bluuebunny/crossref_metadata_2025 huggingface dataset:
Utilization of Ensiled Cassava Leaf and Taro Mixed with Waste of Soybean Meal as Protein Sources Fed Different Sources of Energy on Fattening Crossbred Pigs
Authors: Chheng Botum , Chiv Phiny , Bun Tean , Prak Kea
Published: April 2024

The experiment was carried out at the station of Agricultural Research of Svay Rieng University, and was carried out from 15th April to 17th June 2023 with the title “Utilization of ensiled cassava leaf and taro mixed with waste of soybean meal as protein sources fed different sources of energy on fattening crossbred pigs”. This research has 3 objectives: 1. To compare DM feed intake of crossbred pigs, 2. To compare the growth rate of crossbred pigs, and 3. To compare the feed conversion of crossbred pigs. There were 12 pigs were arranged within 2*2 factorial design through Randomized Complete Block Design (RCBD) with 3 replications. The factor with the energy source: rice bran or broken rice and the factor with the protein source: Ensiled taro mixed with waste of soybean meal or Ensiled cassava leaf mixed with waste of soybean meal. The treatments of the study are: T1: Ensiled Taro 30% + Waste of Soybean Meal 20% + Rice Bran 49% + Premix 1%; T2: Ensiled Taro 30% + Waste of Soybean Meal 20% + Broken Rice 49% + Premix 1%; T3: Ensiled Cassava Leaf 25% + Waste of Soybean Meal 25% + Rice Bran 49% + Premix 1%; and T4: Ensiled Cassava Leaf 25% + Waste of Soybean Meal 25% + Broken Rice 49% + Premix 1%. The period of experiment were 60 days only. All data were collected daily such as feed offer and feed refusals. Those pigs were weighed at every 10 days from the starting of the experiment until the end of experiment. The experiments’ results were found that total intake on the energy sources were higher significant differently in broken rice compared with the rice bran (P<0.01) as total intake on the protein sources were higher significant differently on the ensiled taro mixed with waste of soybean meal (P<0.01). Growth rate on the energy sources were higher significant differently in broken rice as compared to the rice bran (P<0.01) while growth rate for the ensiled taro plus waste of soybean meal was slightly high if compared with the ensile cassava plus waste of soybean meal (P>0.05). Feed conversion ratio on the energy sources was better significant in broken rice compared to the rice bran (P<0.01) while feed conversion for ensiled taro mixed with waste of soybean meal was slightly better as compared with the ensile cassava plus waste of soybean meal (P>0.05). In conclusion, the use of the protein source from the ensiled taro mixed with waste of soybean meal plus the energy source of broken rice or rice bran, the pigs were increased on total intake, live weight gain and good improvement of feed conversion rather than the use of ensiled cassava leaf mixed with waste of soybean meal plus broken rice or rice bran. The research outcome indicated that efficiency of protein offering is asked to use the ensiled taro with waste of soybean meal combination with basal diet of broken rice or rice bran on reproduction of sows in the next trial.
Human Activity Recognition using Machine Learning
Authors: Mandave Omkar , Phad Abhishek , Patekar Sameer , Karlekar Nandkishor
Published: April 2024

Working information and classification is one of the most important problems in computer science. Recognizing and identifying actions or tasks performed by humans is the main goal of intelligent video systems. Human activity is used in many applications, from human-machine interaction to surveillance, security and healthcare. Despite continuous efforts, working knowledge in a limitless field is still a difficult task and faces many challenges. In this article, we focus on some of the current research articles on various cognitive functions. This project includes three popular methods to define projects: vision-based (using estimates), practical devices, and smartphones. We will also discuss some advantages and disadvantages of the above methods and give a brief comparison of their accuracy. The results will also show how the visualization method has become a popular method for HAR research today.
Advancing Healthcare Predictions: Harnessing Machine Learning for Accurate Health Index Prognosis
Authors: Bhaskar P. , Kumar Burramsetty V. S. Rithesh , Pinnaka Bhavya , Teja Kalapala Brahma , Kumar Tanguturi V. S. Sudheer
Published: April 2024

This study investigates how machine learning (ML) techniques may be used to forecast health indicators' accuracy, which is important for efficient medical monitoring and diagnosis. Numerous machine learning techniques, such as Support Vector Machines and Random Forest, are evaluated by using a heterogeneous dataset that includes vital signs, lab findings, and patient information. Model performance is optimised by careful preprocessing and feature engineering, which includes managing missing variables and normalisation. Model accuracy is further improved via hyperparameter tuning strategies, which are measured using metrics like precision and recall. The findings show that machine learning (ML) models can accurately predict health index accuracy, which may help with early illness identification and individualised treatment plans. The study highlights the potential of machine learning in healthcare decision-making and provides guidance for raising the standard of patient care. Future projects could look into adding more functionality and integrating real-time data for.
The Impact of Digital Marketing Dimensions on Customer Satisfaction
Authors: Naeem Sabeeh Mustafa , Sihab Alaboodi Ridha Kareem , Ghazi Al-ARAJL Hussein Faisal
Published: April 2024

This study aims to examine the role of digital marketing in customer satisfaction at Al-Warith Al- Anbiya University and Al-Zahra University in the holy city of Karbala. Due to the scarcity of studies and research attempting to understand the role of this topic, this study is considered one of the first to address these variables. Moreover, this study addresses a real problem that directly affects the educational sector in the relationship. A questionnaire was used to collect data, with (40) questionnaires distributed and (32) responses obtained from employees at Al-Warith Al-Anbiya University and Al-Zahra University in Karbala, using a random sampling method. The study concluded that there is a positive and significant relationship between the use of digital marketing and customer satisfaction in the researched universities. The report makes many recommendations, including expanding the use of digital marketing as a successful strategy for achieving customer satisfaction and offering staff members ongoing training and development opportunities to advance their knowledge of digital marketing and stay abreast of technical advancements in the industry. The report also suggests doing more extensive research in the future to determine the digital marketing tactics and best practices that these colleges might employ to raise customer satisfaction.
Auditor's Independence and Experience on Inspectorate Quality with Auditor's Professional Ethics as a Moderation Variable
Authors: TRISNAYANTI Ni Kadek Dwi , PUTRA I Made Wianto , KRISTIANTARI I Dewa Ayu
Published: April 2024

Purpose:This research empirically tests the influence of auditor independence and experience on the audit quality of the Badung Regency Government Inspectorate with auditor professional ethics as a moderating variable.Methodology:The type of data used in this research is quantitative data. The 36 samples used in this research were obtained using non-probability sampling with the Saturated Sampling method. The primary data source in this research is obtained by distributing questionnaires directly. The data analysis technique used in this research is PLS-SEM (Partial et al. Equation Modeling) analysis with the help of SmartPLS version 3.0 software.Findings:The results of this research show that (1) Auditor independence has a positive effect on the audit quality of the Badung Regency Government Inspectorate, (2) Auditor experience has a positive effect on the audit quality of the Badung Regency Government Inspectorate.Implication:(3) The auditor's professional ethics can moderate the influence of auditor independence on the audit quality of the Badung Regency Government Inspectorate, and (4) The auditor's professional ethics are not able to moderate the influence of the auditor's experience on the audit quality of the Badung Regency Government Inspectorate.
The Influence of Service Quality and Employee Performance on Customer Satisfaction in the Maja Langu Savings and Loan Cooperative, Denpasar
Authors: WIRAGUNA Anak Agung Ngurah Bagus , PURNAMI A. A. Sri , JAYAWARSA A.A Ketut , WULANDARI I Gusti Ayu Athina
Published: April 2024

Purpose:In the competitive business world, customer satisfaction is the main focus for companies. It is not just a goal but also the key to attracting and retaining consumers. This study investigates the influence of service quality and employee performance on customer satisfaction at the Maja Langu Savings and Loans Cooperative, Denpasar.Methodology:64 respondents were successfully obtained by applying the purposive random sampling method and considered representative of the overall sample. Data analysis was carried out using multiple linear regression after undergoing instrument and classical assumption tests. The approach used is quantitative, with a questionnaire as the data collection instrument.Findings:The research results show that service quality and employee performance positively and significantly impact customer satisfaction at KSP Maja Langu, Denpasar. These two factors individually have a positive and significant influence on customer satisfactionImplication:These findings have important implications for the management and employees of KSP Maja Langu in maintaining and increasing customer satisfaction. Improving service quality and employee performance will help increase customer satisfaction at the Maja Langu Denpasar Savings and Loans Cooperative.
DÉBORA
(1927) DE PABLO PALACIO. DE PUNTILLAS SOBRE LA CIUDAD, SU PLANO SERÍA UN CUERO TENDIDO A SECAR. UNA POÉTICA DE LA NO-NOVELA
Authors: Caicedo Alicia Ortega
Published: April 2024

Este artículo inicia con una referencia a la coyuntura cultural y política de la escena contemporánea a Pablo Palacio, con el propósito de señalar el alcance de polémicas y debates que la noción de vanguardia generó en Ecuador entre 1918 y 1934. Desarrolla algunos apuntes en torno a los vínculos políticos del escritor, así como a las revistas que marcaron la ruta vanguardista de la época y el lugar de su escritura en el marco de los diferentes derroteros que eligió la literatura ecuatoriana hacia la segunda década del siglo pasado. Luego de señalar algunos elementos fundamentales del universo vital y narrativo de Pablo Palacio, el texto centra su atención en
Débora
, desde una lectura interesada en destacar, sobre todo, sus apuestas teóricas en torno a la novela (o no-novela): el
fustigamiento
teórico que el autor desarrolla frente al hecho literario en el acto mismo de su creación.
Length and Velocity Scales in Protoplanetary Disk Turbulence
Authors: Sengupta Debanjan , Cuzzi Jeffrey N. , Umurhan Orkan M. , Lyra Wladimir
Published: April 2024

In the theory of protoplanetary disk turbulence, a widely adopted ansatz, or assumption, is that the turnover frequency of the largest turbulent eddy, Ω
L
, is the local Keplerian frequency Ω
K
. In terms of the standard dimensionless Shakura–Sunyaev α parameter that quantifies turbulent viscosity or diffusivity, this assumption leads to characteristic length and velocity scales given respectively by

α

H

and

α

c

, in which H and c are the local gas scale height and sound speed. However, this assumption is not applicable in cases when turbulence is forced numerically or driven by some natural processes such as vertical shear instability. Here, we explore the more general case where Ω
L
≥ Ω
K
and show that, under these conditions, the characteristic length and velocity scales are respectively

α

/

R
′

H

and

α
R
′

c

, where

R
′
≡

Ω

L

/

Ω

K

is twice the Rossby number. It follows that

α
=

α

˜

/

R
′

, where

α

˜

c

is the root-mean-square average of the turbulent velocities. Properly allowing for this effect naturally explains the reduced particle scale heights produced in shearing box simulations of particles in forced turbulence, and it may help with interpreting recent edge-on disk observations; more general implications for observations are also presented. For

R
′
>
1

, the effective particle Stokes numbers are increased, which has implications for particle collision dynamics and growth, as well as for planetesimal formation.
Exploring Changing-look Active Galactic Nuclei with the Sloan Digital Sky Survey V: First Year Results
Authors: Zeltyn Grisha , Trakhtenbrot Benny , Eracleous Michael , Yang Qian , Green Paul , Anderson Scott F. , LaMassa Stephanie , Runnoe Jessie , Assef Roberto J. , Bauer Franz E. , Brandt W. N. , Davis Megan C. , Frederick Sara E. , Fries Logan B. , Graham Matthew J. , Grogin Norman A. , Guolo Muryel , Hernández-García Lorena , Koekemoer Anton M. , Krumpe Mirko , Liu Xin , Martínez-Aldama Mary Loli , Ricci Claudio , Schneider Donald P. , Shen Yue , Śniegowska Marzena , Temple Matthew J. , Trump Jonathan R. , Xue Yongquan , Brownstein Joel R. , Dwelly Tom , Morrison Sean , Bizyaev Dmitry , Pan Kaike , Kollmeier Juna A.
Published: April 2024

“Changing-look” active galactic nuclei (CL-AGNs) challenge our basic ideas about the physics of accretion flows and circumnuclear gas around supermassive black holes. Using first-year Sloan Digital Sky Survey V (SDSS-V) repeated spectroscopy of nearly 29,000 previously known active galactic nuclei (AGNs), combined with dedicated follow-up spectroscopy, and publicly available optical light curves, we have identified 116 CL-AGNs where (at least) one broad emission line has essentially (dis-)appeared, as well as 88 other extremely variable systems. Our CL-AGN sample, with 107 newly identified cases, is the largest reported to date, and includes ∼0.4% of the AGNs reobserved in first-year SDSS-V operations. Among our CL-AGNs, 67% exhibit dimming while 33% exhibit brightening. Our sample probes extreme AGN spectral variability on months to decades timescales, including some cases of recurring transitions on surprisingly short timescales (≲2 months in the rest frame). We find that CL events are preferentially found in lower-Eddington-ratio (f
Edd) systems: Our CL-AGNs have a f
Edd distribution that significantly differs from that of a carefully constructed, redshift- and luminosity-matched control sample (Anderson–Darling test yielding p
AD ≈ 6 × 10−5; median f
Edd ≈ 0.025 versus 0.043). This preference for low f
Edd strengthens previous findings of higher CL-AGN incidence at lower f
Edd, found in smaller samples. Finally, we show that the broad Mg ii emission line in our CL-AGN sample tends to vary significantly less than the broad Hβ emission line. Our large CL-AGN sample demonstrates the advantages and challenges in using multi-epoch spectroscopy from large surveys to study extreme AGN variability and physics.
A New Window for Studying Intermediate Polars and Tilted Accretion Disk Precession
Authors: Sun Qi-Bin , Qian Sheng-Bang , Zhu Li-Ying , Liao Wen-Ping , Zhao Er-Gang , Li Fu-Xing , Shi Xiang-Dong , Li Min-Yu
Published: April 2024

TV Col is a long-period eclipsing intermediate polar (IP) prototype star for the negative superhump (NSH) system. We investigate the eclipse minima, eclipse depth, and NSH amplitude based on TESS photometry. Using the same analytical method as for SDSS J081256.85+191157.8, we find periodic variations of O − C for eclipse minima and NSH amplitudes with periods of 3.939(25) days and 3.907(30) days, respectively. The periodic variation of the NSH amplitude of TV Col confirms that periodic changes in NSH amplitude in response to the tilted disk precession may be universal, which is further evidence that the origin of the NSHs is related to the tilted disk precession. We suggest that the variation in NSH amplitude may be similar to the superorbital signal, coming from the periodic change in visual brightness of the energy released by streams touching the tilted disk with tilted disk precession. Finally, we find for the first time that the eclipse depth exhibits biperiodic variations with periods of P
1 = 3.905(11) days and P
2 = 1.953(4) days, respectively. P
2 is about half of P
1 and the disk precession period (P
1 ≈ P
prec ≈ 2 × P
2). We suggest that P
1 may come from the periodic change in the brightness of the eclipse center due to tilted disk precession, while P
2 may come from two accretion curtains precessing together with the tilted disk, but more verification and discussion are necessary. The discovery of biperiodic variations in eclipse depth provides a new window for studying IPs and tilted disk precession.
Picking pearls from the Silk Road: Insights into the spider (Arachnida, Araneae) diversity in Georgia from the Caucasus Barcode of Life (CaBOL) project. Part III
Authors: Seropian Armen , Bulbulashvili Natalia , Krammer Hans-Joachim , Thormann Jana , Hein Nils , Karalashvili Elisabeth , Kachlishvili Nino , Datunashvili Anastasia
Published: April 2024

The present study contains information about a small number of spiders collected between 2018 and 2023. The spiders were determined to species level and partially assisted by genetic barcoding of the COI gene. Among the resulting 33 species, 23 species and three genera, are recorded from Georgia for the first time, of which 11 are new records for the entire Caucasus. Of the 26 barcoded specimens representing 18 species, six species (Agroeca maculataL. Koch, 1879;Aituaria eriashvilii(Marusik, 1987);Evippa sjostedtiSchenkel, 1936; Lathys cf. lehtineni Kovblyuk, Kastrygina & Omelko, 2014;Zelotes segrex(Simon, 1878);Lathys lehtineniKovblyuk, Kastrygina & Omelko, 2014;Maculoncus obscurusTanasevitch, Ponomarev & Chumachenko, 2016) are barcoded for the first time. In addition, 12 new regional records for 10 species are provided. The collecting information for each record is provided in detail, together with a discussion of the barcoding results and remarks, supported by 48 diagnostic drawings of 22 first-recorded species. A potential synonymy ofPelecopsis odontophora(Kulczyński, 1895) withP. parallela(Wider, 1834) andBassaniodes loeffleri(Roewer, 1955) withB. pseudorectilineus(Wunderlich, 1995) is discussed.
Analytical and numerical investigation of viscous fluid-filled spherical slip cavity in a spherical micropolar droplet
Authors: Alharbi Abdulaziz H. , Salem Ahmed G.
Published: April 2024

This article presents an analytical and numerical investigation on the quasi-steady, slow flow generated by the movement of a micropolar fluid drop sphere of at a concentrical position within another immiscible viscous fluid inside a spherical slip cavity. Additionally, the effect of a cavity with slip friction along with the change in the micropolarity parameter on the movement of the fluid sphere is introduced. When Reynolds numbers are low, the droplet moves along a diameter that connects their centres. The governing and constitutive differential equations are reduced to a computationally convenient form using appropriate transformations. By using the resulting linear partial differential equations for the stream functions and using the method of separation variables, we can obtain their solutions. General solutions for velocity fields are found using spherical coordinate systems, which are based on the concentric point of the cavity; this allows to obtain solutions to the Navier-Stokes equations internal and external to the spherical droplet. The vorticity-microrotation boundary condition is used in regard to the micropolar droplet case in a viscous fluid. The normalised drag forces acted upon the micropolar drop are illustrated via graphs and tables for diverse values of the viscosity ratio and drop-to-wall radius ratio, with the change of the spin parameter that attaches the microrotation to vorticity. The correction wall factor is shown to increase with an increase in the drop-to-wall radius ratio, when moving from the gas bubble case to the solid sphere case, with an increase in the micropolarity parameter, and with an increase in the slip frictional resistance. This study is relevant due to its potential uses in a variety of biological, natural, and industrial processes, including the creation of raindrops, the investigation of blood flow, fluid-fluid extraction, the forecasting of weather conditions, the rheology of emulsions, and sedimentation phenomena.
Exploring unconventional optical soliton solutions for a novel $ \mathfrak{q} $-deformed mathematical model
Authors: Ali Khalid K. , Alharbi Weam G.
Published: April 2024

This paper presents a significant contribution in the form of a new general equation, namely the $ \mathfrak{q} $-deformed equation or the $ \mathfrak{q} $-deformed tanh-Gordon equation. The introduction of this novel equation opens up new possibilities for modeling physical systems that exhibit violated symmetries. By employing the $ (G'/G) $ expansion method, we have successfully derived solitary wave solutions for the newly defined $ \mathfrak{q} $-deformed equation under specific parameter regimes. These solutions provide valuable insights into the behavior of the system and its dynamics. To further validate the obtained analytical results, the numerical solution of the $ \mathfrak{q} $-deformed equation has been constructed by using the finite difference method. This numerical approach ensures the accuracy and reliability of the findings. To facilitate a comprehensive understanding of the results, we have included two- and three-dimensional tables and figures, which provide visual representations and comparisons between the analytical and numerical solutions. These graphical illustrations enhance the clarity and interpretation of the obtained data. The significance of the $ \mathfrak{q} $-deformation lies in its ability to model physical systems that exhibit deviations from standard symmetry properties, such as extensivity. This type of modeling is increasingly relevant in various fields, as it allows for a more accurate representation of real-world phenomena.
Au fil d’une vie politique réflexions biographiques sur la carrière de Jens Otto Krag
Authors: Olesen Niels Wium , Laursen Johnny
Published: April 2024

Jens Otto Krag fut l’un des hommes politiques les plus influents de l’histoire du Danemark d’après-guerre. Cet article étudiera deux épisodes clefs de sa carrière politique : il fut un idéologue et stratège de la planification en 1944-1945 (Krag est un des pères fondateurs du modèle danois d’État providence), et il façonna la position du Danemark vis-à-vis de la CEE en 1957. Mais cet article est aussi une réflexion théorique sur l’historiographie : Krag fut son propre biographe et l’historien officieux du parti social-démocrate ; de plus, il était conscient de sa position dans l’histoire de son pays et laissa des sources pour les historiens du futur tout en organisant sa propre mise en scène et son héritage historique. Les historiens devront déjouer « l’illusion biographique » chère à Bourdieu afin de comprendre quelle vision à long terme Krag avait réellement de l’État providence et de l’intégration européenne.
Ólafur Ragnar Grímsson et l’évolution du statut de la fonction présidentielle en Islande
Authors: Óskarsdóttir Stefanía
Published: April 2024

La crise financière qui toucha l’Islande en 2008 affecta son système politique en profondeur. L’un des changements qui en découlèrent fut l’accroissement du rôle du président de la République. Jusqu’alors, les acteurs politiques islandais adhéraient à une interprétation du parlementarisme selon laquelle la majorité contrôlait l’ordre du jour législatif, décidait de l’attribution des portefeuilles ministériels et déterminait les politiques publiques. Le pouvoir du président était donc limité et d’ordre plutôt symbolique. À présent, au contraire, la présidence apparaît comme l’émissaire de la majorité parlementaire. Les changements relatés dans cet article ne furent pas le résultat d’une révision formelle de la Constitution. Ils ont puisé leurs sources dans le jeu politique qui a favorisé une réinterprétation du rôle du président. La nouvelle interprétation bénéficie d’un soutien populaire et reflète l’érosion de la confiance dans le Parlement et dans la classe politique depuis la crise.
Long-Term Analysis of the Usability and Performance of Photovoltaic Systems
Authors: Koussa Assia , Boudra Sabrina , Saheb Djohra , Koussa Mustapha , Razagui Abdelhak , Ounis Houdaifa
Published: April 2024

The PVF-Chart method consists of a combination of correlations and fundamental expressions for the hourly calculations of solar radiation at a given location. It uses long-term monthly average solar radiation and ambient temperature to predict the annual performance of a photovoltaic array. The chapter aims to analyse the long-term performances of a PV system used to cover an individual household situated in different Algerian climatic zones. The obtained results are compared to those presented in the literature and from which it can be concluded that the developed software program permits to calculate the PV system performances with a good exactitude which confirm its uses as a tool to analyse the long-term performance of PV system at any climate where only the monthly mean daily weather data is available. From these later, the hourly, the daily, and the annual performance of a photovoltaic array are predicted. For the preliminary assessment of the energy-producing of the studied PV project, the LCOE was calculated for three interest rates and two lifetimes.
Safe Green Hydrogen
Authors: Hassan Shuaibu Chiroma , Kumar Sanjeev
Published: April 2024

Energy has been a vital resource in our daily lives and, simultaneously, one of the worldwide main problems. However, energy is an essential resource in almost everything in our daily lives, ranging from transportation, cooking, warming residential places, electronics, factories, schools, banks, etc. The most popular forms of energy used are from biofuels, which pollute the environment and cause some hazards to humans and plants, in the states of coal, oil and gas, nuclear, and so on. Due to these concerns, a new form of energy is needed that is environmentally friendly, cost-effective, and more sustainable. With the various types of research carried out across the globe, different forms of energy were identified and tested: solar, wind, reusable batteries, etc. Various research institutions, organizations, multinational organizations, and companies are embarking on research to find meaningful solutions to the problems. None has been shown to hold much promise like the green hydrogen (GH). Against this backdrop, this research will be conducted to show how much potential it holds.
AI-Powered Dialogue System for Business Exploring GPT3's Impact
Authors: Dar Amir Ahmad , Jain Akshat , Malhotra Mehak , Khan Mohammad Shahfaraz , Khanday Manzoor Ahmad
Published: April 2024

Transformative breakthroughs appear every few decades, revolutionizing the globe and vastly enhancing our quality of life. With the introduction of ChatGPT, another paradigm-shifting event has now occurred. This Study examine the importance of GPT-3 in the context of business. In essence, GPT-3 has the potential to transform a variety of business processes by streamlining workflows, enhancing client relationships, and spurring creative breakthroughs. This transformative tool can substantially advance businesses by amplifying effectiveness, refining customer engagements, fostering innovation, and simplifying an array of operations. This research will explore the capacity to influence diverse sectors, notably the financial market. Moreover, GPT-3 holds the potential to profoundly shape the functions of financial brokers, augmenting their efficiency, customer service, and decision-making protocols. Ultimately, we will examine how GPT-3 can elevate effectiveness, customer engagement, decision-making, and creative advancement
Revolutionizing Healthcare
Authors: Kaur Jaspreet
Published: April 2024

The healthcare industry is currently experiencing a groundbreakingly revolution as cloud robotics and artificial intelligence (AI) come together. This research examines the synergistic relationship between these two advanced technologies and their significant influence on improving patient care. Through the utilisation of cloud-based computing power and the advanced capabilities of intelligent robotics, healthcare systems can attain unparalleled levels of efficiency, accessibility, and personalisation. Integrating AI algorithms with robotic systems enables enhanced diagnosis, treatment planning, and real-time monitoring, ultimately resulting in enhanced patient outcomes. This chapter examines the present condition of these technologies, explores instances where they have been effectively put into practice, and highlights the possible obstacles and ethical concerns. In this era of healthcare transformation, it is essential to recognise the collaborative capabilities of cloud robotics and AI in designing the future of patient-centred, data-driven healthcare systems.
Exploring Artificial Intelligence in Evolving Healthcare Environments
Authors: Barua Ranjit
Published: April 2024

Artificial intelligence (AI) is assuming an increasingly significant role in our daily lives, primarily due to the array of benefits it offers when applied. These advantages encompass round-the-clock availability, an exceptionally low error rate, the capacity to deliver real-time insights, and swift data analysis. In the domain of clinical medical and dental healthcare, AI is experiencing growing utilization with noteworthy applications that encompass disease diagnosis, risk assessment, treatment planning, and drug discovery. This chapter undertakes a comprehensive analysis of AI's integration into healthcare from a multidisciplinary perspective, with a specific focus on the healthcare sector.
Application of AI in Big Data Processing
Authors: Bhatt Chandradeep , Shukla Devang , Kumar Indrajeet , Agrawal Krishna Kant
Published: April 2024

With the rapid growth of quantity of data in various domains, the necessity for resourceful processing systems has become dominant. This paper investigates the Artificial Intelligence (AI) for solving real word problems using big data processing. AI sub fields such as computer vision, machine learning and natural language processing are heavily used in the field of big data processing. By using these emerging technologies, mining complex data and fetching useful information become an easy task. After pandemic, all sectors including medical and health care used the AI technologies for data processing in order to get vaccine fast. This paper also investigates how all sector planned their action to deal with new challenges and opportunities. To enhance the precision, scalability and accuracy in big data processing and manipulating tasks, the integration of multiple new technologies required like Hadoop Spark and distributed computing. This work also covers these integration and innovation so that we can take more advantage of existing things. Moreover, there exist real world case studies from a different sector such as healthcare, finance, business etc which shows the revolution in big data processing.
Applications of Artificial Intelligence in Clinical Validation, Device Approval, and Insurance Coverage
Authors: Pujahari Rakesh Mohan , Khan Rijwan
Published: April 2024

Different areas of medicine will probably be impacted by artificial intelligence (AI). Using algorithms of AI for diagnosis and prediction, this paper tries to clarify the underlying ideas guiding device approval, clinical validation and decisions related to insurance coverage. The sensitivity, dice similarity coefficient, specificity, and operating characteristic curves, conventional or free-response receiver are frequently used to assess the discrimination accuracy of AI algorithms. It is important to evaluate calibration accuracy, particularly for algorithms that give users probability. Due to the restricted generalizability of present AI algorithms to practice at real world level. Diagnostic case-control or diagnostic cohort designs may be used in external testing. The validity(clinical) and accuracy of AI are tested in the latter study using samples that represent the target patients in actual clinical circumstances, whereas a detailed case-control diagnostic study assesses the validity on technical ground and how accurate AI is studied. Randomized clinical trials are the optimal method for checking the clinical utility of AI, which is the examination of AI's effect on patient outcomes. The technical validity/accuracy of a device is normally verified before it is approved, therefore this does not necessarily mean that for patient care the device is suitable or that patient outcomes are improved. It also cannot definitively answer the problem of AI's restricted generalizability. It is the responsibility of the professionals practicing in the field of medicine to decide if AI algorithms approval are useful for actual patient related medical care after the device has received approval. Insurance coverage determinations often demand a clinical utility demonstration showing how AI is used has resulted in better patient health improvement outcomes.
Applications of AI and Deep Learning in Biomedicine and Healthcare
Authors: Pujahari Rakesh Mohan , Khan Rijwan , Yadav Satya Praksh
Published: April 2024

The rapid development in the field of medicine and healthcare in the recent years initiated as a large volumes of data are gathered. Taking this advancement, there is a requirement of the process and system needs both technological and analysis methods are to be used to deal with these data. The data which are gathered is health data which are accumulated electronically on the basis of patients' readings, texts, speeches or images as per convenience. The study of models that computer systems use to self-learn instructions based on the weight of parameters without being given explicit instructions is clearly one way to achieve artificial intelligence (AI). Over the past ten years, there has been a noticeable increase in the optimisation of machine learning algorithms and tools in tandem with advances in biomedicine. One of the more intriguing tools of these algorithms that is becoming increasingly important is deep learning. It's an artificial neural network that uses computer design to create multi-layered models that learn several degrees of abstraction from data representations. Deep learning is receiving a lot of attention these days since a lot of research indicates that it may be superior to earlier algorithms that relied just on machine learning and that its results have greater predictive performance. Deep learning has special and broad applications in health informatics and biomedicine, given its many levels of representation and outcomes that outperform human accuracy. In particular, these fall under the umbrella of molecular diagnostics, which includes the interpretation of experimental data involving gene splicing and DNA sequencing, protein structure prediction and classification, biomedical imaging, pharmacogenomics and pathogenic variant identification, drug discovery, and more. This chapter's only goal is to showcase these applications and go into further detail about how they are helping to advance healthcare and medicine in the contemporary world. Deep learning algorithms have enhanced capabilities for identifying patterns and obtaining features from intricate datasets. This chapter will first introduce deep learning and the latest advancements in artificial neural networks. It will then go over its applications in the healthcare industry and, lastly, how they are being used in biomedical informatics and computational biology-related public health research. Furthermore, the applicability of deep learning algorithms would be emphasised from the standpoint of contemporary healthcare.
Artificial Intelligence-Based Computer Vision on Medical Images
Authors: M. Murali , Zahraddeen Yakuu Ismail
Published: April 2024

The trajectory in medical imaging is characterized by a growing volume of data that is increasingly considered an inherent demand across various medical disciplines. With advancements in computer technology has become swifter, resulting in a surge of data for physicians to analyze. Artificial Intelligence (AI) falls within the realm of computer science. Its primary goal is to create machines capable of cognitive functions such as perception, learning, and problem-solvingthat surpass human capabilities. AI encompasses the spectrum of computer technology that displays characteristics reminiscent of human intelligence. Computer vision applied to medical images is an influential domain that utilizes various methods, including image processing and deep learning, to analyze, interpret, and extract valuable insights from medical imagery.
Analysis of Convolutional Neural Networks With and Without Attention Mechanisms for Pneumonia and COVID-19 Disease Prediction
Authors: Mohd Noor , Singh Sumit , Shukla Tanmay , Sharma Jaya , Kumar Sandeep , Singh Anupam , Rawat Saurabh
Published: April 2024

Pandemic has led dramatically loss of human life and a challenge in livelihoods. An international pandemic has been caused by the contagious viral illness COVID-19. A considerable number of COVID-19 deaths are brought on by pneumonia, which is a frequent consequence of the illness. In COVID-19 patients, early diagnosis of pneumonia can enhance clinical outcomes and save lives. In this study, we suggest a modified Convolutional Neural Network-CNN model to predict patients with pneumonia and COVID-19. Dropout regularization, additional convolutional layers, and transfer learning improve the suggested model's performance. In this study attempts to develop a model that has been trained and evaluated using a publicly available dataset of chest X-ray images from COVID-19 patients, pneumonia patients, and healthy individuals.The results demonstrate that the modified CNN model outperforms the original CNN model which gives clear insight for better results on image identification.
Emotion-Infused Multimodal Biometric Fusion for Elevating User Authentication and Interaction
Authors: Chauhan Rahul , Rawat Jyoti
Published: April 2024

This study combines many biometric techniques with emotions and provides a viable method for user engagement and authentication. The study uses recurrent neural networks, which contain LSTM (Long-Short-Term Memory) cells, while convolutional neural networks (CNN) are used for feature extraction. This work provides a standard extraction strategy that combines multiple biometric data sources, such as voice, fingerprints, irises, and faces. This allows the system to more easily\understand the user's emotional state temporal and geographical dimensions. After emotional expressions have changed over time, Using LSTM-based RNNs further improves the system. The overall accuracy and reliability of the emotion recognition model is improved because each biometric form has separate branches. Development of flexible models for advanced fusion techniques, their practical use, consideration of privacy concerns and continuous learning are possible directions for further development of this field. Overall, our work paves the way for safe, understandable, and compassionate technology by making significant advances\in user identification and engagement. The future offers many interesting features for biometric technology as well. Keywords: long-short-term memory (LSTM) cells, user authentication, interaction, biometric fusion with emotions, recurrent neural networks, convolutional neural networks, and multimodal biometric fusion.
Enhancing an Image Using Point Processing
Authors: Hiremath Shilpa , Rani A. Shobha
Published: April 2024

Enhancing an image is the highly demanded digital image processing task, as all the other piece of work depend on how well the image is conveying the information. The core purpose of enhancing an image is in identifying the information details hidden in an image. Image Enhancement is necessary in improvising the quality of image for human perception. Contrast adjustment, noise removal from an image, highlighting of an image detail are few operations of image enhancement. Enhancing an image can be broadly done in two ways by considering only the pixel of interest or by considering the neighbouring pixels of the interested pixel i.e spatial domain or by converting the image into Fourier transform i.e frequency domain. In our work we have worked on matlab database images and natural images with noise intensities varying from 0.01 to 0.6, three types of noises i.e Gaussian noise, salt & pepper noise and speckle noise and different spatial techniques for enhancing the image. Thus, the contribution of this paper is to various techniques to enhance an image in spatial domain.
Planning Ahead
Authors: Morris Gareth Richard , Cao Qing Qing , Weng Wendy , Tesema Fiseha Berhanu , Zhao Tingting
Published: April 2024

This chapter considers the important area of transnational higher education (TNHE) leadership and management through considering the reflections, insights, and suggestions of three highly experienced, successful, and well-regarded academic leaders. Drawing on decades of practice, the three case study examples provide advice to new academic managers in the hope of enabling them to be more effective and successful in their new roles. The suggestions provided here also offer timely and pertinent reminders to existing leaders and managers, which is equally if not more important, as busy and stressful situations can make it easy to forget some of the fundamental principles of leading and managing people, processes, and practice. This chapter should be of interest to a diverse readership given the focus.
Enhancing Cost Savings and Performance Through Employment Retention Initiatives
Authors: Morris Gareth Richard , Ye Jinting , Tesema Fiseha Berhanu , Cao Qing Qing , Culbert Jonathan
Published: April 2024

What motivates someone to seek out employment at a given institution can be influenced by many factors such as work-related aspects, personal considerations, and move convenience. The subsequent satisfaction or dissatisfaction that an employee experiences is often affected by personal interactions, the work, recognition, and progression, alongside the employment package. The importance of features such as expectation and developments over time can also be telling. The role that the institutional and local community exert over an employee, for better or worse, can likewise have a powerful effect. On that note, this chapter will draw on distinct case study examples at two different transnational higher education (TNHE) providers and consider a collection of individual employment motivation and subsequent job satisfaction experiences. This work will also analyse the individuals' perceptions of their communities and personal wellbeing and the impact all of this has had on their employment.
Intelligent Logistics Vehicle Path Planning Using Fused Optimization Ant Colony Algorithm With Grid
Authors: Chu Liyang , Guo Haifeng , Meng Qingshi
Published: April 2024

Aiming at the problem that the environmental planning path of intelligent logistics vehicles on urban roads and remote mountainous areas cannot fit the actual driving scene well. This study creates the algorithm model that combines an ant colony algorithm with a dynamic window algorithm and a Bessel smoothing strategy. Compared to the traditional colony algorithm with the same parameters, this fusion algorithm makes the path smoother by 72.2% when used on an urban highway. It also follows the right-hand rule for right-turn intersections. When the vehicle's height is determined in a mountain environment, this fusion algorithm reduces the driving's mean square deviation of height by 81.5% and shortens the path distance by 38.7%. The fusion algorithm can plan the target path of intelligent logistics vehicles and has the characteristics of scenarios available, multiple factors coordinated, and driving safety. It has provided certain research value and ideas for the digital transformation of the logistics industry.
Does Scarce Inventory Information Disclosure Strategy Promote Online Sales?
Authors: Xi Xi , Ren Feifei , Liu Weiqian , Chen Yuanyuan
Published: April 2024

In pursuit of promoting sales, certain e-commerce vendors post scarce inventory messages on product pages to signal impending stockouts. To explore how disclosure information strategy regarding scarce inventory influences online sales, this study utilizes Chinese e-commerce data collected from February 1st to April 30th, 2023 and constructs an empirical model that delves into the relationship between the disclosure of scarce inventory information and online sales based on the signal theory. The empirical findings reveal a positive impact of scarce inventory information disclosure on online sales, with this impact being more pronounced under the moderating role of the commodity discount rate. These results hold substantial theoretical implications and offer valuable insights for practical applications in the e-commerce domain.
Optimizing Financial Risk Models in Digital Transformation-Deep Learning for Enterprise Management Decision Systems
Authors: Zhao Xingli , Wang Wenjie , Liu Guochao , Vakharia Vinay
Published: April 2024

The digital transformation of enterprises has amplified the complexity of financial risks, underscoring the significance of optimizing financial risk warning models to ensure sustainable development. This study integrates several deep learning techniques, including Back Propagation Neural Network (BPNN), Bi-Long Short-Term Memory (Bi-LSTM), and transfer learning, to enhance the risk warning system and improve the accuracy and efficiency of financial risk prediction models. The results demonstrate that the proposed algorithm surpasses the baseline models in various metrics. For instance, on the Altman's Z-Score dataset, there is an improvement of 1.4% in accuracy, a reduction of over 48.8% in FLOP, and an enhancement of 43.5% in MAPE. These outcomes underscore the significant advancements in risk identification, decision support, and proactive risk management facilitated by the proposed model. As a result, enterprises can derive benefits from more precise and reliable financial risk warning tools, and effectively address the challenges brought about by digital transformation.
Experimental Study of the Liquid Cold Plate Cooling System for Lithium-Ion Battery in Electric Vehicle
Authors: Hilmy Muhammad Raihan , Rahman Muhammad Aulia , Pranoto Indro , Khasani Khasani , Waluyo Joko
Published: April 2024

The battery remains the primary choice of energy and power storage in electric vehicles. To optimize battery performance, manufacturers have developed various technologies, including the battery thermal management system (BTMS). In this study, an experimental investigation was conducted to determine the cooling performance and characteristics of a liquid cold plate cooling system for 18650 lithium-ion battery. The testing involved an 18650 SONY 2100 mAh lithium-ion battery and a 400 W DC Load Tester. Testing was carried out by discharging the battery module and taking data on battery temperature and working fluid temperature to get the heat transfer coefficient. The discharge current varied at 1.25 C, 1.5 C, 1.75 C, and 2 C, while the flow rate was set at 0.2 LPM, 0.5 LPM, and 0.8 LPM. The results indicate that the cooling rate increases as the flow rate rises. The battery temperature rises quicker near the negative pole than at the further location. Furthermore, the pressure drop on the liquid cold plate increases as the flow rate rises, while the highest heat transfer coefficient to pressure drop ratio is observed at 0.2 LPM.
Africa: A stepchild of Globalization
Authors: Vassiliev Alexei
Published: April 2024

No abstract available.