bluuebunny/crossref_metadata_2025 huggingface dataset:
Bibliometric Computation Mapping Analysis of Publication Machine and Deep Learning for Food Crops Mapping using VOSviewer
Authors: Ridwana Riki Ridwana , Kamal Muhammad , Arjasakusuma Sanjiwana , Sugandi Dede , Sakti Anjar Dimara
Published: August 2024

Machine learning and deep learning are currently widely used in various fields, including remote sensing for food security. However, there is no research that specifically examines the interests, developments, and trends of this research in the future. This study aims to examine the development of machine and deep learning research for mapping food crops through a bibliometric approach with computational mapping analysis using VOSviewer. Article data was obtained from the Google Scholar database using the publish or perish reference manager application. The title and abstract of the article were used to guide the search process by referring to the keyword “Machine and Deep Learning Mapping Food Crops”. 114 relevant articles were discovered. Google Scholar-indexed articles over the last ten years, from 2014 to 2023, were used as study material. The results show that machine research and deep learning for mapping food crops can be separated into three terms: machine learning, deep learning, and plant mapping. The term “Crop Mapping” has 57 links for a total of 199 links. The term "machine learning" has 41 links for a total of 79 links, and the term "deep learning" has 26 links for a total of 41 links. The results of the analysis of machine development and deep learning publications for mapping food crops in the last 10 years show a constant increase. The peak of the increase occurred in 2021 and 2022, namely 25 articles published per year, respectively. This means that this research topic is still relatively new in terms of interest and exploration, therefore there is still room further research. We examine numerous articles that have been published on machine and deep learning for crop mapping and their relation to the field studied with VOSviewer. This review can serve as a starting point for further research in different domains
PERKEMBANGAN MORFOLOGI PEMUKIMAN DI DESA KARYA BARU
Authors: Nurmiah Nurmiah
Published: August 2024

Desa Karya baru merupakan desa yang berada di kecamatan Dengilo Kabupaten Pohuwato provinsi Gorontalo memiliki luas lahan 18.874 hektar.  Data penduduk Desa Karya Baru pada tahun 2017  penduduk desa berjumlah 1.151 jiwa. Kemudian pada tahun 2021 jumlah penduduk meningkat menjadi 1.168 jiwa. Seiring berjalannnya waktu Desa Karya Baru mengalami perkembangan pada fasilitas publik berupa, listrik dan jaringan, bangunan, jalan dan jembatan serta  drainase. Perkembangan tidak hanya bermakna kemajuan tetapi juga kemunduran (Dr. Masganti Sit, 2015). Perkembangan mencakup hal-hal yang bersifat kualitatif dan kuantitatif Tujuan penelitian ini untuk mengetahui bentuk morfologi permukiman di desa karya baru. Metode penelitian menggunakan deskriptif analisis dengan pendekatan kualitatif. Data dari hasil penelitian deskripsi kualitatif kemudian dianalisis dengan menggunakan metode Tisseu Analysis untuk menemukan perubahan morfologi yang ada di Desa Karya Baru. Dari penelitian di dapatkan beberapa perubahan morfologi di Desa Karya Baru terjadi pada bangunan rumah tinggal, fasilitas jalan dan jembatan, dan bangunan Pendidikan.
KENYAMANAN TERMAL DI KAWASAN TENGAH KOTA JAKARTA
Authors: Layosibana Akhirun
Published: August 2024

This research is to determine the level of thermal comfort based on occupant perception, microclimate and is associated with architectural engineering in human adaptation to urban heat island (UHI). One of the problems in cities is the increase in earth temperature caused by climate change and vertical obstruction. The impact of climate change and vertical obstruction will affect the level of human comfort in their environment. Thermal comfort is an absolute need for the human body, and with its ability, humans also create a built environment to fulfill their comfort both outside and inside a building. This research takes a case study at The Lavande Residence Apartment, which is a residence in the central area of ​​Jakarta. This research uses a descriptive analysis method from data obtained primarily through direct observation and interviews and several other secondary data. The research results show that the suitability of the comfort level based on climate simulations and the comfort calculator is at a neutral level and compared with the perception of residents who still feel comfortable. This perception of thermal comfort after being analyzed is due to architectural engineering efforts carried out by humans in the context of adaptation to climate change.
Göttliches Ich
Authors: Helmer Karl
Published: August 2024

In der vorliegenden Ausgabe der Kieler Berichte fragt Karl Helmer nach der Bedeutung der Renaissance für die Individualisierung des Menschen.
Der Schwerpunkt liegt auf der Apotheose des Individuums, die in philosophischen und künstlerischen Entwicklungen der Zeit Ausdruck findet.
Helmer analysiert, wie sich das göttliche Ich des Menschen im Denken von Nikolaus von Kues, Giannozzo Manetti und Giovanni Pico della Mirandola
und in Werken Michelangelos und Dürers manifestiert. In der Renaissance, so das Fazit, komme es zu einer grundlegenden Neubestimmung des Menschen.
In jüngerer Zeit ließen Arbeiten von Cattelan und Picasso erkennen, dass diese Vorstellungen brüchig geworden seien.
PENGARUH EDUKASI GIZI SEIMBANG TERHADAP PENGETAHUAN, SIKAP DAN POLA KONSUMSI SAYUR DAN BUAH ANAK PADA MASA COVID-19
Authors: Siagian Ernawaty
Published: August 2024

School-age children is a period of transition from children to adults. There are mental, physical, and emotional growth that develop quite fast at this time. School children are one of the most vulnerable populations in terms of nutrition. Increasing knowledge and attitudes about balanced nutritional needs and consumption patterns vegetables and fruit minimize the occurrrence of nutritional problems in children. Aim of this research is to analyzing the effect of providing balanced nutrition education on knowledge, attitudes and consumption patterns of vegetables and fruit in elementary school children. This study used a pre-experimental design method. The form of experimental design used by OneGroup is pretest-posttest design. Sampling using total sampling technique sample. The researcher first gave questions as pre-test data and then given interventions in the form of education using video media, lectures and intervention leaflets for 3 meetings then carried out the post-test again. The population is grade 4 SD Advent Bandar Lampung students. The statistical test used is the parametric test (Dependent paired T-test). The result of the analysis showed that there was a significant relationship between the level of knowledge before and after the intervention was given with a sig value of 0.00 (p<.0.5). There is a significant relationship between attitudes before and after the intervention is given with a sig value of 0.00 (p<0.05). And there was no significant relationship between attitudes before and after the intervention was given with a sig value of 0.112 (p<0.005). There is a relationship between the effect of balanced nutrition education on knowledge and attitudes as well as vegetable consumption patterns and no significant changes in fruit consumption patterns. The result of this study recommends that parents, especially mothers should support their children by providing food according to balanced nutrition and teachers consistently provide balanced nutrition education to students.
Replacing Missing Teeth with Screw-Cement Retained Implant Supported Fixed Partial Denture: A Case Report
Authors: Bhattacharjee Rahul Bhattacharjee , Upadhya Mithun Upadhya , Shenoy Vidya K Shenoy , Miranda Glynis Anita Miranda
Published: August 2024

An accurate impression with proper interocclusal relationship are the two essential criteria used for the best fit and functionality of the final prosthesis when replacing a partly edentulous arch with an implant-supported fixed partial denture. A detailed process about the open tray impression technique and registering the interocclusal relationship for implant- supported partial denture has been described in this case report.
Designing a construction supply chain model using backup supplier aiming at optimizing resiliency against disruption
Authors: Badkoubeh Mahsa , Ghannadpour Seyed Farid
Published: August 2024

Resilience is a topic that has recently emerged concerning the basics of the construction project supply chain and we can consider it as a response to disruption in the supply chain of the project. Disruption also is an unavoidable reality in today’s complex and dynamic construction supply chain, the occurrence of which can cause irretrievable damages to the system, such as financial losses. Successful companies seek to minimize disruption and maintain adequate supply chain performance before disruption occurs, rather than looking for costly and challenging post-disruption solutions. This paper covers this gap by proposing a scenario-based mixed integer-programming model aiming to minimize logistics costs and delays, while scheduling projects to address selecting the appropriate supplier at risk of disruption. So far, this quantitative view was not presented in discussions about disruptions in the project supply chain, therefore different scenarios are applied in the process to validate the model. To improve its resilience level, this model benefits from back-up suppliers’ strategy. This study focuses on providing the required materials for the project site in an emergency without incurring additional costs using a back-up supplier. Results reveal the model’s suitability in confronting the unavailability of a supplier due to disruption.
Rotation and Hα Emission in a Young SMC Cluster: A Spectroscopic View of NGC 330
Authors: Cristofari Paul I. , Dupree Andrea K. , Milone Antonino P. , Walker Matthew G. , Mateo Mario , Dotter Aaron , Bailey John I.
Published: August 2024

We present an analysis of high-resolution optical spectra recorded for 30 stars of the split extended main-sequence turnoff of the young (∼40 Myr) Small Magellanic Cloud globular cluster NGC 330. Spectra were obtained with the Michigan/Magellan Fiber System and Magellan Inamori Kyocera Echelle spectrographs located on the Magellan-Clay 6.5 m telescope. These spectra revealed the presence of Be stars, occupying primarily the cool side of the split main sequence. Rotational velocity (

v
sin
i

) measurements for most of the targets are consistent with the presence of two populations of stars in the cluster: one made up of rapidly rotating Be stars (

〈
v
sin
i
〉
≈
200

km s−1) and the other consisting of warmer stars with slower rotation (

〈
v
sin
i
〉
≈
50

km s−1). Core emission in the Hδ photospheric lines was observed for most of the Hα emitters. The shell parameter computed for the targets in our sample indicates that most of the observed stars should have inclinations below 75°. These results confirm the detection of Be stars obtained through photometry but also reveal the presence of narrow Hα and Hδ features for some targets that cannot be detected with low-resolution spectroscopy or photometry. Asymmetry variability of Hα line profiles on the timescales of a few years is also observed and could provide information on the geometry of the decretion disks. Observations revealed the presence of nebular Hα emission, strong enough in faint targets to compromise the extraction of spectra and to impact narrow-band photometry used to assess the presence of Hα emission.
Climate Regimes across the Habitable Zone: A Comparison of Synchronous Rocky M and K Dwarf Planets
Authors: Lobo Ana H. , Shields Aomawa L.
Published: August 2024

M and K dwarf stars make up 86% of the stellar population and host many promising astronomical targets for detecting habitable climates in the near future. Of the two, M dwarfs currently offer greater observational advantages and are home to many of the most exciting observational discoveries in the last decade. But K dwarfs could offer even better prospects for detecting habitability by combining the advantages of a relatively dim stellar flux with a more stable stellar environment. Here we explore the climate regimes that are possible on Earth-like synchronous planets in M and K dwarf systems, and how they vary across the habitable zone. We focus on surface temperature patterns, water availability, and implications for habitability. We find that the risk of nightside cold trapping decreases with increased orbital radius and is overall lower for K dwarf planets. With reduced atmospheric shortwave absorption, K dwarf planets have higher dayside precipitation rates and less day-to-night moisture transport, resulting in lower nightside snow rates. These results imply a higher likelihood of detecting a planet with a moist dayside climate in a habitable “eyeball” climate regime orbiting a K dwarf star. We also show that “terminator habitability” can occur for both M and K dwarf land planets, but would likely be more prevalent in M dwarf systems. Planets in a terminator habitability regime tend to have slightly lower fractional habitability, but offer alternative advantages including instellation rates more comparable to Earth in regions that have temperatures amenable to life.
Chemical Doppelgangers in GALAH DR3: The Distinguishing Power of Neutron-capture Elements among Milky Way Disk Stars
Authors: Manea Catherine , Hawkins Keith , Ness Melissa K. , Buder Sven , Martell Sarah L. , Zucker Daniel B.
Published: August 2024

The observed chemical diversity of Milky Way stars places important constraints on Galactic chemical evolution and the mixing processes that operate within the interstellar medium. Recent works have found that the chemical diversity of disk stars is low. For example, the Apache Point Observatory Galactic Evolution Experiment (APOGEE) “chemical doppelganger rate,” or the rate at which random pairs of field stars appear as chemically similar as stars born together, is high, and the chemical distributions of APOGEE stars in some Galactic populations are well-described by two-dimensional models. However, limited attention has been paid to the heavy elements (Z > 30) in this context. In this work, we probe the potential for neutron-capture elements to enhance the chemical diversity of stars by determining their effect on the chemical doppelganger rate. We measure the doppelganger rate in GALactic Archaeology with HERMES DR3, with abundances rederived using The Cannon, and find that considering the neutron-capture elements decreases the doppelganger rate from ∼2.2% to 0.4%, nearly a factor of 6, for stars with −0.1 < [Fe/H] < 0.1. While chemical similarity correlates with similarity in age and dynamics, including neutron-capture elements does not appear to select stars that are more similar in these characteristics. Our results highlight that the neutron-capture elements contain information that is distinct from that of the lighter elements and thus add at least one dimension to Milky Way abundance space. This work illustrates the importance of considering the neutron-capture elements when chemically characterizing stars and motivates ongoing work to improve their atomic data and measurements in spectroscopic surveys.
The Universe SPHEREx Will See: Empirically Based Galaxy Simulations and Redshift Predictions
Authors: Feder Richard M. , Masters Daniel C. , Lee Bomee , Bock James J. , Chiang Yi-Kuan , Choi Ami , Doré Olivier , Hemmati Shoubaneh , Ilbert Olivier
Published: August 2024

We simulate galaxy properties and redshift estimation for SPHEREx, the next NASA Medium Class Explorer. To make robust models of the galaxy population and test the spectrophotometric redshift performance for SPHEREx, we develop a set of synthetic spectral energy distributions based on detailed fits to COSMOS2020 photometry spanning 0.1–8 μm. Given that SPHEREx obtains low-resolution spectra, emission lines will be important for some fraction of galaxies. Here, we expand on previous work, using better photometry and photometric redshifts from COSMOS2020 and tight empirical relations to predict robust emission-line strengths and ratios. A second galaxy catalog derived from the GAMA survey is generated to ensure the bright (m

AB
< 18 in the i band) sample is representative over larger areas. Using template fitting to estimate photometric continuum redshifts, we forecast the recovery of 19 million galaxies over 30,000 deg2 with redshifts better than σ

z
< 0.003(1 + z), 445 million with σ

z
< 0.1(1 + z), and 810 million with σ

z
< 0.2(1 + z). We also find through idealized tests that emission-line information from spectrally dithered flux measurements can yield redshifts with accuracy beyond that implied by the naive SPHEREx channel resolution, motivating the development of a hybrid continuum–line redshift estimation approach.
Action-angle Variables for Axisymmetric Potentials via Birkhoff Normalization
Authors: Hadden Sam
Published: August 2024

We describe a method for calculating action-angle (AA) variables in axisymmetric galactic potentials using Birkhoff normalization, a technique from Hamiltonian perturbation theory. An advantageous feature of this method is that it yields explicit series expressions for both the forward and inverse transformations between the AA variables and position–velocity data. It also provides explicit expressions for the Hamiltonian and dynamical frequencies as functions of the action variables. We test this method by examining orbits in a Milky Way model potential and compare it to the popular Stäckel approximation method. When vertical actions are not too large, the Birkhoff normalization method achieves fractional errors smaller than a part in 103 and outperforms the Stäckel approximation. We also show that the range over which Birkhoff normalization provides accurate results can be extended by constructing Padé approximants from the perturbative series expressions developed with the method. Numerical routines in Python for carrying out the Birkhoff normalization procedure are made available.
5–25 μm Galaxy Number Counts from Deep JWST Data
Authors: Stone Meredith A. , Alberts Stacey , Rieke George H. , Bunker Andrew J. , Lyu 吕 Jianwei 建伟 , Pérez-González Pablo G. , Shivaei Irene , Zhu Yongda
Published: August 2024

Galaxy number counts probe the evolution of galaxies over cosmic time and serve as a valuable comparison point to theoretical models of galaxy formation. We present new galaxy number counts in eight photometric bands between 5 and 25 μm from the Systematic Mid-infrared Instrument Legacy Extragalactic Survey and the JWST Advanced Deep Extragalactic Survey deep MIRI parallel, extending to unprecedented depth. By combining our new MIRI counts with existing data from Spitzer and AKARI, we achieve counts across 3–5 orders of magnitude in flux in all MIRI bands. Our counts diverge from predictions from recent semianalytical models of galaxy formation, likely due to their treatment of mid-IR aromatic features. Finally, we integrate our combined JWST−Spitzer counts at 8 and 24 μm to measure the cosmic infrared background (CIB) light at these wavelengths; our measured CIB fluxes are consistent with those from previous mid-IR surveys but larger than predicted by models based on TeV blazar data.
In-field Phasing at the Upgraded GMRT
Authors: Kudale Sanjay , Roy Jayanta , Chengalur Jayaram N. , Sharma Shyam , Kumari Sangita
Published: August 2024

In time-domain radio astronomy with arrays, voltages from individual antennas are added together with proper delay and fringe correction to form the beam in real time. In order to achieve the correct phased addition of antenna voltages, one has to also correct for the ionospheric and instrumental gains. Conventionally this is done using observations of a calibrator source located near to the target field. This scheme is suboptimal since it does not correct for the variation of the gains with time and position in the sky. Further, since the ionospheric phase variation is typically most rapid at the longest baselines, the most distant antennas are often excluded while forming the beam. We present here a different methodology (“in-field phasing”), in which the gains are obtained in real-time using a model of the intensity distribution in the target field, which overcomes all of these drawbacks. We present observations with the upgraded Giant Metrewave Radio Telescope (uGMRT) which demonstrates that in-field phasing does lead to a significant improvement in sensitivity. We also show, using observations of the millisecond pulsar J1120−3618 that this in turn leads to a significant improvement of measurements of the Dispersion Measure and Time of Arrival. Finally, we present test observations of the GMRT discovered eclipsing black widow pulsar J1544+4937 showing that in-field phasing leads to improvement in the measurement of the cut-off frequency of the eclipse.
The Dzhanibekov Effect as a Possible Source of Magnetar Activity
Authors: Wada Tomoki , Shimoda Jiro
Published: August 2024

Magnetars, which are neutron stars with strong magnetic fields, exhibit occasional bursting activities. The shape of a magnetar is not perfectly spherical due to the Lorentz force exerted by its strong magnetic fields and is described as a triaxial body. We study the unstable free precession in a triaxial magnetar; one of the principal axes undergoes an upside-down flip. This flip is known as the Dzhanibekov effect. We find that during the flip, the Euler force can suddenly disturb the force balance on the surface layer of the magnetar, potentially leading to plastic flow of the layer. This, in turn, may trigger different forms of magnetar activity, such as the emission of the bursts and/or of gravitational waves.
An Extragalactic Widefield Search for Technosignatures with the Murchison Widefield Array
Authors: Tremblay C. D. , Tingay S. J.
Published: August 2024

It is common for surveys that are designed to find artificial signals generated by distant civilizations to focus on galactic sources. Recently, researchers have started focusing on searching for all other sources within the field observed, including the vast population of background galaxies. Toward a population of galaxies in the background toward the Vela supernova remnant, we search for technosignatures, spectral and temporal features consistent with our understanding of technology. We set transmitter power limits for the detection of signals in a population of over 1300 galaxies within a single field of view observed with the Murchison Widefield Array.
A review of unsupervised k-value selection techniques in clustering algorithms
Authors: Pegado-Bardayo Ana , Lorenzo-Espejo Antonio , Muñuzuri Jesús , Escudero-Santana Alejandro
Published: August 2024

Purpose: Automatic grouping of data according to certain characteristics is made possible by clustering algorithms, which makes them an essential tool when working with large datasets. However, although they are unsupervised tools, they generally require the specification of the number of clusters to be formed, k, a task that may be simple for a human, but quite complex to automate. Despite the most commonly used k-value selection techniques offer acceptable results, they are not without shortcomings, suggesting that there is ample room for improvement. This paper briefly introduces clustering techniques, discusses the main shortcomings of conventional k-value selection techniques and examines the advantages and limitations of nine promising alternatives presented in recent years.Design/methodology/approach: An evaluation of the main shortcomings of classic k-value estimation techniques has been carried out, and the newest proposals have been explained and compared.Findings: New k-value estimation indices and methodologies proposed by authors guarantee better results, extending the use of these techniques to large volumes of data, and complex shapes and structures. However, no generical methodology able to overcome all the described shortcomings has still been developed.Research limitations/implications: This research is limited to the newest developed techniques for k-value estimation, including proposals published since 2019. Older proposals have not been considered, as the newest ones overcome the former’s shortcomings. A k-value estimation techniques review published in 2019 is cited in the test as a base reference.Practical implications: Although the examples listed in the text apply to industry, the techniques described and discussed in this review are applicable to any area of science that can benefit from the use of clustering techniques.Originality/value: To date, there has been no paper comparing the new k-value estimation techniques. Although there are literature reviews comparing the classical methods, these methods are nowadays nearly obsolete due to the complexity of the data usually faced.
Investment strategies to maintain the state of water networks
Authors: Robles Velasco Alicia , Aparicio-Ruiz Pablo , Cortés Pablo , Onieva Luis
Published: August 2024

Purpose: This article focuses on the problem of deciding the annual investment that a company should allocate to the rehabilitation of its water distribution and sanitation networks. The objective is to find the investment amount necessary to maintain an adequate quality and sustainability of the infrastructure. It is not a simple decision, as there are different criteria that may be of interest to the managing company. In this paper, we consider four criteria related to the reliability of individual pipes and the complete network. These indicators are the infrastructure value index, the average age of network pipes, the risk index and the probability of failure.Design/methodology/approach: A methodology is proposed to estimate the best annual investment by analysing the evolution of these indicators. Concretely, two strategies are tested. The first one is a minimax-based approach that seeks a balanced solution for all the indicators. The second one is named as minimal deviation strategy and seeks to minimise the deviation of all the indicators in the last year of the time horizon compared to the initial year.Findings: In order to obtain a realistic sample of the performance of both strategies, 201 scenarios, i.e. 201 different annual investments have been tested. According to the first strategy, an annual investment of 55.5 M€ is the best option, while the minimal deviation strategy presents an annual investment of 39.5 M€ as the best decision. The study reveals that different evaluation functions lead to completely different annual investment. Concretely, the minimax evaluation function is more conservative than the minimal deviation strategy.Originality/value: This study proposes an original approach to address the decision problem of investments in asset management. To the best of the authors’ knowledge, it is the first attempt to treat that problem using this kind of evaluation functions. However, it is still a simple proposal and there are many options to continue this line of research.
Age-dependent trends in adverse reactions of SARS-CoV-2 vaccines: A narrative review
Authors: Noor Laiba , Ishtiaq Syeda Momna , Batool Farhat , Arshad Muhammad Imran
Published: August 2024

Since the initiation of immunization against SARS-CoV-2, the adverse reactions of vaccines have remained a major contributor to vaccine hesitancy. Their occurrence has been attributed to the immune response against the administered antigen, which is affected by different factors including the age of the vaccine recipient. The trends of the adverse reactions of COVID-19 vaccines have shown a higher frequency in younger adults (18–64 years old) than in the older population (≥65 years). One of the potential reasons is the healthy and efficient immune systems of younger individuals, which provide a robust pro-inflammatory response upon antigen encounter. Their adaptive immune system has a diverse array of naïve T and B lymphocytes, which aids in antigen elimination with the development of a long-lasting immunological memory. Age-related changes in the immune system of the elderly are referred to as immunosenescence, which reduces its efficiency. The phenomenon of cellular senescence and inflammageing can lower the vaccine responses of older recipients. Thymic involution and ageing of the bone marrow can lead to decreased levels of naïve T-cells and memory B-cells. This could compromise the ability to either fight new infections or effectively respond to vaccines and develop a lasting immune memory. Along with clinical trials of senolytic drugs, lifestyle interventions are being studied to mitigate such age-related changes. Further insights into vaccine responses in the elderly population and the means to alleviate the hallmarks of immunosenescence are required.
Adaptive exponential synchronization of impulsive coupled neutral stochastic neural networks with Lévy noise and probabilistic delays under non-Lipschitz conditions
Authors: Ma Shuo , Li Jiangman , Li Qiang , Liu Ruonan
Published: August 2024

In this paper, we investigated the adaptive exponential synchronization problem of impulsive coupled neutral stochastic neural networks with Lévy noise and probabilistic delays under non-Lipschitz conditions. A stochastic variable with a Bernoulli distribution was utilized to transform the information regarding probabilistic delays into a model featuring deterministic time delays and stochastic parameters. In the context of adaptive controllers, exponential synchronization conditions depending on the delay, noise intensity, and impulse factor were derived using Lyapunov-Krasovskii functions, the nature of Lévy noise, and some inequality methods. To provide further support for the proposed approach, two numerical illustrations were presented.
Multi-modal feature fusion with multi-head self-attention for epileptic EEG signals
Authors: Huang Ning , Xi Zhengtao , Jiao Yingying , Zhang Yudong , Jiao Zhuqing , Li Xiaona
Published: August 2024

It is important to classify electroencephalography (EEG) signals automatically for the diagnosis and treatment of epilepsy. Currently, the dominant single-modal feature extraction methods cannot cover the information of different modalities, resulting in poor classification performance of existing methods, especially the multi-classification problem. We proposed a multi-modal feature fusion (MMFF) method for epileptic EEG signals. First, the time domain features were extracted by kernel principal component analysis, the frequency domain features were extracted by short-time Fourier extracted transform, and the nonlinear dynamic features were extracted by calculating sample entropy. On this basis, the features of these three modalities were interactively learned through the multi-head self-attention mechanism, and the attention weights were trained simultaneously. The fused features were obtained by combining the value vectors of feature representations, while the time, frequency, and nonlinear dynamics information were retained to screen out more representative epileptic features and improve the accuracy of feature extraction. Finally, the feature fusion method was applied to epileptic EEG signal classifications. The experimental results demonstrated that the proposed method achieves a classification accuracy of 92.76 ± 1.64% across the five-category classification task for epileptic EEG signals. The multi-head self-attention mechanism promotes the fusion of multi-modal features and offers an efficient and novel approach for diagnosing and treating epilepsy.
A Geo-Fencing Approach for a Location-Based Alert System
Authors: J. Jayapradha , M. Uma Devi , T. Senthil Kumar , Singh Jusjeet
Published: August 2024

The advancement in technology has brought with it a plethora of resources that can be used to make one's life easier. The Global Positioning System (GPS) is one such resource. Knowing the location of the user can come in handy and provide a wide range of services. Location-based alerts are a new type of location-aware, just-in-time service, which can alert the user of the location at the right time and the right place. The objective of the project is to create a GPS-based application that can sound an alarm to notify users when they arrive at a predetermined location. The final solution enables the user to quickly set off an alarm, and based on the user's remembered position, the alert will sound when the user arrives at the desired area. This location-based alert service will help those who frequently go to new locations or people who do not want to miss their destination for a variety of other reasons not to miss it.
Applications of New Technology in Operations and Supply Chain Management
Authors: Ilic Biljana S. , Ostojic Bojana , Stojanovic Dragica , Djukic Gordana Petar
Published: August 2024

Due to the importance of supply chains, it is no wonder that most of the trends in this area relate to the improvement of the chain itself through the increasing use of new business models. Supply chain technology also uses robotics, internet of things (IoT) and blockchain which are predicted to speed up the chain and reduce the occurrence of adverse events. Apart from supply chain statistics, it is good to be aware of the latest trends in the field. If the organization becomes familiar with trends and problems as soon as possible, it will be able to adapt to the changes that are coming faster. Human resources are of great importance, because without well-qualified employees, business in organizations cannot progress and follow new ways of working. The chapter will provide an overview of certain development assumptions for supply chains in the near future, with a focus on human resources and their importance for modern “green” activities. Together, it paves the way for sustainable development.
Applications of New Technology in Operations and Supply Chain Management
Authors: Mira Mohammed Saud
Published: August 2024

This chapter provides a comprehensive overview of the literature on the application of chatbots in customer service and recruiting, emphasizing the function of chatbots in improving customer service. As a tool for streamlining hiring procedures and automating customer service encounters, chatbots have grown in popularity in recent years. The literature review indicates that chatbots significantly impact the recruitment process, such as improved efficiency, increased candidate engagement, and reduced workload for HR personnel. Chatbots have proven effective in improving customer service interactions, with benefits including increased customer satisfaction, reduced wait times, and improved customer engagement. However, the review also highlights several challenges that must be addressed to realize the potential of chatbots in these areas fully. These challenges include issues related to user acceptance and perceptions of chatbot technology and concerns about privacy and data security. Therefore, this study provides recommendations for addressing these challenges.
E-Commerce Logistics Unleashed
Authors: Munuhwa Shakerod , Chibaro Munyaradzi
Published: August 2024

The chapter explores the broad implications of automation and artificial intelligence (AI) on e-commerce logistics. It discusses how these technologies are altering supply chains through automated warehousing, order processing, and artificial intelligence-driven demand forecasting and personalization. Despite their transformational potential, the chapter highlights issues such as data security, integration, workforce adaptation, and ethical considerations. It also includes real-world case studies demonstrating the successes of businesses that have embraced automation and artificial intelligence, as well as glimpses into the future of e-commerce logistics, stressing emerging technology and projected trends. Finally, the chapter serves as a comprehensive guide for businesses wishing to harness the potential of automation and artificial intelligence in the ever-changing world of e-commerce logistics.
The Impact of 5G Technology on the Digital Supply Chain and Operations Management Landscape
Authors: Natraj N. A. , Abirami T. , Ananthi K. , Arockia Venice J. , Chandru R. , Rathish C. R.
Published: August 2024

The chapter discusses 5G's supply chain and operations management transformation possibilities. With its high bandwidth, low latency, and extensive connectivity, 5G changes supply chains. Statistical evidence shows 5G's rapid integration, growing market value, and worldwide economic impacts. This technology brings real-time visibility, predictive maintenance, efficient logistics, optimised inventory management, and data-driven demand forecasting to traditional practises. Organisations use 5G to improve efficiency, responsiveness, and competitiveness in case studies. 5G is depicted as a driving force in the digitalization revolution, necessary for organisations managing rapid change and unrelenting innovation. This chapter emphasises 5G's centrality to the digitalization revolution. It is more than a technological upgrade for organisations in a time of constant change and innovation. The authors aim to help businesses adapt, optimise, and thrive in the ever-changing supply chain and operations management landscape by revealing how 5G technology is changing supply chains.
The Impact of the Experience Quality of Smart Office on Employee Engagement
Authors: Phan Luc Tan
Published: August 2024

This study explores aspects of the experience quality of a smart office and the impact of these aspects on employee engagement. In the first stage, exploratory factor analysis and confirmatory factor analysis were used to study the dimensions and structure of the experience quality of a smart office. In the second stage, partial least squares structural equation modeling was used to test the hypotheses. The research results show that a framework for the experience quality of a smart office has been developed, including human-machine interactions, intelligent systems, and employee self-service. Additionally, the research shows that intelligent systems and human-machine interactions have an impact on physical engagement, emotional engagement, and cognitive engagement, while employee self-service has no impact on any of the three aspects of employee engagement. The research advances the subject of intelligent office technology and offers managers helpful advice on how to strengthen the bond between workers and the company.
Crafting Effective Sustainable Fashion Communication on Social Media
Authors: Sengun Damlasu Uyug , Kalkan Gamze , Tugrul Tugba Orten
Published: August 2024

Sustainable consumption is an issue that firms try to encourage through different tools. A common approach to influencing environmental consumer behaviors and attitudes is message framing. Specifically, gain-frames highlight the benefits of engaging in a behavior, while loss-frames emphasize the costs of not doing so. The goals of this study are twofold: to examine the effects of sustainable message framing on customer engagement intention and to investigate the moderating effect of attitude towards sustainable message on this relationship. An experimental study was conducted with 66 participants equally distributed between two framing groups. Drawing on the prospect theory, it was proposed that, compared to loss-framed messages, gain-framed brand messages on social media may produce higher customer engagement intention. Based on the theory of customer engagement marketing, it was anticipated that increases in the level of additional attitude towards sustainable message could strengthen the effect of gain message framing. Results provide empirical support for both hypotheses.
AI in DeFi
Authors: Sharma Swati , Kishor Jugal , Bhaskar Preeti , Dias Rui
Published: August 2024

Over the past few years, the crypto industry has encountered various obstacles, including regulatory obstacles, unsuccessful exchanges, and projects that have collapsed. These challenges have paved the way for groundbreaking solutions that are revolutionising the realm of decentralised finance (DeFi). An emerging solution with immense potential is the incorporation of artificial intelligence (AI) into financial ecosystems. The integration of DeFi and AI presents a transformative collaboration that has the potential to revolutionise the industry, enhance its adaptability, and establish a solid groundwork for long-term sustainability. AI and DeFi have emerged as significant technological advancements in recent years, garnering widespread attention and adoption. Incorporating AI into DeFi offers a range of advantages, including automated decision-making, more accurate risk assessment, and enhanced user experiences. These benefits contribute to the continuous development of DeFi.
Fraud Detection in the Era of AI
Authors: Bansal Urshita , Bharatwal Sunita , Bagiyam Dhana S. , Kismawadi Early Ridho
Published: August 2024

Fraudulent activities have increased along with the new prospects of the digital economy's quick growth for both consumers and enterprises. Conventional techniques of fraud detection are insufficient to keep up with these ever-evolving fraudulent strategies. In this sense, machine learning (ML) and artificial intelligence (AI) have become potent instruments to prevent and detect fraud and guarantee the safety of online transactions. This study examines the function of AI and ML and shows how these technologies can spot irregularities and intricate patterns that would be challenging to find with conventional methods. The study includes various methods of AI-based fraud detection and analyses important ethical issues related to these practices. Furthermore, the study looks at developing technology and trends that will probably influence fraud detection in the future. In conclusion, the revolutionary potential of AI and ML in building a safer digital economy is analysed.
Green Banking and SDGs
Authors: Bansal Jahanvi , Shukla Parag , Tripathi Pankaj Kumar , Dubey Suchi
Published: August 2024

Green banking's potential to foster green economies and ensure a sustainable future is profound, emphasizing the critical importance of green finance in advancing SDGs and mitigating global environmental challenges. Central Asian banks, including India, have increasingly embraced green finance, acknowledging its pivotal role in addressing climate risks. The study has proposed a conceptual framework that depicts the interplay between drivers, facilitators, and accelerators of green banking and sustainable development goals (SDGs), especially SDG 7 (affordable and clean energy), SDG 11 (sustainable cities and communities), SDG 12 (responsible consumption and production) and SDG 13 (climate action), highlighting collaborative efforts needed for global sustainability. This article sheds light on the transformational potential of green banking projects by discussing both the practical and social aspects of the proposed initiatives.
The Future of Smart Contracts
Authors: Iyer Rajiv , Maralapalle Vedprakash , Patil Deepak , Irfan Mohammad
Published: August 2024

The future of smart contracts in decentralized finance (DeFi) is a dynamic and evolving field that holds immense potential for transforming traditional financial systems The integration of artificial intelligence (AI) with smart contracts is enhancing their capabilities, enabling efficient processing of data and intelligent decision-making. Smart contracts provide an open and effective replacement for traditional financial structures as they continue to gain popularity. The potential of smart contracts to improve transparency, optimize operating procedures, and transform industries like healthcare, finance, and education is what will determine their future. To support blockchain-based apps and grow the DeFi ecosystem, smart contract platforms like Polkadot, Cardano, and Ethereum must continue to innovate and evolve. The future of smart contracts is largely being shaped by the possibility of multi-chain smart contracts, the incorporation of AI and machine learning technology, and the support for decentralized autonomous organizations (DAOs).
Can AI Change the Way We See the World?
Authors: Kumari Lalita , Gautam Akansha , Goud P. Varaprasad , Abdulkarim Fatima Muhammad
Published: August 2024

The study investigate how female B.Tech. students view of artificial intelligence (AI) in their personal life and determine how their views about AI relate to the advancements they encounter. The study is cross-sectional across only a limited sample and it is important to understand the comparison for different set of students to assess the real situation of Artificial Intelligence. The chapter attempts to construct awareness index and perception index using principle component analysis which further uses ANOVA on a sample of 150 engineering students to better understand the numerous phenomena arising from AI. The results found use of AI has a significant impact on creating better career opportunities. The results of AI are also favourable towards better awareness, curiosity, knowledge, and learning.
AI vs. Traditional Portfolio Management
Authors: Anand Yash , Irfan Mohammad , Hasan Mahadi
Published: August 2024

This research chapter investigates the dynamics between artificial intelligence (AI) and traditional portfolio management strategies, specifically focusing on the attitudes and preferences of investors in the Indian market. The study aims to elucidate the comparative performance, risk-adjusted returns, and behavioral aspects associated with AI-driven portfolio management as opposed to traditional methods. Utilizing a methodology tailored to the unique characteristics of the Indian investment landscape, this research engages investors with varying degrees of experience in the stock market. Through a meticulous collection of data during October and November 2023, employing convenience sampling, the authors explore the factors influencing investor perceptions and decisions in adopting AI-based portfolio management strategies. These findings contribute to the existing discourse by shedding light on the role of trust, subjective norms, perceived usefulness, perceived ease of use, and attitudes as critical variables shaping the adoption of AI in portfolio management.
AI in Managing Perishable Goods Inventory
Authors: Şimşek Ahmet Bahadır
Published: August 2024

Managing perishable goods, such as food and medicine, is challenging due to their short shelf life and the need for high quality. Traditional methods often fail, leading to overstocking, stock-outs, and spoilage. This study explores how artificial intelligence (AI) can transform this field using machine learning, predictive analytics, and computer vision. AI improves demand forecasting by analysing data such as past sales and weather patterns. Predictive analytics uses real-time IoT sensor data to predict shelf life, while computer vision ensures quality monitoring. Case studies from Coles, Walmart, and Migros show AI's success in reducing waste, optimizing inventory, and boosting customer satisfaction. However, challenges include data integration, high costs, and employee training. Future trends suggest integrating AI with blockchain for better transparency and sustainability. AI can revolutionize perishable goods inventory management, making it more efficient, adaptable, and environmentally friendly.
Navigating the Future
Authors: Kaur Ravneet , Aggarwal Kapil Kumar
Published: August 2024

Artificial intelligence is revolutionizing various industries, including travel, education, health, and investment. Robo advisory models (RAs) have increased assets under management (AUM) due to price, transparency, and competitiveness. Although not widely used, robo-advisors are becoming more cost-effective than traditional human advice services. This study explores the current state of robo-advisors and their potential to transform wealth management by facilitating effective decision-making processes and introducing a new era of financial advising services.