bluuebunny/crossref_metadata_2025 huggingface dataset:
The Interaction of Dietary Pectin, Inulin, and Psyllium with Copper Nanoparticle Induced Changes to the Cardiovascular System
Authors: Majewski Michał , Gromadziński Leszek , Cholewińska Ewelina , Ognik Katarzyna , Fotschki Bartosz , Juśkiewicz Jerzy
Published: August 2023

We aimed to analyze how supplementation with a standard (recommended, 6.5 mg/kg) or enhanced (two-times higher, 13 mg/kg) dose of copper (Cu), in the form of nanoparticles (NPs) along with dietary intervention via the implementation of diverse types of fiber, affects the cardiovascular system in rats. Nine-week-old male Wistar Han rats (n/group = 10) received, for an additional 6 weeks, a controlled diet with cellulose as dietary fiber and ionic Cu (in the form of carbonate salt). The experimental groups received cellulose, pectin, inulin, and psyllium as dietary fiber, together with CuNPs (6.5 or 13 mg/kg diet). After the experimental feeding, samples of blood, hearts, and thoracic arteries were collected for further analysis. Compared to pectin, and under a standard dose of CuNPs, inulin and psyllium beneficially increased the antioxidant capacity of lipid- and water-soluble compounds in the blood, and decreased heart malondialdehyde. Moreover, pectin decreased heart catalase (CAT) and cyclooxygenase (COX)-2 in the aortic rings compared to inulin and psyllium under standard and enhanced doses of copper. When the dose of CuNPs was enhanced, inulin and psyllium potentiated vasodilation to acetylcholine by up-regulation of COX-2-derived vasodilator prostanoids compared to both cellulose and pectin, and this was modulated with selective inducible nitric oxide synthase (iNOS) inhibitor for psyllium only. Moreover, inulin decreased heart CAT compared to psyllium. Our results suggest that supplementation with dietary fiber may protect the vascular system against potentially harmful metal NPs by modulating the antioxidant mechanisms.
A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons
Authors: Gui Sibo , Shi Meng , Li Zhaolong , Wu Haitao , Ren Quansheng , Zhao Jianye
Published: August 2023

We apply the Empirical Mode Decomposition (EMD) algorithm and the Time Convolutional Network (TCN) structure, predicated on Convolutional Neural Networks, to successfully enable feature extraction within high-precision optical time-frequency signals, and provide effective identification and alerts for abnormal link states. Experimental validation confirms that the proposed method not only delivers an efficacy on par with traditional manual techniques, but also excels in swiftly identifying anomalies that typically elude conventional approaches. This investigation furnishes novel theoretical backing and forecasting tools for high-precision optical transmission.
Effects of Topography on Vegetation Recovery after Shallow Landslides in the Obara and Shobara Districts, Japan
Authors: Zhong Chenxi , Oguchi Takashi , Lai Roxanne
Published: August 2023

Intense rainfall-induced shallow landslides can have severe consequences, including soil erosion and vegetation loss, making in-depth research essential for disaster risk management. However, vegetation recovery processes after shallow landslides and their influencing multivariate factors are not well known. This study aims to address this gap by investigating the vegetation recovery processes after shallow landslides and the impact of topography on this recovery. We focus on two regions in Japan: the Shobara district in Hiroshima Prefecture and the Obara district in Aichi Prefecture. The Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) derived from long-term Landsat images, as well as aerial photographs and environmental datasets, are used to measure vegetation recovery. Then, statistical analysis and the Seasonal Autoregressive Integrated Moving Averages (SARIMA) model were employed to investigate the dynamic response of vegetation under different combinations of environmental conditions using NDVI and EVI time series. Historical aerial photographs and vegetation index trend analysis suggest that vegetation in the study areas will take more than ten years to return to a stable state. The results also demonstrate the influence of atmospheric and land cover conditions when monitoring vegetation response using NDVI and EVI. In Obara, concave and convergent terrain positively influenced NDVI, while non-steep, low-elevation, and north-facing terrain positively influenced EVI. In Shobara, gentle and northwest-facing slopes were positively correlated with NDVI, and gentle and west-facing slopes were positively correlated with EVI. SARIMA modeling found that NDVI is more suitable for modeling the middle and late stages of vegetation recovery within 10–25 years after the landslide. In comparison, EVI is better for modeling the early stage of vegetation recovery within 10 years after the landslide.
Exploring Random Forest Machine Learning and Remote Sensing Data for Streamflow Prediction: An Alternative Approach to a Process-Based Hydrologic Modeling in a Snowmelt-Driven Watershed
Authors: Islam Khandaker Iftekharul , Elias Emile , Carroll Kenneth C. , Brown Christopher
Published: August 2023

Physically based hydrologic models require significant effort and extensive information for development, calibration, and validation. The study explored the use of the random forest regression (RFR), a supervised machine learning (ML) model, as an alternative to the physically based Soil and Water Assessment Tool (SWAT) for predicting streamflow in the Rio Grande Headwaters near Del Norte, a snowmelt-dominated mountainous watershed of the Upper Rio Grande Basin. Remotely sensed data were used for the random forest machine learning analysis (RFML) and RStudio for data processing and synthesizing. The RFML model outperformed the SWAT model in accuracy and demonstrated its capability in predicting streamflow in this region. We implemented a customized approach to the RFR model to assess the model’s performance for three training periods, across 1991–2010, 1996–2010, and 2001–2010; the results indicated that the model’s accuracy improved with longer training periods, implying that the model trained on a more extended period is better able to capture the parameters’ variability and reproduce streamflow data more accurately. The variable importance (i.e., IncNodePurity) measure of the RFML model revealed that the snow depth and the minimum temperature were consistently the top two predictors across all training periods. The paper also evaluated how well the SWAT model performs in reproducing streamflow data of the watershed with a conventional approach. The SWAT model needed more time and data to set up and calibrate, delivering acceptable performance in annual mean streamflow simulation, with satisfactory index of agreement (d), coefficient of determination (R2), and percent bias (PBIAS) values, but monthly simulation warrants further exploration and model adjustments. The study recommends exploring snowmelt runoff hydrologic processes, dust-driven sublimation effects, and more detailed topographic input parameters to update the SWAT snowmelt routine for better monthly flow estimation. The results provide a critical analysis for enhancing streamflow prediction, which is valuable for further research and water resource management, including snowmelt-driven semi-arid regions.
DSA-Net: Infrared and Visible Image Fusion via Dual-Stream Asymmetric Network
Authors: Yin Ruyi , Yang Bin , Huang Zuyan , Zhang Xiaozhi
Published: August 2023

Infrared and visible image fusion technologies are used to characterize the same scene using diverse modalities. However, most existing deep learning-based fusion methods are designed as symmetric networks, which ignore the differences between modal images and lead to source image information loss during feature extraction. In this paper, we propose a new fusion framework for the different characteristics of infrared and visible images. Specifically, we design a dual-stream asymmetric network with two different feature extraction networks to extract infrared and visible feature maps, respectively. The transformer architecture is introduced in the infrared feature extraction branch, which can force the network to focus on the local features of infrared images while still obtaining their contextual information. The visible feature extraction branch uses residual dense blocks to fully extract the rich background and texture detail information of visible images. In this way, it can provide better infrared targets and visible details for the fused image. Experimental results on multiple datasets indicate that DSA-Net outperforms state-of-the-art methods in both qualitative and quantitative evaluations. In addition, we also apply the fusion results to the target detection task, which indirectly demonstrates the fusion performances of our method.
Non-Fungible Tokens Based on ERC-4519 for the Rental of Smart Homes
Authors: Arcenegui Javier , Arjona Rosario , Baturone Iluminada
Published: August 2023

The rental of houses is a common economic activity. However, there are many inconveniences that arise when renting a property. The lack of trust between the landlord and the tenant due to fraud or squatters makes it necessary to involve third parties to minimize risk. A blockchain (such as Ethereum) provides an ideal solution to act as a low-cost intermediary. This paper proposes the use of non-fungible tokens (NFTs) based on ERC-4519 for smart home tokenization. The ERC-4519 is an Ethereum standard for describing NFTs tied to physical assets, allowing smart homes (assets) to be linked to NFTs so that the smart homes can interact with the blockchain and perform transactions, know their landlord (owner) and assigned tenant (user), whether they are authenticated or not, and know their operating mode (NFT state). The payments associated with the rental process are made using the NFT, eliminating the need for additional fungible tokens and simplifying the process. The entire rental process is described and illustrated with a proof of concept using a Pycom Wipy 3.0 as a smart home gateway and a smart contract programmed in Solidity, which is deployed on the Goerli Testnet for Ethereum. Experimental results show that the smart home gateway takes a few tens of milliseconds to complete a transaction, and the transaction costs of the relevant functions of the smart contract are quite affordable.
EWOD Chip with Micro-Barrier Electrode for Simultaneous Enhanced Mixing during Transportation
Authors: Gao Shang , Rui Xichuan , Zeng Xiangyu , Zhou Jia
Published: August 2023

Digital microfluidic platforms have been extensively studied in biology. However, achieving efficient mixing of macromolecules in microscale, low Reynolds number fluids remains a major challenge. To address this challenge, this study presents a novel design solution based on dielectric electro-wetting (EWOD) by optimizing the geometry of the transport electrode. The new design integrates micro-barriers on the electrodes to generate vortex currents that promote mixing during droplet transport. This design solution requires only two activation signals, minimizing the number of pins required. The mixing performance of the new design was evaluated by analyzing the degree of mixing inside the droplet and quantifying the motion of the internal particles. In addition, the rapid mixing capability of the new platform was demonstrated by successfully mixing the sorbitol solution with the detection solution and detecting the resulting reaction products. The experimental results show that the transfer electrode with a micro-barrier enables rapid mixing of liquids with a six-fold increase in mixing efficiency, making it ideal for the development of EWOD devices.
Master–Slave Control System for Virtual–Physical Interactions Using Hands
Authors: Liu Siyuan , Sun Chao
Published: August 2023

Among the existing technologies for hand protection, master–slave control technology has been extensively researched and applied within the field of safety engineering to mitigate the occurrence of safety incidents. However, it has been identified through research that traditional master–slave control technologies no longer meet current production and lifestyle needs, and they have even begun to pose new safety risks. To resolve the safety risks exposed by traditional master–slave control, this research fuses master–slave control technology for hands with virtual reality technology, and the design of a master–slave control system for hands based on virtual reality technology is investigated. This study aims to realize the design of a master–slave control system for virtual–physical interactions using hands that captures the position, orientation, and finger joint angles of the user’s hand in real time and synchronizes the motion of the slave interactive device with that of a virtual hand. With amplitude limiting, jitter elimination, and a complementary filtering algorithm, the original motion data collected by the designed glove are turned into a Kalman-filtering-algorithm-based driving database, which drives the synchronous interaction of the virtual hand and a mechanical hand. As for the experimental results, the output data for the roll, pitch, and yaw were in the stable ranges of −0.1° to 0.1°, −0.15° to 0.15°, and −0.15° to 0.15°, respectively, which met the accuracy requirements for the system’s operation under different conditions. More importantly, these data prove that, in terms of accuracy and denoising, the data-processing algorithm was relatively compatible with the hardware platform of the system. Based on the algorithm for the virtual–physical interaction model, the authors introduced the concept of an auxiliary hand into the research, put forward an algorithmic process and a judgement condition for the stable grasp of the virtual hand’s, and solved a model-penetrating problem while enhancing the immersive experience during virtual–physical interactions. In an interactive experiment, a dynamic accuracy test was run on the system. As shown by the experimental data and the interactive effect, the system was satisfactorily stable and interactive.
Efficient Fault Detection of Rotor Minor Inter-Turn Short Circuit in Induction Machines Using Wavelet Transform and Empirical Mode Decomposition
Authors: Rehman Attiq Ur , Jiao Weidong , Sun Jianfeng , Sohaib Muhammad , Jiang Yonghua , Shahzadi Mahnoor , Khan Muhammad Ijaz
Published: August 2023

This paper introduces a novel approach for detecting inter-turn short-circuit faults in rotor windings using wavelet transformation and empirical mode decomposition. A MATLAB/Simulink model is developed based on electrical parameters to simulate the inter-turn short circuit by adding a resistor parallel to phase “a” of the rotor. The resulting high current in the new phase indicates the presence of the short circuit. By measuring the rotor and stator three-phase currents, the fault can be detected as the currents exhibit asymmetric behavior. Fluctuations in the electromagnetic torque also occur during the fault. The wavelet transform is applied to the rotor current, revealing an effective analysis of sideband frequency components. Specifically, changes in amplitude and frequency, particularly in d7 and a7, indicate the presence of harmonics generated by the inter-turn short circuit. The simulation results demonstrate the effectiveness of wavelet transformation in analyzing these frequency components. Additionally, this study explores the use of empirical mode decomposition to detect faults in their early stages, observing substantial changes in the instantaneous amplitudes of the first three intrinsic mode functions during fault onset. The proposed technique is straightforward and reliable, making it suitable for application in wind turbines with simple electrical inputs.
FMCW Radar System Interference Mitigation Based on Time-Domain Signal Reconstruction
Authors: Xu Zhengguang , Wei Shanyong
Published: August 2023

In this study, an interference detection and mitigation method is proposed for frequency-modulated continuous-wave radar systems based on time-domain signal reconstruction. The interference detection method uses the difference in one-dimensional fast Fourier transform (1D-FFT) results between targets and interferences. In the 1D-FFT results, the target appears as a peak at the same frequency point for all chirps within one frame, whereas the interference appears as the absence of target peaks within the first or last few chirps within one frame or as a shift in the target peak position in different chirps. Then, the interference mitigation method reconstructs the interference signal in the time domain by the estimated parameter from the 1D-FFT results, so the interference signal can be removed from the time domain without affecting the target signal. The simulation results show that the proposed interference mitigation algorithm can reduce the amplitude of interference by about 25 dB. The experimental results show that the amplitude of interference is reduced by 20–25 dB, proving the effectiveness of the simulation results.
Simultaneous Estimation of Azimuth and Elevation Angles Using a Decision Tree-Based Method
Authors: Carballeira Anabel Reyes , de Figueiredo Felipe A. P. , Brito Jose Marcos C.
Published: August 2023

This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter’s signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model’s generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm. Our results demonstrate an average reduction of over 90% in the prediction error and 50% in the prediction time achieved by our proposal when compared to the MUSIC algorithm. These results establish DTs as competitive alternatives for DOA estimation in signal reception systems.
Hybrid Distributed Optical Fiber Sensor for the Multi-Parameter Measurements
Authors: Zhou Xiao , Wang Feng , Yang Chengyu , Zhang Zijing , Zhang Yixin , Zhang Xuping
Published: August 2023

Distributed optical fiber sensors (DOFSs) are a promising technology for their unique advantage of long-distance distributed measurements in industrial applications. In recent years, modern industrial monitoring has called for comprehensive multi-parameter measurements to accurately identify fault events. The hybrid DOFS technology, which combines the Rayleigh, Brillouin, and Raman scattering mechanisms and integrates multiple DOFS systems in a single configuration, has attracted growing attention and has been developed rapidly. Compared to a single DOFS system, the multi-parameter measurements based on hybrid DOFS offer multidimensional valuable information to prevent misjudgments and false alarms. The highly integrated sensing structure enables more efficient and cost-effective monitoring in engineering. This review highlights the latest progress of the hybrid DOFS technology for multi-parameter measurements. The basic principles of the light-scattering-based DOFSs are initially introduced, and then the methods and sensing performances of various techniques are successively described. The challenges and prospects of the hybrid DOFS technology are discussed in the end, aiming to pave the way for a vaster range of applications.
Same Wavelength Noise Filtering via Quantum Orbital Angular Momentum Emission
Authors: Jia Fan , Zhang Zijing , Cen Longzhu , Sun Yanhui , Zhao Yuan
Published: August 2023

In laser active detection, detection performance is affected by optical noise, laser interference, and environmental background interference. Conventional methods to filter optical noise take advantage of the differences between signal and noise in wavelength and polarization. Due to the limitations of traditional methods in the physical dimension, noise cannot be completely filtered out. In this manuscript, a new method of noise filtering based on the spatial distribution difference between the quantum orbital angular momentum beam and the background noise is proposed. The use of beams containing quantum orbital angular momentum can make the signal light have a new physical dimension and enrich the information of emitted light. We conduct a complete theoretical analysis and provide a proof-of-principle experiment. The experimental results are in good agreement with the theoretical analysis results, and there is a signal-to-noise ratio improvement of more than five times in laser active detection. Our method meets the urgent needs of laser active detection and can be applied in the field of high-quality target detection.
Online Correction of Laser Head Nozzle Position for Laser Metal Deposition Using a Chromatic Confocal Displacement System
Authors: Koruba Piotr , Iskierka Grzegorz , Poskart Bartosz , Mazur Jakub , Zakrzewski Adrian
Published: August 2023

The stability and repeatability of laser metal deposition is particularly important when processing multiple layers or depositing material on complex component surfaces, and requires the use of process parameter control including the stand-off distance between the laser head and the substrate. The system proposed in this paper for correcting the stand-off parameter is based on a chromatic confocal sensor integrated into a laser head. Then, the spectral signal acquired from the measurement system is processed by using the developed application to compensate for the movement of an additional axis of the kinematic system. This study used an independent verification system based on the digital image correlation method. The validation tests were carried out using the system for correcting the stand-off parameter with different control algorithms and given motion trajectories and substrate materials. The results demonstrate that the developed system can be useful for laser metal deposition.
Compensation of Heat Effect in Dielectric Barrier Discharge (DBD) Plasma System for Radar Cross-Section (RCS) Reduction
Authors: Jung Jinwoo , Cho Changseok , Choi Minsu , You Shinjae , Ha Jungje , Lee Hyunsoo , Kim Cheonyoung , Oh Ilyoung , Lee Yongshik
Published: August 2023

In this study, the problems encountered in radar cross-section (RCS) measurement experiments utilizing a dielectric barrier discharge (DBD) plasma system are examined and an effective solution is proposed. A DBD plasma system generates heat due to the high bias voltage required for plasma generation. The thermal-induced structural deformation of the DBD structure caused by this high voltage and its impact on RCS measurements are analyzed. In addition, techniques for minimizing the thermal-induced deformation and compensation methods for addressing the minimized deformation are proposed. Furthermore, RCS measurements are conducted on two kinds of DBD structures using the proposed method to experimentally demonstrate the improved agreement between the simulation and measurement results. For both structures, the RCS experimental results are in very good agreement with the simulation results, which enables accurate plasma characterization. In conclusion, it can be expected that the proposed method can be used to provide more accurate RCS measurements on various DBD structures that generate high heat.
Characterization of Fiber-Optic Vector Magnetic Field Sensors Based on the Magneto-Strictive Effect
Authors: Li Ning , Chen Yuren , Zhang Chaofan , Nong Jie , Xu Wenjie , Wang Zhencheng , Yang Junbo , Yu Yang , Zhang Zhenrong
Published: August 2023

Fiber-optic magnetic field sensors have garnered considerable attention in the field of marine monitoring due to their compact size, robust anti-electromagnetic interference capabilities, corrosion resistance, high sensitivity, ease of multiplexing and integration, and potential for large-scale sensing networks. To enable the detection of marine magnetic field vector information, we propose an optical fiber vector magnetic field sensor that integrates three single-axis sensors in an orthogonal configuration. Theoretical analysis and experimental verification are conducted to investigate its magnetic field and temperature sensing characteristics, and a sensitivity matrix is established to address the cross-sensitivity between the magnetic field and temperature; experimental tests were conducted to assess the vector response of the three-dimensional (3D) vector sensor across the three orthogonal axes; the obtained experimental results illustrate the commendable magnetic field vector response exhibited by the sensor in the orthogonal axes, enabling precise demodulation of vector magnetic field information. This sensor presents several advantages, including cost-effectiveness, easy integration, and reliability vectorially. Consequently, it holds immense potential for critical applications in marine magnetic field network detection.
End-Cloud Collaboration Navigation Planning Method for Unmanned Aerial Vehicles Used in Small Areas
Authors: Xiong Huajie , Yu Baoguo , Yi Qingwu , He Chenglong
Published: August 2023

Unmanned aerial vehicle (UAV) collaboration has become the main means of indoor and outdoor regional search, railway patrol, and other tasks, and navigation planning is one of the key, albeit difficult, technologies. The purpose of UAV navigation planning is to plan reasonable trajectories for UAVs to avoid obstacles and reach the task area. Essentially, it is a complex optimization problem that requires the use of navigation planning algorithms to search for path-point solutions that meet the requirements under the guide of objective functions and constraints. At present, there are autonomous navigation modes of UAVs relying on airborne sensors and navigation control modes of UAVs relying on ground control stations (GCSs). However, due to the limitation of airborne processor computing power, and background command and control communication delay, a navigation planning method that takes into account accuracy and timeliness is needed. First, the navigation planning architecture of UAVs of end-cloud collaboration was designed. Then, the background cloud navigation planning algorithm of UAVs was designed based on the improved particle swarm optimization (PSO). Next, the navigation control algorithm of the UAV terminals was designed based on the multi-objective hybrid swarm intelligent optimization algorithm. Finally, the computer simulation and actual indoor-environment flight test based on small rotor UAVs were designed and conducted. The results showed that the proposed method is correct and feasible, and can improve the effectiveness and efficiency of navigation planning of UAVs.
Automated Identification of Hidden Corrosion Based on the D-Sight Technique: A Case Study on a Military Helicopter
Authors: Katunin Andrzej , Synaszko Piotr , Dragan Krzysztof
Published: August 2023

Hidden corrosion remains a significant problem during aircraft service, primarily because of difficulties in its detection and assessment. The non-destructive D-Sight testing technique is characterized by high sensitivity to this type of damage and is an effective sensing tool for qualitative assessments of hidden corrosion in aircraft structures used by numerous ground service entities. In this paper, the authors demonstrated a new approach to the automatic quantification of hidden corrosion based on image processing D-Sight images during periodic inspections. The performance of the developed processing algorithm was demonstrated based on the results of the inspection of a Mi family military helicopter. The nondimensional quantitative measurement introduced in this study confirmed the effectiveness of this evaluation of corrosion progression, which was in agreement with the results of qualitative analysis of D-Sight images made by inspectors. This allows for the automation of the inspection process and supports inspectors in evaluating the extent and progression of hidden corrosion.
Digitalization and Smartification of Urban Services to Enhance Urban Resilience in the Post-Pandemic Era: The Case of the Pilgrimage City of Makkah
Authors: Aina Yusuf A. , Abubakar Ismaila Rimi , Almulhim Abdulaziz I. , Dano Umar Lawal , Maghsoodi Tilaki Mohammad Javad , Dawood Sharifah R. S.
Published: August 2023

The COVID-19 pandemic has significantly disrupted human socioeconomic activities, leaving an everlasting impact on urban systems. As a result, there is a growing scholarly focus on exploring how urban planning strategies and tools can help create resilient cities. In Saudi Arabia, the pilgrimage city of Makkah, which has always faced the challenge of managing crowds during the annual pilgrimage, was left deserted due to lockdowns and social distancing measures. To quickly revive socioeconomic and pilgrimage activities in the city, a set of digital tools and communication technologies were deployed to manage crowds and enforce social distancing to minimize the spread of the COVID-19 virus. This study examines the role of digitalization and smartification in reviving the city and the importance of context in building urban resilience. This study used desktop research and case study analysis to highlight the transformation to the new normal and the development of future smart technologies for the city. Smart solutions provided valuable support in reducing the impacts of the pandemic and restarting Makkah’s economy. Although most activities have been restored, some facilities and services are still operating below capacity. Digitalization and smartification of urban services could play a major role in improving service delivery and urban resilience.
AraMAMS: Arabic Multi-Aspect, Multi-Sentiment Restaurants Reviews Corpus for Aspect-Based Sentiment Analysis
Authors: AlMasaud Alanod , Al-Baity Heyam H.
Published: August 2023

The abundance of data on the internet makes analysis a must. Aspect-based sentiment analysis helps extract valuable information from textual data. Because of limited Arabic resources, this paper enriches the Arabic dataset landscape by creating AraMA, the first and largest Arabic multi-aspect corpus. AraMA comprises 10,750 Google Maps reviews for restaurants in Riyadh, Saudi Arabia. It covers four aspect categories—food, environment, service, and price—along with four sentiment polarities: positive, negative, neutral, and conflict. All AraMA reviews are labeled with at least two aspect categories. A second version, named AraMAMS, includes reviews labeled with at least two different sentiments, making it the first Arabic multi-aspect, multi-sentiment dataset. AraMAMS has 5312 reviews covering the same four aspect categories and sentiment polarities. Both corpora were evaluated using naïve biased (NB), support vector classification (SVC), linear SVC, and stochastic gradient descent (SGD) models. In the AraMA corpus, the aspect categories task achieved a 91.41% F1 measure result using the SVC model, while in the AraMAMS corpus, the best F1 measure result for aspect categories task reached 91.70% using the linear SVC model.
Advancements in PET Packaging: Driving Sustainable Solutions for Today’s Consumer Demands
Authors: Raj Beenu , Rahul Jitin , Singh Pramod K. , Rao Velidandi V. L. Kanta , Kumar Jagdish , Dwivedi Neetu , Kumar Pravita , Singh Diksha , Strzałkowski Karol
Published: August 2023

This work provides an overview of the importance of recycling PET waste to reduce the environmental impact of plastic waste, conserve natural resources and energy, and create jobs in the recycling industry. Many countries have implemented regulations and initiatives to promote the recycling of PET waste and reduce plastic pollution, such as extended producer responsibility (EPR) systems, bans on certain single-use plastics, and deposit–return systems for plastic bottles. The article further underscores the versatility of recycled PET, as it can be transformed into various products such as fibers, sheets, film, and strapping. These recycled materials find applications in numerous sectors including clothing, carpets, upholstery, and industrial fibers. Recognizing the importance of collaboration among governments, industries, and individuals, we emphasize the need for sustainable PET waste management practices and the promotion of recycled materials. The article also provides information on India’s experiences with PET waste management and regulations in other countries. It is important to note that the global production and consumption of PET have increased significantly in recent years, with the packaging industry being the largest consumer of PET. This has resulted in a significant increase in the generation of PET waste, which poses a significant environmental and health hazard if not managed properly. PET waste can end up in landfills, where it can take hundreds of years to decompose, or it can end up in the oceans, where it can harm marine life and the environment. Therefore, the proper management and recycling of PET waste are essential to mitigate these negative impacts. In terms of India’s experiences with PET waste management, several initiatives have been implemented to promote the recycling of PET waste. For example, the government has launched the Swachh Bharat Abhiyan campaign, which aims to promote cleanliness and sanitation in the country to promote waste segregation and recycling.
The Implication of Steel-Intensity-of-Use on Economic Development
Authors: Wandebori Harimukti , Murtyastanto
Published: August 2023

This paper identifies statistical relationships between the Steel-Intensity-of-Use and the Gross Domestic Product per Capita based on the data of ten selected countries based on judgmental sampling from the World Steel Association. Economic data such as Gross Domestic Product, Gross National Income, Gross Domestic Product per Capita, government spending, investment, Export of Goods and Services, and manufacturing are obtained. Based on regression analysis and exploration of economic data, the relationship differs in terms of economic development stratification. We find a pattern of a waveform in terms of Correlation and Significance between Steel-Intensity-of-Use and the Gross Domestic Product per Capita along the country’s economic classification. Each classification implies that the country needs to enhance the economy related to the required steel or non-steel-related industry and the accompanying government policy.
The Role of Resource Acquisition in Achieving Sustainable Competitive Performance for SMEs in an Emerging Market: A Moderated Mediation Analysis
Authors: Traboulsy Omar Radwan
Published: August 2023

The extant literature shows that due to resource limitations, many small businesses fail to survive in the long term, particularly in developed countries. However, the effect of resource limitations on the performance of small and medium-sized enterprises (SMEs) remains largely ignored, particularly in the context of emerging economies. Therefore, using the resource-based view and upper-echelon theories, this study aims to fill this knowledge gap and examine the role of resource acquisition in SMEs in achieving sustainable competitive performance in the context of the emerging country of Lebanon. To achieve this purpose, we conducted a comprehensive survey of 426 managers and owners of SMEs in the top five provinces in Lebanon, where the majority of its SMEs are located. The empirical results show that resource acquisition has positive direct and indirect effects (through structural flexibility) on sustainable competitive performance. In addition, they reveal that structural flexibility has a positive effect on sustainable competitive performance and partially mediates the resource acquisition–sustainable competitive performance relationship. Furthermore, the results highlight that management commitment to strategic performance moderates the direct resource acquisition–structural flexibility and resource acquisition–sustainable competitive performance nexuses, and the positive effect is further enhanced (or diminished) in SMEs with high (or low) management commitment to strategic performance. The results are robust and have important policy implications.
Biodegradable Zn−1wt.%Mg(−0.5wt.%Mn) Alloys: Influence of Solidification Microstructure on Their Corrosion Behavior
Authors: Vida Talita , Cruz Clarissa , Barros André , Cheung Noé , Brito Crystopher , Garcia Amauri
Published: August 2023

The development of biodegradable Zn-based alloys for implants that effectively mimic the functionality of native bone throughout the healing process is a multifaceted challenge; this is particularly evident in the task of achieving appropriate corrosion rates. This work explores the incorporation of 0.5wt.%Mn into a Zn−1wt.%Mg alloy, with focus on the relationship between corrosion behavior and microstructure. Electrochemical corrosion tests were carried out in a 0.06 M NaCl solution using as-solidified samples with two distinct microstructural length scales. Mn addition was found to induce significant electrochemical active behavior. Localized corrosion was predominant in interdendritic regions, with the ternary alloy exhibiting a higher susceptibility. For both alloys, the coarsening of the microstructure promoted a slight inclination to accelerate the corrosion rates in both biodegradable Zn alloys. The corrosion rate showed an increase of about nine-times with Mn addition for coarser eutectic spacings, while for finer ones, the increase was by about 22 times.
Uterine Smooth Muscle Tumor of Uncertain Malignant Potential: A Retrospective, Monocentric Cohort Study
Authors: Ettore Carla , Incognito Giosuè Giordano , Gulino Ferdinando Antonio , Russo Emanuele , Cannone Francesco , Ettore Giuseppe
Published: August 2023

Smooth muscle tumors of unknown malignant potential (STUMP) represent a group of heterogeneous uterine neoplasms showing worrisome histological features, raising concerns for a malignant behavior, but do not satisfy the diagnostic criteria for leiomyosarcoma. The existing literature remains scarce due to its rarity; therefore, there is no consensus regarding diagnostic criteria, malignant potential, treatment of choice, and follow-up. The authors report their case series to analyze the heterogeneous features of this poorly understood neoplasm. A retrospective, monocentric cohort study of all patients who underwent surgery diagnosed with STUMP at the Garibaldi-Nesima Hospital in Catania from February 2018 to August 2021 was conducted. Each patient’s age at diagnosis, symptoms, surgical treatment, tumor features, postoperative management, and follow-up were recorded. Nine STUMP cases were included. All patients reported menometrorrhagia and pelvic pain. In all women, the tumor lesion was single and intramural, and the mean diameter was 7.5 cm. Four women underwent laparotomic conservative surgery. The mean postoperative follow-up time was 36 months, and recurrences were observed in two patients. In conclusion, STUMP cases should be systematically discussed by tumor boards, pathologists with good expertise in gynecological diseases should examine the histological samples, and close surveillance is mandatory because of the possibility of recurrence or metastasis.
Black Holes Hint towards De Sitter Matrix Theory
Authors: Susskind Leonard
Published: August 2023

De Sitter black holes and other non-perturbative configurations can be used to probe the holographic degrees of freedom of de Sitter space. For small black holes, evidence was first provided in the seminal work of Banks, Fiol, and Morrise and follow-ups by Banks and Fischler, showing that dS is described by a form of matrix theory. For large black holes, the evidence provided here is new: Gravitational calculations and matrix theory calculations of the rates of exponentially rare fluctuations match one another in surprising detail. The occurrences of Nariai geometry and the “inside-out” transition are particularly interesting examples, which I explain in this paper.
An Integrated Ordered Probit Model for Evaluating University Commuters’ Satisfaction with Public Transport
Authors: Ismael Karzan , Duleba Szabolcs
Published: August 2023

Transport policymakers need to have an in-depth understanding of public transport (PT) customers in order to effectively manage transport systems and maintain the attractiveness of these systems to potential users. This research aims to compare the perceptions and satisfaction levels of two groups of PT users (habitual and occasional) among university staff and students regarding the quality of PT through a new integrated approach. A sample of 500 participants from Budapest, Hungary was used. Two stages of analysis were conducted: a descriptive analysis was conducted in the first stage, and Student’s t-tests of two independent samples were applied to identify the varying perceptions and overall satisfaction. Second, a new integrated ordered probit model (OPM) and an importance–performance analysis (IPA) were used to envisage how best to prioritize actions for transport enhancement. The results show that in the circle of commuters, the habitual PT users were more satisfied with the existing PT service than the occasional PT users. According to the findings of the IPA, for habitual users, the attribute “information provided” has a high priority for improvement, whereas the cost for both user types was found to be significant for all models, contributing to overall satisfaction. This factor was included in the possible overkill quadrant, suggesting that there might be more cost resources than needed. The new model, along with the case study results, may help policymakers and transport operators to make better decisions regarding the identification of service priority areas.
Prevalence of Different Courses of Feline Leukaemia Virus Infection in Four European Countries
Authors: Giselbrecht Juliana , Jähne Stéphanie , Bergmann Michèle , Meli Marina L. , Pineroli Benita , Boenzli Eva , Teichmann-Knorrn Svenja , Zablotski Yury , Pennisi Maria-Grazia , Layachi Nicolas , Serra Rodrigo , Bo Stefano , Hofmann-Lehmann Regina , Hartmann Katrin
Published: August 2023

Prevalence of progressive feline leukaemia virus (FeLV) infection is known to still be high in cats in Europe, especially in Southern Europe, but the prevalence of other outcomes of FeLV infection has not been determined in most countries. The present study aimed to investigate the prevalence of progressive, regressive, abortive, and focal infection in four European countries, two with a high (Italy, Portugal) and two with a low expected prevalence (Germany, France). Blood samples of 934 cats (Italy: 269; Portugal: 240; France: 107; Germany: 318) were evaluated for the p27 antigen, as well as anti-whole virus, anti-SU, and anti-p15E antibodies by enzyme-linked immunosorbent assay (ELISA) in serum and for proviral DNA by quantitative polymerase chain reaction (qPCR) in whole blood. Positive p27 antigen ELISA results were confirmed by reverse transcriptase-qPCR (RT-qPCR) detecting viral RNA in saliva swabs and/or blood. The outcome of FeLV infection was categorised as progressive (antigen-positive, provirus-positive), regressive (antigen-negative, provirus-positive), abortive (antigen- and provirus-negative, antibody-positive), and focal (antigen-positive, provirus-negative) infection. Overall FeLV prevalence was 21.2% in Italy, 20.4% in Portugal, 9.5% in Germany, and 9.3% in France. Prevalence of progressive, regressive, abortive, and focal infection in Italy was 7.8%, 4.5%, 6.3%, and 2.6%; in Portugal 3.8%, 8.3%, 6.7%, and 1.7%; in Germany 1.9%, 1.3%, 3.5%, and 2.8%; in France 1.9%, 3.7%, 2.8%, and 0.9%, respectively. In conclusion, overall FeLV prevalence is still very high, especially in Southern European countries. Therefore, testing, separation of infected cats, and vaccination are still important measures to reduce the risk of FeLV infection.
The 2022 Outbreaks of African Swine Fever Virus Demonstrate the First Report of Genotype II in Ghana
Authors: Spinard Edward , Rai Ayushi , Osei-Bonsu Jehadi , O’Donnell Vivian , Ababio Patrick T. , Tawiah-Yingar Daniel , Arthur Daniel , Baah Daniel , Ramirez-Medina Elizabeth , Espinoza Nallely , Valladares Alyssa , Faburay Bonto , Ambagala Aruna , Odoom Theophlius , Borca Manuel V. , Gladue Douglas P.
Published: August 2023

African swine fever (ASF) is a lethal disease of domestic pigs that has been causing outbreaks for over a century in Africa ever since its first discovery in 1921. Since 1957, there have been sporadic outbreaks outside of Africa; however, no outbreak has been as devastating and as far-reaching as the current pandemic that originated from a 2007 outbreak in the Republic of Georgia. Derivatives with a high degree of similarity to the progenitor strain, ASFV-Georgia/2007, have been sequenced from various countries in Europe and Asia. However, the current strains circulating in Africa are largely unknown, and 24 different genotypes have been implicated in different outbreaks. In this study, ASF isolates were collected from samples from swine suspected of dying from ASF on farms in Ghana in early 2022. While previous studies determined that the circulating strains in Ghana were p72 Genotype I, we demonstrate here that the strains circulating in 2022 were derivatives of the p72 Genotype II pandemic strain. Therefore, this study demonstrates for the first time the emergence of Genotype II ASFV in Ghana.
Understanding the Immunomodulatory Effects of Bovine Colostrum: Insights into IL-6/IL-10 Axis-Mediated Inflammatory Control
Authors: Grigalevičiūtė Ramunė , Matusevičius Paulius , Plančiūnienė Rita , Stankevičius Rolandas , Radzevičiūtė-Valčiukė Eivina , Balevičiūtė Austėja , Želvys Augustinas , Zinkevičienė Auksė , Zigmantaitė Vilma , Kučinskas Audrius , Kavaliauskas Povilas
Published: August 2023

Bovine colostrum (COL), the first milk secreted by lactating cows postpartum, is a rich source of bioactive compounds that exert a significant role in the survival, growth, and immune development of neonatal calves. This study investigated the immunomodulatory effects of COL on cytokine production in vitro using a Caco-2/THP-1 macrophage co-culture model stimulated with Phorbol 12-myristate 13-acetate (PMA). COL pretreatment significantly reduced IL-6 (241.3 pg/mL) production induced by PMA (p < 0.05), while increasing IL-10 production (45.3 pg/mL), in comparison to PMA control (441.1 and 12.5 pg/mL, respectively). Further investigations revealed that the IL-6 suppressive effect of colostrum was heat-sensitive and associated with components of higher molecular mass (100 kDa). Moreover, colostrum primarily influenced THP-1 macrophages rather than Caco-2 epithelial cells. The effects of colostrum on IL-6 production were associated with reduced NF-κB activation in THP-1 macrophages. In calf-FMT transplanted C57BL/6 murine model, colostrum decreased intestinal permeability, reduced immune cell infiltration and intestinal score, and suppressed IL-6 (142.0 pg/mL) production during S. typhimurium infection, in comparison to control animals (215.2 pg/mL). These results suggest the immunomodulatory activity of bovine colostrum and its potential applications in inflammatory disorders. Further studies are needed to elucidate the underlying mechanisms and validate the findings in bovine models.