bluuebunny/crossref_metadata_2025 huggingface dataset:
Pengaruh Disiplin Kerja Dan Lingkungan Kerja Terhadap Kinerja Karyawan Melalui Kepuasan Kerja Pada Direktorat Intelkam Polda Ntt (Literatur Review Manajemen Sumber Daya Manusia)
Authors: Asrul Hendrawan Karabi  , John E. H. J. FoEh
Published: April 2024

Studi sebelumnya atau studi yang sesuai memiliki peran vital dalam penulisan karya ilmiah karena mampu menguatkan teori atau korelasi dan pengaruh antarvariabel. Karya ilmiah berbentuk artikel review ini hendak mengulas apakah disiplin kerja memengaruhi kinerja karyawan melalui kepuasan kerja di Direktorat Intelkam Polda NTT. Hasil dari kajian pustaka  dan studi hasil penelitian sejenis, dapat mempergunakan metode analisis SEM PLS, maka mendapat konklusi, yaitu disiplin kerja (X1) memberikan pengaruhnya pada (Z); lingkunngan kerja (X2) memberikan pengaruhnya pada kepuasan kerja (Z); kepuasan kerja (Z) memberikan pengaruhnya pada kinerja pegawai (Y); disiplin kerja (X1) memberikan pengaruhnya pada kinerja pegawai (Y); lingkungan kerja (X2) memberikan pengaruhnya pada kinerja pegawai (Y); kepuasan kerja (Z)  dapat memedasi displin kerja (X1) dalam memengaruhi kinerja pegawai (Y); dan kepuasan kerja (Z) dapat memediasi lingkungan kerja (X2) dalam memengaruhi kinerja pegawai (Y)
Literature Review: Peran Situational Leadership dalam Meningkatkan Kinerja Guru
Authors: Rusmiati Rusmiati , Djumarno Djumarno , Farida Elmi  , Anik Herminingsih
Published: April 2024

Kinerja guru berperan dalam meningkatkan motivasi dan hasil belajar siswa. Penelitian ini bertujuan untuk memberikan gambaran mengenai hubungan kepemimpinan situasional dengan kinerja. Metode penelitian yang digunakan adalah kualitatif dengan tinjauan pustaka. Telusuri artikel menggunakan Google Scholar dengan kata kunci “kepemimpinan situasional” dan “kinerja karyawan”. Artikel yang dipilih adalah dari tahun 2016 hingga 2023. Sebanyak 30 artikel penelitian dipilih dan kemudian diberikan gambarannya. Hasil penelitian menemukan bahwa 11 dari 30 artikel menyatakan hubungan yang tidak signifikan antara kepemimpinan situasional dengan kinerja dan keberlanjutan menyatakan hubungan yang signifikan. Hanya 1 dari 30 artikel yang diteliti bernuansa pendidikan. Artikel penelitian sebagian besar berasal dari Indonesia. Penelitian selanjutnya dapat menggunakan mediasi dalam menghubungkan kepemimpinan situasional dengan kinerja guru seperti kepuasan kerja, motivasi kerja, kesinambungan karyawan, perilaku kewarganegaraan organisasi, total quality manajemen.
Pengaruh Motivasi, Disiplin Kerja dan Lingkungan Kerja Terhadap Kinerja Karyawan dengan Komitmen Sebagai Variabel Intervening (Literature Review Manajemen Sumber Daya Manusia)
Authors: Jovanka Pittaloka  , John E. H. J. FoEh
Published: April 2024

Setiap organisasi harus dapat mengelola sumber daya manusia yang tersedia. Mengelola sumber daya manusia erat kaitannya dengan faktor pegawai yang bisa mendapatkan tujuan organisasi. Ada beberapa faktor yang berimbas pada rendahnya kinerja pegawai di suatu organisasi sehingga penulisan artikel ini memfokus pada pengaruh motivasi, disiplin kerja maupun lingkungan kerja dengan komitmen merupakan variabel intervening. Hasil kajian pustaka dan memakai metode analisis SEM PLS sehingga mendapat konklusi motivasi memberi pengaruhnya bagi kinerja pegawai; disiplin kerja memberi pengaruhnya bagi kinerja pegawai; lingkungan kerja memberi pengaruhnya bagi kinerja; motivasi memberi pengaruhnya bagi komitmen; disiplin kerja memberi pengaruhnya bagi komitmen; lingkungan kerja mempengaruhi komitmen; komitmen mempengaruhi kinerja pegawai; motivasi, disiplin kerja, dan lingkungan kerja memberi pengaruhnya bagi kinerja karyawan melalui komitmen.
Analisis Strategi Digital Marketing dalam Meningkatkan Penjualan Bisnis Online Pada Suhada Grup
Authors: Laila Fitria  , Fauzi Arif Lubis  , Nurbaiti Nurbaiti
Published: April 2024

Untuk memperdalam pengetahuan kita tentang pemasaran online penelitian ini mengenai, Analisis Strategi Digital Marketing Dalam Meningkatkan Penjualan Bisnis Online Pada Suhada Group, fokus pada fungsi strategi ini dalam meningkatkan penjualan untuk Suhada Group. Tindakan yang optimal untuk meningkatkan jumlah penjualan di toko online Suhada Group akan ditentukan dan diukur dalam penelitian ini. Metode analisis yang digunakan adalah metodologi analisis SWOT (Strength, Weakness, Opportunity, Threat). Responden dalam penelitian ini adalah pemilik toko online. Sebelum dilakukan pengujian, kegiatan penelitian diolah dengan menggunakan matriks Internal Factors Analysis (IFA) dan External Factors Analysis (EFA). Berkenaan dengan hasil penelitian, IFA mendapat skor 3,72 dan EFA mendapat skor 3,54. Hasil pemetaan menunjukkan bahwa "Toko Online Suhada Group" berada di kuadran I pemasaran. Hal ini menunjukkan bahwa perusahaan ini kompetitif, atau bersaing dengan cara yang kuat, artinya pengembangan perusahaan akan membutuhkan rencana yang tepat agar dapat terus tumbuh atau mencapai tahap ekspansi. Untuk mempertahankan posisi ini, ada dua strategi pemasaran yang harus dilakukan: promosi dan penciptaan produk baru.
Social Listening Reports, Health Priorities and Systemic Review of Social Listening Reports
Authors: Combrink Herkulaas , Mkungeka Phelokazi
Published: April 2024

This project involves conducting a systematic review with a focus on health intelligence derived from South African national social listening reports spanning from 2021 to 2023. These reports, totaling 91 in number, represent a comprehensive collection of data obtained through social listening platforms, capturing public discussions, perceptions, and concerns related to health, particularly amidst the COVID-19 pandemic. Using machine learning and natural language processing techniques, the review aims to extract valuable insights regarding health priorities and types of misinformation circulating within the South African population. By adopting a health intelligence perspective, the study seeks to understand the nuances of health-related discourse and misinformation dynamics, informing future public health strategies, risk communication efforts, and community engagement initiatives.
Comparing the effectiveness of pitfall traps and active sampling methods for ants and spiders in a Chromolaena odorata invaded site
Authors: Lauchande Vanessa , Mntambo Sinenhlahla , Hlongwane Zabentungwa , Munyai Thinandavha  Caswell
Published: April 2024

Background: Active and passive arthropod sampling techniques have their specific limitations. Pitfall trapping is a commonly used passive sampling method, and bush beating, aerial hand collection above the knee, aerial hand collection below the knee cryptic and non-cryptic are widely used active sampling techniques.
Objective and method: Pitfall traps and four active sampling techniques were used in a Chromolaena odorata invaded site to compare the methods used in sampling arthropods in Buffelsdraai Conservancy outside the city of Durban, South Africa.
Results: Pitfall traps were the most efficient and the most effective sampling technique with high species richness for both the ant (78%) and spider (76%) samples. One explanation for these differences could be the longer sampling time for passive sampling compared to active sampling.
Conclusion: Compared to the subjective identification of species by collectors in active techniques, the non-selective capturing of species by pitfall traps improves its efficiency. The fewest taxa and individuals were collected by aerial hand collection techniques but these techniques are recommended to supplement pitfall traps. The combination of methods allows for the adequate sampling of the various strata found in vegetatively complex sites. An investigation into the possible use of canopy techniques in C. odorata sites would be beneficial, as it considers the various vegetation strata when sampling for biodiversity.
Is the RSGC4 (Alicante 8) Cluster a Real Star Cluster? Peculiar Radial Velocities of Red Supergiant Stars
Authors: Chun Sang-Hyun , Myeong GyuChul , Lee Jae-Joon , Oh Heeyoung
Published: April 2024

Young massive star clusters, like the six red supergiant clusters in the Scutum complex, provide valuable insights into star formation and galaxy structures. We investigated the high-resolution near-infrared spectra of 60 RSG candidates in these clusters using the Immersion Grating Infrared Spectrograph. Among the candidates in RSGC4, we found significant scattering in radial velocity (−64 to 115 km s−1), unlike other clusters with velocities of ∼100 km s−1. Most candidates in RSGC4 have Q

GK

s

values larger than 1.7, suggesting that they could be early AGB stars. Four candidates in RSGC4 exhibit infrared excess and distinct absorption features absent in other candidates. Two of these stars exhibit absorption lines resembling those of D-type symbiotic stars, showing radial velocity changes in multiepoch observations. Analysis of relative proper motions revealed no runaway/walkaway stars in RSGC4. The dynamic properties of RSGC4 and RSGC1 differ from the disklike motions of other clusters: RSGC4 has low normalized horizontal action J
hor = J

ϕ
/J
tot and vertical action J
ver = (J
z − J
R)/J
tot values and high eccentricities, while RSGC1 has vertical motions with high J
ver values and inclinations. We propose that RSGC4 may not be a genuine star cluster but rather a composite of RSGs and AGB stars distributed along the line of sight at similar distances, possibly originating from various environments. Our results suggest a complex and hierarchical secular evolution of star clusters in the Scutum complex, emphasizing the importance of considering factors beyond density crowding when identifying star clusters in the bulge regions.
Ion Heating by a Fast Magnetosonic Turbulence in the Solar Corona
Authors: Markovskii S. A. , Vasquez Bernard J.
Published: April 2024

Observational data at heliocentric distances of tens of solar radii suggest that fast magnetosonic modes make up a considerable fraction of the solar wind fluctuations. Furthermore, this fraction appears to increase closer to the Sun. We carry out three-dimensional kinetic simulations with particle ions and fluid electrons to evaluate the proton and alpha-particle heating produced by the damping of the fast waves in the solar corona. Realistic parameters at 5 solar radii, including the fluctuation amplitude, are used. We show that, due to the cyclotron resonance, the alphas are heated preferentially perpendicularly to the magnetic field and much more strongly than the protons. The presence of the alpha particles alters the energy partition by reducing the heating of the protons. Nevertheless, the proton heating is sufficient to account for the solar wind acceleration.
The Host Galaxy of a Dormant, Overmassive Black Hole at z = 6.7 may be Restarting Star Formation
Authors: Pacucci Fabio , Loeb Abraham , Juodžbalis Ignas
Published: April 2024

JWST is discovering a large population of z > 4 supermassive black holes (SMBHs) that are overmassive with respect to the stellar content of their hosts. A previous study developed a physical model to interpret this overmassive population as the result of quasar feedback acting on a compact host galaxy. In this Note, we apply this model to JADES GN 1146115, a dormant SMBH at z = 6.7 whose mass is ∼40% of the host’s mass in stars and accreting at ∼2% of the Eddington limit. The host has been forming stars at the low rate of ∼1 M
⊙ yr−1 for the past ∼100 Myr. Our model suggests that this galactic system is on the verge of a resurgence of global star formation activity. This transition comes after a period of domination by the effect of its overmassive black hole, whose duration is comparable to typical quasar lifetimes.
Successions of terrestrial invertebrate communities during the Tsey Glacier retreat, Central Caucasus
Authors: Makarova Olga L. , Antipova Maria D. , Babenko Anatoly B. , Bushueva Irina S. , Chulei Artemii D. , Golovatch Sergei I. , Doroshina Galina Y. , Kolesnikov Vasiliy B. , Mazei Yuri A. , Makarov Kirill V. , Palatov Dmitry , Ponomarev Alexander V. , Popov Konstantin P. , Rapoport Irina B. , Semionenkov Oleg I. , Snegovaya Nataly Y. , Sheftel Boris I. , Tsyganov Andrey N. , Turbanov Ilya S. , Zuev Roman V.
Published: April 2024

In the Caucasus, the total area taken up by glaciers is known to have reduced by 23% over the last 20 years. This natural experiment allows for additive and replacement models of autogenic succession of biocoenoses within paraglacial landscapes to be tested. A certain risk of the extinction of cryophilic species also exists. However, montane paraglacial successions of invertebrate assemblages have hitherto been studied neither in the Caucasus nor in Russia as a whole. Structural changes of taxocoenoses were traced in a spatial and temporal sequence of ten properly dated paraglacial sites in the Tsey Gorge, North Ossetia − Alania (1–170-years old) among the testate amoebae, earthworms, molluscs, myriapods, mites, spiders, harvestmen, pseudoscorpions, collembolans, and beetles. As the glacier retreats, in place of bare paraglacial wastelands, grassland communities are formed that, after 10–14 years, are replaced by shrub vegetation and, on 30–35-year old surfaces, by forest communities. Most of the invertebrate groups, once “appearing” along a postglacial transect, were recorded from most older plots as well. Yet, their taxocoenoses underwent considerable transformations through increasing (or an increase turning into some decline in beetles) the species diversity and a strong, often complete change in the taxonomic composition and dominance structure. The most considerable transformations were observed at all major vegetation changes. The “appearance” of some groups in the transect was determined not only by dispersion capacities but mainly by the environmental conditions of particular habitats. When comparing the composition of the pioneer postglacial species complex of the study region with that in the mountains of Europe’s south and north, its high-degree regional specificity was noted, sometimes shown at the family level (in spiders). Spatial β-diversity of all larger taxa studied was mainly attributed to turnover (due to “the replacement model” of succession). The general level of change diminished towards the later succession stages. Endemic arthropod species were revealed both in pioneer grassland and developed forest communities.
Super-resolution reconstruction algorithm for dim and blurred traffic sign images in complex environments
Authors: Ma Yan , Kong Defeng
Published: April 2024

In poor lighting and rainy and foggy bad weather environments, road traffic signs are blurred and have low recognition, etc. A super-resolution reconstruction algorithm for complex lighting and bad weather traffic sign images was proposed. First, a novel attention residual module was designed to incorporate an aggregated feature attention mechanism on the jump connection side of the base residual module so that the deep network can obtain richer detail information; second, a cross-layer jump connection feature fusion mechanism was adopted to enhance the flow of information across layers as well as to prevent the problem of gradient disappearance of the deep network to enhance the reconstruction of the edge detail information; and lastly, a positive-inverse dual-channel sub-pixel convolutional up-sampling method was designed to reconstruct super-resolution images to obtain better pixel and spatial information expression. The evaluation model was trained on the Chinese traffic sign dataset in a natural scene, and when the scaling factor is 4, the average values of PSNR and SSIM are improved by 0.031 when compared with the latest release of the deep learning-based super-resolution reconstruction algorithm for single-frame images, MICU (Multi-level Information Compensation and U-net), the average values of PSNR and SSIM are improved by 0.031 dB and 0.083, and the actual test average reaches 20.946 dB and 0.656. The experimental results show that the reconstructed image quality of this paper's algorithm is better than the mainstream algorithms of comparison in terms of objective indexes and subjective feelings. The super-resolution reconstructed image has a higher peak signal-to-noise ratio and perceptual similarity. It can provide certain technical support for the research of safe driving assistive devices in natural scenes under multi-temporal varying illumination conditions and bad weather.
Computational analysis of peripheral blood RNA sequencing data unravels disrupted immune patterns in Alzheimer's disease
Authors: Anatolou Dimitra , Krokidis Marios G.
Published: April 2024

The central nervous system (CNS) and the immune system collectively coordinate cellular functionalities, sharing common developmental mechanisms. Immunity-related molecules exert an influence on brain development, challenging the conventional view of the brain as immune-privileged. Chronic inflammation emerges as a key player in the pathophysiology of Alzheimer's disease (AD), with increased stress contributing to the disease progression and potentially exacerbating existing symptoms. In this study, the most significant gene signatures from selected RNA-sequencing (RNA-seq) data from AD patients and healthy individuals were obtained and a functional analysis and biological interpretation was conducted, including network and pathway enrichment analysis. Important evidence was reported, such as enrichment in immune system responses and antigen processes, as well as positive regulation of T-cell mediated cytotoxicity and endogenous and exogenous peptide antigen, thus indicating neuroinflammation and immune response participation in disease progression. These findings suggest a disturbance in the immune infiltration of the peripheral immune environment, providing new challenges to explore key biological processes from a molecular perspective that strongly participate in AD development.
Gestes et formes esthétiques désobéissants.
Authors: Corp Mathieu
Published: April 2024

Initié par un acte de désobéissance civile au cours du mois d’octobre 2019, l’estallido social chileno (« l’explosion sociale chilienne ») est rapidement devenu une mobilisation massive, interrompue en mars 2020 par les mesures de restrictions de libertés imposées par le gouvernement de Sebastián Piñera pour enrayer la pandémie de Covid-19. Cette mobilisation sociale se distingue notamment par la diversité et la récurrence des gestes et interventions mis en œuvre dans l’espace public, partageant certaines caractéristiques des actes dits de désobéissance civile et s’appuyant sur des ressources esthétiques et artistiques : des gestes iconoclastes mis en œuvre sur les statues et monuments, des performances organisées par différents collectifs féministes, aux projections lumineuses du collectif Delight Lab. Hétérogènes dans leurs formes, leurs protagonistes et leurs fonctions – critique, prospective ou interpellative – ces interventions ont contribué à la spectacularisation et à la médiatisation du mouvement social, alimentant le dissensus dont elle a procédé.
#DeactivHate: An Educational Experience for Recognizing and Counteracting Online Hate Speech
Authors: Cignarella Alessandra Teresa , Frenda Simona , Lai Mirko , Patti Viviana , Bosco Cristina
Published: April 2024

The possibility of raising awareness –especially in young generations– about misbehavior online such as hate speech, could help society to reduce its impact, and thus, its negative consequences. In the last years, the Computer Science Department of the University of Turin has designed various technologies that support educational projects and activities in this perspective. In this paper, we describe the creation of a laboratory called #DeactivHate, specifically designed for secondary school students. The laboratory aims at countering hateful phenomena online and making students aware of technologies that they use on a daily basis. We describe the teaching experience of the first year of life of the laboratory held in different high school classes between April 2021 and March 2022 and the outcomes of the technologies and activities tested. In this extended version of the paper, some sections are especially devoted to observe and analyze the impact of the course on students and their perspective on the laboratory.
Introducing the Ultimate NOUN Dataset for Online Handwritten Alphabet Recognition
Authors: Belbachir Khadidja ElKobra , Tlemsani Redouane
Published: April 2024

For decades, researchers in the field of machine learning have been trying to raise the features of systems and develop methods and approaches, but they often suffer from a scarcity of data in various machine learning fields. For this and due to the urgent need in our research that we have developed in the field of online handwriting recognition, we had to create a special data set for the Arabic language characters written simultaneously on a graphical tablet. In this work, we created a data set called “Noon” in university neighbourhood laboratories and used its first version in research work that was published in international refereed journals. The construction details have gone through phases, which will be presented in the following sections in this chapter.
Development of Cargo Port Infrastructure in the Asian Region
Authors: Satghare Harshada Rajeev
Published: April 2024

The growth and achievements of the cargo and shipping industry of the Asian region are significant. Increasing cargo movement and recognition of the cargo industry as an important economic sector resulted in the development of the port infrastructure, national policies, funds allocation and intense regional competition. Therefore, the present research is a genuine attempt to study the availability of infrastructure at prominent cargo ports of the various countries of the region, like China, Singapore, Malaysia, India, Sri Lanka, Taiwan, Thailand, and South Korea. The study provided a precise picture of the regional port infrastructure and analysed the present challenges of the development. The researcher has provided the contemporary port infrastructure parameters—modern and technology-enabled infrastructure, sustainability, and competitiveness of the infrastructure. A detailed port infrastructure development plan has been given. The provided suggestions are guiding notes for policymakers and planners of the countries of the Asian region.
Landlocked Horizons to Coastal Challenges
Authors: Datta Bivek
Published: April 2024

Encompassed by the intricate landscapes of South Asia, Nepal, and India, close neighbours with diverse cultures and economies, share a vital reliance on efficient shipping for trade and economic growth. This chapter delves into the challenges and opportunities faced by their shipping sectors amid global disruptions and regional complexities. It explores the multifaceted dynamics, examining policy considerations, technological innovations, and collaborative frameworks crucial for the future of shipping. As Nepal and India navigate a dynamic global landscape, policymakers and industry leaders must foster collaboration, adapt to emerging technologies, and commit to resilient and sustainable shipping practices. This effort will not only fortify their maritime sectors but also contribute significantly to the broader discourse on global trade resilience and connectivity.
AI-Based Mindfulness
Authors: Ramana Yerravalli Venkat Santosh , Peri Pinakapani
Published: April 2024

This chapter explores the integration of artificial intelligence (AI) into mindfulness practices, specifically focusing on healthcare and customer service. The purpose is to evaluate the substantial benefits of AI in encouraging mindfulness in both contexts, with the potential to manage pain better, lower stress levels, and improve overall health outcomes. The chapter begins by defining mindfulness and its potential benefits for mental and physical well-being. It then explores the pioneering work of Jon Kabat-Zinn in integrating mindfulness into medical settings. The chapter discusses how AI is used to augment mindfulness practices, such as video recording for guided breathing exercises and smartphone-based pedometers for physical activity monitoring. The chapter then investigates how AI can assist mindfulness by tailoring interventions to individual needs and monitoring progress over time. It also discusses the potential benefits of AI in encouraging mindfulness in both customer service and healthcare contexts.
Consumer Acceptance and Adoption Challenges of Digital Healthcare Technology in India
Authors: Katke Kadambini S.
Published: April 2024

India, with its large population exceeding 1.3 billion, faces strain on its healthcare system due to an aging population and various factors impacting service quality. Digital healthcare technology offers a solution by providing efficient and accessible services, reducing economic burdens, and enhancing care quality. Understanding users' perceptions is crucial, explored through Davis's technology adoption model (TAM). This model assesses factors like perceived usefulness and ease of use, crucial for technology adoption. Government initiatives support digital healthcare infrastructure. Focus on diseases like diabetes is essential, requiring accessible technology for monitoring and reducing healthcare costs. Digital healthcare providers must develop user-friendly products to ease adoption. Despite India's high diabetic population, healthcare apps struggle to penetrate the market. A digital healthcare ecosystem connecting various healthcare elements can reduce costs and improve accessibility.
TABST
Authors: Nag Diotima , Mandal Unmesh
Published: April 2024

Recommendation systems (RS) are indispensable for personalized marketing, shaping user experiences in online shopping and OTT media. RS aims to deliver timely, relevant content with minimal user effort, drawing insights from prior data, including preferences, timing, and contextual choices. This study explores the temporal dimension of RS using the Transformer model. The proposed time aware behavior sequence transformer (TABST) predicts a target item's rating on a specific day and time by analyzing users' sequential timing history. TABST employs transformer encoders to extract latent temporal and sequential information, recommending a target item with an optimal consumption time. Comparative evaluations validate TABST's superiority over state-of-the-art algorithms, positioning it as a cutting-edge solution for personalized recommendations. In summary, this research contributes to the evolution of RS by introducing TABST, addressing personalized choices within the temporal and contextual behaviours of users, and ultimately enriching the user experience.
Unleashing Entrepreneurial Potential
Authors: Muchenje Chenjerai , Mtengwa Edwill , Maregere Lena Lainah
Published: April 2024

In today's rapidly changing business landscape, entrepreneurs are constantly seeking innovative ways to enhance customer experiences, streamline processes, and gain a competitive edge. Artificial intelligence (AI) has revolutionized these efforts, automating tasks and extracting valuable information from vast datasets. This chapter explores the significant impact of AI on entrepreneurial endeavors, highlighting its role in promoting creativity, problem-solving, productivity, and new opportunities for success. AI aids entrepreneurs in making market-driven decisions through predictive analytics and machine learning-based product recommendations. It also explores AI's potential for collaboration, consumer engagement, supply chain optimization, and security. As entrepreneurs navigate the changing business landscape, AI-driven technologies are crucial for continuous learning and innovation.
The Effectiveness of Machine Translating Tools to Enhance Higher Education Students' Knowledge in Non-English-Speaking Countries
Authors: Sukur Silvester Goridus , Dardjito Hanandyo , Sumekto Didik Rinan
Published: April 2024

The low level of competence in English is one of the obstacles for the university students in Indonesia in increasing their knowledge. This problem is rooted in the fact that English in Indonesia is not a second language, but a foreign language. Students do not use English outside of class. To overcome this problem, students often use translating tools. Unfortunately, students do not understand how to use translation tools to produce quality translations. This research aims to discuss effective ways which are best practices for using translation tools that help students in Indonesia understand texts written in a foreign language. The data sources for this research were the results of in-depth interviews with graduate students, their translated documents, and the results of the researcher's best practice reflections as a translation-course lecturer in Indonesia. The results of this research provide a significant contribution to students in countries where English is a foreign language in that they know the best way to use translation tools in translation.
Cultural Diversity in Language Learning
Authors: Molema Tlou
Published: April 2024

This abstract explores the complex relationship between cultural diversity and language acquisition in English teaching. It examines how diverse cultural backgrounds intricately shape learners' approaches to and proficiency in English. Applying cross-cultural perspectives becomes necessary to optimize learners' potential and enrich their English language learning journey. The discussion emphasized the importance of recognizing and valuing diverse cultural perspectives in the classroom. It explores strategies for creating an inclusive learning environment that promotes intercultural dialogue among students, promoting a deeper understanding of linguistic and cultural diversity. By recognizing the profound impact of cultural backgrounds on language learning, educators can tailor their approaches to cater to diverse learners, thus enhancing the overall effectiveness of English language education. This abstract underlines the importance of integrating multicultural perspectives into pedagogical practices to create an enriching and inclusive space for language acquisition.
Supporting Low-Proficiency L2 Learners Through Scaffolded Peer Feedback on L2 Writing Tasks
Authors: Casinto Carlo Domingo Cania
Published: April 2024

In the context of post-pandemic higher education, this chapter constructs a language instructional model that has the potential to enhance teaching and learning experiences by building on the strengths of learners' engagement, interaction, and educators' feedback which are primarily compromised during the pandemic when instruction shifted remotely online. The chapter discusses the potential benefits of scaffolded peer feedback, as an L2 teaching strategy, for low-proficiency students, particularly on how they can improve their writing by giving and receiving peer feedback. Its discourse will be anchored on an empirical study that is framed under Vygotsky's sociocultural theory.
Enhancing Literacy Education in Higher Institutions With AI Opportunities and Challenges
Authors: Vashishth Tarun Kumar , Sharma Vikas , Sharma Kewal Krishan , Kumar Bhupendra
Published: April 2024

Enhancing literacy education in higher institutions through the integration of artificial intelligence (AI) presents significant opportunities and challenges. This chapter explores the role of AI in literacy education, showcasing its potential to revolutionize teaching methodologies, personalized learning, and student outcomes. AI-powered tools, such as natural language processing (NLP), chatbots, and adaptive learning platforms, offer unique opportunities to engage students, improve language proficiency, and address individual learning needs effectively. The chapter delves into the various opportunities AI presents in higher education literacy, emphasizing the benefits of AI-driven content recommendation systems, personalized feedback, and early intervention for struggling students. However, along with the opportunities come several challenges, including ethical considerations surrounding data privacy, faculty readiness, and the potential displacement of traditional teaching approaches.
Navigating the AI Landscape
Authors: Roberts Leesha Nicole , Solomon Fanta N. , Cummings Reccia
Published: April 2024

This chapter examines AI's role in Caribbean high school and college assessments, analyzing teacher and student perspectives. A quantitative study surveyed 160 students and 102 teachers via Google Forms in September 2023, investigating AI tool usage, its effects on grading and feedback, fairness, and ethical concerns. Key findings include students' prevalent use of Grammarly and ChatGPT and plagiarism software by teachers, with significant AI encounters at the high school level. Positive correlations emerged between teachers' views on AI's grading efficiency, optimism for its future, and students' appreciation for AI's timely feedback. Concerns about AI-induced discrimination showed no significant differences across countries or educational levels, highlighting ethics and transparency as crucial. The need for targeted AI integration training is emphasized, suggesting future research should address AI biases and explore new tools for enhancing Caribbean educational outcomes.
Transforming Classroom Dynamics
Authors: Vashishth Tarun Kumar , Sharma Vikas , Sharma Kewal Krishan , Kumar Bhupendra , Chaudhary Sachin , Panwar Rajneesh
Published: April 2024

The integration of artificial intelligence (AI) in education is reshaping classroom dynamics and fostering a social impact on teaching and learning. This chapter explores the historical evolution of classroom dynamics, from traditional settings to 21st-century learning environments, highlighting the pivotal role of AI in this transformative journey. Understanding AI in education involves delving into its historical overview and current landscape. The research investigates AI's influence on teacher support and development, encompassing AI-assisted lesson planning, teacher training, and enhanced classroom management. The conclusion reflects on the future directions and trends, emphasizing the global potential of AI to democratize education, address language barriers, and provide inclusive learning experiences. The study envisions a future where AI optimally contributes to creating a more equitable, interconnected, and culturally sensitive educational landscape globally.
Digital Transformation of Wealth Management
Authors: Bhatnagar Mukul , Sharma Rajat , Singh Mandeep
Published: April 2024

The significance of robo-advisor adoption and portfolio allocation in risk mitigation enhancement based on this research has been brought to light. With the discussion presented in this chapter, financial managers will be able to integrate these views on harmonization and unification; hence, a successful approach towards risk management strategies and even adjusting their activities in line with various laws geared towards reducing issues of risks within financial sectors. The research underlines the value of using technological innovations and argues for financial institutions to implement robo-advisory services to manage risk in a quicker, more effective manner. This research offers financial managers with actionable knowledge on how to best model their frameworks for risk management in the optimization of achieving a better financial outcome for both institutions and their customers in an industry based on rapid changes.
Utilization of Robo-Advisory Tools in Decision Support Systems
Authors: Kashif Mohammad , David Arokiaraj , Azam Md. Sikandar , Kumar Sanjeet
Published: April 2024

Decision Support Systems are essential tools for helping business organizations navigate the decision-making processes. Through the use of technology, Decision Support Systems significantly improve decision-making processes by offering thorough, data-driven insights by utilizing robo advisory tools. Automated, algorithm-driven platforms are known as robo advisory tools. Robo advisers are renowned for being easily accessible, reasonably priced, and able to offer financial services to a larger range of clients. The integration of robo advising tools with Decision Support Systems provides decision-makers with advanced algorithms and data analytics functionalities. With technology and regulations changing constantly, using robo advising tools in Decision Support Systems is becoming a trend. This chapter has emphasized how critical it is to recognize the constraints, legal framework, and compliance issues related to the use of robo advisors.
Human Resource Management Insights on Social Robots Integration in the Workplace
Authors: Singh Manvinder , Arora Nilesh
Published: April 2024

The research delves into the significance of efficient alliance amongst people & robots. After examining literature on HRM practices, it became evident that there is a notable gap in research concerning teamwork dynamics with social robots and their managerial oversight. To address this gap, the study scrutinizes the determinants that shape individuals' willingness to collaborate with social robots. Employing a structured methodology, the research focuses on key factors such as Performance Expectancy, Trust, Effort Expectancy, and peer and managerial endorsements. These elements collectively influence an individual's inclination or intention to engage with an interactive robotic companion based on their relational preferences. The findings underscored that individuals' perceptions of their task proficiency, perceived effort, and apprehensions about technology significantly shape their willingness to engage with social robots.
Robo-Advisors as Catalysts for Change in the Textile Industry
Authors: Bindu ­ , Kaur Hardeep
Published: April 2024

As part of its embrace of digital transformation and related technical advances, India's textile sector has taken a significant stride forward by using robo-advisors, or automation advisers, to improve performance levels by streamlining operations and optimizing decision-making processes. The present research is conducted to measure the impact of self-efficacy and organizational commitment on worker performance in the presence of work culture as a mediator and Robo Advisor as a moderator. The general learnings derived from the findings suggest that Organizational Commitment is a key driver of both Work Culture and Worker Performance, underscoring the importance of fostering commitment within the workforce to cultivate a positive work environment and enhance productivity. The influence of Robo-Advisors on Worker Performance, while present, is not statistically significant, hinting that technology alone may not be a strong determinant of performance without the support of other organizational factors.
Enhancing Robotic Autonomy and Deep Reinforcement Learning Applications
Authors: Goel Ridhima , Singla Jagdeep
Published: April 2024

The integration of Deep Reinforcement Learning (DRL) into the realm of robotics and autonomous systems has emerged as a groundbreaking paradigm shift, empowering machines to tackle intricate tasks through interaction with their environments. This chapter offers a comprehensive examination of the current research landscape at the intersection of DRL and robotics within this dynamic field. This chapter navigates through the conceptualization of DRL and explores its diverse applications in controlling robotics and object manipulation. The chapter showcases the autonomy and adaptability enabled by DRL while addressing prevalent challenges such as sample efficiency, safety concerns, and scalability. In conclusion, this chapter serves as a valuable resource for future researchers and practitioners intrigued by the intersection of DRL and robotics. It synthesizes current knowledge, underscores significant progress made, and maps out exciting avenues for further exploration, ultimately propelling the advancement of robotic systems in the era of machine learning and artificial intelligence.
Embedding Ethical Practices
Authors: Herath Siriyama Kanthi , Herath Laksitha Maheshi
Published: April 2024

This chapter aims to explore the challenges involved in considering ethical actions as part of CSR. This chapter will review some of the challenges organizations encounter in ensuring that their business strategies align with socially responsible initiatives. It will address some important issues, such as ethical leadership, stakeholder involvement, CSR impact measurement, and global approaches to corporate social responsibility, among others, like resistance towards embracing change within the field of CSR. The chapter focuses on providing feasible solutions and inventive strategies to overcome the difficulties and potential challenges related to CSR implementation. This chapter, therefore, stands as one of the crucial information sources for organizations aiming to synchronize their ethical rules and CSR effectively.
Implication of Artificial Intelligence (AI) in Finance in the Case of Bangladesh
Authors: Mohammad Niaz , Derbali Abdelkader Mohamed Sghaier
Published: April 2024

AI is going to open new technical possibilities that will upend the banking and non-banking financial sector. Numerous opportunities and benefits will result from the changes for the industry, its clients, and society at large. Examples of the benefits include increased efficiency and cost savings, improved customer service offerings, and more consistent adherence to compliance rules. In this chapter, the authors examine the application of AI in the financial sector of Bangladesh. They bring up several general concerns in addition to ethical and legal accountability concerns. Additional moral issues that need to be discussed in public debate include the personalization of service offers, data collecting ownership and security, the possibility of destruction of many jobs in the financial sector, and the potential systemic concerns associated with AI-driven consolidation. Companies listed on the Dhaka Stock Exchange are taken into consideration. The comparison will add to the conversation on how to deal with AI in society by generating moral implications in a developing economy like Bangladesh.
Investigation Into the Barriers to AI Adoption in ESG Integration and Identification of Strategies to Overcome These Challenges
Authors: Herath Siriyama Kanthi , Herath Laksitha Maheshi
Published: April 2024

This chapter examines AI and its role within the landscape of business and technology, teeming with ESG integration. It sheds light on how AI presents meaningful alternatives to existing environmental problems and suggests ways regional and national governments can find solutions to sustainability issues. The ethical elements of these technologies form the focus of the essay, while forecasts, ethical supply chains, and enforcement of regulations loom ahead. The AI-ESGI convergence provides growing space for firms whose focus is sustainability to conquer a more diversified customer base by fostering green strategies. It is this alignment of smart approaches and efficient technologies that promises the future of sustainable development. A glimmer of hope is ignited.
Converging Pharmacy Science and Engineering in Computational Drug Discovery
Authors: Eswaran Ushaa
Published: April 2024

This chapter provides an in-depth exploration of the intersection between pharmacy science and engineering within computational drug discovery. It covers interdisciplinary collaborations, computational chemistry, bioinformatics, engineering principles, target identification, lead optimization, machine learning, and personalized medicine. Additionally, it examines the integration of experimental and computational data, addresses various challenges encountered, and offers insights into the future directions of computational drug discovery.
Artificial Intelligence-Enabled Cutting-Edge Technologies
Authors: Ahmad Sayed Sayeed , Almobaideen Wesam , Abdelhakim Mohamed Noureldin , Rani Rashmi
Published: April 2024

AI combined with other modern technologies, such as the Internet of Things, blockchain, and augmented reality, elevated inventiveness. The age of smart society or the smart era is in full swing. The technologies' collaboration has already evolved numerous enterprises, healthcare institutions, and social institutions. AI-powered healthcare 5.0 technologies enable developments in diagnostics, tailor-made treatment, and patient care. It is possible to achieve more accurate diagnostics, personalised approaches to treatment, and the best possible patient outcomes by employing machine learning algorithms.In this chapter, we will briefly summarize all areas with the advanced AI technologies and their implementation into Industry 5.0, Healthcare 5.0, and Society 5.0. The implications of these advances together create the potential for a smarter, more interconnected future that can be less detrimental to the Earth. This work creates a new future for where research could potentially solve many social problems in the future.
Industrial Automation in Drug Discovery
Authors: Hemamalini V. , Tyagi Amit Kumar , Vasuki P. , Kumari Shabnam
Published: April 2024

This chapter explores the transformative impact of industrial automation on drug discovery, specifically within the context of the emerging paradigm of Industry 5.0. The convergence of advanced technologies, including robotics, artificial intelligence, and the internet of things (IoT), is reshaping the landscape of pharmaceutical manufacturing, leading to the development of smart manufacturing processes tailored for the intricacies of drug discovery. Industry 5.0, characterized by the integration of cyber-physical systems with human-centric approaches, provides a framework for the evolution of pharmaceutical manufacturing towards increased automation and intelligence. In drug discovery, industrial automation streamlines processes such as compound synthesis, high-throughput screening, and quality control, resulting in enhanced efficiency, precision, and reproducibility. Robotic systems, equipped with AI-driven algorithms, play a pivotal role in automating labor-intensive tasks, reducing human error, and expediting the drug development pipeline.
Getting Started With Computational Drug Discovery
Authors: Kumari Rashmi , Das Subhranil , Singh Raghwendra Kishore , Kohli Anvi , Sunil Arya , Dadhich Arushi
Published: April 2024

This manuscript explores the transformative impact of computational drug discovery in pharmaceutical research, emphasizing the integration of algorithms, simulations, and modeling to expedite the development of therapeutic agents. It highlights the multidisciplinary nature of this approach, leveraging insights from computer science, chemistry, biology, and pharmacology. The narrative underscores the crucial role of artificial intelligence (AI) and machine learning (ML) technologies in enhancing the efficiency and precision of drug discovery. These technologies enable the analysis of complex biological data, facilitating the identification of novel drug targets and the prediction of drug efficacies and side effects with unprecedented accuracy. Additionally, the chapter discusses the significance of computational methodologies in improving the speed, cost-effectiveness, and success rates of developing new drugs. Through high-throughput screening and detailed molecular analysis, these methods allow for the rapid identification of promising compounds and offer insights into disease mechanisms, paving the way for targeted therapeutic interventions. This overview aims to showcase the critical role of computational drug discovery in advancing personalized, effective, and patient-centered treatments, marking a significant shift towards more innovative and efficient drug development processes.