bluuebunny/crossref_metadata_2025 huggingface dataset:
TELEPROM Psoriasis: Enhancing patient-centered care and health-related quality of life (HRQoL) in moderate-to-severe plaque psoriasis
Authors: Mercadal-Orfila Gabriel , López Sánchez Piedad , Pou Alonso Aranzazu , Ibarra-Barrueta Olatz , Monte-Boquet Emilio , Borrás Blasco Joaquin , Padullés Zamora Nuria , Sanmartin-Fenollera Patricia , Capilla Montes Cristina , Bernabéu Martínez M. Ángeles , Herrera-Pérez Salvador
Published: December 2024

Background and purpose

Psoriasis is a chronic, immune-mediated inflammatory skin disease that significantly impacts patients’ quality of life. The integration of telepharmacy has the potential to enhance patient care by providing flexible and personalized pharmaceutical follow-up. This study (TELEPROM Psoriasis) evaluates a telepharmacy model for evaluating electronic Patient-Reported outcomes (ePROMs) for individuals with moderate to severe plaque psoriasis in Spain with biological treatment.

Experimental approach

This multicenter prospective quasi-experimental study included 258 adult patients initiating or switching biological/immunomodulatory therapy for moderate to severe plaque psoriasis. Patients were recruited from public hospitals in Spain and monitored through the NAVETA telepharmacy platform over a six-month period. PROMs assessed were the Psoriasis Symptoms and Signs Diary and the Dermatology Life Quality Index at baseline, 1 month, 3 months, and 6 months. Data were analyzed using ANOVA, Student’s t-test, multiple regression, and machine learning algorithms to evaluate ePROMs evolution and response and satisfaction with Telepharmacy follow up.

Key results

The analysis revealed significant influences of gender, employment status, educational level, and daily activity, but no effect of age, on responses to Patient-Reported Outcomes questionnaires. Machine learning models, particularly Random Forest (AUC = 0.98) and Support Vector Machine (AUC = 0.96), effectively predicted patient engagement. DLQI scores significantly decreased from 9.33 ± 7.75 at baseline to 4.34 ± 5.86 at 6 months. Similarly, the PSSD - 7 Days questionnaire showed major reductions, with scores dropping from 55.43 ± 29.94 to 30.73 ± 30.66 at 6 months, and 53% of patients reaching a score of 20 or less. Notably, women reported worse scores at all time points compared to men. Regression analysis explained only 13.2% of the variance in PROMs scores, identifying Employment Status and BMI Range as key contributors.

Conclusion

This study demonstrates the efficacy of biologic treatments in significantly improving HRQoL for psoriasis patients. Addressing demographic variables, such as gender, is key for optimizing treatment outcomes and improving ePROMs response rates. Tailored strategies and ML techniques can help identify low-engagement patients and mitigate disparities. Integrating sociodemographic factors into clinical decision-making and patient engagement strategies is fundamental for delivering equitable and comprehensive care.
The chicken cecal microbiome alters bile acids and riboflavin metabolism that correlate with intramuscular fat content
Authors: Long Xiaoxia , Zhang Fuping , Wang Liqi , Wang Zhong
Published: December 2024

Intramuscular fat (IMF) is a key indicator of chicken meat quality and emerging studies have indicated that the gut microbiome plays a key role in animal fat deposition. However, the potential metabolic mechanism of gut microbiota affecting chicken IMF is still unclear. Fifty-one broiler chickens were collected to identify key cecal bacteria and serum metabolites related to chicken IMF and to explore possible metabolic mechanisms. The results showed that the IMF range of breast muscle of Guizhou local chicken was 1.65 to 4.59%. The complexity and stability of ecological network of cecal microbiota in low-IMF chickens were higher than those in high-IMF chickens. Cecal bacteria positively related to IMF were Alistipes, Synergistes and Subdoligranulum, and negatively related to IMF were Eubacterium_brachy_group, unclassified_f_Lachnospiraceae, unclassified_f_Coriobacteriaceae, GCA-900066575, Faecalicoccus, and so on. Bile acids, phosphatidylethanolamine (Pe) 32:1 and other metabolites were enriched in sera of high-IMF chickens versus low-IMF chickens while riboflavin was enriched in sera of low-IMF chickens. Correlation analysis indicated that specific bacteria including Alistipes promote deposition of IMF in chickens via bile acids while the Eubacterium_brachy group, and Coriobacteriaceae promoted formation of riboflavin, glufosinate, C10-dats (tentative), and cilastatin and were not conducive to the IMF deposition.
Lowering the target daily light integrals following days with excessive lighting can reduce lettuce production costs
Authors: Mayorga-Gomez Andres M. , van Iersel Marc W. , Ferrarezi Rhuanito Soranz
Published: December 2024

Given the fluctuating availability of natural lighting throughout the year, supplemental light is frequently employed to maintain the optimal daily light integral (DLI) levels necessary for adequate plant growth. However, the use of supplemental light translates into higher operational costs. Recent reports suggest that plants can tolerate a day with low DLI following exposure to a day with high DLI from natural light. This was referred to as the ‘carryover’ effect. In such cases, supplemental lighting may not be necessary, resulting in energy savings. In this study, we determined if plants can withstand such DLI fluctuations over multiple days without compromising plant growth. Additionally, we calculated the energy requirements for trese treatments to evaluate the potential energy savings of the carryover effect. To test this, we cultivated lettuce plants (Lactuca sativa cv. ‘Waldmand’s Dark Green’ and ‘Rouxai’) in a walk-in grow chamber, subjecting them to six different lighting treatments. Each treatment consisted of a day with a high DLI of 22.5 mol·m-2·d-1 followed by a varying number of consecutive days with low DLI, ranging from 1 to 5 days, with DLIs of 7.5, 11.25, 12.5, 13.13, and 13.5 mol·m-2·d-1 respectively. The combined DLI for each treatment, calculated as the average DLI across high and low DLI days, was maintained at 15 mol·m-2·d-1. Additionally, we included a control treatment where plants were exposed to a constant DLI of 15 mol·m-2·d-1. We measured plant growth rate, final fresh and dry weights, leaf number, leaf area, specific leaf area, light use efficiency, and relative pigment content to assess differences in plant growth under the different lighting regimes. We observed a decrease in biomass accumulation, as indicated by a 13% reduction in final dry weight only for the treatment involving one day of high DLI followed by one day of low DLI, compared to our control. We discovered that plants can tolerate multiple days of low DLI following a day with high DLI, in contrast to the optimal values reported in the literature. This finding can lead to reduced energy consumption for supplemental lighting and consequent operational cost savings.
An emotion regulation skills training for adolescents and parents: perceptions and acceptability of methodological aspects
Authors: Holmqvist Larsson Kristina , Zetterqvist Maria
Published: December 2024

Introduction

Difficulties with emotion regulation are associated with the development and maintenance of psychiatric symptoms. Focusing on emotion regulation can be beneficial when treating symptoms and behavioral problems. Here we describe a seven-session transdiagnostic approach to regulating emotions, delivered jointly to adolescents and parents in a child and adolescent psychiatric outpatient setting, and we explore the perceptions and acceptability of the methodological aspects of the intervention.

Methods

Quantitative and qualitative data were analyzed. Participants (n = 117) were adolescents and parents.

Results

Participants reported positive attitudes toward the skills training and would recommend it to others. Three overarching themes were developed. “Treatment components” relates to the content of the skills training. Participants appreciated focusing on ER skills but had different perceptions of the content. “The learning climate” contains process-related experiences, such as the skills trainers’ approach and the timing of the intervention. Participants preferred when skills trainers used self-disclosure. “Pedagogical aspects”, describes the structure of the training with perceptions of group constellations, length of sessions and homework. Adolescents particularly requested variety in the pedagogy and found long sessions to be challenging.

Discussion

Recommendations for therapeutic practices are discussed.
An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach
Authors: Niu Tengfei , Cao Shiwei , Cheng Jingyu , Zhang Yu , Zhang Zitong , Xue Ruiling , Ma Jingxi , Ran Qian , Xian Xiaobing
Published: December 2024

Background

Early detection of anxiety symptoms can support early intervention and may help reduce the burden of disease in later life in the elderly with abdominal obesity, thereby increasing the chances of healthy aging. The objective of this research is to formulate and validate a predictive model that forecasts the probability of developing anxiety symptoms in elderly Chinese individuals with abdominal obesity.

Method

This research’s model development and internal validation encompassed 2,427 participants from the 2017-2018 Study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Forty-six variables were defined based on the Health Ecology Model (HEM) theoretical framework. Key variables were screened using LASSO regression, and the XGBoost (Extreme Gradient Boosting) model was further introduced to forecast the risk of developing anxiety symptoms in the elderly with abdominal obesity. SHapley Additive exPlanations (SHAP) was adopted to further interpret and show how the eigenvalues contributed to the model predictions.

Results

A total of 240 participants (9.89%) with anxiety symptoms out of 2,427 participants were included. LASSO regression identified nine key variables: looking on the bright side, self-reported economic status, self-reported quality of life, self-reported health status, watching TV or listening to the radio, feeling energetic, feeling ashamed/regretful/guilty, feeling angry, and fresh fruits. All the evaluation indicators of the XGBoost model showed good predictive efficacy. Based on the significance of the features identified by SHAP (Model Interpretation Methodology), the feature ‘looking on the bright side’ was the most important, and the feature ‘self-reported quality of life’ was the least important. The SHAP beeswarm plot illustrated the impacts of features affected by XGBoost.

Conclusion

Utilizing machine learning techniques, our predictive model can precisely evaluate the risk of anxiety symptoms among elderly individuals with abdominal obesity, facilitating the timely adoption of targeted intervention measures. The integration of XGBoost and SHAP offers transparent interpretations for customized risk forecasts.
Breakfast consumption was associated with suicidal ideation, plan, and attempt: a population-based cross-sectional study
Authors: Zhang Yunshu , Li Keqing , Zhang Lili , Sun Long
Published: December 2024

Background

Although several studies have explored the association between breakfast consumption and suicidal behaviors among children and adolescents, such associations have been less frequently reported among adults and seniors.

Method

This population-based cross-sectional study was conducted in Hebei Province, China. A total of 21, 376 community residents aged 18 years and older were interviewed. This study evaluated the breakfast frequency per week (BFF), full breakfast frequency (FBF), suicidal ideation, suicide plans, and suicide attempts of the participants. Sociodemographic variables, living alone, and sleep quality were also measured.

Results

The prevalence of suicidal ideation, plans, and attempts were 1.4, 0.3, and 0.2%, respectively. Compared to ≥6 days/week BFF, 2–3 days/week BFF was positively associated with suicidal ideation (OR = 1.93, p < 0.01), suicide plan (OR = 2.73, p < 0.05), and suicide attempts (OR = 3.46, p < 0.05). In addition, participants with 2–3 days/week FBF was also at higher risk of suicidal ideation (OR = 2.06, p < 0.001), comparing with never FBF.

Conclusion

The reported prevalence of suicidal ideation, plans, and attempts were lower compared to other countries. Lower breakfast frequency was positively associated with suicidal behaviors, and participants with 2–3 days/week of full breakfast consumption were also at a higher risk of suicidal ideation.
The common prosperity effect of rural households' financial participation: a perspective based on multidimensional relative poverty
Authors: Li Zhengyin , Hu Xiangdong
Published: December 2024

Introduction

Common prosperity holds significant importance in ensuring social equity, promoting sustainable economic growth, and achieving long-term national security. The management of multidimensional relative poverty is a crucial pathway to realizing the common prosperity of all individuals. It is worthwhile to investigate whether the formal and informal financial involvement of rural households can synergistically alleviate multidimensional relative poverty, ultimately contributing to the realization of common prosperity.

Methods

Using data from 5,303 farm households in the 2018 China Family Panel Studies, this study employs multiple linear regression, instrumental variable methods, and propensity score matching to empirically analyze the common prosperity effect of formal and informal financial participation from the perspective of multidimensional relative poverty.

Results

The research demonstrates that both formal and informal financial participation can alleviate multidimensional relative poverty, with formal financial participation exhibiting a more pronounced poverty reduction effect compared to informal financial participation. Mechanism analysis reveals that both forms of financial participation mitigate multidimensional relative poverty by facilitating land transfer and non-farm employment. Heterogeneity analysis reveals that formal financial participation yields a more pronounced poverty reduction effect among rural households experiencing lower levels of multidimensional relative poverty, whereas informal financial participation is more effective in reducing poverty among rural households facing higher levels of multidimensional relative poverty.

Discussion

To achieve common prosperity and enhance the precision of financial interventions for poverty alleviation, it is recommended to leverage the strengths of formal finance over informal finance, enhance financial assistance for land transfer and non-farm employment, and implement tailored financial support policies.
The influence of NOx, temperature, wind and total radiation on the level of ozone concentration in the Upper Silesian agglomeration
Authors: Kobza Joanna , Dul Lechosław , Geremek Mariusz
Published: December 2024

In 2019, ozone was responsible for about 365,000 premature deaths worldwide (6.21 million healthy life years lost) and acute ozone exposure led to 16,800 premature deaths in the European Union. The aim of the study was to estimate the influence of NO, NO2, wind direction (WD) wind speed (WS), air temperature (TA), and total radiation (GLR) on ozone concentration levels. Data provided by 3 automatic air quality monitoring stations of the Regional Environmental Protection Inspectorate in Katowice, were used in this study. The measurements were conducted in from January 1 2009 to December 31 2017. The data obtained from the measuring stations were statistically analysed. The study showed that the strongest influencing factors for O3 values are air temperature and total radiation, with each showing a high correlation with ozone concentration. NO and NO2 had a dual effect on O3 concentration, causing an increase in ozone concentration at low NO and NO2 concentrations and a decrease in ozone concentration at higher NO and NO2 concentrations. We noted that the direction of the wind had very little effect on the concentration of O3. The influence of wind speed on the O3 level was also small, but stronger than that of the wind direction. The research shows that in the analysed years for selected measuring stations the strongest factors influencing O3 concentration are air temperature and total radiation, the NO and NO2 concentrations had a dualistic effect on the O3 concentration.
Attendance rate and perceived relevance related to type, content, and delivery of current rehabilitation programmes after surgical resection for non-small cell lung cancer
Authors: Kaasgaard Mette , Bodtger Uffe , Løkke Anders , Jakobsen Erik , Hilberg Ole
Published: December 2024

Background

Surgical resection is the preferred treatment for localised non-small cell lung cancer (NSCLC). Rehabilitation is central in the management of the associated impaired quality of life, high symptom burden, deconditioning, and social-existential vulnerability. Yet, optimal content and delivery of rehabilitation are not yet defined. Therefore, we aimed to investigate the current rehabilitation offers, attendance rate, and perceived relevance related to content or delivery. Moreover, we investigated the current symptom burden in the patients.

Methods

We conducted an observational cohort study in patients who had undergone surgical resection for NSCLC 4–6 months earlier at Odense University Hospital, Denmark. We retrieved demographic data from patient registries, and interviewed patients via telephone concerning availability, uptake, and attendance rate of any rehabilitation offer in their local primary care setting; content and delivery; benefits of attending, experienced relevance and “symptom burden generally” (specially developed questions); and “symptom burden here and now” [Edmonton Symptom Assessment Scale (ESAS)].

Results

We approached 128 patients, reached 115, and interviewed the 100 (87%) patients who consented. In total, 88% (88/100) had received a rehabilitation offer, and 75% (66/88) had participated in programmes that either targeted NSCLC (23%) or were general cancer rehabilitation (33%), pulmonary rehabilitation (12%), online (1%), or other (33%). Disease-specific rehabilitation was significantly related to the highest attendance rate and perception of relevance. High attendance (≥75%) was, moreover, significantly related to the offer being delivered by a physiotherapist and having a focus on physical exercise. General symptoms were physically oriented [dyspnoea (65%), pain (47%), fatigue (78%)] and “mild” in ESAS scoring. No differences were observed in any baseline characteristics.

Conclusions

Rehabilitation after surgical resection for localised NSCLC is delivered heterogeneously in Denmark. Disease-specific rehabilitation was positively related to attendance rate and to the perceived relevance of the offer.
What emerging adults say about the appropriateness of sexual and reproductive health programmes: evidence from a suburb in Accra, Ghana
Authors: Sowah Laud R. , Biney Adriana A. E. , Atiglo D. Yaw , Badasu Delali , Boateng Angela A. , Sarfoh Kwadwo Ohene , Ankomah Augustine
Published: December 2024

Introduction

Young people's access to appropriate health information in Ghana has been marginal, hence their utilisation of existing services remains poor. Most sexual and reproductive health (SRH) policies and outreach programmes target adolescents, neglecting emerging adults who are equally vulnerable to SRH risks. This study seeks to elicit emerging adults’ knowledge and experiences with SRH programmes, and their recommendations to improve the services for their needs.

Methods

Using data from 30 in-depth interviews and 10 focus group discussions with youth aged 18–24 years in a suburb of Accra, we provide insights on emerging adults’ experiences with SRH programmes and their recommendations for their improvement, as well as young men's perspectives on SRH programmes, in particular.

Results

The participants were in three socio-economic groups: tertiary students, informal workers and apprentices. The main SRH education that the emerging adults had received was from their earlier formal education in Junior and Senior High Schools but not in their current places of school or work. However, they indicate that the SRH education programmes and information they received earlier in life were inadequate to tackle pragmatic issues that contemporary youth face. Furthermore, SRH programmes operate in unfriendly environments with negative messages that cause them to lack vital information.

Discussion

For the success of SRH programmes, the youth should be targeted with diverse contemporary approaches specific for their SRH needs. Key recommendations comprised making available SRH education tailored for emerging adults’ current demographic and socio-economic groups, and providing appropriate SRH content and youth-friendly community centres.
Comparative Analysis of Genomic Prediction for Production Traits Using Genomic Annotation and a Genome-Wide Association Study at Sequencing Levels in Beef Cattle
Authors: Zhao Zhida , Niu Qunhao , Wu Tianyi , Liu Feng , Wang Zezhao , Gao Huijiang , Li Junya , Zhu Bo , Xu Lingyang
Published: December 2024

Leveraging whole-genome sequencing (WGS) that includes the full spectrum of genetic variation provides a better understanding of the biological mechanisms involved in the economically important traits of farm animals. However, the effectiveness of WGS in improving the accuracy of genomic prediction (GP) is limited. Recent genetic analyses of complex traits, such as genome-wide association study (GWAS), have identified numerous genomic regions and potential genes, which can provide valuable prior information for the improvement of genomic selection (GS). In this study, we applied different genome prediction methods to integrate GWAS results and gene feature annotations, which significantly improved the accuracy of GS for beef production traits. The Bayesian models incorporating genomic features showed the highest prediction accuracy, particularly for average daily gain (ADG) and bone weight (BW). Compared to prediction models based on WGS data, GP including biological prior can optimize the prediction accuracy by up to 11.56% for ADG and 14.60% for BW. Also, GP using GBLUP and Bayesian methods integrating biological priors for single-trait GWAS can significantly increase the prediction accuracy. Bayesian methods generally outperformed GBLUP models, with average improvements of 2.25% for ADG, 5.04% for BW, and 3.44% for live weight (LW). Our results indicate that leveraging biological prior knowledge can significantly refine GS models and underline the potential of combining WGS data with biological prior knowledge to further enhance the breeding process.
GLS-YOLO: A Lightweight Tea Bud Detection Model in Complex Scenarios
Authors: Li Shanshan , Zhang Zhe , Li Shijun
Published: December 2024

The efficiency of tea bud harvesting has been greatly enhanced, and human labor intensity significantly reduced, through the mechanization and intelligent management of tea plantations. A key challenge for harvesting machinery is ensuring both the freshness of tea buds and the integrity of the tea plants. However, achieving precise harvesting requires complex computational models, which can limit practical deployment. To address the demand for high-precision yet lightweight tea bud detection, this study proposes the GLS-YOLO detection model, based on YOLOv8. The model leverages GhostNetV2 as its backbone network, replacing standard convolutions with depthwise separable convolutions, resulting in substantial reductions in computational load and memory consumption. Additionally, the C2f-LC module is integrated into the improved model, combining cross-covariance fusion with a lightweight contextual attention mechanism to enhance feature recognition and extraction quality. To tackle the challenges posed by varying poses and occlusions of tea buds, Shape-IoU was employed as the loss function to improve the scoring of similarly shaped objects, reducing false positives and false negatives while improving the detection of non-rectangular or irregularly shaped objects. Experimental results demonstrate the model’s superior performance, achieving an AP@0.5 of 90.55%. Compared to the original YOLOv8, the model size was reduced by 38.85%, and the number of parameters decreased by 39.95%. This study presents innovative advances in agricultural robotics by significantly improving the accuracy and efficiency of tea bud harvesting, simplifying the configuration process for harvesting systems, and effectively lowering the technological barriers for real-world applications.
Automated Seedling Contour Determination and Segmentation Using Support Vector Machine and Image Features
Authors: Samsuzzaman  , Reza Md Nasim , Islam Sumaiya , Lee Kyu-Ho , Haque Md Asrakul , Ali Md Razob , Cho Yeon Jin , Noh Dong Hee , Chung Sun-Ok
Published: December 2024

Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R2 up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity.
A Comparative Study on Rapeseed Sprayed with Film Antitranspirant Under Two Contrasting Rates of Soil Water Depletion
Authors: Xiang Jie , Hare Martin C. , Vickers Laura H. , Kettlewell Peter S.
Published: December 2024

Rapeseed (Brassica napus L.), as one of the most important oil crops around the world, has been affected by drought considerably, particularly at flowering when crops are most sensitive to water stress. It has been shown that film antitranspirant (AT) can effectively reduce the yield loss of droughted crops if applied at the critical stage. However, the mechanism remains unclear by which AT mitigates drought damage to plants under different rates of water depletion. Two experiments in randomised complete block designs were conducted on spring rapeseed with two levels of irrigation, well-watered (WW) and water-stressed (WS), where slow and fast soil water depletion were imposed during the flowering stage in mesocosms (Expt 1_SD) and pots (Expt 2_FD), respectively, and different concentrations of AT, 0, 0.25%, 0.5%, and 1% and 0, 0.5%, and 1%, were applied. Leaf physiological traits, seed yield, and yield components were determined. The results showed that compared to WW, water stress reduced leaf relative water content (RWC) by 2% and 6% in Expt 1_SD and Expt 2_FD, respectively, and had detrimental effects on stomatal conductance, CO2 assimilation rate, and intrinsic water use efficiency. Following AT application, a positive linear relationship was observed in leaf RWC against AT concentrations, albeit with large variations. In Expt 1_SD, seed dry weight and aboveground biomass increased significantly with increasing AT concentrations, while no yield responses were observed in Expt 2_FD, indicating that soil water status may play an important role in rapeseed responses to AT application regardless of concentrations. Therefore, the timing of AT application needs to consider soil water conditions in addition to the growth development of rapeseed plants.
Seeing Beyond Noise: Improving Cryptocurrency Forecasting with Linear Bias Correction
Authors: Syed Sibtain , Talha Syed Muhammad , Iqbal Arshad , Ahmad Naveed , Alshara Mohammed Ali
Published: December 2024

Cryptocurrency is recognized as a leading digital currency by its peer-to-peer transfer capabilities and secure features. Accurately forecasting cryptocurrency price trends holds substantial significance for investors and traders, as they inform critical decisions regarding the acquisition, divestment, or retention of cryptocurrencies, guided by expectations of value, risk assessment, and potential returns. This study also aims to identify a resourceful technique to efficiently forecast prices of cryptocurrencies such as Bitcoin (BTC), Binance (BNB), Ripple (XRP), and Tether (USDT) using optimal data-driven models (LSTM, GRU, and BiLSTM models) using bias correction. The proposed methodology includes collecting cryptocurrency data and precious metal data from Coindesk and BullionVault, respectively, and then finding the optimal model input combination for each cryptocurrency by lag adjustment and correlating feature selection. Hyperparameter tuning was performed by trial-and-error technique, and an early stopping function was applied to minimize time and space complexity. Bias correction (BC) is applied to model-forecasted price trends to reduce errors in evaluation and to enhance accuracy by adjusting model outputs to reduce prediction bias, providing a refined alternative to traditional unadjusted deep learning methods. GRU-BC outperformed other models in forecasting Bitcoin (with MAE 25.291, RMSE 31.266, MAPE 2.999) and USDT (with MAE 0.0006, RMSE 0.0012, MAPE 0.0622) price trends, while BiLSTM-BC was superior in predicting XRP (with MAE 0.0129, RMSE 0.0171, MAPE 2.9013) and BNB (with MAE 2.2759, RMSE 2.8357, MAPE 1.9785) market price flow.
Developing a Robust Fuzzy Inference Algorithm in a Dog Disease Pre-Diagnosis System for Casual Owners
Authors: Kim Kwang Baek , Song Doo Heon , Park Hyun Jun
Published: December 2024

While the pet market is continuously rapidly increasing in Korea, pet dog owners feel uncomfortable in coping with pet dog’s health problems in time. In this paper, we propose a pre-diagnosis system based on neuro-fuzzy learning, enabling non-expert users to monitor their pets’ health by inputting observed symptoms. To develop such a system, we form a disease–symptom database based on several textbooks with veterinarians’ guidance and filtering. The system offers likely disease predictions and recommended coping strategies based on fuzzy inference. We evaluated three fuzzy inference algorithms—PFCM-R, FHAL, and MNFL. While PFCM-R achieved high accuracy with clean data, it struggled with noisy inputs. FHAL showed better noise tolerance but lower precision. PFCM-R is a variant of well-known fuzzy unsupervised learner FCM, and FHAL is the hybrid fuzzy inference engine based on Fuzzy Association Memory and a double-layered FCM we developed. To make the system more robust, we propose the multi-layered neuro-fuzzy learner (MNFL) in this paper, which effectively weakens the association strength between the disease and the observed symptoms, less related to the body part on which the abnormal symptoms are observed. In experiments that are designed to examine how the inference system reacts under increasing noisy input from the user, MNFL achieved 98% accuracy even with non-erroneous inputs, demonstrating superior robustness to other inference engines. This system empowers pet owners to detect health issues early, improving the quality of care and fostering more informed interactions with veterinarians, ultimately enhancing the well-being of companion animals.
The Bioaccumulation, Fractionation and Health Risk of Rare Earth Elements in Wild Fish of Guangzhou City, China
Authors: Miao Xiongyi , Wei Xueqin , Zhao Xiqian , Hao Yupei , Bao Wei
Published: December 2024

In this study, the total content of REEs ranged from 1.32 to 67.74 μg/kg, with a predominant presence of light REEs. The ΔEu and ΔCe values, which exceeded and approached 1, respectively, indicated positive Eu anomalies and low Ce anomalies. Wild fish were categorized into high-, medium-, and low-REEs-bioaccumulation groups using cluster analysis. Higher LRs/HRs and ΔEu values, coupled with lower ΔCe values, in fish from the high-bioaccumulation group suggested that increased bioaccumulation mitigated fractionation. Omnivorous fish with higher REEs levels and lower LRs/HRs indicated broader feeding sources may enhance REE bioaccumulation and diminish fractionation. Elevated REEs concentrations and LRs/HRs in demersal fish highlighted a preferential accumulation of light REEs in the benthic environment. Smaller fish with higher REEs levels but lower LRs/HRs were likely associated with complex feeding sources. Regression analysis revealed that fish with lengths and weights of less than 18 cm and 130 g, respectively, were more susceptible to REEs bioaccumulation. Despite higher ADI values indicating a greater risk for children and Pelteobagrus fulvidraco, all ADI values within 70 μg/(kg·d) suggested that fish consumption poses no risk. This study confirmed that the fractionation of REEs in fish can be used to trace their bioconversion.
LncRNAs Involved in Antioxidant Response Regulation as Biomarkers of Gestational Diabetes: A Study on H19, MALAT1 and MEG3
Authors: Stevanović Jovana , Petrović Uroš , Penezić Ana , Radojičić Ognjen , Ardalić Daniela , Mandić Milica , Mandić-Marković Vesna , Miković Željko , Brkušanin Miloš , Nedić Olgica , Dobrijević Zorana
Published: December 2024

Recent findings highlighted the potential of long non-coding RNAs (lncRNAs) as novel indicators of gestational diabetes mellitus (GDM), as they demonstrate altered expression in metabolic disorders, oxidative stress (OS) and inflammation (IFM). The aim of this study was to evaluate the diagnostic potential and prognostic significance of the OS/IFM-related lncRNAs H19, MALAT1 and MEG3 in GDM and their correlations with redox status-related parameters. The relative quantification of selected lncRNAs from peripheral blood mononuclear cells (PBMCs) of GDM patients and controls (n = 50 each) was performed by qPCR. The expression levels were tested for correlations with metal ion concentrations, NRF2 expression, activities of glutathione reductase (GR), superoxide dismutase (SOD), catalase (CAT), serum thiol content, protein carbonyl level and thiobarbituric acid reactive substances. MALAT1 and H19 were significantly downregulated in GDM patients (p = 0.0095 and p = 0.012, respectively). A correlation was observed between H19 expression and zinc levels in both GDM patients and controls. MALAT1 expression positively correlated with NFE2L2 levels in GDM patients (p = 0.026), while H19 exhibited a positive correlation with GR activity in controls (p = 0.018) and an inverse correlation with SOD activity (p = 0.048). Our data show the disturbance of OS/IFM-lncRNAs in GDM pathogenesis and illustrate the biomarker potential of the analyzed lncRNAs, as well as of certain redox status parameters.
Effects of Lactic Acid Bacterial Fermentation on the Biochemical Properties and Antimicrobial Activity of Hemp Seeds
Authors: Čižeikienė Dalia , Gaidė Ieva , Žadeikė Daiva , Bašinskienė Loreta
Published: December 2024

Recently, there has been an increase in the application of lactic acid bacteria (LAB) for seed fermentation because of the improved functional, technological, and nutritional properties of the fermented seeds. The purpose of this study was to evaluate the effects of selected LAB on the biochemical and antimicrobial features of fermented hemp seeds and their water-, salt-, and ethanol-soluble protein fractions. The results showed that hemp seed medium was suitable for Lactiplantibacillus plantarum, Levilactobacillus brevis, and Lactobacillus acidophilus multiplication (with a LAB count > 109 colony-forming units/g). The biochemical and antimicrobial properties of the fermented hemp seeds strictly depended on the LAB strain used for fermentation. The hemp seeds fermented with L. brevis, which was previously isolated from rye sourdough, presented the highest total phenolic content and phytase, amylase, protease, and antioxidant activities. The hemp seeds fermented with L. acidophilus showed the broadest spectrum of antimicrobial activity against foodborne pathogens. The ethanol-soluble protein fractions from the fermented hemp seeds inhibited the growth of Escherichia coli, Staphylococcus aureus, Salmonella typhimurium, Bacillus subtilis, and Bacillus cereus, while the ethanol-soluble protein fraction from unfermented hemp seeds did not have an antimicrobial effect. The results of ABTS·+ radical scavenging and antimicrobial activity assays by agar well diffusion method confirmed that the tested LAB strains for hemp seed fermentation influenced the antioxidant and antimicrobial activities of protein fractions.
A Comprehensive Approach to Bias Mitigation for Sentiment Analysis of Social Media Data
Authors: Venugopal Jothi Prakash , Subramanian Arul Antran Vijay , Sundaram Gopikrishnan , Rivera Marco , Wheeler Patrick
Published: December 2024

Sentiment analysis is a vital component of natural language processing (NLP), enabling the classification of text into positive, negative, or neutral sentiments. It is widely used in customer feedback analysis and social media monitoring but faces a significant challenge: bias. Biases, often introduced through imbalanced training datasets, can distort model predictions and result in unfair outcomes. To address this, we propose a bias-aware sentiment analysis framework leveraging Bias-BERT (Bidirectional Encoder Representations from Transformers), a customized classifier designed to balance accuracy and fairness. Our approach begins with adapting the Jigsaw Unintended Bias in Toxicity Classification dataset by converting toxicity scores into sentiment labels, making it suitable for sentiment analysis. This process includes data preparation steps like cleaning, tokenization, and feature extraction, all aimed at reducing bias. At the heart of our method is a novel loss function incorporating a bias-aware term based on the Kullback–Leibler (KL) divergence. This term guides the model toward fair predictions by penalizing biased outputs while maintaining robust classification performance. Ethical considerations are integral to our framework, ensuring the responsible deployment of AI models. This methodology highlights a pathway to equitable sentiment analysis by actively mitigating dataset biases and promoting fairness in NLP applications.
Influence of Ice Fragments on the Scraping Effect of Rock–Ice Avalanches: Insights from Physical Model Experiments
Authors: Lin Ziyi , Li Zhouyi , Luo Shuang , Huo Miao , Yang Ping
Published: December 2024

Research on the scraping effects of rock–ice avalanches remains relatively limited. This study investigates the evolution of rock–ice avalanches with varying ice content and initial accumulation forms during motion, scraping, and deposition using laboratory physical model experiments. Changes in pre- scraping velocity, scraping length, scraping depth, maximum deposition length, and deposition thickness were analyzed as functions of ice content. The analysis revealed the influence of ice content and initial accumulation on scraping effects, as well as on motion and deposition characteristics. The experimental results indicate that, compared to typical debris flows (without ice), the presence of ice significantly enhances the mobility, deposition features, and scraping effects of rock–ice avalanches. Through analysis, it is shown that the low friction of ice debris enhances the kinetic energy of ice-rock debris flows, thereby increasing the energy required for scraping.
Analysis of the Factors Leading to the Degradation of Wooden Cultural Heritage Based on the Example of a 16th-Century Sacred Monument in Poland
Authors: Andres Bogusław , Betlej Izabela , Nowacka Małgorzata , Boruszewski Piotr
Published: December 2024

Microorganisms are one of the most critical factors involved in the degradation of buildings. Fungi that develop in historic spaces not only cause the degradation of monuments (immovable and movable) but can also negatively affect those who visit or use such spaces. This article is a case study of the 16th-century St. Catherine Church in Cięcina (Poland), whose walls became severely damp as a result of changes made to the church’s surroundings (relocation of the riverbed of the Cięcinka River, raising the level of St. Catherine Street and building a wooden fence on a concrete foundation around the church). The effect of the severe dampness of the building was the intensive development of Serpula lacrymans fungus and mold. This article highlights the importance of a multidisciplinary approach to hazard diagnosis in historic buildings.
Effect of Different Stocking Densities on Growth, Survival and Blood Parameters of Pacific Fat Sleeper Dormitator latifrons in a Small-Scale Aquaponic System
Authors: Vargas-Ceballos Manuel A. , Ruiz-González Luis E. , Flores-Rodríguez Dulce M. , Badillo-Zapata Daniel , Galavíz-Parada Juan Diego , Vega-Villasante Fernando
Published: December 2024

Aquaponics is the integration between aquaculture and hydroponics, where bacteria mediate the interaction between fish and plants, facilitating a mutually beneficial system. Although there have been numerous studies on aquaponics, there have been few studies focused on optimizing management parameters in small-scale systems for family farming. A key aspect is to find the appropriate culture density to maximize the production of both fish and plants. In aquaculture, introduced species have predominated due to their ease of management, great adaptability, and high growth rates; however, it is important to consider alternatives such as Dormitator latifrons. To determine the effect of density on the yield of D. latifrons and chard Beta vulgaris in an aquaponic culture, three culture densities of 10, 30, and 50 fish m−3 were evaluated in triplicate with a fixed plant density of 20 plants m−2. Nine experimental units (EU) were implemented consisting of 300 L Rotoplas® troughs, a settler (80 L), a biofilter (80 L), and a submersible water pump connected to a nutrient film technique (NFT) system that represented the hydroponic component. The results obtained suggest that an intermediate stocking density (30 fish m−3) could provide a favorable balance between fish and plant performance and fish health. These findings contribute to the knowledge on the culture of this species in aquaponics.
Heart Rate Index as a Measure of Physical Workload in Chainsaw Operations
Authors: Abramuszkinová Pavlíková Eva , Nevrkla Pavel , Röhrich Martin
Published: December 2024

Timber harvesting operations, including manual and motor-manual activities, require workers who are in good health to be able to work effectively. The aim of our paper was to introduce a simplified index methodology for workload assessment. Generally available wearable technology, namely Garmin, Biostrap, and Whoop devices, were used. The dependence of the heart rate (HR) on physical workload was examined to calculate the Heart Rate Index. The case study was performed with several variations of chainsaw devices cutting the poplar wood. It was proved that the use of a heavier work tool, MS 500i/90 cm 9.3 kg, contributes both to the creation of a non-ergonomic working position and to an increase in the energy required to perform work, which was represented by an increase in heart rate. With a lighter work tool and a shorter cutting blade, both a decrease in heart rate and a reduction in the working time performed in a non-ergonomic position were achieved. The results can be used in common practice for workers’ self-assessment to increase safety and health protection at work or work productivity, not only in forestry-related professions.
Research on Detection of Icing Cover Transmission Lines Under Different Weather Conditions Based on Wide-Field Dynamic Convolutional Network LDKA-NET
Authors: Dong Xinsheng , Wan Yuanhao , Zhu Yongcan , Ji Chao
Published: December 2024

The safety of transmission lines is a crucial guarantee for the operation of the power grid. To address the issue of low detection accuracy for icing transmission line defects with existing models, this paper proposes a defect detection algorithm for icing transmission line defects under different weather conditions based on a Large Dynamic Kernel Aggregation Net (LDKA-NET). First, a wide field of view convolutional network (WFVC Net) is introduced to enhance the network’s perception and generalization capabilities, enabling better adaptation to complex scene targets. Secondly, a full-dimensional dynamic convolutional feature fusion network is proposed, which strengthens the model’s feature extraction ability by learning linear combinations of multiple convolution kernels and their weighted input-related attention. Finally, an Expectation Maximization Dynamic Convolutional Attention (EM-DCA) mechanism is introduced, which focuses on and utilizes important information in the input data to help the model better allocate attention, thereby improving its generalization and robustness. Experimental results show that on the dataset proposed in this paper, the average accuracy of the improved algorithm (mAP@0.5) reaches 99.01%. The model parameters decreased by 47.7 M compared with the baseline model and 91.26 M compared with the Faster region with CNN feature (Faster R-CNN) model. The accuracy is 4.81% and 3.11% higher than the SSD model and YOLOv5-L model, respectively. Compared with the existing models, our model is smaller and has higher detection accuracy. It can accurately detect ice-covered wires and complete the task of the ice-covered detection of transmission lines.