Miracle-dz/UniFAR-FTUs huggingface dataset:
Encyclopedia of Artificial Intelligence Information Sci Library of Congress Cataloging-in-publication Data Ea Multi-model Selection for Svm

Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Encyclopedia of artificial intelligence / Juan Ramon Rabunal Dopico, Julian Dorado de la Calle, and Alejandro Pazos Sierra, editors. p. cm. Includes bibliographical references and index. Summary: "This book is a comprehensive and in-depth reference to the most recent developments in the field covering theoretical developments, techniques , technologies, among others"-All work contributed to this encyclopedia set is new, previously-unpublished material. The views expressed in this encyclopedia set are those of the authors, but not necessarily of the publisher. If a library purchased a print copy of this publication, please go to http://www.igi-global.com/agreement for information on activating the library's complimentary electronic access to this publication. INTRODUCTION Evolutionary algorithms (EA) (Rechenberg, 1965) belong to a family of stochastic search algorithms inspired by natural evolution. In the last years, EA were used successfully to produce efficient solutions for a great number of hard optimization problems (Beasley, 1997). These algorithms operate on a population of potential solutions and apply a survival principle according to a fitness measure associated to each solution to produce better approximations of the optimal solution. At each iteration, a new set of solutions is created by selecting individuals according to their level of fitness and by applying to them several operators. These operators model natural processes, such as selection, recombina-tion, mutation, migration, locality and neighborhood. Although the basic idea of EA is straightforward, solutions coding, size of population, fitness function and operators must be defined in compliance with the kind of problem to optimize. Multi-class problems with binary SVM (Support Vector Machine) classifiers are commonly treated as a decomposition in several binary sub-problems. An open question is how to properly choose all models for these sub-problems in order to have the lowest error rate for a specific SVM multi-class scheme. In this paper, we propose a new approach to optimize the generalization capacity of such SVM multi-class schemes. This approach consists in a global selection of models for sub-problems altogether and is denoted as multi-model selection. A multi-model selection can outperform the classical individual model selection used until now in the literature, but this type of selection defines a hard optimisation problem, because it corresponds to a search a efficient solution …
Plant Prenylflavonoids and Prenyltransferases Related to their Biosynthesis

Abstract As the most widely distributed phenolic compounds in the plant kingdom, flavonoids play an integral role in plant reproduction and defense. Also, they represent many important quality traits of edible plants like color and antioxidants, and have a variety of biological activities beneficial to human health. To diversify the functions of synthesized flavonoids, plants have evolved various enzymes to perform structural modifications on different flavonoid backbones. One of these modifications is prenylation, which refers to the attachment of an isoprenoid moiety, most commonly a prenyl (C5) group. Numerous structure-activity analyses of prenylflavonoids have shown that isopentenyl substitutions at specific sites can significantly expand and enhance their chemical properties, bioactivities and potential health benefits. This review summarizes prenylflavonoids reported so far in all plant species and highlights the current knowledge on naturally occurring prenyltransferases from different biological sources that can act on plant flavonoids to synthesize prenylflavonoids. Most of them have strict flavonoid substrate- and regio-specificities, and they provide a valuable gene repository to facilitate the efficient scale-up production of flavonoids with specific prenylation patterns in cell factories. To truly achieve this goal, it is necessary to explore more diversified natural prenyltransferases, and to optimize the bioreactors system such as pathway regulation and modular co-culture engineering in the future.
RELIGIOUS HARMONY INDEX IN SPECIAL REGION OF YOGYAKARTA

The existence of the Special Region of Yogyakarta as a city of students, culture, and tourism holds diversity both ethnically, culturally, and religiously; therefore, the Special Region of Yogyakarta is called the city of tolerance. The plurality of the Special Region of Yogyakarta may give rise to conflicts and divisions, including inter-religious conflicts if they are not appropriately managed. One of the successes of developing religious harmony can be assessed by looking at the most basic indicators of harmony at the national and regional levels. This study aims to measure inter-religious harmony in the Special Region of Yogyakarta. Variables that become measuring instruments have three dimensions: tolerance, dimensions, and dimensions of cooperation. The study used quantitative methods with a total sample of 400 respondents spread across Yogyakarta City, Bantul Regency, Kulon Progo Regency, and Gunung Kidul Regency. The research findings show that the index of inter-religious harmony in the Special Region of Yogyakarta is 78.90, or in the high level of religious harmony category. For this reason, maintenance must continue improving to achieve a harmonious, harmonious, and harmonious religious life. Although, in general, it is in a high category, there are still sub-dimensions that need special attention, namely caring for the construction of other places of worship and being president or regional head regardless of religion.
Seismogenic structure behaviour revealed by spatial clustering of seismicity in the Umbria-Marche Region (Central Italy)

Time variations in the spatial distribution of earthquake epicentres are analyzed by application of the fractal correlation dimension method. The zone under investigation is located in Central Italy, bounded in longitude by 12.0 and 14.4 degrees east and in latitude by 42.0 and 43.6 degrees north. From 1st January 1978 to 5th October 1997, 2028 events with a magnitude above Ml= 2.5 constitute the database.Evolution of the spatial fractal dimension Ds permits the identification of seismic cycles that are connected to the occurrence of main earthquakes.In particular, it is possible to recognize a division within each cycle, between a period of random background seismicity and a spatial clustering of events where shocks of magnitude Ml ³occur. Moreover, the decrease in Ds prior to such events, evidences a structural relationship between foreshocks and the occurrence of a main shock, even if not in close territorial proximity.This feature indicates a new, more extensive definition of seismogenic structure which can includes several interconnected structures within a large area.
Our Common Journey:: A Transition Toward Sustainability

World human population is expected to reach upwards of 9 billion by 2050 and then level off over the next half-century. How can the transition to a stabilizing population also be a transition to sustainability? How can science and technology help to ensure that human needs are met while the planet's environment is nurtured and restored? Our Common Journey examines these momentous questions to draw strategic connections between scientific research, technological development, and societies' efforts to achieve environmentally sustainable improvements in human well being. The book argues that societies should approach sustainable development not as a destination but as an ongoing, adaptive learning process. Speaking to the next two generations, it proposes a strategy for using scientific and technical knowledge to better inform future action in the areas of fertility reduction, urban systems, agricultural production, energy and materials use, ecosystem restoration and biodiversity conservation, and suggests an approach for building a new research agenda for sustainability science. Our Common Journey documents large-scale historical currents of social and environmental change and reviews methods for "what if" analysis of possible future development pathways and their implications for sustainability. The book also identifies the greatest threats to sustainability--in areas such as human settlements, agriculture, industry, and energy--and explores the most promising opportunities for circumventing or mitigating these threats. It goes on to discuss what indicators of change, from children's birth-weights to atmosphere chemistry, will be most useful in monitoring a transition to sustainability.
Extended concrete capacity design method for anchors close to non‐rectangular edges

Fastenings in concrete are widely applicable in construction. A significant benefit of fastening technology lies in the fact that it can adapt to various geometries and construction sequences. As a result, the need to design and realize a fastening in structural elements with unconventional geometric configurations can be encountered. Existing design standards rely on the concrete capacity design (CCD) method for concrete‐related failures, which typically covers detailing situations with rectangular configurations of the fastenings and the concrete members. However, the lack of guidelines for a wider range of applications may lead to over‐conservative dimensioning or design constraints. This study investigates the influence of beveled concrete edges on the load‐bearing behavior of anchors. As part of the research, a series of experiments and numerical simulations were conducted. The results derived from the investigations are used to develop an analytical concept that extends the area of applicability of the CCD method and can be used for reliable dimensioning of fastenings close to non‐rectangular concrete edges and geometrical boundaries.
Scalable Distributed Data-Driven State Estimation Algorithm via Gaussian Processes With Guaranteed Stability

In this article, we focus on the scalable distributed data-driven state estimation problem using Gaussian processes (GPs). The framework includes two parts: 1) the data-driven training approach and 2) the state-estimation architecture. First, the objective is to obtain the transition and measurement functions of the considered state-space model by a data-driven training strategy via distributed GPs. In particular, to improve the training efficiency, we employ an online conditioning algorithm, which reduces the computational burden significantly. Then, all nodes exchange their own trained GPs with their respective neighbors. Furthermore, to achieve consensus on local trained GPs, we propose a Wasserstein weighted average consensus algorithm, which differs from the current Kullback–Leibler average consensus on probability densities. Second, based on the training results, we propose a distributed state estimation algorithm to perform fresh state estimation. After obtaining the state estimation results (mean and covariance) and exchanging them with their neighboring nodes, we then execute the Wasserstein weighted average to achieve consensus on state estimations. Also, we analyze the stability and robustness of the proposed distributed state estimation by using GP. Finally, numerical and real-world examples are provided to validate the effectiveness of the proposed training method and data-driven state estimation algorithms.
On the Reconstruction and Prediction Improvements of the Deterministic Sea Wave Predictable Zone Using Spatio-Temporal Coherent Radar Measurements

In the study of deterministic sea waves, the predictable zone refers to the area where the phase-resolvable wave field can be fully reconstructed and accurately predicted based on measured data. Under certain marine environmental and radar measurement parameter conditions, expanding the predictable zone is of great significance for domains such as quiescent period prediction (QPP) for ships and optimizing wave energy harvesting. Prior research on the predictable zone mostly relied on either temporal or spatial measurements, significantly constraining the range of the predictable zone. In this article, based on coherent microwave radar, we present a novel method combining spatial and temporal measurements to expand the predictable zone. First, the boundary of the predictable zone under a single spatial measurement and multiple spatial measurements (spatial-temporal measurement) is analyzed, and the closed-form expressions for the predictable zone are derived for both the unidirectional and multidirectional wave fields. The quantitative relationship between the predicted time at the desired spatial location and the radar parameters and wave spectra is obtained. Subsequently, the theoretical analysis of the predictable zone is validated using the numerical simulation by comparing the theoretically predictable zone with the error distributions for the unidirectional and multidirectional wavefield cases. Finally, the analysis of experimental data from Weihai, China, in 2021 also indicates that the joint spatial-temporal measurements can effectively expand the predictable zone.
Integrative evidence construction for resveratrol treatment of nonalcoholic fatty liver disease: preclinical and clinical meta-analyses

Background: Resveratrol, a polyphenol found in various plants, is known for its diverse bioactivities and has been explored in relation to nonalcoholic fatty liver disease (NAFLD). However, no high-quality evidence exists regarding its efficacy. Objective: a meta-analysis was conducted to evaluate the potential efficacy of resveratrol in treating nonalcoholic fatty liver disease by analyzing both preclinical studies and clinical trials. Method: PubMed, Embase and Web of Science were searched for the included literature with the criteria for screening. Quantitative synthesis and meta-analyses were performed by STATA 16.0. Results: Twenty-seven studies were included, and the results indicated that resveratrol effectively improved liver function, reduced fatty liver indicators, and affected other indices in preclinical studies. The effective dosage ranged from 50 mg/kg-200 mg/kg, administered over a period of 4–8 weeks. While there were inconsistencies between clinical trials and preclinical research, both study types revealed that resveratrol significantly reduced tumor necrosis factor-α levels, further supporting its protective effect against nonalcoholic fatty liver disease. Additionally, resveratrol alleviated nonalcoholic fatty liver disease primarily via AMPK/Sirt1 and anti-inflammatory signaling pathways. Conclusion: Current meta-analysis could not consistently verify the efficacy of resveratrol in treating nonalcoholic fatty liver disease, but demonstrated the liver-protective effects on nonalcoholic fatty liver disease. The large-sample scale and single region RCTs were further needed to investigate the efficacy.
The use of infinity in pure number theory and algebra

What is meant here by pure number theory is elementary number theory from Fermat to Kronecker, what I call Fermat-Kronecker arithmetic, that is the method of infinite descent combined with the theory of forms or homogeneous polynomials which extends to algebraic number theory and beyond. I contend that these two motives still shape to some extent the contemporary number-theoretic foundations of arithmeticalgebraic geometry as I emphasize the work of the great French arithmetician André Weil. Mathematics Subject Classification: 11R04, 11RO9, 11S05, 12E05, 12E20, 12E30
A Compact Textile Monopole Antenna for Monitoring the Healing of Bone Fractures Using Un-Supervised Machine Learning Algorithm

In this paper, a novel approach to monitoring the healing process of bone fractures using a compact textile monopole antenna integrated into a wearable device. A planar wearable textile ultra-wideband monopole (TUM) antenna is proposed as a sensing element. The antenna is fabricated on the jeans fabric with a thickness of 1 mm, an overall size of $18\times19$ mm2, and operates in an ultra-wideband frequency range (3.22-10.9 GHz). The human arm tibia model is developed in a full-wave simulator in order to estimate the radiation exposure caused by the antenna at near and far field distances. At both field distances, the average specific absorption rate (SAR) values of the model are below the FCC limit. The proposed antenna is experimentally tested on a donor bovine tibia. The proposed system utilizes Unsupervised machine learning (ML) techniques to analyze the received signals from the antenna and provide real-time feedback on the progression of bone fracture healing. The compact and flexible nature of the textile antenna allows for comfortable and unobtrusive wear, making it suitable for long-term monitoring. The integration of Unsupervised ML techniques enables automated analysis of the received signals, eliminating the need for manual interpretation. This work aims to improve the monitoring and assessment of bone fracture healing, leading to more effective treatment strategies and faster recovery times. The experimental results and comparison study with previous literature show that the proposed TUM antenna has several advantages and is suitable for diagnosing bone fractures.
On the construction of solutions of general linear boundary value problems for systems of functional differential equations

For the linear boundary value problem on the closed interval I (cid:26) R , where p W C.I;R n / ! L.I;R n / is a strongly bounded linear operator, l W C.I;R n / ! R n is the bounded linear functional, q 2 L.I;R n / and c 0 2 R n , we describe a method of construction of its solution using successive approximations by the sequence of the solutions of simple boundary value problems. We prove the conditions which guarantee the convergence of the above mentioned sequences in general and special cases, we prove the stability of the convergence in some sense. Also, for illustration, we solve some typical problems in M APLE .
Empirical Scale for Revenge Buying Behaviour: A Curious Consequence of Pandemic

The present paper aims to explain the new phenomenon of revenge buying behaviour witnessed among consumers as an aftermath of the pandemic. This paper also aims to develop and validate a new scale that can empirically measure the revenge buying behaviour. Recommendations and observations made by Churchill in 1979 are used to develop and validate the revenge buying behaviour scale. The factor analysis results indicate empirical evidence for the 13-item revenge buying behaviour scale. The results show that the scale is valid and reliable. Added purchasing power, feel store environment, feel good factor and desire satiation were noted as the four factors forming a part of the empirical scale for the revenge buying behaviour. The study also suggests that people hold special value for ‘in-store’ shopping. This research is among the first to present an empirical scale for the revenge buying behaviour, and it adds value to the domain of consumer buying behaviour post the changes brought in by the pandemic.
Bigger Helium Nanodroplets without the Swirls

T he frictionless environments of helium nanodroplets makes them perfect for studying the self-organization of atoms andmolecules. However, if there are vortices inside these nanodroplets, this can hinder the assembly of some of these nanostructures. Now Anatoli Ulmer from the Technical University of Berlin and colleagues have developed a method for generating vortex-free helium nanodroplets with 1000 more helium atoms than previously possible [1]. The advance could enable researchers to study the self-assembly of a larger range of molecules.
Accessory material for: Estimating a nitrous oxide emission factor for animal urine from some New Zealand pastoral soils

The Intergovernmental Panel on Climate Change methodology estimates that over 50% of total nitrous oxide (N2O) emissions in New Zealand derive from animal excreta-N deposited during grazing. The emission factor for excreta-N as used by this methodology has an important impact on New Zealand’s total N2O inventory. The objectives of this study were to refine the N2O emission factor for urine by simultaneously measuring N2O emissions from 5 pastoral soils of different drainage class, in 3 different regions in New Zealand following a single application of urine; plus test various aspects of the soil cover method for determining emission factors. Cow urine and synthetic urine was applied to pastoral soils in autumn 2000 and N2O emissions were measured using closed flux chambers at regular intervals for 4–18 months following application. The N2O emission factors for cow urine estimated for the first 4 months after urine application varied greatly depending on rainfall and soil drainage class, and ranged from 0.3 to 2.5% of the urine-N applied, suggesting that adopting a single emission factor for New Zealand may be inappropriate. The largest emission factor was found in a poorly drained soil, and the lowest emission factor was found in a well-drained stony soil. Ongoing measurements on one of the soils resulted in an increase in emission factors as the N2O emissions had not reached background levels 4 months after urine application. To characterise urine-induced N2O emissions, we recommend measurements continue until N2O emissions from urine-amended soil return to background levels. Furthermore, we recommend using real animal urine rather than synthetic urine in studies when determining the N2O emission factor for urine. SR8 Nss C Ain et Additional keywords: cow urine, emission factor, nitrous oxide inventory, soil cover, soil drainage class.
Influence of Exciting Field on Electromagnetic Torque of Novel Switched Reluctance Motor

In this paper, a novel switched reluctance motor with two magnetically dependent stator and rotor sets (TMDSRM) is proposed and tested. The novelty of the TMDSRM is that there is an assistant exciting winding (short for exciting winding), which is placed between the two-stator sets and can be energized by controllable dc current to boost the air-gap flux density and to improve the output torque and torque density. What deserves to be mentioned the most is that the air-gap flux density is compensated when armature winding is in commutation because of the exciting winding carrying dc current and being permanently energized, and thus, the torque ripple is reduced. This paper describes the magnetic structure and the principle of the proposed motor in detail. Moreover, finite-element analysis has been carried out to investigate the influence of exciting field on electromagnetic torque of the motor. A prototype TMDSRM is manufactured, and the experimental results are presented.
Efficiency Improvement Under Coupler Misalignment for Dual-Transmitter and Single-Receiver Capacitive Power Transfer System

This article proposes an efficiency improvement method for the dual-transmitter (Tx) and single-receiver capacitive power transfer (CPT) system under misaligned conditions. A ten-plate-based dual-Tx capacitive coupler, including transmitting, receiving, and shielding plates, is established and modeled. The equivalent circuit of the dual-Tx CPT system is built and analyzed. The relationship between the maximum efficiency point (MEP) and input impedances of two Txs is derived theoretically. The MEP can be achieved by regulating the input voltages of two Txs when the input impedances of the two Txs are the same under misaligned conditions. To validate the feasibility of the proposed efficiency improvement method, a 1.6-kW prototype is constructed. To ensure the fairness, all efficiency comparisons are based on the premise that the output power is equal. The experimental results show that the system efficiency is highest when the dual-Tx CPT system's input resistance is equal under any misaligned conditions. Even when the misalignment distance is more than 15 mm and the total air gap of two Txs is 60 mm, the optimal efficiency remains stable.