bluuebunny/crossref_metadata_2025 huggingface dataset:
Incidence trends, histological subtypes, and topographical distribution of bladder cancer in Iran: a study based on the Iranian National Cancer Registry during 2006-2015
Authors: Naserghandi Alvand , Azizmohammad Looha Mehdi , Jameie Melika , Moradian Haft Cheshmeh Zeynab , Namakin Kosar , Golmakani Najmeh , Feyzi Aydin , Shabanipour Hadis , Tofighi Zavareh Mohammad Amin , Allameh Farzad , Akbari Mohammad Esmaeil
Published: October 2024

Background

Bladder cancer (BCa) is a significant public health concern. This study aimed to analyze the incidence trends, histological subtypes, and topographical distribution of BCa in Iran over a decade.

Methods

This retrospective registry-based study analyzed data on BCa patients diagnosed between March 20, 2006, and March 20, 2015. Following data quality control, age-standardized incidence rates (ASIRs) were calculated for BCa overall, by sex and histological subtype using the new World Health Organization (WHO) standard population.

Results

We identified 51,379 BCa cases (81.97% male) with a mean age of 65.10 ± 14.89 years. The overall ASIR was 8.92 per 100,000 (95% CI: 8.84-9.00). While a modest increase in ASIR was observed overall (8.77 in 2006 to 9.64 in 2015) and among males (14.13 in 2006 to 15.95 in 2015) during the study period, males consistently had a significantly higher ASIR compared to females (approximately 4.5:1 ratio). BCa incidence showed a progressive increase across older age groups, particularly those aged 40-44 to 80-84 years. Urothelial cell carcinoma (UCC) was the most prevalent histological type (ASIR: 8.19 to 7.93), followed by adenocarcinoma (AC; ASIR: 0.13 to 0.11). Squamous cell carcinoma (SCC) displayed a decreasing trend (ASIR: 0.11 to 0.06). Both UCC and AC were more frequent in males (approximately 5 and 3 times higher than females, respectively). Malignant neoplasm of the bladder, unspecified, constituted over 95% of BCa topography classifications.

Conclusion

This study identified a modest rise in BCa incidence, with male predominance and a higher burden in older adults. Further investigation into potential risk factors contributing to this increase is warranted.
The mutated in colorectal cancer (MCC) gene can serve as a potential biomarker of glioblastoma
Authors: Nguyen Huonggiang , Huang Qingzhi , Juang Uijin , Gwon Suhwan , Jung Woohyeong , Lee Soohyeon , Lee Beomwoo , Kwon So Hee , Kim In Soo , Park Jongsun , Kim Seon-Hwan
Published: October 2024

Introduction

The mutated in colorectal cancer (MCC) gene was initially identified as a candidate tumor suppressor gene in colorectal cancer, acting as a negative regulator of cell cycle progression. However, its functional roles in brain tumors, particularly glioblastoma, remain largely unexplored. This study reveals a significant association between MCC status and glioblastoma.

Methods

We explored MCC expression in the glioblastoma database, patient samples, and cell lines. We investigated the proliferation and migration of the cell lines in MCC gene knockdown using small interfering RNA.

Results

In vitro analyses revealed elevated protein and mRNA levels of MCC in several glioblastoma cell lines (U118MG and T98G). Silencing MCC expression via siRNA-mediated knockdown resulted in increased proliferation and migration of these cell lines. Supporting these findings, analyses of The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Genotype-Tissue Expression (GTEx) databases confirmed higher MCC expression in glioblastoma tumors than in normal brain tissue. Importantly, we observed that high MCC expression was associated with poor prognosis in glioblastoma patients, highlighting its potential role in disease progression. Additionally, this study identifies a nuclear localization of MCC in the glioblastoma cell line.

Discussion

These findings indicate that MCC expression is significantly upregulated in glioblastoma and may play a role in its pathophysiology, warranting further investigation.
Causal links between circulatory inflammatory cytokines and risk of digestive polyps: a Mendelian randomization analysis
Authors: Yan Ziqi , Zheng Hongming , Feng Jieni , Li Yiting , Hu Zhifan , Wu Yuan , Liao Guibin , Miao Taosheng , Qiu Zexin , Mo Qiaolan , Li Jia , Lai Ailin , Lu Yue , Chen Bin
Published: October 2024

Background

There is a high morbidity of polyps in the digestive tract, and certain subtypes of polyps are thought to induce cancer progression and often recur, which may be associated with chronic inflammation. Mendelian randomization (MR) can help identify potential causative relationships and inform early treatment action.

Methods

We performed a bidirectional two-sample MR analysis implementing the results from genome-wide association studies for 41 serum cytokines from 8,293 Finnish individuals, and three types of polyps from European ancestry, respectively, including gastric polyp (6,155 cases vs. 341,871 controls), colonic polyp (22,049 cases vs. 332,368 controls) and gallbladder polyp (458 cases vs. 340,083 controls). Inverse-variance weighted (IVW), weight median (WM), and MR-Egger methods were used for calculating causal estimates. Furthermore, Bayesian model averaging MR (MR-BMA) method was employed to detect the dominant causal circulatory cytokines with adjustment for pleiotropy effects.

Results

Our univariable MR using inverse-variance weight method identified causal associations of IL-2ra (OR: 0.892, 95%CI: 0.828–0.961, p = 0.003), MIG (OR: 1.124, 95%CI: 1.046–1.207, p = 0.001) and IL-18 (OR: 0.912, 95%CI: 0.852–0.977, p = 0.008) with gastric polyp, MIP1b (OR: 0.956, 95%CI: 0.927–0.987, p = 0.005) and IL-6 (OR: 0.931, 95%CI: 0.870–0.995, p = 0.035) with colonic polyp and IL-9 (OR: 0.523, 95%CI: 0.345–0.794, p = 0.0007) with gallbladder polyp. Finally, our MR-BMA analysis prioritized MIG (MIP = 0.332, MACE = 0.022; PP: 0.264, MSCE = 0.059), IL-18 (MIP = 0.302, MACE = −0.020; PP: 0.243, MSCE = −0.059) and IL-2ra (MIP: 0.129; MACE: −0.005; PP: 0.112, MSCE: −0.031) for gastric polyp, and MIP1b (MIP = 0.752, MACE = −0.033; PP: 0.665, MSCE = −0.044) and IL-6 (MIP: 0.196; MACE: −0.012; PP: 0.140, MSCE: −0.064) for colonic polyp, and IL-9 (MIP = 0.936, MACE = −0.446; PP: 0.781, MSCE = −0.478) for gallbladder polyp as the top-ranked protective factors.

Conclusion

Our research advances the current understanding of the function of certain inflammatory biomarker pathways in the genesis and malignant mutation of polyps in the digestive tract. Deeper substantiation is necessary to assess the potential of these cytokines as pharmacological or lifestyle targets for digestive polyps prevention.
Gender differences in autonomic and psychological stress responses among educators: a heart rate variability and psychological assessment study
Authors: Calderón-García Andrea , Álvarez-Gallardo Estela , Belinchón-deMiguel Pedro , Clemente-Suárez Vicente Javier
Published: October 2024

Introduction

This study explores the gender differences in psychological stress perception and autonomic modulation among teachers.

Methods

Utilizing heart rate variability (HRV) as a measure of autonomic function and a suite of validated psychological tests, the study examines the discrepancies in stress, anxiety, burnout, and personality traits between male and female educators.

Results

Results indicate that despite higher reported levels of stress and anxiety, women demonstrate a higher HRV, suggesting a stronger parasympathetic response.

Discussion

These findings highlight the complex interplay between psychological stressors and physiological responses, emphasizing the need for gender-specific interventions in stress management within the educational sector. Implications for enhancing educators’ well-being and performance through tailored strategies are discussed.
A study on music therapy aimed at psychological trauma recovery for bereaved families driven by artificial intelligence
Authors: Wang Zixi , Guan Xin , Li Enhui , Dong Bo
Published: October 2024

Introduction

This study explores the effectiveness of music therapy in repairing psychological trauma in bereaved families, aiming to provide a comprehensive understanding of its potential therapeutic impact. It begins with an analysis of the current situation faced by bereaved families, identifying the psychological challenges they experience.

Methods

The research design included the recruitment of participants from bereaved families, who were then divided into an experimental group and a control group. An optimized Long Short-Term Memory (LSTM) network model was constructed to analyze music therapy data, tailored specifically to capture the nuances of this therapeutic process. The experimental procedure detailed the specific operations involved in the music therapy sessions and established a clear comparison framework between the two groups.

Results

The performance of the proposed LSTM model demonstrated high accuracy (0.85), precision (0.86), recall (0.84), and F1-score (0.85), indicating its effectiveness in predicting treatment outcomes. When compared to the Self-Reporting Inventory-90 (SCL-90) scale, the model captured the trend of treatment effects with a high level of accuracy and reliability. Notably, participants numbered 2, 6, and 8 in the experimental group showed substantial improvement rates of 67.21%, 71.45%, and 75.67%, respectively, in their psychological health issues.

Discussion

The comparative analysis between the experimental and control groups confirmed that the music therapy approach, as guided by the proposed LSTM model, led to a more significant improvement in psychological health issues for bereaved families. This suggests that the model offers a promising avenue for enhancing the effectiveness of music therapy in this context.
Epigenetic changes driven by environmental pollutants in lung carcinogenesis: a comprehensive review
Authors: Zhang Aijia , Luo Xuexing , Li Yu , Yan Lunchun , Lai Xin , Yang Qianxu , Zhao Ziming , Huang Guanghui , Li Zheng , Wu Qibiao , Wang Jue
Published: October 2024

Lung cancer remains the leading cause of cancer-related mortality globally, with environmental pollutants identified as significant risk factors, especially for nonsmokers. The intersection of these pollutants with epigenetic mechanisms has emerged as a critical area of interest for understanding the etiology and progression of lung cancer. Epigenetic changes, including DNA methylation, histone modifications, and non-coding RNAs, can induce alterations in gene expression without affecting the DNA sequence and are influenced by environmental factors, contributing to the transformation of normal cells into malignant cells. This review assessed the literature on the influence of environmental pollutants on lung cancer epigenetics. A comprehensive search across databases such as PubMed, Web of Science, Cochrane Library, and Embase yielded 3,254 publications, with 22 high-quality papers included for in-depth analysis. These studies demonstrated the role of epigenetic markers, such as DNA methylation patterns of genes like F2RL3 and AHRR and alterations in the miRNA expression profiles, as potential biomarkers for lung cancer diagnosis and treatment. The review highlights the need to expand research beyond homogenous adult male groups typically found in high-risk occupational environments to broader population demographics. Such diversification can reduce biases and enhance the relevance of findings to various clinical contexts, fostering the development of personalized preventive and therapeutic measures. In conclusion, our findings underscore the potential of innovative epigenetic therapies, such as DNA demethylating drugs and histone modification agents, to counter environmental toxins’ carcinogenic effects. The growing interest in miRNA therapies and studies aiming to correct aberrant methylation patterns indicate significant strides toward better lung cancer management and a healthier future for global communities.
Sustainability Orientation of Generation Z and Its Role in Their Choice of Employer—A Comparative Qualitative Inquiry of India and United States
Authors: Sengupta Debashish , Mathews Manisha , Bridges Lasondrick , D’Costa Roshan , Bastian Bettina Lynda
Published: October 2024

This article critically analyses and compares the sustainability orientation of Generation Z in India and the United States and examines its role in their employer selection, using the Theory of Planned Behaviour. Our qualitative study employs the Narrative Inquiry method and includes 30 participants—15 from India and 15 from the United States—aged between 19 and 26 years, who are either currently employed or preparing to enter the workforce within the next few years. Data were collected through in-depth interviews. The findings of our research show that subjective norms and contextual realities can play a significant role in shaping Generation Z’s personal attitudes towards sustainability and their perceived behavioural control in making important life decisions, such as choosing an employer. These findings contribute significantly to the literature on Generation Z’s sustainability orientation and bear important implications for the higher education sector and employers.
Linking Entrepreneurial Orientation, Learning Orientation, Organization Structure and the Growth of Government
Authors: Alyammahi Alyaa Hamed , Sarker Abu Elias , Zervopoulos Panagiotis
Published: October 2024

The growth of government (GoG) has garnered significant attention among scholars and practitioners across disciplines such as economics, political science, and public management. Despite the optimism surrounding this concept, its future trajectory in the twenty-first century remains uncertain. Extant literature explaining the rationales for organizational growth in the public sector is inconclusive which pertains mostly to the absence of an appropriate methodological approach. Therefore, this research underscores the need for a comprehensive framework to examine the determinants of the growth. Particularly, the burgeoning literature on new public management (NPM) as well as behavioural public administration provides huge potentials to examine the role of the interplay of organizational structure and learning orientation in exacting the impact of entrepreneurial orientation on the growth of government on GoG. The conceptual framework was guided by the hermeneutic approach that focuses on interpreting texts or phenomena by understanding the contextual elements to uncover deeper meaning and insights. It does so by drawing upon different streams of literature including entrepreneurial orientation, learning orientation, organizational structure, and the growth of government. In order to operationalize the framework, a succinct set of propositions is proposed. The proposed conceptual framework along with suggested research propositions will potentially guide future research to further extend the growth of government literature.
Leveraging ICT and Generative AI in Higher Education for Sustainable Development: The Case of a Lebanese Private University
Authors: Boustani Nada Mallah , Sidani Dina , Boustany Zaher
Published: October 2024

This study investigates the interaction of information and communication technology (ICT) and higher education in driving sustainable development, with a special emphasis on the rising significance of generative AI tools such as ChatGPT. This quantitative study, conducted in a Lebanese private university, looks into the impact of ICT, specifically generative AI, on promoting sustainability in higher education. A questionnaire was distributed to university instructors to determine how these technologies contribute to sustainable practices in academic environments. The results, which were evaluated using descriptive statistics and correlation analysis, show that ICT tools, professional instructor development, and the upkeep of educational infrastructure all contribute considerably to Lebanon’s sustainable development. Furthermore, integrating generative AI into educational processes has the potential to improve personalized learning, resource efficiency, and content production, all of which contribute to sustainability goals. The study concludes by exploring the findings’ broader implications, acknowledging their shortcomings, and suggesting future research possibilities.
Integrated Technologies for Anti-Deicing Functions and Structures of Aircraft: Current Status and Development Trends
Authors: Cui Yanchao , Chen Weijian , Dai Ning , Han Chuang
Published: October 2024

With the increasing adoption of composite materials in aircraft construction, traditional anti-icing technologies face significant challenges due to the low thermal conductivity and heat resistance of composite resins. These limitations have spurred the development of lightweight, efficient, durable, and cost-effective integrated anti-icing technologies as a critical area of research. This paper begins with an overview of advancements in electrothermal anti-icing and de-icing technologies for aircraft. It then explores the configurations and applications of functional-structural integration technology for anti-icing and de-icing, emphasizing pivotal technologies and current challenges in this field. Finally, the study forecasts the development trends in the multifunctional integration of thermal conductivity/insulation, anti-icing, and electromagnetic wave transparency/wave-absorbing properties. These advancements are driven by the evolution of composite materialization in aircraft and the progression of multi-electrical/all-electrical technologies. The objective is to provide a comprehensive guide for technological development in anti-icing, aiding researchers and relevant departments to further enhance the application of anti-icing technology in composite material aircraft.
Investigating the Variation between Lignin Content and the Fracture Characteristics in Capsicum annuum Mutant Stems
Authors: Pápai Bánk , Kovács Zsófia , Tóth-Lencsés Kitti Andrea , Bedő Janka , Chan Khin Nyein , Kovács-Weber Mária , Pap Tibor István , Csilléry Gábor , Szőke Antal , Veres Anikó
Published: October 2024

The cultivation of horticultural plants in controlled greenhouse environments is a pivotal practice in modern agriculture, offering the potential to enhance crop productivity and mitigate climate change effects. This study investigates the biomechanical properties and lignin content of various Capsicum annuum mutant lines—‘fragile-plant’ (frx), ‘tortuous internodi’ (tti), and ‘puffy-structured stem’ (pfi)—in comparison to a commercially established variety, ‘Garai Fehér’. We employed the acetyl bromide method to quantify lignin content and conducted three-point bending tests to assess rigidity in three distinct regions of the stem. Gene expression analysis of key lignin biosynthetic pathway genes (PAL, C4H, 4CL, CCoAOMT, CAD) was performed using qRT-PCR. The results revealed significant differences in lignin content and breaking force among the genotypes and stem regions. The tti mutants exhibited similar lignin content to the control but lower breaking strength, likely due to elongated internodes. The frx mutants showed uniformly reduced lignin content, correlating with their fragile stems. The pfi mutants displayed abnormally high lignin content in the top region yet demonstrated the lowest stem rigidity in every region. Overexpression of CAD and CCoAOMT was detected in the mutants in specific regions of the stem, suggesting alterations in lignin biosynthesis; however, we could not confirm the correlation between them. Our findings indicate that while lignin content generally correlates with stem rigidity, this trait is complex and influenced by more factors.
Crude Protein as an Indicator of Pasture Availability and Quality: A Validation of Two Complementary Sensors
Authors: Serrano João , Shahidian Shakib , Moral Francisco J.
Published: October 2024

This study evaluated the possibility of using two complementary electronic sensors (rising plate meter (RPM) and active optical sensor (AOS)) to obtain a global indicator, pasture crude protein (CP) in kg ha−1. This parameter simultaneously integrates two essential dimensions: pasture dry matter availability (dry matter (DM) in kg ha−1) measured by RPM, and pasture quality (measured by AOS), and supports management decisions, particularly those related to the stocking rates, supplementation, or rotation of animals between grazing parks. The experimental work was carried out on a dryland biodiverse and representative pasture, and consisted of sensor measurements, followed by the collection of a total of 144 pasture samples, distributed between three dates of the pasture vegetative cycle of 2023/2024 (Autumn—December 2023; Winter—February 2024; and Spring—May 2024). These samples were subjected to laboratory reference analysis to determine DM and CP. Sensor measurements (compressed height (HRPM) in the case of RPM, and normalized difference vegetation index (NDVI) in the case of AOS) and the results of reference laboratory analysis were used to develop prediction models. The best correlations between CP (kg ha−1) and “HRPM × NDVI” were obtained in the initial and intermediate phases of the cycle (autumn: R2 = 0.86 and LCC = 0.80; and Winter; R2 = 0.74 and LCC = 0.81). In the later phase of the cycle (spring), the accuracy of the forecasting model decreased dramatically (R2 = 0.28 and LCC = 0.42), a trend that accompanies the decrease in the pasture moisture content (PMC) and CP. The results of this study show not only the importance of extending the database to other pasture types in order to enhance the process of feed supplement determination, but also the potential for the research and development of proximal and remote sensing tools to support pasture monitoring and animal production management.
Effects of Long-Term Sustainable Inorganic Fertilization on Rice Productivity and Fertility of Quaternary Red Soil
Authors: Liu Yiren , Hou Hongqian , Lan Xianjin , Ji Jianhua , Liu Xiumei , Lv Zhenzhen , Chen Liumeng
Published: October 2024

Soil microbial communities play a critical role in soil fertility and crop productivity. The present study investigated the impact of long-term chemical fertilization on microbial communities, rice productivity, and fertility of Quaternary red soil. A long-term experiment was conducted from 1984 to 2018 with the following treatments: unbalanced nitrogen (N), phosphorus (P), potassium (K) fertilization (NP, NK, and PK) and balanced inorganic fertilization (NPK) and non-fertilization (CK) as control. The results indicate that alkaline hydrolyzed nitrogen (AhN), available phosphorus (AP), and available potassium (AK) were higher with the application of NPK fertilizers than in the initial stage of the experiment. The crop yield of fertilizer groups was also higher than that of CK, and the maximum yield was observed in the NPK group. The relative abundance of dominant bacteria, such as Acidobacteriaceae and Proteobacteria, was significantly different among different fertilizer treatments. Different fertilization strategies also had significant effects on soil fungi. For instance, Mortierella had a positive correlation with the soil N content, and Arnium showed a negative correlation with the balanced fertilization of N and P. Therefore, long-term balanced inorganic fertilization can effectively improve rice productivity and fertility of Quaternary red soil.
Automated Audible Truck-Mounted Attenuator Alerts: Vision System Development and Evaluation
Authors: Owor Neema Jakisa , Adu-Gyamfi Yaw , Zhang Linlin , Sun Carlos
Published: October 2024

Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety.
A Bag-of-Words Approach for Information Extraction from Electricity Invoices
Authors: Sánchez Javier , Cuervo-Londoño Giovanny A.
Published: October 2024

In the context of digitization and automation, extracting relevant information from business documents remains a significant challenge. It is typical to rely on machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new model for extracting key values from electricity invoices, including customer data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach relies on a bag-of-words strategy and custom-designed features tailored for electricity data. We validate our method on the IDSEM dataset, which includes 75,000 electricity invoices with eighty-six fields. The model converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of unseen documents. The precision obtained with Support Vector Machines is 91.86% on average, peaking at 98.47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices.
An Update on Australian Policy around Lamb Marking with Examination of Potential Drivers. Comment on Johnston et al. How Well Does Australian Animal Welfare Policy Reflect Scientific Evidence: A Case Study Approach Based on Lamb Marking. Animals 2023, 13, 1358
Authors: Johnston Charlotte H. , Errington Amanda J. , Hutchinson Mark R. , Whittaker Alexandra L.
Published: October 2024

This commentary provides an update and clarification on the legislative landscape surrounding mulesing in Australia since the publication of the 2023 study, “How Well Does Australian Animal Welfare Policy Reflect Scientific Evidence: A Case Study Approach Based on Lamb Marking”. The article explores legislative changes mandating the use of pain relief for mulesing in various states, emphasising Victoria’s original role, and highlighting the fragmented state-based legislative approach to animal welfare. It discusses the impact of these legislative changes on industry practices and animal welfare outcomes. The commentary highlights the complexities of policy development in this area, due in part to the diverse and often conflicting interests of stakeholders and the public. It underscores the importance of transparency, stakeholder collaboration, and scientifically informed policymaking to effectively enhance animal welfare standards.
Morphology of Larger Salivary Glands in Peccaries (Pecari tajacu Linnaeus, 1758)
Authors: Júnior Carlos Magno Oliveira , Júnior Hélio Noberto de Araújo , Tertulino Moisés Dantas , Guerra Ricardo Romão , Rola Luciana Diniz , Silva Alexandre Rodrigues da , Moura Carlos Eduardo Bezerra de , Oliveira Moacir Franco de
Published: October 2024

This work aims to study the major salivary gland morphology of peccaries during their growth. The glands were analyzed using macroscopic description, light microscopy, electron microscopy, histochemistry, and immunohistochemistry. Topographically, the salivary glands resemble other animals, including domestic animals and pigs. During growth, the parotid enlarges and mandibular gland loses weight. Histologically, the parotid has serous production, and sublingual has mucous production, resembles most species, however, mandibular gland produces mucous, unlike other animals, including pigs, which produce seromucous secretion. Histochemically, parotid produces more acidic mucins than pigs and it undergoes maturation during development; mandibular, and especially the sublingual gland, produce more acidic and basic mucopolysaccharides than pigs. The results found with transmission and scanning electron microscopy techniques corroborate the histological and histochemistry findings. The major salivary glands were positive to different lecithins (Com-A, BSA-I-B4, WGA and PNA), which were also more positive than in pigs and sheep. We conclude that collared peccaries have a salivary secretion that facilitates the digestion of carbohydrates, and biometric characteristics and positivity to lecithins that facilitate adaptation to foods with antinutritional factors.
Role of Metabolomics and Metagenomics in the Replacement of the High-Concentrate Diet with a High-Fiber Diet for Growing Yushan Pigs
Authors: Xie Min , Fei Dan , Guang Yelan , Xue Fuguang , Xu Jun , Zhou Yaomin
Published: October 2024

The objective of this study was to investigate the regulatory effects of a high-fiber content feed on the productive performance, meat quality, and fat acid composition. A total of 18 120-day-old Yushan pigs with similar initial body weight were randomly allotted into high-concentrate diet (high energy, HE) and high-fiber diet (low energy, LE) treatments for the determination of regulatory effects on productive performance, meat quality, and fatty acid content. Further, blood metabolomic, gut microbiota, and liver energy-related gene expression measurements were used to investigate the underlying mechanisms. Results showed that the LE treatment significantly increased ADFI while decreasing carcass weight, fat percentage, and IMF. Metabolomic results showed that the high-fiber treatment significantly down-regulated metabolites that participated in lipid metabolism such as cyclic ADP-ribose and hippuric acid, while up-regulated metabolites were mainly enriched in nitrogen metabolism such as DL-arginine and propionylcarnitine (p < 0.05). Microbial results showed relative abundances of Lactobacillus and Bifidobacterium are significantly proliferated in the high-fiber feeding treatments (p < 0.05). Transcriptomic results showed that genes mainly enriched into the lipid metabolism are significantly up-regulated under the high-fiber dietary treatment (p < 0.05). Conclusion: higher dietary fiber significantly reduced dietary energy provision, effectively decreased the backfat and abdominal fat content of Yushan pigs through proliferating intestinal fiber-degradable bacteria, and up-regulating the hepatic lipolysis-related gene expression.
New Technology of Rumen-Protected Bypass Lysine Encapsulated in Lipid Matrix of Beeswax and Carnauba Wax and Natural Tannin Blended for Ruminant Diets
Authors: Inô Claudiney Felipe Almeida , Pereira Filho José Morais , de Oliveira Roberto Matheus Tavares , de Oliveira Juliana Felipe Paula , da Silva Filho Edson Cavalcanti , Nascimento Ariane Maria da Silva Santos , Oliveira Ronaldo Lopes , do Nascimento Romilda Rodrigues , de Lucena Kevily Henrique de Oliveira Soares , Bezerra Leilson Rocha
Published: October 2024

Tannins are compounds present in forage plants that, in small quantities in the diet of ruminants, produce protein complexes that promote passage through the rumen and use in the intestine. This study tested the hypothesis that beeswax (BW) and carnauba wax (CW) lipid matrices are effective encapsulants for creating bypass lysine (Lys) for ruminants, with tannin extracted from the Mimosa tenuiflora hay source enhancing material protection. Microencapsulated systems were made using the fusion–emulsification technique with a 2:1 shell-to-core ratio and four tannin levels (0%, 1%, 2%; 3%). The following eight treatments were tested: BWLys0%, BWLys1%, BWLys2%, BWLys3%, CWLys0%, CWLys1%, CWLys2%, and CWLys3%. Tannin inclusion improved microencapsulation yield and efficiency. CWLys3% had the highest microencapsulation efficiency and retained Lys. Lysine in BW and CW matrices showed higher thermal stability than in its free form. Material retention was greater in BW than CW. Rumen pH and temperature remained unaffected, indicating that BW and CW as the shell and tannin as the adjuvant are efficient encapsulants for Lys bypass production. The formulation CWLys3% is recommended as it is more efficient in protecting the lysin amino acid from rumen degradation.
Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System
Authors: Gao Junchai , Han Haorui , Yang Jiamin , Li Hanshan
Published: October 2024

In order to evaluate the tracking capability of optoelectric tracking for an orbital target, the limit magnitude detection performance calculation model and its calculation method are studied. Combining the optical signal characteristics of the tracked orbital target, the background, and the CCD noise, the framework of the limit magnitude calculation model of the system for dynamic target detection is constructed. The relationships between the limit magnitude and the signal-to-noise ratio threshold of the optical signal characteristics, the exposure time of the CCD camera, and the dark current of the CCD imaging are studied and analyzed while considering the sunlight illumination condition, so that the calculation function and its change curve are given. The limit magnitude detection capability of the system is verified by the simulated experiment and the synchronized tracking test, and the detection distance maximum error of the model calculation is 3.6 m. The results show that under certain illumination conditions, when the exposure time of the CCD camera is longer and the SNR threshold is lower, the limit magnitude detection performance of the system is better, and the tracking performance of the system is more stable.
An Application of Mist Generator as a Way to Reduce Particulate Matter during High Concentration Episodes in Urban Forests
Authors: Yoo Sin-Yee , Kim Taehee , Choi Sumin , Park Chan-Ryul , Song Dong-Ha
Published: October 2024

Previous conventional mist devices can induce a detrimental effect of leaf burn by intense, focused sunlight in summer. A mist generator is designed to prevent particulate matter (PM) damage to trees by combining mist with PM during high PM episodes. We measured changes in microclimate conditions and the concentration of PM before, during, and after mist spraying in urban parks (Yangjae Citizen Forest, YCF; Cheongdam Road Park, CRP) from May 6 to 8, 2020. PM changes in YCF and CRP were observed immediately after mist spraying and were found to return to the previous concentrations. Mist spraying had no significant effects on the meteorological traits of air temperature, humidity, and wind speed but had significant effects on the concentration of PMx and the ratio of PM during a short time. Also, the ratio of PMx was partially affected by mist spraying. During the morning rush hour and lunch, mist, high wind speed, and low relative humidity conditions were related to the increase in mist movement, resulting in increasing PM (2.5–10 μm) and the deposition of these PM. During the evening rush hour, high relative humidity and low wind speed affected PM concentrations more than mist. This prototype of mist spraying could effectively condense and deposit the PM during high PM episodes.
Military Equipment Entity Extraction Based on Large Language Model
Authors: Liu Xuhong , Yu Zhipeng , Liu Xiulei , Miao Lin , Yang Tao
Published: October 2024

The technology of military equipment entity extraction, a crucial component in constructing military knowledge bases, holds significant research value and theoretical importance for guiding the development and improvement of equipment support forces. In the military domain, equipment entities exhibit a phenomenon of nesting, where one entity is contained within another, and abbreviations or codes are frequently used to represent these entities. To address this complexity, this paper proposes a method named CoTNER for extracting entities. Initially, a large-scale language model is used to perform data augmentation with chain-of-thought on the original dataset, providing additional semantic and contextual information. Subsequently, the augmented dataset is fine-tuned on a small-scale language model to adapt it to the task of military equipment entity extraction and to enhance its ability to learn complex rules specific to the domain of military equipment. Additionally, a high-quality data filtering strategy based on instruction-following difficulty scoring is proposed to address the catastrophic forgetting issue that may occur during the fine-tuning of large language models. The experimental results demonstrate that the proposed military equipment entity extraction method outperforms mainstream traditional deep learning methods, validating the effectiveness of CoTNER.
Enhancing Clustering Performance of Failed Test Cases during HIL Simulation: A Study on Deep Auto-Encoder Structures and Hyperparameter Tuning
Authors: Abboush Mohammad , Knieke Christoph , Rausch Andreas
Published: October 2024

Over the last decade, hardware-in-the-loop (HIL) simulation has been established as a safe, efficient, reliable, and flexible method for performing real-time simulation. Furthermore, in the automotive sector, the HIL system has been recommended in the ISO 26262 standard as a powerful platform for performing realistic simulation during system integration testing. As a result of performing HIL black-box tests, the results of executing test cases (TCs) are reported as pass/fail without determining the nature and root causes of the underlying failures. The conventional analysis process of the failed TCs relies on expert knowledge. The higher the number of failed TCs, the higher the cost of manual analysis in terms of time and effort. In light of the shortcomings of existing methodologies, this study presents a novel intelligent framework capable of analyzing failed TCs without the need for expert knowledge or code access. To this end, a convolutional auto-encoder-based deep-learning approach is proposed to extract representative features from the textual description of the failed TCs. Furthermore, k-means-based clustering is used to categorize the extracted features according to their respective failure classes. To illustrate the effectiveness and validate the performance of the proposed method, a virtual test drive with real-time HIL simulation is presented as a case study. The proposed model exhibits superior clustering performance compared to other standalone k-means algorithms, as demonstrated by the David Bouldin Index (DBI) and accuracy values of 0.5184 and 94.33%, respectively. Furthermore, the proposed model shows a significant advantage in terms of feature extraction and clustering performance compared to the current state-of-the-art fault-analysis method. The proposed approach not only supports the validation process and improves the safety and reliability of the systems but also reduces the costs of manual analysis in terms of time and effort.
Climate Change and Building Renovation: The Impact of Historical, Current, and Future Climatic Files on a School in Central Italy
Authors: Lops Camilla , Serpilli Fabio , D’Alessandro Valerio , Montelpare Sergio
Published: October 2024

Climate change significantly affects the operating environment of buildings. These changes impact both energy efficiency and occupants’ comfort and remain crucial even in building restoration, where design decisions typically rely on historical data, yet performance depends on anticipated future scenarios. The present work evaluates the impact of different climate datasets on dynamic energy simulations for an educational building in Central Italy, focusing on estimating heating demands across historical, current, and future climatic scenarios. The assessment considers both the building’s current state and potential energy-efficient retrofits. Initially, various meteorological datasets, including measured and model-generated data, are selected to predict key weather parameters. The analysis reveals the potential and limitations of regional climate models (RCMs) in estimating these variables, with the MM5 dataset emerging as the most reliable. Subsequently, the energy performance of the reference building and its vulnerability to climate change are assessed. Our results show significant differences in energy demand based on construction periods, with the oldest section consuming 29% to 54% more energy monthly than the newer sections. Moreover, using non-representative climatic files can lead to prediction errors of up to 199%. Finally, the building’s energy behaviour is analysed under future climate conditions by generating typical meteorological years (TMYs) for 2030, 2050, and 2070. This analysis evaluates the energy requirements for both existing and retrofitted building configurations. The findings confirm that retrofit interventions with high-performance insulation and upgraded windows significantly enhance the building’s energy efficiency and resilience to future climate conditions, leading to annual energy savings of 50% to 57%.
An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
Authors: Pavlopoulos Adrianos , Rachiotis Theodoros , Maglogiannis Ilias
Published: October 2024

This study aims to evaluate the utilization and effectiveness of artificial intelligence (AI) applications in managing symptoms of anxiety and depression. The primary objectives are to identify current AI tools, analyze their practicality and efficacy, and assess their potential benefits and risks. A comprehensive literature review was conducted using databases such as ScienceDirect, Google Scholar, PubMed, and ResearchGate, focusing on publications from the last five years. The search utilized keywords including “artificial intelligence”, “applications”, “mental health”, “anxiety”, “LLMs” and “depression”. Various AI tools, including chatbots, mobile applications, wearables, virtual reality settings, and large language models (LLMs), were examined and categorized based on their functions in mental health care. The findings indicate that AI applications, including LLMs, show significant promise in symptom management, offering accessible and personalized interventions that can complement traditional mental health treatments. Tools such as AI-driven chatbots, mobile apps, and LLMs have demonstrated efficacy in reducing symptoms of anxiety and depression, improving user engagement and mental health outcomes. LLMs, in particular, have shown potential in enhancing therapeutic chatbots, diagnostic tools, and personalized treatment plans by providing immediate support and resources, thus reducing the workload on mental health professionals. However, limitations include concerns over data privacy, the potential for overreliance on technology, and the need for human oversight to ensure comprehensive care. Ethical considerations, such as data security and the balance between AI and human interaction, were also addressed. The study concludes that while AI, including LLMs, has the potential to significantly aid mental health care, it should be used as a complement to, rather than a replacement for, human therapists. Future research should focus on enhancing data security measures, integrating AI tools with traditional therapeutic methods, and exploring the long-term effects of AI interventions on mental health. Further investigation is also needed to evaluate the effectiveness of AI applications across diverse populations and settings.
Derivation of Analytical Equations for the Fundamental Period of Framed Structures Using Machine Learning and SHAP Values
Authors: Karampinis Ioannis , Morfidis Konstantinos , Iliadis Lazaros
Published: October 2024

The fundamental period is one of the most important parameters for the design of new structures as well as for estimating the capacity of existing ones. Thus, to estimate it, various design codes and researchers have adopted several approximate analytical equations based on a number of key structural parameters. To this end, the present study introduces a novel methodology for deriving the analytical equations for the fundamental period of reinforced concrete structures. The methodology is based on machine learning explainability techniques, specifically the so-called SHapley Additive exPlanations values. These values are commonly employed as an explainability tool. However, in the proposed novel approach they are employed as a basis to fit analytical curves, which allows the resulting equations to be constructed sequentially and in an informed manner while controlling the balance between accuracy and complexity. An extended dataset consisting of 4026 data points is employed, on which a Gradient Boosting Machine model is fitted. The model achieves excellent accuracy, with a coefficient of determination R2≈0.99, while the equations derived from the proposed formulation achieve an R2≈0.95 and Mean Absolute Error ≈0.12. This demonstrates the potential applicability of the proposed methodology in a wide array of similar engineering challenges.
Correction: Califano et al. Critical Ergonomics and (Dis)Comfort Factors While Performing Tasks with Hand Tools on a Ladder: A Pilot Study. Appl. Sci. 2024, 14, 2398
Authors: Califano Rosaria , Auricchio Antonio , Carbone Mario , Dessì Lucio , Frasci Valentino , Landi Angelo , Naddeo Alessandro
Published: October 2024

In the original publication [...]
Real-Time Forward Head Posture Detection and Correction System Utilizing an Inertial Measurement Unit Sensor
Authors: Park Gyumin , Jung Im Y.
Published: October 2024

Forward head posture (FHP) has become a prevailing health issue in modern society as people spend more time on computers and smartphones. FHP is a posture where the head is forward and the anterior and posterior curvatures of the lower cervical and upper thoracic spines are both, respectively, exaggerated. FHP is often associated with neck pain, bad static balance, and hunched shoulders or back. To prevent this, consciously maintaining good posture is important. Therefore, in this study, we propose a system that gives users real-time, accurate information about their neck posture, and it also encourages them to maintain a good posture. This inexpensive system utilizes a single inertial measurement unit sensor and a Raspberry Pi system to detect the changes in state that can progress to an FHP. It retrieves data from the sensor attached to the user’s cervical spine to indicate their real-time posture. In a real-world office environment experiment with ten male participants, the system accurately detected the transition to the FHP state for more than 10 s, with a delay of less than 0.5 s, and it also provided personalized feedback to encourage them to maintain good posture. All ten participants recognized that their average craniovertebral angle had to be increased after receiving visual alerts regarding their poor postures in real time. The results indicate that the system has potential for widespread applications.