bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessment of Coating Properties in Car Body by Ultrasonic Method
Authors: Ulbrich Dariusz , Psuj Grzegorz , Bartkowski Dariusz , Bartkowska Aneta
Published: September 2024

Adhesive bonds, including car putty coatings, are used in the construction of modern motor vehicles. Therefore, it is important to improve methods that allow nondestructive evaluation of the properties of these joints. The main objective of this study was to evaluate selected properties of putty coatings such as the width of the applied coating and adhesion to the substrate based on changes in ultrasonic wave parameters. The research was carried out in two stages. In the first, the values of the surface wave amplitude were determined as a function of the width of the coating to the substrate. It was found that as the width of the coating increases, the amplitude of the surface wave pulse decreases. The second stage involved correlation studies to relate the reflection coefficient |r| to the adhesion of the coating to the substrate. Based on the results, it was found that as the value of the reflection coefficient decreases, the value of the coating’s adhesion to the substrate increases. The determined values of this parameter range from 0.30 to 0.86, which correspond to the adhesion of the range 1.51 to 18.97 MPa. The obtained test results have practical significance and can be used in evaluating the condition of coatings in vehicle body repair shops.
Research on Key Roof-Cutting Parameters for Surrounding Rock Stability Control in Gob-Side Entry Retention without Coal Pillars in Karst Mountainous Area
Authors: Liu Yutao , Guo Wenhao , Fan Gangwei , Yu Wei , Chai Yujian , Yue Xin , Han Xuesen
Published: September 2024

The differential distribution of original rock stress and stress concentration caused by the variation in coal seam depth in karst topography are critical factors influencing the selection of roof-cutting parameters. Based on this, this study explores a method to determine reasonable roof-cutting parameters by incorporating the characteristics of coal seam depth variation in karst mountainous areas. A mechanical model of the cantilever beam structure for roof cutting in gob-side entry retention (GSER) is constructed, and the critical values and reasonable ranges of roof-cutting height and angle under different burial depths are derived. Furthermore, the displacement and stress evolution characteristics of surrounding rocks in gob-side entry retention under different coal seam burial depths, roof-cutting heights, and roof-cutting angles within the reasonable range of roof-cutting parameters are analyzed. The results show that there is a positive correlation between roof-cutting height and tensile stress in the uncut portion of the main roof, while roof-cutting angle and coal seam depth are negatively correlated with tensile stress. From the perspective of impact, roof-cutting height has a greater impact than roof-cutting angle, followed by coal seam depth. As for the distribution characteristics of the reasonable roof-cutting parameter range, the fan-shaped area of reasonable roof-cutting parameters gradually decreases with increasing coal seam depth. Taking the geological conditions of Anshun Coal Mine as an example, when the burial depth increases from 350 m to 550 m, adjusting the roof-cutting height to 6 m, 7 m, and 8 m, respectively, and setting the roof-cutting angle at 10° can effectively achieve the stability of the surrounding rock in the GSER. The research findings can provide a scientific basis and engineering references for selecting roof-cutting parameters in mines with similar geological conditions.
Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
Authors: Wang Mingde , Liu Zhijing
Published: September 2024

The security and privacy of a system are urgent issues in achieving secure and efficient learning-based systems. Recent studies have shown that these systems are susceptible to subtle adversarial perturbations applied to inputs. Although these perturbations are difficult for humans to detect, they can easily mislead deep learning classifiers. Noise injection, as a defense mechanism, can offer a provable defense against adversarial attacks by reducing sensitivity to subtle input changes. However, these methods face issues of computational complexity and limited adaptability. We propose a multilayer filter defense model, drawing inspiration from filter-based image denoising techniques. This model inserts a filtering layer after the input layer and before the convolutional layer, and incorporates noise injection techniques during the training process. This model substantially enhances the resilience of image classification systems to adversarial attacks. We also investigated the impact of various filter combinations, filter area sizes, standard deviations, and filter layers on the effectiveness of defense. The experimental results indicate that, across the MNIST, CIFAR10, and CIFAR100 datasets, the multilayer filter defense model achieves the highest average accuracy when employing a double-layer Gaussian filter (filter area size of 3×3, standard deviation of 1). We compared our method with two filter-based defense models, and the experimental results demonstrated that our method attained an average accuracy of 71.9%, effectively enhancing the robustness of the image recognition classifier against adversarial attacks. This method not only performs well on small-scale datasets but also exhibits robustness on large-scale datasets (miniImageNet) and modern models (EfficientNet and WideResNet).
Two-Level Approach for Simultaneous Component Assignment and Layout Optimization with Applications to Spacecraft Optimal Layout
Authors: Gamot Juliette , Balesdent Mathieu , Wuilbercq Romain , Tremolet Arnault , Melab Nouredine
Published: September 2024

Optimal layout problems consist in positioning a given number of components in order to minimize an objective function while satisfying geometrical or functional constraints. Such kinds of problems appear in the design process of aerospace systems such as satellite or spacecraft design. These problems are NP-hard, highly constrained and dimensional. This paper describes a two-stage algorithm combining a genetic algorithm and a quasi-physical approach based on a virtual-force system in order to solve multi-container optimal layout problems such as satellite modules. In the proposed approach, a genetic algorithm assigns the components to the containers while a quasi-physical algorithm based on a virtual-force system is developed for positioning the components in the assigned containers. The proposed algorithm is experimented and validated on the satellite module layout problem benchmark. Its global performance is compared with previous algorithms from the literature.
Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
Authors: Li Guoxin , Zhang Ruolei , Qi Dawei , Ni Haiming
Published: September 2024

In recent years, deep-learning methods have significantly improved the classification results in the field of plant-leaf recognition. However, limited by the model input, the original image needs to be compressed to a certain size before it can be input into the convolutional neural network. This results in great changes in the shape and texture information of some samples, thus affecting the classification accuracy of the model to a certain extent. Therefore, a minimum enclosing quadrate (MEQ) method is proposed to standardize the sample datasets. First, the minimum enclosing rectangle (MER) of the leaf is obtained in the original image, and the target area is clipped. Then, the minimum enclosing quadrate of the leaf is obtained by extending the short side of the rectangle. Finally, the sample is compressed to fit the input requirements of the model. In addition, in order to further improve the classification accuracy of plant-leaf recognition, an EC-ResNet50 model based on transfer-learning strategy is proposed and further combined with the MEQ method. The Swedish leaf, Flavia leaf, and MEW2012 leaf datasets are used to test the performance of the proposed methods, respectively. The experimental results show that using the MEQ method to standardize datasets can significantly improve the classification accuracy of neural networks. The Grad-CAM visual analysis reveals that the convolutional neural network exhibits a higher degree of attention towards the leaf surface features and utilizes more comprehensive feature regions during recognition of the leaf samples processed by MEQ method. In addition, the proposed MEQ + EC-ResNet50 method also achieved the best classification results among all the compared methods. This experiment provides a widely applicable sample standardization method for leaf recognition research, which can avoid the problem of sample deformation caused by compression processing and reduce the interference of redundant information in the image to the classification results to a certain degree.
Elimination of the Solid Graininess Issue with Different Micro-Pattern Structures at Flexo Printing
Authors: Kesan Nehar , Sahinbaskan Turkun
Published: September 2024

Flexo printing is a relief printing system, and ink transfers on the solid areas are not transferring well during the printing. That is why graininess is increasing and pinholes are occurring on the solid areas. This is a well-known issue in the flexo printing system. Micro-patterns usually eliminate these pinholes. Using correct micro-patterns allows homogeneous ink laydown and increases solid ink density. Micro-pattern holes behave like gravure cylinders on plate surfaces, and this makes for better ink transfer to the substrate. In this study, a more successful micro-pattern structure than the ones currently used was found by examining the solid ink density (SID) values and ink laydown obtained from the structure by producing eight different micro-pattern structures of 1000 LPI and 1500 LPI (line per inch) pattern screen frequencies with the same polymer structure and type. Densitometric values of solid prints made with the developed micro-patterns were measured. By eliminating the pinholes formed in solid prints and at the screen dot shapes, the ink is distributed more homogeneously without graininess. It has been determined that this results in a more stable measurement of ink density and eliminates the measurement of excess ink and low density.
A Method for Assisting GNSS/INS Integrated Navigation System during GNSS Outage Based on CNN-GRU and Factor Graph
Authors: Zhao Hailin , Liu Fuchao , Chen Wenjue
Published: September 2024

In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network–gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods.
High-Resolution Monitored Data Analysis of EV Public Charging Stations for Modelled Grid Impact Validation
Authors: Estrada Poggio Aaron , Rotondo Giuseppe , Prina Matteo Giacomo , Zubaryeva Alyona , Sparber Wolfram
Published: September 2024

As electric vehicle adoption grows, understanding the impact of electric vehicle charging on electricity grids becomes increasingly important. Accurate grid impact modelling requires high-quality charging infrastructure data. This study examined the electric vehicle recharging infrastructure and usage patterns in a region of the Italian Alps over a three-year period from 2021 to 2023. The primary objectives were to analyze the growth and distribution of electric vehicle charging stations, assess energy consumption, and evaluate charging behaviours across various recharging points. The research involved collecting empirical data from 411,800 recharging sessions and simulated data using the emobpy tool to model energy consumption and charging behavior. Key findings reveal a substantial increase in the number of recharging points, from 673 in 2021 to 970 in 2023, with the total energy delivered increasing from 938 MWh in 2021 to 4133 MWh in 2023. The data showed distinct temporal trends: AC points were primarily used during the day, while DC points saw higher usage during morning and late afternoon peaks, aligning with travelling times. The study’s validation of simulation results against empirical data emphasized the importance of high-quality input for accurate grid impact assessments. These findings suggest the necessity for strategic placement of recharging infrastructure and provide practical insights for policymakers, urban planners, and utility companies to support sustainable electric vehicle integration.
A Comparison of Automatic Bone Age Assessments between the Left and Right Hands: A Tool for Filtering Measurement Errors
Authors: Lee Kyu-Chong , Kang Chang Ho , Ahn Kyung-Sik , Lee Kee-Hyoung , Lee Jae Joon , Cho Kyu Ran , Oh Saelin
Published: September 2024

This study aimed to investigate whether the left and right hands yield the same bone age using the automated bone age assessment (BAA) system and proposed the right-hand BAA as a tool for filtering out measurement errors. The Bland–Altman, Passing–Bablok, and Spearman correlation coefficients were analyzed to compare the automated BAA results for each hand. The absolute difference between each hand obtained by the model (ADBH model) was calculated. The mean absolute difference (MAD) was estimated between the automatic BAA results for each hand and the reference standard. The mean of the ADBH model was 0.23 ± 0.19 years; 92.2% of the participants showed an ADBH model result of <0.5 years. The Passing–Bablok regression analysis revealed an excellent overall correlation between the BAAs of both hands. Of the total cases, 59 participants showed an ADBH model result >0.5 years, with a MAD between the model and the reference standard of 0.409 years for the left hand and 0.424 years for the right hand; both MADs were higher than those of previous studies using the same model. Given the excellent overall correlation of the BAA between both hands using the model, the high ADBH model value may indicate BAA measurement errors and serve as a cue for manual supervision.
Speaker-Attributed Training for Multi-Speaker Speech Recognition Using Multi-Stage Encoders and Attention-Weighted Speaker Embedding
Authors: Kim Minsoo , Jang Gil-Jin
Published: September 2024

Automatic speech recognition (ASR) aims at understanding naturally spoken human speech to be used as text inputs to machines. In multi-speaker environments, where multiple speakers are talking simultaneously with a large amount of overlap, a significant performance degradation may occur with conventional ASR systems if they are trained by recordings of single talkers. This paper proposes a multi-speaker ASR method that incorporates speaker embedding information as an additional input. The embedding information for each of the speakers in the training set was extracted as numeric vectors, and all of the embedding vectors were stacked to construct a total speaker profile matrix. The speaker profile matrix from the training dataset enables finding embedding vectors that are close to the speakers of the input recordings in the test conditions, and it helps to recognize the individual speakers’ voices mixed in the input. Furthermore, the proposed method efficiently reuses the decoder from the existing speaker-independent ASR model, eliminating the need for retraining the entire system. Various speaker embedding methods such as i-vector, d-vector, and x-vector were adopted, and the experimental results show 0.33% and 0.95% absolute (3.9% and 11.5% relative) improvements without and with the speaker profile in the word error rate (WER).
Future Scenarios of Design Rainfall Due to Upcoming Climate Changes in NSW, Australia
Authors: Hossain Iqbal , Gato-Trinidad Shirley , Imteaz Monzur , Rayburg Scott
Published: September 2024

The occurrence of rainfall is significantly affected by climate change around the world. While in some places this is likely to result in increases in rainfall, both winter and summer rainfall in most parts of New South Wales (NSW), Australia are projected to decrease considerably due to climate change. This has the potential to impact on a range of hydraulic and hydrologic design considerations for water engineers, such as the design and construction of stormwater management systems. These systems are currently planned based on past extreme rain event data, and changes in extreme rainfall amounts due to climate change could lead to systems being seriously undersized (if extreme precipitation events become more common and/or higher in magnitude) or oversized (if extreme rainfall events become less frequent or decrease in magnitude). Both outcomes would have potentially serious consequences. Consequently, safe, efficient, and cost-effective urban drainage system design requires the consideration of impacts arising from climate change on the approximation of design rainfall. This study examines the impacts of climate change on the probability of occurrence of daily extreme rainfall in New South Wales (NSW), Australia. The analysis was performed for 29 selected meteorological stations located across NSW. Future design rainfall in this research was determined from the projected rainfall for different time periods (2020 to 2039, 2040 to 2059, 2060 to 2079, and 2080 to 2099). The results of this study show that design rainfall for the standard return periods was, in most cases, lower than that derived employing the design rainfall obtained from the Australian Bureau of Meteorology (BoM). While most of the analysed meteorological stations showed significantly different outcomes using the climate change scenario data, this varied considerably between stations and different time periods. This suggests that more work needs to be performed at the local scale to incorporate climate change predicted rainfall data into future stormwater system designs to ensure the best outcomes.
Hierarchical Progressive Image Forgery Detection and Localization Method Based on UNet
Authors: Liu Yang , Li Xiaofei , Zhang Jun , Li Shuohao , Hu Shengze , Lei Jun
Published: September 2024

The rapid development of generative technologies has made the production of forged products easier, and AI-generated forged images are increasingly difficult to accurately detect, posing serious privacy risks and cognitive obstacles to individuals and society. Therefore, constructing an effective method that can accurately detect and locate forged regions has become an important task. This paper proposes a hierarchical and progressive forged image detection and localization method called HPUNet. This method assigns more reasonable hierarchical multi-level labels to the dataset as supervisory information at different levels, following cognitive laws. Secondly, multiple types of features are extracted from AI-generated images for detection and localization, and the detection and localization results are combined to enhance the task-relevant features. Subsequently, HPUNet expands the obtained image features into four different resolutions and performs detection and localization at different levels in a coarse-to-fine cognitive order. To address the limited feature field of view caused by inconsistent forgery sizes, we employ three sets of densely cross-connected hierarchical networks for sufficient interaction between feature images at different resolutions. Finally, a UNet network with a soft-threshold-constrained feature enhancement module is used to achieve detection and localization at different scales, and the reliance on a progressive mechanism establishes relationships between different branches. We use ACC and F1 as evaluation metrics, and extensive experiments on our method and the baseline methods demonstrate the effectiveness of our approach.
A Deep Learning-Enhanced Compartmental Model and Its Application in Modeling Omicron in China
Authors: Deng Qi , Wang Guifang
Published: September 2024

The mainstream compartmental models require stochastic parameterization to estimate the transmission parameters between compartments, whose calculation depend upon detailed statistics on epidemiological characteristics, which are expensive, economically and resource-wise, to collect. In addition, infectious diseases spread in three dimensions: temporal, spatial, and mobile, i.e., they affect a population through not only the time progression of infection, but also the geographic distribution and physical mobility of the population. However, the parameterization process for the mainstream compartmental models does not effectively capture the spatial and mobile dimensions. As an alternative, deep learning techniques are utilized in estimating these stochastic parameters with greatly reduced dependency on data particularity and with a built-in temporal–spatial–mobile process that models the geographic distribution and physical mobility of the population. In particular, we apply DNN (Deep Neural Network) and LSTM (Long-Short Term Memory) techniques to estimate the transmission parameters in a customized compartmental model, then feed the estimated transmission parameters to the compartmental model to predict the development of the Omicron epidemic in China over the 28 days for the period between 4 June and 1 July 2022. The average levels of predication accuracy of the model are 98% and 92% for the number of infections and deaths, respectively. We establish that deep learning techniques provide an alternative to the prevalent compartmental modes and demonstrate the efficacy and potential of applying deep learning methodologies in predicting the dynamics of infectious diseases.
Transcriptomic and Metabolomic Analysis Reveal the Effects of Light Quality on the Growth and Lipid Biosynthesis in Chlorella pyrenoidosa
Authors: Zhu Tingting , Guan Ge , Huang Lele , Wen Lina , Li Linxuan , Ren Maozhi
Published: September 2024

Light quality has significant effects on the growth and metabolite accumulation of algal cells. However, the related mechanism has not been fully elucidated. This study reveals that both red and blue light can promote the growth and biomass accumulation of Chlorella pyrenoidosa, with the enhancing effect of blue light being more pronounced. Cultivation under blue light reduced the content of total carbohydrate in Chlorella pyrenoidosa, while increasing the content of protein and lipid. Conversely, red light decreased the content of protein and increased the content of carbohydrate and lipid. Blue light induces a shift in carbon flux from carbohydrate to protein, while red light transfers carbon flux from protein to lipid. Transcriptomic and metabolomic analysis indicated that both red and blue light positively regulate lipid synthesis in Chlorella pyrenoidosa, but they exhibited distinct impacts on the fatty acid compositions. These findings suggest that manipulating light qualities can modulate carbon metabolic pathways, potentially converting protein into lipid in Chlorella pyrenoidosa.
Pro-Inflammatory Characteristics of Extracellular Vesicles in the Vitreous of Type 2 Diabetic Patients
Authors: Shan Shengshuai , Alanazi Abdulaziz H. , Han Yohan , Zhang Duo , Liu Yutao , Narayanan S. Priya , Somanath Payaningal R.
Published: September 2024

Diabetic retinopathy (DR) is a leading cause of blindness, yet its molecular mechanisms are unclear. Extracellular vesicles (EVs) contribute to dysfunction in DR, but the characteristics and functions of vitreous EVs are unclear. This study investigated the inflammatory properties of type 2 diabetic (db) vitreous EVs. EVs isolated from the vitreous of db and non-db donors were used for nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), immunogold staining, Western blotting, and proteomic analysis by mass spectrometry. Intracellular uptake of vitreous EVs by differentiated macrophages was evaluated using ExoGlow membrane labeling, and the impact of EVs on macrophage (THP-1) activation was assessed by cytokine levels using RT-qPCR. NTA and TEM analysis of db and non-db vitreous EVs showed non-aggregated EVs with a heterogeneous size range below 200 nm. Western blot detected EV markers (Alix, Annexin V, HSP70, and Flotillin 1) and an upregulation of Cldn5 in db EVs. While the db EVs were incorporated into macrophages, treatment of THP-1 cells with db EVs significantly increased mRNA levels of TNFα and IL-1β compared to non-db EVs. Proteomic and gene enrichment analysis indicated pro-inflammatory characteristics of db EVs. Our results suggest a potential involvement of EC-derived Cldn5+ EVs in triggering inflammation, offering a novel mechanism involved and presenting a possible therapeutic avenue for DR.
Hematologic Inflammation Indices for Differentiating between Brucella, Pyogenic, and Tuberculous Spondylodiscitis
Authors: Baran Ali Irfan , Binici Irfan , Arslan Yusuf , Hakseven Karaduman Zekiye , Ilter Server , Tarcan Tayyar , Unal Murat
Published: September 2024

Infectious spondylodiscitis is a life-threatening disease and has some challenges in terms of diagnostic, differentiative, and therapeutic processes. Therefore, rapid and effective management of infectious spondylodiscitis is necessary. Hematological inflammation indices (HIIs) such as the neutrophil/lymphocyte ratio and aggregate index of systemic inflammation are derived from blood cells and used as diagnostic, prognostic, predictive, and treatment monitoring indicators. This study aimed to evaluate HIIs for discriminating between infectious spondylodiscitis pathogens. This retrospective comparative study included 116 patients with infectious spondylodiscitis. According to the responsible infectious pathogens, three types of infectious spondylodiscitis were defined: Brucella (n = 51), pyogenic (n = 43), and tuberculous (n = 22). The HIIs were derived from baseline complete blood counts. The three types of infectious spondylodiscitis were statistically compared for the HII scores. We found that the Brucella group had significantly lower HII scores than the pyogenic group (p < 0.05). Also, the Brucella group had significantly lower HII scores than the tuberculous group (p < 0.05). However, no significant differences were found between the pyogenic and tuberculous groups regarding HIIs (p > 0.05). In conclusion, the HIIs may be considered in the differentiation between Brucella spondylodiscitis and other types of infectious spondylodiscitis.
The Relationship between Smoking and Susceptibility to HIV Infection: A Two-Sample Mendelian Randomization Analysis
Authors: Yu Min-Rui , Hu Wei , Yan Song , Qu Meng-Meng , Jiao Yan-Mei , Wang Fu-Sheng
Published: September 2024

Smoking is prevalent among people living with the human immunodeficiency virus (HIV), and it increases morbidity and mortality in this population. However, due to ethical constraints, there is limited information on the effects of smoking on susceptibility to HIV infection. To investigate whether smoking is associated with an increased susceptibility to HIV infection, we conducted a two-sample Mendelian randomization (MR) study using summary statistics from genome-wide association studies of individuals of European ancestry who have ever smoked (n = 99,996) and have HIV (n = 412,130). The random-effects inverse-variance weighted estimation method was used as the study’s primary approach, with the MR-Egger regression and the weighted-median method as complementary approaches. Using 100 single-nucleotide polymorphisms of genome-wide significance as instrumental variables for smoking, we observed a significant association between smoking and HIV infection (odds ratio 5.790, 95% confidence interval [1.785, 18.787], and p = 0.003). Comparable results were obtained using the weighted-median method. Our findings implied that smoking is probably associated with increased susceptibility to HIV infection. Given the exploratory nature of this study, further research is needed to confirm this relationship.
Unraveling the Liver–Brain Axis: Resveratrol’s Modulation of Key Enzymes in Stress-Related Anxiety
Authors: Tseilikman Vadim E. , Tseilikman Olga B. , Shevyrin Vadim A. , Yegorov Oleg N. , Epitashvili Alexandr A. , Aristov Maxim R. , Karpenko Marina N. , Lipatov Ilya A. , Pashkov Anton A. , Shamshurin Maxim V. , Buksha Irina A. , Shonina Anna K. , Kolesnikova Alexandra , Shatilov Vladislav A. , Zhukov Maxim S. , Novak Jurica
Published: September 2024

Stress-related anxiety disorders and anxiety-like behavior in post-traumatic stress disorder (PTSD) are associated with altered neurocircuitry pathways, neurotransmitter systems, and the activities of monoamine and glucocorticoid-metabolizing enzymes. Resveratrol, a natural polyphenol, is recognized for its antioxidant, anti-inflammatory, and antipsychiatric properties. Previous studies suggest that resveratrol reduces anxiety-like behavior in animal PTSD models by downregulating key enzymes such as 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD-1) and monoamine oxidases (MAOs). However, the underlying mechanisms remain unclear. In this study, we explored the efficacy of resveratrol in treating stress-induced anxiety using a chronic predator stress model in rats. Resveratrol was administered intraperitoneally at 100 mg/kg following a 10-day stress exposure, and anxiety behavior was assessed with an elevated plus maze. Our results indicated that stress-related anxiety correlated with increased activities of brain MAO-A, MAO-B, and hepatic 11β-HSD-1, alongside elevated oxidative stress markers in the brain and liver. Resveratrol treatment improved anxiety behavior and decreased enzyme activities, oxidative stress, and hepatic damage. We demonstrate that resveratrol exerts antianxiogenic effects by modulating glucocorticoid and monoamine metabolism in the brain and liver. These findings suggest resveratrol’s potential as a therapeutic agent for anxiety disorders, warranting further clinical investigation.
Comparative Short- and Long-Term Effectiveness and Safety of Pramipexole and Aripiprazole Augmentation in Treatment-Resistant Unipolar Depression: An Observational Study
Authors: Tundo Antonio , Betrò Sophia , de Filippis Rocco , Felici Roberto , Lucangeli Chiara , Iommi Marica
Published: September 2024

Background: This study compares the short- and long-term effectiveness and safety of pramipexole augmentation (PA) and aripiprazole augmentation (AA) for unipolar treatment-resistant depression (TRD). Methods: Patients were recruited in a private out-patients clinic specializing in mood disorders. At intake and at each visit, depressive and (hypo)manic symptoms, clinical status, and level of functioning were evaluated with appropriate scales. The trend of outcomes was analyzed using mixed-effect linear regression models. Results: The study includes 81 patients with unipolar TRD treated with PA and 51 with AA. After 12 and 24 weeks of treatment with PA, the predicted response (64.1% and 76.2%) and remission rates (49.7% and 72.7%) were significantly higher than the predicted response (32.2% and 38.0%) and remission rates (18.9% and 28.1%) for AA. The improvement in psychosocial functioning was significantly greater and faster in PA than in AA. PA showed significant superiority over AA as a maintenance strategy (time spent ill and psychosocial functioning) up to 12 months. No difference in safety was found at each time point. Conclusions: PA could be an alternative option for the short- and long-term treatment of unipolar TRD, more effective than AA and similar in safety. These preliminary results need confirmation from randomized clinical trials.
Building Social Support and Moral Healing on Nursing Units: Design and Implementation of a Culture Change Intervention
Authors: Usset Timothy J. , Godzik Cassandra , Harris J. Irene , Wurtz Rebecca M. , Pyne Jeffrey M. , Edmonds Stephanie W. , Prunty April , Brown Rebecca J. L. , Bardach Shoshana H. , Bradley Joel M. , Hubble Christopher L. , Oliver Brant J. , Pepin Renee L. , Currier Joseph , Smith Andrew J.
Published: September 2024

The healthcare industry continues to experience high rates of burnout, turnover, and staffing shortages that erode quality care. Interventions that are feasible, engaging, and impactful are needed to improve cultures of support and mitigate harm from exposure to morally injurious events. This quality improvement project encompassed the methodical building, implementation, and testing of RECONN (Reflection and Connection), an organizational intervention designed by an interdisciplinary team to mitigate the impact of moral injury and to increase social support among nurses. This quality improvement project was conducted in a medical intensive care unit (MICU) in a rural, academic medical center. We employed an Evidence-Based Quality Improvement (EBQI) approach to design and implement the RECONN intervention while assessing the feasibility, acceptability, and preliminary effectiveness via surveys (n = 17). RECONN was found acceptable and appropriate by 70% of nurses who responded to surveys. Preliminary effectiveness data showed small to moderate effect sizes for improving social support, moral injury, loneliness, and emotional recovery. Further evaluation is warranted to establish the effectiveness and generalizability of RECONN to other healthcare settings.
Proactive Personality and Turnover Intention: The Mediating Role of Career Aspiration and the Moderating Effect of Organizational Career Management
Authors: Wang Qiong , Shang Zhe , Zuo Chenhui , Fan Huaye , Xu Chen , Cai Zijun , Shi Wei
Published: September 2024

As proactivity becomes vital for organizational success, retaining proactive employees becomes increasingly important, making the relationship between a proactive personality and turnover intention a key research topic. While existing studies have largely depicted turnover as a consequence of dissatisfaction and have identified negative indirect relationships, this study seeks to challenge that perspective by proposing that, in today’s boundaryless career environment, people also engage in voluntary turnover for career advancement. Using a self-regulation career management model, we propose that proactive employees set ambitious career goals influenced by career aspirations, leading them to seek external opportunities and thus exhibit higher turnover intention. However, when organizations implement career management practices, this relationship weakens as proactive employees perceive opportunities to achieve their goals within their current organizations. We tested these hypotheses with a sample of 342 respondents using the SPSS macro PROCESS. The findings support our propositions, revealing a positive indirect effect through career aspirations, which diminishes when perceived organizational career management is strong.
The Detailed Axial Compression Behavior of CFST Columns Infilled by Lightweight Concrete
Authors: Alnemrawi Bara’a R. , Al-Rousan Rajai
Published: September 2024

The utilization of lightweight aggregate concrete (LWC) plays a major role in reducing the self-weight of CFST (concrete-filled steel tube) columns, which is reflected in the behavior of the structural system. This paper aims to investigate the characteristics of lightweight concrete-filled steel tubular (LWCFST) columns under an axial compressive load, using a total of (48) LWCFST column models. The simulated models were divided into four groups with different concrete compressive strength, length-to-diameter ratios (L/D), and diameter-to-thickness ratios (D/t). Four concrete compressive values were examined (30, 40, 50, and 60) MPa, three length-to-diameter ratios short (L/D = 3), medium (L/D = 6), and long (L/D = 9), and four diameter-to-thickness ratios (36, 31, 26, and 21). The method of nonlinear finite element analysis (NLFEA) was used to fulfill the objective of this study where results were presented as graphical plots between the compressive loading versus the axial and lateral strains along with the failure modes. In addition, the results were compared with the AISC360-16 and EC4 codes predictions to examine their applicability on the LWCFST columns where the AISC was overpredicted in most cases with higher percentages under lower (L/D) values, whereas the EC2 was underestimated in most cases with high percentages up to 28%, which become closer to the NLFEA predictions at higher (L/D) values. It has been revealed that the utilization of steel tubes significantly improves the LWCFST column’s mechanical performance, ductility, compressive strength, and toughness. Moreover, the structural behavior of the LWCFST columns and their associated failure modes was found to be highly affected by the geometrical properties of the CFST column (i.e., L/D ratio and D/t ratio) where specimens with small tube thickness show bad behavior. Finally, the utilization of high-strength concrete has a favorable performance compared to the utilization of thick steel tubes.
A Study on the Relationship between Campus Environment and College Students’ Emotional Perception: A Case Study of Yuelu Mountain National University Science and Technology City
Authors: Peng Zhimou , Zhang Ruiying , Dong Yi , Liang Zhihao
Published: September 2024

The campus environment directly impacts college students’ psychological and emotional well-being, influencing their behavioral performance and the development of their personalities. Investigating the complex relationship between the campus spatial environment and students’ emotions is crucial for designing urban environments that support mental health. Using Yuelu Mountain National University Science and Technology City as a case study, this research developed a framework to analyze campus environment characteristics and emotional perception. The study quantitatively assessed emotional perceptions, examined the specific contributions of different campus environment elements to individual emotions, and created an emotion prediction map to explore these relationships in depth. The results indicate that “campus greenery” and “diversity” negatively affect “disappointment” and “depression”, while “sky views” positively impact “happiness” and “sense of security”. Additionally, “diversity” positively affects “relaxation”, and “campus greenery” and “diversity” have negative effects on “disappointment” and “depression”, with “diversity” having a particularly strong positive effect on “relaxation”. The pronounced spatial clustering of emotional perceptions on campus further underscores the significant influence of the campus environment on individual emotional experiences. As the first study to explore the mechanisms underlying the emotional perceptions of Chinese college students in relation to the campus environment, this research overcomes the limitations of traditional environmental assessment indicators by identifying campus environmental elements and psychological factors that better align with the psychological needs of college students. This provides a scientific basis for optimizing campus environments based on the emotional perceptions of students, thereby supporting mental health promotion and guiding campus environment construction. Moreover, the research methodology is broadly applicable. The integration of campus environment image data and deep learning offers a significant tool for assessing campus space and environmental perception, thereby enhancing human-centered environmental assessment and prediction while more accurately reflecting architectural space perception.
Effects of the Natural Environment on the Subjective and Psychological Well-Being of Older People in the Community in China
Authors: Gong Kangcheng , Wang Chendi , Yin Jun
Published: September 2024

As people age, their activity radius gradually narrows, leading to increased interaction with the community environment. The natural environment (NE) within the community may influence the subjective and psychological well-being (SPWB) of older people (i.e., air quality, noise, green space, and blue space). To enhance the NE and promote the SPWB of older people, this study explored the effect of the community NE on the SPWB of older people. A questionnaire survey involving 180 older people from three communities was collected, alongside observations and measurements of the NE within the community. Finally, a NE-SPWB model was established, indicating that smell (air), ambient noise, green space areas, water landscapes, and smell (water) all positively influenced the SPWB of older people. Based on these findings, recommendations were proposed to enhance community environmental conditions, such as improving water landscapes and green spaces, to further enhance the well-being of older people.
Place-Making Research: A Bibliometric, Visualization, and Thematic Analysis
Authors: Yan Wenwen , Liao Jianfeng , Zhai Hui
Published: September 2024

Place-making is recognized for its significant role in enhancing community cohesion, preserving cultural heritage, and promoting environmental sustainability. In recent years, place-making has garnered increasing attention as a multidisciplinary research topic from scholars in various fields. However, despite the increasing amount of research, systematic reviews and analyses of existing studies remain relatively scarce. Therefore, this study aims to provide a comprehensive bibliometric overview of the field of place-making through bibliometric and visualization analyses. We used CiteSpace for data analysis and visualization, focusing on the most influential articles, journals, countries, research categories, keywords, and themes in this field. The results indicate a significant increase in publications on placemaking since 2013. Major research institutions are concentrated in the United States, the United Kingdom, and Australia. Research hotspots are centered around “city”, “politics”, and “place-making”, among others. Current research is primarily categorized into four core themes: Urban Design and Public Space, Social Participation and Cultural Identity, Economic Development and Environmental Sustainability, and Governance, Policy, and Community Development. The research trajectory in this field shows trends of diversification and integration. Future research should emphasize interdisciplinary integration, combining urban planning, digital technology, and sociology.
Radiomics-Based Computed Tomography Urogram Approach for the Prediction of Survival and Recurrence in Upper Urinary Tract Urothelial Carcinoma
Authors: Alqahtani Abdulsalam , Bhattacharjee Sourav , Almopti Abdulrahman , Li Chunhui , Nabi Ghulam
Published: September 2024

Upper tract urothelial carcinoma (UTUC) is a rare and aggressive malignancy with a poor prognosis. The accurate prediction of survival and recurrence in UTUC is crucial for effective risk stratification and guiding therapeutic decisions. Models combining radiomics and clinicopathological data features derived from computed tomographic urograms (CTUs) can be a way to predict survival and recurrence in UTUC. Thus, preoperative CTUs and clinical data were analyzed from 106 UTUC patients who underwent radical nephroureterectomy. Radiomics features were extracted from segmented tumors, and the Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select the most relevant features. Multivariable Cox models combining radiomics features and clinical factors were developed to predict the survival and recurrence. Harrell’s concordance index (C-index) was applied to evaluate the performance and survival distribution analyses were assessed by a Kaplan–Meier analysis. The significant outcome predictors were identified by multivariable Cox models. The combined model achieved a superior predictive accuracy (C-index: 0.73) and higher recurrence prediction (C-index: 0.84). The Kaplan–Meier analysis showed significant differences in the survival (p < 0.0001) and recurrence (p < 0.002) probabilities for the combined datasets. The CTU-based radiomics models effectively predicted survival and recurrence in the UTUC patients, and enhanced the prognostic performance by combining radiomics features with clinical factors.
Dynamic Mitotic Localization of the Centrosomal Kinases CDK1, Plk, AurK, and Nek2 in Dictyostelium amoebae
Authors: Krüger Stefan , Pfaff Nathalie , Gräf Ralph , Meyer Irene
Published: September 2024

The centrosome of the amoebozoan model Dictyostelium discoideum provides the best-established model for an acentriolar centrosome outside the Opisthokonta. Dictyostelium exhibits an unusual centrosome cycle, in which duplication is initiated only at the G2/M transition and occurs entirely during the M phase. Little is known about the role of conserved centrosomal kinases in this process. Therefore, we have generated knock-in strains for Aurora (AurK), CDK1, cyclin B, Nek2, and Plk, replacing the endogenous genes with constructs expressing the respective green fluorescent Neon fusion proteins, driven by the endogenous promoters, and studied their behavior in living cells. Our results show that CDK1 and cyclin B arrive at the centrosome first, already during G2, followed by Plk, Nek2, and AurK. Furthermore, CDK1/cyclin B and AurK were dynamically localized at kinetochores, and AurK in addition at nucleoli. The putative roles of all four kinases in centrosome duplication, mitosis, cytokinesis, and nucleolar dynamics are discussed.
Small Extracellular Vesicles and Oral Mucosa: The Power Couple in Regenerative Therapies?
Authors: Borowiec Blanka Maria , Dyszkiewicz-Konwińska Marta , Bukowska Dorota , Nowicki Michał , Budna-Tukan Joanna
Published: September 2024

Although ongoing debates persist over the scope of phenomena classified as regenerative processes, the most up-to-date definition of regeneration is the replacement or restoration of damaged or missing cells, tissues, organs, or body parts to full functionality. Despite extensive research on this topic, new methods in regenerative medicine are continually sought, and existing ones are being improved. Small extracellular vesicles (sEVs) have gained attention for their regenerative potential, as evidenced by existing studies conducted by independent research groups. Of particular interest are sEVs derived from the oral mucosa, a tissue renowned for its rapid regeneration and minimal scarring. While the individual regenerative potential of both sEVs and the oral mucosa is somewhat understood, the combined potential of sEVs derived from the oral mucosa has not been sufficiently explored and highlighted in the existing literature. Serving as a broad compendium, it aims to provide scientists with essential and detailed information on this subject, including the nature of the materials employed, isolation and analysis methodologies, and clinical applications. The content of this survey aims to facilitate the comparison of diverse methods for working with sEVs derived from the oral mucosa, aiding in the planning of research endeavors and identifying potential research gaps.