bluuebunny/crossref_metadata_2025 huggingface dataset:
The Nação Rules: A Comparative Analysis of the Bylaws of Western Sephardic Congregations in the Early Modern Atlantic
Authors: Vieira Carla
Published: November 2023

Religious persecution, segregation, and commercial networks triggered the diaspora of Iberian New Christians and Sephardic Jews throughout the Atlantic in the Early Modern period. Despite the geographical dispersion, the diverse host environments, and the complex religious experiences, the so-called Western Sephardic Diaspora was founded on the principles of unity and preservation, which were cemented by the notion of belonging to the Nação. Thus, as a cross-border community connected by common geographical origins, collective cultural identity, and a shared New Christian background, the concept of Nação was structural for the definition and evolution of the diasporic experience of Portuguese and Spanish exiles and their descendants from the sixteenth to the eighteenth centuries. This article overviews the most recent findings and perspectives on the concept of the Nação as a unifying element of the Western Sephardic Diaspora. Then, it problematizes the interpretation of this concept in different Sephardic communities established in Atlantic port cities. This problem is approached by analyzing these communities’ internal bylaws (ascamot) under the lens of two critical questions: (1) the concept of Nação as defining the borders of belonging to the community and (2) the unity and preservation of the Nação as essential drifts of the organization and management of the community. This analysis emphasizes the tension between the dynamism and particularities of each community and the conservatism of the idea of Nação, promoted by small social and economic elites that exercised increasing control over the communities and their interaction with the surrounding environments.
Deciphering Complex Morphology and Structural Connectivity of High-Magnitude Deep-Seated Landslides via Airborne Laser Scanning: A Case Study in the Vrancea Seismic Region, Romanian Carpathians
Authors: Micu Mihai , Vasile Mirela , Miron Florin , Onaca Alexandru , Sîrbu Flavius ,   Skyline Drones Team
Published: November 2023

In the Vrancea seismic region (Romanian Carpathians; the most important intermediate-depth seismic source of Europe), the morphology of the slopes is often marked by the existence of numerous high-magnitude, deep-seated active, dormant or relict landslides, which are the subjects of many cases of functional and structural connectivity. Due to the compact and extensive (coniferous and broad leaved) forest coverage and because of the lack of publicly available regional high-resolution DEMs, it is usually difficult to fully understand the morphogenetic framework of such large, deep-seated landslides in order to assess their frequency–magnitude relationship, a key issue in hazard quantification. However, the high impact of such landslides on river networks requires an in-depth understanding of the multi-hazard framework, as cascading effects are likely to affect the presently growing human activities developing along the valleys. Within a case study represented by a 2.5 km long deep-seated landslide, that caused a 500 m lateral occlusion of Buzău River, we used integrated remote sensing technologies (UAV laser scanning) and in situ (geomorphic mapping and ERT investigations) techniques, which allowed us to better understand the structural connectivity which conditions the landslide hazard in such complex morphogenetic conditions, outlining the present potential of the regional seismo-climatic context to trigger potential high-magnitude chain effects.
LAGRS-Soil: A Full-Polarization GNSS-Reflectometry Model for Bare Soil Applications in FY-3E GNOS-R Payload
Authors: Wu Xuerui , Ouyang Xinqiu , Xia Junming , Yan Zhe , Wang Fang
Published: November 2023

The Land Surface GNSS Reflection Simulator (LAGRS)-Soil model represents a significant advancement in soil moisture detection with the aid of Global Navigation Satellite System (GNSS) Occultation Sounder-Reflectometry (GNOS-R) technology, which is one payload of the Fengyun-3E (FY-3E) satellite that was launched on 5 July 2021. To fully exploit the properties of noncoherent scattering, the LAGRS-Soil model has the capability to calculate DDM information for different observational geometries, which relies on the random surface scattering models employed in LAGRS-Soil. This will provide a comprehensive understanding of soil moisture dynamics across diverse terrains and environments. One of the most notable features of LAGRS-Soil is its ability to obtain DDMs for full polarizations, which enhances soil moisture retrievals compared to current methods that only utilize the commonly used LR polarization (left-hand circular polarization received and right-hand circular polarization transmitted). Meanwhile, the model can also capture frozen soil DDMs which holds immense potential for near-surface Freezing/Thawing (F/T) detection, opening up new research and application opportunities in cold climate regions. LAGRS-Soil is built on microwave scattering models, making it a robust and efficient theoretical model for the FY-3E GNOS-R payload. This model can support ongoing soil moisture retrieval efforts by combining physical models with investigations of diffuse scattering and polarization capabilities for soil moisture detection.
An Evaluation of CRA40 and ERA5 Precipitation Products over China
Authors: Zhou Zelan , Chen Sheng , Li Zhi , Luo Yongming
Published: November 2023

Precipitation datasets derived from reanalysis products play a crucial role in weather forecasting and hydrological applications. This paper aims to evaluate the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The evaluation is based on continuous and categorical statistical indicators with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations. The findings of this study can be summarized as follows: (1) CRA40 demonstrates a clear superiority over ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 exhibits better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). (2) Both reanalyzed precipitation products generally exhibit an overestimation of precipitation in mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., QZ, XJ), while wet regions (e.g., CJ, HN) demonstrate relatively less overestimation. (3) ERA5 shows better performance in the detection of daily precipitation. Neither CRA40 nor ERA5 can capture heavy precipitation events well. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China.
Domain-Invariant Feature and Generative Adversarial Network Boundary Enhancement for Multi-Source Unsupervised Hyperspectral Image Classification
Authors: Xu Tuo , Han Bing , Li Jie , Du Yuefan
Published: November 2023

Hyperspectral image (HIS) classification, a crucial component of remote sensing technology, is currently challenged by edge ambiguity and the complexities of multi-source domain data. An innovative multi-source unsupervised domain adaptive algorithm (MUDA) structure is proposed in this work to overcome these issues. Our approach incorporates a domain-invariant feature unfolding algorithm, which employs the Fourier transform and Maximum Mean Discrepancy (MMD) distance to maximize invariant feature dispersion. Furthermore, the proposed approach efficiently extracts intraclass and interclass invariant features. Additionally, a boundary-constrained adversarial network generates synthetic samples, reinforcing the source domain feature space boundary and enabling accurate target domain classification during the transfer process. Furthermore, comparative experiments on public benchmark datasets demonstrate the superior performance of our proposed methodology over existing techniques, offering an effective strategy for hyperspectral MUDA.
Comparative Evaluation of State-of-the-Art Semantic Segmentation Networks for Long-Term Landslide Map Production
Authors: Hu Zekun , Yi Bangjin , Li Hui , Zhong Cheng , Gao Peng , Chen Jiaoqi , Yao Qianxiang , Guo Haojia
Published: November 2023

The production of long-term landslide maps (LAM) holds crucial importance in estimating landslide activity, vegetation disturbance, and regional stability. However, the availability of LAMs remains limited in many regions, despite the application of various machine-learning methods, deep-learning (DL) models, and ensemble strategies in landslide detection. While transfer learning is considered an effective approach to tackle this challenge, there has been limited exploration and comparison of the temporal transferability of state-of-the-art deep-learning models in the context of LAM production, leaving a significant gap in the research. In this study, an extensive series of tests was conducted to evaluate the temporal transferability of typical semantic segmentation models, specifically U-Net, U-Net 3+, and TransU-Net, using a 10-year landslide-inventory dataset located near the epicenter of the Wenchuan earthquake. The experiment results disclose the feasibility and limitations of implementing transfer-learning methods for LAM production, particularly when leveraging the power of U-Net 3+. Furthermore, following an assessment of the effects of varying data volumes, patch sizes, and time intervals, this study recommends appropriate settings for LAM production, emphasizing the balance between efficiency and production performance. The findings from this study can serve as a valuable reference for devising an efficient and reliable strategy for large-scale LAM production in landslide-prone regions.
RST: Rough Set Transformer for Point Cloud Learning
Authors: Sun Xinwei , Zeng Kai
Published: November 2023

Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with applications encompassing object classification, part segmentation, and point cloud recognition. Leveraging the global learning capacity of dot product attention, transformers have recently exhibited outstanding performance in point cloud learning tasks. Nevertheless, existing transformer models inadequately address the challenges posed by uncertainty features in point clouds, which can introduce errors in the dot product attention mechanism. In response to this, our study introduces a novel global guidance approach to tolerate uncertainty and provide a more reliable guidance. We redefine the granulation and lower-approximation operators based on neighborhood rough set theory. Furthermore, we introduce a rough set-based attention mechanism tailored for point cloud data and present the rough set transformer (RST) network. Our approach utilizes granulation concepts derived from token clusters, enabling us to explore relationships between concepts from an approximation perspective, rather than relying on specific dot product functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method. Our method establishes concepts based on granulation generated from clusters of tokens. Subsequently, relationships between concepts can be explored from an approximation perspective, instead of relying on specific dot product or addition functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method.
Simulation of Electronic Waste Reverse Chains for the Sao Paulo Circular Economy: An Artificial Intelligence-Based Approach for Economic and Environmental Optimizations
Authors: Oliveira Neto Geraldo Cardoso de , de Araujo Sidnei Alves , Gomes Robson Aparecido , Alliprandini Dario Henrique , Flausino Fabio Richard , Amorim Marlene
Published: November 2023

The objective of this study was to apply simulation and genetic algorithms for the economic and environmental optimization of the reverse network (manufacturers, waste managers, and recyclers in Sao Paulo, Brazil) of waste from electrical and electronic equipment (WEEE) to promote the circular economy. For the economic evaluation, the reduction in fuel, drivers, insurance, depreciation, maintenance, and charges was considered. For the environmental evaluation, the impact of abiotic, biotic, water, land, air, and greenhouse gases was measured. It was concluded that the optimized structure of the WEEE reverse chains for Sao Paulo, Brazil provided a reduction in the number of collections, thus making the most of cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Therefore, the proposed approach is replicable in organizational practice, which is mainly required to meet the 2030 agenda of reducing the carbon footprint generated by transport in large cities. Thus, this study can guide companies in structuring the reverse WEEE chains in Sao Paulo, Brazil, and other states and countries for economic and environmental optimization, which is an aspect of great relevance considering the exponential generation of WEEE.
Domain-Specific Processing Stage for Estimating Single-Trail Evoked Potential Improves CNN Performance in Detecting Error Potential
Authors: Farabbi Andrea , Mainardi Luca
Published: November 2023

We present a novel architecture designed to enhance the detection of Error Potential (ErrP) signals during ErrP stimulation tasks. In the context of predicting ErrP presence, conventional Convolutional Neural Networks (CNNs) typically accept a raw EEG signal as input, encompassing both the information associated with the evoked potential and the background activity, which can potentially diminish predictive accuracy. Our approach involves advanced Single-Trial (ST) ErrP enhancement techniques for processing raw EEG signals in the initial stage, followed by CNNs for discerning between ErrP and NonErrP segments in the second stage. We tested different combinations of methods and CNNs. As far as ST ErrP estimation is concerned, we examined various methods encompassing subspace regularization techniques, Continuous Wavelet Transform, and ARX models. For the classification stage, we evaluated the performance of EEGNet, CNN, and a Siamese Neural Network. A comparative analysis against the method of directly applying CNNs to raw EEG signals revealed the advantages of our architecture. Leveraging subspace regularization yielded the best improvement in classification metrics, at up to 14% in balanced accuracy and 13.4% in F1-score.
Baseline-Free Damage Imaging of Composite Lap Joint via Parallel Array of Piezoelectric Sensors
Authors: Barzegar Mohsen , Ribeiro Artur L. , Pasadas Dario J. , Asokkumar Aadhik , Raišutis Renaldas , Ramos Helena G.
Published: November 2023

This paper presents a baseline-free damage imaging technique using a parallel array of piezoelectric sensors and a control board that facilitates custom combinations of sensor selection. This technique incorporates an imaging algorithm that uses parallel beams for generation and reception of ultrasonic guided waves in a pitch–catch configuration. A baseline-free reconstruction algorithm for probabilistic inspection of defects (RAPID) algorithm is adopted. The proposed RAPID method replaces the conventional approach of using signal difference coefficients with the maximum signal envelope as a damage index, ensuring independence from baseline data. Additionally, conversely to the conventional RAPID algorithm which uses all possible sensor combinations, an innovative selection of combinations is proposed to mitigate attenuation effects. The proposed method is designed for the inspection of lap joints. Experimental measurements were carried out on a composite lap joint, which featured two dissimilar-sized disbonds positioned at the lap joint’s borderline. A 2D correlation coefficient was used to quantitatively determine the similarity between the obtained images and a reference image with correct defect shapes and locations. The results demonstrate the effectiveness of the proposed damage imaging method in detecting both defects. Additionally, parametric studies were conducted to illustrate how various parameters influence the accuracy of the obtained imaging results.
Automated Cow Body Condition Scoring Using Multiple 3D Cameras and Convolutional Neural Networks
Authors: Summerfield Gary I. , De Freitas Allan , van Marle-Koster Este , Myburgh Herman C.
Published: November 2023

Body condition scoring is an objective scoring method used to evaluate the health of a cow by determining the amount of subcutaneous fat in a cow. Automated body condition scoring is becoming vital to large commercial dairy farms as it helps farmers score their cows more often and more consistently compared to manual scoring. A common approach to automated body condition scoring is to utilise a CNN-based model trained with data from a depth camera. The approaches presented in this paper make use of three depth cameras placed at different positions near the rear of a cow to train three independent CNNs. Ensemble modelling is used to combine the estimations of the three individual CNN models. The paper aims to test the performance impact of using ensemble modelling with the data from three separate depth cameras. The paper also looks at which of these three cameras and combinations thereof provide a good balance between computational cost and performance. The results of this study show that utilising the data from three depth cameras to train three separate models merged through ensemble modelling yields significantly improved automated body condition scoring accuracy compared to a single-depth camera and CNN model approach. This paper also explored the real-world performance of these models on embedded platforms by comparing the computational cost to the performance of the various models.
Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform
Authors: Xu Xichen , Tang Qunfeng , Chen Zhencheng
Published: November 2023

We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was −0.04 L/min with a −1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between −20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.
Local Weather Station Design and Development for Cost-Effective Environmental Monitoring and Real-Time Data Sharing
Authors: Rivera Antonio , Ponce Pedro , Mata Omar , Molina Arturo , Meier Alan
Published: November 2023

Current weather monitoring systems often remain out of reach for small-scale users and local communities due to their high costs and complexity. This paper addresses this significant issue by introducing a cost-effective, easy-to-use local weather station. Utilizing low-cost sensors, this weather station is a pivotal tool in making environmental monitoring more accessible and user-friendly, particularly for those with limited resources. It offers efficient in-site measurements of various environmental parameters, such as temperature, relative humidity, atmospheric pressure, carbon dioxide concentration, and particulate matter, including PM 1, PM 2.5, and PM 10. The findings demonstrate the station’s capability to monitor these variables remotely and provide forecasts with a high degree of accuracy, displaying an error margin of just 0.67%. Furthermore, the station’s use of the Autoregressive Integrated Moving Average (ARIMA) model enables short-term, reliable forecasts crucial for applications in agriculture, transportation, and air quality monitoring. Furthermore, the weather station’s open-source nature significantly enhances environmental monitoring accessibility for smaller users and encourages broader public data sharing. With this approach, crucial in addressing climate change challenges, the station empowers communities to make informed decisions based on real-time data. In designing and developing this low-cost, efficient monitoring system, this work provides a valuable blueprint for future advancements in environmental technologies, emphasizing sustainability. The proposed automatic weather station not only offers an economical solution for environmental monitoring but also features a user-friendly interface for seamless data communication between the sensor platform and end users. This system ensures the transmission of data through various web-based platforms, catering to users with diverse technical backgrounds. Furthermore, by leveraging historical data through the ARIMA model, the station enhances its utility in providing short-term forecasts and supporting critical decision-making processes across different sectors.
Person-Specific Template Matching Using a Dynamic Time Warping Step-Count Algorithm for Multiple Walking Activities
Authors: Filippou Valeria , Backhouse Michael R. , Redmond Anthony C. , Wong David C.
Published: November 2023

This study aimed to develop and evaluate a new step-count algorithm, StepMatchDTWBA, for the accurate measurement of physical activity using wearable devices in both healthy and pathological populations. We conducted a study with 30 healthy volunteers wearing a wrist-worn MOX accelerometer (Maastricht Instruments, NL). The StepMatchDTWBA algorithm used dynamic time warping (DTW) barycentre averaging to create personalised templates for representative steps, accounting for individual walking variations. DTW was then used to measure the similarity between the template and accelerometer epoch. The StepMatchDTWBA algorithm had an average root-mean-square error of 2 steps for healthy gaits and 12 steps for simulated pathological gaits over a distance of about 10 m (GAITRite walkway) and one flight of stairs. It outperformed benchmark algorithms for the simulated pathological population, showcasing the potential for improved accuracy in personalised step counting for pathological populations. The StepMatchDTWBA algorithm represents a significant advancement in accurate step counting for both healthy and pathological populations. This development holds promise for creating more precise and personalised activity monitoring systems, benefiting various health and wellness applications.
A Novel Machine-Learning Framework Based on a Hierarchy of Dispute Models for the Identification of Fish Species Using Multi-Mode Spectroscopy
Authors: Sueker Mitchell , Daghighi Amirreza , Akhbardeh Alireza , MacKinnon Nicholas , Bearman Gregory , Baek Insuck , Hwang Chansong , Qin Jianwei , Tabb Amanda M. , Roungchun Jiahleen B. , Hellberg Rosalee S. , Vasefi Fartash , Kim Moon , Tavakolian Kouhyar , Kashani Zadeh Hossein
Published: November 2023

Seafood mislabeling rates of approximately 20% have been reported globally. Traditional methods for fish species identification, such as DNA analysis and polymerase chain reaction (PCR), are expensive and time-consuming, and require skilled technicians and specialized equipment. The combination of spectroscopy and machine learning presents a promising approach to overcome these challenges. In our study, we took a comprehensive approach by considering a total of 43 different fish species and employing three modes of spectroscopy: fluorescence (Fluor), and reflectance in the visible near-infrared (VNIR) and short-wave near-infrared (SWIR). To achieve higher accuracies, we developed a novel machine-learning framework, where groups of similar fish types were identified and specialized classifiers were trained for each group. The incorporation of global (single artificial intelligence for all species) and dispute classification models created a hierarchical decision process, yielding higher performances. For Fluor, VNIR, and SWIR, accuracies increased from 80%, 75%, and 49% to 83%, 81%, and 58%, respectively. Furthermore, certain species witnessed remarkable performance enhancements of up to 40% in single-mode identification. The fusion of all three spectroscopic modes further boosted the performance of the best single mode, averaged over all species, by 9%. Fish species mislabeling not only poses health-related risks due to contaminants, toxins, and allergens that could be life-threatening, but also gives rise to economic and environmental hazards and loss of nutritional benefits. Our proposed method can detect fish fraud as a real-time alternative to DNA barcoding and other standard methods. The hierarchical system of dispute models proposed in this work is a novel machine-learning tool not limited to this application, and can improve accuracy in any classification problem which contains a large number of classes.
Research on Similarity Simulation Experiment of Mine Pressure Appearance in Surface Gully Working Face Based on BOTDA
Authors: Zhang Dingding , Huang Zhiming , Ma Zhe , Yang Jianfeng , Chai Jing
Published: November 2023

In order to study the mountain deflection characteristics and the pressure law of the working face after the mining of a shallow coal seam under the valley terrain, a geometric size of 5.0 × 0.2 × 1.33 m is used in the physical similarity model. Brillouin optical time domain analysis (BOTDA) technology is applied to a similar physical model experiment to monitor the internal strain of the overlying rock. In this paper, the strain law of the horizontal optical fiber at different stages of the instability of the mountain structure is analyzed. Combined with the measurement of the strain field on the surface of the model via digital image correlation (DIC) technology, the optical fiber strain characteristics of the precursor of mountain instability are given. The optical fiber characterization method of working face pressure is proposed, and the working face pressures at different mining stages in gully terrain are characterized. Finally, the relationship between the deflection instability of the mountain and the strong ground pressure on the working face is discussed. The sudden increase in the strain peak point of the horizontally distributed optical fiber strain curve can be used to distinguish the strong ground pressure. At the same time, this conclusion is verified by comparing the measured underground ground pressure values. The research results can promote the application of optical fiber sensing technology in the field of mine engineering.
Computing Offloading Based on TD3 Algorithm in Cache-Assisted Vehicular NOMA–MEC Networks
Authors: Zhou Tianqing , Xu Ming , Qin Dong , Nie Xuefang , Li Xuan , Li Chunguo
Published: November 2023

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm.
High Dynamic Pixel Structure Based on an Adaptive Integrating Capacitor
Authors: Liu Suiyang , Guo Zhongjie , Xu Ruiming , Yu Ningmei
Published: November 2023

Infrared image sensing technology has received widespread attention due to its advantages of not being affected by the environment, good target recognition, and high anti-interference ability. However, with the improvement of the integration of the infrared focal plane, the dynamic range of the photoelectric system is difficult to improve, that is, the restrictive trade-off between noise and full well capacity is particularly prominent. Since the capacitance of the inversion MOS capacitor changes with the gate–source voltage adaptively, the inversion MOS capacitor is used as the capacitor in the infrared pixel circuit, which can solve the contradiction between noise in low light and full well capacity in high light. To this end, a highly dynamic pixel structure based on adaptive capacitance is proposed, so that the capacitance of the infrared image sensor can automatically change from 6.5 fF to 37.5 fF as the light intensity increases. And based on 55 nm CMOS process technology, the performance parameters of an infrared image sensor with a 12,288 × 12,288 pixel array are studied. The research results show that a small-size pixel of 5.5 µm × 5.5 µm has a large full well capacity of 1.31 Me− and a variable conversion gain, with a noise of less than 0.43 e− and a dynamic range of more than 130 dB.
Detection of Broken Rotor Bars in Cage Induction Motors Using Machine Learning Methods
Authors: Chisedzi Lloyd Prosper , Muteba Mbika
Published: November 2023

In this paper, the performance of machine learning methods for squirrel cage induction motor broken rotor bar (BRB) fault detection is evaluated. Decision tree classification (DTC), artificial neural network (ANN), and deep learning (DL) methods are developed, applied, and studied to compare their performance in detecting broken rotor bar faults in squirrel cage induction motors. The training data were collected through experimental measurements. The BRB fault features were extracted from measured line-current signatures through a transformation from the time domain to the frequency domain using discrete Fourier Transform (DFT) of the frequency spectrum of the current signal. Eighty percent of the data were used for training the models, and twenty percent were used for testing. A confusion matrix was used to validate the models’ performance using accuracy, precision, recall, and f1-scores. The results evidence that the DTC is less load-dependent, and it has better accuracy and precision for both unloaded and loaded squirrel cage induction motors when compared with the DL and ANN methods. The DTC method achieved higher accuracy in the detection of the magnitudes of the twice-frequency sideband components induced in stator currents by BRB faults when compared with the DL and ANN methods. Although the detection accuracy and precision are higher for the loaded motor than the unloaded motor, the DTC method managed to also exhibit a high accuracy for the unloaded current when compared with the DL and ANN methods. The DTC is, therefore, a suitable candidate to detect broken rotor bar faults on trained data for lightly or thoroughly loaded squirrel cage induction motors using the characteristics of the measured line-current signature.
Does the Installation or the Improvement of Existing Outdoor Parks Increase Physical Activity Levels? A Systematic Review
Authors: Peralta Miguel , Viscioni Gianluca , Melo Xavier , Gouveia Élvio R. , Griesser Thorsten , Blocher Alexander , Bertollo Maurizio , Di Blasio Andrea , Marques Adilson
Published: November 2023

Investment in outdoor parks is proposed as a promising large-scale strategy to promote physical activity (PA). This study aimed to systematically review the impact of park renovations or installing new ones in increasing PA. Searches were conducted using predefined terms in three databases (PubMed, Scopus, and Web of Science) in March 2022. Studies examining the effectiveness of park renovations or developing new ones in increasing PA and having control or comparison were eligible for inclusion. Study quality was assessed using the Quality Assessment Tool for Quantitative Studies. Data were extracted from the included records using a predefined extraction table. The database search yielded 959 records, and 26 were included. For park renovations (n = 17), 11 (65%) studies presented findings supporting a positive effect on PA. The other six (35%) studies found no PA benefits when compared to control or pre-renovations/improvement levels. Regarding new installations (n = 9), five (56%) studies presented improvements in PA, and four (44%) did not. A promising positive impact of park renovations and new installations on park use and PA was observed. The review findings reflect the need to understand the context, daily routines, and interests of the surrounding population before renovating or installing new outdoor parks.
On the Partition Temperature of Massless Particles in High-Energy Collisions
Authors: Qian Wei-Liang , Lin Kai , Yue Rui-Hong , Hama Yogiro , Kodama Takeshi
Published: November 2023

Although partition temperature derived using the Darwin–Fowler method is exact for simple scenarios, the derivation for complex systems might reside in specific approximations whose viability is not ensured if the thermodynamic limit is not attained. This work elaborates on a related problem relevant to relativistic high-energy collisions. On the one hand, it is simple enough that closed-form expressions can be obtained precisely for the one-particle distribution function. On the other hand, the resulting expression is not an exponential form, and therefore, it is not straightforward that the notion of partition function could be implied. Specifically, we derive the one-particle distribution function for massless particles where the phase-space integration is performed exactly for the underlying canonical ensemble consisting of a given number of particles. We discuss the viability of the partition temperature in this case. Possible implications of the obtained results regarding the observed Tsallis distribution in transverse momentum spectra in high-energy collisions are also addressed.
Biodetoxification Using Intravenous Lipid Emulsion, a Rescue Therapy in Life-Threatening Quetiapine and Venlafaxine Poisoning: A Case Report
Authors: Cobilinschi Cristian , Mirea Liliana , Andrei Cosmin-Andrei , Ungureanu Raluca , Cotae Ana-Maria , Avram Oana , Isac Sebastian , Grințescu Ioana Marina , Țincu Radu
Published: November 2023

The administration of intravenous lipid emulsion (ILE) is a proven antidote used to reverse local anesthetic-related systemic toxicity. Although the capacity of ILE to generate blood tissue partitioning of lipophilic drugs has been previously demonstrated, a clear recommendation for its use as an antidote for other lipophilic drugs is still under debate. Venlafaxine (an antidepressant acting as a serotonin–norepinephrine reuptake inhibitor (SNRI)) and quetiapine (a second-generation atypical antipsychotic) are widely used in the treatment of psychotic disorders. Both are lipophilic drugs known to induce cardiotoxicity and central nervous depression. We report the case of a 33-year-old man with a medical history of schizoaffective disorder who was admitted to the emergency department (ED) after having been found unconscious due to a voluntary ingestion of 12 g of quetiapine and 4.5 g of venlafaxine. Initial assessment revealed a cardiorespiratory stable patient but unresponsive with a GCS of 4 (M2 E1 V1). In the ED, he was intubated, and gastric lavage was performed. Immediately after the admission to the intensive care unit (ICU), his condition quickly deteriorated, developing cardiovascular collapse refractory to crystalloids and vasopressor infusion. Junctional bradycardia occurred, followed by spontaneous conversion to sinus rhythm. Subsequently, frequent ventricular extrasystoles, as well as patterns of bigeminy, trigeminy, and even episodes of non-sustained ventricular tachycardia, occurred. Additionally, generalized tonic–clonic seizures were observed. Alongside supportive therapy, antiarrhythmic and anticonvulsant therapy, intravenous lipid emulsion bolus, and continuous infusion were administered. His condition progressively improved over the following hours, and 24 h later, he was tapered off the vasopressor. On day 2, the patient repeated the cardiovascular collapse and a second dose of ILE was administered. Over the next few days, the patient’s clinical condition improved, and he was successfully weaned off ventilator and vasopressor support. ILE has the potential to become a form of rescue therapy in cases of severe lipophilic drug poisoning and should be considered a viable treatment for severe cardiovascular instability that is refractory to supportive therapy.
Effects of the Toxic Non-Protein Amino Acid β-Methylamino-L-Alanine (BMAA) on Intracellular Amino Acid Levels in Neuroblastoma Cells
Authors: Violi Jake P. , Pu Lisa , Pravadali-Cekic Sercan , Bishop David P. , Phillips Connor R. , Rodgers Kenneth J.
Published: November 2023

The cyanobacterial non-protein amino acid (AA) β-Methylamino-L-alanine (BMAA) is considered to be a neurotoxin. BMAA caused histopathological changes in brains and spinal cords of primates consistent with some of those seen in early motor neuron disease; however, supplementation with L-serine protected against some of those changes. We examined the impact of BMAA on AA concentrations in human neuroblastoma cells in vitro. Cells were treated with 1000 µM BMAA and intracellular free AA concentrations in treated and control cells were compared at six time-points over a 48 h culture period. BMAA had a profound effect on intracellular AA levels at specific time points but in most cases, AA homeostasis was re-established in the cell. The most heavily impacted amino acid was serine which was depleted in BMAA-treated cells from 9 h onwards. Correction of serine depletion could be a factor in the observation that supplementation with L-serine protects against BMAA toxicity in vitro and in vivo. AAs that could potentially be involved in protection against BMAA-induced oxidation such as histidine, tyrosine, and phenylalanine were depleted in cells at later time points.
Phosphorylation of Human Polyomavirus Large and Small T Antigens: An Ignored Research Field
Authors: Moens Ugo , Passerini Sara , Falquet Mar , Sveinbjørnsson Baldur , Pietropaolo Valeria
Published: November 2023

Protein phosphorylation and dephosphorylation are the most common post-translational modifications mediated by protein kinases and protein phosphatases, respectively. These reversible processes can modulate the function of the target protein, such as its activity, subcellular localization, stability, and interaction with other proteins. Phosphorylation of viral proteins plays an important role in the life cycle of a virus. In this review, we highlight biological implications of the phosphorylation of the monkey polyomavirus SV40 large T and small t antigens, summarize our current knowledge of the phosphorylation of these proteins of human polyomaviruses, and conclude with gaps in the knowledge and a proposal for future research directions.
Osteopontin Concentration in Prostates Fractions: A Novel Marker of Sperm Quality in Dogs
Authors: Tekin Koray , Kurtdede Efe , Salmanoğlu Berrin , Uysal Ongun , Stelletta Calogero
Published: November 2023

This study aimed to investigate the relationship between the sperm quality and the osteopontin (OPN) concentration in the prostates of Malakli shepherd dogs. Ejaculates were collected once by digital manipulation from 39 male dogs aged between 2 and 4 years and older. The first and third fractions of the ejaculate were centrifuged at 5000× g for 30 min, and supernatants were stored at −80 °C for further analysis of OPN using a double-antibody sandwich method (SEA899CA, Cloude-Clone Corp, Houston, TX, USA). Meanwhile, the second fractions were evaluated for sperm motility, concentration, viability, and rate of abnormal spermatozoa (head, acrosome, midpiece and tail abnormalities). The average concentration of OPN was 8.7 ± 5.2 ng/mL, and it differed significantly between the 1st 10.4 ± 5.3 ng/mL and 3rd 7.4 ± 5 ng/mL fractions. According to ROC (receiver operating characteristic curve) analysis, the OPN concentration had a better diagnostic ability for sperm motility (p < 0.001) than for the rate of abnormal spermatozoa (p < 0.05). Additionally, the OPN concentration was negatively correlated with poor sperm morphology and motility. In conclusion, the OPN concentration in prostate-derived secretions may be a possible marker of sperm quality in dogs. Further research could explore the involvement of OPN in sperm motility during cryopreservation and in vivo fertility.
In Vitro and In Silico Evaluations of the Antileishmanial Activities of New Benzimidazole-Triazole Derivatives
Authors: Eser Mustafa , Çavuş İbrahim
Published: November 2023

Benzimidazole and triazole rings are important pharmacophores, known to exhibit various pharmacological activities in drug discovery. In this study, it was purposed to synthesize new benzimidazole-triazole derivatives and evaluate their antileishmanial activities. The targeted compounds (5a–5h) were obtained after five chemical reaction steps. The structures of the compounds were confirmed by spectral data. The possible in vitro antileishmanial activities of the synthesized compounds were evaluated against the Leishmania tropica strain. Further, molecular docking and dynamics were performed to identify the probable mechanism of activity of the test compounds. The findings revealed that compounds 5a, 5d, 5e, 5f, and 5h inhibited the growth of Leishmania tropica to various extents and had significant anti-leishmanial activities, even if some orders were higher than the reference drug Amphotericin B. On the other hand, compounds 5b, 5c, and 5g were found to be ineffective. Additionally, the results of in silico studies have presented the existence of some interactions between the compounds and the active site of sterol 14-alpha-demethylase, a biosynthetic enzyme that plays a critical role in the growth of the parasite. Therefore, it can be suggested that if the results obtained from this study are confirmed with in vivo findings, it may be possible to obtain some new anti-leishmanial drug candidates.
Optimal Placement and Active Control Methods for Integrating Smart Material in Dynamic Suppression Structures
Authors: Moutsopoulou Amalia , Stavroulakis Georgios E. , Petousis Markos , Pouliezos Anastasios , Vidakis Nectarios
Published: November 2023

To simulate a lightweight structure with integrated actuators and sensors, two-dimensional finite elements are utilized. The study looks at the optimal location and active vibration control for a piezoelectric smart flexible structure. Intelligent applications are commonly used in engineering applications. In computational mechanics, selecting the ideal position for actuators to suppress oscillations is crucial. The structure oscillates due to dynamic disturbance, and active control is used to try to reduce the oscillation. Utilizing an LQR and Hinfinity controller, optimization is carried out to determine the best controller weights, which will dampen the oscillation. Challenging issues arise in the design of control techniques for piezoelectric smart structures. Piezoelectric materials have been investigated for use in distributed parameter systems (for example airplane wings, intelligent bridges, etc.) to provide active control efficiently and affordably. Still, no full suppression of the oscillation with this approach has been achieved so far. The controller’s order is then decreased using optimization techniques. Piezoelectric actuators are positioned optimally according to an enhanced optimization method. The outcomes demonstrate that the actuator optimization strategies used in the piezoelectric smart single flexible manipulator system have increased observability in addition to good vibration suppression results.
Impact of Nano–Sized Polyethylene Terephthalate on Microalgal–Bacterial Granular Sludge in Non–Aerated Wastewater Treatment
Authors: Du Chao , Xiong Wenxuan , Zhu Guangya , Ji Bin
Published: November 2023

The widely used plastics in our daily lives have resulted in ubiquitous microplastics and nanoplastics in wastewater, such as polyethylene terephthalate (PET). As an emerging green process for wastewater treatment and resource recovery, microalgal–bacterial granular sludge (MBGS) aligns with the concept of the circular economy. However, it is unclear whether the tiny PET can affect the MBGS process. Thus, this study investigated the impact of nano–sized PET (nPET) on the MBGS process. The results showed that 10 to 30 mg/L nPET had no obvious impact on pollutant removal as compared with the control group. However, the performance of the MBGS with the addition of 50 mg/L nPET became worse after 15 days. Scanning electron microscopy revealed that the MBGS adsorbed nPET by generating extracellular polymeric substances. Further microbial analyses showed that the algal abundance in prokaryotes slowly declined with increasing concentrations of nPET, while the reduced energy storage and electron transfer in eukaryotes might lead to an inferior performance at 50 mg/L nPET. Overall, the MBGS was demonstrated to exhibit good adaptability to nPET–containing wastewater, which showed the potential to be applied for the treatment of municipal wastewater containing nanoplastics.