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The present study investigates the repaired components of mechanical, microstructural, and grain structures developed in initial layers of stainless steel 316L samples manufactured by directed energy deposition. The microstructural changes happening in the interface region owing to the substrate variation are studied by considering two different substrates, namely powder bed fusion substrate and hot rolled steel substrate. The novelty of this study is to make the repair work by additive manufacturing (directed energy deposition) process, which has to build the structure with different substrates (powder bed fusion and hot-rolled steel). The purpose of this study is to investigate the significance of the beginning layers of the directed energy deposition process. Alternative welding concepts for repair work applications the directed energy deposition process is used to rework or re-join a broken component's metal. Different regions of the directed energy deposition deposited samples are studied through an optical microscope, field emission scanning electron microscope, and electron backscatter diffraction analysis. The very fine crystal grains are found just above the interface region on the powder bed fusion substrate depending on the high cooling rate. The microhardness on the initial layers is based on the substrate cooling rate. Furthermore, microhardness values of 230.7 HV for PBF substrates and 225.2 HV for hot rolled steel substrates have been obtained. Tensile test results show that the TE of PBF-DED is 7% higher than that of HRS-DED samples. The YS value of 74 MPa is also higher. The UTS of PBF-DED was obtained at 582 MPa, while the HRS-DED sample reached 529 MPa. The results of PBF-DED are 53 MPa higher than that of HRS-DED.

There has been a recent spurt in application of platelet-rich plasma (PRP) in dermatology and aesthetic medicine. However, the details regarding use of PRP in various dermatological indications ranging from hair restoration to chronic ulcers are dispersed in literature, herein we have tried to focus all under one heading. Overall, PRP seems to be a promising therapeutic modality but the level of evidence as of now, from the available published data is low. This review will also stimulate readers to carry out well designed, larger population based trials, so as to validate its use in dermatology practice.

Psychodermatology is an interesting domain of dermatology that overlaps with psychiatry. This arena in dermatology has received limited diligence, partly due to lack of training in this realm. We present here a comprehensive review of salient features and treatment updates in primary psychiatric dermatoses and have also discussed the role of psyche in psychophysiological cutaneous disorders. Secondary psychiatric morbidity is relatively common among patients visiting the dermatologists but often overlooked and uncared for. Dermatologist should be able to initiate basic pharmacotherapy, should be knowledgeable about various non-pharmacological treatments and know the right time to refer the patient to the psychiatrist. Awareness and pertinent treatment of psychodermatological disorders among dermatologists will lead to a more holistic treatment approach and better prognosis in this unique group of patients.

Artificial Intelligence (AI) and Machine Learning (ML) have become foundational technologies transforming marketing and service ecosystems through predictive intelligence, automation, personalization, and generative capabilities. Recent significant advancements in deep learning, large language models (LLMs), real-time analytics, and AI governance frameworks have reshaped how organizations create value. This paper presents a systematic Review and Taxonomy of Applications and Artificial Intelligence and Machine Learning in Marketing and Service Ecosystems. The study develops a taxonomy of AI applications across five domains: (1) customer intelligence, (2) decision support systems, (3) operational automation, (4) innovation capability, and (5) financial and strategic performance impact. The review identifies growing adoption of generative AI in marketing content creation, AI-driven service robots in frontline services, predictive analytics in financial services, and AI-enabled customer journey orchestration. Despite measurable performance gains, persistent challenges remain in algorithmic transparency, ethical governance, data quality, workforce transformation, and regulatory compliance. A conceptual framework linking AI capabilities, organizational readiness, adoption intensity, and performance outcomes is proposed. Future research directions include responsible AI governance, SME-focused adoption models, and human–AI collaboration in service ecosystems.

Human Action Recognition (HAR) in still images is a well-established computer vision task with applications in sports, security, and human-computer interaction scenarios. Current trends in HAR research indicate that deep learning applications are proliferating, and a few publication hot spots are leading to systematic fair evaluations of the state of the art in convolutional neural networks (CNNs). This work conducts a systematic evaluation of seven popular CNN families, ResNet, Inception, MobileNet, DenseNet, VGGNet, EfficientNet, and EfficientNetV2, as well as representative instance variants within each family. The models are trained and evaluated on the Stanford-40 Human Action Recognition dataset using a common experimental setup, constrained to a limited training budget (three epochs) to allow comparability across a large number of model evaluations. The models are assessed utilizing a variety of accuracy, precision, recall, and F1 score metrics. The experiment results reveal model performance trends that are related to model family and model depth. Mid-range models, like ResNet-50 and DenseNet variants yield the best trade-offs between performance and resource consumption. Lightweight models perform the worst but justify the sacrifice in performance with training efficiency. The best overall model is EfficientNetV2-L, which achieves the best performance across all evaluated metrics. It achieves this performance through training-aware model design, improved compound model scaling, and fused MB-Conv blocks as well as greater input resolution, all of which enable effective learning even under the study’s low training budget. In contrast with previous studies that advocate for highly hybridized specialized models, this study provides a framework for systematic evaluation of CNN families under the same training budget. Overall, this study provides realistic HAR baselines for images and informs the reader of relative model selection strategies and trade-offs that may be employed in budget-limited training contexts.

The present work aims to adopt the directed energy deposition (DED) technique for repairing the hot rolled steel (HRS) by depositing stainless steel 316L (SS316L) material over the hot rolled steel. The mechanical and microstructural characteristics of the as-built DED sample, HRS sample, and repaired samples are evaluated in the present work. The mechanical characteristics such as microhardness and tensile strength of the repaired samples are studied. A succinct comparison is made between the HRS samples and the as-built DED samples. The ultimate tensile strength of the repaired sample (hybrid sample), HRS sample, and the as-built DED sample is found to be 541, 590, and 598 Mpa, respectively. The DED sample has an average microhardness of 228HV, while the HRS sample has an average microhardness of 215HV, and the average microhardness of the interface region is 236HV. This work clarified the difference in microstructural characteristics of the interface region, as-built DED samples, and HRS samples. The samples repaired by DED process possess required metallurgical bonding, and hence, DED process can be used to repair HRS components.

Electronic health records (EHR) have revolutionized healthcare by providing efficient access to patient information, but their implementation poses various challenges. This paper examines the ethical and legal issues surrounding EHR adoption, particularly focusing on the healthcare landscape in India. Ethical considerations, including patient autonomy, confidentiality, beneficence, and justice, must guide EHR implementation to protect patient rights and privacy. Legal issues such as medical errors, malpractice, data breaches, and billing inaccuracies underscore the importance of robust policies and security measures. Threats to EHRs, such as phishing attacks, malware, encryption vulnerabilities, and insider threats, emphasize the need for comprehensive cybersecurity strategies. Overcoming challenges in EHR implementation requires meticulous planning, financial investment, staff training, and stakeholder support. Despite the complexities involved, the benefits of EHR adoption in improving patient care and operational efficiency justify the efforts required to address legal, ethical, and technical concerns. Embracing EHRs while mitigating associated risks is essential for delivering high-quality healthcare in the digital age.

In general, numerous inbuilt diagnosis complications are due to improper or missing data. Thus, it becomes mandatory to perform proper imputation of the missed values to predict the diseases accurately. Imputation operations will be crucial when we encounter incompletely recorded patient data. The measurement of blood glucose level is considered to be the most important health-conscious effort that one does periodically since the false diagnosis of it leads to misinterpretation of patient health conditions that might cause fatal outcomes. But predicting those measures has become a tedious task in the course of diabetic treatment of these days. This paper focuses on the aim of the imputation of the missing patient-specific diabetic data, especially to overcome the existing methods’ demerits of yielding lesser accuracy and more time. This work attempts to predict the blood glucose levels by analyzing time-series data along with the patient activities. The patient activities are being thoroughly investigated here in this work; for instance, with the first 20-day diabetic data of a patient, the diabetic forecast for the next 10 days is made in the considered month. This prediction of patient diabetic conditions is done by proposing a novel approach for predicting the blood glucose levels with the aid of Maclaurin series-based expectation maximization, estimation of correlation relationship and dissimilarities, kernel-based Hilbert–Schmidt optimization, optimized features, and classification using the deep learning methodology of RNN—recurrent neural network. Finally, we make the performance analysis with the performance metrics like accuracy, Kappa, TN, TP, FN, FP, precision, recall, Jaccard coefficient, F1-measure, and error.

Mobile Ad Hoc Network (MANET) is a group of node that would interrelate among each other through one multi-hop wireless link, wherein the nodes were able to move in response to sudden modifications. The objective of MANET routing protocol is to quantify the route and compute the best path, but there exists a major decrease in energy efficiency, difficulty in hop selection, cost estimation, and efficient load-balancing. In this paper, a novel least common multipath-based routing has been proposed. Multipath routing is used to find a multipath route from source and destination. Load balancing is of primary importance in the mobile ad-hoc networks, due to limited bandwidth among the nodes and the initiator of the load routing discovery phase in the multipath routing protocol. Fuzzy logic for load balancing multipath routing in MANETs is proposed, which ensures the data packets are sent through a path with the variance of binary sets to predict the original transformation of the data to be received in the system. The main objective of the proposed system is to reduce the routing time of data packets and avoid the traffic based on multipath source and destination. The experimental results have to verify 96.7% efficiency in balancing the load.

Shrimp price volatility vis-a-vis increased production costs puts the shrimp farmers in debt, reduces further investments, and threatens the sustainability of shrimp farming. The cost and price analysis using the time series data drawn from primary and secondary sources substantiated that the production cost per kg of shrimp has increased gradually whereas the corresponding prices reveal a declining trend across the years. Further analyses indicated that shrimp price instability was higher for the largely supplied 21–30 g shrimp size vis-a-vis small- (15–20 g) and large-sized shrimps (> 30 g) over a period of time. Moreover, price trend scrutiny revealed that the price of small- and large-sized shrimps were higher in January, February, September and November months across the years. Likewise, higher price trends were observed in winter, spring, and monsoon seasons, whereas in summer, the price tended to decline. The ARIMA model fitted to predict the shrimp prices for the immediate future, forecasted an increasing price trend for 15 to 20 g size shrimps. Therefore, market based farming with phase-wise stocking of ponds with the adoption of on-farm nursery that would supply quality seed for a scattered stocking and produce different sized shrimps meeting the market demand is the prudent strategy to minimize the price risk. Similarly, partial harvesting of shrimps at 15 g size and its sale in the domestic markets could secure the investments made and continuing the crop for large size shrimps for the niche market would minimize the price risk and sustain the profitability. Further, insurance cover for shrimp price volatility and social capital development in the form of fish farmer producer organizations for collectively procuring inputs and sale of shrimps are suggested as strategies towards reducing the price risk and sustain the profitability of shrimp farming in India.