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                <text>Hassan A., Tourism Consultants Network, The Tourism Society, London, United Kingdom; Sharma A., Department of Commerce and Management, University of Kota, Rajasthan, Kota, India; Kennell J., School of Hospitality and Tourism Management, University of Surrey, Guildford, United Kingdom; Mohanty P., Department of Business Administration, Christ (Deemed to be University), Bengaluru, India</text>
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                <text>Sarkar S., Department of Electronics and Communication Engineering, RCC Institute of Information Technology, Kolkata, India; Basu A., Department of Electronics and Communication Engineering, RCC Institute of Information Technology, Kolkata, India; Bhattacharyya S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India</text>
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                <text>Abstract: The current era focuses not only on producing solar energy but also preserving it for future use. Dye-sensitized solar cells (DSSC) and supercapacitors (SC) are such energy-based devices. DSSCs capture the solar energy and SCs store this captured energy. A natural anthocyanin dye extracted from Garcinia indica (kokum fruit) was used in the DSSCs. SnO2, one of the promising electrode materials for DSSC, was synthesized via a microwave technique. Blend polymer electrolytes (BPE) were prepared through a solution casting technique. A polyvinyl pyrrolidone (PVP) and polyvinyl alcohol (PVA) blend with varying concentrations of potassium iodide, along with iodine dopant, was prepared as a BPE electrolyte composition. The best of the PVA-PVP/KI composition was chosen using Nyquist plots of electrochemical impedance spectroscopy (EIS). Varying the temperature, the dielectric and conductivity study of the chosen composition was studied in detail. A fast/single-step synthesis technique, namely a laser-engraved approach, was used for few-layer graphene synthesis. This graphene serves as a common platform for the DSSC-SC integrated device: as a counter electrode in DSSC and graphene-graphene symmetric electrode in SC. A DSSC-SC integrated device was fabricated and characterized using various analytical and microscopy techniques. The integrated device showed a 0.42 fill factor and 0.56% efficiency. The discharge time for integrated DSSC-SC cells was found to be increased threefold. Graphical Abstract: [Figure not available: see fulltext.]  2020, The Author(s).</text>
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                <text>Sangeetha D.N., Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India; Hegde N., Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India; Poojari V., Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India; Devadiga D., Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India; Sudhakar Y.N., Department of Chemistry, Christ (Deemed to be University), Bangalore Dharmaram Campus, Bengaluru, 560029, Karnataka, India; Santosh M.S., Centre for Incubation, Innovation, Research, and Consultancy (CIIRC), Jyothy Institute of Technology, Thataguni, Bangalore, 560082, Karnataka, India; Selvakumar M., Department of Chemistry, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India</text>
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                <text>Digitalization across all spheres of life has given rise to issues like data ownership and privacy. Privacy-Preserving Machine Learning (PPML), an active area of research, aims to preserve privacy for machine learning (ML) stakeholders like data owners, ML model owners, and inference users. The Paper, CoTraIn-VPD, proposes private ML inference and training of models for vertically partitioned datasets with Secure Multi-Party Computation (SPMC) and Differential Privacy (DP) techniques. The proposed approach addresses complications linked with the privacy of various ML stakeholders dealing with vertically portioned datasets. This technique is implemented in Python using open-source libraries such as SyMPC (SMPC functions), PyDP (DP aggregations), and CrypTen (secure and private training). The paper uses information privacy measures, including mutual information and KL-Divergence, across different privacy budgets to empirically demonstrate privacy preservation with high ML accuracy and minimal performance cost.  2023 SCPE.</text>
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                <text>Scalable Computing, Vol-24, No. 4, pp. 1065-1076.</text>
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                <text>Human rights and Intellectual Property Rights (IPRs) have developed independently. Human rights are inalienable rights associated with human dignity while IPRs are the rights with the goal of promoting innovations and the interests of select communities to further economic and technological growth. The economic and personal interests of the individual have received prime attention under the international intellectual property law. Economic growth is given priority over human rights in the international criteria for IPRs in global trade. Whereas, it has a significant impact on the implementation of human rights for both individuals and communities, including the rights to adequate food, health, environment, and education. IPRs are gravely at odds with human rights, even though a connection between the two rights can be found in General Comment No. 17 on Article 15(1)(c) of the International Covenant on Economic, Social, and Cultural Rights (ICESCR) and Article 27 of the Universal Declaration of Human Rights (UDHR). According to the UDHR, intellectual property is a human right in and of itself, but its enforcement often infringes other human rights. In light of the above perspective, the authors explore the interrelationship between IPRs and human rights and also analyze the evolving IPRs, in different fields of its application, causing adversarial impacts on several other human rights.  The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.</text>
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                <text>To address congestion in the MAC layer and enhance overall performance, the HS-MLCA is proposed. This algorithm incorporates the principles of both Harmony Search and Laying Chicken Algorithm to optimize resource allocation and congestion control. At the MAC layer, HS-MLCA offers several advantages over traditional congestion control schemes. Firstly, it leverages the Harmony Search algorithm, which is known for its ability to exploit the best outcomes in search processes. By exploring the solution space and exploiting promising regions, HS-MLCA optimizes resource allocation in the MAC layer. The integration of the Laying Chicken Algorithm (LCA) further enhances performance by improving convergence speed and solution accuracy. This hybrid approach leverages the strengths of both Harmony Search (HS) and LCA, resulting in more efficient and effective resource management. The Laying Chicken Algorithm simulates the behavior of laying hens in terms of resource allocation and competition. This approach contributes to provide the solution in quality and convergence speed, as the algorithm adapts to the dynamic nature of the MAC layer and the varying traffic conditions in VANETs. By combining the strengths of Harmony Search and Laying Chicken Algorithm, HS-MLCA offers improved performance in terms of congestion control in the MAC layer. It optimizes resource allocation, minimizes collisions and packet loss, reduces delay, and enhances overall network efficiency. These improvements ultimately lead to better quality of service, increased network capacity, and enhanced user experience in VANETs. It is worth noting that the specific performance improvements and benefits of HS-MLCA may vary depending on the implementation details, network conditions, and the specific VANET scenario.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="18402">
                <text>English</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="51">
            <name>Type</name>
            <description>The nature or genre of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="18403">
                <text>Patent</text>
              </elementText>
            </elementTextContainer>
          </element>
        </elementContainer>
      </elementSet>
    </elementSetContainer>
  </item>
</itemContainer>
