bluuebunny/crossref_metadata_2025 huggingface dataset:
Antibiotic Resistance in Campylobacter: A Systematic Review of South American Isolates
Authors: Portes Ana Beatriz , Panzenhagen Pedro , Pereira dos Santos Anamaria Mota , Junior Carlos Adam Conte
Published: March 2023

In recent years, Campylobacter has become increasingly resistant to antibiotics, especially those first-choice drugs used to treat campylobacteriosis. Studies in South America have reported cases of antibiotic-resistant Campylobacter in several countries, mainly in Brazil. To understand the current frequency of antibiotic-resistant Campylobacter in humans, farm animals, and food of animal origin in South America, we systematically searched for different studies that have reported Campylobacter resistance. The most commonly reported species were C. jejuni and C. coli. Resistance to ciprofloxacin was found to be ubiquitous in the isolates. Nalidixic acid and tetracycline showed a significantly expressed resistance. Erythromycin, the antibiotic of first choice for the treatment of campylobacteriosis, showed a low rate of resistance in isolates but was detected in almost all countries. The main sources of antibiotic-resistant Campylobacter isolates were food of animal origin and farm animals. The results demonstrate that resistant Campylobacter isolates are disseminated from multiple sources linked to animal production in South America. The level of resistance that was identified may compromise the treatment of campylobacteriosis in human and animal populations. In this way, we are here showing all South American communities the need for the constant surveillance of Campylobacter resistance and the need for the strategic use of antibiotics in animal production. These actions are likely to decrease future difficulties in the treatment of human campylobacteriosis.
Mitochondria Need Their Sleep: Redox, Bioenergetics, and Temperature Regulation of Circadian Rhythms and the Role of Cysteine-Mediated Redox Signaling, Uncoupling Proteins, and Substrate Cycles
Authors: Richardson Richard B. , Mailloux Ryan J.
Published: March 2023

Although circadian biorhythms of mitochondria and cells are highly conserved and crucial for the well-being of complex animals, there is a paucity of studies on the reciprocal interactions between oxidative stress, redox modifications, metabolism, thermoregulation, and other major oscillatory physiological processes. To address this limitation, we hypothesize that circadian/ultradian interaction of the redoxome, bioenergetics, and temperature signaling strongly determine the differential activities of the sleep–wake cycling of mammalians and birds. Posttranslational modifications of proteins by reversible cysteine oxoforms, S-glutathionylation and S-nitrosylation are shown to play a major role in regulating mitochondrial reactive oxygen species production, protein activity, respiration, and metabolomics. Nuclear DNA repair and cellular protein synthesis are maximized during the wake phase, whereas the redoxome is restored and mitochondrial remodeling is maximized during sleep. Hence, our analysis reveals that wakefulness is more protective and restorative to the nucleus (nucleorestorative), whereas sleep is more protective and restorative to mitochondria (mitorestorative). The “redox–bioenergetics–temperature and differential mitochondrial–nuclear regulatory hypothesis” adds to the understanding of mitochondrial respiratory uncoupling, substrate cycling control and hibernation. Similarly, this hypothesis explains how the oscillatory redox–bioenergetics–temperature–regulated sleep–wake states, when perturbed by mitochondrial interactome disturbances, influence the pathogenesis of aging, cancer, spaceflight health effects, sudden infant death syndrome, and diseases of the metabolism and nervous system.
Protein Fractions from Flaxseed: The Effect of Subsequent Extractions on Composition and Antioxidant Capacity
Authors: Waszkowiak Katarzyna , Mikołajczak Beata , Polanowska Katarzyna , Wieruszewski Marek , Siejak Przemysław , Smułek Wojciech , Jarzębski Maciej
Published: March 2023

Flaxseed proteins exhibit functionalities interesting for the food industry, including antioxidant capacity. Antioxidant activity depends on the protein composition and the presence of phenolic compounds extracted with them from the matrix. The research focused on the effect of subsequent protein extractions (water, salt and alkaline) of flaxseed meals (of three cultivars) on the protein fraction composition and its relations to antioxidant capacity. The protein and phenolic profiles and antioxidant functionalities (in antiradical ORAC and emulsion assays) were analysed. Spectroscopic characteristics of the fractions (fluorometric and FT-IR analysis) were also included. Our study has shown the effect of fractionation on the share of proteins at MW from 56–38 kDa (globulin-like) and <15 kDa (albumin-like) in the protein profiles. The highest globulin share was in the alkaline-extracted fractions (AEF) and albumin in the salt-extracted (SEF) ones. SDG (secoisolariciresinol diglucosides) and phenolic acids (p-coumaric and ferulic) were extracted with flaxseed proteins. Their contents were fraction-dependent and the highest in AEF. The concentration of phenolics in AEF corresponded with the highest antiradical capacity (ORAC) compared with the other fractions. However, the SEF showed a higher ability to inhibit oxidation in emulsions than AEF, which could be associated with the higher content of the low MW proteins.
Antioxidant and Anti-Inflammatory Effects of Carotenoids in Mood Disorders: An Overview
Authors: Rasmus Paweł , Kozłowska Elżbieta
Published: March 2023

Depression has a multifactorial etiology comprising family history and unemployment. This review aims to summarize the evidence available for the antioxidant and anti-inflammatory effects of carotenoids in mood disorders. This review article’s methodologies were based on a search of the PubMed database for all linked published papers. Epidemiological studies indicate that a diet rich in vegetables, fruits, nuts, fish, and olive oil may prevent the development of depression. Antioxidant supplementation has been found to combat various stress-induced psychiatric disorders, including depression and anxiety. A growing body of evidence indicates that carotenoids have both antioxidant and anti-inflammatory. Studies also suggest that poor dietary intake, particularly low intakes of fruit and vegetables and high intakes of fast food and other convenience foods, may increase the risk of developing depression. Thus, dietary interventions have the potential to help mitigate the risk of mental health decline in both the general population and those with mood disorders. Considering that carotenoids have both antioxidant and anti-inflammatory effects, it is expected that they might exert a promising antidepressant effect. Nevertheless, further studies (including interventional and mechanistic studies) assessing the effect of carotenoids on preventing and alleviating depression symptoms are needed.
Effect of Thallium(I) on Growth, Nutrient Absorption, Photosynthetic Pigments, and Antioxidant Response of Dittrichia Plants
Authors: Espinosa Francisco , Ortega Alfonso , Espinosa-Vellarino Francisco L. , Garrido Inmaculada
Published: March 2023

Dittrichia plants were exposed to thallium (Tl) stress (10, 50, and 100 µM) for 7 days. The Tl toxicity altered the absorption and accumulation of other nutrients. In both the roots and the leaves, there was a decline in K, Mg, and Fe content, but an increase in Ca, Mn, and Zn. Chlorophylls decreased, as did the photosynthetic efficiency, while carotenoids increased. Oxidative stress in the roots was reflected in increased lipid peroxidation. There was more production of superoxide (O2.−), hydrogen peroxide (H2O2), and nitric oxide (NO) in the roots than in the leaves, with increases in both organs in response to Tl toxicity, except for O2.− production in the roots, which fluctuated. There was increased hydrogen sulfide (H2S) production, especially in the leaves. Superoxide dismutase (SOD), ascorbate peroxidase (APX), dehydroascorbate reductase (DHAR), monodehydroascorbate reductase (MDHAR), and glutathione reductase (GR) showed increased activities, except for APX and MDHAR in the roots and GR in the leaves. The components of the ascorbate–glutathione cycle were affected. Thus, ascorbate (AsA) increased, while dehydroascorbate (DHA), reduced glutathione (GSH), and oxidized glutathione (GSSG) decreased, except for in the roots at 100 µM Tl, which showed increased GSH. These Tl toxicity-induced alterations modify the AsA/DHA and GSH/GSSG redox status. The NO and H2S interaction may act by activating the antioxidant system. The effects of Tl could be related to its strong affinity for binding with -SH groups, thus altering the functionality of proteins and the cellular redox state.
Glutamine Starvation Affects Cell Cycle, Oxidative Homeostasis and Metabolism in Colorectal Cancer Cells
Authors: Spada Martina , Piras Cristina , Diana Giulia , Leoni Vera Piera , Frau Daniela Virginia , Serreli Gabriele , Simbula Gabriella , Loi Roberto , Noto Antonio , Murgia Federica , Caria Paola , Atzori Luigi
Published: March 2023

Cancer cells adjust their metabolism to meet energy demands. In particular, glutamine addiction represents a distinctive feature of several types of tumors, including colorectal cancer. In this study, four colorectal cancer cell lines (Caco-2, HCT116, HT29 and SW480) were cultured with or without glutamine. The growth and proliferation rate, colony-forming capacity, apoptosis, cell cycle, redox homeostasis and metabolomic analysis were evaluated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide test (MTT), flow cytometry, high-performance liquid chromatography and gas chromatography/mass spectrometry techniques. The results show that glutamine represents an important metabolite for cell growth and that its deprivation reduces the proliferation of colorectal cancer cells. Glutamine depletion induces cell death and cell cycle arrest in the GO/G1 phase by modulating energy metabolism, the amino acid content and antioxidant defenses. Moreover, the combined glutamine starvation with the glycolysis inhibitor 2-deoxy-D-glucose exerted a stronger cytotoxic effect. This study offers a strong rationale for targeting glutamine metabolism alone or in combination with glucose metabolism to achieve a therapeutic benefit in the treatment of colon cancer.
Reduced-Order Model Based on Volterra Series for Aerodynamics of the Bridge Deck Section and Flutter Critical Wind Speed Prediction
Authors: Wei Ziran , Liu Zhiwen , He Fawei
Published: March 2023

This study proposes a novel reduced-order model (ROM), based on the Volterra series, for the aerodynamic force of the bridge deck section. Moreover, the ROM of the aerodynamic force of the streamlined box girder section of the Great Belt East Bridge (GBEB) is identified with computational fluid dynamic (CFD) simulations. Furthermore, an analysis method combining ROM aerodynamic force and Newmark-β integration is established to simulate the aeroelastic responses of the bridge deck section. Finally, the wind-induced vibration responses of the GBEB section are calculated near the flutter critical wind speed based on the Volterra series-based aeroelastic analysis and the fluid–structure interaction (FSI) numerical simulations in ANSYS Fluent, respectively. Moreover, to verify the applicability of the proposed method, the aeroelastic responses of the main deck section with the crash barriers of Lingdingyang Bridge (LDYB) are also simulated via the Volterra model and Newmark-β integration near the flutter critical wind speed. The results show that the first-order truncated Volterra model established in this study can accurately capture the aerodynamic response of the main girder, and the results are in good agreement with those of the CFD numerical simulation under forced vibration. Furthermore, the proposed method combined with ROM aerodynamic force and Newmark-β integration can effectively calculate the FSI of the bridge girder. The numerical results of the flutter critical wind speed and flutter frequency of GBEB and LDYB are consistent with the numerical results by the FSI method based on ANSYS Fluent and the existing numerical and experimental results, respectively.
Fuzzy Neural Network with Ordered Fuzzy Numbers for Life Quality Technologies
Authors: Apiecionek Łukasz , Moś Rafał , Ewald Dawid
Published: March 2023

The general goal of the research in this article is to devise an artificial neural network that requires less computational power than an ordinary one for assessing overall life satisfaction—a term often referred to as quality of life (QoL). The development of the mentioned ANN was possible due to the application of fuzzy logic, especially ordered fuzzy numbers (OFN). Research on the appliance of OFN aims at different issues such as the detection of an attack on a computer network, the anticipation of server load, management of multiplexing of data transmission paths, or transmission error rate forecasting that allows the improvement of the quality of life. It occurs due to, for instance, reduced energy demand, savings through better data transmission, and the distribution of computers’ power used in the cloud. Finally, the application of OFN on single neurons of a deep ANN allows achieving a network that is able to solve the same problem as a normal network, but with a lower number of neurons. Such networks in the future may be implemented easier in small solutions, such as solutions for the Internet of Things to improve the quality of human life. This approach is unique and has no equivalent in the literature. Due to the application of OFN in an ANN, fewer requirements for network architecture were needed to solve the same problems, and as a result, there is less demand for processor power and RAM.
Research on Microstructure and Mechanical Properties of Laser Welding of BR1500HS Joints
Authors: Kang Yongwang , Xia Xi , Zhu Chundong , Wu Raofu
Published: March 2023

In this research, BR1500HS high strength steel was laser welded under various process parameters. The welded samples were heated in a furnace for 5 min with a temperature of 900 °C and then water-quenched. The effects of process parameters and quenching process on mechanical properties and microstructure of the joints were investigated. The results show that when the heat input is 1440 J/cm, the best mechanical properties of the welded joint can be obtained. When the welding speed increases, the minimum hardness increases, and the width of the softened area decreases; while the laser power increases, the width of the softened area increases, and the minimum hardness does not change significantly. As the welding speed increases, the front-bead width A, back-bead width B, and the height of smooth segment H all decrease; while the laser power increases, the back-bead width B increases, the front-bead width A and the height of the smooth segment H increase first and then decrease. The front-bead width A reaches the maximum when the laser power is 3000 W and the height of the smooth segment H does not change much.
Human and Environmental Factors Analysis in Traffic Using Agent-Based Simulation
Authors: Moreno Ariadna Claudia , Moreno Mailyn , Porras Cynthia , Pavón Juan
Published: March 2023

Traffic congestion is a frequent problem on most urban roads. This may be due to incorrect configuration of traffic signals but planning analysis should also include a study of human behavior, which, often imprudent, contributes to traffic congestion. The aim of this paper is to analyze the influence of human factors and their reaction to the environment on the impact of traffic performance through waiting time. For this purpose, an agent-based simulation is developed to represent the autonomous and social behavior of road users. The waiting of vehicles at signals is modeled on the basis of a queuing system. Simulations and experiments are based on the analysis of the age of the people and the condition of the pavement. Results show that people’s age is the most important factor influencing their behavior on the road. It is also shown external factors that also affect driver response and thus signal impact, such as the condition of the pavement. Finally, traffic performance, measured by waiting time, depends strongly on the behavior of people facing signals, according to their characteristics and factors present in the environment.
The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
Authors: Shen Lu , Chen Bidong , Wei Jianjing , Xu Hui , Tang Su-Kit , Mirri Silvia
Published: March 2023

Offline handwritten Chinese recognition is an important research area of pattern recognition, including offline handwritten Chinese character recognition (offline HCCR) and offline handwritten Chinese text recognition (offline HCTR), which are closely related to daily life. With new deep learning techniques and the combination with other domain knowledge, offline handwritten Chinese recognition has gained breakthroughs in methods and performance in recent years. However, there have yet to be articles that provide a technical review of this field since 2016. In light of this, this paper reviews the research progress and challenges of offline handwritten Chinese recognition based on traditional techniques, deep learning methods, methods combining deep learning with traditional techniques, and knowledge from other areas from 2016 to 2022. Firstly, it introduces the research background and status of handwritten Chinese recognition, standard datasets, and evaluation metrics. Secondly, a comprehensive summary and analysis of offline HCCR and offline HCTR approaches during the last seven years is provided, along with an explanation of their concepts, specifics, and performances. Finally, the main research problems in this field over the past few years are presented. The challenges still exist in offline handwritten Chinese recognition are discussed, aiming to inspire future research work.
Developing an Integrated Energy Demand-Supply Modeling Framework for Scenario Analysis of the Low Carbon Emission Energy System in Zambia
Authors: Daka Precious P. , Farzaneh Hooman
Published: March 2023

This study develops an integrated modeling approach to analyze the effects of different scenarios compared to a baseline (2019) scenario on energy demand and supply in Zambia. Aiming at minimizing costs, the model combines available resources and technologies, adhering to techno-economic and environmental limits. On the demand side, the study uses time series analysis to forecast future electricity demand in Zambia. The supply-side energy model identifies the optimal combination of resources and technologies needed to satisfy exogenously specified electricity demand levels at the least cost. The baseline scenario results revealed that the power outages in Zambia, especially at peak times, have been attributed to reliance on imported fuel for power generation, the slow-paced integration of renewable resources, and not fully utilizing resource potential for the supply side. To overcome the power outages and meet the electricity demand in 2035, a comprehensive scenario analysis was conducted, including the main scenarios of 30% integration of renewable energy, no coal power generation, and low emission targets (10%, 20%, 30%, 40%, and 50% reduction from the baseline). The expected CO2 reductions from the renewable energy scenario, no coal scenario, and 50% low emission target are estimated at 5222 kilotons in 2035, respectively.
Value Creation with Digital Twins: Application-Oriented Conceptual Framework and Case Study
Authors: Barth Linard , Schweiger Lukas , Galeno Gianluca , Schaal Nikolas , Ehrat Matthias
Published: March 2023

The internet of things, digital twins of smart connected products, and thereby enabled smart services are topics of great interest and have been gaining traction for many years. However, many questions concerning the application-oriented usage of digital twins still need to be scrutinized. Therefore, this paper examines the question of an application-oriented framework for value creation with digital twins using design science research approaches. A conceptual reference framework is presented based on earlier research and iteratively developed within workshops with three companies. The framework incorporates primary dimensions of external and internal value creation and data resources. Further, it discusses the product life cycle, the real-world counterpart, value creation in the ecosystem, and the generational aspect of the digital twins. Furthermore, applying the framework to a use case with an industrial research partner helps to show the contributions to the industrial sector. The framework provides utility to practitioners as a means of creating a common sense in interdisciplinary teams, communicating digital twin projects to internal and external stakeholders, and as a toolbox for specific challenges concerning digital twins. In addition, the framework distinguishes itself from existing approaches by including the service ecosystem and its actors while considering the principles of product life cycle management. Therefore, using the framework in other use cases will test the approach on different industries and products. Furthermore, there is a need to develop approaches for implementing and developing an existing case.
Classification of Mineral Foam Flotation Conditions Based on Multi-Modality Image Fusion
Authors: Jiang Xiaoping , Zhao Huilin , Liu Junwei
Published: March 2023

Accurate and rapid identification of mineral foam flotation states can increase mineral utilization and reduce the consumption of reagents. The traditional flotation process concentrates on extracting foam features from a single-modality foam image, and the accuracy is undesirable once problems such as insufficient image clarity or poor foam boundaries are encountered. In this work, a classification method based on multi-modality image fusion and CNN-PCA-SVM is proposed for work condition recognition of visible and infrared gray foam images. Specifically, the visible and infrared gray images are fused in the non-subsampled shearlet transform (NSST) domain using the parameter adaptive pulse coupled neural network (PAPCNN) method and the image quality detection method for high and low frequencies, respectively. The convolution neural network (CNN) is used as a trainable feature extractor to process the fused foam images, the principal component analysis (PCA) reduces feature data, and the support vector machine (SVM) is used as a recognizer to classify the foam flotation condition. After experiments, this model can fuse the foam images and recognize the flotation condition classification with high accuracy.
Special Issue on Current Trends and Future Directions in Voice Acoustics Measurement
Authors: Ternström Sten
Published: March 2023

The human voice production mechanism implements a superbly rich communication channel that at once tells us what, who, how, and much more [...]
Nursing Professionals’ Role in the Comprehensive Management of Obstructive Sleep Apnoea: A Literature Review
Authors: El Arab Rabie Adel , Sánchez-de-la-Torre Manuel , Valenzuela-Pascual Fran , Rubinat-Arnaldo Esther , Blanco-Blanco Joan , Rubí-Carnacea Francesc , Martinez-Navarro Oriol , Mediano Olga , Gea-Sánchez Montserrat
Published: March 2023

Background: obstructive sleep apnoea is a common and burdensome condition, characterised by obstruction of the airway during sleep at the level of the pharynx, which may occur with symptoms or without any symptoms. The most common place for obstructive sleep apnoea management is in specialised sleep units. Aim: to identify what is known about nurses’ role in comprehensive management of obstructive sleep apnoea, and to determine the effectiveness of nurse-led interventions as well as the cost effectiveness of management of obstructive sleep apnoea in primary care settings. Methods: a scoping review was carried out by searching PubMed/Medline, CINAHL, Scopus, Cochrane Database of Systematic Reviews and ScienceDirect. The study findings were synthesised using a thematic analysis approach. Results: In this review, 12 articles were included, and three key themes emerged, namely the role of nurses in the diagnosis of obstructive sleep apnoea, role of nurses in the follow up and support of obstructive sleep apnoea patients, and role of nurses in the management of obstructive sleep apnoea in primary care and the cost-effectiveness. Conclusion: Nurses can play a critical role in obstructive sleep apnoea. There are many ways in which nurses can contribute, including screening, assessment, diagnosis, promotion of therapy adherence, and following up with patients, including monitoring for any side effects associated with the CPAP machine, such as irritation of the face. Additionally, nurses can provide patient education and coordinate with other health care providers. Nursing interventions such as patient education have been demonstrated to be highly effective in promoting adherence to PAP therapy in both sleep units and primary care settings. Based on the findings of this review, the primary care model is more cost-effective than the sleep unit model in the management of obstructive sleep apnoea patients. The role of nurses in managing Obstructive Sleep Apnoea in both paediatric and adult populations shares similarities, but also includes differences that should be carefully considered and explored. In this review, one article only explored the role of nurses in paediatric OSA care. Thus, there is a need to identify the potential role of nursing in the comprehensive management of paediatric obstructive sleep apnoea, as well as to explore alternative cost-effective approaches that include primary care settings.
Effect of Carbon and Steel Fibers on the Strength Properties and Electrical Conductivity of Fiber-Reinforced Cement Mortar
Authors: Jun Hyung-Min , Seo Dong-Ju , Lim Doo-Yeol , Park Jong-Gun , Heo Gwang-Hee
Published: March 2023

Cement-based composites are generally non-conductors with high electrical resistivity, but they can be used as conductors by incorporating conductive materials. Recently, research has been actively conducted to develop high-conductive fiber-reinforced cement mortar (FRCM) due to increased interest in multifunctional cement mortar required by the market today. Thus, the present paper investigated the effect of the developed conductive FRCM containing carbon and steel fibers on the fresh and mechanical properties as well as electrical conductivity. The performance of conductivity FRCM was studied based on flow, unit weight, air content, three-point flexural, compression, and electrical resistance tests. In addition, their performance was also compared and reviewed with plain mortar (PM). Furthermore, the surface shape and element components of the developed conductive FRCM fracture surface were analyzed using a scanning electron microscopy (SEM) and an energy dispersive X-ray spectrometer (EDS). The results showed that the addition of steel fibers slightly decreased the relative flow value, whereas the incorporation of carbon fibers is very disadvantageous in terms of fluidity due to the fiber ball phenomenon. The unit weight of mixture containing carbon fibers was somewhat decreased, whereas the changes in the amount of air contents were relatively insignificant regardless of the fiber volume fractions. The flexural strength of conductive carbon fiber-reinforced cement mortar (CFRCM) and steel fiber-reinforced cement mortar (SFRCM) was significantly improved compared to that of PM. The compressive strength of CFRCM decreased significantly as the volume fraction of carbon fibers increased. Overall, even if the steel fibers were added up to 1.25%, the effect of improving the electrical conductivity of SFRCM was insignificant. In the case of the CFRCM used in this study, it was found that the percolation threshold existed between 0.3% and 0.4% fibers, and it was thus thought that the optimum dosage of carbon fiber should be secured by at least more than 0.4% in terms of electrical conductivity. Therefore, the most important factor for the electrical conductivity effect was found to be carbon fiber, whereas the effect of steel fiber was insignificant.
Energy Hub Optimal Scheduling and Management in the Day-Ahead Market Considering Renewable Energy Sources, CHP, Electric Vehicles, and Storage Systems Using Improved Fick’s Law Algorithm
Authors: Alghamdi Ali S. , Alanazi Mohana , Alanazi Abdulaziz , Qasaymeh Yazeed , Zubair Muhammad , Awan Ahmed Bilal , Ashiq Muhammad Gul Bahar
Published: March 2023

Coordinated energy scheduling and management strategies in the energy hub plan are essential to achieve optimal economic performance. In this paper, the scheduling and management framework of an energy hub (EH) is presented with the aim of energy profit maximization in partnership with electricity, natural gas, and district heating networks (EGHNs) considering the coordinated multi-energy management based on the day-ahead market. The optimum capacity of EH equipment, including photovoltaic and wind renewable energy sources, a combined heat and power system (CHP), a boiler, energy storage, and electric vehicles is determined in the day-ahead market using the improved Fick’s law algorithm (IFLA), considering the energy profit maximization and also satisfying the linear network and hub constraints. The conventional FLA is inspired by the concept of Fick’s diffusion law, and, in this study, its performance against premature convergence is improved by using Rosenbrock’s direct rotational method. The performance of the IFLA when applied to EH coordinated scheduling and management problems with the aim of profit maximization is compared with the conventional FLA, particle swarm optimization (PSO), and manta ray foraging optimization (MRFO) methods. The results show that the proposed scheduling and multi-energy management framework achieves more energy profit in the day-ahead electricity, gas, and heating markets by satisfying the operation and EH constraints compared to other methods. Furthermore, according to the findings, the increased (decreased) demand and the forced outage rate caused a decrease (increase) in the EH profit. The results show the effectiveness of the proposed framework to obtain the EH maximum energy profit in the day-ahead market.
A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and Future Realms
Authors: Pitafi Shahneela , Anwar Toni , Sharif Zubair
Published: March 2023

In the field of data mining, clustering has shown to be an important technique. Numerous clustering methods have been devised and put into practice, and most of them locate high-quality or optimum clustering outcomes in the field of computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning. This research provides a modern, thorough review of both classic and cutting-edge clustering methods. The taxonomy of clustering is presented in this review from an applied angle and the compression of some hierarchical and partitional clustering algorithms with various parameters. We also discuss the open challenges in clustering such as computational complexity, refinement of clusters, speed of convergence, data dimensionality, effectiveness and scalability, data object representation, evaluation measures, data streams, and knowledge extraction; scientists and professionals alike will be able to use it as a benchmark as they strive to advance the state-of-the-art in clustering techniques.
Multi-Defect Identification of Concrete Piles Based on Low Strain Integrity Test and Two-Channel Convolutional Neural Network
Authors: Wu Chuan-Sheng , Ge Man , Qi Ling-Ling , Zhuo De-Bing , Zhang Jian-Qiang , Hao Tian-Qi , Peng Yang-Xia
Published: March 2023

Defects in different positions and degrees in pile foundations will affect the building structure’s safety and the foundation’s bearing capacity. The efficiency and accuracy of using traditional methods to identify multi-defect types of pile foundations are very low, so finding suitable methods to improve their related indicators for pile foundation safety and engineering applications is necessary. In this paper, under the condition of secondary development of finite element software ABAQUS to obtain the time-domain signal database of six kinds of multi-defect pile foundations, a multi-defect type identification method of pile foundations based on two-channel convolutional neural network (TC-CNN) and low-strain pile integrity test (LSPIT) is proposed. Firstly, simulated time-domain signals of the dynamic measurements that match the experimental results performed wavelet packet denoising. Secondly, the 1D time-domain signals before and after denoising and the corresponding 2D wavelet time–frequency maps are inputs to retain more data information and prevent overfitting. Finally, TC-CNN achieved the multi-defect type identification of concrete piles. Compared with the single-channel convolutional neural network, this method can effectively fuse 1D and 2D features, extract more potential features, and make the classification accuracy reach 99.17%.
Fast and Effective Tag Searching for Multi-Group RFID Systems
Authors: Yan Na , Chen Honglong , Lin Kai , Li Zhe , Liu Yuping
Published: March 2023

In RFID-assisted applications, the customers often request to provide the tag searching service to determine which specific tags are present in the system. In practice, tags are usually divided into different groups to represent different categories or brands. We found that the traditional tag searching protocols are not very appropriate for multi-group RFID scenarios because they cannot ensure that the searching results of each group satisfy the predefined reliability requirements. Therefore, we develop a series of effective multi-group tag searching schemes. B-Search is a basic method that leverages the filter vector and the indicator vector to perform tag searching for each group sequentially. G-Search and A-Search are two parallel multi-group tag searching schemes. G-Search selects the longest frame length as the frame length of all groups, while A-Search can adaptively adjust the frame length of each group to improve the searching efficiency. We evaluate the performance of the proposed protocols through theoretical analysis and discuss the optimal parameter settings to minimize the execution time. Extensive simulations illustrate that both G-Search and A-Search can achieve fast and effective multi-group tag searching.
Machine Learning-Based Pain Severity Classification of Lumbosacral Radiculopathy Using Infrared Thermal Imaging
Authors: Rim Jinu , Ryu Seungjun , Jang Hyunjun , Zhang Hoyeol , Cho Yongeun
Published: March 2023

Pain is subjective and varies among individuals. Doctors determine pain severity based on a patient’s self-reported symptoms. In such situations, a language barrier may prevent patients from expressing their pain accurately, which may cause doctors to underestimate their pain degree. Moreover, patients’ subjective descriptions of pain can determine their eligibility for secondary benefits, as in the case of compensation for traffic or industrial accidents. Therefore, to perform a multiclass prediction of the severity of lumbar radiculopathy, the authors applied digital infrared thermographic imaging (DITI) to a machine-learning (ML) algorithm. The DITI dataset included data from a healthy population and patients with radiculopathy with herniated lumbar discs at the L3/4, L4/5, and L5/S1 levels. The dataset of 1000 patients was split into training and test datasets in a 7:3 ratio to evaluate the model’s performance. For the training dataset, the average accuracy, precision, recall, and F1 score were 0.82, 0.76, 0.72, and 0.74, respectively. For the test dataset, these values were 0.77, 0.71, 0.75, and 0.73, respectively. Applying the ML algorithm to a pain-severity classification using thermographic images will aid in the treatment of lumbosacral radiculopathy and allow providers to monitor the therapeutic effect of interventions through an assessment of physiological evidence.
Laser Seed Pretreatment Alters the Silybin Content and Anti-Dictyostelium discoideum Cell Growth Activity of Silybum marianum (L.) Fruit
Authors: El Sherif Fadia , Yap Yun-Kiam , Alamer Sarah , Althumairy Duaa , Khattab Salah
Published: March 2023

Silybum marianum is a medicinal plant that produces silybins A and B, which exhibit liver-protective activities. The He–Ne laser pre-sowing seed treatment has been used as a physical biostimulator in numerous plants to promote seed germination, plant growth, and secondary metabolite production. Thus, in this study, a He–Ne laser at 632.8 nm was used to irradiate S. marianum seeds with the aim of promoting plant growth as well as enhancing the production of silybin (A + B). The seeds were irradiated with a He–Ne laser for 2, 4, 6, 8, and 10 min. The growth attributes of the plants, as well as the silybin (A + B) content and bioactivities of fruit extracts from various treatment groups, were compared. The results indicated that all laser seed treatments resulted in a subsequent increase in plant growth, with the highest yield observed in the 10 min laser treatment. An increase in fruit silybin (A + B) content and in chalcone synthase gene expressions were observed in plants from the 6-, 8-, and 10-min laser-irradiated seeds. The highest silybin (A + B) contents were observed in plants from the 6-min-irradiated seeds. Furthermore, the corresponding fruit extract also exhibited elevated cell growth inhibition in the Dictyostelium discoideum cell model.
Influence of Discontinuous Linear Deformation on the Values of Continuous Deformations of a Mining Area and a Building Induced by an Exploitation of Hard Coal Seam
Authors: Orwat Justyna
Published: March 2023

In this article, the impact of a ground step on a residential building and a mining terrain has been presented. Influence of a discontinuous linear deformation by the changes of the inclination and curvatures’ values (continuous deformations) has been observed. Discontinuous and continuous deformations have been caused by an exploitation of the 405/1 hard coal seam, located at a depth of 550 m. Extraction was carried out with the use of the longwall system to the south of the building. A discontinuous linear deformation occurred parallel to a north wall of the building and had a height of 0.2 m. The inclination and curvatures’ values have been calculated on the basis of the results of the geodesic surveys. Vertical displacements and horizontal distances between the measuring points in the ground and on the building’s walls have been measured. Points in two perpendicular directions (parallel and perpendicular to a ground step) have been stabilized. The observational network consisted of 12 points (3 points in each direction, in the ground and on the walls). Research has shown that inclinations and curvatures have different values on a terrain’s surface and in buildings, which means that a deformation process takes place in a different way in the ground and a building. The obtained results indicate that an occurrence of a discontinuous linear deformation near the building reduces the values of inclinations and curvatures of a terrain surface and a building in a parallel direction to its longitudinal axis, and it increases the values of the continuous deformations in the direction perpendicular to it.
e-Archeo: A Pilot National Project to Valorize Italian Archaeological Parks through Digital and Virtual Reality Technologies
Authors: Pietroni Eva , Menconero Sofia , Botti Carolina , Ghedini Francesca
Published: March 2023

Commissioned to ALES spa by the Ministry of Culture (MiC), the e-Archeo project was born with the intention of enhancing and promoting knowledge of some Italian archaeological sites with a considerable narrative potential that has not yet been fully expressed. The main principle that guided the choice of the sites and the contents was of illustrating the various cultures and types of settlements present in the Italian territory. Eight sites were chosen, spread across the national territory from north to south, founded by Etruscans, Greeks, Phoenicians, natives and Romans. e-Archeo has developed multimedia, integrated and multi-channel solutions for various uses and types of audiences, adopting both scientific and narrative and emotional languages. Particular attention was paid to multimedia accessibility, technological sustainability and open science. The e-Archeo project was born from a strong synergy between public entities, research bodies and private industries thanks to the collaboration of MiC and ALES with the CNR ISPC, 10 Italian Universities, 12 Creative Industries and the Italian National Television (RAI). This exceptional and unusual condition made it possible to realise all the project’s high-quality contents and several outputs in only one and a half years.
Tax Fraud Reduction Using Analytics in an East European Country
Authors: Ruzgas Tomas , Kižauskienė Laura , Lukauskas Mantas , Sinkevičius Egidijus , Frolovaitė Melita , Arnastauskaitė Jurgita
Published: March 2023

Tax authorities face the challenge of effectively identifying companies that avoid paying taxes, which is not unique to European Union countries. Limited resources often constrain tax administrators, who traditionally rely on time-consuming and labour-intensive tax audit tools. As a result of this established practice, governments are losing a lot of tax revenue. The main objective of this study is to increase the efficiency of the detection of tax evasion by applying data mining methods in the East European country Lithuania, which has a rapidly developing economy, by applying data mining methods concerning affluence-related impacts. The study develops various models for segmentation, risk assessment, behavioral templates, and tax crime detection. Results show that the data mining technique can effectively detect tax evasion and extract hidden knowledge that can be used to reduce revenue losses resulting from tax evasion. This study’s methods, software, and findings can assist decision-makers, experts, and scientists in developing countries in predicting tax fraud detection.
Further Closed Formulae of Exotic 3F2-Series
Authors: Chu Wenchang
Published: March 2023

By making use of the linearization method, we examine a class of nonterminating 3F2-series with five free integer parameters that yields twenty summation formulae. Under the Kummer and Thomae transformations, six classes of exotic 3F2-series are consequently evaluated in closed forms. There are overall 100 identities recorded in the present paper
Prediction of the Heat Generation Rate of Lithium-Ion Batteries Based on Three Machine Learning Algorithms
Authors: Cao Renfeng , Zhang Xingjuan , Yang Han
Published: March 2023

The heat generation rate (HGR) of lithium-ion batteries is crucial for the design of a battery thermal management system. Machine learning algorithms can effectively solve nonlinear problems and have been implemented in the state estimation and life prediction of batteries; however, limited research has been conducted on determining the battery HGR through machine learning. In this study, we employ three common machine learning algorithms, i.e., artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR), to predict the battery HGR based on our experimental data, along with cases of interpolation and extrapolation. The results indicated the following: (1) the prediction accuracies for the interpolation cases were better than those of extrapolation, and the R2 values of interpolation were greater than 0.96; (2) after the discharge voltage was added as an input parameter, the prediction of the ANN was barely affected, whereas the performance of the SVM and GPR were improved; and (3) the ANN exhibited the best performance among the three algorithms. Accurate results can be obtained by using a single hidden layer and no more than 15 neurons without the additional input, where the R2 values were in the range of 0.89–1.00. Therefore, the ANN is preferable for predicting the HGR of lithium-ion batteries.
Real-Time Attention Monitoring System for Classroom: A Deep Learning Approach for Student’s Behavior Recognition
Authors: Trabelsi Zouheir , Alnajjar Fady , Parambil Medha Mohan Ambali , Gochoo Munkhjargal , Ali Luqman
Published: March 2023

Effective classroom instruction requires monitoring student participation and interaction during class, identifying cues to simulate their attention. The ability of teachers to analyze and evaluate students’ classroom behavior is becoming a crucial criterion for quality teaching. Artificial intelligence (AI)-based behavior recognition techniques can help evaluate students’ attention and engagement during classroom sessions. With rapid digitalization, the global education system is adapting and exploring emerging technological innovations, such as AI, the Internet of Things, and big data analytics, to improve education systems. In educational institutions, modern classroom systems are supplemented with the latest technologies to make them more interactive, student centered, and customized. However, it is difficult for instructors to assess students’ interest and attention levels even with these technologies. This study harnesses modern technology to introduce an intelligent real-time vision-based classroom to monitor students’ emotions, attendance, and attention levels even when they have face masks on. We used a machine learning approach to train students’ behavior recognition models, including identifying facial expressions, to identify students’ attention/non-attention in a classroom. The attention/no-attention dataset is collected based on nine categories. The dataset is given the YOLOv5 pre-trained weights for training. For validation, the performance of various versions of the YOLOv5 model (v5m, v5n, v5l, v5s, and v5x) are compared based on different evaluation measures (precision, recall, mAP, and F1 score). Our results show that all models show promising performance with 76% average accuracy. Applying the developed model can enable instructors to visualize students’ behavior and emotional states at different levels, allowing them to appropriately manage teaching sessions by considering student-centered learning scenarios. Overall, the proposed model will enhance instructors’ performance and students at an academic level.
Transcriptome Analysis on Hepatopancreas Reveals the Metabolic Dysregulation Caused by Vibrio parahaemolyticus Infection in Litopenaeus vannamei
Authors: Miao Miao , Li Shihao , Liu Yuan , Yu Yang , Li Fuhua
Published: March 2023

Acute hepatopancreas necrosis disease (AHPND) has caused massive deaths of shrimp and has led to huge economic losses in aquaculture. Vibrio parahaemolyticus (VPAHPND) carrying a plasmid encoding binary toxins homologous to the photorhabdus insect-related (Pir) toxins is one of the main pathogens causing this disease. Previous studies have reported many immune-related genes of shrimp in response to this pathogenic bacteria. However, few studies have so far focused on the metabolic changes in Litopenaeus vannamei upon VPAHPND infection. In the present study, comparative transcriptomic analysis was performed on the hepatopancreas of shrimp at different times during VPAHPND infection. Functional analyses on the differentially expressed genes (DEGs) during infection showed that pathways related to glucose, energy and amino acid metabolism, as well as nucleic acid synthesis, were obviously changed in the hepatopancreas after VPAHPND infection. Additionally, three signaling pathways, which could regulate metabolic processes, including HIF-1 signaling pathway, PI3K-Akt signaling pathway and NF-KappaB signaling pathway, also changed significantly. Collectively, these data reveal a close relationship between host metabolism processes and Vibrio infection. The information will enrich our understanding of the interaction mechanism between the shrimp and Vibrio.