bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of potato leaf area index based on spectral information and Haralick textures from UAV hyperspectral images
Authors: Fan Jiejie , Liu Yang , Fan Yiguang , Yao Yihan , Chen Riqiang , Bian Mingbo , Ma Yanpeng , Wang Huifang , Feng Haikuan
Published: November 2024

The Leaf Area Index (LAI) is a crucial parameter for evaluating crop growth and informing fertilization management in agricultural fields. Compared to traditional methods, UAV-based hyperspectral imaging technology offers significant advantages for non-destructive, rapid monitoring of crop LAI by simultaneously capturing both spectral information and two-dimensional images of the crop canopy, which reflect changes in its structure. While numerous studies have demonstrated that various texture features, such as the Gray-Level Co-occurrence Matrix (GLCM), can be used independently or in combination with crop canopy spectral data for LAI estimation, limited research exists on the application of Haralick textures for evaluating crop LAI across multiple growth stages. In this study, experiments were conducted on two early-maturing potato varieties, subjected to different treatments (e.g., planting density and nitrogen levels) at the Xiaotangshan base in Beijing, during three key growth stages. Crop canopy spectral reflectance and Haralick textures were extracted from ultra-low-altitude UAV hyperspectral imagery, while LAI was measured using ground-based methods. Three types of spectral data—original spectral reflectance (OSR), first-order differential spectral reflectance (FDSR), and vegetation indices (VIs)—along with three types of Haralick textures—simple, advanced, and higher-order—were analyzed for their correlation with LAI across multiple growth stages. A model for LAI estimation in potato at multiple growth stages based on spectral and textural features screened by the successive projection algorithm (SPA) was constructed using partial least squares regression (PLSR), random forest regression (RFR) and gaussian process regression (GPR) machine learning methods. The results indicated that: (1) Spectral data demonstrate greater sensitivity to LAI than Haralick textures, with sensitivity decreasing in the order of VIs, FDSR and OSR; (2) spectral data alone provide more accurate LAI estimates than Haralick textures, with VIs achieving an accuracy of R² = 0.63, RMSE = 0.38, NRMSE = 28.36%; and (3) although Haralick textures alone were not effective for LAI estimation, they can enhance LAI prediction when combined with spectral data, with the GPR method achieving R² = 0.70, RMSE = 0.30, NRMSE = 20.28%. These findings offer a valuable reference for large-scale, accurate monitoring of potato LAI.
Factors influencing utilization of healthcare services for internal migrant older adults in Xuzhou, China: based on Anderson’s model
Authors: Li Guiyuan , Qi Zijian , Yu Wenxin , Wang Qingzhi , Hou Hao , Miao Chunxia , Yan Wenjun , Gao Xiuyin
Published: November 2024

Background

As population migration increases and the aging process accelerates, the number of migrant older adults is growing significantly. This trend presents a substantial challenge to urban health services in regions experiencing an influx of older adults individuals. Consequently, examining the healthcare service utilization of the migrant older adults population is crucial to promote healthy aging.

Methods

A multi-stage random sampling method was employed to select a study population of 568 migrant older adults individuals, aged 60 years and above, residing in urban Xuzhou City. Multivariate logistic regression analysis, based on Anderson’s model, was conducted to explore the factors influencing outpatient and inpatient health service utilization among this population.

Results

Among the 568 migrants, 73 (12.9%) had received outpatient services within the past 2 weeks, while 109 (19.2%) had received inpatient services within the past year. Migrant older adults with education level, time to health centers, and self-assessed health status negatively influenced the outpatient services utilization among migrant older adults. Possession of pension insurance, convenience to visit, sick for the past 2 weeks, and health services need positively influenced the outpatient services utilization among migrant older adults. Furthermore, age, possession of pension insurance and health insurance, convenience to visit, number of chronic diseases, sick for the past 2 weeks, and healthcare service need positively influenced inpatient service utilization among migrant older adults. Education level, self-assessed health status, and time to health centers negatively influenced the inpatient services utilization among migrant older adults.

Conclusion

The overall utilization of healthcare services by migrant older adults in Xuzhou remains inadequate. Addressing this issue requires enhanced medical policy support and assistance, stronger health education initiatives, and improved social integration for the older adults. Additionally, efforts should be made to reduce their financial burdens and improve the accessibility of healthcare services.
Case studies on community care in Japan: considerations for mitigating social isolation and loneliness in older adults with dementia
Authors: Chen Li-Mei , Inoue Megumi , Buckley Nina
Published: November 2024

This article explores dementia care in Japan’s aging population with a focus on mitigating social isolation and loneliness in older adults with dementia. Through an in-depth examination of case studies, the study highlights several community-based interventions, including Community Cafés, the Dementia Supporter Caravan, and the Omuta City Dementia Care model. These cases provide insights into how these initiatives foster community engagement and inclusive environments. Using a socio-ecological (SE) framework, the analysis focuses on the effectiveness of leveraging social capital to address the social challenges faced by people living with dementia (PLwD) and their caregivers. The case studies emphasize context-sensitive strategies tailored to Japan’s cultural and demographic landscape, offering lessons for reducing isolation and promoting community support for older adults with dementia.
Social distancing mediates clinical work and depression: impact of the COVID-19 pandemic on nurses
Authors: Chen Shu-Chen , Lee Tony Szu-Hsien , Chao En
Published: November 2024

Background

The psychological well-being of nurses, during the COVID-19 pandemic has become a critical area of concern. This study examines the psychological impact of the pandemic on nurses, focusing on the mediating role of perceived social distance between work units and depression.

Methods

This study employed a cross-sectional design with respondent driven sampling. Anonymous questionnaire consisting of demographics, perceived social distancing and depression were distributed through email, Facebook, LINE, and other online platforms by key informants of nurses. A total of 1,064 volunteered questionnaires were collected, consisting of 1,032 females and 32 males.

Results

Data showed that 517 (48.9%) nurses may have depression. Results from the structure equation modeling indicated that perception of social distancing mediates between individuals in units of care and depression, while unit and tenure of nursing work were negatively associated with depression.

Conclusion

The study findings support that perceived social distancing due to COVID-19 pandemic from others toward nurses may have influential impact on elevated depression, especially for nurses worked in the acute critical care. Thus, emotional support should be emphasized, and avenues for stress relief should be provided as well as creating a supportive workplace environment is crucial to nurses and patient safety.
Exploring trust dynamics in health information systems: the impact of patients’ health conditions on information source preferences
Authors: Song Mingming , Elson Joel , Nguyen Tin , Obasi Sharon , Pintar John , Bastola Dhundy
Published: November 2024

Introduction

Health information systems (HISs) should provide accessible and high-quality information to patients. However, the challenge lies in understanding patients’ trust preferences for health information. This study explores how different information sources (e.g., online platforms, interpersonal sources) are trusted under varying health conditions, focusing on symptom intensity and disease type.

Methods

Using a 2 × 2 × 4 between-subject design, 243 participants from a US college were presented with vignettes of acute or chronic diseases with varying symptom intensities and information sources. Participants rated their trust levels, including both cognitive and behavioral trust, in the health information and recommendations provided by one of the information sources, which was randomly assigned. Logistic regression and ANOVA were employed for the statistical analysis.

Results

The analysis results revealed that trust is generally higher for interpersonal sources like doctors and family/friends compared to online sources like WebMD and Wikipedia when patients are making health decisions. Doctors are the most trusted source during health-related decision making. However, there are no significant differences in cognitive trust among interpersonal sources or among online sources. Furthermore, symptom intensity and disease type did not significantly alter trust levels across various information sources. These findings suggest that people prefer professional medical advice regardless of their health conditions.

Discussion

The study highlights the need for HIS to incorporate features that provide “doctor-verified” information and promote interactive engagement to enhance patients’ trust in information source. Additionally, it distinguishes between cognitive and behavioral trust, revealing distinct trust patterns that can inform the strategic development of HIS for varied health conditions. Understanding these trust dynamics can inform the design of effective, patient-centered HIS that better support health education, information seeking, and decision-making.
Diffusion-weighted MRI in the identification of renal parenchymal involvement in children with a first episode of febrile urinary tract infection
Authors: Anfigeno Lorenzo , La Valle Alberto , Castagnola Elio , Verrina Enrico Eugenio , Piaggio Giorgio , Degl'Innocenti Maria Ludovica , Piccotti Emanuela , Wolfler Andrea , Lembo Francesca Maria , Bodria Monica , Formigoni Clelia , Boetto Alice , Santini Lucia , Damasio Maria Beatrice
Published: November 2024

Aims

This study aims to assess the diagnostic accuracy of diffusion-weighted Magnetic Resonance Imaging (DW-MRI) and determine the inter-reader agreement between two expert radiologists in detecting pyelonephritic foci during the initial episode of febrile urinary tract infection (fUTI) in children aged 0–5 years. Also, we aim to establish the correlation between clinical data and DW-MRI findings.

Methods

Children aged 0–5 years presenting with their first episode of fUTI were included in the study and underwent DW-MRI and Ultrasound (US) examinations within 72 h of admission. Inter-observer agreement between the two expert radiologists in assessing DW-MRI scans was evaluated using Cohen's kappa statistic. Clinical and laboratory data were subjected to statistical analysis.

Results

84 children (40 male, 44 female) with a mean age of 7.3 (SD 6.2) months were enrolled. DW-MRI detected pyelonephritis in 78 out of 84 cases (92.9%), with multiple foci observed in 73 out of 78 cases (93.6%). There was a “substantial” level of agreement between the two expert radiologists (κ = 0.725; observed agreement 95.2%). Renal US revealed pyelonephritis in 36 out of 78 cases (46.2%). White blood cell (WBC) count (p = 0.04) and lymphocyte count (p = 0.01) were significantly higher in patients with positive DW-MRI. Although not statistically significant, patients with positive DW-MRI had higher mean values of C-Reactive Protein, Procalcitonin, and neutrophil WBC count (7.72 mg/dl, 4.25 ng/dl, and 9,271 /μl, respectively).

Conclusions

DW-MRI exhibited excellent diagnostic performance in detecting pyelonephritic foci, with substantial inter-reader agreement among expert radiologists, indicating the reliability of the technique. However, a weak correlation was observed between laboratory parameters and DW-MRI results, potentially because of the low rate of negative DW-MRI findings.
Building trust in long-term care settings using assistive technology: a systematic review
Authors: Zheng Kangjie , Han Fred , Yang Siyu , Li Nanxin
Published: November 2024

Background

This review investigates the dynamics of trust between caregivers and care receivers in long-term care settings, where the implementation of assistive technology also becomes chronically crucial. Trust is essential in the care receiver-caregiver relationship as it impacts the effectiveness of care and the care receiver's participation in treatment. Moreover, integrating assistive technology significantly affects the quality of care by increasing care receivers' autonomy and reducing caregivers' workload. Despite its significance, the mechanisms of trust involving assistive technology in long-term care have not been clarified.

Methods

To address this gap, this review systematically analyzed 32 articles published in English since 2,000, sourced from Web of Science, PubMed, Scopus, and Science Direct databases.

Results

The review identified the dynamics of trust in long-term care settings involving assistive technology. Based on this trust dynamics, three critical factors were analyzed: care receiver-related, caregiver-related, and assistive technology-related.

Discussion

The findings provide a detailed understanding of the factors affecting trust in long-term care settings involving assistive technology. These insights contribute to long-term care facility operators making informed decisions regarding technology adoption in care practice and care service strategies, ultimately enhancing trust and the quality of care in long-term care settings.
Performance evaluation and finite element modeling of heat, mass, and fluid flow inside a hybrid solar dryer during drying of paddy grains
Authors: Jha Aprajeeta , Tripathy P. P.
Published: November 2024

Introduction

A comprehensive assessment of a photovoltaic (PV) integrated hybrid solar dryer (HSD) for drying paddy was undertaken in the present investigation. Performance evaluation of the system along with finite element model of HSD at no-load and load conditions were successfully developed.

Methods

A three-dimensional PV aided hybrid solar dryer assembly model was created in indoor simulations using COMSOL Multiphysics version 5.3 a. Solidworks 16 was used to build the dryer’s collector, drying chamber, chimney, and thin food grain layer.Mesh refinement tests verified the mesh size independence of the panel temperature forecast finite element model.

Results and Discussion

Collector efficiency ranged from 49.24% to 81.19% and peaked at 750 W/m2 thermal intensity. System evaporation ranged from 0.25 to 0.39 kg/h. We also compared HSD, tray dryer (TD), and mixed-mode solar dryer (MMSD) system efficiency, specific energy consumption (SEC), and specific moisture extraction rate (SMER). HSD SEC values were 72% and 46% lower than TD and MMSD. HSD, MMSD, and TD had SMER values of 0.27, 0.15, and 0.08 kg/kWh. HSD dried paddy samples 36.36% and 84.61% faster than TD and MMSD. Hybrid solar dryers saved 33% and 50% more time than mixed mode and tray dryers, respectively. Simulations of the collector and chamber air distribution profiles showed a dead zone where air velocity drops below 0.5 m/s. I must note that the model accurately predicted the dryer’s temperature, wetness, and air dispersion pattern at load and no-load. PV-assisted HSD is suitable for sustainable food grain drying, according to this study.
Design of an intelligent wearable device for real-time cattle health monitoring
Authors: Yu Zhenhua , Han Yalou , Cha Lukas , Chen Shihong , Wang Zeyu , Zhang Yang
Published: November 2024

In the realm of precision cattle health monitoring, this paper introduces the development and evaluation of a novel wearable continuous health monitoring device designed for cattle. The device integrates a sustainable solar-powered module, real-time signal acquisition and processing, and a storage module within an animal ergonomically designed curved casing for non-invasive cattle health monitoring. The curvature of the casing is tailored to better fit the contours of the cattle’s neck, significantly enhancing signal accuracy, particularly in temperature signal acquisition. The core module is equipped with precision temperature sensors and inertial measurement units, utilizing the Arduino MKR ZERO board for data acquisition and processing. Field tests conducted on a cohort of ten cattle not only validated the accuracy of temperature sensing but also demonstrated the potential of machine learning, particularly the Support Vector Machine algorithm, for precise behavior classification and step counting, with an average accuracy of 97.27%. This study innovatively combines real-time temperature recognition, behavior classification, and step counting organically within a self-powered device. The results underscore the feasibility of this technology in enhancing cattle welfare and farm management efficiency, providing clear direction for future research to further enhance these devices for large-scale applications.
Re-Evaluating the Transplant Glomerulopathy Lesion—Beyond Donor-Specific Antibodies
Authors: Chutani Arun , Guevara-Pineda Daniel , Lerner Gabriel B. , Menon Madhav C.
Published: November 2024

There have been significant advances in short-term outcomes in renal transplantation. However, longer-term graft survival has improved only minimally. After the first post-transplant year, it has been estimated that chronic allograft damage is responsible for 5% of graft loss per year. Transplant glomerulopathy (TG), a unique morphologic lesion, is reported to accompany progressive chronic allograft dysfunction in many cases. While not constituting a specific etiologic diagnosis, TG is primarily considered as a histologic manifestation of ongoing allo-immune damage from donor-specific anti-HLA alloantibodies (DSA). In this review article, we re-evaluate the existing literature on TG, with particular emphasis on the role of non-HLA-antibodies and complement-mediated injury, cell-mediated immune mechanisms, and early podocyte stress in the pathogenesis of Transplant Glomerulopathy.
Reduced Soil Quality but Increased Microbial Diversity in Cultivated Land Compared to Other Land-Use Types in the Longzhong Loess Plateau
Authors: Xiang Hang , Xu Jingjing , Yang Hang , Song Jianchao , Yu Xiaojun
Published: November 2024

Soil microorganisms, as a vital part of terrestrial ecosystems, play a key role in sustaining essential soil functions. However, the impact of cultivated land (CL) on soil quality and microbial communities compared to other land-use types is still unclear. This study investigated the soil quality index (SQI) along with bacterial and fungal communities across various land-use types, including abandoned land, cultivated land, forest land, and grassland, in the Longzhong region of the Loess Plateau. The results showed that CL had the lowest SQI, but the diversity of soil bacterial and fungal communities in CL was significantly higher than that of other land-use types. The relative abundance of Ascomycota in CL fungal communities is significantly higher than that of other land-use types. Soil water content, organic matter, alkaline nitrogen, total nitrogen, and nitrate nitrogen all have an impact on soil bacterial and fungal communities in CL. The diversity of soil bacterial and fungal communities is mainly influenced by pH, nitrate nitrogen, and available phosphorus. This study emphasizes the impact of human activities such as tillage on soil quality, as well as the structure and diversity of soil microbial communities, in cultivated land compared to other different land-use methods.
Effects of Potassium Fertilizer on Sugarcane Yields and Plant and Soil Potassium Levels in Louisiana
Authors: Johnson Richard M. , Richard Katie A. , Read Quentin D.
Published: November 2024

The influence of potassium fertilizer on sugarcane (interspecific hybrids of Saccharum Spp.) yields and leaf and soil potassium levels was evaluated at six locations in Louisiana. The objective of this study was to determine if the sugarcane yields in Louisiana could be improved with potassium application. Different rates of potassium fertilizer (0–179 kg K2O ha−1) were applied to plant cane and ratoon sugarcane fields in Louisiana. Soil samples and sugarcane leaf samples were also collected from all experiments. Yield data were collected by harvesting plots with a single row, chopper harvester and a field transport wagon equipped with electronic load sensors. At all locations and soil types, potassium fertilizer did not increase cane or sugar yields. Soil properties data showed that significant increases in soil potassium levels did not occur until the second ratoon crop, where soil potassium increased by 30% for the high rate. Increases in plant potassium were also not observed until the second ratoon crop, where plant potassium increased by 10.5% for the high rate. The potential cause of the observed lack of response may be explained by interference from calcium and magnesium, combined with fixation by smectite and vermiculite clay minerals. Our soil and plant uptake data would suggest that repeated K applications at recommended rates, which currently vary from 90 to 157 kg ha−1, may be required to achieve the potential benefits of K fertilizer in Louisiana sugarcane soils. However, this must be verified by additional on-farm trials.
GMN+: A Binary Homologous Vulnerability Detection Method Based on Graph Matching Neural Network with Enhanced Attention
Authors: Zhao Zheng , Zhang Tianhao , Fan Xiaoya , Mao Qian , Wang Dafeng , Zhao Qi
Published: November 2024

The widespread reuse of code in the open-source community has led to the proliferation of homologous vulnerabilities, which are security flaws propagated across diverse software systems through the reuse of vulnerable code. Such vulnerabilities pose serious cybersecurity risks, as attackers can exploit the same weaknesses across multiple platforms. Deep learning has emerged as a promising approach for detecting homologous vulnerabilities in binary code due to their automated feature extraction and high efficiency. However, existing deep learning methods often struggle to capture deep semantic features in binary code, limiting their effectiveness. To address this limitation, this paper presents GMN+, which is a novel graph matching neural network with enhanced attention for detecting homologous vulnerabilities. This method comprehensively considers the information contained in instructions and incorporates types of input instruction. Masked Language Modeling and Instruction Type Prediction are developed as pre-training tasks to enhance the ability of GMN+ in extracting semantic information from basic blocks. GMN+ utilizes an attention mechanism to focus concurrently on the critical semantic information within functions and differences between them, generating robust function embeddings. Experimental results indicate that GMN+ outperforms state-of-the-art methods in various tasks and achieves notable performance in real-world vulnerability detection scenarios.
Detection of Pest Feeding Traces on Industrial Wood Surfaces with 3D Imaging
Authors: Sioma Andrzej , Nagashima Keiko , Lenty Bartosz , Hebda Arkadiusz , Nakata Yasutaka , Harada Kiichi
Published: November 2024

This paper presents a method for detecting holes and grooves made by wood-boring pests. As part of the production process automation, wood delivered from sawmills is checked for defects visible on its surface. One of the critical defects that disqualifies wood from further processing is the presence of feeding marks left by various types of pests on its surface. This paper proposes a method for detecting this type of damage based on analysis of three-dimensional images of the wood surface. Three-dimensional imaging methods and the image resolutions resulting from the adopted imaging system’s configurations are discussed. An analysis of the advantages and disadvantages of the methods investigated is presented, together with an assessment of their potential use in the implementation of the assigned control task, i.e., the detection of holes and grooves made by pests. Three-dimensional image parameters and interferences affecting the quality of the recorded image are described, along with the designed algorithm for identifying holes and grooves and the parametric description of the identified defect. The imaging effects for selected surfaces bearing signs of pest damage and the parameters describing the effectiveness of the present industrial solution are also presented. This paper demonstrates that it is possible to build a three-dimensional image to identify damage effectively within a minimum diameter of 1mm. It makes it possible to observe the damage carried out by most wood-boring pests.
Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
Authors: Santiago Júnior Valdivino Alexandre de
Published: November 2024

Few-shot learning (FSL) is a learning paradigm which aims to address the issue of machine/deep learning techniques which traditionally need huge amounts of labelled data to work out. The remote sensing (RS) community has explored this paradigm with numerous published studies to date. Nevertheless, there is still a need for clear pieces of evidence on FSL-related issues in the RS context, such as which of the inference approaches is more suitable: inductive or transductive? Moreover, how does the number of epochs used during training, based on the meta-training (base) dataset, relate to the number of unseen classes during inference? This study aims to address these and other relevant questions in the context of FSL for scene classification in RS images. A comprehensive evaluation was conducted considering eight FSL approaches (three inductive and five transductive) and six scene classification databases. Some conclusions of this research are as follows: (1) transductive approaches are better than inductive ones. In particular, the transductive technique Transductive Information Maximisation (TIM) presented the best overall performance, where in 20 cases it got the first place; (2) a larger number of training epochs is more beneficial when there are more unseen classes during the inference phase. The most impressive gains occurred particularly considering the AID (6-way) and RESISC-45 (9-way) datasets. Notably, in the AID dataset, a remarkable 58.412% improvement was achieved in 1-shot tasks going from 10 to 200 epochs; (3) using five samples in the support set is statistically significantly better than using only one; and (4) a higher similarity between unseen classes (during inference) and some of the training classes does not lead to an improved performance. These findings can guide RS researchers and practitioners in selecting optimal solutions/strategies for developing their applications demanding few labelled samples.
Anxiety Detection System Based on Galvanic Skin Response Signals
Authors: Al-Nafjan Abeer , Aldayel Mashael
Published: November 2024

Anxiety is a significant mental health concern that can be effectively monitored using physiological signals such as galvanic skin response (GSR). While the potential of machine learning (ML) algorithms to enhance the classification of anxiety based on GSR signals is promising, their effectiveness in this context remains largely underexplored. This study addresses this gap by investigating the performance of three commonly used ML algorithms, support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF), in classifying anxiety and stress activity using a benchmark dataset. We employed two feature extraction methods: traditional statistical feature extraction and an innovative automatic feature extraction approach utilizing a 14-layer autoencoder, aimed at improving classification performance. Our findings demonstrate the effectiveness of using GSR signals and the robust performance of the KNN algorithm in accurately classifying anxiety levels. The KNN algorithm achieved the highest accuracy in both the statistical and automatic feature extraction approaches, with results of 96.9% and 98.2%, respectively. These findings highlight the effectiveness of KNN for anxiety detection and emphasize the need for advanced feature extraction techniques to enhance classification outcomes in mental health monitoring.
Effect of the Inoculation Method on the Potential Plant Growth-Promoting Activity of a Microbial Synthetic Consortium
Authors: Abou Jaoudé Renée , Ficca Anna Grazia , Luziatelli Francesca , Ruzzi Maurizio
Published: November 2024

Plant microbiomes represent a reservoir of adaptive traits that can enhance plant resilience and productivity. In this study, we investigated the potential of a synthetic microbial consortium (SynCom), composed of five nitrogen-fixing Bacillaceae derived from the phyllosphere of Pistacia lentiscus L., to promote the growth of Lactuca sativa L. under nutrient-limiting availability. The short-term effects of the SynCom were evaluated in a hydroponic system, and four inoculation treatments were compared. The root and leaf inoculation (RL) significantly increased total plant biomass, driven by higher above-ground (+33%) and below-ground (+31%) biomass. The number of leaves per plant and leaf mass per area were also significantly enhanced in RL (+12% and +34%, respectively). While root-only inoculation (R) did not alter plant biomass, structure, or leaf traits, foliar spraying (L) significantly decreased the total leaf area and increased root biomass and the root-to-shoot ratio compared to non-inoculated plants, suggesting a direct influence of microbial metabolites on root growth and nutrient uptake. Compared to the individual R and L treatments, the synergistic effect observed in RL highlights the complex interplay between plant–microbe interactions in the rhizosphere and phyllosphere and the importance of ensuring adequate nutrient availability to nitrogen-fixing bacteria to achieve their growth-promoting potential fully. These findings suggest the potential for utilizing SynComs as bioinoculants to promote plant growth, emphasizing the need to optimize application strategies, considering both the SynCom composition and the host plant’s nutritional status to ensure efficacy.
The Importance of Genetic Screening on the Syndromes of Colorectal Cancer and Gastric Cancer: A 2024 Update
Authors: Lupan Iulia , Silaghi Ciprian , Stroe Claudia , Muntean Adriana , Deleanu Diana , Bintintan Vasile , Samasca Gabriel
Published: November 2024

Gastrointestinal cancers (GIC), encompassing colonic, rectal, and gastric malignancies, rank among the most prevalent cancer types globally, contributing significantly to cancer-related mortality. In the scientific literature, various syndromes associated with colorectal and gastric cancers have been elucidated, highlighting the intricate interplay between genetic factors and disease manifestation. The primary objective of this study was to conduct a genetic exploration aimed at elucidating these associations and identifying shared genetic determinants across these cancer types. Notably, considerable research has focused on the KRAS gene mutations, polymorphisms in nucleic acids, the Wnt signaling pathway, and the role of chemokine ligands in tumorigenesis. While investigations into natural plant extracts as potential therapeutic agents are still in their nascent stages, they represent a promising avenue for future research. Ongoing studies are essential to uncover suitable biomarkers that could facilitate the identification and understanding of the genetic links between these GIC. This exploration not only seeks to enhance our comprehension of the underlying genetic architecture but also aims to inform the development of targeted therapies and preventive strategies.
Integrative Multi-Omics Analysis Reveals Critical Molecular Networks Linking Intestinal-System Diseases to Colorectal Cancer Progression
Authors: Ji Shiliang , Hu Haoran , Zhu Ruifang , Guo Dongkai , Liu Yujing , Yang Yang , Li Tian , Zou Chen , Jiang Yiguo , Liu Guilai
Published: November 2024

Background/Objectives: Colorectal cancer (CRC) frequently co-occurs with intestinal system diseases (ISDs), yet their molecular interplay remains poorly understood. We employed a comprehensive bioinformatics approach to elucidate shared genetic signatures and pathways between CRC and ISDs. Methods: We systematically analyzed 12 microarray and RNA-seq datasets encompassing 989 samples across seven ISDs and CRC. Differentially expressed genes (DEGs) were identified using Limma and DESeq2. Functional enrichment analysis was performed using clusterProfiler. Protein–protein interaction networks were constructed via STRING and visualized with Cytoscape to identify hub genes. Clinical significance of shared genes was further assessed through survival analysis and validated by immunohistochemistry staining of 30 paired CRC–normal tissue samples. Results: Integrating bioinformatics and machine learning approaches, we uncovered 160 shared DEGs (87 upregulated, 73 downregulated), which predominantly enriched cell metabolism, immune homeostasis, gut–brain communication, and inflammation pathways. Network analysis revealed nine key hub proteins linking CRC and ISDs, with seven upregulated (CD44, MYC, IL17A, CXCL1, FCGR3A, SPP1, and IL1A) and two downregulated (CXCL12 and CCL5). Survival analysis demonstrated the prognostic potential of these shared genes, while immunohistochemistry confirmed their differential expression in CRC tissues. Conclusions: Our findings unveil potential biomarkers and therapeutic targets, providing insights into ISD-influenced CRC progression and offering a robust foundation for improved diagnostic and treatment strategies in ISD-associated CRC.
The HuRaA Trial—The Radiocapitellar Line Shows Significant Posterior Translation in Healthy Elbows: A Prospective Analysis of 53 Healthy Individuals
Authors: Schamberger Christian T. , Grossner Tobias , Rehnitz Christoph , Findeisen Sebastian , Ferbert Thomas , Suda Arnold J. , Schmidmaier Gerhard , Stein Stephan
Published: November 2024

Background: The elbow joint is stabilized by complex interactions between bony structures and soft tissues, notably the lateral and medial collateral ligaments. Posterolateral rotatory instability (PLRI), a form of elbow instability, is challenging to diagnose due to overlapping symptoms with other conditions. The radiocapitellar line (RCL) is a radiographic tool for assessing humeroradial alignment and elbow stability, but its diagnostic accuracy remains debated. This study aims to provide normative data on RCL deviations in healthy individuals to improve diagnostic criteria for PLRI. Methods: A prospective study was conducted with 53 healthy individuals (27 males, 26 females) aged 18–45 years. MRI scans of the participants’ elbows were performed in maximum extension and supination to assess radiocapitellar deviations (RCDs). Two orthopedic surgeons independently analyzed the images to evaluate RCDs and assess interobserver reliability. Statistical analyses, including independent t-tests and Pearson correlations, were used to explore the relationship between RCDs, demographic factors, and elbow stability. Results: The average RCD in the cohort was 1.77 mm (SD 1.06 mm). Notably, 62.9% of participants had deviations greater than 1.2 mm, while 12.9% exceeded 3.4 mm, thresholds traditionally used to diagnose PLRI. Gender and age did not significantly influence RCD values. The interobserver reliability was almost good (ICC = 0.87), supporting the consistency of the RCL measurements. Conclusions: Significant RCDs occur even in asymptomatic individuals, challenging the current diagnostic thresholds for PLRI based solely on RCL measurements. A comprehensive assessment that includes clinical, anatomical, and functional evaluations is essential for accurate diagnosis. These findings highlight the need for refined diagnostic criteria and further research into elbow stability.
Patient Safety Incidents in Inpatient Psychiatric Settings: An Expert Opinion Survey
Authors: Russotto Sophia , Conti Andrea , Vanhaecht Kris , Mira José Joaquín , Panella Massimiliano
Published: November 2024

Patient safety in psychiatric inpatient facilities remains under-researched despite its crucial importance. This study aims to address this gap by using expert opinion to estimate the frequency of diverse patient safety incidents (PSIs) in psychiatric settings and to compare it with the existing literature. Utilizing a seven-step approach, a questionnaire based on the World Health Organization’s International Classification for Patient Safety was developed and deployed. A total of 33 expert opinions were collected. Results showed a higher estimated incidence of PSIs in psychiatric settings compared to general healthcare, highlighting categories such as patient behavior, medication, and infrastructure as significant contributors. Experts emphasized the prevalence of incidents related to behavioral issues and inadequate infrastructure, areas often overlooked in the existing literature. Unlike general settings, psychiatric facilities appear more vulnerable to specific PSIs, such as those related to medication and building safety, underscoring the need for targeted safety measures. Our study suggests the existence of significant discrepancies between expert opinion and available research, with several underexplored domains in psychiatric patient safety.
The Stability of Slopes and Building Structures Using an Energy Visualization Procedure
Authors: Yao Yi , Zhang Jianjun , Li Xiaoyong , Tu Yiliang , Zhong Zuliang
Published: November 2024

Many building structures in the southwest of China are constructed on slopes due to its mountainous terrain characteristics. Therefore, it is crucial to accurately study the stability of slopes and building structures during the construction and operation stages. Traditional numerical simulation methods for slope stability often analyze from the perspectives of stress and strain. However, due to the complex changes in stress and strain inside the slope, the traditional methods are not only complex but also result in some errors. The slope failure is essentially a procedure of energy transformation, dissipation, and mutation. Therefore, the slope stability can be analyzed more effectively from the perspective of energy changes. In this paper, an energy field visualization procedure is developed and applied to analyze the failure mechanism of slopes. First, the energy calculation principle of slopes was derived based on the principle of thermodynamics. Then, FLAC3D7.0 was used to develop the energy visualization procedure for slope. It was applied to a classical two-dimensional slope to calculate the safety factor of slopes and then compared with the traditional methods. Finally, the procedure was applied to two practical slopes and building structure engineering cases to study their stability and provide suggestions for practical construction. The research results show that the energy visualization procedure can correctly simulate the energy evolution principle in the procedure of slope failure. The sudden change of energy can be used to determine the safety factor and sliding surface of slopes. The error of the slope safety factor calculated by this procedure is only 0.02, indicating that the procedure is correct. The deformation and failure of slopes are essentially driven by energy. There are corresponding relationships between the energy stability stage and the slope equilibrium stage, the energy dissipation stage and the slope deformation stage, and the energy mutation stage and the slope failure stage. The preferred backfill scheme of high-fill slope engineering is one with less variation in gravitational potential energy and a greater increase in elastic strain energy. Pile foundation and building structure are effective methods to increase slope stability. Therefore, the energy visualization procedure developed in this paper can more intuitively and accurately analyze the stability of slopes and building structures.
Influence of Boundary Conditions on the Estimation of Thermal Properties in Insulated Building Walls
Authors: Rendu Manon , Le Dréau Jérôme , Salagnac Patrick , Doya Maxime
Published: November 2024

The objective of this study is to evaluate the ability of inverse techniques to estimate the resistance and the capacity of a highly insulated multilayer wall under real weather conditions. The wall is equipped with temperature sensors inside and on its inner and outer surfaces, and the boundary conditions have been measured over a 14-day period. Uncertainties on various parameters of the model are evaluated, including internal and external convective heat transfer coefficients (±20% and ±7 W.m-².K−1 respectively), external long-wave heat transfer coefficient (±0.15 W.m−2.K−1) and solar absorption coefficient (±0.06). A sensitivity analysis demonstrated the high correlation with some parameters defining the thermal performance of the walls (thermal resistance or capacity). A solution is proposed to limit the number of identified parameters, while allowing the identification of the thermal resistance and the thermal capacity of the walls. There are two cases: either the weather conditions are accurately measured (temperature, short- and long-wave radiation) and the thermal characteristics can be assessed, or intrusive sensors are installed, and the thermal characteristics can be evaluated more accurately.
A Study on the Factors Influencing Willingness to Use Virtual Reality Systems for External Evaluation of Buildings
Authors: Liu Yuanzhao , Konduri Sreenidhi , Park Changbae
Published: November 2024

Integrating new visualization methods based on virtual reality (VR) in the design evaluation process remains a challenge despite its expanding use in architectural design in recent years. This article proposes a VR-based evaluation model to improve the efficiency and quality of the overall architectural design process. By adopting a structural equation model in conjunction with the Technology Acceptance Model (TAM), the study examines users’ intention and perceived ease of use of VR in the design evaluation process based on a questionnaire survey using the proposed VR model of architectural and non-architectural professionals and students. The findings of the study show that the output quality plays a significant role in increasing behavioral intention to use the system, and perceived ease of use has a positive effect on perceived immersion and perceived usefulness of VR in the evaluation process. The study illustrates the importance of understanding “user perspective” and “willingness to use” in the development of VR-based systems to increase their practical use in architecture and design fields.
A Rare Case of Posterior Fossa Syndrome Associated with Neuropathic Pain Successfully Treated with a Combination of Gabapentin, Diazepam and Baclofen—A Case Report and Literature Review
Authors: Giglio Mariateresa , Corriero Alberto , Perillo Teresa , Varrassi Giustino , Puntillo Filomena
Published: November 2024

Background: Posterior fossa syndrome (PFS), also known as cerebellar mutism syndrome, occurs in about 25% of pediatric patients undergoing resection of a posterior cranial fossa medulloblastoma. It is characterized primarily by mutism or reduced/impaired speech and may include variable symptoms such as motor dysfunction (apraxia, ataxia, hypotonia), supranuclear cranial nerve palsies, neurocognitive changes, and emotional lability. Long-term multidisciplinary rehabilitation is typically required, with recovery taking approximately six months, though many children experience long-term residual deficits. Neuropathic pain associated with PFS is rarely reported in pediatric patients, and evidence for its management is limited. Methods: This case report describes a 10-year-old boy who developed PFS following incomplete resection of a medulloblastoma. Clinical presentation included mutism, irritability, emotional lability, sleep disturbances, and neuropathic pain localized at the C5 level. The patient was treated with a combination of gabapentin, diazepam, and baclofen. Results: The combined pharmacological approach resulted in successful management of the patient’s neuropathic pain and other symptoms associated with PFS, improving his overall condition. Conclusions: This case highlights the potential effectiveness of a multimodal pharmacological regimen for treating neuropathic pain and associated symptoms in pediatric patients with PFS. Further research is needed to explore optimal treatment strategies for this rare but challenging complication.
Exploring the Impacts of Lifestyle Changes in the Global Energy Transition: Insights from a Model-Based Analysis Using PROMETHEUS
Authors: Fragkos Panagiotis , Zisarou Eleftheria , Andreou Andreas
Published: November 2024

A global clean energy transition is required for achieving ambitious climate goals and ensuring sustainable development. While technological advancements are crucial, they are not sufficient on their own to meet Paris Agreement (PA) climate targets. Integrating lifestyle changes, particularly in sectors such as transport and residential use of energy, into climate policies and energy modeling framework is gaining recognition in energy transition research. This study explores the impact of lifestyle changes on the global energy system and CO2 emissions using the PROMETHEUS model, an advanced energy–economy–environment system model. In this research we present scenarios in which lifestyle changes, such as reduced private car use and increased adoption of public transport and energy-savings behavior in households, are gradually introduced and complement technological and policy measures within the energy transition framework. We explore the impacts of scenarios with different levels of climate policies and lifestyle changes to evaluate the effects of various behavioral shifts on global energy consumption and CO2 emissions. Results show that even under current climate policies, lifestyle changes can reduce global energy demand by 5% by 2030 and 10% by 2050. When combined with ambitious decarbonization policies, the reductions are much more significant, leading to a 35% reduction by 2050 compared to the baseline scenario. Overall, the findings suggest that lifestyle changes, when effectively integrated with climate policy measures, can reduce energy demand and carbon emissions, alleviate the pressure on energy supply, and reduce the cost burden for energy producers and consumers.
Design of a Single-Edge Nibble Transmission Signal Simulation and Acquisition System for Power Machinery Virtual Testing
Authors: Li He , Li Zhengyu , Cai Yanbin , Zhang Jiwei , Liu Hongyu , Cui Wei , Wang Qingxin , Zhang Shutao , Cui Wenrui , Zhao Feiyang , Yu Wenbin
Published: November 2024

With the advancement of technology, the Single-Edge Nibble Transmission (SENT) protocol has become increasingly prevalent in automotive sensor applications, highlighting the need for a robust SENT signal simulation and acquisition system. This paper presents a real-time SENT signal acquisition system based on NI Field Programmable Gate Array (FPGA) technology. The system supports a range of message and data frame formats specified by the SAE J2716 SENT protocol, operates autonomously within a LabVIEW self-compiled environment, and is compatible with NI hardware-in-the-loop (HIL) systems for virtual electronic control units (ECU) calibration. This innovative, self-developed SENT system accommodates four message formats, seven data frame formats, and three pause pulse modes. Benchmarking tests were conducted by integrating this system with the dSPACE SCALEXIO HIL (located in Paderborn, Germany) system for SENT signal simulation and acquisition. The results confirm that the system effectively simulates and acquires SENT signals in accordance with SAE J2716 standards, establishing it as an invaluable asset in the electronification, intelligentization, informatization, and smart sensing of automotive and agricultural machinery.