Seawater desalination is an affordable and viable solution to the growing freshwater scarcity problem in water scarce regions. The current study focuses on cost analysis of Vacuum Membrane Distillation (VMD) setup for removing salts from water. The membrane used in the flat sheet VMD module was Polytetrafluoroethylene (PTFE) with 250 mm × 200 mm dimensions and 165 µm thickness. The experiments were carried out with variations in parameters such as velocity, pressure, concentration, and temperature. For the cost analysis, the operational, maintenance, instrumentation, and capital cost of the lab model was considered and then upscaled. A range of experiments was performed for NaCl and KCl under variations of operating parameters. It was noted that, for the NaCl solution, the increase in temperature from 50 °C to 70 °C doubled the permeate flux. However, for the conditions tested, the concentration shift from 0.25 M to 0.75 M decreased the permeate flux by 1.4% because the increase in ion concentrations along the membrane lowers the vapor pressure, restricting the permeate flux. The results trend for the KCl solution was similar to the NaCl; at temperature T1, it was noted that increased concentration from 0.25 M to 0.75 M significantly reduces the permeate flow. The reduction in permeate flow was nonlinear for a given pressure 30 kPa and velocity 5.22 m/s, but linear for all other variables. It was also observed that with an increase in temperature from 60 °C to 70 °C, the permeate flux for concentration 0.25 M was 49% for all the combinations of pressure and velocity. In addition, permeate flow increased 53% from temperature 50 °C to 60 °C and 49% from temperature 60 °C to 70 °C for both the solutions at a concentration of 0.25 M. This shows that the temperature also had a profound impact on the permeate flux. The economic analysis and market survey shows that the cost of clean water at the lab level was high which can be significantly reduced using a large-scale setup providing 1,000,000 L/H of distilled water.

Current medical treatments for diastasis recti often involve exercises to strengthen the core muscles, along with abdominal binders or supports. However, there is limited evidence comparing the effectiveness of surface electromyography (SEMG) biofeedback-assisted core strengthening exercises combined with kinesiotaping to other approaches. This study aimed to assess the impact of three interventions on core strength, inter-rectus distance, and quality of life in postpartum women with diastasis recti. The interventions included core strengthening exercises with kinesiotaping and SEMG biofeedback-assisted core strengthening with kinesiotaping. This randomized controlled trial (NCT05897255) included 24 postpartum women divided into three groups. We measured inter-rectus distance, quality of life using the Short Form Health Survey 36, and core strength using the McGill torso battery test. The SEMG biofeedback provided auditory and visual cues. We used one-way analysis of variance to compare outcomes between groups, while a t-test for within-group analysis. Both the SEMG biofeedback-assisted and non-assisted core strengthening exercises with kinesiotaping groups showed significantly greater improvements in energy, bodily pain, general health, physical functioning, and limitations due to physical problems than the core strengthening group. Additionally, the SEMG biofeedback-assisted group demonstrated a greater reduction in inter-rectus distance. There were no statistically significant differences in core strength improvement among the three groups. Core strengthening exercises with SEMG-assisted kinesiotaping were superior to core strengthening alone in reducing inter-rectus distance, enhancing physical function, energy levels, and general health, and decreasing bodily pain and limitations due to physical problems. Core strength improvements were similar across all groups.

This comprehensive survey examines the transformative impact of machine learning (ML), deep learning (DL), and natural language processing (NLP) across diverse business and finance sectors. From micro-level operational efficiencies to macro-level strategic decision-making, we delve into the multifaceted applications of AI and its influence on traditional practices. Our analysis highlights the increasing prominence of ML in enhancing decision-making and achieving greater accuracy and efficiency. We also identify key challenges such as data quality, modelinterpretability, and linguistic information processing. By addressing these challenges, we can further unlock the potential of ML in revolutionizing business and finance. We conducted a systematic literature review using the PRISMA technique to identify relevant research published within the last five years. Our findings reveal the growing adoption of ML techniques in various business and finance applications, such as fraud detection, risk assessment, customer relationship management, and algorithmic trading. We identified relevant publications through comprehensive searches in leading academic databases, including Scopus, Web of Science, and Google Scholar. We also discuss the challenges associated with implementing ML models in these domains, including data quality issues, model interpretability, and ethical considerations. Byunderstanding the applications, challenges, and opportunities associated with AI, businesses, policymakers, researchers, and investors can make informed decisions and leverage AI to drive innovation and growth in the business and finance sectors.