bluuebunny/crossref_metadata_2025 huggingface dataset:
Supercooling: a promising technique for prolonged preservation in solid organ transplantation, and early perspectives in vascularized composite allografts
Authors: Berkane Yanis , Hayau Justine , Filz von Reiterdank Irina , Kharga Anil , Charlès Laura , Mink van der Molen Abele B. , Coert J. Henk , Bertheuil Nicolas , Randolph Mark A. , Cetrulo Curtis L. , Longchamp Alban , Lellouch Alexandre G. , Uygun Korkut
Published: October 2023

Ex vivo preservation of transplanted organs is undergoing spectacular advances. Machine perfusion is now used in common practice for abdominal and thoracic organ transportation and preservation, and early results are in favor of substantially improved outcomes. It is based on decreasing ischemia-reperfusion phenomena by providing physiological or sub-physiological conditions until transplantation. Alternatively, supercooling techniques involving static preservation at negative temperatures while avoiding ice formation have shown encouraging results in solid organs. Here, the rationale is to decrease the organ's metabolism and need for oxygen and nutrients, allowing for extended preservation durations. The aim of this work is to review all advances of supercooling in transplantation, browsing the literature for each organ. A specific objective was also to study the initial evidence, the prospects, and potential applications of supercooling preservation in Vascularized Composite Allotransplantation (VCA). This complex entity needs a substantial effort to improve long-term outcomes, marked by chronic rejection. Improving preservation techniques is critical to ensure the favorable evolution of VCAs, and supercooling techniques could greatly participate in these advances.
Survival and persistence of foodborne pathogenic and indicator bacteria in spatially separated integrated crop-livestock farms
Authors: Goodwyn Brian , Millner Patricia , Punchihewage-Don Anuradha Jeewantha , Schwarz Melinda , Hashem Fawzy , Bowers John , Biswas Debabrata , Parveen Salina
Published: October 2023

Certified organic production prohibits chemical fertilizer use but permits the application of biological soil amendments of animal origin (BSAAOs) to croplands for fertilizing soil. For fresh produce likely to be consumed uncooked, the United States Department of Agriculture National Organic Program (USDA-NOP) stipulates a 90-day withholding period between BSAAO application and crop harvest for produce not in direct contact with soil to reduce concerns about potential pathogen contamination from BSAAOs. For fresh produce crops in direct contact with soil this withholding period is 120-days. In this study, survival and pathogenesis of three foodborne pathogens and indicator bacteria in BSAAO-amended soils were analyzed with their potential to transfer to produce harvested from three organic, integrated crop-livestock farms (ICLFs) on the Maryland Eastern Shore. In total, 428 manure/compost, soil, untreated produce, and water samples were collected before/after BSAAO incorporation and monthly for 180-days. Samples were assessed for the presence of Listeria monocytogenes (L. monocytogenes), Salmonella, and Shiga toxin-producing E. coli (STEC) with associated virulence factor (VF) genes (STEC/VF-genes), and for aerobic plate count (APC), generic E. coli (gEC), and total coliform levels using standard methods and polymerase chain reaction (PCR). Overall, 18.4% (26/141) of produce samples were positive for at least one tested pathogen, with STEC/VF-genes being detected the most. Produce was contaminated with at least one pathogen and indicators 90- and 120-days post-BSAAO incorporation. Salmonella wasn’t detected in manure/compost samples, and 6.7% (1/15) and 66.7% (10/15) of manure/compost samples were positive for L. monocytogenes and STEC/VF-genes, respectively. In BSAAO-amended soils, 29.1% (74/254) were positive for at least one of the tested pathogens in this study. STEC/VF-genes were detected in 24.0% (61/254) of soils, while L. monocytogenes and Salmonella were detected in 4.3% (11/254) and 0.8% (2/254) of soils, respectively. These three pathogens were detected in soils directly after BSAAO incorporation but were followed by a reduced prevalence. However, pathogens were detected in soils 90- and/or 120-days post-application. The results of this study indicated pathogens associated with BSAAOs (contaminated manures/composts) can persist after soil incorporation and transfer to harvestable produce grown on ICLF’s on the Maryland Eastern Shore. Therefore, further research into withholding periods and other mitigation strategies is necessary to mitigate pathogen contamination risks on ICLFs.
Application of Opposition-Based Learning Jumping Spider Optimization Algorithm in Gas Turbine Coupled Cooling System
Authors: Wang Dazhi , Li Tianyi , Ni Yongliang , Song Keling , Li Yanming
Published: October 2023

A gas turbine cooling system is a typical multivariable, strongly coupled, nonlinear system; however, the randomness and large disturbances make it difficult to control the variables precisely. In order to solve the problem of precise process control for multi-input and multi-output coupled systems with flow, pressure, and temperature, this article conducts the following research: (1) Designing a secondary circuit for waste hot water and establishing a water-circulating gas turbine cooling system to improve the efficiency of waste heat utilization. (2) Identifying the coupled system model and establishing a mathematical model of the coupling relationship based on the characteristic data of input and output signals in the gas turbine cooling system. (3) Designing a coupled-system decoupling compensator to weaken the relationships between variables, realizing the decoupling between coupled variables. (4) An Opposition-based Learning Jumping Spider Optimization Algorithm is proposed to be combined with the PID control algorithm, and the parameters of the PID controller are adjusted to solve the intelligent control problems of heat exchanger water inlet flow rate, pressure, and temperature in the gas turbine cooling system. After simulation verification, the gas turbine cooling system based on an Opposition-based Learning Jumping Spider Optimization Algorithm can realize the constant inlet flow rate, with an error of no more than 1 m3/h, constant inlet water temperature, with an error of no more than 0.2 °C, and constant main-pipe pressure, with an error of no more than 0.01 MPa. Experimental results show that a gas turbine cooling system based on the Opposition-based Learning Jumping Spider Optimization Algorithm can accurately realize the internal variable controls. At the same time, it can provide a reference for decoupling problems in strongly coupled systems, the controller parameter optimization problems, and process control problems in complex systems.
Optimization and Accuracy Analysis of a Soil–Planter Model during the Sowing Period of Wheat after a Rice Stubble Based Discrete Element Method
Authors: Luo Weiwen , Chen Xulei , Guo Kai , Qin Mingyang , Wu Feng , Gu Fengwei , Hu Zhichao
Published: October 2023

The soil during the sowing period of wheat after rice stubble cannot be accurately described by existing models and parameters with DEM because of its high moisture content and strong viscosity. The purpose of this study is to conduct an overall simulation of high-viscosity paddy soil and to analyze the accuracy of the model. Based on the results of an unconfined compression test and shear test, the range of bond parameters is preliminarily determined by a simulation test. Through the P-BD test and RSM test, the parameters with significant influence are determined to be normal stiffness per unit area (SN), shear stiffness per unit area (SS), and critical shear stress (CS), and an optimized combination of these parameters is obtained. Based on the optimized model, the error range and error generation mechanism of the model are analyzed under different operating parameters. The results show that the optimal parameter combination is SN of 1.07 × 107 N/m3, SS of 0.70 × 107 N/m3, and CS of 0.35 × 105 Pa, corresponding to a compression force of 120.1 N and a shear force of 7.70 N. With an increase in forward speed or seeding quantity or a decrease in rotary plowing speed, the model accuracy tends to increase, and the range of relative errors was found to be from 8.8% to 28.4%. The results can provide a research basis for the study of the motion state of seeds under soil. It can also further enrich parameter data of soil discrete element simulation models and provide a reference for related research studies.
The Application of Artificial Intelligence Models for Food Security: A Review
Authors: Sarku Rebecca , Clemen Ulfia A. , Clemen Thomas
Published: October 2023

Emerging technologies associated with Artificial Intelligence (AI) have enabled improvements in global food security situations. However, there is a limited understanding regarding the extent to which stakeholders are involved in AI modelling research for food security purposes. This study systematically reviews the existing literature to bridge the knowledge gap in AI and food security, focusing on software modelling perspectives. The study found the application of AI models to examine various indicators of food security across six continents, with most studies conducted in sub-Saharan Africa. While research organisations conducting AI modelling were predominantly based in Europe or the Americas, their study communities were in the Global South. External funders also supported AI modelling research on food security through international universities and research institutes, although some collaborations with local organisations and external partners were identified. The analysis revealed three patterns in the application of AI models for food security research: (1) the exclusive utilisation of AI models to assess food security situations, (2) stakeholder involvement in some aspects of the AI modelling process, and (3) stakeholder involvement in AI modelling for food security through an iterative process. Overall, studies on AI models for food security were primarily experimental and lacked real-life implementation of the results with stakeholders. Consequently, this study concluded that research on AI, which incorporates feedback and/or the implementation of research outcomes for stakeholders, can contribute to learning and enhance the validity of the models in addressing food security challenges.
One Welfare: Assessing the Effects of Drought and the COVID-19 Pandemic on Farmers’ Well-Being and Their Perception of Goats’ Welfare
Authors: Larrondo Cristian , Guevara Raúl David , Calderón-Amor Javiera , Munoz Carolina , Cáceres Carolina , Alvarado Mabeley , Fresno Marcela , Di Pillo Francisca
Published: October 2023

Considering the interconnections between human well-being, animal welfare, and the environment, this study aimed to investigate the impacts of drought and the COVID-19 pandemic on small-scale goat farmers’ well-being and their perception of goats’ welfare following the One Welfare framework. Using a telephone survey, close-ended questions, and Likert scales, we assessed the impacts of drought and the COVID-19 pandemic on human well-being and animal welfare in the Coquimbo region of Chile. The DASS-21 questionnaire was used to evaluate farmers’ mental health. Goat farmers perceived the scarcity of water and food for animals as factors that negatively affected animal productivity and welfare and caused an increase in farmers’ stress levels. Farmers who had not been visited by a veterinarian showed higher levels of stress than those who received one visit during the year (M = 10 vs. 2, p = 0.025). Additionally, farmers who perceived better welfare of their animals showed lower levels of depression (rs = −0.17, p = 0.048), anxiety (rs = −0.21, p = 0.016), and stress (rs = −0.33, p < 0.001). These findings emphasize the importance of addressing farmers’ mental health and veterinary support as crucial aspects to ensure both goat welfare and farm productivity.
Genome-Wide Analysis of Escherichia coli Isolated from Dairy Animals Identifies Virulence Factors and Genes Enriched in Multidrug-Resistant Strains
Authors: Haley Bradd J. , Kim Seon Woo , Salaheen Serajus , Hovingh Ernest , Van Kessel Jo Ann S.
Published: October 2023

The gastrointestinal tracts of dairy calves and cows are reservoirs of antimicrobial-resistant bacteria (ARB), which are present regardless of previous antimicrobial therapy. Young calves harbor a greater abundance of resistant bacteria than older cows, but the factors driving this high abundance are unknown. Here, we aimed to fully characterize the genomes of multidrug-resistant (MDR) and antimicrobial-susceptible Escherichia coli strains isolated from pre-weaned calves, post-weaned calves, dry cows, and lactating cows and to identify the accessory genes that are associated with the MDR genotype to discover genetic targets that can be exploited to mitigate antimicrobial resistance in dairy farms. Results indicated that both susceptible and resistant E. coli isolates recovered from animals on commercial dairy operations were highly diverse and encoded a large pool of virulence factors. In total, 838 transferrable antimicrobial resistance genes (ARGs) were detected, with genes conferring resistance to aminoglycosides being the most common. Multiple sequence types (STs) associated with mild to severe human gastrointestinal and extraintestinal infections were identified. A Fisher’s Exact Test identified 619 genes (ARGs and non-ARGs) that were significantly enriched in MDR isolates and 147 genes that were significantly enriched in susceptible isolates. Significantly enriched genes in MDR isolates included the iron scavenging aerobactin synthesis and receptor genes (iucABCD-iutA) and the sitABCD system, as well as the P fimbriae pap genes, myo-inositol catabolism (iolABCDEG-iatA), and ascorbate transport genes (ulaABC). The results of this study demonstrate a highly diverse population of E. coli in commercial dairy operations, some of which encode virulence genes responsible for severe human infections and resistance to antibiotics of human health significance. Further, the enriched accessory genes in MDR isolates (aerobactin, sit, P fimbriae, and myo-inositol catabolism and ascorbate transport genes) represent potential targets for reducing colonization of antimicrobial-resistant bacteria in the calf gut.
Spine Deformity Assessment for Scoliosis Diagnostics Utilizing Image Processing Techniques: A Systematic Review
Authors: Amran Nurhusna Najeha , Basaruddin Khairul Salleh , Ijaz Muhammad Farzik , Yazid Haniza , Basah Shafriza Nisha , Muhayudin Nor Amalina , Sulaiman Abdul Razak
Published: October 2023

Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, hypo/hyperlordosis, or hypo/hyperkyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person’s diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review the recent works on spinal deformity assessment for scoliosis diagnosis utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022 using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.
Special Issue on Artificial Intelligence in Medical Imaging: The Beginning of a New Era
Authors: Nardi Cosimo
Published: October 2023

Artificial intelligence (AI) can be considered the real revolution of the 21st century [...]
Predictive Torque Control of the Vehicle’s Permanent-Magnet Synchronous-Motor Model Based on Multi-Objective Sorting
Authors: Zheng Weiguang , Geng Quanfu , Xu Xiaohong , Liu Zhixiang
Published: October 2023

The permanent-magnet synchronous motor (PMSM), with the advantages of low energy consumption and stable operation, is considered a green power source to replace gasoline engines. Motor control is the core problem of the electric-drive system, so it is important to study the high-performance motor control algorithm. The traditional PMSM control strategy has problems such as torque pulsation, large overshoot, and parameters which are not easy to adjust. This work proposes a new model-predictive torque control (MPTC) based on multi-objective ranking for these issues. The Romberg observer was utilized to accurately estimate motor flux and torque across a wide range of speeds and ensure optimal performance of the MPTC. The optional voltage vectors were classified using graph theory. The model’s cost function was optimized and the control delay caused by hardware processing was compensated by a modified Euler method. A multi-objective ranking method was used to avoid the offline selection of MPTC weight coefficients. Additionally, one ranking method was used to reduce the complexity of the algorithm for multiple objectives. Based on the simulation results, the newly proposed MPTC method, when compared with traditional approaches, reduced the total harmonic distortion from 2.78% to 2.26%. Torque ripple decreased by approximately 58.4%, and the switching frequency was reduced by 3.05%, lowering the inverter’s switching losses. Therefore, the newly proposed MPTC had faster torque response, reduced computation time, and less torque pulsation, which further improved the dynamic performance of the permanent-magnet synchronous motor.
City Architectural Color Recognition Based on Deep Learning and Pattern Recognition
Authors: Zhuang Yi , Guo Chenyi
Published: October 2023

The collection of information about buildings and their colors is an important aspect of urban planning. The intelligent recognition of buildings using image information plays a significant role in the development of smart cities and urban planning. This thesis proposes a building color-recognition technique based on morphological features utilizing convolutional neural networks and the K-means clustering algorithm of image-recognition technology. The proposed method can identify buildings in images and classify them into two categories, buildings and panoramas, for color extraction and matching. This method involves training convolutional neural networks on deep learning so that the buildings can be differentiated and segmented. Subsequently, the K-means algorithm extracts colors from the segmented building images. The extracted building category, color, and text information were analyzed to obtain a comparison and analysis results of buildings and panoramas. The results demonstrated that the system is capable of accurately segmenting buildings, as well as extracting colors from both buildings and panoramas separately. It can also contribute to the extraction and presentation of color schemes in smart city planning and provide valuable insights for the future development of urban colors.
Receding Horizon Optimization for Cooperation of Connected Vehicles at Signal-Free Intersections under Mixed-Automated Traffic
Authors: Gong Jian , Chen Weijie , Zhou Ziyi
Published: October 2023

This paper proposes a distributed coordination scheme for connected vehicles, including automated vehicles (AVs) and manual vehicles (MVs), at signal-free intersections. The cooperation issue of vehicles at an intersection is formulated into a multi-objective optimization problem that aims to eliminate conflicts and improve traffic mobility and fuel economy. For this purpose, the future trajectories of AVs and MVs are predicted by the respective car-following models, and are shared with neighboring vehicles in conflict relationships. The proposed scheme optimizes the sum of the performance of AVs within the cooperative zone in a prediction horizon. A distributed optimization algorithm in the receding horizon is presented to obtain the local optimal solutions, and is tested in simulations with different demand levels and penetration rates of AVs. The results show that the proposed scheme reduces travel time by 29.7–45.5% and 34.5–49.2%, and decreases fuel consumption by 27.6–35.3% and 21.6–29.9% under 70–100% penetration rates of AVs, compared to the no-control operation and fixed-time signal control strategy. In addition, a comparison simulation with the strategy of jointly optimizing the vehicle trajectory and signal timing is conducted to evaluate the relative merits of the proposed scheme.
Multi-Camera Person Re-Identification Based on Trajectory Data
Authors: Mendes Diogo , Correia Simão , Jorge Pedro , Brandão Tomás , Arriaga Patrícia , Nunes Luís
Published: October 2023

This study presents a trajectory-based person re-identification algorithm, embedded in a tool to detect and track customers present in a large retail store, in a multi-camera environment. The customer trajectory data are obtained from video surveillance images captured by multiple cameras, and customers are detected and tracked along the frames that compose the videos. Due to the characteristics of a multi-camera environment or the occurrence of occlusions, caused by objects such as shelves or counters, different identifiers are assigned to each person when, in fact, they should be identified with a unique identifier. Thus, the proposed tool tries to solve this problem in a scenario where there are constraints in using images of people due to data privacy concerns. The results show that our method was able to correctly re-identify the customers present in the store with a re-identification rate of 82%.
Time-Varying Topology Formation Reconfiguration Control of the Multi-Agent System Based on the Improved Hungarian Algorithm
Authors: Zhang Yingxue , Chen Meng , Chen Jinbao , Chen Chuanzhi , Yu Hongzhi , Zhang Yunxiao , Deng Xiaokang
Published: October 2023

Distributed time-varying formation technology for multi-agent systems is recently become a research hotspot in formation control field. However, the formation reconfiguration control technology for agents that randomly appeared to fail during maneuvers is rarely studied. In this paper, the topological relations between intelligence are designed by graph theory to simplify the cooperative interaction between multi-agent systems. Moreover, this paper constructs the time-varying configuration of the target formation based on the rigidity graph theory and leader–follower strategy. Drawing on the establishment of the expert experience database in a collaborative process, we innovatively propose the establishment of a graphic library to help the multi-agent system quickly form an affine transformation as soon as it is disabled. Secondly, the improved Hungarian algorithm is adopted to allocate the target point when the first failure occurs. This algorithm incorporates a gradient weighting factor from the auction algorithm to improve the speed of system reconfiguration with minimum path cost. On this basis, a distributed multi-agent control law based on consistency theory is established, and the system’s stability can be guaranteed via Lyapunov functions. Finally, the simulation results demonstrate the feasibility and effectiveness of the proposed formation reconfiguration control algorithm in a collaborative environment.
Analytic Investigation of Hooked Stirrups on Seismic Behavior of Reinforced Concrete 3D Frame Buildings
Authors: Karasin Ibrahim Baran
Published: October 2023

Ensuring the safety and stability of buildings during earthquakes is of utmost importance. This can be achieved by assessing the seismic performance of reinforced concrete structures with consideration of design details. This study focused on the seismic behavior of reinforced concrete buildings by comparing the effects of two different types of stirrups, namely those with a 135° angled end-hook shape and straight hooks, with variation of concrete strength. Pushover analysis of a sample building was performed to determine the effect of hook shape on stirrup reinforcement with a constant volumetric ratio for various concrete strength classes. The results of the analysis indicated significant differences in concrete strength and seismic behavior between the two stirrup configurations. The hooked stirrups demonstrated superior energy dissipation capability and ductility, which led to better seismic performance compared to unhooked stirrups across varying levels of concrete strength. To extend the investigation, the study compared the Mander et al., Kent–Scott–Park, and Kappos–Konstantinidis concrete models with different concrete classes (C50-C25-C20-C16-C10). The findings emphasized the importance of stirrup configuration in the design of earthquake-resistant structures. The study concluded that RC structural performance with the 135-degree hooked concrete members exhibited much better behavior of the 90-degree members for the various concrete strength. In this way, it has been revealed the arrangement and detailing of reinforcement in the construction beams and columns improves the governing effect on seismic structural performance.
Optimal Control Strategy for Floating Offshore Wind Turbines Based on Grey Wolf Optimizer
Authors: Ferahtia Seydali , Houari Azeddine , Machmoum Mohamed , Ait-Ahmed Mourad , Saim Abdelhakim
Published: October 2023

Due to the present trend in the wind industry to operate in deep seas, floating offshore wind turbines (FOWTs) are an area of study that is expanding. FOWT platforms cause increased structural movement, which can reduce the turbine’s power production and increase structural stress. New FOWT control strategies are now required as a result. The gain-scheduled proportional-integral (GSPI) controller, one of the most used control strategies, modifies the pitch angle of the blades in the above-rated zone. However, this method necessitates considerable mathematical approximations to calculate the control advantages. This study offers an improved GSPI controller (OGSPI) that uses the grey wolf optimizer (GWO) optimization method to reduce platform motion while preserving rated power output. The GWO chooses the controller’s ideal settings. The optimization objective function incorporates decreasing the platform pitch movements, and the resulting value is used to update the solutions. The effectiveness of the GWO in locating the best solutions has been evaluated using new optimization methods. These algorithms include the COOT optimization algorithm, the sine cosine algorithm (SCA), the African vultures optimization algorithm (AVOA), the Harris hawks optimization (HHO), and the whale optimization algorithm (WOA). The final findings show that, compared to those caused by the conventional GSPI, the suggested OGSPI may successfully minimize platform motion by 50.48%.
Research on the Comprehensive Evaluation Method of Driving Behavior of Mining Truck Drivers in an Open-Pit Mine
Authors: Zhang Zhao , Zhang Ruixin , Sun Jiandong
Published: October 2023

Trucking is an important production link in most open-pit mines, and its transportation cost accounts for more than 50% of the total production cost of open-pit mines. The quality of the driver’s driving behavior plays a crucial role in the fine control of the production cost of transportation. Different from the previous evaluation studies of drivers’ driving behavior in open-pit mines, which mainly took safety driving behavior index as a factor variable, this paper puts forward a comprehensive evaluation method of driving behavior of mining truck drivers, which takes both safety driving and transportation cost as factor variables. Taking the mining truck as the research object, firstly, a scientific and reasonable data collection scheme is established, and the data information characterizing the transport state of the mining truck is obtained through data collection and analysis. Secondly, the RKNN algorithm of time series prediction and the wavelet analysis method are used to achieve noise reduction and missing processing of the original data so as to obtain accurate sample data. Then, taking the principal component analysis method as the entry point, through constructing the principal component analysis theory model, the key index system representing safe driving behavior and transportation cost is established to realize the comprehensive evaluation of the driving behavior of mining truck drivers, and the evaluation system of “standard driving”, “prudent driving” and “aggressive driving” of mining truck drivers is formulated. The results show that after noise reduction, the accuracy of mining car operation data can be improved by 7~12%, and the transportation cost can be reduced by about 5% after the driver’s operation behavior is standardized.
Validation of MERRA-2 AOT Modeling Data over China Using SIAVNET Measurement
Authors: Shi Shuaiyi , Zhu Hao , Wang Xing
Published: October 2023

The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) Aerosol Optical Thickness (AOT) dataset is a consistent and comprehensive dataset combining observations from various satellite instruments and other sources with a numerical model, supporting climate studies, atmospheric modeling, air quality monitoring, and environmental research. Due to the uneven and sparse distribution of the Aerosol Robotic Network (AERONET) in China, the validation for the MERRA-2 AOT dataset over China is inadequate. The construction of the National Civil Space Infrastructure Satellite Aerosol Product Validation Network (SIAVNET) is helpful to compensate for MERRA-2 AOT dataset validation over China. The validation results show that the accuracy of the MERRA-2 AOT goes down along with the aerosol loading in the atmosphere increase. In general, when the AOT is less than 1.0, the slope can reach 0.712 with R2 = 0.584. The percentage of data pairs that fall within the GCOS minimum requirement is less than 60%. Research also shows that MERRA-2 has a lower simulation quality of AOT at high altitudes than at low altitudes in China. Additionally, MERRA-2’s AOT simulation quality varies by season. Simulated quality is worst in spring, improving in subsequent seasons. During the winter season, simulations are of the highest quality.
Immune Checkpoints in Solid Organ Transplantation
Authors: Del Bello Arnaud , Treiner Emmanuel
Published: October 2023

Allogenic graft acceptance is only achieved by life-long immunosuppression, which comes at the cost of significant toxicity. Clinicians face the challenge of adapting the patients’ treatments over long periods to lower the risks associated with these toxicities, permanently leveraging the risk of excessive versus insufficient immunosuppression. A major goal and challenge in the field of solid organ transplantation (SOT) is to attain a state of stable immune tolerance specifically towards the grafted organ. The immune system is equipped with a set of inhibitory co-receptors known as immune checkpoints (ICs), which physiologically regulate numerous effector functions. Insufficient regulation through these ICs can lead to autoimmunity and/or immune-mediated toxicity, while excessive expression of ICs induces stable hypo-responsiveness, especially in T cells, a state sometimes referred to as exhaustion. IC blockade has emerged in the last decade as a powerful therapeutic tool against cancer. The opposite action, i.e., subverting IC for the benefit of establishing a state of specific hypo-responsiveness against auto- or allo-antigens, is still in its infancy. In this review, we will summarize the available literature on the role of ICs in SOT and the relevance of ICs with graft acceptance. We will also discuss the possible influence of current immunosuppressive medications on IC functions.
Advancements in Complementary Metal-Oxide Semiconductor-Compatible Tunnel Barrier Engineered Charge-Trapping Synaptic Transistors for Bio-Inspired Neural Networks in Harsh Environments
Authors: Lee Dong-Hee , Park Hamin , Cho Won-Ju
Published: October 2023

This study aimed to propose a silicon-on-insulator (SOI)-based charge-trapping synaptic transistor with engineered tunnel barriers using high-k dielectrics for artificial synapse electronics capable of operating at high temperatures. The transistor employed sequential electron trapping and de-trapping in the charge storage medium, facilitating gradual modulation of the silicon channel conductance. The engineered tunnel barrier structure (SiO2/Si3N4/SiO2), coupled with the high-k charge-trapping layer of HfO2 and high-k blocking layer of Al2O3, enabled reliable long-term potentiation/depression behaviors within a short gate stimulus time (100 μs), even under elevated temperatures (75 and 125 °C). Conductance variability was determined by the number of gate stimuli reflected in the maximum excitatory postsynaptic current (EPSC) and the residual EPSC ratio. Moreover, we analyzed the Arrhenius relationship between the EPSC as a function of the gate pulse number (N = 1–100) and the measured temperatures (25, 75, and 125 °C), allowing us to deduce the charge trap activation energy. A learning simulation was performed to assess the pattern recognition capabilities of the neuromorphic computing system using the modified National Institute of Standards and Technology datasheets. This study demonstrates high-reliability silicon channel conductance modulation and proposes in-memory computing capabilities for artificial neural networks using SOI-based charge-trapping synaptic transistors.
Isothermal Amplification and Hypersensitive Fluorescence Dual-Enhancement Nucleic Acid Lateral Flow Assay for Rapid Detection of Acinetobacter baumannii and Its Drug Resistance
Authors: Wang Qian , Zheng Shuai , Liu Yong , Wang Chongwen , Gu Bing , Zhang Long , Wang Shu
Published: October 2023

Acinetobacter baumannii (A. baumannii) is among the main pathogens that cause nosocomial infections. The ability to rapidly and accurately detect A. baumannii and its drug resistance is essential for blocking secondary infections and guiding treatments. In this study, we reported a nucleic acid fluorescent lateral flow assay (NFLFA) to identify A. baumannii and carbapenem-resistant A. baumannii (CRAB) in a rapid and quantitative manner by integrating loop-mediated isothermal amplification (LAMP) and silica–based multilayered quantum dot nanobead tag (Si@MQB). First, a rapid LAMP system was established and optimised to support the effective amplification of two bacterial genes in 35 min. Then, the antibody-modified Si@MQB was introduced to capture the two kinds of amplified DNA sequences and simultaneously detect them on two test lines of a LFA strip, which greatly improved the detection sensitivity and stability of the commonly used AuNP-based nucleic acid LFA. With these strategies, the established LAMP-NFLFA achieved detection limits of 199 CFU/mL and 287 CFU/mL for the RecA (house-keeping gene) and blaOXA-23 (drug resistance gene) genes, respectively, within 43 min. Furthermore, the assay exhibited good repeatability and specificity for detecting target pathogens in real complex specimens and environments; thus, the proposed assay undoubtedly provides a promising and low-cost tool for the on-site monitoring of nosocomial infections.
Research Progress in Diffusion Spectrum Imaging
Authors: Sun Fenfen , Huang Yingwen , Wang Jingru , Hong Wenjun , Zhao Zhiyong
Published: October 2023

Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications.
Influence of Positive Thinking Ideology on Physician Representations of Cancer
Authors: Higuita-Gutiérrez Luis Felipe , Estrada-Mesa Diego Alejandro , Salas-Zapata Walter Alfredo , Cardona-Arias Jaiberth Antonio
Published: October 2023

To understand the influence of positive thinking ideology on cancer representations among physicians in the city of Medellín. Methods: This qualitative study was conducted on the basis of the theoretical and methodological elements of Corbin and Strauss’s grounded theory. Fourteen physicians were included and selected according to the criteria of maximum variation for education, years of study, and personal and family history of cancer. The information was collected through semi-structured interviews and analyzed with open, axial, and selective coding. Results: the ideology of positive thinking has managed to permeate the medical discourse and the representations that they form about the etiology and treatment of cancer. Physicians place the mind, emotions, attitude, and positive thinking as determinants of the origin of the disease and the response to therapy. To argue this link, they use two strategies: (i) a sophisticated and specialized discourse that involves relationships among thoughts, genetics, the neurological, immune and endocrine system and (ii) a mystical and less rational discourse that emphasizes the omnipotence of the mind and thoughts. In no case was the idea of positive thinking rejected or in disagreement with this style of thinking expressed. Conclusion: The fact of linking the disease with mental factors refers to the mind–body dualism and generates a responsibility of the patients on the etiology and therapeutics of the disease, as well as an erasure of the social and political determinants of cancer. The technical discourse and the symbolic capital of physicians offer scientific legitimacy to these ideas and can become performative for patients.
The Mental Health Implications of Corporate Social Responsibility: The Significance of the Sense-Making Process and Prosocial Motivation
Authors: Kim Byung-Jik , Kim Min-Jik , Lee Dong-gwi
Published: October 2023

As corporate social responsibility (CSR) has progressively ascended to prominence among academics and industry professionals, numerous studies have embarked on examining its impact on employees’ perceptions, attitudes, and behaviors. Notwithstanding, the current body of research has predominantly overlooked the influence of CSR on employees’ mental health, encompassing depression, anxiety, and burnout. In order to acknowledge the critical role of employee mental health within an organization, our exploration is focused on discerning the effect of CSR on depressive states. Furthermore, our paper undertakes a thorough analysis of the link between CSR and depression, probing its underlying processes and potential contingent factors. We posit that CSR can alleviate the incidence of employee depression by amplifying the sense of meaningfulness that work provides. Moreover, the element of prosocial motivation among employees may act as a positive moderating variable that intensifies the beneficial effect of CSR on the sense of meaningfulness derived from work. By relying on data obtained through a tripartite online survey involving 214 South Korean workers, this paper scrutinized the proposed hypotheses via the application of moderated mediation analysis with structural equation modeling. We contend that the insights yielded by this study bear significant theoretical and practical implications.
A Review of Construction Program Delivery Attributes: Bibliometric Analysis of Two Decades
Authors: Taheriboshrouyeh Mehdi , Sandanayake Malindu , Fragomeni Sam
Published: October 2023

In scholarly construction management, “program” denotes terminologies like “mega-project” and “infrastructure project”. Within this framework, the Construction Program Delivery (CPD) system is an indispensable mechanism affecting the entire lifecycle of these complex endeavours. The CPD system harmonises an arrangement of crucial delivery attributes to achieve successful outcomes, rendering the elucidation of these attributes a scholarly imperative. Numerous studies have identified multiple attributes that impact delivery strategies in the construction industry. However, only a limited number of studies have focused explicitly on the CPD attributes. Hence, the study aims to explore the main drivers of CPD methods based on a systematic review, including a bibliometric analysis over the current century in existing literature. It also addresses current research trends and gaps in the delivery context concerning mega projects. The two major-step research methodology involves a bibliometric assessment and determining key delivery attributes. A bibliometric analysis was conducted using 639 journal articles focused on CPD. Ultimately, the analysis of the findings and existing knowledge of the CPD literature have revealed that researchers, as well as construction agencies, have emphasised regulatory and technical aspects embedded within a socio-economic context conveying normative and cultural attributes when addressing CPD. These triple aspects of the delivery strategy have been considered by scholars simultaneously for a successful construction program.
Exploring the Potential of Polypropylene Fibers and Bacterial Co-Culture in Repairing and Strengthening Geopolymer-Based Construction Materials
Authors: Griño Albert A. , Soriano Hannah Shane P. , Promentilla Michael Angelo B. , Ongpeng Jason Maximino C.
Published: October 2023

This study explored self-healing in geopolymer mortar cured at ambient temperature using polypropylene fibers and bacterial co-cultures of Bacillus subtilis and Bacillus megaterium. Damage degree, compressive strength, ultrasonic pulse velocity (UPV), strength-regain percentage, and self-healing percentage were evaluated. A full factorial design was used, which resulted in an eight-run complete factorial design with four levels in the first factor (polypropylene content: 0%, 0.25%, 0.5%, and 0.75%) and two levels in the second factor (bacteria concentration: 0 (without) and 1 (with)). The results indicate that increasing the polypropylene fiber content enhanced strength regains up to 199.97% with 0.75% fibers and bacteria. The bacteria alone improved strength-regain percentages by 11.22% through mineral precipitation. The analysis of variance (ANOVA) showed no interaction between fibers and bacteria, but both independently improved the compressive strength. Only bacterial samples exhibited positive self-healing, ranging from 16.77 to 147.18%. The analysis using a scanning electron microscope with energy dispersive X-ray (SEM-EDX) and X-ray fluorescence (XRF) also revealed greater calcite crystal formation in bacterial samples, increasing the strength-regain and self-healing percentages. The results demonstrate that polypropylene fibers and bacteria cultures could substantially enhance the strength, durability, and self-healing percentage of geopolymer mortars. The findings present the potential of a bio-based self-healing approach for sustainable construction and repair materials.
Damage Analysis and Quality Control of Carbon-Reinforced Concrete Beams Based on In Situ Computed Tomography Tests
Authors: Liebold Frank , Wagner Franz , Giese Josiane , Grzesiak Szymon , de Sousa Christoph , Beckmann Birgit , Pahn Matthias , Marx Steffen , Curbach Manfred , Maas Hans-Gerd
Published: October 2023

Carbon-reinforced concrete (CRC) is increasingly utilized in construction, due to its unique properties, such as corrosion resistance, high-tensile strength, and durability. Understanding its behavior under different loads is crucial to ensuring its safe and effective use in various construction applications. In this study, three-point bending tests were performed in combination with large-scale in situ computed tomography (CT). This paper presents the related three- and four-dimensional evaluation methods, with emphasis on crack width and quality control. The focus was on large CRC beams, with cross-sectional sizes of up to 80 mm by 160 mm. Such dimensions require extremely high energy during a CT scan. Therefore, a new experimental setup with energies of up to 8 MeV was used in this study. However, such high energies posed new challenges to the analysis methods. Therefore, two methods (digital volume correlation and grayscale profile analysis) for accurate crack width estimation were adapted and applied to the 3D reconstructions. In addition, a photogrammetric stereo image sequence was acquired and analyzed, using digital image correlation to cross-validate the results derived from the 3D crack width estimates. The 3D CT images also played a key role in the quality control measures, including the localization of the carbon-reinforcement and the assessment of porosity within the concrete structure.