bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Caffeine on Movement-Related Cortical Potential Morphology and Detection
Authors: Jochumsen Mads , Lavesen Emma Rahbek , Griem Anne Bruun , Falkenberg-Andersen Caroline , Jensen Sofie Kirstine Gedsø
Published: June 2024

Movement-related cortical potential (MRCP) is observed in EEG recordings prior to a voluntary movement. It has been used for e.g., quantifying motor learning and for brain-computer interfacing (BCIs). The MRCP amplitude is affected by various factors, but the effect of caffeine is underexplored. The aim of this study was to investigate if a cup of coffee with 85 mg caffeine modulated the MRCP amplitude and the classification of MRCPs versus idle activity, which estimates BCI performance. Twenty-six healthy participants performed 2 × 100 ankle dorsiflexion separated by a 10-min break before a cup of coffee was consumed, followed by another 100 movements. EEG was recorded during the movements and divided into epochs, which were averaged to extract three average MRCPs that were compared. Also, idle activity epochs were extracted. Features were extracted from the epochs and classified using random forest analysis. The MRCP amplitude did not change after consuming caffeine. There was a slight increase of two percentage points in the classification accuracy after consuming caffeine. In conclusion, a cup of coffee with 85 mg caffeine does not affect the MRCP amplitude, and improves MRCP-based BCI performance slightly. The findings suggest that drinking coffee is only a minor confounder in MRCP-related studies.
A Novel Simulation Method for 3D Digital-Image Correlation: Combining Virtual Stereo Vision and Image Super-Resolution Reconstruction
Authors: Chen Hao , Li Hao , Liu Guohua , Wang Zhenyu
Published: June 2024

3D digital-image correlation (3D-DIC) is a non-contact optical technique for full-field shape, displacement, and deformation measurement. Given the high experimental hardware costs associated with 3D-DIC, the development of high-fidelity 3D-DIC simulations holds significant value. However, existing research on 3D-DIC simulation was mainly carried out through the generation of random speckle images. This study innovatively proposes a complete 3D-DIC simulation method involving optical simulation and mechanical simulation and integrating 3D-DIC, virtual stereo vision, and image super-resolution reconstruction technology. Virtual stereo vision can reduce hardware costs and eliminate camera-synchronization errors. Image super-resolution reconstruction can compensate for the decrease in precision caused by image-resolution loss. An array of software tools such as ANSYS SPEOS 2024R1, ZEMAX 2024R1, MECHANICAL 2024R1, and MULTIDIC v1.1.0 are used to implement this simulation. Measurement systems based on stereo vision and virtual stereo vision were built and tested for use in 3D-DIC. The results of the simulation experiment show that when the synchronization error of the basic stereo-vision system (BSS) is within 10−3 time steps, the reconstruction error is within 0.005 mm and the accuracy of the virtual stereo-vision system is between the BSS’s synchronization error of 10−7 and 10−6 time steps. In addition, after image super-resolution reconstruction technology is applied, the reconstruction error will be reduced to within 0.002 mm. The simulation method proposed in this study can provide a novel research path for existing researchers in the field while also offering the opportunity for researchers without access to costly hardware to participate in related research.
Smart Partitioned Blockchain
Authors: Assiri Basem , Alnami Hani
Published: June 2024

Blockchain is a developing technology that promises advancements when it is applied to other fields. Applying blockchain to other systems requires a customized blockchain model to satisfy the requirements of different application fields. One important area is to integrate blockchain with smart spaces and the Internet of Things to process, manage, and store data. Actually, smart spaces and Internet of Things systems include various types of transactions in terms of sensitivity. The sensitivity can be considered as correctness sensitivity, time sensitivity, and specialization sensitivity. Correctness sensitivity means that the systems should accept precise or approximated data in some cases, whereas time sensitivity means that there are time bounds for each type of transaction, and specialization sensitivity applies when some transactions are processed only by specialized people. Therefore, this work introduces the smart partitioned blockchain model, where we use machine learning and deep learning models to classify transactions into different pools according to their sensitivity levels. Then, each pool is mapped to a specific part of the smart partitioned blockchain model. The parts can be permissioned or permissionless. The permissioned parts can have different sub-parts if needed. Consequently, the smart partitioned blockchain can be customized to meet application-field requirements. In the experimental results, we use bank and medical datasets with a predefined sensitivity threshold for classification accuracy in each system. The bank transactions are critical, whereas the classification of the medical dataset is speculative and less critical. The Random Forest model is used for bank-dataset classification, and its accuracy reaches 100%, whereas Sequential Deep Learning is used for the medical dataset, which reaches 91%. This means that all bank transactions are correctly mapped to the corresponding parts of the blockchain, whereas accuracy is lower for the medical dataset. However, acceptability is determined based on the predefined sensitivity threshold.
SC-AOF: A Sliding Camera and Asymmetric Optical-Flow-Based Blending Method for Image Stitching
Authors: Chang Jiayi , Li Qing , Liang Yanju , Zhou Liguo
Published: June 2024

Parallax processing and structure preservation have long been important and challenging tasks in image stitching. In this paper, an image stitching method based on sliding camera to eliminate perspective deformation and asymmetric optical flow to solve parallax is proposed. By maintaining the viewpoint of two input images in the mosaic non-overlapping area and creating a virtual camera by interpolation in the overlapping area, the viewpoint is gradually transformed from one to another so as to complete the smooth transition of the two image viewpoints and reduce perspective deformation. Two coarsely aligned warped images are generated with the help of a global projection plane. After that, the optical flow propagation and gradient descent method are used to quickly calculate the bidirectional asymmetric optical flow between the two warped images, and the optical-flow-based method is used to further align the two warped images to reduce parallax. In the image blending, the softmax function and registration error are used to adjust the width of the blending area, further eliminating ghosting and reducing parallax. Finally, by comparing our method with APAP, AANAP, SPHP, SPW, TFT, and REW, it has been proven that our method can not only effectively solve perspective deformation, but also gives more natural transitions between images. At the same time, our method can robustly reduce local misalignment in various scenarios, with higher structural similarity index. A scoring method combining subjective and objective evaluations of perspective deformation, local alignment and runtime is defined and used to rate all methods, where our method ranks first.
A Data Matrix Code Recognition Method Based on L-Shaped Dashed Edge Localization Using Central Prior
Authors: Liu Yi , Song Yang , Gu Guiqiang , Luo Jianan , Wang Taoan , Jiang Qiuping
Published: June 2024

The recognition of data matrix (DM) codes plays a crucial role in industrial production. Significant progress has been made with existing methods. However, for low-quality images with protrusions and interruptions on the L-shaped solid edge (finder pattern) and the dashed edge (timing pattern) of DM codes in industrial production environments, the recognition accuracy rate of existing methods sharply declines due to a lack of consideration for these interference issues. Therefore, ensuring recognition accuracy in the presence of these interference issues is a highly challenging task. To address such interference issues, unlike most existing methods focused on locating the L-shaped solid edge for DM code recognition, we in this paper propose a novel DM code recognition method based on locating the L-shaped dashed edge by incorporating the prior information of the center of the DM code. Specifically, we first use a deep learning-based object detection method to obtain the center of the DM code. Next, to enhance the accuracy of L-shaped dashed edge localization, we design a two-level screening strategy that combines the general constraints and central constraints. The central constraints fully exploit the prior information of the center of the DM code. Finally, we employ libdmtx to decode the content from the precise position image of the DM code. The image is generated by using the L-shaped dashed edge. Experimental results on various types of DM code datasets demonstrate that the proposed method outperforms the compared methods in terms of recognition accuracy rate and time consumption, thus holding significant practical value in an industrial production environment.
A Curiosity Estimation in Storytelling with Picture Books for Children Using Wearable Sensors
Authors: Ohnishi Ayumi , Kosaka Sayo , Hama Yasukazu , Saito Kaoru , Terada Tsutomu
Published: June 2024

Storytelling is one of the most important learning activities for children since reading aloud from a picture book stimulates children’s curiosity, emotional development, and imagination. For effective education, the procedures for storytelling activities need to be improved according to the children’s level of curiosity. However, young children are not able to complete questionnaires, making it difficult to analyze their level of interest. This paper proposes a method to estimate children’s curiosity in picture book reading activities at five levels by recognizing children’s behavior using acceleration and angular velocity sensors placed on their heads. We investigated the relationship between children’s behaviors and their levels of curiosity, listed all observed behaviors, and clarified the behavior for estimating curiosity. Furthermore, we conducted experiments using motion sensors to estimate these behaviors and confirmed that the accuracy of estimating curiosity from sensor data is approximately 72%.
Methane Quantification Performance of the Quantitative Optical Gas Imaging (QOGI) System Using Single-Blind Controlled Release Assessment
Authors: Ilonze Chiemezie , Wang Jiayang (Lyra) , Ravikumar Arvind P. , Zimmerle Daniel
Published: June 2024

Quantitative optical gas imaging (QOGI) system can rapidly quantify leaks detected by optical gas imaging (OGI) cameras across the oil and gas supply chain. A comprehensive evaluation of the QOGI system’s quantification capability is needed for the successful adoption of the technology. This study conducted single-blind experiments to examine the quantification performance of the FLIR QL320 QOGI system under near-field conditions at a pseudo-realistic, outdoor, controlled testing facility that mimics upstream and midstream natural gas operations. The study completed 357 individual measurements across 26 controlled releases and 71 camera positions for release rates between 0.1 kg Ch4/h and 2.9 kg Ch4/h of compressed natural gas (which accounts for more than 90% of typical component-level leaks in several production facilities). The majority (75%) of measurements were within a quantification factor of 3 (quantification error of −67% to 200%) with individual errors between −90% and 831%, which reduced to −79% to +297% when the mean of estimates of the same controlled release from multiple camera positions was considered. Performance improved with increasing release rate, using clear sky as plume background, and at wind speeds ≤1 mph relative to other measurement conditions.
S5Utis: Structured State-Space Sequence SegNeXt UNet-like Tongue Image Segmentation in Traditional Chinese Medicine
Authors: Song Donglei , Zhang Hongda , Shi Lida , Xu Hao , Xu Ying
Published: June 2024

Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important step in the examination process. The segmentation and processing of the tongue image directly affects the results of intelligent Traditional Chinese Medicine diagnosis. As intelligent Traditional Chinese Medicine continues to develop, remote diagnosis and patient participation will play important roles. Smartphone sensor cameras can provide irreplaceable data collection capabilities in enhancing interaction in smart Traditional Chinese Medicine. However, these factors lead to differences in the size and quality of the captured images due to factors such as differences in shooting equipment, professionalism of the photographer, and the subject’s cooperation. Most current tongue image segmentation algorithms are based on data collected by professional tongue diagnosis instruments in standard environments, and are not able to demonstrate the tongue image segmentation effect in complex environments. Therefore, we propose a segmentation algorithm for tongue images collected in complex multi-device and multi-user environments. We use convolutional attention and extend state space models to the 2D environment in the encoder. Then, cross-layer connection fusion is used in the decoder part to fuse shallow texture and deep semantic features. Through segmentation experiments on tongue image datasets collected by patients and doctors in real-world settings, our algorithm significantly improves segmentation performance and accuracy.
Real-World Spatial Synchronization of Event-CMOS Cameras through Deep Learning: A Novel CNN-DGCNN Approach
Authors: Mizrahi Dor , Laufer Ilan , Zuckerman Inon
Published: June 2024

This paper presents a new deep-learning architecture designed to enhance the spatial synchronization between CMOS and event cameras by harnessing their complementary characteristics. While CMOS cameras produce high-quality imagery, they struggle in rapidly changing environments—a limitation that event cameras overcome due to their superior temporal resolution and motion clarity. However, effective integration of these two technologies relies on achieving precise spatial alignment, a challenge unaddressed by current algorithms. Our architecture leverages a dynamic graph convolutional neural network (DGCNN) to process event data directly, improving synchronization accuracy. We found that synchronization precision strongly correlates with the spatial concentration and density of events, with denser distributions yielding better alignment results. Our empirical results demonstrate that areas with denser event clusters enhance calibration accuracy, with calibration errors increasing in more uniformly distributed event scenarios. This research pioneers scene-based synchronization between CMOS and event cameras, paving the way for advancements in mixed-modality visual systems. The implications are significant for applications requiring detailed visual and temporal information, setting new directions for the future of visual perception technologies.
Millimeter-Wave Radar-Based Identity Recognition Algorithm Built on Multimodal Fusion
Authors: Guo Jian , Wei Jingpeng , Xiang Yashan , Han Chong
Published: June 2024

Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user’s vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%.
GMSA-Net: A Transmission Line Ice Thickness Identification Network Based on Global Micro Strip Awareness
Authors: Zhang Yu , Dou Yinke , Jiao Yangyang , Zhao Liangliang , Guo Dongliang
Published: June 2024

Ice-covered transmission lines seriously affect the normal operation of the power transmission system. Resonance deicing based on different ice thicknesses is an effective method to solve the issue of ice-covered transmission lines. In order to obtain accurate ice thickness of transmission lines, this paper designs an ice thickness of transmission line recognition model based on Global Micro Strip Awareness Net (GMSA-Net) and proposes a Mixed Strip Convolution Module (MSCM) and a global micro awareness module (GMAM). The MSCM adapts to the shape of ice-covered transmission lines by using strip convolutions with different receptive fields, improving the encoder’s ability to extract ice-covered features; the GMAM perceives through both global and micro parts, mining the connections between semantic information. Finally, the ice thickness of the generated segmented image is calculated using the method of regional pixel statistics. Experiments are conducted on the dataset of ice-covered transmission lines. The mean Intersection over Union (mIoU) of image segmentation reaches 96.4%, the balanced F-Score (F1-Score) is 98.1%, and the identification error of ice thickness is within 3.8%. Experimental results prove that this method can accurately identify the ice thickness of transmission lines, providing a control basis for the application of resonant deicing engineering.
Identification and Analysis of the Geohazards Located in an Alpine Valley Based on Multi-Source Remote Sensing Data
Authors: Yang Yonglin , Zhao Zhifang , Zhou Dingyi , Lai Zhibin , Chang Kangtai , Fu Tao , Niu Lei
Published: June 2024

Geohazards that have developed in densely vegetated alpine gorges exhibit characteristics such as remote occurrence, high concealment, and cascading effects. Utilizing a single remote sensing datum for their identification has limitations, while utilizing multiple remote sensing data obtained based on different sensors can allow comprehensive and accurate identification of geohazards in such areas. This study takes the Latudi River valley, a tributary of the Nujiang River in the Hengduan Mountains, as the research area, and comprehensively uses three techniques of remote sensing: unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR), Small Baseline Subset interferometric synthetic aperture radar (SBAS-InSAR), and UAV optical remote sensing. These techniques are applied to comprehensively identify and analyze landslides, rockfalls, and debris flows in the valley. The results show that a total of 32 geohazards were identified, including 18 landslides, 8 rockfalls, and 6 debris flows. These hazards are distributed along the banks of the Latudi River, significantly influenced by rainfall and distribution of water systems, with deformation variables fluctuating with rainfall. The three types of geohazards cause cascading disasters, and exhibit different characteristics in the 0.5 m resolution hillshade map extracted from LiDAR data. UAV LiDAR has advantages in densely vegetated alpine gorges: after the selection of suitable filtering algorithms and parameters of the point cloud, it can obtain detailed terrain and geomorphological information on geohazards. The different remote sensing technologies used in this study can mutually confirm and complement each other, enhancing the capability to identify geohazards and their associated hazard cascades in densely vegetated alpine gorges, thereby providing valuable references for government departments in disaster prevention and reduction work.
Fast Low-Sidelobe Pattern Synthesis Using the Symmetry of Array Geometry
Authors: Zhang Ming , Liu Yongxi , Zhou Haidong , Zhang Anxue
Published: June 2024

Array pattern synthesis with low sidelobe levels is widely used in practice. An effective way to incorporate sensor patterns in the design procedure is to use numerical optimization methods. However, the dimension of the optimization variables is very high for large-scale arrays, leading to high computational complexity. Fortunately, sensor arrays used in practice usually have symmetric structures that can be utilized to accelerate the optimization algorithms. This paper studies a fast pattern synthesis method by using the symmetry of array geometry. In this method, the problem of amplitude weighting is formulated as a second-order cone programming (SOCP) problem, in which the dynamic range of the weighting coefficients can also be taken into account. Then, by utilizing the symmetric property of array geometry, the dimension of the optimization problem as well as the number of constraints can be reduced significantly. As a consequence, the computational efficiency is greatly improved. Numerical experiments show that, for a uniform rectangular array (URA) with 1024 sensors, the computational efficiency is improved by a factor of 158, while for a uniform hexagonal array (UHA) with 1261 sensors, the improvement factor is 284.
Associations of Physical Activity and Heart Rate Variability from a Two-Week ECG Monitor with Cognitive Function and Dementia: The ARIC Neurocognitive Study
Authors: Marino Francesca R. , Wu Hau-Tieng , Etzkorn Lacey , Rooney Mary R. , Soliman Elsayed Z. , Deal Jennifer A. , Crainiceanu Ciprian , Spira Adam P. , Wanigatunga Amal A. , Schrack Jennifer A. , Chen Lin Yee
Published: June 2024

Low physical activity (PA) measured by accelerometers and low heart rate variability (HRV) measured from short-term ECG recordings are associated with worse cognitive function. Wearable long-term ECG monitors are now widely used, and some devices also include an accelerometer. The objective of this study was to evaluate whether PA or HRV measured from long-term ECG monitors was associated with cognitive function among older adults. A total of 1590 ARIC participants had free-living PA and HRV measured over 14 days using the Zio® XT Patch [aged 72–94 years, 58% female, 32% Black]. Cognitive function was measured by cognitive factor scores and adjudicated dementia or mild cognitive impairment (MCI) status. Adjusted linear or multinomial regression models examined whether higher PA or higher HRV was cross-sectionally associated with higher factor scores or lower odds of MCI/dementia. Each 1-unit increase in the total amount of PA was associated with higher global cognition (β = 0.30, 95% CI: 0.16–0.44) and executive function scores (β = 0.38, 95% CI: 0.22–0.53) and lower odds of MCI (OR = 0.38, 95% CI: 0.22–0.67) or dementia (OR = 0.25, 95% CI: 0.08–0.74). HRV (i.e., SDNN and rMSSD) was not associated with cognitive function. More research is needed to define the role of wearable ECG monitors as a tool for digital phenotyping of dementia.
Determination of Partial Propagation Velocity and Partial Isentropic Compressibility Coefficient in Water–Ethanol System
Authors: Reyna Carlos A. B. , Franco Ediguer E. , Lopes Jose H. , Tsuzuki Marcos S. G. , Buiochi Flávio
Published: June 2024

This study introduces an innovative approach to the layered model, emphasizing the physical–chemical characterization of miscible liquid systems through ultrasonic techniques, with a specific focus on the water–ethanol system used in pharmaceutical formulations. Traditional characterization methods, while effective, face challenges due to the complex nature of solutions, such as the need for large pressure variations and strict temperature control. The proposed approach integrates partial molar volumes and partial propagation velocity functions into the layered model, enabling a nuanced understanding of miscibility and interactions. Ultrasonic techniques are used to calculate the isentropic compressibility coefficient for each component of the mixture as well as the total value using an additive mixing rule. Unlike conventional methods, this technique uses tabulated and experimental data to estimate the propagation velocity in the mixture, leading to a more precise computation of the isentropic compressibility coefficient. The results indicate a significant improvement in predicting the behavior of the water–ethanol system compared to the classical layered model. The methodology demonstrates the potential to provide new physicochemical insights that can be applied to other miscible systems beyond water–ethanol. This research has implications for improving the efficiency and accuracy of liquid medication formulations in the pharmaceutical industry.
Distributed Vibration Monitoring System for 10 kV-400 kVA 3D Wound Core Transformer under Progressive Short-Circuit Impulses
Authors: Tao Jiagui , Zhang Sicong , Dai Jianzhuo , Zhu Jinwei , Zhao Heng
Published: June 2024

As large-scale, high-proportion, and efficient distribution transformers surge into the grids, anti-short circuit capability testing of transformer windings in efficient distribution seems necessary and prominent. To deeply explore the influence of progressively short-circuit shock impulses on the core winding deformation of efficient power transformers, a finite element theoretical model was built by referring to a three-phase three-winding 3D wound core transformer with a model of S20-MRL-400/10-NX2. The distributions of internal equivalent force and total deformation of the 3D wound core transformer along different paths under progressively short-circuit shock impulses varying from 60% to 120% were investigated. Results show that the equivalent stress and total deformation change rate reach their maximum as the short-circuit current increases from 60% to 80%, and the maximum and average variation rate for the equivalent stress reach 177.75% and 177.43%, while the maximum and average variation rate for the total deformation corresponds to 178.30% and 177.45%, respectively. Meanwhile, the maximum equivalent stress and maximum total deformation reach 29.81 MPa and 38.70 μm, respectively, as the applied short-circuit current increased to 120%. In light of the above observations, the optimization and deployment of wireless sensor nodes was suggested. Therefore, a distributed monitoring system was developed for acquiring the vibration status of the windings in a 3D wound core transformer, which is a beneficial supplement to the traditional short-circuit reactance detection methods for an efficient grid access spot-check of distribution transformers.
Formulation and Physicochemical Characterization of Terpenocannabinoid-Functionalized Hemp Oil Emulsifier: Assessment of Topical Anti-Inflammatory, Antinociceptive, Wound Healing Activity and Cutaneous Toxicity Effects
Authors: Metouekel Amira , Zejli Hind , Chebaibi Mohamed , Lefrioui Youssra , Bousta Dalila , El Amri Hamid , El Fahime Elmostafa , El Kazzouli Saïd , El Brahmi Nabil
Published: June 2024

This article presents a comprehensive study on the formulation and physicochemical characterization of a novel terpenocannabinoid-functionalized hemp oil emulsifier (AMCana-Oil) for potential applications in topical anti-inflammatory, antinociceptive, and wound healing treatments. The emulsifier exhibits interesting properties, meets international acidity index requirements, and has a room temperature density comparable to liquid oils. The prepared emulsifier (AMCana-Oil), AMCana-Oil (10% TC) and AMCana-Oil (20% TC), contains a diverse array of cannabinoids, including cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC), alongside bioactive compounds such as benzenepropanoic acid and oleamide. Physicochemical properties of AMCana-Oil and MCana-Oil (20% TC) were found, respectively, as follows: density value of 0.9872 ± 0.001 mg/mL: g/cm³ and 0.9882 ± 0.002 mg/mL: g/cm³; an acidity index of 1.599 ± 0.002 mgKOH/g and 1.605 ± 0.001 mgKOH/g; an average peroxide value encompassing a range from 12.982 ± 0.351 to 23.320 ± 0.681 (mEq O2/kg), and a K of 1.575 ± 0.004 and 1.535 ± 0.0039, which underscore the fluidity, stability, and quality of emulsifiers studied. Preliminary pharmacological examinations reveal significant antioxidant, anti-inflammatory, antinociceptive, and wound healing potentials. Moreover, in silico predictions confirm the safety profile of the prepared emulsifiers. These findings emphasize the multifaceted nature of the terpenocannabinoid-functionalized emulsifier, paving the way for its potential applications in topical formulations.
Sinkhole Risk-Based Sensor Placement for Leakage Localization in Water Distribution Networks with a Data-Driven Approach
Authors: Medio Gabriele , Varra Giada , İnan Çağrı Alperen , Cozzolino Luca , Della Morte Renata
Published: June 2024

Leakages from damaged or deteriorated buried pipes in urban water distribution networks may cause significant socio-economic and environmental impacts, such as depletion of water resources and sinkhole events. Sinkholes are often caused by internal erosion and fluidization of the soil surrounding leaking pipes, with the formation of soil cavities that may eventually collapse. This in turn causes road disruption and building foundation damage, with possible victims. While the loss of precious water resources is a well-known problem, less attention has been paid to anthropogenic sinkhole events generated by leakages in water distribution systems. With a view to improving urban smart resilience and sustainability of urban areas, this study introduces an innovative framework to localize leakages based on a Machine learning model (for the training and evaluation of candidate sets of pressure sensors) and a Genetic algorithm (for the optimal sensor set positioning) with the goal of detecting and mitigating potential hydrogeological urban disruption due to water leakage in the most sensitive/critical locations. The application of the methodology on a synthetic case study from literature and a real-world case scenario shows that the methodology also contributes to reducing the depletion of water resources.
The Impact of Natural Disasters on Corporate ESG Performance: Evidence from China
Authors: Huang Lulu , Liu Qiannan
Published: June 2024

Over the past decades, the frequency and economic losses from natural disasters have continued to escalate globally. Disasters pose significant challenges to global sustainable economic and social development. As a critical part of the economy, a firm’s ESG performance is a key indicator of sustainability. Whereas the determinants of firms’ ESG (environmental, social, and governance) performance have gained growing attention, limited research focuses on the impact of natural disasters on ESG performance. This paper provides the first empirical study on the short-, medium-, and long-term impacts of natural disasters on firms’ ESG performance through the lensof earthquakes. By exploiting a difference-in-differences approach based on panel data of Chinese listed firms and a dataset of seismic events, this paper shows that earthquakes have a significant positive impact on affected firms’ ESG performance, with short-term positive effects on environmental and social performance and long-term positive effects on social and governance performance. Heterogeneity analysis reveals that the positive effects are more pronounced for state-owned firms, relatively smaller firms, and firms located in cities with frequent earthquake occurrences. This study highlights the distinct temporal effects on different ESG dimensions and provides new insights for policymakers to employ ESG practices to alleviate the negative effects of disasters.
Sustainable Practices in Manufacturing SMEs: The Importance of Technological Collaboration between Supply Chain Partners
Authors: López-Cózar-Navarro Cristina , Priede-Bergamini Tiziana , Cuello-de-Oro-Celestino Diego
Published: June 2024

The development of new technologies within Industry 4.0 (I4.0) may serve as a catalyst in the development of sustainable practices (SP), particularly in the case of small and medium enterprises (SMEs). This paper contributes to the literature by examining the development of technological collaboration agreements between supply chain partners as an influence factor that may impact the environmental commitment of SMEs. Specifically, we study whether the type of partner within the supply chain impacts a firm’s spending or its investment decision toward SP. To that end, we propose an analysis model that considers supplier and customer relationships separately, and the SP measure to be taken distinguishing between expenses and investments. Additionally, empirical research comprising descriptive statistics and econometrics using the regression technique has been conducted on a sample of 1808 Spanish industrial firms. Our results confirm that there are significant differences between SMEs and large companies when developing SP, and that these SP in SMEs depend on the chosen partner and measure. Finally, we offer our main conclusions, as well as relevant recommendations for managers, public administrators, suppliers, and customers, who are essential players in the I4.0 revolution and are under social pressure to promote sustainable development.
Reverse Logistics and Sustainability: A Bibliometric Analysis
Authors: Salas-Navarro Katherinne , Castro-García Lia , Assan-Barrios Karolay , Vergara-Bujato Karen , Zamora-Musa Ronald
Published: June 2024

Currently, reverse logistics and sustainability are recognized as strategies to enhance the performance of supply chain processes and customer service and to reduce environmental impact, which is reflected in the planning of and reduction in costs throughout the production process. This article developed a bibliometric review that considered the growth of reverse logistics and sustainability from the perspective of different organizations, authors, thematic subareas, countries, journals, keywords, and volume of citations and publications. For this, a methodology was developed that consisted of reviewing previous research, obtaining the Scopus data set, applying the analysis with Microsoft Excel 365 and VOSviewer version 1.6.18 to determine the applications and trends of future research, and identifying the global impact in the last six years on organizations. The search equation with the application of filters resulted in 22,625 articles. The Sustainability Switzerland journal provided the most significant number of contributions in terms of publications, and the Journal of Cleaner Production stood out for its number of citations. Tseng, M.L. and Govindan, K. were the most active authors. China, the United States, and the United Kingdom were the most notable countries. Chinese Academy of Sciences and the Ministry of Education of the People’s Republic of China were the most influential institutions. The main findings were the recognition of the potential research lines and industry 4.0 technologies applied in supply chains and the development of sustainable processes with the fusion of reverse logistics, sustainability, and circular economy.
The Integration of Urban Freight in Public Transportation: A Systematic Literature Review
Authors: El Amrani Amine Mohamed , Fri Mouhsene , Benmoussa Othmane , Rouky Naoufal
Published: June 2024

Urban logistics face growing complexity as cities seek sustainable development pathways; one solution is integrating freight transportation with public transit. The purpose of this study is to evaluate current practices, identify gaps, and highlight opportunities for advancement in this field. Through a comprehensive literature review guided by four research questions, this study evaluates methodologies, vehicle choices, the application of intermodal containers, and the shared use of public transportation infrastructure for freight delivery. A meticulous search methodology was employed to select references, which were then analyzed and classified into two main tables. Findings reveal a progression from initial pilot projects and cost analyses to sophisticated planning and optimization challenges, with a focus on metro networks and buses. This study uncovers a predilection for certain research types, the utilization of varied transportation modes, underexplored intermodal container use, and an emerging emphasis on transshipment operations. The integration of freight and public transportation within urban settings is identified as a dynamic and diversifying field, with significant potential for contributing to sustainable urban development. We outline nine areas for future research, including the incorporation of external costs and congestion effects into optimization models, and the exploration of unmanned vehicles, thereby laying the groundwork for more inclusive and efficient urban logistics solutions.
Can Process Digitization Improve Firm Innovation Performance? Process Digitization as Job Resources and Demands
Authors: Qin Yize , Shen Yuqing
Published: June 2024

Process digitization as a means to enhance innovation performance has garnered increasing attention from researchers and firms. Digital-driven innovation helps businesses achieve sustainable development. Following the job demands–resources model, we argue that process digitization contributes to job resources and job demands simultaneously, both of which are related to innovation performance. Process digitization offers additional job resources that contribute to enhanced work engagement and foster innovation performance at lower levels, whereas increasing job demands results in exhaustion and hinders innovation performance at higher levels. Therefore, we propose that firms with moderate levels of process digitization are more likely to have higher innovation performance. We further propose that employee training and pay can buffer the negative impact of process digitization on innovation performance. Training serves as an ex ante measure that enables knowledge-based employees to effectively respond to job demands without becoming exhausted. Conversely, pay serves as an ex post measure that compensates for resource depletion caused by excessive job demands, thereby alleviating the adverse effect of exhaustion on innovation performance. Our empirical results based on data from 3823 Chinese firms strongly support our hypotheses.
Environmental Factors, Personal Factors, and the Entrepreneurial Intentions of University Students from the Perspective of the Theory of Planned Behavior: Contributions to a Sustainable Vision of Entrepreneurship in the Business Area
Authors: Slomski Vilma Geni , Tavares de Souza Junior Antonio Vicente , Lavarda Carlos Eduardo Facin , Simão Kaveski Itzhak David , Slomski Valmor , Frois de Carvalho Ronaldo , Fontes de Souza Vasconcelos Ana Lucia
Published: June 2024

This study analyzed the relationship between environmental and cognitive factors and the entrepreneurial intentions of business students from the perspective of the Planned Behavior Theory. Data were collected in the classroom through a questionnaire that was distributed to 229 freshmen in the first year of the Accounting, Economics and Administration course, and the analysis was carried out using the Partial Least Squares - Structural Equation Model (PLS-SEM). It was found that education and conducive business environments are predictive of cognitive factors, such as a favorable evaluation of an entrepreneurial career choice and social pressures, with indirect effects on intentions to undertake entrepreneurship (EI). In addition, entrepreneurial intentions are influenced by cognitive factors, such as a positive evaluation of an entrepreneurial career and perceived entrepreneurial ability. This demonstrates that environmental factors potentiate cognitive factors and, indirectly, the intention to undertake entrepreneurship (EI). It was identified that public policies do not influence the perceived entrepreneurial capacity and that social pressures are not predictive of the surveyed students’ intentions to undertake entrepreneurship. These data require the attention of universities and governments to create environments that are conducive to entrepreneurship and students’ decisions to engage with sustainable projects and business models. It is concluded that environmental factors are predictive of cognitive aspects with positive and significant effects on the entrepreneurial career intentions of university students. An entrepreneurial attitude and the ability to transform ideas into actions create space for the promotion of a student micro-entrepreneur and the cultivation of a sustainable entrepreneurial culture in higher education.
Genetic Reassortment in a Child Coinfected with Two Influenza B Viruses, B/Yamagata Lineage and B/Victoria-Lineage Strains
Authors: Matsuzaki Yoko , Sugawara Kanetsu , Kidoguchi Yuko , Kadowaki Yoko , Shimotai Yoshitaka , Katsushima Yuriko , Katsushima Fumio , Tanaka Shizuka , Matoba Yohei , Komabayashi Kenichi , Aoki Yoko , Mizuta Katsumi
Published: June 2024

We identified a child coinfected with influenza B viruses of B/Yamagata and B/Victoria lineages, in whom we analyzed the occurrence of genetic reassortment. Plaque purification was performed using a throat swab specimen from a 9-year-old child, resulting in 34 well-isolated plaques. The genomic composition of eight gene segments (HA, NA, PB1, PB2, PA, NP, M, and NS genes) for each plaque was determined at the lineage level. Of the 34 plaques, 21 (61.8%) had B/Phuket/3073/2013 (B/Yamagata)-like sequences in all gene segments, while the other 13 (38.2%) were reassortants with B/Texas/02/2013 (B/Victoria)-like sequences in 1–5 of the 8 segments. The PB1 segment had the most B/Victoria lineage genes (23.5%; 8 of 34 plaques), while PB2 and PA had the least (2.9%; 1 of 34 plaques). Reassortants with B/Victoria lineage genes in 2–5 segments showed the same level of growth as viruses with B/Yamagata lineage genes in all segments. However, reassortants with B/Victoria lineage genes only in the NA, PB1, NP, or NS segments exhibited reduced or undetectable growth. We demonstrated that various gene reassortments occurred in a child. These results suggest that simultaneous outbreaks of two influenza B virus lineages increase genetic diversity and could promote the emergence of new epidemic strains.
Hepatitis C Virus as a Possible Helper Virus in Human Hepatitis Delta Virus Infection
Authors: Crobu Maria Grazia , Ravanini Paolo , Impaloni Clotilde , Martello Claudia , Bargiacchi Olivia , Di Domenico Christian , Faolotto Giulia , Macaluso Paola , Mercandino Alessio , Riggi Miriam , Quaglia Vittorio , Andreoni Stefano , Pirisi Mario , Smirne Carlo
Published: June 2024

Previous studies reported that the hepatitis C virus (HCV) could help disseminate the hepatitis D virus (HDV) in vivo through the unrelated hepatitis B virus (HBV), but with essentially inconclusive results. To try to shed light on this still-debated topic, 146 anti-HCV-positive subjects (of whom 91 HCV/HIV co-infected, and 43 with prior HCV eradication) were screened for anti-HDV antibodies (anti-HD), after careful selection for negativity to any serologic or virologic marker of current or past HBV infection. One single HCV/HIV co-infected patient (0.7%) tested highly positive for anti-HD, but with no positive HDV-RNA. Her husband, in turn, was a HCV/HIV co-infected subject with a previous contact with HBV. While conducting a thorough review of the relevant literature, the authors attempted to exhaustively describe the medical history of both the anti-HD-positive patient and her partner, believing it to be the key to dissecting the possible complex mechanisms of HDV transmission from one subject to another, and speculating that in the present case, it may have been HCV itself that behaved as an HDV helper virus. In conclusion, this preliminary research, while needing further validation in large prospective studies, provided some further evidence of a role of HCV in HDV dissemination in humans.
Evolution of an Extended Pathogenicity Motif in VP2 of Infectious Pancreatic Necrosis Virus Isolates from Farmed Rainbow Trout in Turkey
Authors: Tamer Cuneyt , Ulrich Kristina , Di Paola Nicholas , Kurucay Hanne Nur , Albayrak Harun , Weidmann Manfred
Published: June 2024

Infectious pancreatic necrosis virus (IPNV) causes economic losses with a highly variable mortality rate worldwide, especially in rainbow trout. The virus has a double-stranded bi-partite RNA genome designated segment A and B. New complete genome sequences of nine rainbow trout isolates from Turkey were determined and subjected to phylogenetic analysis, identifying all as genotype 5 (serotype Sp). A time-dependent change in the extended pathogenicity motif of VP2 from P217T221A247 (PTA) to PTE P217T221E247 over a period of 10 years was identified. A wider analysis of 99 IPNV sequences from Turkey and Iran revealed the emergence of the motif PTE from 2007 to 2017, inducing significant morbidity in fry by 2013. In fact, displacement of the PTA motif, by the PTE motif in IPNV isolates appeared to be connected to a production peak of rainbow trout in 2013. An additional CAI analysis provided more evidence, indicating that rainbow trout culture in Turkey has an influence on the evolution of IPNV.
Combinations of Bacteriophage Are Efficacious against Multidrug-Resistant Pseudomonas aeruginosa and Enhance Sensitivity to Carbapenem Antibiotics
Authors: Kovacs Christopher J. , Rapp Erika M. , Rankin William R. , McKenzie Sophia M. , Brasko Brianna K. , Hebert Katherine E. , Bachert Beth A. , Kick Andrew R. , Burpo F. John , Barnhill Jason C.
Published: June 2024

The Gram-negative ESKAPE bacterium Pseudomonas aeruginosa has become a pathogen of serious concern due its extensive multi-drug resistance (MDR) profile, widespread incidences of hospital-acquired infections throughout the United States, and high occurrence in wound infections suffered by warfighters serving abroad. Bacteriophage (phage) therapy has received renewed attention as an alternative therapeutic option against recalcitrant bacterial infections, both as multi-phage cocktails and in combination with antibiotics as synergistic pairings. Environmental screening and phage enrichment has yielded three lytic viruses capable of infecting the MDR P. aeruginosa strain PAO1. Co-administration of each phage with the carbapenem antibiotics ertapenem, imipenem, and meropenem generated enhanced overall killing of bacteria beyond either phage or drug treatments alone. A combination cocktail of all three phages was completely inhibitory to growth, even without antibiotics. The same 3× phage cocktail also disrupted PAO1 biofilms, reducing biomass by over 75% compared to untreated biofilms. Further, the phage cocktail demonstrated broad efficacy as well, capable of infecting 33 out of 100 diverse clinical isolate strains of P. aeruginosa. Together, these results indicate a promising approach for designing layered medical countermeasures to potentiate antibiotic activity and possibly overcome resistance against recalcitrant, MDR bacteria such as P. aeruginosa. Combination therapy, either by synergistic phage-antibiotic pairings, or by phage cocktails, presents a means of controlling mutations that can allow for bacteria to gain a competitive edge.