Purpose – This paper seeks to understand how users know when to stop searching for more information when the information space is so saturated that there is no certainty that the relevant information has been identified. Design/methodology/approach – Faculty, undergraduate and graduate students participated in focus group interviews to investigate what leads them to satisfice their information needs. Findings – Academic library users describe both qualitative and quantitative criteria, which lead them to make rational choices determining when “enough” information satisfices their need. The situational context of both the participants' specific information need and their role in academic society affects every stage of their search – from the selection of the first resource, to ongoing search strategies, to decisions on how much information is enough. Originality/value – These findings broaden the scope of earlier user research, which tends to focus on the more static views of habitual information-seeking and -searching behavior, by applying theoretical frameworks for a richer understanding of the users' experiences.

Almost 85% of companies polled said they were looking into anomaly detection (AD) technologies for their industrial image anomalies. The present problem concerns detecting anomalies often occupied by redundant data. It can be either in images or in videos. Finding a correct pattern is a challenging task. AD is crucial for various applications, including network security, fraud detection, predictive maintenance, fault diagnosis, and industrial and healthcare monitoring. Many researchers have proposed numerous methods and worked in the area of AD. Multiple anomalies and considerable intraclass variation make industrial datasets tough. Further, research is needed to create robust, efficient techniques that generalize datasets and detect anomalies in complex industrial images. The outcome of this study focuses on various AD methods from 2019 to 2023. These techniques are categorized further into machine learning (ML), deep learning (DL), and federated learning (FL). It explores AD approaches, datasets, technologies, complexities, and obstacles, emphasizing the requirement for effective detection across domains. It explores the results achieved in various ML, DL, and FL AD methods, which helps researchers explore these techniques further. Future research directions include improving model performance, leveraging multiple validation techniques, optimizing resource utilization, generating high‐quality datasets, and focusing on real‐world applications. The paper addresses the changing environment of AD methods and emphasizes the importance of continuing research and innovation. Each ML and DL AD model has strengths and shortcomings, concentrating on accuracy and performance while applying quality parameters for evaluation. FL provides a collaborative way to improve AD using distributed data sources and data privacy.

Data visualization is a key component to business and data analytics and it is common to think of statistical graphics and data visualization as relatively modern developments in statistics. The most fundamental step in data visualization is formulating a research question, gathering data, cleaning the data, selecting a chart type and tool, preparing data, and finally creating a chart. In today’s data-driven corporate environment, data visualization is critical, and it has been widely employed to aid decision-making that is closely tied to the key revenues of many industrial businesses. However, owing to the rising need for data processing in terms of volume, velocity, and validity of data, database specialists are increasingly needed to assist with effective and accurate data processing. In response to the increasing need, this study examines different data visualisation phases; its types and numerous applications. Data visualization identifies descriptions, creates hypotheses, and develops ideas from large volumes contributing to scientific research and prediction.

Machine learning and deep learning are one of the most sought-after areas in computer science which are finding tremendous applications ranging from elementary education to genetic and space engineering. The applications of machine learning techniques for the development of smart cities have already been started; however, still in their infancy stage. A major challenge for Smart City developments is effective waste management by following proper planning and implementation for linking different regions such as residential buildings, hotels, industrial and commercial establishments, the transport sector, healthcare institutes, tourism spots, public places, and several others. Smart City experts perform an important role for evaluation and formulation of an efficient waste management scheme which can be easily integrated with the overall development plan for the complete city. In this work, we have offered an automated classification model for urban waste into multiple categories using Convolutional Neural Networks. We have represented the model which is being implemented using Fine Tuning of Pretrained Neural Network Model with new datasets for litter classification. With the help of this model, software, and hardware both can be developed using low-cost resources and can be deployed at a large scale as it is the issue associated with healthy living provisions across cities. The main significant aspects for the development of such models are to use pre-trained models and to utilize transfer learning for fine-tuning a pre-trained model for a specific task.

The bacterial isolate KC1 was screened from the rhizosphere of castor plants (Ricinus communis) indigenous to agricultural fields of Bihar. The isolate was Gram negative, non-spore forming, and exhibited fluorescence under UV light. Its molecular characterization is based on the sequencing of 16S rDNA (1450bp) and alignment at GeneBank (NCBI, MaryLand). The strain has been validated as Pseudomonas aeruginosa (HM195190). The bacterium grew at 4–42°C, with a temperature optima of 30°C. The strain KC1 was found to produce cyanide (4.78nmoll−1) over a period of 36h. Data revealed enhanced cyanogenesis (6.98nmoll−1), when glycine was provided in the King’s B medium. Seed bacterization exhibited reduction in root length, shoot length of weed seedlings (Amaranthus spinosus, Portulaca oleracea), which was significant (p<0.05) in both laboratory and glasshouse experiments. Biomass was significantly reduced (p<0.05) for the weed seedlings in glasshouse experiments. However, KC1 inoculated crop seedlings (Triticum aestivum) were found to be less inhibitory as compared to weed seedlings. The observations are significant to establish, that the secondary metabolites producing KC1 rhizobacterium, P. aeruginosa KC1 could be exploited as a weed biocontrol agent. The innate potential of KC1 could be further formulated and utilized in field applications for agricultural sustainability

The Internet of Things (IoT) is the present and future of every field, influencing everyone’s lives by making everything smart. Almost every industry, including agriculture and healthcare, has been rebuilt as a result of the quick rise of Internet-of-Things-based technologies. Agriculture’s significant developments with the deployment of IoT are changing the landscape of traditional farming methods by making them not only more productive but also more financially viable and sustainable for farmers.Health is the most pressing concern for the bulk of the population, irrespective of age. IoT has shown potential in connecting a variety of medical devices, sensors, and healthcare experts in order to provide high-quality medical treatment. The flow of this current study gives a complete collection of information on the IoT environment,including most commonly utilized IoT sensors. The significance of integrating IoT technologies into the agriculture and healthcare domains has also been presented herein in the tabular form. Incorporating Agriculture- IoT and Health-IoT systems into a real- time scenario can provide ample scope for research in predicting, processing, and analyzing circumstances, as well as boosting timely actions.