bluuebunny/crossref_metadata_2025 huggingface dataset:
PERSEPSI GURU TERHADAP PLATFORM MERDEKA MENGAJAR : MERESPON TRANSFORMASI PENDIDIKAN DI ERA DIGITAL
Authors: Istiqomah Nur Indah , Santosa Rochmad Budi , Pepsi Febriyanti
Published: May 2024

Penelitian ini bertujuan untuk mengeksplorasi bagaimana para guru memanfaatkan Platform Merdeka Mengajar yang diluncurkan oleh Kementerian Pendidikan dan Kebudayaan sebagai media untuk mendukung para guru dalam menciptakan pembelajaran yang kreatif dan inovatif. Penelitian ini menggunakan Metode mix method, yaitu menggunakan kombinasi penelitian kualitatif dengan metode studi kasus dan pendekatan kuantitatif dengan metodologi deskriptif. Teknik pengumpulan data melalui kuesioner yang telah divalidasi oleh para ahli dan studi dokumentasi. Hasil penelitian menunjukkan bahwa sebagian besar guru menggunakan Platform Merdeka Mengajar (PMM) secara teratur untuk berbagai alasan yang berbeda. Kebanyakan guru percaya bahwa PMM relevan dengan kebutuhan pendidikan saat ini. Para guru sangat tertarik pada keragaman materi pengajaran dan kemudahan akses pada PMM. PMM dianggap sebagai alat yang efektif dan relevan dalam pendidikan saat ini. Para responden menganggap PMM sangat membantu dalam transformasi pendidikan di era digital. Saran dan masukan guru tentang PMM sangat beragam terkait konten, pelatihan, dan aksesibilitas platform.
ANALISIS AWAL KEMAMPUAN LITERASI SISWA SD KELAS RENDAH
Authors: Wahyuni Maria Eni , Nugrahanta Gregorius Ari , Prasetya Andreas Erwin
Published: May 2024

Artikel ini bertujuan untuk mengetahui kemampuan literasi siswa SD kelas rendah. Penelitian yang telah dilakukan menggunakan pendekatan deskriptif kualitatif dan dilaksanakan di SD Santo Yosef Sioban Mentawai pada Agustus sampai Oktober 2023.  Data penelitian dikumpulkan melalui observasi partisipatif, wawancara mendalam dan dokumentasi. Subjek dalam penelitian ini pendidik, orang tua peserta didik dan peserta didik kelas satu SD Santo Yosef Sioban Mentawai. Objek dalam penelitian ini yakni analisis awal kemampuan literasi siswa SD kelas rendah. Keabsahan data diperiksa menggunakan triangulasi. Data dianalisis menggunakan analisis secara kualitatif Miles & Huberman yakni mengumpulkan suatu data, reduksi suatu data, menyajikan suatu data dan menarik kesimpulan. Hasil atau kesimpulannya yakni kemampuan literasi siswa SD Santo Yosef Sioban Mentawai masih rendah. Hal ini dibuktikan dari 1) rendahnya kemampuan literasi siswa, dan 2)  minimnya fasilitas sebagai penunjang keberhasilan proses belajar.
PENGEMBANGAN PERMAINAN MULTIPLY CARD PADA MATERI OPERASI HITUNG PERKALIAN DALAM PEMBELAJARAN KONTEKSTUAL DI SEKOLAH DASAR
Authors: Karlina Lilis , Yandari Indhira Asih Vivi , Alamsyah Trian Pamungkas
Published: May 2024

Penelitian  ini bertujuan untuk mengetahui proses pengembangan, kelayakan media, dan respon peserta didik terhadap media permainan Multiply Card pada materi operasi hitung perkalian dalam pembelajaran kontekstual di kelas IV sekolah dasar. Metode yang digunakan pada penelitian menggunakan metode  Research and Development (R&D) dengan model pengembangan 4D yang terdiri dari 4 fase atau tahapan, yaitu (Define, Design, Development, Disseminate). Teknik pengumpulan data diperoleh melalui wawancara, angket, observasi, dan dokumentasi. Subjek pada penelitian ini adalah 36 peserta didik kelas IV SDN Ranca Iyuh IV. Hasil penelitian menunjukkan kelayakan media permainan Multiply Card yang telah dikembangkan, berdasarkan hasil uji validasi produk dari ahli media memperoleh kriteria sangat layak, dan dari hasil uji validasi ahli materi memperoleh kriteria sangat layak. Hasil penilaian kelayakan media permainan Multiply Card juga di dukung oleh penilaian angket hasil respon peserta didik. Hasil angket respon peserta didik memperoleh kriteria sangat baik. Dengan demikian hasil penelitian pengembangan media permainan Multiply Card sangat layak dan sangat baik untuk digunakan dalam proses pembelajaran peserta didik kelas IV SD.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS MULTIMEDIA INTERAKTIF PADA MATA PELAJARAN GEOGRAFI
Authors: Puspitasari Henny , Nurbani  , Fatmawati Erni
Published: May 2024

Permasalahan dalam penelitian ini yaitu kurangnya minat siswa dalam proses pembelajaran karena media pembelajaran yang digunakan masih berupa buku cetak dan  peran guru sangat mendominasi dalam proses pembelajaran pada matapelajaran Geografi. Oleh karena itu perlu adanya media pembelajaran berbasis multimedia interaktif agar siswa aktif belajar dan menciptakan kemandirian belajar siswa. Adapun tujuan dari penelitian ini yaitu: 1) untuk mengetahui pengembangan media pembelajaran berbasis multimedia interaktif pada mata pelajaran Geografi, 2) untuk mengetahui kelayakan media pembelajaran berbasis multimedia interaktif pada interaktif pada mata pelajaran Geografi, 3) untuk mengetahui  siswa setelah menggunakan media pembelajaran berbasis multimedia interaktif pada interaktif pada mata pelajaran Geografi. Metode penelitian yang digunakan dalam yaitu Research and Development (R&D). Metode ini bertujuan mengembangkan produk dan dilakukan penelitian agar layak digunakan oleh siswa. Model pengembangan yang digunakan yaitu Borg and Gall. Alat pengumpul data berupa angket. Teknik analisis data yang digunakan dalam penelitian ini berupa data display dan deskriptif kuantitatif. Hasil penelitian yaitu rata-rata skor penilaian ahli materi adalah 110 sehingga dikategorikan layak. Sedangkan rata-rata skor penilaian ahli media adalah 97 sehingga dikategorikan layak.  Hasil penilaian respon siswa memiliki skor rata-rata 4 dengan kategori baik.
PENERAPAN PENDEKATAN PENDIDIKAN MATEMATIKA REALISTIK BERBASIS ETNOMATEMATIKA UNTUK MENINGKATKAN HASIL BELAJAR SISWA
Authors: Saubaki Ellen A , Leton Samuel Igo , Aleksius Madar
Published: May 2024

Tujuan dari penelitian ini untuk mendeskripsikan penerapan pendekatan Pendidikan Matematika Realistik (PMR) berbasis etnomatematika untuk meningkatkan hasil belajar siswa SMA Negeri 2 Kupang Tengah. Metode penelitian yang digunakan adalah Penelitia Tindakan Kelas (PTK) mengacu pada model Kemmis & McTaggrat, dengan jenis data kualitatif dan kuantitatif. Penelitian ini dilaksanakan di SMA Negeri 2 Kupang Tengah. Pelaksanaan Penelitian Tindakan Kelas (PTK) ini dilaksanakan dengan 4 tahap yaitu : Perencanaan (planning), Pelaksanaan Tindakan (action), Pengamatan (Observation), dan Refleksi (reflection). Hasil penelitian menunjukkan penerapan pendekatan Pendidikan Matematika Realistik (PMR) berbasis etnomatematika dapat meningkatkan hasil belajar SMA Negeri 2 Kupang Tengah. Hal ini dibuktikan dengan presentase belajar siswa yang tuntas pada siklus I yaitu 68.75% kemudian pada siklus II meningkat menjadi 84,375%.
ANALISIS AWAL ASPEK KEBAHASAAN DAN SITUASI KELAS DALAM KEGIATAN LITERASI MENGGUNAKAN BAHASA IBU SEBAGAI BAHASA TRANSISI DALAM PEMBELAJARAN SD
Authors: Sae Floranida Nari , Lawe Yosefina Uge , Laksana Dek Ngurah Laba , Raga Bergita , Lodo Noni Stephana Ariany , Sayangan Yohanes Vianey
Published: May 2024

Penelitian ini bertujuan untuk melakukan analisis awal tentang aspek kebahasaan dan situasi kelas berbasis bahasa ibu. Jenis penelitian ini adalah penelitian deskripstif kualitatif dengan metode pengumpulan data menggunakan metode observasi, wawancara dan dokumentasi. Hasil penelitian menunjukkan bahwa dari aspek analisis kebahasaan dari 14 peserta didik yang kini duduk di kelas 1, peserta didik yang mampu menggunakan bahasa daerah atau bahasa ibu terdiri dari 5 orang sehingga persentasi 35,7% dan 9 orang peserta didik menggunakan bahasa Indonesia sehingga persentasi 64,2%. Sedangkan pada aspek situasi kelas peserta didik dengan kategori senang belajar dengan menggunakan media pembelajaran terdiri dari 5 peserta didik dan memperoleh persentase 35,7%, peserta didik dengan kategori senang belajar menggunakan metode cerita terdiri dari 4 peserta didik sehingga memperoleh persentase 28,5%, peserta didik dengan kategori senang belajar dengan bermain peran terdiri dari 5 peserta didik sehingga memperoleh persentase 35,7%. Dapat ditarik sebuah kesimpulan bahwa aspek kebahasaan berbasis bahasa ibu di UPTD SDN Sobo mencapai kategori sangat rendah dengan persentase 35,7%. Berdasarkan hasil analisis data aspek kebahasaan dan situasi kelas di atas dapat disimpulkan bahwa pembelajaran berbasis bahasa ibu di UPTD SDN Sobo dapat di terapkan untuk mengembangkan literasi siswa.
The Perceptions of Indigenous Language and Cultural Synergy in Ghana and Sierra Leone: A Cross-Cultural Study
Authors: Adom Dickson , Thulla Philip Foday Yamba , Fofanah Ibrahim Mustapha , Nyadu-Addo Ralph , Brewu John Kofi
Published: May 2024

This study examined how young people perceive the revitalization of indigenous languages in multicultural environments. The research team gathered data from a stratified random sample of 200 participants hailing from diverse rural and urban areas in Ghana and Sierra Leone. Employing a convergent parallel mixed-methods design, the study employed questionnaires and interviews to gauge local community perceptions toward indigenous language revitalization in Ghana and Sierra Leone. The team also conducted focus groups to gain qualitative insights. The two data sets were analyzed using both descriptive and inferential statistics, as well as thematic qualitative analysis to reveal the perspectives of study participants on the importance of indigenous language in maintaining cultural legacy. The findings underscored the significance of indigenous languages beyond communication, emphasizing their exposure to external threats such as cultural assimilation and the dominance of foreign languages. The research team calls on the Ministries in charge of culture in Ghana and Sierra Leone to prioritize initiatives aimed at recovering and safeguarding indigenous languages, as a critical step for cultural transmission and preservation.

Keywords: Native Culture, Linguistic Traditions, Cultural Ecosystems, Language, Indigenous Languages, Cultural Synergy
DYNAMIC CAPABILITIES AND PERFORMANCE OF FAMILY BUSINESSES IN EMERGING ECONOMIES
Authors: Rossignoli Cevallos Gina , Guevara Sánchez Daniel
Published: May 2024

Dynamic capabilities analyze the sources and methods of better performance and wealth creation and capture by firms operating in environments of rapid technological changes. Based on this, The objective of this research was to analyze the relationship between absorptive, adaptive, and innovation capabilities on financial and non-financial performance of family businesses was analyzed in the context of emerging economies, a relationship that has not been analyzed in this context. Through the application of structural equation modeling in a sample of 235 family businesses of agricultural supplies and machinery, located in the G46 and G47 categories of the International Standard Industrial Classification – ISIC, the results allowed us to identify that absorptive capability has a positive influence on financial performance, while innovation capability has a positive influence on no-financial performance. No evidence was found that other capabilities were related to the performance of the organizations analyzed. In addition, it was shown that the size of the companies does not generate any moderating effect in the relationship between these variables. This study contributes to dynamic capabilities theory by exploring how absorptive and innovative capabilities influence financial and non-financial performance in a specific and underexplored context: family businesses in emerging economies. Furthermore, the importance of developing and enhancing absorptive and innovation capabilities is highlighted. This could lead to the implementation of training programs, investment in R&D, and adoption of knowledge management practices.
On the Structure of the Sagittarius Spiral Arm in the Inner Milky Way
Authors: Bian S. B. , Wu Y. W. , Xu Y. , Reid M. J. , Li J. J. , Zhang B. , Menten K. M. , Moscadelli L. , Brunthaler A.
Published: May 2024

We report measurements of trigonometric parallax and proper motion for two 6.7 GHz methanol and two 22 GHz water masers located in the far portion of the Sagittarius spiral arm as part of the BeSSeL Survey. Distances for these sources are estimated from parallax measurements combined with three-dimensional kinematic distances. The distances of G033.64−00.22, G035.57−00.03, G041.15−00.20, and G043.89−00.78 are 9.9 ± 0.5, 10.2 ± 0.6, 7.6 ± 0.5, and 7.5 ± 0.3 kpc, respectively. Based on these measurements, we suggest that the Sagittarius arm segment beyond about 8 kpc from the Sun in the first Galactic quadrant should be adjusted radially outward relative to previous models. This supports the suggestion of Xu et al. that the Sagittarius and Perseus spiral arms might merge in the first quadrant before spiraling inward to the far end of the Galactic bar.
Age Analysis of Extrasolar Planets: Insight from Stellar Isochrone Models
Authors: Swastik C. , Banyal Ravinder K. , Narang Mayank , Unni Athira , Sivarani T.
Published: May 2024

There is growing evidence from stellar kinematics and galactic chemical evolution suggesting that giant planets (M

P
≥ 0.3M

J
) are relatively young compared to the most commonly occurring population of small planets (M

P
< 0.3M

J
). To further test the validity of these results, we analyzed the ages for a large number of 2336 exoplanet hosting stars determined using three different but well-established isochrone fitting models, namely, PARSEC, MIST, and Yonsei Yale. As input parameters, we used Gaia DR3 parallaxes, magnitudes, and photometric temperature, as well as spectroscopically determined more accurate temperatures and metallicities from the Sweet Catalog. Our analysis suggests that ∼50%–70% of stars with planets are younger than the Sun. We also find that, among the confirmed exoplanetary systems, stars hosting giant planets are even younger compared to small planet hosts. The median age of ∼2.61–3.48 Gyr estimated for the giant planet-hosting stars (depending on the model input parameters) suggests that the later chemical enrichment of the galaxy by the iron-peak elements, largely produced from Type Ia supernovae, may have paved the way for the formation of gas giants. Furthermore, within the giant planet population itself, stars hosting hot Jupiters (orbital period ≤10 days) are found to be younger compared to the stellar hosts of cool and warm Jupiters (orbital period >10 days), implying that hot Jupiters could be the youngest systems to emerge in the progression of planet formation.
The Complexity of Emerging Magnetic Flux during the Lifetime of Solar Ephemeral Regions
Authors: Yang Hanlin , Jin Chunlan , Wang Zifan , Wang Jingxiu
Published: May 2024

As a relatively active region, the ephemeral region (ER) exhibits a highly complex pattern of magnetic flux emergence. We aim to study in detail the secondary flux emergences (SFEs), which we define as bipoles that appear close to ERs and finally coalesce with ERs after a period of time. We study SFEs during the whole process, from the emergence to the decay of five ERs observed by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. The maximum unsigned magnetic flux for each ER is around 1020 Mx. Each ER has tens of SFEs with an average emerging magnetic flux of approximately 5 × 1018 Mx. The frequency of normalized magnetic flux for all the SFEs follows a power-law distribution with an index of −2.08. The majority of SFEs occur between the positive and negative polarities of an ER, and their growth time is concentrated within 1 hr. The magnetic axis of SFE is found to exhibit a random distribution in the five ERs. We suggest that the relationship between SFEs and ERs can be understood by regarding the photospheric magnetic field observations as cross sections of an emerging magnetic structure. Tracking the evolution of ERs, we propose that these SFEs in ERs may have emerged consequentially from the bundle of flux tubes of ERs and that SFEs are partially emerged Ω-loops.
Exploring the Maximum Magnitude versus Rate of Decline Relation for Novae in M31
Authors: Clark J. Grace , Hornoch Kamil , Shafter Allen W. , Kučáková Hana , Vraštil Jan , Kušnirák Peter , Wolf Marek
Published: May 2024

The results of a two-decade-long R-band photometric survey of novae in M31 are presented. From these data, R-band light curves have been determined for 180 novae with data sufficient for estimating the peak brightness and subsequent rate of decline. The data show a weak correlation of peak brightness with fade rate consistent with the well-known maximum magnitude versus rate of decline (MMRD) relation. As generally appreciated for Galactic novae, the large scatter in the MMRD relation precludes its use in determining distances to individual novae. The novae at maximum light are distributed with standard deviation σ = 0.89 mag about a mean R-band absolute magnitude given by 〈M

R
〉 = −7.57 ± 0.07. The overall M31 luminosity distribution is in excellent agreement with that found for Galactic novae suggesting that the nova populations in M31 and the Galaxy are quite similar. The notion that all novae can be characterized by a standard luminosity 15 days after maximum light (M
15) is also explored. Surprisingly, the distribution of M
15 values is characterized by a standard deviation only slightly smaller than that for novae at maximum light and thus offers little promise for precise extragalactic distance determinations. A dozen faint and fast novae that are likely to be previously unidentified recurrent novae have been identified from their position in the MMRD plot and in the M
15 distribution.
An improved approximate method for solving two-dimensional time-fractional-order Black-Scholes model: a finite difference approach
Authors: Prathumwan Din , Khonwai Thipsuda , Phoochalong Narisara , Chaiya Inthira , Trachoo Kamonchat
Published: May 2024

In this paper, we considered the two-dimensional fractional-order Black-Scholes model in the Liouville-Caputo sense. The Black-Scholes model was an important tool in the financial market, used for determining option prices in the European-style market. However, finding a closed-form analytical solution for the fractional-order partial differential equation was challenging. To address this, we introduced an improved finite difference method for approximating the solution of the two-dimensional fractional-order Black-Scholes model in the Liouville-Caputo sense, based on the Crank-Nicolson finite difference method. This method combined the concepts of the finite difference method for solving the multidimensional Black-Scholes model and the finite difference method for solving the fractional-order heat equation. We analyzed the conditional stability and the order of convergence. Furthermore, numerical examples were provided to illustrate the determination of option prices.
Phenomenology of Caste Violence in Education and Limitation of Empathy: A Vimukta Perspective
Authors: Goswami Shweta
Published: May 2024

This article is written from the author’s position as a Vimukta woman and an advocate of feminism. The article attempts to include the unique situatedness of Vimukta in the discourse on caste violence in education. In doing so, it critically revisits the decade-old ongoing debate over the question, can Savarna feminists write on the experience of Dalit, Adivasi, and Vimukta women? The article highlights the limitations of the claim that empathy makes the experiences of Dalit, Adivasi, and Vimukta women accessible to Savarna feminists. It engages with the writings on the website “Savari” in which women belonging to Dalit, Adivasi, Vimukta, and backward communities share their articulation about their situation in caste society and call out the violence in Savarna feminists’ academic endeavors.
Detection Avoidance in UK Border Immigration Crime
Authors: de Oliveira Julia
Published: May 2024

As record uncontrolled migration takes place across the world driven by war and other factors, border related immigration crime has soared. In the UK's case, criminal human trafficking gangs and other individuals employ a range of detection avoidance measures to achieve their aim of moving people or themselves across sea, land, and national borders. Such crime is problematic because it is often a mere precursor to other intended crimes; for example, engaging in modern slavery practices or furthering terrorism. A wide range of detection avoidance methods may be used to meet criminal objectives, including disguise of identity as well as identity document or technology abandonment and destruction. The variable nature of these methods has meant the Government's strategy has invoked a plethora of countermeasures in response. Drawing also on approaches in other countries, this chapter analyses the response by public authorities in the UK to this persistent and growing issue.
Constructive Manoeuvring of the Interconnected World
Authors: Kanojia Siddharth
Published: May 2024

The practice of outsourcing tasks to a large group or community, typically through an open call on the internet or mass media, with the aim of harnessing collective intelligence, skill sets, and creativity to address a critical problem or achieve a specific goal, is known as 'crowdsourcing.' In criminal investigations, law enforcement agencies often employ this approach to gather information, tips, or leads from the public via social media platforms, online forums, or mobile applications. Crowdsourcing emerges as a potent tool for police investigations due to its capacity to swiftly reach a broad audience and uncover information that might otherwise remain unnoticed. Moreover, it fosters trust between law enforcement and the community, showcasing a collaborative effort to solve crimes. This chapter critically analyses the efficacy and inefficacy of employing crowdsourcing in erstwhile criminal investigations by drawing insights from prominent cases influenced by this method.
Privilege Escalation
Authors: Özdemir Gencay , Tuna Gurkan
Published: May 2024

Considering we use technology in almost every area of our daily life, and the fact that the internet has become a part of our lives, the size of the risks and threats it brings has grown considerably. The expansion of the cyber environment day by day has transformed cyber attack methods into a system that updates itself day by day. Many methods continue to be developed to ensure information security in the cyberspace environment. The main objective of this chapter is to examine the vulnerabilities of privilege escalation used by cyber attackers and to explain what can be encountered in possible attack scenarios; measures that can be taken and methods that can be applied.
Touch DNA
Authors: Neole Nupoor Gopal
Published: May 2024

Touch DNA analysis has emerged as a powerful tool in forensic science for the identification and profiling of individuals based on the DNA left behind through direct contact with surfaces. This chapter is on touch DNA analysis, focusing on its principles, methodologies, applications, and challenges. Touch DNA refers to the cellular material transferred from the skin to objects during routine human activities, such as handling, grasping, or touching. Touch DNA analysis plays a crucial role in criminal investigations, allowing forensic scientists to link individuals to crime scenes, objects, or victims, even in the absence of visible biological evidence. Overall, touch DNA analysis represents a valuable forensic tool for identifying perpetrators, exonerating the innocent, and contributing to the administration of justice in criminal proceedings.
An Investigation Into Training and Mentoring Practices Within the Prison Estate
Authors: Leonard Liam J.
Published: May 2024

This chapter will investigate the basis for the teaching of integrity-based competencies to prison officers as part of their training. This training underpins the performance of prison officers in the execution of their daily workplace duties. At the heart of this study is a desire to understand and explain how a prison officer can be taught to go beyond what is the basic requirement in their tasks, in order to deliver the ‘safe and humane' service required of them in the prison system. The degree of success in achieving this form of elevated integrity within the prison can be seen to impact upon the lives of the prisoners in the officer's care, and on wider society as a whole. For instance, the challenges of dealing with concealment and detection avoidance of illicit substances can create problems for the inexperienced officer. Therefore, their training becomes important in successfully overcoming concealment of illegal materials. The chapter will also investigate mentoring as a key form of learning within prisons. While the world of prison is one which is closed to many in society, the author gained insights when he worked as an ‘embedded criminologist,' working as a lecturer on a prison training programme for five years between 2008 and 2013. This provided him with valuable criminological and penological understandings of the hidden world of the prison system, as well as the officers who work behind their walls.
Friend or Foe?
Authors: Scheidt Nancy
Published: May 2024

In this day and age, it is difficult to imagine technology not being part of our everyday life. However, such can also hold the power to be used for activities that an average consumer may not partake in. This chapter focuses on anti-digital forensics and digital forensics methods. Hence, it examines detection avoidance strategies and establishes current investigation and prevention methods when a crime is committed with the help of technologies within cyberspace, reaching from device forensics to data hiding. The cases of the San Bernardino shooting, hacktivist group ‘Anonymous,' EncroChat, and the Shadowz Brotherhood are discussed, examining how offenders utilise technologies such as encryption and data wiping to try to ‘outrun' authorities as well as methods authorities implement to keep up with technological advances to prevent and detect these criminal activities.
Cyber Threat Intelligence for Lightweight Trust Architectures in Medical IoT Environments
Authors: Chaki Shreeja , Bandyopadhyay Saubhik
Published: May 2024

We explore the integration of cyber threat intelligence (CTI) in the internet of medical things (IoMT) with lightweight trust architectures. Addressing the unique vulnerabilities of IoMT devices, the authors survey for resource-efficient CTI architectures suitable for the constrained nature of medical IoT devices. The framework's core is the discussion of adaptive algorithms and models designed for real-time threat detection and response, essential in a landscape where cyber threats are increasingly sophisticated. The authors emphasize the synergy between CTI and lightweight cryptographic solutions, demonstrating their combined effectiveness in reinforcing IoMT security. The novelty aspect of the survey lies in the discussion of lightweight models in practical use-cases. The chapter outlines development roadmap for robust and trustworthy medical IoT ecosystems, highlighting the need for ongoing innovation in this critical area. This contribution aims to guide future efforts in securing IoMT environments, ensuring patient safety and data integrity in an increasingly digital healthcare landscape.
Designing a Secure and Lightweight Ecosystem for Internet of Medical Things (IoMT) in Healthcare
Authors: Joel M. Robinson , Ebenezer V. , Jenefa A. , Sagayam K. Martin , Rajan J. Jerlin , Mandali Deepak
Published: May 2024

The surge in internet of things (IoT) devices, especially in healthcare's internet of medical things (IoMT), demands a nuanced balance between security and resource constraints. This chapter outlines a design framework for a lightweight and secure IoMT ecosystem, focusing on lightweight cryptography, secure routing protocols, and critical trade-offs. Careful selection of cryptographic algorithms like AES-CCM and Keccak addresses medical device limitations. Proposing routing protocol for low power and lossy networks (RPL) for resource-constrained contexts, the chapter advocates secure neighbor discovery algorithms and cryptographic protocols to fortify the network, ensuring communication legitimacy. Building a secure IoMT involves navigating trade-offs, balancing latency vs security, scalability vs security, and maintaining current security protocols while minimizing damage. Reliable authentication and access control are crucial, verifying identities and safeguarding medical information security and privacy.
Digital Watermarking Strategies for Healthcare Data Security
Authors: Kumari Aditi , Madhukar Harshek , Haider Shagufa Ali , Majumder Anandaprova , Kundu Sumana
Published: May 2024

This chapter extensively delves into the realm of digital image watermarking, encompassing comparisons with analogous information security methodologies, elucidations of watermark embedding and extraction procedures, discussions on watermark attributes and applications, overviews of prevalent watermarking tactics, and a concise recapitulation of secure watermarking approaches. The primary focus of this discourse lies in advanced strategies for shielding healthcare data using digital watermarking techniques, with special emphasis on the integration of discrete wavelet transform (DWT), discrete cosine transform (DCT), and singular value decomposition (SVD). Recognizing the inherent challenges in safeguarding medical data amidst the dynamic healthcare landscape, the investigation explores multifaceted watermarking algorithms. Leveraging foundational principles from DWT, DCT, and SVD, the study amalgamates prior research and provides comparative evaluations of the efficacy of these methodologies through tabular representations.
Early Parkinson's Disease Diagnosis Using Multi-Modal CASENet CNN-LSTM
Authors: Gayathri N. , Rakesh Kumar S. , Reddy U. Janardhan , Reddy Midde Ranjit , Ravikanth G.
Published: May 2024

By analyzing the deviation of features earlier stages can be segmented with subtle patterns in patients' handwriting dynamics and voice recordings, this innovative method showcases deep learning's potential to revolutionize medical diagnostics. By applying Casenet convolutional neural network framework, a hybrid architecture incorporating CNNs and improved long short-term memory networks is implemented using Kaggle datasets, which excels in spatial feature extraction from handwriting features with individual cases. while LSTM captures temporal patterns from voice recordings. Demonstrating a robust 94.6% accuracy rate, the model proves its effectiveness in Parkinson's disease prediction in earlier stages that can support complete diagnosis. Model assessment includes precision, recall, and F1-score evaluations using Principal Component Analysis (PCA) by integrating the Casenet CNN framework to enhance the diagnosis system and reliable accuracy that can predict early detection of Parkinson's disease from multimodal data.
IoMT and the Urban Tapestry
Authors: Kaur Jaspreet
Published: May 2024

The incorporation of the internet of medical things (IoMT) into the urban tapestry inside smart city frameworks holds the potential to bring about a significant change in the way healthcare services are provided. This study utilizes a variety of research methodologies, such as surveys, interviews, data analytics, and existing datasets, to collect insights from healthcare professionals, city planners, and consumers. The results demonstrate the significant capacity of IoMT to improve preventive healthcare, optimize resource allocation, and promote innovative healthcare models. Privacy and teamwork are identified as crucial factors. The integration of IoMT into urban infrastructure offers a strategic plan for developing smart cities that are not only more interconnected, but also promote better health and resilience.
Influencer Marketing in Navigating Trends and Ethical Considerations
Authors: Nadeem Iram , Tunga Jashmitha , Badsheshi Shradha , Reddy Vijaya Paidi
Published: May 2024

In the rapidly evolving landscape of marketing and consumer science, influencer marketing has surged to the forefront as a transformative force in shaping brand relationships. This book chapter delves comprehensively into the dynamic realm of influencer marketing, intricately exploring its integration within the evolving service economy and its profound impact on shaping consumer preferences. A key focus is directed toward understanding how internet platforms, artificial intelligence, and big data synergize to enhance marketing strategies through influencers.
“The Sense” and Its Manifestations Launch New Trends in Marketing
Authors: Nair Lakshmi R.
Published: May 2024

The chapter investigates the latest developments in marketing and consumer science. The study deepens and strengthens the prospects of marketing. In the process, human sense plays a vital role. The study seeks the assistance of hermeneutics to understand the permutations and combinations generated by a plethora of multifarious conceptual variations in both perception and aesthetics. The “sense” influences marketing of goods and products in business. The proposed configurations relate business functions with aesthetic characteristics. The first part by the research picks the application of senses in the perception level. The second part the researcher chooses sense in the cognition level. The whole study substantiates how Aesthetics and Sense during last decades generate a fast progress in business and marketing.
Bridging Genomic Data and CRM
Authors: Behare Nitesh , Mahajan Rashmi D. , Behare Shubhada Nitesh , Somani Pritesh Pradeep , Iyer Vishwanathan , Megha Ashwin , Gupta Shital
Published: May 2024

The combination of genetic data and CRM systems has pushed customer engagement into a new world, totally transforming the traditional marketing strategies. This chapter explores the impact of the relationship between genomics and CRM together on understanding consumer behaviour, personalization, and targeted marketing. This chapter starts with the overview of using genomic data for understanding marketing. The discussion then progresses to the core concepts of CRM and the importance of the need of integrating CRM strategies with genomic data. Real world case studies are demonstrated highlighting the benefits of integrating genomic data and CRM. Emphasising the importance of handling the data with responsibility and maintaining the customer privacy, this chapter also addresses the arising challenges, explores the enhancements in the technological infrastructure required to facilitate the integration, and predicts the emerging trends. It also provides practical tips to find ways to adapt to this changing landscape and set the foundation for a customer centric future driven by genomic data.
Leveraging Text Analytics to Enhance Marketing Insights From Digital Customer Experiences
Authors: Tuna Murat F.
Published: May 2024

The manuscript underscores the growing importance of digital customer experiences and the role of text analytics in enhancing marketing insights. It emphasizes the crucial role of customer experience, stressing the necessity to comprehend and analyze customer feedback for service improvement. The text details how companies can use text analytics to scrutinize customer reviews, discern emotions, and understand sentiments. Additionally, the text explores the evolution of customer experience (CX) and the shift towards digital customer experiences (DCX) due to innovative technologies like mobile devices, Web 3.0, artificial intelligence, robotics, and the internet of things. The literature review covers various aspects of customer experience, including value co-creation, brand loyalty, and satisfaction. It also examines the impact of DCX in sectors such as healthcare, hospitality, tourism, and retailing, exploring the effects of advanced technologies like mobile devices, Web 3.0, artificial intelligence, robotics, and the internet of things on enhancing digital customer experiences.
Revisiting Visual Communication
Authors: Gkiouzepas Lampros , Tarnanidis Theodore
Published: May 2024

A new approach to advertising visuals has been advocated that sees images as complex figurative arguments, which are processed cognitively rather than peripherally and automatically. Have visuals therefore acquired a status similar to that of words in advertising? Consumer research has not yet demonstrated the ability of advertising visuals to directly persuade consumers about products' possession of specific attributes. The establishment of the ability of visuals to make direct claims and therefore persuade viewers seems to be an important step towards the recognition of images as an independent system of argumentation. To this aim, the authors develop a theoretical framework that explains how a categorization-based approach to images can help understand visual rhetoric in advertising. Building on categorization theory, the present chapter delineates how consumers process visual ads claim that vary in strength.
Harnessing the Power of AI for Information Management and User Engagement in Next-Generation Libraries
Authors: Vijesh Jaya K.
Published: May 2024

In this era of digital transformation, libraries are evolving into next-generation knowledge hubs by harnessing the power of artificial intelligence (AI). This research explores the integration of AI to enhance information management and user engagement in libraries. The study investigates diverse AI applications, methodological approaches, and their impact on library systems. By examining intelligent systems and algorithms, the research aims to uncover how AI technologies can streamline information organization, retrieval, and dissemination, ultimately contributing to a more efficient and user-friendly library experience. Additionally, the study delves into user engagement strategies, leveraging AI to tailor services based on individual preferences and behaviors.
Exploring the Top ChatGPT Libraries for Powerful Conversational AI
Authors: Thaliyan Delma , Joseph Joe , Helen Josephine V. L.
Published: May 2024

This research analyzes the need for top ChatGPT libraries and their usage today. It focuses on the features, benefits, and drawbacks of ChatGPT through in-depth interviews to uncover nuanced insights into library preferences and usage patterns. The findings reveal diverse perspectives among developers, highlighting preferences for libraries based on ease of integration and customization. Participants emphasize the significance of user-friendly interfaces and robust documentation in shaping positive experiences with ChatGPT libraries. Additionally, themes emerge around the need for continuous updates to address evolving AI challenges. Through in-depth insights from developers, the study elucidates the importance of user-friendly interfaces, adaptability, and efficient documentation, providing valuable guidance for developers seeking to optimize their conversational AI projects. This research contributes essential qualitative dimensions to the evaluation of ChatGPT libraries, offering actionable insights for the development community.
AI for Accessibility
Authors: Kishore Henry S. , Solomon Paul Raj D. , Senthilkumar K. R.
Published: May 2024

This chapter examines integrating artificial intelligence (AI) technologies to enhance library services for users with disabilities, aiming to contribute to the accessibility and inclusivity of library resources. The study adopts a mixed-methods approach, combining quantitative user experience surveys with qualitative interviews to assess the impact of AI-driven tools—such as voice recognition, text-to-speech, and AI-powered search and recommendation systems—on the accessibility of library services for individuals with visual, auditory, and mobility impairments.
Phytochemical, antimicrobial and proximate analysis of Costus spectabilis grown in different soil types
Authors: Bankole Abimbola Esther , Ebabhi Abosede Margaret , Umebese Caroline , Owagboriaye Folarin , Thomas Emmanuel
Published: May 2024

The search for medicinal plants with novel properties for therapeutical potentials is a continuum. The preliminary phytochemical analysis, antimicrobial properties and proximate contents of Costus spectabilis leaf and root harvested from different soil types were studied following standard protocols. The ethyl ethanoate extracts were assayed against six microbial cultures of Staphylococcus aureus (ATCC 29523), Pseudomonas aeruginosa (ATCC 27853), Bacillus subtilis (ATCC 6633), Aspergillus niger, Candida albicans, and Aspergillus clavatus using the agar well diffusion methods. The phytochemical analysis revealed the presence of alkaloids, saponin, phenol, flavonoids and anthraquinone in all samples at varying concentrations. Terpenes and phlobatannins were not observed in any extract. Phenolic compound was observed to have the highest quantity of 6.18±0.00 mg/g from the leaf extract of the plant grown in loamy soil. Tannin and flavonoids were only detected in very insignificant quantities. The extracts showed significant inhibitory potential against the bacterial strains. The highest zone of inhibition was recorded as 33.02±1.45 mm against B. subtilis in the leaf extracts of the plant grown in clay soil. The leaf extracts from the plant obtained from clay soil showed fungicidal activity against the Aspergillus species, while the other extracts only showed fungistatic effects against all sampled fungal species. The proximate content analysis revealed that carbohydrates and protein are the dominant nutrients in the leaf of C. spectabilis. Costus spectabilis is a plant that can be exploited for its medicinal, nutritional and economic values.
Neutrophil-Derived Peptidyl Arginine Deiminase Activity Contributes to Pulmonary Emphysema by Enhancing Elastin Degradation
Authors: Murphy Mark P , Hunt David , Herron Malcolm , McDonnell Jake , Alshuhoumi Rashed , McGarvey Lorcan P , Fabré Aurelie , O’Brien Helen , McCarthy Cormac , Martin S Lorraine , McElvaney Noel G , Reeves Emer P
Published: May 2024

In chronic obstructive pulmonary disease (COPD), inflammation gives rise to protease-mediated degradation of the key extracellular matrix protein, elastin, which causes irreversible loss of pulmonary function. Intervention against proteolysis has met with limited success in COPD, due in part to our incomplete understanding of the mechanisms that underlie disease pathogenesis. Peptidyl arginine deiminase (PAD) enzymes are a known modifier of proteolytic susceptibility, but their involvement in COPD in the lungs of affected individuals is underexplored. In this study, we showed that enzyme isotypes PAD2 and PAD4 are present in primary granules of neutrophils and that cells from people with COPD release increased levels of PADs when compared with neutrophils of healthy control subjects. By examining bronchoalveolar lavage and lung tissue samples of patients with COPD or matched smoking and nonsmoking counterparts with normal lung function, we reveal that COPD presents with markedly increased airway concentrations of PADs. Ex vivo, we established citrullinated elastin in the peripheral airways of people with COPD, and in vitro, elastin citrullination significantly enhanced its proteolytic degradation by serine and matrix metalloproteinases, including neutrophil elastase and matrix metalloprotease-12, respectively. These results provide a mechanism by which neutrophil-released PADs affect lung function decline, indicating promise for the future development of PAD-based therapeutics for preserving lung function in patients with COPD.
A Novel Deep Learning Model for Pancreas Segmentation: Pascal U-Net
Authors: Kurnaz Ender , Ceylan Rahime , Bozkurt Mustafa Alper , Cebeci Hakan , Koplay Mustafa
Published: May 2024

A robust and reliable automated organ segmentation from abdomen images is a crucial problem in both quantitative imaging analysis and computer aided diagnosis. Especially, automatic pancreas segmentation from abdomen CT images is most challenging task which based on in two main aspects (1) high variability in anatomy (like as shape, size, etc.) and location across different patients (2) low contrast with neighboring tissues. Due to these reasons, achievement of high accuracies in pancreas segmentation is hard image segmentation problem. In this paper, we propose a novel deep learning model which is convolutional neural network-based model called Pascal U-Net for pancreas segmentation. Performance of the proposed model is evaluated on The Cancer Imaging Archive (TCIA) Pancreas CT database and abdomen CT dataset which is taken from Selcuk University Medicine Faculty Radiology Department. During the experimental studies, k-fold cross-validation method is used. Furthermore, results of the proposed model are compared with results of traditional U-Net. If results obtained by Pascal U-Net and traditional U-net for different batch size and fold number is compared, it can be seen that experiments on both datasets validate the effectiveness of Pascal U-Net model for pancreas segmentation.
Machine Learning-based Extrapolation of Crop Cultivation Cost
Authors: Bari Poonam , Ragha Lata
Published: May 2024

It is important to comprehend the relation between operational expenses such as labour, seed, irrigation, insecticides, fertilizers and manure costs necessary for the cultivation of crops. A precise cost for the cultivation of crops can offer vital information for agricultural decision-making. The main goal of the study is to compare machine learning (ML) techniques to measure relationships among operational cost characteristics for predicting crop cultivation costs before the start of the growing season using the dataset made available by the Ministry of Agriculture and Farmer Welfare of the Government of India. This paper describes various ML regression techniques, compares various learning algorithms as well as determines the most efficient regression algorithms based on the data set, the number of samples and attributes. The data set used for predicting the cost with 1680 instances includes varying costs for 14 different crops for 12 years (2010-2011 to 2021-2022). Ten different ML algorithms are considered and the crop cultivation cost is predicted. The evaluation results show that Random Forest (RF), Decision Tree (DT), Extended gradient boosting (XR) and K-Neighbours (KN) regression provide better performance in terms of coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) rate while training and testing time. This study also compares different ML techniques and showed significant differences using the statistical analysis of variance (ANOVA) test. The optimal hyperparameters for the ML models are found using the gridsearchCV and randomizedsearchCV functions, which improves the model's capacity for generalisation.