bluuebunny/crossref_metadata_2025 huggingface dataset:
Formulation of Catalytic Deep Eutectic Solvent for H2S Oxidative Absorption at Ambient Condition with Assistance of Cosmo-Rs
Authors: Nor Fariza Abd Mutalib  , Muhammad Shazwan Shabani  , Mohamad Azmi Bustam
Published: February 2024

H2S is very well known for its toxicity, corrosivity, flammability and explosiveness which in overall lead to operational difficulty. Hence, raising the need for H2S sweetening process. However, the current conventional sweetening process is very energy intensive besides suffer from catalyst deactivation issue. Therefore, raising the need for more environmentally and economical friendly approach such as utilizing green solvent like deep eutectic solvent. Thus, the objective of this research is to determine the best combination of HBD and HBA components for catalytic DESs to convert H2S at room temperature. The study utilized COSMO-RS computational to assist in selecting the appropriate components, synthesized and characterized the chosen catalytic DES, and performed oxidative absorption of H2S using the selected DES. Initially, the solubility and selectivity performance of various HBD and HBA were evaluated, and the components with the best performance were combined with CuCl2.2H2O to form catalytic DES. The synthesized catalytic DES was characterized using FTIR and tested for its oxidation absorption capability. Based on the COSMO-RS screening, 1-butyl-3-methylimidazoilum chloride [bmim][Cl] and ethylene glycol [EG] were determined to be the best HBD and HBA, respectively. The catalytic DES formed from these components was homogeneous and brown in colour. The results of oxidative absorption showed that the catalytic DES rapidly produced a yellow precipitate, which was later confirmed to be sulphur. The study concludes that COSMO-RS computational can be used to formulate DESs for H2S oxidative absorption, and catalytic DES maintained its properties before and after H2S oxidation. The research also suggests that oxidative absorption of H2S to sulphur using catalytic DESs is a feasible and rapid.
Blockchain Technology in Overcoming Security Threats for Smart Manufacturing System - A Systematic Literature Review
Authors: Ahmed Mohamed Gaafar Mahmoud Ali  , Kelvin Chong Boon Kai  , Zool Hilmi Ismail
Published: February 2024

Blockchain is a new emerging technology that is mainly used to add security layers to many applications. Studies showed that businesses and manufacturing industries suffer from cyber-attacks, especially after the emergence of Industry 4.0 and IoT devices connected to the cloud for data sharing purposes, these interactions between IoT devices and the information involved in the transactions between companies and customers produce a massive amount of data daily. This phenomenon becomes an easy target for attackers due to the security limitations, data corruption and compromised reliability of the traditional manufacturing systems during data transferring. Many have focused on the methods to address these security limitations of the manufacturing businesses. This paper shed the light on the major smart manufacturing sectors that can potentially benefit from blockchain technologies, including cloud manufacturing, collaborative manufacturing, digital twin manufacturing, robonomics based smart manufacturing and the supply chain industry. The blockchain can enhance the security by creating a decentralized environment for the data to be stored, which will make the data stored immutable for improved security. These immutable data are nearly impossible to manipulate, replace, or falsify the data stored on the blockchain secured network.
Evaluation of Power Storage Circuit for Piezoelectric Energy Harvester in Impact Mode
Authors: Amat Amir Basari  , Seiji Hashimoto
Published: February 2024

Power storage circuit is crucial to the performance of the vibration energy harvesting system as it will affect the amount of energy that can be harvested. The objective of this paper is to discuss the design and evaluation of power storage circuit for the vibration-based impact mode piezoelectric energy harvester. The proposed power storage circuit consists of rectifier circuit with rectifier capacitor, voltage regulator and the storage device. Evaluation on the rectifier circuit and the power storage circuit is carried out with variation in the resistive load and some other components. Results show that for the multiple impact input, full wave rectifier produces higher output than that of the other circuit. In addition to that, the proposed storage circuit can produce a stable output voltage and output power at about 40 Hz of operating frequency range. This show that piezoelectric energy harvester in impact mode can operate well with the proposed power storage circuit.
Implementasi Akad Musyarakah Mutaqisah Pada Pembiayaraan Kpr Di Bank Panin Dubai Syariah KC Malang
Authors: Safitri Linda , Cahyani Lineke Regita , Susanto Feri , Meliza Meza Alim , Anjaswari Tutik
Published: February 2024

This article aims to analyze the implementation of the musyarakah mutanaqishah contract in mortgage financing at Bank Panin Dubai Syariah KC Malang. The research method used is descriptive qualitative method by conducting interviews. The results of the analysis show that the implementation of the musyarakah mutanaqisah contract on mortgages at Bank Panin Dubai Syariah is in accordance with the DSN Fatwa Number 73/DSNMUI/XI/2008 concerning the Musyarakah Mutanaqisah Agreement, which is to cooperate between the bank and the customer to buy an asset which will then reduce ownership rights. one party through a payment mechanism. Where there is a minimum capital requirement of 20% that must be met in it.
Stepping up with ChatGPT
Authors: Combrink Herkulaas , Brokensha Susan , Scheltema-Van Wyk Cornelle , Matlhoko Ketshepileone Shiela , van der Merwe Sean , van der Merwe Tiana
Published: February 2024

This guide is intended to provide general information on the use of ChatGPT at the University of the Free State. The information contained within this guidebook is not intended to be a substitute for professional advice or guidance. Additionally, to ensure the accuracy and relevance of the information provided in this guide, the authors collaborated with ChatGPT to address common questions and concerns related to its use in an academic setting. However, the authors and publishers of this guide cannot guarantee the completeness or accuracy of the information provided, as ChatGPT's responses are generated by a machine learning algorithm that may have limitations in contextual understanding and accuracy. The authors and publishers of this guide are not responsible for any errors or omissions or any outcomes resulting from the use of this information. The reader assumes full responsibility for their use of the information contained in this guide.
Consistent Estimates of the Parameters of the multiparameter fractional Brownian motion
Authors: Kurchenko O. O. , Syniavska O. O.
Published: February 2024

The consistent estimators of the multiplicative parameter c and Hurst parameter H of the covariance function of the multiparameter fractional Brownian motion are constructed.
IID Time Series Testing
Authors: Sarantsev Andrey
Published: February 2024

Traditional white noise testing, for example the Ljung-Box test, studies only the autocorrelation function (ACF). Time series can be heteroscedastic and therefore not i.i.d. but still white noise (that is, with zero ACF). An example of heteroscedasticity is financial time series: times of high variance (financial crises) can alternate with times of low variance (calm times). Here, absolute values of time series terms are not white noise. We could test for white noise separately for original and absolute values, for example using Ljung-Box tests for both. In this article, we create an omnibus test which combines these two tests. Moreover, we create a general framework to create various i.i.d. tests. We apply tests to simulated data, both autoregressive linear and heteroscedastic.
A Survey of Lyα Emission around Damped Lyα Absorbers at z ≈ 2 with the Keck Cosmic Web Imager
Authors: Oyarzún Grecco A. , Rafelski Marc , Kanekar Nissim , Prochaska J. Xavier , Neeleman Marcel , Jorgenson Regina A.
Published: February 2024

We present Keck Cosmic Web Imager Lyα integral field spectroscopy of the fields surrounding 14 damped Lyα absorbers (DLAs) at z ≈ 2. Of these 14 DLAs, nine have high metallicities ([M/H] > − 0.3), and four of those nine feature a CO-emitting galaxy at an impact parameter ≲30 kpc. Our search reaches median Lyα line flux sensitivities of ∼2 × 10−17 erg s−1 cm−2 over apertures of ∼6 kpc and out to impact parameters of ∼50 kpc. We recover the Lyα flux of three known Lyα-emitting H i-selected galaxies in our sample. In addition, we find two Lyα emitters at impact parameters of ≈50–70 kpc from the high-metallicity DLA at z ≈ 1.96 toward QSO B0551-366. This field also contains a massive CO-emitting galaxy at an impact parameter of ≈15 kpc. Apart from the field with QSO B0551-366, we do not detect significant Lyα emission in any of the remaining eight high-metallicity DLA fields. Considering the depth of our observations and our ability to recover previously known Lyα emitters, we conclude that H i-selected galaxies associated with high-metallicity DLAs at z ≈ 2 are dusty and therefore might feature low Lyα escape fractions. Our results indicate that complementary approaches—using Lyα, CO, Hα, and [C ii] 158 μm emission—are necessary to identify the wide range of galaxy types associated with z ≈ 2 DLAs.
One Fits All: A Unified Synchrotron Model Explains GRBs with FRED-shape Pulses
Authors: Yan Zhen-Yu , Yang Jun , Zhao Xiao-Hong , Meng Yan-Zhi , Zhang Bin-Bin
Published: February 2024

The analysis of gamma-ray burst (GRB) spectra often relies on empirical models lacking a distinct physical explanation. Previous attempts to couple physical models with observed data focus on individual burst studies, fitting models to segmented spectra with independent physical parameters. However, these approaches typically neglect to explain the time evolution of observed spectra. In this study, we propose a novel approach by incorporating the synchrotron radiation model to provide a self-consistent explanation for a selection of single-pulse GRBs. Our study comprehensively tests the synchrotron model under a unified physical condition, such as a single injection event of electrons. By tracing the evolution of cooling electrons in a decaying magnetic field, our model predicts time-dependent observed spectra that align well with the data. Using a single set of physical parameters, our model successfully fits all time-resolved spectra within each burst. Our model suggests that the rising phase of the GRB light curve results from the increasing number of radiating electrons, while the declining phase is attributed to the curvature effect, electron cooling, and the decaying magnetic field. Our model provides a straightforward interpretation of the peak energy’s evolution, linked to the decline of the magnetic field and electron cooling due to the expansion of the GRB emission region. Our findings strongly support the notion that spectral and temporal evolution in GRB pulses originates from the expansion of the GRB emission region, with an initial radius of approximately 1015 cm, and synchrotron radiation as the underlying emission mechanism.
Magnetic Relaxation Seen in a Rapidly Evolving Light Bridge in a Sunspot
Authors: Song Donguk , Lim Eun-Kyung , Chae Jongchul , Kim Yeon-Han , Katsukawa Yukio , Yurchyshyn Vasyl
Published: February 2024

We report a magnetic relaxation process inside a sunspot associated with the evolution of a transient light bridge (LB). From high-resolution imaging and spectro-polarimetric data taken by the 1.6 m Goode Solar Telescope installed at Big Bear Solar Observatory, we observe the evolutionary process of a rapidly evolving LB. The LB is formed as a result of the strong intrusion of filamentary structures with relatively horizontal fields into the vertical umbral field region. A strong current density is detected along a localized region where the magnetic field topology changes rapidly in the sunspot, especially in the boundary region between the LB and the umbra, and bright jets are observed intermittently and repeatedly in the chromosphere along this region through magnetic reconnection. In the second half of our observation, the horizontal component of the magnetic field diminishes within the LB, and the typical convection structure within the sunspot, which manifests itself as umbral dots, is restored. Our findings provide a comprehensive perspective not only on the evolution of an LB itself but also on its impacts in the neighboring regions, including the chromospheric activity and the change of magnetic energy of a sunspot.
Potent antioxidant activity of black grass jelly (Mesona palustris BL) leaf extract and fractions
Authors: Andri Deswati Dytha , Anggadiredja Kusnandar , Nuryanti Garmana Afrillia
Published: February 2024

Black grass jelly (Mesona palustris BL) is an Indonesian traditional food that is rich in antioxidants and believed to have potential for treating various diseases such as diabetes, hypertension, and cancer. The present study aimed to determine the antioxidant activity of black grass jelly leaf extract as well as fractions using ABTS, DPPH and FRAP methods. Following ethanol extraction and subsequent fractionations, total flavonoid level was determined, followed by antioxidant activity tests using the ABTS, DPPH and FRAP with vitamin C as references. The tests revealed the following order of antioxidant activities, ethyl acetate fraction>extract>water fraction>n-hexane fraction. All test substances had IC50 of <50 ppm, which categorized them as having very high antioxidant activities. In line with this data, on the basis of reducing power, ethyl acetate extract was shown to be the most potent antioxidant, having a value of 20.24 mgAAE/g sample. Overall, results of the present study suggest the potential use of black grass jelly as a part of the therapeutic armamentarium for oxidative stress-related diseases.
Oral Health Literacy and Tooth Loss and Replacement in Older Adults at a University Dental Clinic in Colombia
Authors: Mafla Ana Cristina , Herrera-López Mauricio , Dorado-Pantoja Gabriela Tatiana , López-Ruano Karent Jheurany , Saa-Valentierra Lilibeth , Gallardo-Pino Carmen , Schwendicke Falk
Published: February 2024

Background:

Oral health literacy (OHL) is the ability of individuals to obtain, process, and understand oral health information and services, allowing them to make appropriate oral health decisions. The association between OHL and tooth loss and replacement have not been well understood.

Objectives:

We aimed to determine the association between OHL and tooth loss and replacement in a Colombia population.

Methods:

A cross-sectional study of 384 older adults age 65 to 89 years from Pasto, Colombia was carried out. The number of lost and replaced teeth was assessed intraorally; sociodemographic and prosthetic characteristics were collected, and the Health Literacy in Dentistry questionnaire was used to evaluate OHL. Generalized linear models were estimated to assess associations between independent variables (including OHL) and the number of lost and replaced teeth.

Key Results:

There were 224 (58.3%) men and 160 (41.7%) women. The mean (standard deviation [SD]) number of lost and replaced teeth was 27.78 (4.03) and 12.53 (9.89), respectively. One hundred fifty five (40.4%) individuals had full removable dental protheses, 122 (31.8%) partial removable dental protheses, 68 (17.7%) fixed prosthetics, and 36 (9.4%) dental implants. OHL was 33.29 (6.59) and significantly positively associated with the number of replaced teeth (
β
= 0.65, 95% confidence interval [CI]: 0.52–0.78,
p
< .001), but not with lost teeth.

Conclusions:

OHL may foster individuals' capabilities to replace lost teeth, although we did not find it associated with reduced tooth loss, likely as tooth loss was highly common in this older population. [

HLRP: Health Literacy Research and Practice
. 2024;8(1):e21–e28.

]
Révolution et amour : Victorien Sardou en Chine (1907-1946)
Authors: Xiaodong Ma
Published: February 2024

L’article porte sur la traduction et l’adaptation des œuvres du dramaturge français Victorien Sardou en Chine (1907-1946). Nous abordons d’abord l’évolution de l’image des œuvres de Sardou en Chine, de « pièces de renommée mondiale » à « pièces bien faites ». Ensuite, nous nous concentrons sur la traduction et l’adaptation des deux pièces du dramaturge les plus jouées sur la scène chinoise, La Tosca et Patrie !, en étudiant la manière dont le thème « révolution et amour », populaire dans la littérature chinoise moderne, a influencé la réception de Sardou dans le pays. Enfin, nous nous attachons à révéler les relations entre commerce, politique et art dans le développement du théâtre chinois moderne, à travers la réception des œuvres de Sardou en Chine.
Beyond a Shared Theme
Authors: Dinger Casey , Jayton Chenthuran
Published: February 2024

In this chapter, the authors provide a descriptive overview of a living and learning community for first year university students aimed at cultivating global perspectives and intercultural learning. Grounded in the values of global citizenship education, they detail how they employ the dialogic method to construct a scaffolded experience for the cohort. The authors highlight key curricular and co-curricular activities that guide students through the complex process of building capacity for global citizenship beginning with the self and their local lived experiences and ending with a reflection of and a commitment to the well-being of a global “other.” The authors and the implementor of the living and learning community situate the learning in the context of the sustainable development goals articulated by the United Nations in 2015. This chapter serves as a case study and a method for delivering a global intercultural learning experience.
Artificial Intelligence Technologies for Sustainability in Business
Authors: Saglam Yesim Can
Published: February 2024

In a fiercely competitive environment, modern technologies have attracted the tremendous attention of businesses for existence. As a consequence of this development, research areas including supply chain management, industrial engineering, operation management, and so on have been influenced by the effect of these cutting-edge technologies. Leading-edge technologies such as artificial intelligence are assisting to remodel the operational management and production systems of businesses. In the literature, increasing consensus and investigations exist that new technologies can provide a variety of advantages for performing and developing sustainability issues. However, empirical insights on how the advantages of artificial intelligence can enhance business sustainability (in terms of economic, environmental, and social sustainability) are relatively scarce. Therefore, this chapter investigates the advantages of artificial intelligence on business sustainability from the angle of three aspects, namely environmental, economic, and social sustainability.
Machine Learning-Based Load Forecast for Energy Markets
Authors: Nandan Mauparna , Musti K. S. Sastry
Published: February 2024

Time series-based forecasting is one of the most popular machine learning methods due to its effectiveness in estimation of future values based on observations, interpolations, and interpretations of past data. Forecasting the load in power distribution networks is an essential step in energy trading and system operation. Machine learning, specifically time series-based forecasting, can be used in the precise prediction of energy consumption in power networks. Precise predictions have the potential to reduce operating and maintenance expenses, enhance the dependability of power supply and delivery systems, and enable informed decisions for future development endeavors. This chapter employs time series analysis to forecast energy usage in 10-minute intervals specifically for the city of Tétouan in Morocco by applying gradient boosting algorithm. Past and present data trends have been presented along with various accuracy parameters such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE).
System Modeling and Functional Analysis of Renewable Energies for Management of Smart Grids
Authors: Wertani Hamza
Published: February 2024

Smart grids studies focus on improving their reliability and reducing the cost of energy, particularly when they are operated onshore. A smart grid can integrate electric vehicles that will greatly modify the urban infrastructure as well as the electrical network. This chapter presents an overview of system modeling for management of smart grids based on renewable energies and functional analysis techniques. In fact, this work allows a discussion of a hybrid system on the one hand and the description of the functional and operational control-command applications method SADT (structured analysis, design technique) and SA-RT (structured analysis real time), on the other hand.
Multifaceted and Diverse Applications of Nanocomposites
Authors: Kulkarni Sunil Jayant
Published: February 2024

The materials produced on nanoscale have a new set of properties. Nanotechnology and nanomaterials have positively affected the human lifestyle. It has an impact on agricultural, energy, petroleum, sports, chemical and catalysis, environmental, paints and coating, medical, beauty, and textile sectors. Feeding the increasing needs of the population has given rise to different innovations in various sectors. The agricultural sector also has benefitted from such innovations. Nanotechnology and nanomaterials have come to the rescue of the farmers. Micronutrients are supplied to the crops through fertilizers. Nanomaterials are explored for fuel borne catalysis for soot prevention. Nanoparticle reinforced tires have greater abrasive resistance and gas permeability. Nanoparticles are used in coating for better gloss, scratch and corrosion resistance, and anti-fog coatings. Nanoparticles and nanomaterials like cubosomes, nanodots, liposomes, dendrimers, nano-emulsions have become common ingredients in cosmetics.
Nanophotocatalyst-Infused Textile Composites for Environmental Remediation
Authors: Sharma Vanshika , Kumar Amit , Jaswal Priya , Porwal Chirag , Gaur Akshay , Vaish Rahul
Published: February 2024

The development of innovative materials for environmental cleaning applications has been the focus of substantial research efforts due to the growing global concern over environmental pollution and the need for sustainable solutions. In light of this, textile composites based on nano photocatalysts have become an exciting new way to tackle a range of environmental issues. Nanophotocatalyst-based textile composites have a wide range of environmental uses, including materials for oil spill cleanup, water purification filters, and self-cleaning textiles and masks for air purification. These composites are appropriate for a variety of real-world situations due to their benefits of scalability, durability, and adaptability. The research status on textile composites based on nano photocatalysts and their uses in environmental remediation is described in this chapter.
Characteristics, Applications, and Limitations of Nanocomposites in Biosensing
Authors: Bhattacharjee Jyoti , Bhakta Debjani , Roy Subhasis
Published: February 2024

To combat the exponentially increasing environmental challenges, there is a greater demand for eco-friendly and sustainable materials in various industries, including sensor technology. Green nanocomposites, which combine biodegradable polymers with nanoparticles obtained from renewable resources, offer a possible solution to the demand for effective biosensors and environmental sustainability. This chapter assesses current advances in developing green nanocomposites for skin cancer biosensors and energy storage devices such as lithium-ion batteries, supercapacitors, and fuel cells, emphasizing their prospective applications and performance enhancements. This chapter provides an in-depth examination of green nanocomposites as sustainable, efficient, and cost-effective biosensors, highlighting their manufacturing, unique features, and numerous sensing applications, and also explores the inclusion of nanomaterials such as cellulose nanofibers, chitosan, and graphene oxide into biopolymer matrices such as starch, polylactic acid, cellulose, proteins, and chitosan to build biosensors.
Nanocellulose Composites as Chemo-/Bio-Sensing Agents and Polymeric Matrixes
Authors: Peña González Paula Tatiana , Celi Inés Hernández , Gómez Jaimes Franci Nathalie , Martínez Bonilla Carlos Andrés
Published: February 2024

This chapter highlights the nanocellose-based composite sensors as powerful tools in multiple sensing/detecting applications and polymeric matrixes. The worldwide need for rapid, precise/accurate, selective/specific detection positions the nanosensors as crucial devices for sensing purposes. The authors discuss the basic sensor conception, a basic architecture applied in different spatial scales and research areas, such as environmental sciences, medicine, and optoelectronics. Chemo- and biosensors are also described in the chapter. The use of nanocellulose as supporting or embedding material in sensors and polymeric matrixes gives the attributes of this noble material, which, combined with active and sensitive materials at the nanoscale, assemble new opportunities in the sensors field.
The Power of Data
Authors: Abrol Vipasha , Gupta Hina , Shukla Aditya
Published: February 2024

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the loss of dopamine-producing neurons in the brain, which leads to impaired movement, cognitive function, and other symptoms. It is an increasingly prevalent neurodegenerative condition predominantly impacting the elderly demographic. The diagnosis of PD is typically reliant on medical assessment and observable clinical manifestations. This approach is subjective and unfortunately does not offer a favorable prognosis. Nonetheless, identifying Parkinson's disease during its initial phases poses challenges. Yet, early intervention can significantly enhance the quality of life for individuals living with the condition. The progress in information technology and bioinformatics has resulted in remarkable achievements in the field of medical sciences. Machine learning has been used to diagnose Parkinson's disease with promising results. This chapter discusses the different types of machine learning methods that have been used for PD diagnosis.
A Functional Gradient Boost Approach for Identifying Parkinson's Disease
Authors: Kumar T. Ananth , Thirshala M. Nidya , Kanimozhi P. , Ananth Christo
Published: February 2024

Parkinson's disease is a neurodegenerative disorder characterised by the manifestation of involuntary and uncontrolled motor symptoms, such as tremors, rigidity, and impaired balance and coordination. Parkinson's disease is characterised by the degeneration of neurons in the substantia nigra, a region located within the brain. The gradient boost method will be employed in the field of machine learning to identify individuals afflicted with Parkinson's disease. This study employs a collection of features derived from the Parkinson's progression markers initiative (PPMI) in order to gain insights into the initiation and progression of brain diseases, as well as to explore potential interventions for mitigating their effects. The method was applied to a cohort of patients selected from the Parkinson's progression markers initiative (PPMI) dataset for evaluation. The utilisation of a machine learning algorithm facilitates the categorization of individuals afflicted with Parkinson's disease into distinct clusters.
Evaluation of Machine Learning Techniques for Classification of Early Parkinson's Disease
Authors: Kumar Amit , Sharma Neha , Anand Abhineet
Published: February 2024

Parkinson's disease (PD) arises from the degeneration of neurons and the subsequent depletion of dopamine, resulting in symptoms such as tremors, muscle rigidity, and bradykinesia. Timely identification is crucial; however, existing techniques do not offer a conclusive remedy. This work aims to fill the existing gap by utilizing open-source Python-trained models to evaluate the potential of auditory data in classifying Parkinson's disease, applying a range of machine learning algorithms, such as neural networks, logistic regression, random forest, adaboost, and k-nearest neighbors, to the UCI telemonitoring dataset, which consists of 31 persons, including 23 with Parkinson's disease. The evaluation is done using parameters including accuracy, precision, and recall. The suggested framework prioritizes data preprocessing, segmentation, algorithm training, and comprehensive evaluation, highlighting the significance of data preparation and algorithmic assessment in predictive modelling for early identification of Parkinson's disease.
Exploring the Landscape of Human Resource Management Research in the Tourism and Hospitality Sector
Authors: Kumar Suneel , Kumar Varinder , Valeri Marco , Devi Nisha , Attri Kamlesh
Published: February 2024

Human resource management (HRM) plays a critical role in the effective management of human capital within the tourism and hospitality sector. This study offers a comprehensive bibliometric analysis of HRM research in tourism and hospitality using Scopus data. Examining 1,171 studies, it reveals increasing interest and significant contributions in recent years. Prominent research themes include HRM, hospitality, tourism, organizational culture, talent management, and training. Co-authorship network analysis showcases collaborative relationships among international researchers. Acknowledging limitations like database reliance and language constraints, the study enriches the understanding of HRM in this sector, providing valuable insights for practitioners, policymakers, and researchers, while guiding future research endeavours.
Green HRM for Sustainable Business
Authors: Kaith Anup , Sachdeva Geeta
Published: February 2024

Green human resources is a concept centered on promoting sustainable employee behaviours, involving every employee in raising awareness about sustainability issues. It encompasses HR activities that are eco-friendly and advocates for the sustainable utilization of resources within organizations. The present study focuses on elaborating on the concept of GHRM in a more comprehensive manner and the various challenges faced by organizations in implementing environmental measures in their general HR processes. Moreover, this chapter will also introduce and discuss the ESG frameworks adopted by the companies. Furthermore, the author will try to present the various green practices adopted by Indian private sector company ITC and state-owned undertaking ONGC. In conclusion, the research underscores that GHRM comprises two fundamental components: environmentally friendly HR practices and the preservation of knowledge capital. This dual approach empowers industry professionals to enhance their awareness of green business and corporate social responsibilities.
Quality of Work-Life Practices in the Indian Hospitality Sector
Authors: Singh Amrik
Published: February 2024

Employees are the most valuable assets for any organization. It becomes very significant for organizations to attract and retain the best talents and maintain them for the longer term in the organization. Moreover, it has been observed in various studies that the working conditions of employees in the hotel industry are unnecessarily more precarious than those in other service industries. However, such conditions differ in certain aspects as well. The present chapter aims to study the broad dimensions of quality of work life such as equal opportunities and growth prospects, adequate fair and compensation, welfare facilities, social security measures, safe and healthy environment, basic facilities related to employee well-being, stress management, motivational dimensions, etc. Further, this study also projects future challenges and solutions for better employee engagement practices and work motivation through quality work practices.
The Evolving Nature of International Collaboration and Partnership
Authors: Chopra Amanpreet Singh , Manohar Sridhar , Zawadski Artur , Jain Mayank
Published: February 2024

The growth aspirations of developing countries supported by industrial prowess of developed economies, while leading to phases of rapid globalisation across the world, also resulted in skewed power and trade dynamics in favour of developed nations. Later, the world economy got hit hard by deglobalization. Today, nations are realigning their policies and national priorities through engaging in collaborative partnership with other economies to overcome the current phase of deglobalization. This chapter will track the phases of global growth and its reverse internationalization from the lens of various black swan events and will also analyse the changing global economic landscape resulting in the emergence of new pillars of global trade and cooperation in the form of economic trade blocs and regional trade agreements leading to a multipolar world and new reality of global trade. From a business perspective, the chapter will dwell on how corporates are realigning their international business practices, in this era of deglobalisation, to maintain their growth trajectory.
Decentralization in the Digital Age
Authors: Sharma Swaty
Published: February 2024

This chapter explores how digital technologies are reshaping decentralization in our fast-evolving technological era. It defines decentralization and examines its diverse aspects in politics, economics, and technology. The chapter emphasizes blockchain technology, highlighting its immutability, decentralized consensus, and smart contracts as pivotal elements driving decentralized applications and disrupting traditional paradigms. To provide context, it conducts a thorough literature review, tracing decentralization's historical evolution, exploring its impact on various sectors like decentralized finance, governance, financial inclusion, and system resilience. The chapter also addresses challenges such as regulatory uncertainties, scalability issues, and security concerns, drawing from authoritative sources and academic research. In conclusion, it sets the stage for a deeper exploration of decentralization's applications and impact across sectors, acknowledging both its potential and complexity as a transformative paradigm shift.
The Digital Transformation
Authors: Yadav Gajalakshmi N. S. , Seranmadevi R.
Published: February 2024

In the business world, the digital transformation has ushered in an era of unprecedented change. The explosive growth of digital technology over the past three decades has profoundly changed how businesses operate, compete, and engage with their customers. Businesses across industries have been forced to navigate this constantly changing environment due to the adoption of cloud computing, data analytics, the growth of e-commerce, and the rise of artificial intelligence. Organizations are forced to reconsider their strategies, operations, and consumer engagement models as a result of this significant instability. This chapter discusses the role of artificial intelligence in aiding customised and personalized marketing strategies, acknowledging the diverse preferences and behaviour of consumers. It shares insights on branding and customer management in an AI-driven environment. It also emphasises on online and technology-mediated customer engagement.
Physiological alterations in ‘Rubinela’ lettuce (Lactuca sativa L.) cultivated in conventional and hydroponic systems
Authors: Zappelini Julia , Pescador Rosete , Girardello Gabriel Menegusso , Souza Priscila  Priscila Fernandes de , Borghezan Marcelo , Oliveira Jorge Luiz Barcelos
Published: February 2024

Lettuce (Lactuca sativa L.) is the most consumed leafy vegetable in Brazil. It is cultivated using at least four distinct systems, the most common of which are conventional and hydroponic systems. These systems provide different cultivation conditions for plants, causing physiological changes that are important for commercial production, such as nutrient uptake and biomass accumulation. However, only a few studies have compared the physiological aspects of these two cultivation systems. The objective of this study was to evaluate the physiological behavior of ‘Rubinela’ lettuce plants grown in hydroponic and conventional pot systems, by comparing dry mass (DM) and fresh mass (FM) production, number of leaves (NF), stomatal density, and contents of chlorophyll, carotenoids, anthocyanin, sugars, and starch. Plants cultivated in hydroponic systems presented significant differences in chlorophyll content, producing more biomass than plants cultivated in conventional pot systems, probably because of better nutritional conditions, primarily with respect to macronutrients, provided by the nutrient solution of the hydroponic system. The lower water availability encountered by plants cultivated in conventional pot systems influenced the increased sugar and starch concentrations, as well as the anthocyanin content, which may be a strategy to mitigate the possible damage caused by hydric stress conditions.
Simulating soil carbon and nitrogen trends under an integrated system in the Brazilian Cerrado
Authors: Vogado Renato Falconeres , Souza Henrique  Antunes de , Althoff Tiago Diniz , Menezes Rômulo Simões Cezar , Gualberto Adriano Veniciús Santana , Cunha João Rodrigues da , Leite Luiz Fernando Carvalho
Published: February 2024

Management systems that include trees tend to provide higher amounts of plant biomass to the soil, contributing to the increase in carbon (C) and nitrogen (N) stocks. This study simulated C and N stocks and their compartments in a crop-livestock-forest integration system in the edafoclimatic conditions of the Maranhão Cerrado using the Century 4.5 model. The evaluated areas were native Cerrado vegetation (NV) and crop-livestock-forest integration (CLFI). The calibration process gradually modified the model parameters to better fit the simulated and observed soil C and N stocks. The best fit between the data was obtained after changes in the main parameters (DEC3(2), DEC4, and DEC5) that controlled the rate of decomposition of soil organic matter. C and N stocks increased by 14% and 15%, respectively, over 14 years after replacing NV with CLFI. The slow compartment of C presented greater sensitivity to changes in management, with an increase of 47% compared with that of NV. The active compartment increased by 31% and the passive compartment remained constant for over 14 years. Future scenarios, where pasture was maintained between the eucalyptus trees and the scenario that allowed the soybean, corn, and Brachiaria rotation between the trees, were more effective, accumulating approximately 37 Mg C ha-1. The continuous contribution of residues from the trees and pasture increased C and N stocks in the long-term in the slow fraction, where the total organic carbon increased from 32 to 36 Mg ha-1 when NV was replaced with CLFI. The model predicted the C and N stocks with accuracies ranging from 1 to 11% of the observed values
Genomic prediction using the lmekin function from the coxme R package
Authors: Souza Clemeson Silva de , Santos Vinícius Silva dos , Martins Filho Sebastião
Published: February 2024

The increasing use of genomic selection (GS) in plant and animal breeding programs has led to the development of software that fits models based on unique scenarios. Accordingly, several R packages have been developed for GS. The lmekin function from the coxme R package was one of the first functions implemented in R to fit models with random family effects using the pedigree–based relationship matrix. The function allows the user to provide the covariance structures for the random effects; thus, the GBLUP model can be fitted. This fitting process consists of replacing, in the traditional BLUP model, the additive relationship matrix derived from a pedigree by the additive relationship matrix derived from markers. Thus, the objective of this study was to employ the lmekin function in the context of genomic prediction by comparing the results of this function with those obtained using five R packages for GS: rrBLUP, BGLR, sommer, lme4qtl, and lme4GS. The comparisons were performed considering the computational times and predicted values for a wheat dataset and simulated big data. In addition, we implemented a 5-fold cross-validation scheme through considering the values predicted by the lmekin function for the wheat dataset. The results indicated that the lmekin function was effective in predicting genomic breeding values considering multiple random effects and relatively small sample sizes. The rrBLUP package processed the fastest for the scenario with only one genetic random effect, and the high temporal efficiency of the sommer package was confirmed for the scenario with more than one genetic random effect. Differences in computational times occurred because of the different algorithms implemented in the packages to estimate the variance components.
Longer cutting intervals on the characteristics of Guinea grass: morphogenetic, productive, and nutritional traits
Authors: Junges Luciana , Gomes Eva Nara Oliveira , Candido Anderson Ramires , Ítavo Luís Carlos Vinhas , Ítavo Camila Celeste Brandão Ferreira , Coelho Elson Martins , Difante Gelson dos Santos , Santos Geraldo Tadeu dos , Dias Alexandre Menezes
Published: February 2024

The aim of this study was to examine the effect of longer cutting intervals on morphogenetic and structural traits, herbage production, nutritional value, and in vitro digestibility of Guinea grass cv. Mombaça (Mombaça grass). Four cutting intervals (49, 63, 77, and 91 days) were evaluated in two crop years (2015-2016 and 2016-2017) during the rainy season, in two replicates. Cutting intervals influenced structural and morphogenetic traits, except for number of live leaves (4.35 leaves tiller-1) and final leaf length (72.94 cm) in the 2015-2016 crop year. As the cutting intervals increased, dry matter yield and stem percentage increased, whereas leaf percentage and leaf-to-stem ratio declined. Regardless of the evaluated crop year, the dry matter, acid detergent fiber, and lignin contents increased linearly; however, the neutral detergent fiber content was unaffected. Cutting intervals affected the crude protein content and in vitro digestibility. Considering leaf appearance rate, stem appearance rate, and leaf-to-stem ratio, the recommended harvest age for Mombaça grass for optimum yield and nutritional value is 77 days.
Non-carcass components of confined lambs in feedlot using red propolis extract
Authors: Vasconcelos Adriano Mendes , Almeida Vitor Visintin Silva de , Oliveira Aline Cardoso , Silva João Wilian Dias , Paixão Tarcísio , Santos Laize Vieira , Silva Ana Paula Gomes da , Lima Júnior Dorgival Morais de , Silva Robério Rodrigues
Published: February 2024

The objective of this work was to evaluate the non-carcass components of crossbred Santa Inês lambs finished in feedlot with the inclusion of different levels of red propolis extract (RPE). Thirty-five crossbred lambs were used with an average weight of 17.08 ± 2.36 kg and mean age of five months, distributed in a completely randomized design with five treatments and seven replications. The treatments evaluated were: 0; 7; 14 and 21 mL of extract/animal/day Before slaughter, each animal was individually weighed to obtain the body weight at slaughter (BWS). The weights and yields of non-carcass components were evaluated, weights and yields of regional dishes (Buchada and Panelada). The addition of different levels of red propolis extract (RPE) did not influence (p > 0.05) the weights and yields of non-carcass components, by-products and adipose deposits, to the weight and yield of regional dishes. The use of red propolis extract in diets up to the level studied (28 mL day-1) did not change the characteristics evaluated.
Combustion analysis of ternary mixtures of diesel oil, biodiesel, and refined soybean oil: a view of cylinder pressure
Authors: Filgueiras Tainã Carvalho Garcia Miranda , Sousa Edson  Alves de , Freire Walber Alair Carvalho , Pinheiro Maick Gleyton Antunes , Saavedra Rodrigo dos Santos , Lameira Pedro Igor Dias , Rocha Hendrick Maxil Zarate , Nogueira Manoel  Fernandes Martins
Published: February 2024

Currently, the high consumption of fossil fuels is causing problems, perhaps, irreversible to the planet, investing in green energy is an essential point for the sustainable development of any country. Concomitant to this, biomass fuels, in particular natural vegetable oils, appear as an option for the total or partial replacement of these fuels in the production of energy in the current world energy matrix. Corroborating this pretext, the objective of this work is to analyze the effects of using different mixtures of Brazilian commercial diesel oil (92% diesel, 8% biodiesel) with refined soybean oil from the effective cylinder pressure of a stationary diesel generator set. To achieve this purpose, the generator set of the Branco brand, model BD-6500, was used, with the aid of the pressure acquisition system of the Austrian company AVL. Analyzes of the requested fuels were carried out in addition to the experimental tests. The results presented show that the use of mixtures with volumetric ratios of soybean oil may show a drop in the cylinder pressure.
Dam failure analysis according to different methods in HEC-RAS, Gökçe Dam, Turkey
Authors: Doğan Emrah , Temiz Temel , Sönmez Osman , Demirci Beytullah , Spor Pinar , Cumhur Alper
Published: February 2024

As in the rest of the world, the impact of climate change is clearly felt in Turkey, while the effects of global warming, drought and flood-induced disasters continue to increase day by day. At the same time, water consumption is crucial for the survival of people. Efficient water use and conservation, facilitated by water resources management, should be recognized as a pressing contemporary concern. Recently, floods that cause a great deal of damage have started to occur frequently in Turkey. A possible flood that may occur in the demolition of Gökçe Dam has been a matter of curiosity. This dam is the most important water source for Yalova province and has been in service for a long time. Therefore, the aim of this study is to examine the flood risk situations of settlements in case of a possible Gökçe Dam failure or basin flooding. Additionally, the study aims to estimate the damages that may occur in houses of flooded areas. Piping and overtopping methods were used in HEC-RAS program to create flood maps, and the Van Eck and Kok damage curves were used to assess the damages. While water level varies between 0-1 meters in the most affected buildings in the overtopping method, this level is 2-3 meters in the piping method. The damage to buildings with a water level of 4 meters and above is quite high in the piping method, and it has been observed that greater damage may occur for lower water levels in the overtopping method. The total damage and number of affected buildings are higher in the piping method. Accordingly, a possible Gökçe dam failure would cause significant losses in both methods, especially in the piping method.