bluuebunny/crossref_metadata_2025 huggingface dataset:
Small-Signal Stability Analysis and MOSMA-Based Optimization Control Strategy of OWF with MMC-HVDC Grid Connection
Authors: Zheng Jie , Li Hui , Zhang Bo , Li Qinghe
Published: December 2023

The recent oscillation events in offshore wind farms (OWFs) connected via a modular multilevel-converter-based HVDC (MMC-HVDC) system are developing towards a wider frequency band, which causes complex a small-signal interaction phenomenon and difficulties in the stability analysis and control. In this paper, the wideband dynamic interaction mechanism is investigated based on the impedance analysis method and an improved control strategy using an optimization algorithm is proposed to improve the small-signal stability and reduce the oscillation risks. First, the detailed impedance models of the grid-connected system are established considering the distribution characteristics of the submarine cable, control delay and frequency coupling effect. Then, combined with the active damping control method, the wideband resonance mechanism is analyzed, and the stability constraints of controller parameters are obtained using the impedance stability criterion. Finally, an improved multi-objective slime mold algorithm (MOSMA)-based coordinated optimization control strategy is proposed to enhance the adaptability of the controller parameters and the wideband damping ability of a grid-connected system, which can improve the wideband stability of the system. The simulation and experimental results verify the proposed control strategy.
The Role of Zn Ions in the Structural, Surface, and Gas-Sensing Properties of SnO2:Zn Nanocrystals Synthesized via a Microwave-Assisted Route
Authors: da Silva Luís F. , Lucchini Mattia A. , Catto Ariadne C. , Avansi Jr. Waldir , Bernardini Sandrine , Aguir Khalifa , Niederberger Markus , Longo Elson
Published: December 2023

Although semiconducting metal oxide (SMOx) nanoparticles (NPs) have attracted attention as sensing materials, the methodologies available to synthesize them with desirable properties are quite limited and/or often require relatively high energy consumption. Thus, we report herein the processing of Zn-doped SnO2 NPs via a microwave-assisted nonaqueous route at a relatively low temperature (160 °C) and with a short treatment time (20 min). In addition, the effects of adding Zn in the structural, electronic, and gas-sensing properties of SnO2 NPs were investigated. X-ray diffraction and high-resolution transmission electron microscopy analyses revealed the single-phase of rutile SnO2, with an average crystal size of 7 nm. X-ray absorption near edge spectroscopy measurements revealed the homogenous incorporation of Zn ions into the SnO2 network. Gas sensing tests showed that Zn-doped SnO2 NPs were highly sensitive to sub-ppm levels of NO2 gas at 150 °C, with good recovery and stability even under ambient moisture. We observed an increase in the response of the Zn-doped sample of up to 100 times compared to the pristine one. This enhancement in the gas-sensing performance was linked to the Zn ions that provided more surface oxygen defects acting as active sites for the NO2 adsorption on the sensing material.
PGGait: Gait Recognition Based on Millimeter-Wave Radar Spatio-Temporal Sensing of Multidimensional Point Clouds
Authors: Dang Xiaochao , Tang Yangyang , Hao Zhanjun , Gao Yifei , Fan Kai , Wang Yue
Published: December 2023

Gait recognition, crucial in biometrics and behavioral analytics, has applications in human–computer interaction, identity verification, and health monitoring. Traditional sensors face limitations in complex or poorly lit settings. RF-based approaches, particularly millimeter-wave technology, are gaining traction for their privacy, insensitivity to light conditions, and high resolution in wireless sensing applications. In this paper, we propose a gait recognition system called Multidimensional Point Cloud Gait Recognition (PGGait). The system uses commercial millimeter-wave radar to extract high-quality point clouds through a specially designed preprocessing pipeline. This is followed by spatial clustering algorithms to separate users and perform target tracking. Simultaneously, we enhance the original point cloud data by increasing velocity and signal-to-noise ratio, forming the input of multidimensional point clouds. Finally, the system inputs the point cloud data into a neural network to extract spatial and temporal features for user identification. We implemented the PGGait system using a commercially available 77 GHz millimeter-wave radar and conducted comprehensive testing to validate its performance. Experimental results demonstrate that PGGait achieves up to 96.75% accuracy in recognizing single-user radial paths and exceeds 94.30% recognition accuracy in the two-person case. This research provides an efficient and feasible solution for user gait recognition with various applications.
Performance Evaluation Method for Intelligent Computing Components for Space Applications
Authors: Xie Man , Wang Lianguo , Ma Miao , Zhang Pengfei
Published: December 2023

The computational performance requirements of space payloads are constantly increasing, and the redevelopment of space-grade processors requires a significant amount of time and is costly. This study investigates performance evaluation benchmarks for processors designed for various application scenarios. It also constructs benchmark modules and typical space application benchmarks specifically tailored for the space domain. Furthermore, the study systematically evaluates and analyzes the performance of NVIDIA Jetson AGX Xavier platform and Loongson platforms to identify processors that are suitable for space missions. The experimental results of the evaluation demonstrate that Jetson AGX Xavier performs exceptionally well and consumes less power during dense computations. The Loongson platform can achieve 80% of Xavier’s performance in certain parallel optimized computations, surpassing Xavier’s performance at the expense of higher power consumption.
Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers
Authors: Nganga Allan , Nganya George , Lützhöft Margareta , Mallam Steven , Scanlan Joel
Published: December 2023

Increasingly disruptive cyber-attacks in the maritime domain have led to more efforts being focused on enhancing cyber resilience. From a regulatory perspective, there is a requirement that maritime stakeholders implement measures that would enable the timely detection of cyber events, leading to the adoption of Maritime Security Operation Centers (M-SOCs). At the same time, Remote Operation Centers (ROCs) are also being discussed to enable increased adoption of highly automated and autonomous technologies, which could further impact the attack surface of vessels. The main objective of this research was therefore to better understand both enabling factors and challenges impacting the effectiveness of M-SOC operations. Semi-structured interviews were conducted with nine M-SOC experts. Informed by grounded theory, incident management emerged as the core category. By focusing on the factors that make M-SOC operations a unique undertaking, the main contribution of this study is that it highlights how maritime connectivity challenges and domain knowledge impact the M-SOC incident management process. Additionally, we have related the findings to a future where M-SOC and ROC operations could be converged.
SchoolAIR: A Citizen Science IoT Framework Using Low-Cost Sensing for Indoor Air Quality Management
Authors: Barros Nelson , Sobral Pedro , Moreira Rui S. , Vargas João , Fonseca Ana , Abreu Isabel , Guerreiro Maria Simas
Published: December 2023

Indoor air quality (IAQ) problems in school environments are very common and have significant impacts on students’ performance, development and health. Indoor air conditions depend on the adopted ventilation practices, which in Mediterranean countries are essentially based on natural ventilation controlled through manual window opening. Citizen science projects directed to school communities are effective strategies to promote awareness and knowledge acquirement on IAQ and adequate ventilation management. Our multidisciplinary research team has developed a framework—SchoolAIR—based on low-cost sensors and a scalable IoT system architecture to support the improvement of IAQ in schools. The SchoolAIR framework is based on do-it-yourself sensors that continuously monitor air temperature, relative humidity, concentrations of carbon dioxide and particulate matter in school environments. The framework was tested in the classrooms of University Fernando Pessoa, and its deployment and proof of concept took place in a high school in the north of Portugal. The results obtained reveal that CO2 concentrations frequently exceed reference values during classes, and that higher concentrations of particulate matter in the outdoor air affect IAQ. These results highlight the importance of real-time monitoring of IAQ and outdoor air pollution levels to support decision-making in ventilation management and assure adequate IAQ. The proposed approach encourages the transfer of scientific knowledge from universities to society in a dynamic and active process of social responsibility based on a citizen science approach, promoting scientific literacy of the younger generation and enhancing healthier, resilient and sustainable indoor environments.
Fog Computing for Control of Cyber-Physical Systems in Industry Using BCI
Authors: Rodríguez-Azar Paula Ivone , Mejía-Muñoz Jose Manuel , Cruz-Mejía Oliverio , Torres-Escobar Rafael , López Lucero Verónica Ruelas
Published: December 2023

Brain-computer interfaces use signals from the brain, such as EEG, to determine brain states, which in turn can be used to issue commands, for example, to control industrial machinery. While Cloud computing can aid in the creation and operation of industrial multi-user BCI systems, the vast amount of data generated from EEG signals can lead to slow response time and bandwidth problems. Fog computing reduces latency in high-demand computation networks. Hence, this paper introduces a fog computing solution for BCI processing. The solution consists in using fog nodes that incorporate machine learning algorithms to convert EEG signals into commands to control a cyber-physical system. The machine learning module uses a deep learning encoder to generate feature images from EEG signals that are subsequently classified into commands by a random forest. The classification scheme is compared using various classifiers, being the random forest the one that obtained the best performance. Additionally, a comparison was made between the fog computing approach and using only cloud computing through the use of a fog computing simulator. The results indicate that the fog computing method resulted in less latency compared to the solely cloud computing approach.
Modeling the Converse Magnetoelectric Effect in the Low-Frequency Range
Authors: Bichurin Mirza , Sokolov Oleg , Ivanov Sergey , Leontiev Viktor , Lobekin Vyacheslav , Semenov Gennady , Wang Yaojin
Published: December 2023

This article is devoted to the theory of the converse magnetoelectric (CME) effect for the longitudinal, bending, longitudinal-shear, and torsional resonance modes and its quasi-static regime. In contrast to the direct ME effect (DME), these issues have not been studied in sufficient detail in the literature. However, in a number of cases, in particular in the study of low-frequency ME antennas, the results obtained are of interest. Detailed calculations with examples were carried out for the longitudinal mode on the symmetric and asymmetric structures based on Metglas/PZT (LN); the bending mode was considered for the asymmetric free structure and structure with rigidly fixed left-end Metglas/PZT (LN); the longitudinal-shear and torsional modes were investigated for the symmetric and asymmetric free structures based on Metglas/GaAs. For the identification of the torsion mode, it was suggested to perform an experiment on the ME structure based on Metglas/bimorphic LN. All calculation results are presented in the form of graphs for the CME coefficients.
Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning
Authors: Jaramillo-Quintanar Daniel , Gomez-Reyes Jean K. , Morales-Hernandez Luis A. , Dominguez-Trejo Benjamin , Rodriguez-Medina David A. , Cruz-Albarran Irving A.
Published: December 2023

Stress is a factor that affects many people today and is responsible for many of the causes of poor quality of life. For this reason, it is necessary to be able to determine whether a person is stressed or not. Therefore, it is necessary to develop tools that are non-invasive, innocuous, and easy to use. This paper describes a methodology for classifying stress in humans by automatically detecting facial regions of interest in thermal images using machine learning during a short Trier Social Stress Test. Five regions of interest, namely the nose, right cheek, left cheek, forehead, and chin, are automatically detected. The temperature of each of these regions is then extracted and used as input to a classifier, specifically a Support Vector Machine, which outputs three states: baseline, stressed, and relaxed. The proposal was developed and tested on thermal images of 25 participants who were subjected to a stress-inducing protocol followed by relaxation techniques. After testing the developed methodology, an accuracy of 95.4% and an error rate of 4.5% were obtained. The methodology proposed in this study allows the automatic classification of a person’s stress state based on a thermal image of the face. This represents an innovative tool applicable to specialists. Furthermore, due to its robustness, it is also suitable for online applications.
Quasi-Zero Stiffness Vibration Sensing and Energy Harvesting Integration Based on Buckled Piezoelectric Euler Beam
Authors: Tuo Jiying , Xu Xiaonan , Li Jun , Dai Tianlang , Liu Zilin
Published: December 2023

This paper presents a novel quasi-zero stiffness vibration sensing and energy harvesting integration system for absolute displacement measurements based on a buckled piezoelectric Euler beam (BPEB) with quasi-zero stiffness (QZS) characteristics. On one hand, BPEB provides negative stiffness to the system, thus creating a vibration-free point within the system and transforming the absolute displacement measurement problem into a relative motion sensing problem. On the other hand, during the measurement process, the BPEB collects the vibration energy from the system, which can provide electrical energy for low-power relative motion sensing devices and remarkably suppress the frequency range of the jump phenomenon, thereby further expanding the frequency domain measurement range of the sensing system. The research results have shown that this system can measure the absolute motion signal of the tested object in low-frequency vibration with small excitation. By adjusting parameters such as the force–electric coupling coefficient and damping ratio, the measurement accuracy of the sensing system can be improved. Furthermore, the system can convert the mechanical energy of vibrations into electrical energy to power the surrounding low-power sensors or provide partial power. This could potentially achieve self-powering integrated quasi-zero stiffness vibration sensing, offering another approach and possibility for the automation development in wireless sensing systems and the Internet of Things field.
Neural Network-Based Adaptive Height Tracking Control of Active Air Suspension System with Magnetorheological Fluid Damper Subject to Uncertain Mass and Input Delay
Authors: Zhao Rongchen , Xie Haifeng , Gong Xinle , Sun Xiaoqiang , Cao Chen
Published: December 2023

In this paper, we present a novel robust adaptive neural network-based control framework to address the ride height tracking control problem of active air suspension systems with magnetorheological fluid damper (MRD-AAS) subject to uncertain mass and time-varying input delay. First, a radial basis function neural network (RBFNN) approximator is designed to compensate for unmodeled dynamics of the MRD. Then, a projector-based estimator is developed to estimate uncertain parameter variation (sprung mass). Additionally, to deal with the effect of input delay, a time-delay compensator is integrated in the adaptive control law to enhance the transient response of MRD-AAS system. By introducing a Lyapunov–Krasovskii (LK) functional, both ride height tracking and estimator errors can robustly converge towards the neighborhood of the desired values, achieving uniform ultimate boundness. Finally, comparative simulation results based on a dynamic co-simulator built in AMESim 2021.2 and Matlab/Simulink 2019(b) are given to illustrate the validity of the proposed control framework, showing its effectiveness to operate ride height regulation with MRD-AAS systems accurately and reliably under random road excitations.
A Machine Learning App for Monitoring Physical Therapy at Home
Authors: Pereira Bruno , Cunha Bruno , Viana Paula , Lopes Maria , Melo Ana , Sousa Andreia
Published: December 2023

Shoulder rehabilitation is a process that requires physical therapy sessions to recover the mobility of the affected limbs. However, these sessions are often limited by the availability and cost of specialized technicians, as well as the patient’s travel to the session locations. This paper presents a novel smartphone-based approach using a pose estimation algorithm to evaluate the quality of the movements and provide feedback, allowing patients to perform autonomous recovery sessions. This paper reviews the state of the art in wearable devices and camera-based systems for human body detection and rehabilitation support and describes the system developed, which uses MediaPipe to extract the coordinates of 33 key points on the patient’s body and compares them with reference videos made by professional physiotherapists using cosine similarity and dynamic time warping. This paper also presents a clinical study that uses QTM, an optoelectronic system for motion capture, to validate the methods used by the smartphone application. The results show that there are statistically significant differences between the three methods for different exercises, highlighting the importance of selecting an appropriate method for specific exercises. This paper discusses the implications and limitations of the findings and suggests directions for future research.
Surface-Enhanced Raman Scattering in Silver-Coated Suspended-Core Fiber
Authors: Xu Yangyang , Zhang Xian , Zhu Xiao-Song , Shi Yi-Wei
Published: December 2023

In this paper, the silver-coated large-core suspended-core fiber (LSCF) probe was fabricated by the dynamic chemical liquid phase deposition method for surface-enhanced Raman scattering (SERS) sensing. The 4-mercaptophenylboronic acid (4-MPBA) monolayer was assembled in the LSCF as the recognition monolayer. Taking advantage of the appropriate core size of the LSCF, a custom-made Y-type optical fiber patch cable was utilized to connect the semiconductor laser, Raman spectrometer, and the proposed fiber SERS probe. The SERS signal is propagated in the silver-coated air channels, which can effectively reduce the Raman and fluorescence background of the silica core. Experiments were performed to measure the Raman scattering spectra of the 4-MPBA in the silver-coated LSCF in a non-enhanced and enhanced case. The experiment results showed that the Raman signal strength was enhanced more than 6 times by the surface plasmon resonance compared with the non-enhanced case. The proposed LSCF for SERS sensing technology provides huge research value for the fiber SERS probes in biomedicine and environmental science. The combination of SERS and microstructured optical fibers offers a potential approach for SERS detection
Ultra-Lightweight Fast Anomaly Detectors for Industrial Applications
Authors: Kocon Michał , Malesa Marcin , Rapcewicz Jerzy
Published: December 2023

Quality inspection in the pharmaceutical and food industry is crucial to ensure that products are safe for the customers. Among the properties that are controlled in the production process are chemical composition, the content of the active substances, and visual appearance. Although the latter may not influence the product’s properties, it lowers customers’ confidence in drugs or food and affects brand perception. The visual appearance of the consumer goods is typically inspected during the packaging process using machine vision quality inspection systems. In line with the current trends, the processing of the images is often supported with deep neural networks, which increases the accuracy of detection and classification of faults. Solutions based on AI are best suited to production lines with a limited number of formats or highly repeatable production. In the case where formats differ significantly from each other and are often being changed, a quality inspection system has to enable fast training. In this paper, we present a fast method for image anomaly detection that is used in high-speed production lines. The proposed method meets these requirements: It is easy and fast to train, even on devices with limited computing power. The inference time for each production sample is sufficient for real-time scenarios. Additionally, the ultra-lightweight algorithm can be easily adapted to different products and different market segments. In this work, we present the results of our algorithm on three different real production data gathered from food and pharmaceutical industries.
Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks
Authors: Fay Cormac D. , Corcoran Brian , Diamond Dermot
Published: December 2023

This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO2 emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8–99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.
Sulfolane Analysis in Environmental Samples: A Critical Review
Authors: Kobarfard Merrik , Górecki Tadeusz
Published: December 2023

Sulfolane, a highly water-soluble industrial solvent, has raised environmental concerns due to its widespread usage and its prolonged persistence once released into the environment. To assess the extent of contamination effectively, reliable analytical methods are essential. In this review article, the existing methodologies for the sample preparation and determination of sulfolane are systematically examined and evaluated. In brief, existing guidelines for sampling from environmental matrices provide a solid foundation for sulfolane analysis. Notably, there is little variation in the choice of final determination methods, with GC-MS or GC-FID being favored across all studies. However, substantial variability emerges in sample preparation methods. Moreover, upon scrutiny of the analytical procedures utilized in sulfolane analysis, it becomes evident that a considerable number of them are characterized by inefficiency or environmental hazards, often relying on substantial quantities of chlorinated solvents. Through this review, valuable insights into the challenges at hand and potential solutions can be gained, offering a foundation for the development of novel sulfolane analysis methods applicable to a range of environmental matrices. Overall, this field calls for further research to devise efficient and environmentally sustainable analytical methods for sulfolane analysis.
Application of the Biomass of Leaves of Diospyros kaki L.f. (Ebenaceae) in the Removal of Metal Ions from Aqueous Media
Authors: Galera Rodrigo Martorelli , da Silva Adrielli Cristina Peres , Jorgetto Alexandre de Oliveira , Wondracek Marcos Henrique Pereira , Saeki Margarida Juri , Schneider José Fabián , Pedrosa Valber de Albuquerque , Martines Marco Autônio Utrera , Castro Gustavo Rocha
Published: December 2023

Using straightforward and cost-effective methods, persimmon leaves were converted into high-quality powder. This powder was applied as an adsorbent for the removal of Cu(II) and Cd(II) from aqueous solutions. Scanning electron microscopy (SEM) revealed the presence of particles with non-homogeneous sizes and rough textures. The biosorbent exhibited a specific surface area of approximately 0.44 ± 0.015 m2 g−1. Elemental analysis and energy-dispersive X-ray spectroscopy (EDX) confirmed the presence of elements such as sulfur, phosphorus, nitrogen, and oxygen. The results of 13C nuclear magnetic resonance (13C-NMR), obtained using the cross-polarization technique, show the presence of groups containing sulfur and oxygen. Infrared spectroscopy (FTIR) indicated the existence of amine and hydroxyl groups. The material was used in the solid-phase extraction of Cu(II) and Cd(II) in batch experiments, and its adsorption capacity was evaluated as a function of time, pH, and analyte concentration. The fraction with a diameter between 63 and 106 μm was selected for the adsorption tests. Kinetic equilibrium was reached within 5 min, and the experimental data were fitted to the pseudo-second-order kinetic model. The optimum pH for the adsorption of both metal species was approximately 5.0. The adsorption isotherms were adjusted using the modified Langmuir equation, and the maximum amount of metal species extracted from the solution was determined to be 0.213 mmol g−1 for Cu(II) and 0.215 mmol g−1 for Cd(II), with high linear correlation coefficients for both metals. Persimmon leaves are typically abundant during the growing season, and because they are seasonal, the Diospyros kaki L.f. tree undergoes the natural process of leaf abscission, ensuring the availability of leaves for application.
Indicators of Fatigue during a Soccer Match Simulation Using GPS-Derived Workload Values: Which Metrics Are Most Useful?
Authors: Snyder Benjamin J. , Maung-Maung Cameron , Whitacre Cameron
Published: December 2023

Research into women’s competitive soccer matches has shown distance and high-speed running (HSR) reductions over time, but the effects on some GPS-derived metrics have not been investigated. The purpose of this project was to examine the utility of common GPS metrics for indicating fatigue from the T-SAFT90 match simulation in collegiate soccer players. Unlike in competitive matches, changes to these metrics occurred as early as 15 min, with HSR, average and max speed, and speed intensity (SI) exhibiting significant declines. HSR and SI were even further decreased in later periods, with HSR lower in minutes 30–40 (T30–35 and T35–40) than T15–20 and lower in T70–85 than T45–60. SI showed a similar pattern of continued decline, reaching its lowest value in the last three time segments. Accelerations and decelerations were also decreased beginning at T15–20 and T20–25, respectively, but the fatigue index (FI), dynamic stress load (DSL), and step balance (SB) were unchanged. It can be concluded that in contrast to competitive matches where players can dictate their own intensity, a match simulation may result in a quicker onset of fatigue, but not all GPS-derived values change as expected in fatiguing environments. Coaches and sports scientists may use these findings to properly monitor fatigue in real time.
Reviewing the Cost–Benefit Analysis and Multi-Criteria Decision-Making Methods for Evaluating the Effectiveness of Lithium-Ion Batteries in Electric Vehicles
Authors: Barić Danijela , Grabušić Silvestar , Jakara Martina , Emanović Marko
Published: December 2023

Lithium-ion batteries (LIBs) have a wide range of applications in different fields, starting with electronics and energy storage systems. The potential of LIBs in the transportation sector is high, especially for electric vehicles (EVs). This study aims to investigate the efficiency and effectiveness of, and justification for, the application of LIBs in the field of transport, primarily in EVs. The research focuses on single and multi-criteria evaluations of the efficiency of LIBs. Previous studies in which LIBs were evaluated using cost–benefit analysis (CBA) and multi-criteria decision-making methods (MCDM) were analysed. An electronic literature search of the Web of Science, Scopus, and other relevant databases was performed. The literature was searched using the keywords: “lithium-ion batteries”; “multi-criteria decision-making”; “cost-benefit analysis”; “energy storage”; “vehicles”; “PROMETHEE” (or other MCDM method)”. A total of 40 scientific articles concerning the application of CBA (of which are 20%) and MCDM methods between 1997 and 2023, worldwide, were analysed. The results show multiple applications of both CBA and MCDM methods. The main findings of the areas of application were summarised and future research was discussed.
Probabilistic Load Flow Analysis Using Nonparametric Distribution
Authors: Bin Li , Abbas Rashana , Shahzad Muhammad , Safdar Nouman
Published: December 2023

In the pursuit of sustainable energy solutions, this research addresses the critical need for accurate probabilistic load flow (PLF) analysis in power systems. PLF analysis is an essential tool for estimating the statistical behavior of power systems under uncertainty. It plays a vital part in power system planning, operation, and dependability studies. To perform accurate PLF analysis, this article proposes a Kernel density estimation with adaptive bandwidth for probability density function (PDF) estimation of power injections from sustainable energy sources like solar and wind, reducing errors in PDF estimation. To reduce the computational burden, a Latin hypercube sampling approach was incorporated. Input random variables are modeled using kernel density estimation (KDE) in conjunction with Latin hypercube sampling (LHS) for probabilistic load flow (PLF) analysis. To test the proposed techniques, IEEE 14 and IEEE 118 bus systems are used. Two benchmark techniques, the Monte Carlo Simulation (MCS) method and Hamiltonian Monte Carlo (HMC), were set side by side for validation of results. The results illustrate that an adaptive bandwidth kernel density estimation with the Latin hypercube sampling (AKDE-LHS) method provides better performance in terms of precision and computational efficiency. The results also show that the suggested technique is more feasible in reducing errors, uncertainties, and computational time while depicting arbitrary distributions of photovoltaic and wind farms for probabilistic load flow analysis. It can be a potential solution to tackle challenges posed by sustainable energy sources in power systems.
Integrated Agent-Based Simulation and Game Theory Decision Support Framework for Cash Flow and Payment Management in Construction Projects
Authors: Dorrah Dalia H. , McCabe Brenda
Published: December 2023

Effective cash flow management has become crucial for projects and stakeholders given the wide payment-related problems and financial risks encountered in the construction industry worldwide. Previous studies mostly addressed cash flow and payments from the perspective of a specific stakeholder, resulting in an imbalanced cash flow management culture that is further intensified by the power asymmetry of the top-down payment decision-making process. This research proposes an adaptive decision support framework for evaluating and negotiating payment options in construction projects while incorporating the individual and collective financial roles of stakeholders. The framework is comprised of three modules for data acquisition, payment simulation, analysis, and negotiation, as well as decision support. It integrates agent-based simulation, data envelopment analysis, and game theory for a multi-level study of project performance while capturing the driving forces of stakeholders in payment negotiations. A case study project is used to demonstrate the framework implementation under varying payment conditions and interest rates. The results provide quantitative profiles of stakeholders to identify incurred charges, balanced payment conditions, and suitable compensation. Finally, the framework can be utilized by stakeholders and jurisdictions to move towards enhanced contractual arrangements that alleviate economic and financial risks with the informed collaboration of its entities.
Eco-Efficient Artificial Stones Produced Using Quartzite Quarry Waste and Vegetable Resin
Authors: Aguiar Mariane Costalonga de , Fernandes Maria Carolyna Sopeletti , Sant’Ana Maria Angelica Kramer , Sagrillo Viviana Possamai Della , Anastácio Alexandre dos Santos , Gadioli Monica Castoldi Borlini
Published: December 2023

The ornamental stone industry plays a crucial role in the world economy, and thus the production process of ornamental stones generates a large volume of waste that is normally destined for landfills. Given the growing market import of artificial stones, there is a need for more sustainable practices to reduce waste and improve the use of natural resources. Thus, the present study aims to develop artificial stones with waste from the ornamental stone extraction process from the quarry, and vegetable polyurethane resin derived from castor oil, in order to analyze its viability in the production of artificial stone slabs. The wastes used in this work were three quartzites, fragmented and mixed with three grain sizes, called coarse, medium, and fine. These wastes were characterized using X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier transform infrared (FTIR), and thermal analysis (TGA/DTG/DSC). Artificial stone slabs were produced using 15 wt.% vegetable polyurethane resin derived from castor oil and 85 wt.% ornamental stone wastes (quartzite). These slabs underwent apparent density, water absorption, porosity, 3-point flexural strength, scanning electron microscopy (SEM), petrography, and X-ray diffraction (XRD) tests. According to the results obtained, it was possible to prove the technical viability of manufacturing artificial stones with ornamental stone wastes and with the use of vegetable polyurethane resin derived from castor oil. The use of these raw materials not only resulted in the manufacture of a new material but also in an environmentally conscious and sustainable approach, following precepts such as the circular economy. The creation of these eco-efficient products is a significant contribution to the search for solutions that value environmental preservation and the optimization of natural resources.
A Novel Scale for Evaluating Digital Readiness toward Earthquakes: A Comprehensive Validity and Reliability Analysis
Authors: Sancar Nuriye , Cavus Nadire
Published: December 2023

New digital technology trends have the potential to mitigate the effects of earthquakes and improve response and recovery efforts such as earthquake prediction, emergency call-out, and earthquake location notification. Earthquake readiness makes it necessary to have a digitally prepared society. However, no scale has been found in the literature that can measure the digital attitudes and skills of individuals regarding earthquakes. For this reason, to fill this gap in the literature, this descriptive research study aimed to develop an original, valid, and reliable scale to determine the digital readiness of individuals toward earthquakes. Data were collected from 621 adult individuals with different socioeconomic characteristics using the convenience sampling method. An item pool was created through a comprehensive literature review, followed by a series of analyses encompassing content validity, construct validity, convergent validity, discriminant validity, criterion-related validity, reliability analysis, and item analysis in the course of the scale’s development process. For the digital readiness toward earthquakes, the Draft scale, with 21 items remaining, a KMO value (0.894), and a Bartlett sphericity test result of χ2 (91) = 2407.76 (p < 0.001) showed the suitability of the data for exploratory factor analysis (EFA). As a result of the EFA, 14 items were categorized into two dimensions based on whether their eigenvalues exceeded 1. The explained variance was 46.823% (eigenvalue = 6.555) in the first factor, while it was 12.832% (eigenvalue = 1.796) in the second factor, and the total variance was 59.655%. After analyzing the scale’s items within these dimensions, the first dimension was named “Technological Skills”, comprising eight items, and the second dimensions was named “Digital Attitudes”, consisting of six items. The factor loadings for these items ranged from 0.562 to 0.900. Confirmatory factor analysis (CFA) affirmed the factorial structure identified by the EFA. For the five-point Likert-type Digital Readiness toward Earthquakes scale, named the DRE scale, with 14 items after validity analyses, Cronbach’s alpha value was obtained as 0.910, demonstrating high internal consistency. Cronbach’s alpha values of the two factors were 0.910 for the “Technological Skills” dimension and 0.837 for the “Digital Attitudes” dimension. It was concluded that the developed scale is a valid and reliable measurement in evaluating the digital readiness of individuals toward earthquakes.
Grain Production in Turkey and Its Environmental Drivers Using ARDL in the Age of Climate Change
Authors: Gurbuz Ismail Bulent , Kadioglu Irfan
Published: December 2023

This study aims to evaluate the long-run and causality relationships between the annual grain production (kg per hectare) in Turkey, fertilizer used in agriculture, the number of tractors, agricultural greenhouse gas emissions, and grain production area from 1988 to 2018. The study’s data for the years 1988–2018 were taken from the World Bank and Turkish Statistical Institute (Turkstat) databases. The autoregressive distributed lag bounds (ARDL) test was applied to estimate the cointegration between the variables. The cointegration test results confirmed a long-run relationship between the variables. The short-run estimation revealed that the error correction coefficient was negative and statistically significant. The result obtained for the error correction term estimated that the deviations from the short-run equilibrium would be corrected, and the system would converge to the long-run equilibrium within 1.05 years. Further, the long-run estimation showed that all variables included in the model had a statistically significant effect on the dependent variable. While this relationship was negative for grain production amount and carbon emission, it was positive for fertilizer use and the number of tractors. The grain areas estimated as the dependent variable in the ARDL model were in a feedback relationship with the current production and number of tractors variables, while the fertilizer and carbon emission variables were in a unidirectional causality relationship towards the grain production area. There is a negative relationship between grain production (kg per hectare) and grain production areas (hectares). A 1% increase in grain production leads to a decrease of approximately 0.30% in grain production areas. Agricultural greenhouse gas emissions, another variable that stands out with its negative impact in ARDL long-run estimation results, indicate that product groups produced as an alternative to grain have a higher emission-generating power. The other long-run estimation results reveal that the tractor variable positively affects grain production areas.
Editorial for Mathematics Section
Authors: Vetro Calogero
Published: December 2023

Mathematics represents a wide area of knowledge and is an essential subject in many technologies and applications [...]
Synthetic Proteins in Dental Applications
Authors: Lopez-Ayuso Christian Andrea , Aranda-Herrera Benjamin , Guzman-Rocha Dulce , Chavez-Granados Patricia Alejandra , Garcia-Contreras Rene
Published: December 2023

Biotechnology and artificial intelligence have sparked a revolution in dentistry, with a focus on restoring natural tissue functions. This transformation has given rise to bioactive materials, inspired by biomimetics, aimed at replicating the processes found in nature. As synthetic biology advances, there is a heightened focus on signaling systems crucial for bio-based diagnostics and therapeutics. Dentistry now harnesses synthetic proteins for tissue regeneration and dental material enhancement. A current research priority is bacterial biofilm inhibition, vital for dental health. Given the role of Streptococcus mutans in dental caries, the development of synthetic antimicrobial peptides targeting this bacterium is underway. The balance of dental enamel between demineralization and remineralization impacts caries formation. Factors such as the presence of hydroxyapatite and salivary peptides influence enamel health. Recent studies have spotlighted salivary protein-inspired peptides for enhanced remineralization. In the realm of bone regeneration, synthetic proteins like bone morphogenetic proteins (BMP) have been spotlighted, earning FDA approval. Research is currently delving into peptides such as cementum protein 1 peptide (CEMP-1-p1) and parathyroid hormone variants like PTH (1-34), underscoring their potential in advancing dental and bone health.
Bacillus amyloliquefaciens B10 Alleviates the Immunosuppressive Effects of Deoxynivalenol and Porcine Circovirus Type 2 Infection
Authors: Xiao Huiping , Qin Zihui , Xu Baocai , Long Miao , Wu Qinghua , Guo Xinyi , Zhang Huayue , Li Zelin , Wu Wenda
Published: December 2023

As one of the most common mycotoxins, deoxynivalenol (DON) can contaminate a wide range of crops and foods. Porcine circovirus 2 (PCV2) is a kind of immunosuppressive virus, which can cause porcine circovirus associated disease (PCVD) in pig farms infected with PCV2. Pigs are extremely sensitive to DON, and PCV2-infected pig farms are often contaminated with DON. Our previous studies indicated that Bacillus amyloliquefaciens B10 (B10) has the potential to alleviate the toxicity of mycotoxins. The research was aimed at investigating the effects of Bacillus amyloliquefaciens B10 on the immunosuppressive effects caused by both DON and PCV2 infection. The results indicated that the expression of the PCV2 capsid protein CAP was significantly decreased after pretreatment with Bacillus amyloliquefaciens B10. Then, the effects of the Bacillus amyloliquefaciens B10 pretreatment on the type I interferon, antiviral protein and the antiviral signal pathway cGAS–STING was further investigated. The findings displayed that the expression of the type I interferon and antiviral protein were increased, while the IL-10 were decreased after pretreatment with Bacillus amyloliquefaciens B10. The inhibition of DON on the cGAS–STING signal pathway was relieved. Furthermore, it was found that this intervention effect was produced by inhibiting autophagy. In summary, Bacillus amyloliquefaciens B10 can mitigate the immunosuppressive effects of PCV2 and DON by inhibiting the production of autophagy.
Use of Capsid Integrity-qPCR for Detecting Viral Capsid Integrity in Wastewater
Authors: Kevill Jessica L. , Farkas Kata , Ridding Nicola , Woodhall Nicholas , Malham Shelagh K. , Jones Davey L.
Published: December 2023

Quantifying viruses in wastewater via RT-qPCR provides total genomic data but does not indicate the virus capsid integrity or the potential risk for human infection. Assessing virus capsid integrity in sewage is important for wastewater-based surveillance, since discharged effluent may pose a public health hazard. While integrity assays using cell cultures can provide this information, they require specialised laboratories and expertise. One solution to overcome this limitation is the use of photo-reactive monoazide dyes (e.g., propidium monoazide [PMAxx]) in a capsid integrity-RT-qPCR assay (ci-RT-qPCR). In this study, we tested the efficiency of PMAxx dye at 50 μM and 100 μM concentrations on live and heat-inactivated model viruses commonly detected in wastewater, including adenovirus (AdV), hepatitis A (HAV), influenza A virus (IAV), and norovirus GI (NoV GI). The 100 μM PMAxx dye concentration effectively differentiated live from heat-inactivated viruses for all targets in buffer solution. This method was then applied to wastewater samples (n = 19) for the detection of encapsulated AdV, enterovirus (EV), HAV, IAV, influenza B virus (IBV), NoV GI, NoV GII, and SARS-CoV-2. Samples were negative for AdV, HAV, IAV, and IBV but positive for EV, NoV GI, NoV GII, and SARS-CoV-2. In the PMAxx-treated samples, EV, NoV GI, and NoV GII showed −0.52–1.15, 0.9–1.51, and 0.31–1.69 log reductions in capsid integrity, indicating a high degree of potentially infectious virus in wastewater. In contrast, SARS-CoV-2 was only detected using RT-qPCR but not after PMAxx treatment, indicating the absence of encapsulated and potentially infectious virus. In conclusion, this study demonstrates the utility of PMAxx dyes to evaluate capsid integrity across a diverse range of viruses commonly monitored in wastewater.
Application of a SARS-CoV-2 Antigen Rapid Immunoassay Based on Active Microfluidic Technology in a Setting of Children and Young Adults
Authors: Leli Christian , Ferrara Lidia , Bottino Paolo , Bara Cristina , Megna Iacopo , Penpa Serena , Felici Enrico , Maconi Antonio , Rocchetti Andrea
Published: December 2023

To carry out effective and quick identification of SARS-CoV-2 from nasopharyngeal swabs and contain outbreaks, reliable and rapid tools are needed. Herein, we compared a rapid antigen test based on active microfluidic technology to an RT-qPCR assay in pediatric and young adult patients admitted to the Pediatric Emergency Unit of a Children’s Hospital. Nasopharyngeal swabs collected from patients with suspected COVID-19 disease and from those without COVID-19 related symptoms, but requiring hospitalization, were performed with both antigen test and RT-qPCR assays. We included 375 patients with a median age of 5 years in the study, with an estimated overall prevalence of 7.2%. Overall, we observed a specificity of 97.4% (95% CI: 94.9–98.7) and a sensitivity of 66.6% (95% CI: 46.0–82.7) with a positive likelihood ratio (LR+) of 25.8 (95% CI: 12.8–51.8). In the subgroup of symptomatic patients, the specificity and the sensitivity were 95.2% (95% CI: 89.4–98.0) and 80.0% (95% CI: 44.2–96.5) respectively; LR+ was 16.6 (95% CI: 7.19–38.6). In the asymptomatic subset, the performance showed a specificity of 98.7% (95% CI: 95.8–99.7), a sensitivity of 58.8% (95% CI: 33.5–80.6), and an LR+ of 43.7 (95% CI: 13.3–144.0). Compared to RT-qPCR, the new microfluidic-based antigen test showed higher specificity (>95%) in the pediatric population, thus representing a suitable point-of-care testing (POCT) in a clinical setting with low prevalence of COVID-19.