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A new extension of hesitant fuzzy set: An application to an offshore wind turbine technology selection process
Wind energy is an energy source that is naturally clean, safe and cheap. It comes from a variety of sources. The electric energy generated by a wind turbine manifests as kinetic energy throughout the earth. The energy received from the wind is clean and is permanently available and can be generated forever. Turbine characteristics also have an impact on wind energy production. The turbine properties within a wind farm are important in estimating the load on power generation and wind turbine energy. The amount of energy released is calculated according to the type of the turbine model applied. In many situations, the choices of turbine model can incur various vague and complicated hesitation situations. To manage this situation, a hesitant fuzzy set with the Multi Criteria Decision Making (MCDM) is used. In the present research, the newly proposed Normal Wiggly Hesitant Fuzzy-Criteria Importance Through Intercriteria Correlation (NWHF-CRITIC) and Normal Wiggly Hesitant Fuzzy-Multi Attribute Utility Theory (NWHF-MAUT) methods were employed to rank turbine models based on quality, power level, voltage, and capacity. As part of this process, the NWHF method was utilized to extract and gather deeper information from the decision-makers. 2021 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology. -
Designing of a Free-Standing Flexible Symmetric Electrode Material for Capacitive Deionization and Solid-State Supercapacitors
In this work, a highly efficient free-standing flexible electrode material for capacitive deionization and supercapacitors was reported. The reported porous carbon shows a high surface area of 2070.4 m2 g-1 with a pore volume of 0.8208 cm3 g-1. The material exhibited a high specific capacitance of 357 F g-1 at 1 A g-1 in a two-electrode symmetric setup. A solid-state supercapacitor device has been fabricated with a total cell capacitance of 152.5 F g-1 at 1 A g-1 in a solid PVA/H2SO4 gel electrolyte with an energy density of 21.18 W h kg-1 at a 501.63 W kg-1power density. A long-run stability test was carried out up to 15,000 cycles at 5 A g-1 that showed capacitance retention of 99% with ?100% Coulombic efficiency. Furthermore, the electrosorption experiment was conducted by a flow-through test by coating on commercially available cellulose thread that was employed, which shows electrosorption ability up to 16.5 mg g-1 at 1.2 V in a 500 mg L-1 NaCl solution. Complete experiments were conducted with a proper procedure, provided by scientific approaches with analytical data. Thus, the reported electrode material showed bifunctional application for energy storage and environmental remediation. 2023 American Chemical Society. -
A Comparative Benchmark of Deep Learning and Classical Models for BLE-Based Indoor Localization
Bluetooth Low Energy (BLE)-based indoor positioning has gained attention as a cost-effective solution for environments where GPS signals are unreliable. Despite advances in ML and DL techniques, few standardized benchmarks exist for comparing models under uniform conditions. This study evaluates seven models - K-Nearest Neighbor, Random Forest, Deep Neural Network, 1D CNN, Long Short-Term Memory, Bi-LSTM, and Transformer - on a publicly available dataset collected across multiple building floors. A preprocessing pipeline was applied to address missing values, refine RSSI signals, and generate temporal features. Performance was assessed using both accuracy metrics (MAE, RMSE) and efficiency metrics such as processing time, and model size. Results show that KNN, Random Forest, and DNN consistently outperformed complex sequential and attention-based models, achieving RMSE as low as 1.297 m. These findings suggest that simpler architectures align more effectively with BLE RSSI data than deeper models. This study establishes a benchmark that can support future work in developing efficient, lightweight, and generalizable indoor positioning systems. 2025 IEEE. -
Unveiling thePower ofBayesian Optimization: Methods, Insights, andApplications
Bayesian optimization (BO) has emerged as a popular approach for optimizing expensive black-box functions, which are common in modern machine learning, scientific research, and industrial design. This paper provides a comprehensive review of the recent advances in Bayesian optimization techniques, addressing new methodological developments such as multi-fidelity optimization, transfer learning, and neural network surrogates. Additionally, we explore the increasing role of BO in complex, high-dimensional, and multi-objective optimization problems, as well as its application in various fields like hyperparameter tuning, reinforcement learning, and neural architecture search. The goal of this review is to offer both theoretical insights and practical guidelines to researchers and practitioners working in areas where BO is a suitable tool. Finally, we discuss key challenges and propose directions for future research in the rapidly evolving field of Bayesian optimization. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
A novel wide slice kronecker forward fractional network for osteoporosis detection using knee X-ray image
Osteoporosis is an asymptomatic and progressive skeletal disorder that maximizes the risk of fractures in people aged 50 to 60. Early and accurate detection is critical, yet challenging, due to the fine structural changes in bone that are often difficult to identify in routine medical images. Knee X-rays are commonly used diagnostic tools, but interpreting them for osteoporosis detection remains complex because of variations in bone geometry and trabecular patterns. To solve these challenges, the novel Wide Slice Kronecker Forward Fractional Network (WKFF-Net) is developed to detect osteoporosis efficiently. Initially, the input image is taken from the database for detection. Here, the denoising process is done using the Non-Local Means (NLM) filter, and the Otsu thresholding method is considered for the segmentation process. Further, a template search method is used for analyzing the femur geometry. Next, features, like spatial, adaptive Local Binary Patterns (aLBP), Convolutional Neural Networks (CNN), and medical-level features, are extracted, and osteoporosis detection is accomplished by the hybrid WKFF-Net model that integrates Deep Kronecker Network (DKN), Wide Slice Residual Network (WISeR), and fractional calculus. The experimental results obtained by the WKFF-Net are 90.868% accuracy, 92.876% True Positive Rate (TPR), 87.766% True Negative Rate (TNR), 89.888% precision, and 91.357% F1-score, for 90% of the training samples. 2026 Elsevier B.V. -
Biological Feature Selection and Classification Techniques for Intrusion Detection on BAT
Privacy is a significant problem in communications networks. As a factor, trustworthy knowledge sharing in computer networks is essential. Intrusion Detection Systems consist of security tools frequently used in communication networks to monitor, detect, and effectively respond to abnormal network activity. We integrate current technologies in this paper to develop an anomaly-based Intrusion Detection System. Machine Learning methods have progressively featured to enhance intelligent Anomaly Detection Systems capable of identifying new attacks. Thus, this evidence demonstrates a novel approach for intrusion detection introduced by training an artificial neural network with an optimized Bat algorithm. An essential task of an Intrusion Detection System is to maintain the highest quality and eliminate irrelevant characteristics from the attack. The recommended BAT algorithm is used to select the 41 best features to address this problem. Machine Learning based SVM classifier is used for identifying the False Detection Rate. The design is being verified using the KDD99 dataset benchmark. Our solution optimizes the standard SVM classifier. We attain optimal measures for abnormal behavior, including 97.2 %, the attack detection rate is 97.40 %, and a false-positive rate of 0.029 %. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
A Comprehensive Review on the Antimicrobial and Photocatalytic Properties of Green Synthesized Silver Nanoparticles
With the advancement of technology, there is a growing demand for new nanoparticles that are viable, eco-friendly, non-toxic, and non-hazardous, as well as having unique chemical and physical properties. Silver nanoparticles are currently promising for antibacterial, antimicrobial, and photocatalytic applications. Because of their toxicity, nanosilver particles are now widely used in various applications, including cosmetics, clothing, sunscreen, medicinal, sensing, antibacterial, antimicrobial, and photocatalytic. The importance of plant extracts in the synthesis of AgNPs is emphasized. The various mechanisms and characterization techniques used in the study of silver nanoparticles will also be covered. This review also discusses the role of green synthesized AgNPs in antimicrobial and photocatalytic applications, which adds to our understanding of improving health, and the environment and preventing contagious diseases. 2022 by the authors. -
AI and IoT in Digital Marketing: Enhancing Automation, Personalization, and Consumer Interaction
An interconnected system of Internet-enabled devices that can collect and transmit statistics via a wireless connection without the assistance of people is known as the Internet of Things (IoTs). Accordingly, the IoT's seductive power is causing significant shifts in the current corporate environment. The digital marketing industry stands to gain the most from this innovation, which is currently causing major shifts in many other sectors. Using a variety of digital marketing strategies, this innovation gathers numerous types of consumer statistics. IoT technological advancements' impact on digital marketing tactics and customer interaction has emerged as a crucial research topic as it begins to pervade many facets of everyday existence. This investigation examines the application of IoT-based machine learning (ML) in digital marketing for the food business. To give ML-based suggestions, consumer data is analyzed, interests are identified, and conduct is predicted using ML approaches. The ensemble technique aggregates the results of multiple ML techniques to produce an individual forecast. The accuracy matrices graphs for the K-nearest neighbor and decision trees produced excellent estimations, with 100% accuracy and 0.0 error, correspondingly. The nae Bayes method achieved 97.2% accuracy with a 0.029 error, successfully identifying the right tags across every category. The guided ensemble of 3 ML methods is demonstrated by effectively enhancing digital marketing tactics in the food distribution industry by reducing duration and expenses. 2025 IEEE. -
Moisture-Sensitive Fe2O3 Nanoparticle-Based Magnetic Soft Actuators
Multifunctional soft robots are emerging as a new-generation intelligent device for challenging environments. To meet the requirements of smart applications and soft robotics, developing a soft actuator capable of multiple functions and mechanical deformation is essential. In this study, we designed a free-standing magnetic soft actuator constructed from iron oxide (Fe2O3) nanoparticles and poly(vinyl alcohol) (PVA), that responds to both moisture and magnetic fields. We used computational modeling (density functional theory and ab initio molecular dynamics) to explain the experimental findings demonstrating the deformation and high-bending angle (?150), which is about twice as large under combined moisture and magnetic field exposure compared to their individual effect. Additionally, a flower-shaped soft robot was designed by using the continuous bending deformation of the actuator in response to moisture changes, performing directional bending in an ambient environment. These findings demonstrate the materials sensitivity to moisture and magnetic fields, opening up new possibilities for designing responsive structures in the smart device industry. 2024 American Chemical Society. -
Nickel Telluride Quantum Dots as a Counter Electrode for an Efficient Dye-Sensitized Solar Cell
Transition-metal dichalcogenides (TMDs) have recently emerged as highly appealing and efficient options for electrodes in dye-sensitized solar cells (DSSCs), effectively substituting the scarce and expensive metal platinum (Pt). In this work, nickel telluride (NiTe2) quantum dots (QDs) were effectively used as a counter electrode for DSSCs by providing a sustainable alternative to the scarce platinum (Pt). The DSSC based on NiTe2 QDs shows a power conversion efficiency (?) of ?8.06%, which is comparatively better than exfoliated NiTe2 (? ? 6.58%). The density functional theory (DFT) was employed to comprehensively understand the underlying mechanisms involved in the charge transfer between the QDs and the electrolyte species. The outcomes demonstrated the benefits of creating diverse structural configurations designed to enhance interfacial transport, ensure an even distribution of active facets, and improve the electrocatalytic performance in the DSSC process.(Figure Presented). 2023 American Chemical Society. -
Automated Leukaemia Prediction and Classification Using Deep Learning Techniques
Leukemia is typically diagnosed based on an abnormal blood count, frequently an elevated White Blood Cell (WBC) count. The diagnosis is established through bone marrow, replaced by neoplastic cells. Acute Lymphoblastic Leukemia (ALL) is a type of leukaemia that affects the blood and bone marrow. Leukaemia primarily affects children and adults around the world. Early leukaemia detection is critical for appropriately treating patients, especially children. This research aims to present a diagnostic method that uses computational intelligence and image processing algorithms to identify blast cells from ALL images. The medical image is prepared initially using the preprocessing and segmentation technique for efficient classification. In this research, the type is accomplished using Bidirectional Associative Memory Neural Networks (BAMNN), where the accuracy is 96.87%, the highest classification rate and outperforms the existing technique. 2023 IEEE. -
An Efficient Quantum Transfer Learning for Cancer Prediction Using Tumour Markers: New Era of Computer in Medical
Ovarian cancer prediction models or algorithms estimate a person's risk of getting the disease based on different variables, such as their medical history, genetics, and biomarkers. Early identification and intervention will enhance patient successive diagnosis outcomes. Tumour markers are chemicals frequently detected in higher concentrations than usual in cancer patient's blood, urine, or tissues. They could be certain chemicals or proteins linked to the presence of tumours or cancer kinds. Tumour markers are employed for diagnosis, prognosis, and treatment response monitoring. Applying information or models from one quantum job to enhance the performance of another requires quantum transfer learning. Transferring knowledge from one domain to another seeks to increase learning effectiveness in novel quantum contexts. The main goal of efficient Quantum Transfer Learning (QTL) is to minimize the resources (computer power, data, or time) necessary to transfer between tasks successfully. In this research work, QTL is used to predict Ovarian Cancer (OC) with the assistance of biomarkers. The Quantum Transfer Learning- Ovarian Cancer (QTL-OC) achieves 93.78% accuracy and outperforms the existing techniques. 2023 IEEE. -
CLASS onboard Chandrayaan-2: Five years around the Moon
Chandrayaan-2 Large Area soft X-ray Spectrometer (CLASS) is a remote X-ray Fluorescence experiment to map the lunar surface elemental abundances. With its large effective area and low energy threshold, CLASS generates the highest spatial resolution maps of all major rock-forming elements on the Moon, such as Mg, Al, Si, Ca, Ti, and Fe. Five years of operation in lunar orbit has resulted in global coverage. With several lunar missions planned for this decade for in situ exploration and sample returns, the 155 km geochemical maps from CLASS will serve as an important dataset. This article highlights the scientific results of CLASS in the last five years and discusses its potential applications. Indian Academy of Sciences 2025. -
A secure and light weight privacy preserving data aggregation algorithm for wireless sensor networks
WSN is a collection of sensors, which senses critical information related to military, opponent tracking, patient health details etc. These sensed critical and private data will be collected and aggregated by aggregators and forward it to the base station. Due to the involvement of sensitive data, there is a demand for secure transmission and privacy preserving data aggregation. In this paper, we propose a light weight, secure, multi party, privacy preserving data aggregation scheme, in which one or more sensors share their private data with aggregator securely without revealing the original content. The aggregators also perform the aggregation operation without knowing the original content. 2020 Alpha Publishers. -
The Influence of Alloying Constituent Fe on Mechanical Properties of NiTi Based Shape Memory Alloys
The influences of Fe-addition on phase transformation behavior, mechanical properties and microstructure of Ti50Ni50-xFex alloys were investigated by means of optical microscopy, scanning electron microscopy (SEM) and X-ray diffraction (XRD). Results indicate that, as a substitute for Ni, Fe added to TiNi alloys can dramatically decrease the martensite transformation temperature and R phase transformation and martensite transformation are accordingly separated. The results show that TiNiFe alloys exhibit two-step martensitic transformation. The start temperature of martensitic transformation increases sharply from 212 K to 267 K when 2% Fe is added in, and then decreases gradually if Fe content further increases. The hardness of TiNiFe ternary alloys before heat treatment is constant for up to 6% of the composition and suddenly increases for 9% composition and also it behaves same for heat treated specimens because of formation of equilibrium precipitates Ni3Ti formation. 2017 Elsevier Ltd. -
Investigation into the Mechanical, Fatigue and Superplastic Characteristics of Shape Memory Alloys (SMA) in CuAlMn, CuAlBeMn, and CuAlFeMn Compositions and Their Composite Variants
Shape memory alloys (SMAs) exhibit high sensitivity to compositional changes in terms of their super elasticity, shape memory effect, and transition temperatures. A deeper comprehension of SMA composition and its impact on mechanical properties can be attained by differential scanning calorimetry. The current study uses experimental work to assess the energy absorption capacity, mean fracture width, residual strength, and cracking strength of samples made of short shape memory alloy (SMA) fibers that are randomly distributed on the specimens tensile side. In this investigation, three samples were synthesized based on the Cu, Al, and Mn proportions found in CuAlMn shape memory alloys (SMA1, SMA2, and SMA3). Moreover, three samples with different ratios of Cu, Al, Mn, Be, and Fe were synthesized for the shape memory alloys CuAlBeMn and CuAlFeMn (SMA2, and SMA3). The synthesized CuAlMn, CuAlBeMn, and CuAlFeMn SMA alloys showed good strain recovery, ranging from 90 to 95%. The martensite that forms and changes when the alloys are heated and quenched mostly controls the strain recovery by the corresponding SMAs. SMA 2 of the CuAlBeMn has a greater strain recovery rate, rising by 8.5% and 44.38%, respectively, in comparison to SMA 1 and SMA 3. CuAlBiMn shape memory alloys demonstrated superior super elasticity and martensite stability in comparison to SMA 1 and SMA 2 respectively. SMA 1 and SMA 2 demonstrated greater residual strength, cracking strength, and energy absorption capacity for all fiber volume fractions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Ethics of digital journalism
Marshall McLuhan proposed that technological changes impact society. Digital media has enabled journalists to reach their audiences instantly with the news. Journalists need more time to decide what to report and how to present it. Ethics is the belief about what is morally correct or acceptable. Traditionally, newspaper reporters devised moral codes to help them in their professional decision-making. There emerged an almost universal set of principles that guided journalists in their profession. Television journalists were compelled to draw up a code of ethics to ward off criticism about sensationalism. Digital media has blurred the distinction between professional and citizen journalists. Twitter allows the man in the street to break the news as it happens. Privacy and copyright are just two of the significant issues that digital journalists must deal with. Digital media throws up these challenges, and this chapter aims to answer them. Is it acceptable to extend the ethical standards of old media to the digital space, or do we need a new set of ethics to guide digital journalists? New principles and ethical standards are being framed to tackle the unique challenges of digital news media. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. All rights reserved. -
A Reflection on the Current Status of Animal-Assisted Therapy in India
The field of animal-assisted therapy is advancing quickly throughout the world gaining popularity as a complementary therapy. Several countries, especially in the East, are still in their nascent phase in utilizing animal-assisted therapy and a more realistic presentation of their status should drive them towards effective initiatives to promote the field. The primary objective of this paper is to throw light on the current scenario of animal-assisted therapy in India. The relevant databases such as Scopus, Google Scholar, Proquest, PubMed, and JSTOR were searched to identify the research literature. The organizational websites, news, and blog articles, as well as institutional repositories, were explored to maximize the evidence. A total of 24 articles were found which included published research articles as well as unpublished conference papers. Results found a dearth of practice and research throughout the country indicating an urgent need to direct steps in promoting the growth of the field. The contemporary issues in the implementation of animal-assisted therapy such as cultural and religious beliefs, lack of awareness, lack of practising organizations and therapists warrant immediate attention. Reducing the research and practice gap alongside focusing on creating awareness, changing public perception, introducing coursework in educational institutions, the publication of evidence-based research will help in the acceptance and growth of this novel therapeutic field. 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Animal-Assisted Therapy for the Promotion of Social Competence: a Conceptual Framework
Developmental disorders have a substantial effect on the social competence of children affecting their overall psychosocial functioning. Social competence entails the process of being socially mature by establishing stable and adaptive patterns of social behavior. Animal-assisted therapy, as an alternative treatment modality, has offered some new prospects for improving social cognition. This conceptual paper, thus, attempts to throw light on how animal-assisted therapy can help improve social competence. The paper draws its knowledge from the existing theories and empirical work done to propose a conceptual framework that can enhance social competence by incorporating therapy animals. It can be concluded that animal-assisted therapy has found to improve different dimensions crucial for development of social competence. This further suggests the dire need to explore the effectiveness of human-animal interactions by utilizing it for improving social competence. 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Animal-assisted therapy for children and adolescents with neurodevelopmental disorders: A review
The increase in neurodevelopmental disorders presents the need for complementary and alternative treatment modalities to support well-being in the maximum possible way. This narrative review was conducted with the aim to explore how animal-assisted therapy as a complementary treatment approach is beneficial for children and adolescents with neurodevelopmental disorders. A search in various databases was conducted to identify articles published in the field of animal-assisted interventions. The review comprised of a total of 32 studies. The discussion of the results was presented in terms of different therapy animals incorporated into the therapeutic environment. The review indicated that animal-assisted therapy has the potential to improve symptoms and various psycho-social variables in individuals suffering from different developmental disabilities. 2024, IGI Global. All rights reserved.