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Adaptive consumer psychology navigating trust and scepticism in automated retail experiences
The rapid deployment of automated retail technologies has brought both opportunities and challenges for consumer acceptance. Trust is an important factor for benefits determination of the automation systems. Sometimes Skepticism often acts as a barrier to adoption. This chapter enlightens into the psychology of consumer trust, perceived security, reliability, and transparency in automated systems. The research identifies key psychological triggers lead to skepticism in such a way that the data misuse or system failures and solution mitigation. The chapter highlights the role of adaptive design in consumer hesitations and positive interactions. Using empirical evidence and practical approach emphasizes technological efficiency balance with emotional reassurance and consumer comfort in automated environments. 2026, IGI Global Scientific Publishing. -
Synthetic Biology Tools for Genome and Transcriptome Engineering of Solventogenic Clostridium
Strains of Clostridium genus are used for production of various value-added products including fuels and chemicals. Development of any commercially viable production process requires a combination of both strain and fermentation process development strategies. The strain development in Clostridium sp. could be achieved by random mutagenesis, and targeted gene alteration methods. However, strain improvement in Clostridium sp. by targeted gene alteration method was challenging due to the lack of efficient tools for genome and transcriptome engineering in this organism. Recently, various synthetic biology tools have been developed to facilitate the strain engineering of solventogenic Clostridium. In this review, we consolidated the recent advancements in toolbox development for genome and transcriptome engineering in solventogenic Clostridium. Here we reviewed the genome-engineering tools employing mobile group II intron, pyrE alleles exchange, and CRISPR/Cas9 with their application for strain development of Clostridium sp. Next, transcriptome engineering tools such as untranslated region (UTR) engineering and synthetic sRNA techniques were also discussed in context of Clostridium strain engineering. Application of any of these discussed techniques will facilitate the metabolic engineering of clostridia for development of improved strains with respect to requisite functional attributes. This might lead to the development of an economically viable butanol production process with improved titer, yield and productivity. Copyright 2020 Kwon, Paari, Malaviya and Jang. -
Hybrid bimetallic sulfide (FeCoS)-doped conductive polymer as efficient oxygen evolution reaction electrocatalyst for direct seawater electrolysis
Seawater electrolysis is critical for sustainable hydrogen production, especially in regions facing freshwater scarcity. However, chloride ions compete through parasitic reactions, such as the chlorine evolution reaction, creating a serious challenge that reduces catalytic activity and durability. Herein, a hybrid electrocatalyst composed of FeCoS embedded in a polyaniline matrix (FCS-PANI) is synthesized using a simple hydrothermal method. This fabricated composite combines the benefits of the high catalytic activity of FeCoS and the corrosion resistance of the conductive polymer (PANI). Structural analysis establishes the formation of a uniform nanocomposite with strong metalsulfur and metalnitrogen interactions. Advanced oxygen evolution reaction (OER) performance with a low overpotential of 327?mV at 30?mA?cm?2 and a Tafel slope of 38.67?mV dec?1 is achieved through electrochemical testing in alkaline seawater. High stability, low degradation (0.3?mV?h?1) over 500?h of operation, and 99.97% hydrogen purity are observed upon integration into an anion exchange membrane water electrolyzer (AEMWE), indicating its practical potential for seawater electrolysis. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Executives Perception about Project Management Practices in BEML Bangalore
International Journal of Research in Commerce, IT & Management Vol.2, No. 7, pp 69-74, ISSN No. 2231-5756 -
Relationship between Emotional Intelligence and Academic Achievement among College Students
Indian Journal of Applied Psychology Vol. 50, pp. 78-81, ISSN No. 0019-5073 -
Chlorella vulgaris-mediated sustainable biogenic synthesis of silver nanoparticles for wastewater remediation and antibacterial applications
This investigation examines the green synthesis of silver nanoparticles (SNPs) using Chlorella vulgaris as a reducing and stabilizing agent. Algae-mediated SNPs (ASNPs) were tested for the potential application in sewage water remediation and as an antibacterial agent. Biogenic ASNPs demonstrated excellent stability and a surface plasmon resonance (SPR) peak at 440 nm. Energy dispersive X-ray (EDAX) spectroscopy analysis and Fourier transform infrared (FTIR) spectroscopy investigation indicated the role of biomolecules originating from the algal extract, which play a crucial role in the green synthesis process of ASNPs. Dynamic light scattering analysis yielded a hydrodynamic mean particle size of 200 nm and a zeta potential of around 18 mV. Observation under electron microscopy presented the morphological diversity with a prominent signature of elemental silver in ASNPs. A domestic waste sewage sample collected from a sewage treatment plant presented elevated levels of alkalinity, salinity, and biological oxygen demand (BOD). ASNP treatment normalises most of the water parameters, while algal extract alone could produce minimal effects. The antibacterial evaluations against Staphylococcus aureus and Escherichia coli, well-known opportunistic pathogens responsible for a wide range of hospital and community-acquired infections, showed dose-dependent effects. These findings highlight the dual functional role of C. vulgaris-mediated SNPs as an effective, eco-friendly solution for both wastewater remediation and antibacterial application. 2025 Elsevier Ltd -
The birth of Be star disks: I. from localized ejection to circularization
Context. Classical Be stars are well known to eject mass to build up a disk, but the details governing the initial distribution and subsequent evolution of this matter into a disk are in general poorly constrained through observations. Aims. By combining high-cadence time-series spectroscopy with contemporaneous space photometry from the Transiting Exoplanet Survey Satellite (TESS), we have sampled about 30 mass ejection events in 13 Be stars. Our goal is to constrain the geometrical and kinematic properties of the ejecta as early as possible, facilitating the investigation into the material's initial conditions and evolution, and understanding its interactions with preexisting material. Methods. The photometric variability is analyzed together with measurements of the at-times rapidly changing emission features in order to identify the onset of outburst events and obtain information about the geometry of the ejecta and how it changes over time. Short-lived line asymmetries display oscillation cycles (tefl frequencies), which are compared to photometric and stable spectroscopic frequencies. Results. All Be stars observed with sufficiently high cadence during an outburst are found to exhibit rapid oscillations of line asymmetry with a single frequency in the days following the start of the event. For a given star this circumstellar frequency may differ only slightly from event to event even when the outbursts they are associated with have different properties. These circumstellar frequencies are typically between 0.5 to 2 d- 1, and are generally near photometric frequencies. They are slightly below prominent (generally stable) spectroscopic frequencies seen in photospheric absorption lines. The emission asymmetry cycles break down after roughly 5- 10 cycles, with the emission line profile converging toward approximate symmetry shortly thereafter. In photometry, several frequencies typically emerge at relatively high amplitude at some point during the mass ejection process. Conclusions. In all observed cases, freshly ejected material was initially constrained within a narrow azimuthal range, indicating it was launched from a localized region on the stellar surface. The material orbits the star with a frequency consistent with the near-surface Keplerian orbital frequency. This material circularizes into a disk configuration after several orbital timescales. This is true whether or not there was a preexisting disk at the time of the observed outburst. We find no evidence for precursor phases prior to the ejection of mass in our sample. The several photometric frequencies that emerge during outburst are at least partially stellar in origin. The Authors 2025. -
Novel Hybrid Machine-Learning Algorithms for Resource Optimization in Cloud
The resource optimization process in the cloud is crucial and can be achieved through the ideal Load Balancing (LB) mechanism. The cloud undergoes several challenges with resource optimization due to poor LB mechanism, where its Virtual Machines (VMs) are either overloaded or idle. The main aim of this experimental-based research is to enhance the LB mechanism of the cloud by implementing and comparing the performance of novel hybrid LB algorithms RLFCFS and RLSJF to optimize the resources. The RLFCFS and RLSJF novel LB algorithms are designed by combining the Reinforcement Learning (RL) technique with the heuristic FCFS and SJF algorithms. The proposed algorithms improve resource optimization in terms of cost and time by facilitating enhanced LB mechanism through RL intelligence mechanism. The performance of RLFCFS and RLSJF LB algorithms is compared with respect to the average (avg.) load managed by the VMs and the avg. percentage (perc.) of deviation observed against the expected load in each experimental stage. The experimental throughput conveys that the RLFCFS LB algorithm managed an aggregate avg. load of 968.77 tasks against the RLSJF LB algorithm, which managed 999.08 tasks aggregately across all experimental stages. Concerning the avg. perc. of deviation, the RLFCFS LB algorithm deviated by 63.44% against the ideal expected load to manage against the RLSJF LB algorithm, which deviated by 64.60%. This shows that the RLFCFS LB algorithm gave better resource optimization results than the RLSJF LB algorithm. Lastly, these results are mathematically validated using the Simple Linear Regression model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
EM-ACO-ARM: An Enhanced Multiple Ant Colony Optimization Algorithm for Adaptive Resource Management in Cloud Environment
Ant Colony Optimization (ACO) is an intelligent algorithm ensuring optimal resource management in cloud environments. This paper proposes an enhanced version of the ACO algorithm called Enhanced Multiple Ant Colony Optimization for Adaptive Resource Management (EM-ACO-ARM). Our approach uses multiple ant colonies undergoing several iterations of optimizations to find the optimal Virtual Machine (VM) and adapt to the convergence uncertain-ties, unlike a single ant colony in the existing ACO, which can hinder Quality of Service (QoS)-based performance parameters. We conducted experiments in a cloud-simulated environment to evaluate EM-ACO-ARM in two phases. In the first phase, we computed real-time Montage tasks using the existing ACO algorithm on VMs across ten scenarios. To ensure an unbiased comparison, the same cloud configuration was maintained in the second phase, and the same tasks were computed using the proposed EM-ACO-ARM algorithm in all ten scenarios. The experimental results demonstrate that EM-ACO-ARM improves Execution Cost and Execution Time, leading to a 14.73% increase in Resource Utilization. This ultimately improves the management of cloud resources. Additionally, a stability evaluation was conducted using regression models, and it outputted EM-ACO-ARM to provide more stability than the existing ACO algorithm. The cloud can provide better QoS with the proposed EM-ACO-ARM algorithm while abiding by Service Level Agreements. 2025 The Authors. Published by Elsevier B.V. -
Hunter Prey Optimization for Optimal Allocation of Photovoltaic Units in Radial Distribution System for Real Power Loss and Voltage Stability Optimization
Renewable Energy (RE) based Distribution Generation (DG), is a widely accepted eco-friendly alternative to conventional energy production. On the basic note, a DG is used to provide a part of or all of a customers real power demand and/or as a standby supply, and of all various existing types of DG technologies, Photovoltaic (PV) type distribution generation is considered for the study. The location of distributed generation is defined as the installation and operation of electric power generation modules connected directly to the distribution network or the network on the customer side of the meter, hence signifying the optimal location and size of the DGs used. This paper proposes a new algorithm of Hunter-Prey Optimization (HPO) to determine the optimal allocation of PV integration in the radial distribution systems (RDS). HPO is a new population-based algorithm inspired by the hunting behavior of a carnivore. The optimal sizing and siting of the PVs are determined by the proposed algorithm of HPO and are tested in MATLABR2021b on standard IEEE-33 and 69 test bus systems. On the basic of comparative study with literature, HPO is performed efficiently for solving multi-variable complex optimization problem. Also, the performance of RDSs is significantly improved with optimal PV allocations. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Dynamic Performance Analysis of D-STATCOMs for Mitigating Charging Station Impacts in Photovoltaic Distribution Systems Using Enhanced HunterPrey Optimization
This study investigates the dynamic operational performance and optimal allocation of Distribution Static Compensators (D-STATCOMs) in PVEV integrated distribution systems to mitigate the impacts of voltage fluctuations, increased power losses, and reactive power imbalances. An Enhanced HunterPrey Optimization (EHPO) algorithm is proposed in the study, incorporating chaotic initialization, adaptive parameter control, and Cauchy's mutation exploration strategy to improve global search capability and convergence reliability. The proposed method is validated on the IEEE 33, 69, and 118-bus distribution test systems under varying PV generation and EV charging demand scenarios. Results show that the EHPO-based D-STATCOM placement significantly reduces active power losses and enhances voltage stability. The findings highlight the effectiveness of combining advanced metaheuristic optimization with custom power devices to ensure the resilient, reliable, and sustainable operation of future EVPVdominated distribution networks. 2026, TUBITAK. All rights reserved. -
Performance Analysis of Distribution Network Under EV Penetration with Different Types of Load Models
Electric Vehicles (EVs), the booming technology in the transport sector, are the eco-friendly solution for existing environmental issues like global warming. In this scenario, analyzing the variation in power flow and voltage profile at certain nodal junctions in a distribution system when an Electric Vehicle has been connected as a load is considered in this paper. The voltage stability analysis on a distribution system with an Electric Vehicle as a load and the other types of loads has been studied. The results provide that the parameters of total active power loss, total reactive power, minimum Voltage at the node, and the voltage stability index vary indefinitely. The load flow analysis is performed using the Backward/Forward Sweep (BFS) method on the IEEE-85 test system taking various loads such as constant power, residential, commercial, industrial, and composite types with Electric Vehicles load. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Beyond apologies: A systematic review of forgiveness in different relationship contexts
Forgiveness is a vital psychological process that contributes significantly to an individuals emotional well-being, fostering personal growth, reducing stress, and alleviating negative emotions. Beyond the individual, forgiveness also positively influences communities by promoting harmony, reducing conflict, and strengthening social bonds, thereby enhancing collective mental health and relational stability. This paper aims to examine how forgiveness has been explored within various interpersonal contexts, including marriage, peer interactions, familial bonds, workplace relationships, and among dating couples. A comprehensive search across ten databases yielded 37 articles that met the inclusion criteria. These studies utilized four primary methodologies: quantitative approaches (surveys, interventions, experiments), qualitative approaches (case studies and interviews), longitudinal designs and mixed method designs. Findings indicate that fostering forgiveness significantly enhances relational health and interpersonal dynamics, alongside improvements in physical, mental and spiritual well-being. The systematic review highlights how forgiveness processes and interventions vary by relationship context, shaped by unique relational dynamics and cultural factors. However, more experimental and intervention based research is necessary to establish evidence based forgiveness practices, tailored to specific relationship dynamics. The promotion and study of forgiveness should be furthered to reinforce its multifaceted benefits across diverse interpersonal relationships. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
A Pre-trained YOLO-v5 model and an Image Subtraction Approach for Printed Circuit Board Defect Detection
Almost every electronic product used regularly contains printed circuit boards, which in addition to being used for business purposes are also used for security applications. Manual visual inspection of anomalies and faults in circuit boards during manufacture and usage is extremely challenging. Due to a shortage of training data and the uncertainty of new abnormalities, identifying undiscovered flaws continues to be complicated. The YOLO-v5 technique on a customized PCB dataset is used in the study to incorporate computer vision to detect six potential PCB defects. The algorithm is designed to be feasible, deliver precise findings, and operate at a considerable pace to be effective. A technique of image subtraction is also implemented to detect flaws in printed circuit boards. The structural similarity index, a perception-based method, gauges how similar non-defective and defective PCB images are to one another. 2023 IEEE. -
Extraction of Web News from Web Pages Using a Ternary Tree Approach
The spread of information available in the World Wide Web, it appears that the pursuit of quality data is effortless and simple but it has been a significant matter of concern. Various extractors, wrappers systems with advanced techniques have been studied that retrieves the desired data from a collection of web pages. In this paper we propose a method for extracting the news content from multiple news web sites considering the occurrence of similar pattern in their representation such as date, place and the content of the news that overcomes the cost and space constraint observed in previous studies which work on single web document at a time. The method is an unsupervised web extraction technique which builds a pattern representing the structure of the pages using the extraction rules learned from the web pages by creating a ternary tree which expands when a series of common tags are found in the web pages. The pattern can then be used to extract news from other new web pages. The analysis and the results on real time web sites validate the effectiveness of our approach. 2015 IEEE. -
Engineering applications of artificial intelligence
Artificial intelligence (AI) has evolved rapidly over the past few decades, permeating various aspects of our lives and transforming industries. This chapter explores the emerging applications of AI across diverse fields, including healthcare, finance, transportation, education, and entertainment. In healthcare, AI is revolutionizing diagnostics, drug discovery, personalized medicine, and patient care. In finance, AI-powered algorithms are enhancing trading strategies, risk assessment, fraud detection, and customer service. The transportation sector is witnessing advancements in autonomous vehicles, traffic management, and logistics optimization through AI technologies. AI is also reshaping education with adaptive learning platforms, personalized tutoring, and educational analytics. Moreover, in the entertainment industry, AI is driving content creation, recommendation systems, and virtual experiences. Despite the remarkable progress, challenges such as ethical concerns, bias mitigation, data privacy, and regulatory frameworks need to be addressed for the responsible deployment of AI. 2024, IGI Global. All rights reserved. -
In Vitro Production of Bioactive Compounds from Plant Cell Culture
Secondary metabolites (SMs) are bioactive compounds widely used in various industries as pharmaceutical agents and food additives and serve as precursor substances for the synthesis of commercially important products. These natural bioactive metabolites are quickly replacing chemicals as efficient coloring, flavoring, texturizing, and preservative agents. Productions of these SMs are hampered due to physiological and technological parameters. Although SMs do not have any significant role in the growth and development of the organisms where they are found, they have commercial importance. Humankind has harnessed its application in every walk of their life. In the medical field, SMs are used as antibiotics, antifungal, antiviral, metabolic inhibitors, anticancer agents, and many more. The biological and pharmacological benefits of medicinal plants are attributed to SM produced by subsidiary pathways that are highly specific to target molecules. Most pharmaceuticals are either directly or indirectly derived from plant sources. Production of SMs from field plants suffers from various limitations like seasonal production, choosing specific plant organs for specific metabolites, low yield, cost of purification, and seasonal variations. Biotechnological approaches such as plant cell, tissue, and organ cultures are the best alternative methods for commercial production. The current chapter focuses on establishment of plant cell culture system for the production of SMs, strategies to improve biomass yield and metabolite content, and biosynthetic pathways. The chapter also emphasizes elicitation strategies, application of CRISPR Cas9 in metabolite synthesis, large-scale production, and commercial aspects of SMs. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
In Vitro Production of Tocopherols
Tocopherols are an essential dietary nutrient for mammals and photosynthetic products produced by green plants. Tocopherols commonly referred to as vitamin E exist in four forms (?-, ?-, ?-, and ?-tocopherol). Synthetic ?-tocopherol is a mixture of eight racemic forms and is less effective than natural tocopherol, thus the demand for plant-derived tocopherols is high. Tocopherols are lipophilic antioxidant and extensively used as therapeutic agents such as anti-inflammatory, anti-infection, anticancer, immune-stimulant, and nephro-protectant. They are also used as food additives and nutraceuticals. Plant cell and tissue culture is one of the promising techniques for mass production of tocopherols to meet the commercial demand. Optimizing physical and chemical factors for in vitro culture system has resulted in better accumulation of the product. Moreover, using bioreactors, precursor feeding, elicitation, biotransformation, and metabolic engineering approaches have resulted in enhanced yield of tocopherols from in vitro cultures. The present chapter deals with various important aspects of tocopherol in vitro production such as biosynthesis of tocopherol with special emphasis on key enzymes involved in the pathway whose modulation in expression can increase the yield of the product. Topics discussed include production of tocopherol from callus, cell and organ culture, metabolic engineering for mass production, different methods employed for extraction and quantification of tocopherols, and their biological activities and commercial applications. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022. -
Bio-Decolorization and Degradation of Reactive Blue 222 by a Novel Isolate Kucoria marina CU2005
In this study, a novel bacterial strain, Kucoria marina CU2005, was isolated and identified using 16S rRNA gene sequencing from an industrial wastewater sludge sample capable of degrading Reactive Blue 222 (RB222) dye. Batch mode bio stimulation studies were performed with minimal salt media to optimize key physiological parameters for effective decolorization of RB222. When cultured at 35 C and pH 7 under static conditions, this bacterium decolorized 82 percent of the dye after 24 hours. Decolorization was monitored using UV-vis spectrophotometry. Isolates ability to decolorize the complex dye was attributed to its degradation potential rather than a passive surface adsorption. FTIR, HPLC, GC-MS studies were used to confirm microbial dye metabolism. The results indicated breakdown of dye upon decolorization as some peaks were shifted and generation of aromatic amine for monosubstituted benzene ring as intermediates of dye degradation in decolorized solutions. This study has shown the potential of Kucoria marina CU2005 to decolorize RB222 dye at a better pace and efficiency than previously reported bacterial strains. Thus, we propose that our isolated strain can be utilized as a potential dye decolorizer in environmental biotechnology as effluent treatment for decolorization of RB 222. 2023, Association of Biotechnology and Pharmacy. All rights reserved. -
Sustainable biodegradation of textile dye reactive blue 222 by the novel strain Enterobacter CU2004, isolated from the industrial waste: A design of experiment based optimization study and characterisation of metabolites
Reactive Blue 222 (RB222) is widely used in textile industries and hence a common recalcitrant pollutant in the industrial effluent. Bioremediation of this dye is of significance as its one of the complex dyes with high molecular weight. In the present study, we isolated a novel bacterial strain Enterobacter CU2004 from the industrial waste and characterize using16S rRNA gene sequencing. Its potential to dye degradation was evaluated in a simple minimal salt media with the parameters namely dye concentration (1001000 ppm), pH (49), temperature (1555C), Carbon source (Lactose, Sucrose, Glucose, Starch, and Fructose), and Nitrogen source (Casein, Yeast extract, Peptone, Tryptone, Ammonium sulphate, and Urea) in a 24 h culture. Finally, data obtained were extended to design of experiment based optimization for the degradation efficacy of Enterobacter CU2004 and to validated design space was established. The novelty is in optimizing the design space parameters for highest percentage of degradation ?90% by the bacterial isolate Enterobacter CU2004 were finalized as 3037C temperature, 133249 ppm dye concentration, Lactose as Carbon source, Yeast extract as Nitrogen source, and the pH as 8. Microbial dye degradation was confirmed by FTIR, HPLC and GCMS studies. Further studies revealed the dye intermediates and the potential of Enterobacter CU2004 toward the degradation of complex, high molecular weight industrial dye RB222. 2024 Vasantha Veerappa Lakshmaiah, et al.