bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Objective Optimisation of Injection Moulding Process for Dashboard Using Genetic Algorithm and Type-2 Fuzzy Neural Network
Authors: Qazani Mohammad Reza Chalak , Moayyedian Mehdi , Amirkhizi Parisa Jourabchi , Hedayati-Dezfooli Mohsen , Abdalmonem Ahmed , Alsmadi Ahmad , Alam Furqan
Published: June 2024

This study examines the use of injection moulding to evaluate mechanical properties in plastic products, such as shear and residual stresses. Key process variables like melt temperature, mould temperature, hold pressure duration, and pure hold duration are meticulously chosen for study. A full factorial experiment design is utilised to determine the best settings. These variables notably influence the end product’s physical and mechanical properties. Computational techniques, like the finite element method, are used to analyse behaviours based on varied input parameters. A CAD model of a dashboard part is incorporated into a finite element analysis to measure shear and residual stresses. Four specific parameters from the injection moulding process are subjected to an in-depth experimental design. It is worth noting that the injection moulding process does not incorporate a type-2 fuzzy neural network (T2FNN). However, in this particular investigation, T2FNN was employed to replicate the mechanical stress model associated with dashboard injection moulding. Its purpose was to estimate shear and residual stress levels. Additionally, the multi-objective genetic algorithm (MOGA) was utilised to extract the most optimal parameters for the injection moulding process, aiming to minimise shear and residual stress and thereby increase the resistance of the final product. The proposed model was developed and implemented using MATLAB software. A Pareto front was derived from the MOGA by employing the T2FNN within the process, identifying fourteen optimal solutions.
The Impact of Surgeon Experience on Surgical Parameters and Complication Rates for the Surgical Management of Adult Spinal Deformities: A Systematic Review and Meta-Analysis
Authors: Anastasio Albert T. , Baumann Anthony N. , Callaghan Megan E. , Walley Kempland C. , Gong Davin C. , Talaski Grayson M. , Conry Keegan T. , Shafer Cole , Hoffmann Jacob C.
Published: June 2024

The surgical management of adult spinal deformities (ASDs) involves a wide variety of complex and technically challenging operative techniques. Despite numerous publications examining the relationship between surgeon experience and outcomes in ASD, no systematic review or meta-analysis exists. This first-time systematic review and meta-analysis examines the impact of surgeon experience on the surgical parameters and complication rates for the surgical management of ASD. Four databases were used for the initial search of this study from database inception until 22 September 2023. The inclusion criteria required articles that examined the outcomes for surgery for ASD, stratified outcomes by surgeon experience and/or the learning curve as a proxy for surgeon experience, and utilized adult patients (>18 years of age). Seven articles met the criteria for final inclusion. Patients in the Experienced Surgeon group had statistically significantly lower levels of EBL with no significant difference in operative time after surgery for ASD compared to patients in the Inexperienced Surgeon group via a meta-analysis of three articles. Patients in the Experienced Surgeon group had a statistically significantly lower total complication rate compared to patients in the Inexperienced Surgeon group via a meta-analysis. Increased surgeon experience resulted in lower levels of EBL, without a significant difference in the operative time after surgery for ASD.
The Current State of Proteomics and Metabolomics for Inner Ear Health and Disease
Authors: Khorrami Motahare , Pastras Christopher , Haynes Paul A. , Mirzaei Mehdi , Asadnia Mohsen
Published: June 2024

Characterising inner ear disorders represents a significant challenge due to a lack of reliable experimental procedures and identified biomarkers. It is also difficult to access the complex microenvironments of the inner ear and investigate specific pathological indicators through conventional techniques. Omics technologies have the potential to play a vital role in revolutionising the diagnosis of ear disorders by providing a comprehensive understanding of biological systems at various molecular levels. These approaches reveal valuable information about biomolecular signatures within the cochlear tissue or fluids such as the perilymphatic and endolymphatic fluid. Proteomics identifies changes in protein abundance, while metabolomics explores metabolic products and pathways, aiding the characterisation and early diagnosis of diseases. Although there are different methods for identifying and quantifying biomolecules, mass spectrometry, as part of proteomics and metabolomics analysis, could be utilised as an effective instrument for understanding different inner ear disorders. This study aims to review the literature on the application of proteomic and metabolomic approaches by specifically focusing on Meniere’s disease, ototoxicity, noise-induced hearing loss, and vestibular schwannoma. Determining potential protein and metabolite biomarkers may be helpful for the diagnosis and treatment of inner ear problems.
Two-Stage Adaptive Network for Semi-Supervised Cross-Domain Crater Detection under Varying Scenario Distributions
Authors: Liu Yifan , Song Tiecheng , Xian Chengye , Chen Ruiyuan , Zhao Yi , Li Rui , Guo Tan
Published: June 2024

Crater detection can provide valuable information for humans to explore the topography and understand the history of extraterrestrial planets. Due to the significantly varying scenario distributions, existing detection models trained on known labelled crater datasets are hardly effective when applied to new unlabelled planets. To address this issue, we propose a two-stage adaptive network (TAN) for semi-supervised cross-domain crater detection. Our network is built on the YOLOv5 detector, where a series of strategies are employed to enhance its cross-domain generalisation ability. In the first stage, we propose an attention-based scale-adaptive fusion (ASAF) strategy to handle objects with significant scale variances. Furthermore, we propose a smoothing hard example mining (SHEM) loss function to address the issue of overfitting on hard examples. In the second stage, we propose a sort-based pseudo-labelling fine-tuning (SPF) strategy for semi-supervised learning to mitigate the distributional differences between source and target domains. For both stages, we employ weak or strong image augmentation to suit different cross-domain tasks. Experimental results on benchmark datasets demonstrate that the proposed network can enhance domain adaptation ability for crater detection under varying scenario distributions.
Radiation Feature Fusion Dual-Attention Cloud Segmentation Network
Authors: He Mingyuan , Zhang Jie
Published: June 2024

In the field of remote sensing image analysis, the issue of cloud interference in high-resolution images has always been a challenging problem, with traditional methods often facing limitations in addressing this challenge. To this end, this study proposes an innovative solution by integrating radiative feature analysis with cutting-edge deep learning technologies, developing a refined cloud segmentation method. The core innovation lies in the development of FFASPPDANet (Feature Fusion Atrous Spatial Pyramid Pooling Dual Attention Network), a feature fusion dual attention network improved through atrous spatial convolution pooling to enhance the model’s ability to recognize cloud features. Moreover, we introduce a probabilistic thresholding method based on pixel radiation spectrum fusion, further improving the accuracy and reliability of cloud segmentation, resulting in the “FFASPPDANet+” algorithm. Experimental validation shows that FFASPPDANet+ performs exceptionally well in various complex scenarios, achieving a 99.27% accuracy rate in water bodies, a 96.79% accuracy rate in complex urban settings, and a 95.82% accuracy rate in a random test set. This research not only enhances the efficiency and accuracy of cloud segmentation in high-resolution remote sensing images but also provides a new direction and application example for the integration of deep learning with radiative algorithms.
A New Dual-Branch Embedded Multivariate Attention Network for Hyperspectral Remote Sensing Classification
Authors: Chen Yuyi , Wang Xiaopeng , Zhang Jiahua , Shang Xiaodi , Hu Yabin , Zhang Shichao , Wang Jiajie
Published: June 2024

With the continuous maturity of hyperspectral remote sensing imaging technology, it has been widely adopted by scholars to improve the performance of feature classification. However, due to the challenges in acquiring hyperspectral images and producing training samples, the limited training sample is a common problem that researchers often face. Furthermore, efficient algorithms are necessary to excavate the spatial and spectral information from these images, and then, make full use of this information with limited training samples. To solve this problem, a novel two-branch deep learning network model is proposed for extracting hyperspectral remote sensing features in this paper. In this model, one branch focuses on extracting spectral features using multi-scale convolution and a normalization-based attention module, while the other branch captures spatial features through small-scale dilation convolution and Euclidean Similarity Attention. Subsequently, pooling and layering techniques are employed to further extract abstract features after feature fusion. In the experiments conducted on two public datasets, namely, IP and UP, as well as our own labeled dataset, namely, YRE, the proposed DMAN achieves the best classification results, with overall accuracies of 96.74%, 97.4%, and 98.08%, respectively. Compared to the sub-optimal state-of-the-art methods, the overall accuracies are improved by 1.05, 0.42, and 0.51 percentage points, respectively. The advantage of this network structure is particularly evident in unbalanced sample environments. Additionally, we introduce a new strategy based on the RPNet, which utilizes a small number of principal components for feature classification after dimensionality reduction. The results demonstrate its effectiveness in uncovering compressed feature information, with an overall accuracy improvement of 0.68 percentage points. Consequently, our model helps mitigate the impact of data scarcity on model performance, thereby contributing positively to the advancement of hyperspectral remote sensing technology in practical applications.
Terrain Shadow Interference Reduction for Water Surface Extraction in the Hindu Kush Himalaya Using a Transformer-Based Network
Authors: Yan Xiangbing , Song Jia
Published: June 2024

Water is the basis for human survival and growth, and it holds great importance for ecological and environmental protection. The Hindu Kush Himalaya (HKH) is known as the “Water Tower of Asia”, where water influences changes in the global water cycle and ecosystem. It is thus very important to efficiently measure the status of water in this region and to monitor its changes; with the development of satellite-borne sensors, water surface extraction based on remote sensing images has become an important method through which to do so, and one of the most advanced and accurate methods for water surface extraction involves the use of deep learning networks. We designed a network based on the state-of-the-art Vision Transformer to automatically extract the water surface in the HKH region; however, in this region, terrain shadows are often misclassified as water surfaces during extraction due to their spectral similarity. Therefore, we adjusted the training dataset in different ways to improve the accuracy of water surface extraction and explored whether these methods help to reduce the interference of terrain shadows. Our experimental results show that, based on the designed network, adding terrain shadow samples can significantly enhance the accuracy of water surface extraction in high mountainous areas, such as the HKH region, while adding terrain data does not reduce the interference from terrain shadows. We obtained the water surface extraction results in the HKH region in 2021, with the network and training datasets containing both water surface and terrain shadows. By comparing these results with the data products of Global Surface Water, it was shown that our water surface extraction results are highly accurate and the extracted water surface boundaries are finer, which strongly confirmed the applicability and advantages of the proposed water surface extraction approach in a wide range of complex surface environments.
Near-Infrared Spectroscopy for Rapid Differentiation of Fresh and Frozen–Thawed Common Carp (Cyprinus carpio)
Authors: Atanassova Stefka , Yorgov Dimitar , Stratev Deyan , Veleva Petya , Stoyanchev Todor
Published: June 2024

This study aimed to investigate near-infrared spectroscopy (NIRS) in combination with classification methods for the discrimination of fresh and once- or twice-freeze–thawed fish. An experiment was carried out with common carp (Cyprinus carpio). From each fish, test pieces were cut from the dorsal and ventral regions and measured from the skin side as fresh, after single freezing at minus 18 °C for 15 ÷ 28 days and 15 ÷ 21 days for the second freezing after the freeze–thawing cycle. NIRS measurements were performed via a NIRQuest 512 spectrometer at the region of 900–1700 nm in Reflection mode. The Pirouette 4.5 software was used for data processing. SIMCA and PLS-DA models were developed for classification, and their performance was estimated using the F1 score and total accuracy. The predictive power of each model was evaluated for fish samples in the fresh, single-freezing, and second-freezing classes. Additionally, aquagrams were calculated. Differences in the spectra between fresh and frozen samples were observed. They might be assigned mainly to the O–H and N–H bands. The aquagrams confirmed changes in water organization in the fish samples due to freezing–thawing. The total accuracy of the SIMCA models for the dorsal samples was 98.23% for the calibration set and 90.55% for the validation set. For the ventral samples, respective values were 99.28 and 79.70%. Similar accuracy was found for the PLS-PA models. The NIR spectroscopy and tested classification methods have a potential for nondestructively discriminating fresh from frozen–thawed fish in as methods to protect against fish meat food fraud.
A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining
Authors: Liu Ruilin , Tian Wenwen
Published: June 2024

Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples.
Detection of Electromagnetic Seismic Precursors from Swarm Data by Enhanced Martingale Analytics
Authors: Harrigan Shane , Bi Yaxin , Huang Mingjun , O’Neill Christopher , Zhai Wei , Sun Jianbao , Zhang Xuemin
Published: June 2024

The detection of seismic activity precursors as part of an alarm system will provide opportunities for minimization of the social and economic impact caused by earthquakes. It has long been envisaged, and a growing body of empirical evidence suggests that the Earth’s electromagnetic field could contain precursors to seismic events. The ability to capture and monitor electromagnetic field activity has increased in the past years as more sensors and methodologies emerge. Missions such as Swarm have enabled researchers to access near-continuous observations of electromagnetic activity at second intervals, allowing for more detailed studies on weather and earthquakes. In this paper, we present an approach designed to detect anomalies in electromagnetic field data from Swarm satellites. This works towards developing a continuous and effective monitoring system of seismic activities based on SWARM measurements. We develop an enhanced form of a probabilistic model based on the Martingale theories that allow for testing the null hypothesis to indicate abnormal changes in electromagnetic field activity. We evaluate this enhanced approach in two experiments. Firstly, we perform a quantitative comparison on well-understood and popular benchmark datasets alongside the conventional approach. We find that the enhanced version produces more accurate anomaly detection overall. Secondly, we use three case studies of seismic activity (namely, earthquakes in Mexico, Greece, and Croatia) to assess our approach and the results show that our method can detect anomalous phenomena in the electromagnetic data.
Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
Authors: Chen Jia-Qi , Chen Chao , Sun Jing-Jing , Zhang Jian-Wei , Liu Zhao-Hui , Qin Li , Ning Yong-Qiang , Wang Li-Jun
Published: June 2024

Narrow-linewidth lasers mainly depend on the development of advanced laser linewidth measurement methods for related technological progress as key devices in satellite laser communications, precision measurements, ultra-high-speed optical communications, and other fields. This manuscript provides a theoretical analysis of linewidth characterization methods based on the beat frequency power spectrum and laser phase noise calculations, and elaborates on existing research of measurement technologies. In addition, to address the technical challenges of complex measurement systems that commonly rely on long optical fibers and significant phase noise jitter in the existing research, a short-delay self-heterodyne method based on coherent envelope spectrum demodulation was discussed in depth to reduce the phase jitter caused by 1/f noise. We assessed the performance parameters and testing conditions of different lasers, as well as the corresponding linewidth characterization methods, and analyzed the measurement accuracy and error sources of various methods.
The Effect of Sensor Feature Inputs on Joint Angle Prediction across Simple Movements
Authors: Hollinger David , Schall Mark C. , Chen Howard , Zabala Michael
Published: June 2024

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs.
A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory
Authors: Deng Zhixing , Wang Wubin , Xu Linrong , Bai Hao , Tang Hao
Published: June 2024

The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.
A Lifestyle Monitoring System for Older Adults Living Independently Using Low-Resolution Smart Meter Data
Authors: Mathunjwa Bhekumuzi M. , Chen Yu-Fen , Tsai Tzung-Cheng , Hsu Yeh-Liang
Published: June 2024

Background: Monitoring the lifestyles of older adults helps promote independent living and ensure their well-being. The common technologies for home monitoring include wearables, ambient sensors, and smart household meters. While wearables can be intrusive, ambient sensors require extra installation, and smart meters are becoming integral to smart city infrastructure. Research Gap: The previous studies primarily utilized high-resolution smart meter data by applying Non-Intrusive Appliance Load Monitoring (NIALM) techniques, leading to significant privacy concerns. Meanwhile, some Japanese power companies have successfully employed low-resolution data to monitor lifestyle patterns discreetly. Scope and Methodology: This study develops a lifestyle monitoring system for older adults using low-resolution smart meter data, mapping electricity consumption to appliance usage. The power consumption data are collected at 15-min intervals, and the background power threshold distinguishes between the active and inactive periods (0/1). The system quantifies activity through an active score and assesses daily routines by comparing these scores against the long-term norms. Key Outcomes/Contributions: The findings reveal that low-resolution data can effectively monitor lifestyle patterns without compromising privacy. The active scores and regularity assessments calculated using correlation coefficients offer a comprehensive view of residents’ daily activities and any deviations from the established patterns. This study contributes to the literature by validating the efficacy of low-resolution data in lifestyle monitoring systems and underscores the potential of smart meters in enhancing elderly people’s care.
Assessment of Measurement Uncertainty for S-Parameter Measurement Based on Covariance Matrix
Authors: Zhu Jiangmiao , Wang Yifan , Zhao Kejia , Wang Yidi , Fu Chaoxian , Man Kaige
Published: June 2024

S-parameters are widely used to detail the scattering parameters of radio frequency (RF) components and microwave circuit modules. The vector network analyzer (VNA) is the most commonly used device for measuring S-parameters. Given the multiple frequency points, complex values, and intricate uncertainty propagation involved, accurately assessing the uncertainty of S-parameter measurements is difficult. In this study, we proposed a new method for assessing S-parameter uncertainty based on the covariance matrices, tracing back to the nominal uncertainty of calibration standards. First, we analyzed the relevant theory of uncertainty assessment using covariance matrices and subsequently deduced the mechanism of Type B uncertainty propagating from calibration standards to error model coefficients and S-parameter measurements to evaluate Type B measurement uncertainty. In this study, a novel measurement system was constructed for measuring grounded coplanar waveguides by using a VNA and calibration standards with 8- and 12-error models. Initially, the model assessed the Type B uncertainty of measuring four S-parameters of a grounded coplanar waveguide. Next, the VNA calibrated with the 12-error model was used to conduct multiple repeated measurements to assess the Type A uncertainty of the grounded coplanar waveguide. Finally, the composite uncertainty was constructed, which demonstrated that the proposed method can be used for assessing the uncertainty of S-parameters.
A Novel Clustering Algorithm Integrating Gershgorin Circle Theorem and Nonmaximum Suppression for Neural Spike Data Analysis
Authors: Patel Sahaj Anilbhai , Yildirim Abidin
Published: June 2024

(1) Problem Statement: The development of clustering algorithms for neural recordings has significantly evolved, reaching a mature stage with predominant approaches including partitional, hierarchical, probabilistic, fuzzy logic, density-based, and learning-based clustering. Despite this evolution, there remains a need for innovative clustering algorithms that can efficiently analyze neural spike data, particularly in handling diverse and noise-contaminated neural recordings. (2) Methodology: This paper introduces a novel clustering algorithm named Gershgorin—nonmaximum suppression (G–NMS), which incorporates the principles of the Gershgorin circle theorem, and a deep learning post-processing method known as nonmaximum suppression. The performance of G–NMS was thoroughly evaluated through extensive testing on two publicly available, synthetic neural datasets. The evaluation involved five distinct groups of experiments, totaling eleven individual experiments, to compare G–NMS against six established clustering algorithms. (3) Results: The results highlight the superior performance of G–NMS in three out of five group experiments, achieving high average accuracy with minimal standard deviation (SD). Specifically, in Dataset 1, experiment S1 (various SNRs) recorded an accuracy of 99.94 ± 0.01, while Dataset 2 showed accuracies of 99.68 ± 0.15 in experiment E1 (Easy 1) and 99.27 ± 0.35 in experiment E2 (Easy 2). Despite a slight decrease in average accuracy in the remaining two experiments, D1 (Difficult 1) and D2 (Difficult 2) from Dataset 2, compared to the top-performing clustering algorithms in these categories, G–NMS maintained lower SD, indicating consistent performance. Additionally, G–NMS demonstrated robustness and efficiency across various noise-contaminated neural recordings, ranging from low to high signal-to-noise ratios. (4) Conclusions: G–NMS’s integration of deep learning techniques and eigenvalue inclusion theorems has proven highly effective, marking a significant advancement in the clustering domain. Its superior performance, characterized by high accuracy and low variability, opens new avenues for the development of high-performing clustering algorithms, contributing significantly to the body of research in this field.
Arteriovenous Malformation of the Prostate Vasculature as a Cause of Torrential Bleeding during Transurethral Resection of the Prostate
Authors: Sitharthan Darshan , Katelaris Athos , O’Neill Gordon
Published: June 2024

An 82-year-old male experienced severe bleeding during transurethral resection of the prostate (TURP) due to an undetected prostatic arteriovenous malformation (AVM). Initial management with a catheter was followed by successful transarterial embolisation. This case highlights the effectiveness of endovascular angioembolisation for unexpected prostatic AVMs.
Prevalence of Genetic Mutations in Patients with Metastatic Prostate Cancer in a Cohort of Mexican Patients
Authors: Rodríguez González Orión Erenhú , Bravo Castro Edgar Iván , Osorio Jesus Eduardo , Pacheco Guerrero Habiram , Suaste Carmona Brenda , Arreola Peralta Luis David , Martinez Juárez Noe Esaul , Izquierdo Luna Juan Samuel , Islas García José de Jesús Oswaldo , Victorio Vargas Omar Dimas , Valdez Flores Rafael Alberto , Torres Gómez Jesús Javier , Campos Salcedo José Gadú
Published: June 2024

Background: Prostate cancer is a malignant neoplasm of the male genitourinary system with the highest incidence worldwide. Susceptibility genes related to aggressiveness and prognosis, such as BRCA1/2, ATM, PTEN, have been identified. Currently, reports related to germline mutations in patients with prostate cancer in Latin American populations are very limited or absent. In the Mexican population, reports are also limited, especially in the context of metastatic prostate cancer. Determining the prevalence of these mutations is relevant to predict the potential aggressiveness of tumors and allow the use of targeted therapies, such as PARPi inhibitors. Objective: Determine the prevalence of germline mutations in patients with metastatic prostate cancer and establish their clinical characteristics at diagnosis. Material and Methods: Sixty-nine patients with metastatic PCa underwent testing and genetic analysis using the Comprehensive Multi-Cancer Hereditary Cancer Panel. The prevalence of germline mutations was assessed, and the cohort was divided into two groups for the evaluation and analysis of clinical characteristics between the mutated and non-mutated populations. Results: We identified mutations in 15 out of 69 patients (21.73%), while 54 patients (78.26%) had no mutations. Pathogenic mutations were observed in 15.9% of patients, Variants of Uncertain Significance (VUS) in 34.78%, and 5.79% had both. The most frequent mutations included ATM (11.54%), BRCA1 (11.54%), BRCA2 (7.69%), FANCA (7.69%), and FANCM (7.69%). No statistically significant differences were found in PSA levels, age at diagnosis, and resistance to castration between the two groups. Conclusions: Our study unveiled a mutation rate of 21.73%, marked by a significant prevalence of ATM, FANCA, FANCM, and Variants of Uncertain Significance (VUS). This pattern deviates from findings in other series, underscoring the necessity for improved access to clinical genetic testing in our population.
A Quality and Completeness Assessment of Testicular Cancer Health Information on TikTok
Authors: Wong Hoi , Yang Lee , S/O Senthamil Selvan Vikneshwaren , Lim Jamie , So Wei , Gauhar Vineet , Tiong Ho
Published: June 2024

TikTok has become a hub for easily accessible medical information. However, the quality and completeness of this information for testicular cancer has not been examined. Our study aims to assess the quality and completeness of testicular cancer information on TikTok. A search was performed on TikTok using the search terms “Testicular Cancer” and “Testicle Cancer”. Inclusion criteria encompassed videos about testicular cancer in English. We excluded non-English videos, irrelevant videos, and videos without audio. We evaluated these videos using the DISCERN instrument and a completeness assessment. A total of 361 videos were considered for screening and 116 videos were included. Of these, 57 were created by healthcare professionals (HCPs). The median video length was 40 s (5–277 s), with >25 million cumulative views and a median of 446,400 views per video. The average DISCERN score was 29.0 ± 5.7, with HCPs providing higher-quality videos than non-HCPs (30.8 vs. 5.5, p < 0.05). HCPs also had more reliable videos (21.2 vs. 18.1, p < 0.05). Overall quality levels were mostly poor or very poor (97.4%), with none being good or excellent. Most HCP videos were poor (63.2%), whilst many non-HCP videos were very poor (61.0%). The most viewed video had 2,800,000 views but scored a 31 on the DISCERN tool and one on the completeness assessment. The highest DISCERN score had 11,700 views. HCP videos better defined the disease and were more complete (p < 0.05). Most videos discussed self-assessment but were lacking in definitions, risk factors, symptoms, evaluation, management, and outcomes. Most of TikTok’s testicular cancer information lacks quality and completeness, whilst higher-quality videos have limited reach.
Rural–Urban Features of Social Innovation: An Exploratory Study of Work Integration Social Enterprises in Ireland
Authors: Olmedo Lucas , Ruiz-Rivera María José , O’Shaughnessy Mary , Chatzichristos Georgios
Published: June 2024

Geography is a significant element of social innovation. This paper focuses on exploring differences and similarities in the characteristics and contributions towards impact of Work Integration Social Enterprises (WISEs), a form of social innovation which provides otherwise unmet services and opportunities to people at risk of social and economic exclusion and distant from the labour market, in rural and urban areas of Ireland. To do so, we use data from 336 surveys from urban (213) and rural (123) WISEs and conduct an exploratory and spatially sensitive analysis to compare the characteristics, in terms of organisational age, legal and governance form, multiplicity of activities, revenue diversification; and contributions towards impact, in terms of geographical focus/reach, employment, volunteers, and income generation. Our analysis shows that WISEs in urban and rural areas present rather similar organisational characteristics and ways of functioning (legal structure, multiactivity, multiple sources of funding), but their contributions to socioeconomic impact differ according to their spatial location, with urban WISEs generating significantly more employment and income than their rural counterparts. Our study illustrates that socially innovative organisations are spatially sensitive, and that context influences their capacity to create sustainable employment opportunities and contribute to the local economy.
Cultural Differences in Body Image: A Systematic Review
Authors: Abdoli Marzieh , Scotto Rosato Marco , Desousa Avinash , Cotrufo Paolo
Published: June 2024

Culture affects individuals’ perceptions and experiences of their bodies. In order to provide the most effective solutions to body image-related issues, it is necessary to understand cultures and their influences on body image in various populations. This paper focuses on the effects of culture on body image. Therefore, a systematic literature search following PRISMA guidelines was performed in the PubMed, Scopus, and Web of Science databases, yielding 2064 articles published between 1990 and 2023. After applying inclusion and exclusion criteria, 54 articles were selected. Our findings showed a strong influence of culture on body image, highlighting the impact of societal expectations on individuals’ mental well-being. Western cultures, with their preference for thinness, differ from non-Western ideals. The findings also showed the impact of regional variations within the same culture and society on body image. Furthermore, the study found that the young demographic, especially females, is the most vulnerable to body image issues; however, emerging research within our review also indicates a growing concern among males. This study underscores the necessity of culturally considering interventions to address body image issues, which are integral to improving mental health concerns like body dissatisfaction, eating disorders, depression, low self-esteem, and anxiety.
Exploring Serum Copeptin and Hematological Profile: A Comparative Analysis after Intradermal versus Intramuscular Porcine Reproductive and Respiratory Syndrome Virus Vaccination in Piglets
Authors: Maragkakis Georgios , Katsogiannou Eleni G. , Papakonstantinou Georgios I. , Korou Laskarina-Maria , Chaintoutis Serafeim C. , Konstantopoulos Panagiotis , Perrea Despoina N. , Christodoulopoulos Georgios , Athanasiou Labrini V. , Papatsiros Vasileios G.
Published: June 2024

This study aimed to investigate the impact of intradermal (ID) and intramuscular (IM) vaccination with a porcine reproductive and respiratory syndrome virus (PRRSV)-modified live vaccine (MLV) in piglets on serum copeptin levels and hematological profile. This study included 104 suckling piglets (2 weeks of age) from a commercial farrow-to-finish pig farm suffering from positive unstable PRRSV status. Animals were assigned to four groups, with two replicates (13 piglets/group/replicate); group A: IM vaccination with a PRRSV MLV vaccine, group B: ID vaccination with the same vaccine, group C: ID of Diluvac Forte, and group D: IM of Diluvac Forte. Blood samples were collected from the same three pigs/group/replicate at 4, 7, and 10 weeks of age. Blood samples were used for the performance of the complete blood count, and they were also examined by PCR for PRRSV and by ELISA for copeptin. No significant differences in serum copeptin levels and the number of blood cell counts (packed cell volume—PCV, numbers of white blood cells—WBCs, and platelets number—PLTs) were noticed in the same group over time and among groups. In conclusion, it seems that the vaccination against PRRSV does not affect the levels of the released copeptin. Based on our results, the measurement of serum copeptin could not be proposed as a potential stress biomarker in pigs.
Digital Financial Inclusion, Land Circulation and High-Quality Development of Agriculture
Authors: Xiong Qi , Guo Xiaoyang , Yang Jingyi
Published: June 2024

With the deep integration of digital technology and inclusive finance, digital inclusive finance has provided a new opportunity for agricultural high-quality development through “overtaking on curves”. This article empirically examines the impact of digital inclusive finance on agricultural high-quality development and the dynamic mechanism of land circulation in its transmission process, utilizing panel data from various provinces in China from 2011 to 2021. The research indicates that digital inclusive finance has a significant improvement effect on agricultural high-quality development, and this conclusion remains valid after a series of endogenous treatments and robustness tests. Meanwhile, intelligent manufacturing has a more pronounced role in promoting agricultural high-quality development in China’s eastern regions, regions with sound infrastructure, and regions with high environmental regulation intensity. Further research reveals that digital inclusive finance can promote agricultural high-quality development through the mechanism of promoting land circulation. The research conclusions provide important empirical evidence and policy implications for achieving coordinated development of agricultural economic growth and environmental protection, thereby realizing the beautiful vision of comprehensive rural revitalization.
Organoclays Based on Bentonite and Various Types of Surfactants as Heavy Metal Remediants
Authors: Perelomov Leonid , Gertsen Maria , Burachevskaya Marina , Hemalatha S. , Vijayalakshmi Architha , Perelomova Irina , Atroshchenko Yurii
Published: June 2024

The rapid industrial development of civilization has led to the need for the development of new materials to clean up chemically contaminated wastewater and soils. Organoclays, based on smectite minerals and various types of surfactants, are one of the most effective sorbents for adsorbing organic and inorganic pollutants. Organoclays are clay minerals that have been modified by the intercalation or grafting of organic molecules. The main mechanism of interaction between organic substances and organoclays involves the adsorption of the substances onto the surface of the clay mineral, which has an expanded structural cell. Various types of surfactants can be used to synthesize organoclays, including cationic, anionic, and amphoteric surfactants. Each type of surfactant has different properties that affect the clay’s ability to sorb. Cationic forms of trace elements, such as heavy metals, can also be adsorbed by organoclays. Data on the adsorption of these substances by organoclays are provided, along with information on how to synthesize them using various surfactants. This review also discusses the main mechanisms of interaction between these substances and clays and the various methods used to create organoclays. It is clear that the adsorption of heavy metals by organoclays is not influenced by their structure or properties, as they belong to the category of surfactant, but rather by their overall chemical structure and characteristics. The wide variety of surfactant types leads to different effects on the adsorption properties of trace elements.
Urban Flood Modeling for Sustainability Management: Role of Design Rainfall and Land Use
Authors: Młyński Dariusz , Halecki Wiktor , Surowiec Karolina
Published: June 2024

This study aimed to evaluate how different methods of determining design rainfall levels and land usage affect flood hydrographs in an urban catchment; specifically, the catchment in southern Poland. The data included daily precipitation records from 1981 to 2020 and land cover information from Corine Land Cover and Urban Atlas databases for 2006 and 2018. The analysis involved examining precipitation data, determining design rainfall levels, analyzing land usage databases, exploring the influence of design rainfall levels on hydrograph characteristics, and investigating the database’s impact on these characteristics. No discernible trend in precipitation was found. The highest design rainfall values followed the GEV distribution, while the lowest followed the Gumbel distribution. Both land usage databases indicated an increasing human influence from 2006 to 2018. This study conclusively showed that the method used for estimating design rainfall and the choice of the land usage database significantly affected hydrograph characteristics. Multivariate analyses are recommended for design rainfall assessments, while the Urban Atlas database is preferred for urban catchment land usage determinations due to its detailed information.
Bird Species Use of Bioenergy Croplands in Illinois, USA—Can Advanced Switchgrass Cultivars Provide Suitable Habitats for Breeding Grassland Birds?
Authors: LaGory Kirk E. , Cacho Jules F. , Zumpf Colleen R. , Lee DoKyoung , Feinstein Jeremy , Dematties Dario , Walston Leroy J. , Namoi Nictor , Negri M. Cristina
Published: June 2024

Grassland birds have sustained significant population declines in the United States through habitat loss, and replacing lost grasslands with bioenergy production areas could benefit these species and the ecological services they provide. Point count surveys and autonomous acoustic monitoring were used at two field sites in Illinois, USA, to determine if an advanced switchgrass cultivar that is being used for bioenergy feedstock production could provide suitable habitats for grassland and other bird species. At the Brighton site, the bird use of switchgrass plots was compared to that of corn plots during the breeding seasons of 2020–2022. At the Urbana site, the bird use of restored prairie, switchgrass, and Miscanthus × giganteus was studied in the 2022 breeding season. At Brighton, Common Yellowthroat, Dickcissel, Grasshopper Sparrow, and Sedge Wren occurred on switchgrass plots more often than on corn; Common Yellowthroat and Dickcissel increased on experimental plots as the perennial switchgrass increased in height and density over the study period; and the other two species declined over the same period. At Urbana, Dickcissel was most frequent in prairie and switchgrass; Common Yellowthroat was most frequent in miscanthus and switchgrass. These findings suggest that advanced switchgrass cultivars could provide suitable habitats for grassland birds, replace lost habitats, and contribute to the recovery of these vulnerable species.
A Review on Nitrogen Flows and Obstacles to Sustainable Nitrogen Management within the Lake Victoria Basin, East Africa
Authors: Masso Cargele , Gweyi-Onyango Joseph , Luoga Hilda Pius , Yemefack Martin , Vanlauwe Bernard
Published: June 2024

The Lake Victoria Basin (LVB) is located in the upper reaches of the Nile River Basin and is shared by five East-African countries. The population in the catchment is growing rapidly and the lake is facing several environmental problems. During the past few decades, numerous efforts have been made across the five countries, with the coordination of the Lake Victoria Basin Commission (LVBC) to reduce the loading of reactive nitrogen (Nr) into the lake and Lake Watershed. However, most of the measures envisaged to ensure long-term sustainable N management are not as easily adopted as planned. This paper reports on a review study on N flows and obstacles in achieving sustainable N management in the LVB, with the objectives of improving the understanding of the N cycle and examining the N management practices and policies that can help reduce the loss of Nr in the region. The scientific literature related to a range of N flows, N management obstacles, and options to overcome obstacles has been analyzed using N prospects developed at the global level for their potential applicability across the LVB. The study showed that an unbalanced use of N input is a serious threat to agricultural productivity leading to extreme soil N mining and degradation, with the majority of LVB farms operating within negative N balances and above the safe operating boundary for N in production systems. From the projections in N input as recommended by various stakeholders, there would likely be changes in both current yield and N use efficiency (NUE) values; however, most small-scale farmers will continue to experience low yields, which remains a challenge for food security in the area. These results suggest that scientists as well as those involved in decision-making and policymaking processes should formulate new targets for fertilizer increment to reduce the yield gap for sustainability, focusing on more integrated soil fertility as a package for nutrient management in cropping systems.