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This study evaluates the effect of steam-assisted pretreatment of Bougainvillea biomass on bioelectricity generation and wastewater treatment in microbial fuel cells (MFCs) utilizing reverse osmosis (RO) reject water as the electrolyte. Bougainvillea, an underutilized lignocellulosic ornamental plant waste, is naturally resistant to microbial degradation due to its high lignin content. To enhance its biodegradability, the biomass was steam-treated at 121 °C and 15 psi for 30 min, resulting in increased porosity and improved microbial accessibility. Both untreated and pretreated biomass were tested as substrates in dual-chamber MFCs inoculated with a pure Pseudomonas aeruginosa (P. aeruginosa), with RO reject water (total dissolved solids (TDS)—655 ± 23 mg/L) serving as the catholyte. Electrochemical characterization through cyclic voltammetry and impedance spectroscopy revealed enhanced redox activity and significantly reduced internal resistance in the system fed with steam-treated biomass. This setup achieved a peak power density of 275 mW/m2 and reduced TDS to ~ 200 mg/L within five days. Fourier Transform Infrared Spectroscopy (FTIR) and microscopy analyses confirmed structural degradation of the lignocellulosic matrix. Moreover, the release of reducing sugars peaked at 215 mg/g, indicating enhanced substrate bioavailability. These findings demonstrate that steam pretreatment is an effective, low-cost strategy to improve both energy recovery and TDS wastewater remediation in MFCs, promoting a sustainable approach to biomass valorization and environmental management.Graphical abstractSteam treatment makes Bougainvillea waste easier to convert into clean bioenergy.The system generates strong electricity and efficiently reduces salts in wastewater.Provides a simple, low-cost method to turn plant and RO reject waste into resources.

Outbound logistics network (OLN) in the downstream supply chain of a firm plays a dominant role in the success or failure of that firm. This paper proposes the design of a hybrid and flexible OLN in multi objective context. The proposed distribution network for a manufacturing supply chain consists of a set of customer zones (CZs) at known locations with known demands being served by a set of potential manufacturing plants, a set of potential central distribution centers (CDCs), and a set of potential regional distribution centers (RDCs). Three variants of a single product classified based on nature of demand are supplied to CZs through three different distribution channels. The decision variables include number of plants, CDCs, RDCs, and quantities of each variant of product delivered to CZs through a designated distribution channel. The goal is to design the network with multiple objectives so as to minimize the total cost, maximize the unit fill rates, and maximize the resource utilization of the facilities in the network. The problem is formulated as a mixed integer linear programming problem and a multiobjective genetic algorithm (MOGA) called non-dominated sorting genetic algorithm—II (NSGA-II) is employed to solve the resulting NP-hard combinatorial optimization problem. Computational experiments conducted on randomly generated data sets are presented and analyzed showing the effectiveness of the solution algorithm for the proposed network.

Project portfolios are considered “powerful strategic weapons” for implementing corporate strategy. Projects are exposed to different types of risks. Studies on project portfolio optimization have addressed risks either by maximizing the expected net present value or including constraints that place an upper bound on portfolio risk score. However, no study has attempted to minimize the risk of severe low returns by adopting a risk-averse measure. The present study contributes by addressing this research gap and utilizes a risk measure conditional value at risk (CVaR) for decision making. The present paper considers a case study of a dairy firm. It captures financial risk in the form of uncertain project cash inflows and evaluates strategic alignment scores and risk scores for technical, schedule, economic and political, organizational, and statutory clearance risks of projects using an analytical hierarchy process. Further, it formulates three project portfolio selection and scheduling models namely, risk-neutral (max_E), risk-averse (max_CVaR) and combined compromise (max_E_CVaR) models. A comparison of results shows that the max_CVaR model ensures that the lowest return in the worst scenario is maximized to the greatest extent possible, thereby yielding high returns even when the confidence levels are low. The model exploits the diversification approach for risk management and its portfolios contain at least one project from each project category (derivative, platform and breakthrough). The results obtained using max_E_CVaR model can be utilized by decision makers to select and schedule project portfolios according to their risk appetite and acceptable trade-off between risk-averse and risk-neutral objectives.

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.

Myocardial infarction (MI; location and extent of infarction) can be determined by late enhancement cardiac magnetic resonance (CMR) imaging, which requires the injection of a potentially harmful gadolinium-based contrast agent (GBCA). Alternatively, emerging research in the area of myocardial strain has shown potential to identify MI using strain values. This study aims to identify the location of MI by developing an applied algorithmic method of circumferential strain (CS) values, which are derived through a novel hierarchical template matching (HTM) method. HTM-based CS H-spread from end-diastole to end-systole was used to develop an applied method. Grid-tagging magnetic resonance imaging was used to calculate strain values in the left ventricular (LV) myocardium, followed by the 16-segment American Heart Association model. The data set was used with k-fold cross-validation to estimate the percentage reduction of H-spread among infarcted and noninfarcted LV segments. A total of 43 participants (38 MI and 5 healthy) who underwent CMR imaging were retrospectively selected. Infarcted segments detected by using this method were validated by comparison with late enhancement CMR, and the diagnostic performance of the applied algorithmic method was evaluated with a receiver operating characteristic curve test. The H-spread of the CS was reduced in infarcted segments compared with noninfarcted segments of the LV. The reductions were 30% in basal segments, 30% in midventricular segments, and 20% in apical LV segments. The diagnostic accuracy of detection, using the reported method, was represented by area under the curve values, which were 0.85, 0.82, and 0.87 for basal, midventricular, and apical slices, respectively, demonstrating good agreement with the late-gadolinium enhancement-based detections. The proposed applied algorithmic method has the potential to accurately identify the location of infarcted LV segments without the administration of late-gadolinium enhancement. Such an approach adds the potential to safely identify MI, potentially reduce patient scanning time, and extend the utility of CMR in patients who are contraindicated for the use of GBCA.

Whether lignite or black cotton soil, they are so expansive that they pose significant challenges to geotechnical engineers due to their high swelling capacity, poor bearing capacity, and low shear strength. This experiment aims to assess the suitability of six geosynthetic materials—namely, geotextile, geogrid, geocell, geomembrane, geomat, and geocomposite—in improving the engineering properties of black cotton soil. Laboratory tests were performed on both processed and unprocessed soils, including Specific Gravity, Atterberg Limits, Standard Proctor Compaction, California Bearing Ratio (CBR), Unconfined Compressive Strength (UCS), Swelling Pressure, Swell Index, and Direct Shear tests on specimens with varying inclusion rates (0.5 - 3.0%) of geosynthetic products. Results showed substantial improvements across all measured properties. The Plasticity Index decreased from 53.40% to as low as 17% with the use of geogrid, while the maximum dry density (MDD) increased to 23.3 g/cc with geotextile. The CBR value doubled, reaching 11.8% with the use of geocomposite and geomembrane, and the UCS increased to 171 kPa. Swelling pressure and Swell Index decreased significantly, with the lowest values being 3.1 kPa and 3.72, respectively, when using the geocomposite. Additionally, the soil cohesion improved to 43.6 kPa with the use of geotextile, and the friction angle reached 23.5° with the use of geogrid. These findings confirm that geosynthetics, especially geocomposites, geomembranes, and geogrids, are highly effective in enhancing the mechanical behaviour of expansive soils. Incorporating them into subgrade stabilization strategies offers a sustainable and cost-effective solution for improving infrastructure performance in areas with problematic soils.

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results.

Purpose The purpose of this study is to examine the impact of corporate governance (CG) on the shareholding level of retail investors in Indian listed firms. Design/methodology/approach Primarily, a broad CG-index was constructed based on the Indian Companies Act, 2013; Clause 49 listing agreement; and Securities Contracts (Regulation) Act, 1956. Thereafter, a panel data approach has been used to examine the association between CG attributes and retail shareholdings (RSs) during 2014–2015 and 2018–2019. Findings Authors find that the firm-level CG quality positively affects retail investors’ shareholding level. The results explain that among various attributes of CG, retail investors pay more attention to firms’ audit and board information while making investment decisions. The results also reveal that the influence of CG attributes on RSs is lesser for group-affiliated, mature and large-sized firms than for stand-alone, young and small-sized firms. Practical implications First, the study provides new insight to the firms for increasing retail-shareholding levels and complying with India’s ongoing minimum public shareholding norms by improving CG practices concerning specific CG mechanisms. Second, it illuminates the regulators and policymakers to monitor and strengthen firms’ governance quality in light of ongoing regulatory reforms. Originality/value This study is a new investigation that explores the impact of CG on investment decisions of retail investors from the perspective of an emerging economy.

Integrated Urban Water Management (IUWM) has gained worldwide popularity for improving urban water supply systems through a more coordinated, responsive, and holistic approach. While urban water supply systems worldwide face many common challenges, developing countries like India face distinct challenges. These include high dependence on external water sources, interstate water conflicts, intermittent supply, inadequate infrastructures, and financial constraints. Moreover, the performance of the IUWM approach to the challenges varies locally. Insights from cities that have implemented IUWM can provide valuable lessons for improving India’s urban water supply system. This paper’s novelty lies in identifying distinct challenges faced by developing regions and analysing how IUWM can be tailored to address them, highlighting its feasibility, benefits, and implementation hurdles. Firstly, the study presents a content analysis of scientific literature on urban water supply system challenges worldwide, especially in India. Secondly, it examines the potential of IUWM reported worldwide. IUWM typically requires system planning and design, decision-support tools, and effective management. However, no universal solution exists as IUWM focuses on context-specific local-based solutions rather than one-size-fits-all. Despite its potential benefits such as ensuring reliable, robust, and self-sufficient urban water supply systems, implementing IUWM in India presents challenges. These include infrastructure financing, implemented scheme management, cost recovery of innovative designs, coordination between several decision-makers, and social acceptance. Finally, the paper highlights the need for future research on IUWM approach to address challenges specific to Indian urban water supply systems.

This study examined the applicability of two mine sludge wastes, mine tailing sludge (MTS) and acid mine drainage sludge (AMDS) as iron-rich bio-stimulant for enhancing organic matter degradation in anaerobic process. Batch treatment of domestic sewage having 343 ± 10 mg/L chemical oxygen demand (COD) using MTS and AMDS as additives mixed with septic tank sludge as anaerobic inoculum produced lower start-up time, higher efficiency of COD removal, enhanced biomass retention, and higher acidogenic and methanogenic activity after stabilization. Biostimulation induced by mine sludge waste additives in anaerobic system were observed to have correlation with percentage of iron content in the additives, as well as difference in surface charge between biomass and the additives. Treatment efficiency induced by the two mine sludge waste based additives were similar at 90% confidence limit, however, was found to be higher than lower iron containing additive laterite soil, while lower than higher iron containing synthetic zero valent nano iron as additives used for comparison. The study was supported by scanning electron microscope, atomic force microscope and optical microscope images of sludge granule sand surface charge measurement.