impacts the sustainability of firms’ operations, but not many studies have investigated this issue. In this context, the aim of this study is to examine how firms’ absorptive capacity and dynamic capability could impact the adoption of UCRM to influence the operational sustainability of the firms and their performance. The study also investigates the moderating role of technology turbulence on the relationship between a firm’s operational sustainability and its performance. Using absorptive capacity theory and dynamic capability view theory and reviewing the existing literature, we developed a conceptual model. The model was then validated using a structural equation modeling technique considering 309 usable respondents from different firms that use UCRM for their operational activities. The study found that firms’ absorptive capacity and dynamic capability significantly and positively impact the adoption of UCRM, which in turn significantly and positively impacts firms’ operational sustainability and improves their performance. The study also shows that there is a significant moderating role of technology turbulence on the relationship between operational sustainability and firm performance.

Cloud data center’s total operating cost is conquered by electricity cost and carbon tax incurred due to energy consumption from the grid and its associated carbon emission. In this work, we consider geo-distributed sustainable datacenter’s with varying on-site green energy generation, electricity prices, carbon intensity and carbon tax. The objective function is devised to reduce the operating cost including electricity cost and carbon cost incurred on the power consumption of servers and cooling devices. We propose renewable-aware algorithms to schedule the workload to the data centers with an aim to maximize the green energy usage. Due to the uncertainty and time variant nature of renewable energy availability, an investigation is performed to identify the impact of carbon footprint, carbon tax and electricity cost in data center selection on total operating cost reduction. In addition, on-demand dynamic optimal frequency-based load distribution within the cluster nodes is performed to eliminate hot spots due to high processor utilization. The work suggests optimal virtual machine placement decision to maximize green energy usage with reduced operating cost and carbon emission.

Cannabis consumption has become the center of much debate globally. The positive public perception of the medicinal benefits of cannabis and the rise of recreational usage of cannabis necessitate dramatic changes in cannabis reform policy. As a consequence, there is an increase in cannabis legalization around the globe, although it is still facing many rejections. It is crucial to understand the factors affecting public acceptance of cannabis use to support the contextualization and success of cannabis legalization. This review aims to address consumer cultural, social and psychological factors regarding the legal use of cannabis. Based on this review, cultures influence the endorsement or rejection of cannabis use depending on political views, religious sentiments and affiliated subcultures (adult, youth and adolescent subcultures). Regarding the social factors, socioeconomic status, measured by income, education level and occupation, is a key determinant of cannabis use. The beliefs opposing cannabis legalization are due to the negative stigma surrounding cannabis use. Nevertheless, growing awareness about the pharmaceutical and therapeutic effects of cannabis has led to an increase in positive attitudes towards cannabis legalization. Thus, dissemination of cannabis use benefits reaffirmed by scientific evidence could be a strategic way to alleviate the public’s negative feedback on cannabis legalization.

Significant changes in society were emphasized as being required to achieve Sustainable Development Goals, a need which was further intensified with the emergence of the pandemic. The prospective society should be directed towards sustainable development, a process in which technology plays a crucial role. The proposed study discusses the technological potential for attaining the Sustainable Development Goals via disruptive technologies. This study further analyzes the outcome of disruptive technologies from the aspects of product development, health care transformation, a pandemic case study, nature-inclusive business models, smart cities and villages. These outcomes are mapped as a direct influence on Sustainable Development Goals 3, 8, 9 and 11. Various disruptive technologies and the ways in which the Sustainable Development Goals are influenced are elaborated. The investigation into the potential of disruptive technologies highlighted that Industry 5.0 and Society 5.0 are the most supportive development to underpin the efforts to achieve the Sustainable Development Goals. The study proposes the scenario where both Industry 5.0 and Society 5.0 are integrated to form smart cities and villages where the prospects of achieving Sustainable Development Goals are more favorable due to the integrated framework and Sustainable Development Goals’ interactions. Furthermore, the study proposes an integrated framework for including new age technologies to establish the concepts of Industry 5.0 and Society 5.0 integrated into smart cities and villages. The corresponding influence on the Sustainable Development Goals are also mapped. A SWOT analysis is performed to assess the proposed integrated approach to achieve Sustainable Development Goals. Ultimately, this study can assist the industrialist, policy makers and researchers in envisioning Sustainable Development Goals from technological perspectives.

In agroecosystems, drought is a critical climatic phenomenon that affects evapotranspiration and induces water stress in plants. The objective in this study was to characterize and forecast water stress in the Hyderabad region of India using artificial intelligence models. The monthly precipitation data for the period 1982–2021 was characterized by the standardized precipitation index (SPI) and modeled using the classical autoregressive integrated moving average (ARIMA) model and artificial intelligence (AI), i.e., artificial neural network (ANN) and support vector regression (SVR) model. The results show that on the short-term SPI3 time scale the studied region experienced extreme water deficit in 1983, 1992, 1993, 2007, 2015, and 2018, while on the mid-term SPI6 time scale, 1983, 1991, 2011, and 2016 were extremely dry. In addition, the prediction of drought at both SPI3 and SPI6 time scales by AI models outperformed the classical ARIMA models in both, training and validation data sets. Among applied models, the SVR model performed better than other models in modeling and predicting drought (confirmed by root mean square error—RMSE), while the Diebold–Mariano test confirmed that SVR output was significantly superior. A reduction in the prediction error of SVR by 48% and 32% (vs. ARIMA), and by 21% and 26% (vs. ANN) was observed in the test data sets for both SPI3 and SPI6 time scales. These results may be due to the ability of the SVR model to account for the nonlinear and complex patterns in the input data sets against the classical linear ARIMA model. These results may contribute to more sustainable and efficient management of water resources/stress in cropping systems.

Understanding groundwater geochemistry is crucial for water supply in arid regions. The present research was conducted in the arid Mo river watershed on the Tibetan plateau to gain insights into the geochemical characteristics, governing processes and quality of groundwater in arid endorheic watersheds. A total of 28 groundwater samples were collected from the phreatic and confined aquifers for hydrochemical analysis. The results showed that the groundwater was slightly alkaline in all aquifers of the watershed. The phreatic groundwater samples (PGs) and confined groundwater samples (CGs) had the TDS value in the ranges of 609.19–56,715.34 mg/L and 811.86–2509.51 mg/L, respectively. PGs were salter than CGs, especially in the lower reaches. Both the PGs and CGs were dominated by the Cl-Na type, followed by the mixed Cl-Mg·Ca type. The toxic elements of NO2− (0.00–0.20 mg/L for PGs and 0.00–0.60 mg/L for CGs), NH4+ (0.00–0.02 mg/L for PGs and 0.00–0.02 mg/L for CGs) and F− (0.00–4.00 mg/L for PGs and 1.00–1.60 mg/L for CGs) exceeded the permissible limits of the Chinese guidelines at some sporadic sites. Water–rock interactions, including silicates weathering, mineral dissolution (halite and sulfates) and ion exchange, were the main contributions to the groundwater chemistry of all aquifers. The geochemistry of PGs in the lower reach was also greatly influenced by evaporation. Agricultural sulfate fertilizer input was responsible for the nitrogen pollutants and salinity of PGs. All CGs and 73.91% of PGs were within the Entropy-weighted water quality index (EWQI) of below 100 and were suitable for direct drinking purposes. Precisely 8.70 and 17.39% of PGs were within the EWQI value in the range of 100–150 (medium quality and suitable for domestic usage) and beyond 200 (extremely poor quality and not suitable for domestic usage), respectively. The electrical conductivity, sodium adsorption ratio, sodium percentage and permeability index indicated that groundwater in most parts of the watershed was suitable for irrigation, and only a small portion might cause salinity, sodium or permeability hazards. Groundwater with poor quality was mainly distributed in the lower reaches. CGs and PGs in the middle-upper reaches could be considered as the primary water resources for water supply. Agricultural pollution should be paid more attention to safeguard the quality of groundwater.