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Impact of Learnability Quotient on Employability of Students: Mediating Role of Spiritual Intelligence
This study investigates the impact of Learnability Quotient (LQ) on Employability, with a particular focus on the mediating role of Spiritual Intelligence (SI). Conducted in southern India with a cross-sectional design, the research utilizes primary data collected from educated adults through surveys. The study aims to elucidate cause-and-effect relationships between LQ and Employability and to test hypotheses regarding these variables. The findings reveal that Learnability Quotient and Employability significantly influence each other, with both being affected by age. Education also plays a crucial role in determining employability, while Spiritual Intelligence and Learnability Quotient are less influenced by educational level. The type of institution does not significantly affect these factors, although the location of the institution does impact Spiritual Intelligence and Employability. Correlation analysis shows that higher Spiritual Intelligence correlates moderately with both Learnability Quotient and Employability, while Learnability Quotient has a strong positive association with Employability. Mediation analysis uncovers a complex dynamic where, despite the positive direct effect of Learnability Quotient on Employability, its impact is diminished when mediated through Spiritual Intelligence, as indicated by a negative Variance Accounted For (VAF). Learnability Quotient is crucial for enhancing employability, while Spiritual Intelligence has a nuanced, potentially counterproductive mediation role. Further research is necessary to refine strategies for improving employability through these variables. 2024, Iquz Galaxy Publisher. All rights reserved. -
Were the recent air pollution and landfill fires in Brahmapuram at odds with Kerala's vision of sustainable development?
Air pollution is a global issue, as is commercial, and industrial waste disposal. Industrialized cities have poor air quality. Emissions from fossil fuel, solid household resources and industry, uncontrolled construction, and human and natural activity pollution are the main sources. The purpose of the study is to investigate answers to the question: Were the recent air pollution and landfill fire in Brahmapuram at odds with Kerala's vision of sustainable development? The study consists of a content analysis of prominent newspaper reports on the Prisma model of sorting articles on Brahmapuram issues to investigate the issue and assess the acceptance of sustainable development in Kochi. The reports cover the period from March 3, 2023, to April 3, 2023. The content analysis revealed that the contractor's failure to meet their obligations was the immediate cause. However, the ineffectiveness of the State's solid waste management policies, from a general failure of waste segregation at source, posed a threat to sustainable development. The researcher classified the causes of the Kochi waste fire under the following reasons, namely, environmental, economic, social, and political. The researcher concluded that the recent landfill fire and air pollution at Brahmapuram were contrary to Keralas vision of sustainable development. 2024 by the authors. -
Environmental and Sustainable Development Policies to Address the Pollution Catastrophe in India
Although the environment, crops, water, air, food and fiber, control the weather, and supply oxygen, its air, water, and soil are polluted too. Humans have altered about 75% of the earth, reducing wildlife and nature's space and harming the environment. Industrialisation, urbanisation, population growth, and globalisation have affected people and the environment. This study aims to investigate the environmental and sustainable development-focussed policies to address the pollution catastrophe. The study is a content analysis of prominent online newspaper media reports from January 1, 2020, to November 30, 2022, on legal, environmental, and sustainable issues to reduce pollution and advocate an Indian environmental and sustainable development policy. Since pollution and environmental degradation pose significant threat to humanity, ecosystems, and sustainable living are at risk. Despite national and international legislative and regulatory actions, the environment remains a significant issue. An environmental strategy that encourages sustainable development for future generations is the need of the times. It was found that there were legal and environmental offenses, the management of unscientific treatment procedures, the lack of fundamental education about existing court orders, and fatality-induced health problems. Therefore, India needs an environmental and sustainable development policy to limit environmental concerns' fatality and protect the earth from pollution. 2024 - IOS Press. All rights reserved. -
Design of reconfigurable multiplier-less filter structure based on IFIR for digital channelizer
The flexibility in frequency allocation is essential for high throughput satellites (HTS). The digital channelizer based transponder system has a crucial role in enhancing the performance of HTS. In this work, the design and implementation of a low computational complexity digital channelizer for HTS is proposed. The proposed reconfigurable filter structure for digital channelizer is to generate non-uniform and sharp transition width FIR filters for transponder of the satellite systems. The multipliers and group delay needed in the digital channelizer that employ the proposed structure are reduced when compared to FRM and traditional IFIR based digital channelizer. An example is used to illustrate the effectiveness of the proposed design. Results reveal that the proposed structure has a lower multiplier complexity than existing techniques. The proposed structure once implemented effectively, the power dissipation and hardware complexity are reduced. With the help of CSD, MOABC, and SIDC-CSE optimization, the filters used in this structure are made multiplier-less. Hence this structure is adaptable for the digital channelizer in the transponder of the satellite communication systems. 2023 Elsevier GmbH -
Reconfigurable non-uniform band-generating filter bank for channelizer
Multi-band channelizer system must choose a specific channel from a broad bandwidth signal. A variety of distinct wireless standards and frequency bands are used in the channelizer. Reconfigurable and non-uniform multi-channels with narrow transition widths are necessary for channelizers. In this paper, a low complexity reconfigurable non-uniform band-generating filter bank (RNBFB) is proposed for multi-band channelizer. The RNBFB is used to generate a variety of non-uniform channels with a narrow transition width. Utilising frequency response masking (FRM) and the cosine modulation (CM) approach, many non-uniform channels are created. Comparing RNBFB to other state-of-the-art techniques, RNBFB generates multi-bands for channelizer with less multiplier complexities. For a better understanding of hardware complexity, the proposed RNBFB is implemented efficiently. A multiplier-free design such as Canonical Signed Digit (CSD), Multi-Objective Artificial Bee Colony (MOABC), and Shift Inclusive Differential Coefficients (SIDC) with a Common Sub-expression Elimination (CSE) are included in the suggested strategy to further optimise the RNBFB. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
A Multiplier-Less FRM-Based Reconfigurable Regulated Bank of Filter for Spectrum Hole Detection in IoT
A promising solution for the detection of spectrum holes in the Internet of Things networks is the cognitive radio (CR) system, which is used to identify spectrum holes effectively. The intention of this work is to design a low-complexity Reconfigurable Regulated Bank of Filter (RRBF) structure for spectrum hole detection in IoT networks. The RRBF structure is designed by utilizing the Frequency Response Masking (FRM) approach and the Cosine Modulation Technique (CMT). Using the RRBF structure, multiple sharp non-uniform channels are generated for efficient spectrum hole detection in IoT networks. With the aid of an example, the performance and computational complexity of the RRBF structure are demonstrated. The result shows that the RRBF structure has a fewer multipliers than other existing methods. To obtain hardware-efficient realization, the RRBF structure is made of multiplier-less by incorporating Canonical Signed Digit (CSD), Multi-Objective Artificial Bee Colony (MOABC), and Shift Inclusive Differential Coefficients (SIDC) with Common Sub-expression Elimination (CSE) optimization techniques. 2024 IETE. -
An efficient reconfigurable band tuning filter design for channelizer in transponder satellite system
For improved performance in a variety of applications, the transponder in satellite systems must be very flexible. The channelizer-dependent transponder system significantly boosts the operation of a satellite system. When channelizing wideband input signals, a digital filter bank is typically used to extract several small sub-bands. In this research, a reconfigurable band tuning (RBT) design for the channelizer in the satellite transponder system is designed and implemented. Cosine modulation, exponential modulation and IFIR filter are the techniques behind the RBT design. The RBT design facilitates the generation of many channels enabling channelization with non-uniform narrow transition width. A number of examples are presented to illustrate how well the RBT design performs. Findings indicate that there are fewer filter coefficients in the RBT design than there are in the current approaches Effective implementation of a properly designed RBT design lowers power consumption and simplifies the hardware. 2024 The Franklin Institute -
Design of Computationally Efficient IFIR based Filter Structure for Digital Channelizer'
A low computational complexity digital channelizer is essential for a wide band system. FRM is a widely used method to generate a sharp transition width sub-bands or channels in a digital channelizer. The aim of this work is to design a uniform and non-uniform sharp transition width FIR filter bank with low computational complexity compared to FRM based digital channelizer. The design parameters of the proposed structure are evaluated in an efficient way. The proposed structure is designed based on IFIR filter and complex exponential modulation technique (CEMT). The performance of the proposed structure is demonstrated with the help of an example. Results show that the number of multipliers of the proposed structure is less compared to other existing methods. 2022 IEEE. -
Design of Computationally Efficient FRM Based Reconfigurable Filter Structure for Spectrum Sensing in Cognitive Radio for IoT Networks
A low computational complexity FIR bank of filters are essential for spectrum sensing in wireless networks. FRM is a widely used method to generate a sharp transition width sub-bands or channels. The intention of this work is to design multiple non-uniform sharp transition width FIR bank of filter with low computational complexity for spectrum sensing in cognitive radio for IoT networks. The design parameters of the proposed structure are calculated in an efficient way. The proposed structure is designed based on the FRM filter and complex exponential modulation technique (CEMT). The performance of the proposed structure is illustrated with the help of an example. Result indicates that the number of multipliers of the proposed structure is less compared to other existing techniques. 2022 IEEE. -
Design of Reconfigurable Filter Structure Based on FRM for Wideband Channelizer?
A reconfigurable FIR bank of filters are essential for digital channelizer in wideband system. FRM is a extensively used method to generate a sharp transition width sub-bands or channels for digital channelizer. The aim of this work is to design multiple non-uniform sharp transition width FIR bank of filters with reduced number of multipliers and group delay for wideband channelizer. The design parameters of the proposed structure are evaluated in an efficient way. The proposed structure is designed based on FRM filters and exponential modulation (EM) technique. The performance of the proposed structure is illustrated with the help of an example. Result shows that the number of multipliers of the proposed structure is less compared to other existing techniques. 2022 IEEE. -
A Low-Complexity Multiplier-Less Filter Bank Based on Modified IFIR for the SDR Channelizer
Digital filter banks are extensively used in an SDR channelizer for channelization. The objective of this research work is to design a low computational complexity FIR filter bank for generating sharp transition width channels for SDR. The design of unified and variable bandwidth channels for SDR using the proposed structure is based on the modified IFIR filter structure and cosine modulation technique (CMT). The performance of the proposed structure is demonstrated with the help of an example. The results show that the multiplier complexity of the proposed structure is less than those of other state-of-the art methods. The optimization techniques are incorporated in this work to further reduce the complexity of the proposed structure. With the help of canonical signed digit (CSD), multi-objective artificial bee colony (MOABC) and shift inclusive differential coefficients (SIDC) common sub-expression elimination (CSE) optimization, the filter used in this structure is made multiplier-less. 2024 IETE. -
The Efficiency of Ensemble Machine Learning Models on Network Intrusion Detection using KDDCup 99 Dataset
With the advent of data communication the increased usage of the technologies results in network intrusions and associated attacks. Consequently, the data violation rates are increased abundantly and that sacrifices Confidentiality, Integrity and Availability. This article focused on the network Intrusion Detection System (IDS) that detects various attacks and types. Machine learning (ML) has the potential to spot known-experience and Zero-day attacks. Consequently, the article has considered ML and ensembled models for the various attack classification. The major contributions of the current article are 3-fold. Initially, to understand the relevance and sufficiency of the dataset through exploratory data analysis. Second, the comprehensive understanding of the various attacks, its nature, various types and classifications and finally, the empirical analysis of the dataset through the potential of various ML models. The article utilized various discriminative models for the execution and all of the models have shown better accuracy. The tree-based ensemble model, Random Forest has outperformed the rest of the models with higher accuracy in the training and testing samples of 99.997 % and 99.969 % respectively. 2023 IEEE. -
A Novel Approach for Machine Reading Comprehension using BERT-based Large Language Models
Teaching machines to learn the information from the natural language documents remains an arduous task because it involves natural language understanding of contexts, excerpting the meaningful insights, and deliver the answer to the questions. Machine Reading Comprehension (MRC) tasks can identify and understand the content from the natural language documents by asking the model to answer questions. The model can extract the answer from the given context or other external repositories. This article proposes a novel Context Minimization method for MRC Span Extraction tasks to improve the accuracy of the models. The Context Minimization method constitutes two subprocedures, Context Reduction and Sentence Aggregation. The proposed model reduced the context with the most relevant sequences for answering by estimating the sentence embedding between the question and the sequences involved in the context. The Context Reduction facilitates the model to retrieve the answer efficiently from the minimal context. The Sentence Aggregation improves the quality of answers by aggregating the most relevant shreds of evidence from the context. Both methods have been developed from the two notable observations from the empirical analysis of existing models. First, the models with minimal context with efficient masking can improve the accuracy and the second is the issue with the scatted sequences on the context that may lead to partial or incomplete answering. The Context Minimization method with Fine-Tuned BERT model compared with the ALBERT, DistilBERT, and Longformer models and the experiments with these models have shown significant improvement in results. 2024 IEEE. -
Unveiling the potential of large language models: Redefining learning in the age of generative AI
Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are transforming industries by fostering innovation, automating tasks, and enhancing creativity. By enabling personalized user interactions, sophisticated content creation, and advanced data analytics, they are revolutionizing industries such as healthcare, education, and customer service. As these technologies evolve, they can fundamentally change communication and decision-making processes and incorporate AI into everyday life. The objective of this book chapter is to examine the architecture and components, features, functionality, domain-specific applications, recent advances, and future developments of LLMs. Ongoing research aims to reduce biases, increase energy efficiency, and facilitate interpretation. As LLMs continue to evolve, they have the potential to transform many industries, including education, customer service, content creation, and more. As a result, they will be essential for the development of future AI-powered applications. 2024, IGI Global. All rights reserved. -
Question-answering versus machine reading comprehension: Neural machine reading comprehension using transformer models
Teaching machines to read and learn natural language documents and seek answers to questions is an elusive task. Traditional question-answering systems were based on rule-based and keyword-searching algorithms without proper natural language understanding. Machine reading comprehension (MRC) belongs to reading comprehension models and facilitates the machines learning from context. MRC can infer the answer from the context through language understanding. Neural machine reading comprehension has built reading comprehension models by employing the advancements of deep neural networks that have shown unprecedented performance compared to other non-neural and feature-based models. The article comprises the MRC span extraction tasks using Transformer models and, in addition, the illustration of the MRC tasks, trends, modules, benchmarked datasets, implementation, and empirical results. 2024 selection and editorial matter, Muskan Garg, Sandeep Kumar and Abdul Khader Jilani Saudagar chapters. -
In vitro Analytical Techniques as Screening Tools to investigate the Metal chelate-DNA interactions
Deoxyribose nucleic acid (DNA) is found to be the most efficient pharmacological target of many synthetic molecules which are deemed as potential drugs with clinical applications. DNA binding agents are known to regulate several cell functions (gene expression and replication) by adopting various protocols which include the annihilation of the cell membrane, interruption in protein synthesis, and irreversible binding to cell receptors. Recently, several studies have explored fundamental aspects of drug-DNA interactions, providing new insights into the driving forces that render the formation of the drug-DNA complex. In order to study and understand these biologically important reaction mechanisms, several screening tools have been devised and the specificity of drug molecules binding to DNA were studied in detail. This review will discuss the utilization of various analytical tools which include UV-vis spectroscopy, fluorescence spectroscopy, circular dichroism, viscosity measurement, Raman spectroscopy, cyclic voltammetry, and DNA fragmentation assay used for studying drug binding mode and the mechanism involved. 2023 Wiley-VCH GmbH. -
Fluorescent carbon nanoparticle hybrids: synthesis, properties and applications
The development of materials in nanoscale morphologies with novel compositions is one of the major focuses of nanoscience and technology, as these materials are imbibed with unique properties that make them suitable for specific applications in a large variety of fields. Combining two or more chemically distinct constituents into a single nanostructure helps to attain desirable attributes of physical and chemical responses that can be efficiently utilized for specific applications. Hybrid nanomaterials constituted as a combination of multiple components into single nanostructures are known to showcase the properties of the individual components in tandem or synergy. Novel functionalities are also known to arise from integrating Fluorescent carbon nanoparticles (FCNPs) with other counterparts. FCNPs, when combined with other materials to form nanohybrids, provide copious functional attributes due to their inherent properties and the augmentation in properties due to the presence of the other materials. Integrating hybrid counterparts with FCNs improves the functional properties, which can be utilized for various applications such as photocatalysis, bioimaging, bio/chemo sensing, and many more. Herein we present an overview of recent and relevant works related to the synthesis, properties, and applications of fluorescent carbon nanoparticle (FCNP) hybrids. Various synthetic routes of FCNP hybrids via physical and chemical methods are summarized. The properties of the hybrid systems and the influence of hybridization on the properties are discussed. Applications of FCNP hybrids in various fields are also discussed in detail. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Challenging the Representation of the HumanAnimal Relationship in The Elephant Whisperers
[No abstract available] -
Cocos nucifera L.-derived porous carbon nanospheres/ZnO composites for energy harvesting and antibacterial applications
Carbon nanomaterials (CNMs) have been the subject of extensive research for their potential applications in various fields, including photovoltaics and medicine. In recent years, researchers have focused their attention on CNMs as their high electrical conductivity, low cost, and large surface area are promising in replacing traditional platinum-based counter electrodes in dye-sensitized solar cells (DSSC). In addition to their electrical properties, CNMs have also displayed antibacterial activity, making them an attractive option for medical applications. The combination of CNMs with metal oxides to form composite materials represents a promising approach with significant potential in various fields, including energy and biology. Here, we introduce porous carbon nanospheres (PCNS) derived from Cocos nucifera L. and its ZnO composite (PCNS/ZnO) as an alternative material, which opens up new research insights for platinum-free counter electrodes. Bifacial DSSCs produced using PCNS-based counter electrodes achieved power conversion efficiencies (PCE) of 3.98% and 2.02% for front and rear illumination, respectively. However, with PCNS/ZnO composite-based counter electrodes, the efficiency of the device increased significantly, producing approximately 5.18% and 4.26% for front and rear illumination, respectively. Moreover, these CNMs have shown potential as antibacterial agents. Compared to PCNS, PCNS/ZnO composites exhibited slightly superior antibacterial activity against tested bacterial strains, including gram-positive Bacillus cereus (B. cereus) and Staphylococcus aureus (S. aureus), and gram-negative Vibrio harveyi (V. harveyi) and Escherichia coli (E. coli) with MIC values of 125, 250, 125, and 62.5g/ml, respectively. It is plausible that the outcomes observed were influenced by the synergistic effects of the composite material. Graphical abstract: (Figure presented.) The Author(s), under exclusive licence to Korean Carbon Society 2024. -
Carbon dots-Zno/TiO2 ternary nanocomposite as a proficient material to enhance the performance of natural DSSC
A novel sustainable approach for enhancing the efficiency of dye-sensitized solar cells (DSSCs) involves the utilization of a combination of ZnO and carbon dots (CDs) derived from Citrus medica fruit extract, along with microwave-synthesized TiO2 nanoparticles for the preparation of the photoanode. Natural dyes such as Hibiscus rosa-sinensis and Allium Cepa peel are employed as sensitizers to reduce production costs. This co-activation method has demonstrated a significant improvement in the output parameters of the devices. Notably, the photoanode co-activated with ZnO-CD composite (ZnO-CD/TiO2) exhibits the most favorable output parameters when combined with Hibiscus rosa-sinensis dye (open circuit voltage (Voc) = 0.80 V, short circuit current density (Jsc) = 6.62 mA/cm2, fill factor (FF) = 64.20 %, photo conversion efficiency (PCE) = 3.40 %) and Allium Cepa peel dye (Voc = 0.81 V, Jsc = 6.79 mA/cm2, FF = 65.70 %, PCE = 3.61 %). When paired with Allium Cepa dye, the CD modified photoanode (CD/TiO2) offers Voc = 0.73 V, Jsc = 6.64 mA/cm2, FF = 61.27 % and PCE = 2.97 %. Similarly, when combined with Hibiscus rosa-sinensis dye, the output parameters of the CD/TiO2 photoanode are Voc = 0.72 V, Jsc = 6.54 mA/cm2, FF = 64.4 % and PCE = 3.03 %. In comparison to all tested devices, the unmodified photoanode (TiO2) displayed the lowest performance, with parameters such as Voc = 0.59 V, Jsc = 6.45 mA/cm2, FF = 52.5 %, PCE = 2.10 % using Allium Cepa peel dye, and Voc = 0.66 V, Jsc = 6 mA/cm2, FF = 51.60 %, PCE = 2.04 % using Hibiscus rosa-sinensis dye. Furthermore, the co-activation process has been shown to enhance the stability of the devices. While the unmodified photoanodes ceased to operate after eight days, the ZnO-CD composite co-activated photoanodes retained their initial efficiencies up to 61.50 % and 68.53 % with the Allium Cepa peel dye and Hibiscus rosa-sinensis dye, respectively. Therefore, this study underscores the potential of the synthesized composite material in enhancing the performance of natural DSSCs. 2024 Elsevier Ltd