Centurion University of Technology and Management

This study presents a hybrid optimization framework combining Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-Objective Ant Colony Optimization (MOACO) to optimize time, cost, quality, and carbon footprint in bridge construction projects. The construction industry faces growing demands for sustainable practices while maintaining efficiency and cost-effectiveness. The proposed framework addresses these challenges by leveraging NSGA-III’s capability to maintain diversity in Pareto-optimal solutions and MOACO’s strength in refining local solutions through pheromone-based exploration. The framework models construction activities with multiple execution modes, each characterized by specific costs, durations, quality levels, and carbon emissions. Four objective functions are developed to minimize time, cost, and carbon footprint while maximizing quality. Constraints such as precedence relationships and resource availability ensure realistic project planning. The framework is validated through a case study of a 300-m reinforced concrete girder bridge, using input data derived from practical project scenarios. Results demonstrate the hybrid framework’s superior performance compared to standalone NSGA-III, MOACO, Multi-Objective Teaching-Learning-Based Optimization, and Multi-Objective Particle Swarm Optimization. Trade-off and correlation analyses provide actionable insights, revealing relationships between objectives and enabling informed decision-making. This study contributes to sustainable project management by integrating advanced optimization techniques and practical decision-support tools.

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research.

In the era of renewable energy integration, precise solar energy modeling in power systems is crucial for optimized generation planning and facilitating sustainable energy transitions. The present research proposes a comprehensive framework for assessing the operational reliability of solar integrated systems, validated using the IEEE RTS 96 test system. A robust uncertainty model has been developed to characterize variations in solar irradiance to address the uncertainties in solar panel output, followed by a multi-state modeling approach to account for the dynamic nature of solar panel output. The research introduces a time series-based ‘non-linear autoregressive neural network’ (NAR-Net) to forecast the solar irradiance levels five days ahead to optimize solar power efficiency. A comparative analysis has been conducted of three other state-of-the-art approaches, such as auto-regressive (AR), auto-regressive with moving average, and multi-layer perceptron, for predicting solar irradiance. Performance metrics, including mean square error, regression, and computational time, were evaluated to demonstrate the efficacy of the NAR-Net. The proposed prediction-based approach enhances the reliability of power generation planning by integrating modeling, which is based on forecasting. It is found that the proposed method achieves an accuracy of 98% w.r.t its counterpart. Moreover, the assessment to optimize the operational reliability of solar-integrated systems and improve generation planning for a sustainable energy future is achieved.

As a matter of fact, although RCA is inferior to that of NA (natural aggregate) in terms of performance, RCA is currently being used extensively in concrete production as a sustainable solution due to global increasing construction waste. To overcome these limitations, several mechanisms and supplementary cementitious materials (SCMs) have been investigated in the recent past. This study investigates the combined effect of natural fiber coconut (0%, 0.5%,1%,1.5% and 2%) and activated fly ash (mechanically and chemically) on performance of recycled concrete aggregate (RCA). Four concrete compositions were studied: a control group with varying RCA percentages and no fly ash, second group with 30% inactive fly ash, third group with 30% mechanically activated fly ash, and a fourth group with 30% chemically activated fly ash. The findings revealed that the addition of 30% chemically activated fly ash with 1.5% CF increased compressive strength by 25%, tensile strength by 17% for 100% RCA mix, While strength losses for the same mix are 7.18% after one month and 22.14% after three months of acid exposure. Scanning electron microscopy further validates the enhanced packing density and effective crack filling in the optimized mixes, highlighting their superior performance. This approach holds significant potential for advancing sustainable development pathways in high-performance structural concrete, particularly in regions prioritizing green building solutions.

Retrofitting projects play a critical role in enhancing the sustainability of existing structures, yet balancing time, cost, and environmental impact remains a significant challenge for decision-makers. This study introduces a Multi-Objective Particle Swarm Optimization (MOPSO) approach to achieve optimal trade-offs among these competing objectives. By leveraging MOPSO’s capability to explore Pareto-efficient solutions, the research provides a robust framework for sustainable decision-making in retrofitting projects. The model evaluates project scenarios based on key metrics such as time efficiency, cost minimization, and reduced carbon emissions. Through the application of MOPSO, the study generates a range of viable solutions, enabling project managers to make informed decisions tailored to specific sustainability goals. A case study is conducted to validate the model’s effectiveness, comparing its performance with conventional optimization techniques. The results demonstrate that MOPSO excels in balancing multiple objectives, delivering superior outcomes in sustainability metrics. This research contributes to the advancement of sustainable construction practices by offering a practical tool for optimizing retrofitting projects in alignment with environmental and economic priorities. The findings provide valuable insights for practitioners seeking to integrate sustainability into decision-making processes, addressing the pressing need for environmentally conscious infrastructure development.

Construction project management often involves optimizing time and cost while ensuring minimal environmental impact. This study presents an innovative hybrid approach combining non-dominated sorting genetic algorithm III (NSGA-III) and multi-objective particle swarm optimization (MOPSO) to address the time-cost-environmental sustainability trade-off (TCEST) in construction projects. The proposed model aims to minimize project completion time and cost while maximizing environmental sustainability. A case study is conducted to validate the model, incorporating diverse construction activities and their respective time, cost, and environmental sustainability metrics. The results reveal Pareto-optimal solutions demonstrating significant trade-offs among the three objectives. The hybrid approach outperforms standalone algorithms in terms of solution diversity, convergence, and hypervolume indicators. Weighted sum methods are employed to select the most suitable solution from the Pareto front based on project priorities. Correlation analysis further explores interdependencies among objectives, emphasizing the feasibility of balancing these critical factors. The study contributes a robust decision-support tool for sustainable project planning, facilitating informed decision-making in modern construction management.

Unconfined Compressive Strength (UCS) is a critical parameter in geotechnical engineering, influencing soil stability, foundation design, and load-bearing capacity. Traditional UCS prediction methods, such as Multiple Linear Regression (MLR), often struggle to capture the non-linear relationships inherent in mixed soil compositions. This study evaluates the effectiveness of Artificial Intelligence (AI)-based models, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), in predicting UCS for clay-dominant and sand-dominant soils. A dataset of 100 soil samples from six geographically diverse regions across India was analyzed, incorporating key soil parameters such as clay content, sand content, liquid limit, plasticity index, and curing period. The models were assessed using R², Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Prediction Interval (PI), and Index of Agreement (IOA). Among the AI models, RFR outperformed others with an R² of 0.93 (training) and 0.84 (testing), a20 accuracy of 95%, and minimal error rates, demonstrating its superiority in UCS prediction. Sensitivity analysis identified clay content (28.7%) and curing period (22.1%) as the most influential factors affecting UCS, reinforcing their geotechnical significance. Regularization techniques such as dropout, batch normalization, and early stopping were implemented to prevent overfitting in ANN, ensuring model generalizability. To enhance interpretability, feature importance analysis and correlation analysis were conducted, allowing insights into how soil parameters influence UCS. The study also discusses potential advancements, including hybrid AI-geotechnical models, ensemble learning approaches, Bayesian optimization for hyperparameter tuning, and expansion of datasets with global soil data. The findings highlight the potential of AI-driven techniques as robust, scalable alternatives to traditional UCS prediction methods, with implications for soil classification, stability assessment, and foundation engineering.

Electrical storage continues to be a vital domain which contributes to more efficient, reliable and sustainable energy systems to meet the ever‐growing demand for useable electrical energy. In recent years, the benefits of excellent mechanical and dielectric performance have led to a significant increase in interest in polymer‐based composites for capacitive energy storage applications. Here, poly[(vinylidene fluoride)‐co‐trifluoroethylene]/0.5(BaZr0.2Ti0.8O)3–0.5(Ba0.7Ca0.3TiO)3/graphene oxide (PVDF‐TrFE/BZT‐BCT/GO) three‐phase composite films were prepared via solvent casting followed by a hot‐pressing method. X‐ray diffraction and Fourier transform infrared and Raman spectroscopies were performed for structural confirmation of the composites. Dielectric study showed a maximum relative permittivity of 19.35 at 1 kHz, which is around 65% of that of pure PVDF‐TrFE film. Static hysteresis loops were traced for all the samples, showing a maximum efficiency of 89.33% for a composite film. Positive up negative down measurements were also done to confirm the data obtained from static hysteresis analysis. Furthermore, the electromechanical coupling coefficient was analyzed using the resonance–antiresonance method, which gives an insight into the electromechanical properties in the synthesized films. The synthesized three‐phase composites can therefore find application for piezoelectric purposes along with capacitive energy storage. © 2025 Society of Chemical Industry.

Soyabean is economically important crop and known for their exceptional nutritional value. Soybean purple seed stain is a prevalent disease that poses a considerable threat to soybean production worldwide. The disease progresses through the development of dark purple to black stains on soybean seeds, leading to substantial reductions in both yield and seed quality. This chapter focuses on the epidemiological aspects of soybean purple seed stain, exploring how the disease spreads within and between soybean fields. Identifying key factors contributing to its dissemination can aid in the development of targeted disease management strategies. Various factors, including weather conditions, agronomic practices, and seed transmission, influence the disease’s epidemiology and severity. It is important to understand the disease’s progression, timely detection, and effective management practices to mitigate losses due to this disease. Disease management strategies such as cultural practices, biological control, varietal resistance, chemical management are discussed. Overall, this chapter provides a comprehensive analysis of the symptomatology, epidemiology, and detection of the disease and briefly describes the conventional and modern trends in disease management practices.

Phytophthora stem and root rot is a catastrophic plant disease, caused due to Phytophthora sojae. The disease manifests through a range of symptoms, including damping-off, rotting, as well as blighting that ultimately lead to plant death. Epidemiological factors, such as environmental conditions and host susceptibility, greatly influence disease development and spread. Understanding the factors contributing to pathogen dispersal and infection can aid in the formulation of efficient management strategies. Phytophthora stem and root rot management approaches primarily encompass on cultural practices, chemical control, and genetic resistance. Crop rotation, proper drainage, and the use of resistant cultivars are essential components of integrated disease management strategies. Genetic resistance presents a promising avenue for sustainable disease management. The identification and deployment of resistant genes through breeding programs can provide durable protection against Phytophthora stem and root rot. This chapter focuses on the symptomatology, epidemiology, and management strategies employed to mitigate its impact.

The present paper deals with the identification of six species of lichenized fungi as new distributional record to India collected from Similipal Biosphere Reserve, Odisha. The species include Anisomeridium consimile, A. truncatum, Graphis litoralis, G. neoelongata, Pyrenula rubrojavanica, and Trypethelium luteolucidum. A brief morpho-taxonomic description of each species is described and illustrated.

Co²⁺ doped La2CuO4 nanoparticles are prepared by combustion method. The prepared samples are analysed such as structural, magnetic, functional and optical characterization. The development of single-phase orthorhombic crystals with a Bmab space group was verified by X-ray diffraction patterns. The monophasic orthorhombic lattice structure of the material has crystallite size that falls between 28 and 41 nm. The lattice constant falls between a = 5.361 Å − 5.349 Å, b = 5.410 Å − 5.399 Å & c = 13.160 Å − 13.148 Å. The XRD of La2CuO4 perovskite nanomaterials correspond well with the calculated ones, with χ² values closer to 1. Energy gap values decreases from 1.75 to 1.68 eV due to the quantum confinement. The hysteresis curves recorded at 300 K show that coercivity ranges from 210.09 to 177.37 Oe as the content decreases, confirming that the crystallite size increase. The samples were with soft ferromagnetic behavior found in the VSM study.

Background The cysteine-rich receptor-like kinases (CRKs) family in plants have been reported to perform multiple functions against various stresses. However, the CRK family in bottle gourd (Lagenaria siceraria) has not been well-explored. Herein, a comprehensive genome-wide identification and characterization of the CRK gene family has been carried out in bottle gourd under Genome-wide characterization of CRK genes in bottle gourds under Fusarium oxysporum f. sp. lagenariae infection. Results A stringent set of bioinformatic analyses identified 18 LsCRKs in the bottle gourd genome. Chromosomal mapping of the identified LsCRKs revealed that the LsCRKs were distributed in 4 chromosomes in the bottle gourd genome. The phylogenetic analysis of LsCRKs divided them into two subgroups on the tree. The synteny and collinearity analysis of the LsCRKs among themselves and other plant CRKs provided insights into their conservancy and expansion. Gene ontology analysis of the identified LsCRKs suggested their possible roles in regulating different physiological processes and stress responses in bottle gourd. To assess the involvement of the LsCRKs under F. oxysporum f. sp. lagenariae infection, bottle gourd seedlings were transplanted into the pots with F. oxysporum-infected soil. The expression analysis revealed that multiple LsCRKs exhibited induced expression, suggesting their involvement in bottle gourd-F. oxysporum interactions. Additionally, the protein-protein interaction analysis suggested some important interacting partners of LsCRKs crucial to different physiological processes in bottle gourd. Conclusions The present work explored and analyzed the LsCRKs in bottle gourd. Functional predictions and interaction network analysis suggested the roles of LsCRKs in modulating multiple physiological processes in bottle gourd. The expression dynamics of LsCKRs under fungal pathogen infection suggest their involvement in stress response in bottle gourds. Overall, the results of the study provide basic information about the CRK family in bottle gourds and their involvement in fungal pathogen response.

The Indoor Positioning System (IPS) based technology involves the positioning system using sensors and actuators, where the Global Positioning System (GPS) lacks. The IPS system can be used in buildings, malls, parking lots and several other application domains. This system can also be useful in the healthcare centre as an assisting medium for medical professionals in the disease of the diagnosis task. This research work includes the development and implementation of an intelligent and automated IPS based model for melanoma disease detection using image sets. A new classification approach called Fused K-nearest neighbor (KNN) is applied in this study. The IPS based Fused-KNN is a fusion of three distinct folds in KNN (3-NN, 5-NN and 7-NN) where the model is developed using input samples from various sensory units while involving image optimization processes such as the image similarity index, image overlapping and image sampling which helps in refining raw melanoma images thereby extracting a combined image from the sensors. The IPS based Fused-KNN model used in the study obtained an accuracy of 97.8%, which is considerably more than the existing classifiers. The error rate is also least with this new model which is introduced. RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) value generated with the proposed IPS base Fused-KNN the model for melanoma detection was as low as 0.2476 and 0.542 respectively. An average mean value computed for accuracy, precision, recall and f-score were found to be 94.45%, 95.2%, 94.4% and 94.9% respectively when validated with 12 different cancer-based datasets. Hence the presented IPS based model can prove to be an efficient and intelligent predictive model for melanoma disease diagnosis, but also other cancer-based diseases in a faster and more reliable manner than existing models.

Among primary brain tumors, glioma has one of the highest fatality rates. Routine chemotherapy often faces off-target drug loss and sub-optimal drug availability at brain tissue. The present study aims at the development of transferrin-conjugated gemcitabine loaded poly (lactic co glycolic acid) nanoparticles (Tf-GB-PLGA-NPs) targeted strategy for brain cancer cell. GB-PLGA-NPs were prepared using solvent evaporation and nanoprecipitation method and then conjugated with Tf. The formulation was characterized for physicochemical parameters, in-vitro release, cytotoxicity, apoptosis (U87MG cell line), and in-vivo pharmacokinetic study. Tf-GB-PLGA-NPs showed 143±6.23 nm of particle size, 0.213 of PDI, –25 mV of zeta potential, and 77.53±1.43% of entrapment efficiency, respectively. Tf-GB-PLGA-NPs exhibited spherical morphology and sustained release of GB (76.54±4.08%) over 24 h. Tf-GB-PLGA-NPs exhibited significant (p < 0.05) cell inhibition against cell line (U87MG) than GB-PLGA-NPs and pure GB. The Tf-GB-PLGA-NPs exhibited higher U87MG apoptosis (61.25%) than GB-PLGA-NPs (31.61%). The Tf-GB-PLGA-NPs exhibited a significantly higher concentration in the brain than pure GB and GB-PLGA-NPs. Tf-GB-PLGA-NPs showed 11.16-fold higher AUC0-t (bioavailability) than pure GB solution and 2.23-fold higher bioavailability than GB-PLGA-NPs. The finding concludes that the Tf-GB-PLGA-NPs are an alternative potent carrier for GB to brain delivery for treating brain cancer. graphical abstract Fullsize Image

Nanotechnology applications in aquaculture involve using nanoparticles, such as zinc oxide nanoparticles (ZnO NPs), in fish feed or other delivery methods to enhance growth and health. However, widespread use of NPs in industries like cosmetics and healthcare leads to their release into aquatic ecosystems, causing risks to aquatic organisms. Likewise, pH is a fundamental environmental parameter that fluctuates in natural water bodies due to multiple factors such as acid rain, pollution, and biological activities. This study focuses on the combined effects of ZnO NPs and pH fluctuations, key environmental factors influenced by acid rain, pollution, and biological activity, on zebrafish (Danio rerio). Five concentrations of ZnO NPs (5, 10, 15, 20, and 25 ppm) were tested alongside a control (0 ppm). Severe gill damage was observed at all concentrations except the control and 5 ppm, with fish mortality starting at 15 ppm. Combined stress from ZnO NPs and pH extremes (pH 5 and pH 9) exacerbated oxidative stress, disrupted cellular homeostasis, and increased mortality, particularly at 10 ppm till 25 ppm concentrations. To mitigate these effects, strategies such as optimizing ZnO NP synthesis, coating NPs with biocompatible materials, reducing reactivity, and incorporating antioxidants or water conditioners are recommended. These approaches aim to reduce toxicity, stabilize water chemistry, and improve fish survivability. The current study aims to provide insights on the affect of combined stress on the survivability of zebrafish, thus a step towards addressing the United Nation’s Sustainable Goal 14 (Life below water).

Background The GATA transcription factors play multifaceted roles in modulating vital physiological processes in plants. However, the GATA transcription factor family in onion (Allium cepa L.) has been explored to a limited extent. In the present study, a genome-wide survey of the GATA family and the subsequent characterization has been carried out in the onion genome. Results In total, 24 A. cepa GATAs (AcGATA1-AcGATA24) have been identified in the onion genome. Chromosomal mapping revealed that all identified genes could be mapped onto different onion chromosomes or scaffolds. The gene duplication, synteny, and collinearity analysis of the AcGATAs suggested their divergence, expansion, and selection in onions. Phylogenetic analysis of the AcGATAs divided them into five groups along with other plant GATAs. Gene ontology and cis-regulatory element analysis results suggested that the AcGATAs could regulate crucial processes, such as growth and development, phytohormone signalling, and stress response. The tissue-specific expression study indicated that the AcGATAs expressed in multiple onion tissues. The expression analysis under subjected chromium and salt stress revealed that multiple AcGATAs get induced in response to the applied stresses. Lastly, the protein interaction network study predicted some key interacting partners of the AcGATAs that can regulate vital physiological processes in onions. Conclusions The present study identified and characterized the GATA gene family in onions. Functional predictions and interaction network analysis suggested the roles of AcGATAs in modulating multiple onion physiological processes. The induced expression of AcGATAs under chromium and salt stress indicated their involvement in abiotic stress response in onions. Overall, the study provides newer insights into the GATA gene family and their possible roles in onions.