bluuebunny/crossref_metadata_2025 huggingface dataset:
Distribution pattern of soil nematode communities along an elevational gradient in arid and semi-arid mountains of Northwest China
Authors: Chen Jingliang , Zhang Yafeng , Liu Chao , Huang Lei
Published: October 2024

Soil nematodes are the most abundant soil metazoans, occupying multiple trophic levels in the soil food web and playing an important role in soil function. Research on the biogeographic distribution patterns of soil nematode communities and their drivers has received greater attention. However, the distribution characteristics of soil nematode communities along the elevational gradient in the arid and semi-arid regions of Northwest China remain unclear. In this study, four elevational gradients (1750–1900, 1900–2100, 2100–2350 and 2350–2560 m) were established on Luoshan Mountain, Ningxia, an arid and semi-arid region in Northwest China, and soil nematodes in the soil layers of 0–10, 10–20 and 20–40 cm were investigated using the improved Baermann funnel method. The results revealed a monotonically decreasing trend in the total number of soil nematodes along the elevational gradient and soil layer depth, decreasing by 63.32% to 79.94% and 73.59% to 86.90%, respectively, while the interactions were not obvious. A total of 1487 soil nematodes belonging to 27 families and 32 genera were identified across the elevational gradient, with Helicotylenchus as the dominant genus, accounting for 10.43% of the total number of nematodes, and bacterivore nematodes as the main trophic groups, accounting for 32.39% to 52.55% of the relative abundance at each elevation, which increased with increasing elevation. Soil nematode community diversity, richness and maturity indices were relatively low at high elevation and decreased by 44.62%, 48% and 54.74%, respectively, with increasing soil layer depth at high elevations. Compared to low elevations, high-elevation soils experienced greater disturbance, reduced structural complexity and nutrient enrichment of the soil food web, and a shift in soil organic matter decomposition from bacterial to fungal pathways as elevation increased. Finally, redundancy analysis showed that soil pH, bulk density, soil moisture, soil organic carbon, available nitrogen, available phosphorus and available potassium were the main soil factors affecting the composition of soil nematode communities, which well explained the differences in nematode communities at different elevations and soil depths. This study can be used as basic information for further research on soil biota in this mountainous region, expanding our further understanding of the spatial ecology of soil nematodes in the arid and semi-arid mountain ecosystems.
Genome-wide identification of the NAC family in Hemerocallis citrina and functional analysis of HcNAC35 in response to abiotic stress in watermelon
Authors: Cao Lihong , Wang Jinyao , Ren Sijia , Jia Yumei , Liu Yue , Yang Shanjie , Yu Junshen , Guo Xinjuan , Hou Xiaojie , Xu Jin , Li Sen , Xing Guoming
Published: October 2024

Introduction

NAC (NAM, ATAF, and CUC) transcription factor family, one of the important switches of transcription networks in plants, functions in plant growth, development, and stress resistance. Night lily (Hemerocallis citrina) is an important horticultural perennial monocot plant that has edible, medicinal, and ornamental values. However, the NAC gene family of night lily has not yet been analyzed systematically to date.

Methods

Therefore, we conducted a genome-wide study of the HcNAC gene family and identified a total of 113 HcNAC members from the Hemerocallis citrina genome.

Results

We found that 113 HcNAC genes were unevenly distributed on 11 chromosomes. Phylogenetic analysis showed that they could be categorized into 16 instinct subgroups. Proteins clustering together exhibited similar conserved motifs and intron–exon structures. Collinearity analysis indicated that segmental and tandem duplication might contribute to the great expansion of the NAC gene family in night lily, whose relationship was closer with rice than Arabidopsis. Additionally, tissue-specific pattern analysis indicated that most HcNAC genes had relatively higher expression abundances in roots. RNA-Seq along with RT-qPCR results jointly showed HcNAC genes expressed differently under drought and salinity stresses. Interestingly, HcNAC35 was overexpressed in watermelon, and the stress resilience of transgenic lines was much higher than that of wild-type watermelon, which revealed its wide participation in abiotic stress response.

Conclusion

In conclusion, our findings provide a new prospect for investigating the biological roles of NAC genes in night lily.
Effects of activity participation and cognitive levels on depression in middle-aged and older adults with chronic illness: a national cross-sectional study
Authors: Jin Lei , Jing Feiyue
Published: October 2024

Introduction

The world population is rapidly aging, and depression mainly affects middle-aged and older adults with chronic diseases and cognitive impairments. The sample for this study was obtained from the China Health and Retirement Longitudinal Study (CHARLS) public database. The sample size for inclusion was 12,767. There were 6,773 females and 5,994 males, with an overall low level of education. This study aims to provide a theoretical and practical reference basis for the clinical non-pharmacological treatment of depression in middle-aged and older adults (age ≥ 50 years) with chronic diseases. Additionally, the study seeks to promote the development of mental health interventions for middle-aged and older adults (age ≥ 50 years) with chronic diseases, ultimately enhancing the sense of well-being and quality of life for this demographic.

Methods

Cognitive functioning and depressive symptoms of the study participants were assessed using the Mini-Mental State Examination Scale (MMSE) and the short version of the Center for Epidemiological Studies Depression Scale (CESD-10).

Results and discussion

Binary logistic regression results showed that among middle-aged and older adults (age ≥ 50 years) with chronic diseases, participation in physical activity [OR = 1.397; 95% CI (1.181–1.651); p < 0.05] was more effective than participation in social activities [OR = 0.997; 95% CI (0.924–1.076); p < 0.05] for preventing depression. Those with cognitive impairment [OR = 1.206; 95% CI (1.089–1.335); p < 0.05] were more likely to experience depression than those without cognitive impairment. Activity participation (physical activity and social activity) had a more significant effect on mild and moderate depression compared to no depression, and cognitive level had a more pronounced effect on moderate depression [OR = 1.491; 95% CI (1.278–1.740); p < 0.05] and major depression [OR = 2.231; 95% CI (1.282–3.884); p < 0.05]. Within the specific cohort of middle-aged and older adults (age ≥ 50 years) with chronic diseases, both activity participation and cognitive level exert a significant influence on the prevention and intervention of depression. Engagement in physical activity, participation in social activities, and enhanced cognitive functioning emerged as protective factors against depression. Therefore, the policy-maker should strengthen the prevention and treatment of depression in a comprehensive manner through the promotion of physical and social activities and the enhancement of cognitive level, so as to safeguard the mental health of middle-aged and older adults with chronic diseases.
Do emotional demands and exhaustion affect work engagement? The mediating role of mindfulness
Authors: Karahan Kaplan Merve , Bozkurt Gözde , Aksu Bumin Çağatay , Bozkurt Serdar , Günsel Ayşe , Gencer Çelik Gülşah
Published: October 2024

Aim

The current paper seeks to elucidate the interrelationships among emotional demands (ED), emotional exhaustion (EE), mindfulness, and work engagement (WE), with an explanation of the mediating role of mindfulness within indicated relationships.

Background

Nurses working in a stress-related environment face some emotional challenges. New methods such as mindfulness should be learned; therefore, positive outcomes occur along with new developments.

Method

Responses were received from nurses through a self-report questionnaire using the convenience sample technique. Four hundred and twenty-nine nurses from health institutions in Istanbul participated in the study. The PLS-SEM technique was used to test the research model.

Results

ED and mindfulness, EE and mindfulness, and mindfulness and WE relationships were found. While mindfulness was a mediator between EE and WE, it was not a mediator between ED and WE.

Conclusion

It has been revealed that nurses cope with job-related challenges by being present at the moment with high awareness. Furthermore, mindful nurses also foster positive outcomes.

Implications for nursing management

Precautions should be taken because of the nursing shortage. It is better to provide mindfulness training to nursing students in schools before their professional lives. Implementing and using technologies might be helpful for nurses.
Intellectual humility and the learning sciences: can self-reports and behavioral measures coexist to understand civic engagement?
Authors: Lira Matthew , McElroy-Heltzel Stacey
Published: October 2024

Recent political events across the globe have illustrated a resurgence in people’s intolerance to ideas different from their own. We mobilize the idea of intellectual humility to assess how extant psychological theories account for individual differences in people’s tolerance for conflicting ideas. Then, we introduce concepts from the Learning Sciences to determine how alternative methodologies could augment research on intellectual humility and civic engagement. Last, we summarize these analyses by pointing to their relations with three intersecting challenges and solutions regarding studying IH in multiple contexts and with new multiple data sources.
Transparency and translucency indices for 1,525 pictograms from the Aragonese Portal of Augmentative and Alternative Communication
Authors: Díez Emiliano , Díez-Álamo Antonio M. , Alonso María A. , Wojcik Dominika Z. , Fernandez Angel
Published: October 2024

This study investigated the transparency and translucency of 1,525 pictograms from the Aragonese Portal of Augmentative and Alternative Communication (ARASAAC). A total of 521 participants took part in tasks that involved providing the word that best described the meaning of a pictogram or rating the relationship between a pictogram and a verbal label. This process allowed us to obtain indices of transparency (the quality of pictograms that makes their meaning easily “guessable” in the absence of their referent) and translucency (the degree of perceived relationship between the pictogram and its referent when the latter is present) which were further analyzed to assess their reliability and comparability with similar studies. Additionally, the relationship of those indices with various visual and psycholinguistic characteristics was explored, particularly focusing on the match between the original ARASAAC pictogram names and the most frequently provided names by the participants (modal names) for the pictograms. Results showed relatively low levels of transparency, as well as high levels of translucency, with nouns displaying the highest values in both metrics. For transparency and translucency, word imageability and concreteness were the most correlated factors, and, together with age of acquisition, they were the most important features related to the name matching with ARASAAC. The norms derived from this study enhance our understanding of pictogram perception, empowering stakeholders to leverage data-driven insights for the development and implementation of pictographic systems, thus improving cognitive accessibility.
The impact of sleep disturbances on treatment efficacy and prognosis in patients with depressive and anxiety disorders
Authors: Zhang Qingyu , Tong Maoqing , Ji Yunxin , Hou Yanbin , Lou Zongze , Wu Danjuan , Mi Yuwei , Miu Pingping , Tian Jiaxin , Zhu Zhenzhen , Ruan Liemin
Published: October 2024

Introduction

Little was known about the relationship between sleep disturbances and depressive and anxiety disorders, as well as the efficacy of treatment regimens.

Methods

During 2021 to 2023, a total of 417 participants were screened by Hamilton Depression Rating Scale (HAMD-17) and Hamilton Anxiety Rating Scale (HAMA-14) for psychological status, and Pittsburgh sleep quality index (PSQI) assessment. 409 participants were finally enrolled, of which 188 (45.97%) were suffered from sleep disorders. All participants were received polysomnography (PSG) followed by six-week pharmacological treatment of escitalopram and zopiclone, and finally assessed by HAMD-17 and HAMA-14 for treatment efficacy.

Result

PSG monitoring indicated that participants with depression experienced prolonged rapid eye movement sleep latency (REMSL) and increased wakefulness after sleep onset (WASO) (P=0.030 and P=0.002, respectively). Those with anxiety disorders demonstrated a significantly higher percentage of non-rapid eye movement sleep (NREM%) and reduced WASO (P=0.013 and P=0.001, respectively). After six-weeks pharmacological treatment, participants with or without sleep disorders exhibited with similar efficacy outcomes of depression and anxiety disorders (P>0.05). However, every point of PSQI increment at baseline would decrease 0.78 and 0.85 times of probability of effective pharmacological treatment of depression and anxiety disorders. Moreover, participants with both effective outcomes of depression and anxiety disorders were found significant shorter sleep onset latency (SOL) (P<0.001).

Discussion

The insights gained underscore the necessity of considering sleep disturbances in enhancing the overall effectiveness of pharmacological treatments for depression and anxiety disorders.
Parental emotional support, self-efficacy, and mental health problems among adolescents in Hong Kong: a moderated mediation approach
Authors: Qian Mengting , Jin Rui , Lu Chunping , Zhao Mingren
Published: October 2024

Introduction

Early signs of mental health issues may develop into severe mental illnesses over time. The influence of parental emotional support on adolescent mental health problems is well acknowledged. However, prior research has predominantly focused on psychological symptoms, often neglecting the somatic symptoms associated with mental health. Additionally, there has been insufficient exploration of the mediating role of self-efficacy and the relationship between socioeconomic status (SES) and adolescent mental health, particularly within the context of Hong Kong.

Methods

Using data from 3,613 adolescents aged 15 years from 109 schools in Hong Kong who participated in the Programme for International Student Assessment 2018, this study explored the relationship between parental emotional support and two dimensions of adolescent mental health symptoms (psychological and somatic symptoms), and the mediating effect of self-efficacy and the moderating role of SES.

Results

We found that parental emotional support significantly reduced psychological and somatic symptoms, primarily by enhancing self-efficacy. MoreovSES moderated these relationships, with the impact of parental emotional support on psychological symptoms being more pronounced among adolescents from lower SES backgrounds.

Discussion

This study deepens the understanding of the mechanisms underlying adolescent mental health in Hong Kong. By highlighting the importance of parental emotional support and self-efficacy, as well as the moderating effect of SES, the findings offer valuable insights for effective interventions aimed at improving adolescent mental health.
Adherence to infection prevention practices and associated factors among healthcare workers in Northeastern Ethiopia, following the Northern Ethiopia conflict
Authors: Berihun Gete , Gizeyiatu Adinew , Berhanu Leykun , Sewunet Birhanu , Ambaw Birhanie , Walle Zebader , Abebe Masresha , Gasheya Kassahun Ayele
Published: October 2024

Background

In resource-limited areas, especially in conflict-affected settings, managing the risk of hospital-acquired infections is difficult due to the destruction of essential facilities in healthcare settings. The study aimed to assess adherence to Infection prevention practices and associated factors among healthcare workers in Northeastern Ethiopia following the Northern Ethiopia conflict.

Methods and materials

A facility-based cross-sectional study was done with 408 healthcare workers. The survey data was collected using a structured questionnaire based on published articles. Data entry and analysis was done using Epi-Data version 4.6 and SPSS version 25.0, respectively. Binary logistic regression was used to determine the association between dependent and outcome variables, with a cut-off p value 0.05 at 95% confidence interval with a p-value less than 0.05 and a 95% confidence interval for determining factors associated with adherence to infection prevention practices among study participants.

Results

The study included 408 healthcare workers with a response rate of 96.7%. The majority of participants were women 206 (50.5%), married 250 (61.3%), Orthodox followers 211 (51.7%), and educational status of master holder and above with a master’s degree or higher 177 (43.4%). Slightly more than half, 53.7% (219) of the respondents demonstrated safe infection prevention practices. Respondents who received training on infection prevention practices (AOR = 2.662, 95% CI: 1.361, 5.120) had an active infection prevention committee (AOR = 2.203, 95% CI: 1.359, 3.572), use infection prevention guidelines in working departments (AOR = 2.090, 95% CI: 1.013, 4.312), and access to adequate personal protective equipment (AOR = 2.773, 95% CI: 1.560, 4.929) were factors significantly associated with adherence to infection prevention practices.

Conclusion

Overall, only half of the respondents practiced safe infection prevention practices. Receive training on infection prevention guidelines, presence of active infection prevention committee and working guidelines, and the availability of personal protective equipment were factors of infection prevention. Hence, essential facilities like Personal protective equipment, working guidelines should be supplied by donors.
Cost analysis of technological vs. conventional upper limb rehabilitation for patients with neurological disorders: an Italian real-world data case study
Authors: Gower Valerio , Aprile Irene , Falchini Francesca , Fasano Alessio , Germanotta Marco , Randazzo Mattia , Spinelli Federico , Trieste Leopoldo , Gramatica Furio , Turchetti Giuseppe
Published: October 2024

Introduction

Most patients suffering from neurological disorders endure varying degrees of upper limb dysfunction, limiting their everyday activities, with only a limited number regaining full arm use. Robotic and technological rehabilitation has been demonstrated to be a feasible solution to guarantee an effective rehabilitation to recover upper limb performance or to prevent complications of upper limb immobility. However, there is currently a lack of studies which analyze the sustainability of robotic and technological rehabilitation by comparing its costs to conventional rehabilitation pathways.

Methods

Since technology-based and conventional rehabilitation of the upper limb have been demonstrated to have comparable efficacy when the rehabilitation dose is matched, our study concentrates on a cost minimization analysis. The aim of the study is to compare the costs of a “mixed” rehabilitation cycle, which combines conventional and technology-based treatments (the latter delivered with a single therapist supervising several patients), with a cycle of purely conventional treatments. This has been done by developing a cost model and retrospectively analyzing the costs sustained by an Italian hospital which has adopted such a mixed model. A sensitivity analysis has been done to identify the parameters of the model that have the greatest influence on cost difference and to evaluate their optimal values in terms of efficiency of mixed rehabilitation. Finally, probabilistic simulations have been applied to consider the variability of model parameters around such optimized values and evaluate the probability of achieving a given level of savings.

Results

We found a cost difference of 49.60 € per cycle in favor of mixed rehabilitation. The sensitivity analysis demonstrated that, in the situation of the hospital under investigation, the parameter having the largest influence on the cost difference is the number of robotic treatments in a mixed rehab cycle. Probabilistic simulations indicate a probability higher than 98% of an optimized mixed rehabilitation cycle being less expensive than a pure conventional one.

Conclusion

Through a retrospective cost analysis, we found that the technology-based mixed rehabilitation approach, within a specific organizational model allowing a single physiotherapist to supervise up to four patients concurrently, allowed cost savings compared to the conventional rehabilitation model.
Analysis of multi-site HPV infection and vaccination willingness among men who have sex with men in Tianjin, China
Authors: Bai Jianyun , Dong Xiaoyue , Ning Tielin , Zhu Jingjin , Wu Ziming , Li Huijuan , Yu Maohe
Published: October 2024

Background

Men who have sex with men (MSM) are vulnerable to HPV infection. This study aims to explore the HPV infection status at different sites among HIV-positive MSM, HIV-negative MSM, and men who have sex with women (MSW), and to investigate their willingness to receive HPV vaccination.

Methods

From September 2023 to April 2024, three groups were recruited in Tianjin, China. Participants completed an electronic self-administered questionnaire, which included demographic information, knowledge related to sexually transmitted diseases, behavioral information, and willingness to receive the HPV vaccine. Samples were collected from the anal region, genitals, and oral cavity for HPV typing.

Results

A total of 1,559 participants were recruited, including 300 HIV-positive MSM, 600 HIV-negative MSM, and 659 MSW. The HPV infection prevalence for any site were 62.0, 53.7 and 8.3%, respectively (p < 0.001). The infection prevalence for HPV genes covered by the 9-valent vaccine were 47.0, 36.8, and 3.5%, respectively (p < 0.001). Co-infection prevalence at anal and genital were 20.3, 14.2, 0.6%, respectively. Co-infection prevalence at anal and genital and oral were 1.3, 0.3%, 0, respectively. A total of 77.0% HIV-positive MSM and 75.3% HIV-negative MSM expressed willingness to receive the HPV vaccine, whereas 58.9% of MSW were unwilling (p < 0.001). Being HIV-positive (aOR, 3.119; 95% CI, 2.213–4.395), being over 46 years old (aOR, 1.994; 95% CI, 1.266–3.142), with an occupation classified as “white collar workers” (aOR, 1.620; 95% CI, 1.111–2.362) and “freelancing” (aOR, 2.025; 95% CI, 1.371–2.993) and a history of homosexual behavior in the past 6 months (aOR, 5.338; 95% CI, 3.802–7.495) were risk factors for HPV infection among men in Tianjin. Consistently using condoms in the past 6 months (aOR, 0.667; 95% CI, 0.513–0.867) were protective factors.

Conclusion

The HPV infection prevalence among MSM in Tianjin is significantly higher than among MSW, with higher prevalence in the anal region compared to the genital and oral region. HPV infection is associated with HIV infection, older age, and homosexual behavior. Most MSM showed a positive willingness to receive the HPV vaccine, indicating the necessity to implement targeted HPV vaccination programs for MSM and to enhance necessary preventive knowledge and behavioral interventions.
Acute effect of resistive aquatic high-intensity interval training on metabolic costs in adults
Authors: Kwok Manny M. Y. , Ng Shamay S. M. , Ng Y. M. , Tan Gordon C. C. , Huang P. P. , Zhang Y. , So Billy C. L.
Published: October 2024

Background

The effects of Aquatic High-Intensity Interval Training (AHIIT) and resistive AHIIT (Resistive AHIIT) to improve metabolic responses were not yet known.

Objective

This study was to compare the metabolic responses and perceived effort in young healthy adults in a single session of AHIIT and resistive AHIIT.

Methods

20 healthy subjects (9 females, 11 males) performed a stationary running at a matched exercise intensity prior AHIIT and resistive AHIIT [10 × 1-min bouts of stationary running at 90% maximum heart rate (HR max) separated by 1-min active recovery] to examine the metabolic and cardiometabolic outcomes. Mixed effects models were applied to analyze the effects of group, time, and the interaction between group and time on both outcomes. The level of correlations between metabolic variables was checked by Pearson's linear correlation.

Results

There are significant differences on pre and post resting energy expenditure (REE) within both AHIIT and resistive AHIIT groups (p < 0.01) respectively as well as the subjective rate of perceived exertion (RPE) (p < 0.01) within RAHIIT group. A moderate correlation found on respiratory exertional ratio (RER) and RPE in resistive AHIIT (r = 0.534). No significant differences between groups in terms of HR max, mean heart rate (HR mean), peak oxygen consumption (VO2 peak) and total energy expenditure (TEE) (p = 0.50, p = 0.48, p = 0.81, p = 0.59).

Conclusion

Resistive AHIIT provides comparable benefits of metabolic outcomes with AHIIT. Comparable results allowed AHIIT and resistive AHIIT prescriptions precisely.
Tacrolimus Dose Requirement in De Novo Adult Kidney Transplant Patients Treated With Adoport® Can Be Anticipated
Authors: Marquet Pierre , Anglicheau Dany , Humeau Antoine , Adrouche Sofian , Saada Lakhdar , Bisiaux Julie , Guillemin Sara , Lardy-Cléaud Audrey , Rostaing Lionel
Published: October 2024

All the factors potentially influencing tacrolimus dose requirement and combinations thereof have never been thoroughly investigated, precluding accurate prediction of tacrolimus starting dose. This prospective, non-interventional, multicenter study in de novo adult kidney transplant recipients over the first year after transplantation aimed to investigate the factors influencing tacrolimus dose-standardized trough blood concentration (C0/D) over the first week post-transplant (D4-D7, primary objective), D8-M3 and M3-M12 (secondary objectives). Statistical analysis employed mixed linear models with repeated measures. Eighteen sites enrolled 440 patients and followed them up for 9.5 ± 4.1 months. Age at baseline (p = 0.0144), end-stage renal disease (p = 0.0092), CYP3A phenotype (p < 0.0001), dyslipidemia at baseline (p = 0.0031), hematocrit (p = 0.0026), total bilirubin (p = 0.0261) and plasma creatinine (p = 0.0484) independently increased with log(C0/D) over D4-D7, explaining together 72.3% of the interindividual variability, and representing a robust model to estimate tacrolimus initial dose. Donor age and CYP3A phenotype were also influential over D8-M3 and M3-12, in addition to recipient age. Corticosteroids, diabetes at baseline, and ASAT yielded inconstant results between D8-M3 and M3-M12. We found no ethnicity effect when CYP3A phenotype was accounted for, and no food effect. Intra-individual variability over M3-M12 was moderate, and significantly lower in patients with chronic hepatic disorder (p = 0.0196) or cancer (p = 0.0132).
Inverse Kinematics of Robotic Manipulators Based on Hybrid Differential Evolution and Jacobian Pseudoinverse Approach
Authors: Hernandez-Barragan Jesus , Plascencia-Lopez Josue , Lopez-Franco Michel , Arana-Daniel Nancy , Lopez-Franco Carlos
Published: October 2024

Robot manipulators play a critical role in several industrial applications by providing high precision and accuracy. To perform these tasks, manipulator robots require the effective computation of inverse kinematics. Conventional methods to solve IK often encounter significant challenges, such as singularities, non-linear equations, and poor generalization across different robotic configurations. In this work, we propose a novel approach to solve the inverse kinematics (IK) problem in robotic manipulators using a metaheuristic algorithm enhanced with a Jacobian step. Our method overcomes those limitations by selectively applying the Jacobian step to the differential evolution (DE) algorithm. The effectiveness and versatility of the proposed approach are demonstrated through simulations and real-world experimentation on a 5 DOF KUKA robotic arm.
Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
Authors: Falcão Dinis , Reis Francisco , Farinha José , Lavado Nuno , Mendes Mateus
Published: October 2024

Predictive maintenance has gained importance due to industrialization. Harnessing advanced technologies like sensors and data analytics enables proactive interventions, preventing unplanned downtime, reducing costs, and enhancing workplace safety. They play a crucial role in optimizing industrial operations, ensuring the efficiency, reliability, and longevity of equipment, which have become increasingly vital in the context of industrialization. The analysis of time series’ stationarity is a powerful and agnostic approach to studying variations and trends that may indicate imminent failures in equipment, thus contributing to the effectiveness of predictive maintenance in industrial environments. The present paper explores the use of the Augmented Dickey–Fuller p-value temporal variation as a possible method for determining trends in sensor time series and thus anticipating possible failures of a wood chip pump in the paper industry.
Response-Aided Score-Matching Representative Approaches for Big Data Analysis and Model Selection under Generalized Linear Models
Authors: Zheng Duo , Li Keren , Yang Jie
Published: October 2024

In this paper, we propose an efficient method called the response-aided score-matching representative (RASMR) approach to facilitate massive data model selection and data analysis with generalized linear models (GLMs) and a predetermined data partition due to data localization. Similar to the original score-matching representative (SMR) approach, RASMR constructs an artificial data point, called the representative, for each data block. It then fits a GLM on the representative dataset, which provides not only an efficient approach for massive data analysis but also an ideal solution in response to privacy concerns by avoiding the transfer of sensitive data. By further splitting the data blocks according to the values of the response variables, RASMR can obtain more accurate parameter estimates than SMR. Furthermore, by theoretical justifications and simulation studies, we show that RASMR can be more efficiently utilized for model selection and variable selection for a massive dataset by approximating the Akaike information criterion (AIC) and the aggregated prediction errors for cross-validation, which are commonly used for choosing the most appropriate statistical model and drawing reliable conclusions. We also apply the proposed RASMR approach to the airline on-time performance data, which consists of 371 data files labeled by month, and show that RASMR can be successfully used for selecting the most appropriate model for real massive data analysis.
Preliminary Results on the Use of Straw Cover and Effective Microorganisms for Mitigating GHG and Ammonia Emissions in Pig Slurry Storage Systems
Authors: Terrero Turbí Martire Angélica , Gómez-Garrido Melisa , El bied Oumaima , Cuevas Bencosme José Gregorio , Cano Ángel Faz
Published: October 2024

Spain has been the largest pork producer in the EU in recent years, leading to significant pig slurry (PS) production that requires proper management to prevent environmental impacts. The objectives of this study were to quantify greenhouse gas (GHG) and ammonia emissions and to characterize the PS in storage pond systems. A straw cover pond (SP) and addition of effective microorganisms (EMs) in a biological pond (BP) were used to treat the slurries. During two periods (autumn and spring), PS was characterized and GHG (CO2, CH4, N2O) and NH3 emissions were measured with a dynamic chamber. After 5 weeks of storage, BP achieved a reduction of 96% for CO2, 98% for CH4 and 59% for NH3 compared to the control pond (CP) in spring, while SP presented a 74% reduction for CO2 in autumn, and 60% and 97% reductions for CH4 and NH3, respectively, in spring. Additionally, the PS samples showed a decreasing trend for EC, dry matter, COD, BOD5, total N, NH4+-N, Org.-N, NO3−-N, and PO43− during both seasons. This preliminary study shows promise in reducing GHG/NH3 emissions and improving PS properties, but further replication is recommended. Varying straw cover thickness, optimizing EM dose, and a pH reduction may enhance outcomes.
Assessment of the Antioxidative Properties of Extracts from the Fruits of Pyrus pyraster (L.) Burgsd and Pyrus ×myloslavensis Czarna & Antkowiak Grown under Natural Environmental Conditions
Authors: Hęś Marzanna , Antkowiak Wojciech , Stuper-Szablewska Kinga , Dziedzic Krzysztof , Jessa Marta , Ratajczak Paulina
Published: October 2024

Analyses were conducted on extracts from the fruits of P. pyraster and P. ×myloslavensis. Extraction with 80% methanol was performed at room temperature. The total phenolic content was determined by spectrophotometry using the Folin–Ciocalteu reagent, with gallic acid as the reference standard. Phenolic compounds and organic acids were identified on a liquid chromatograph. The antioxidative activity of the extracts was tested in relation to linoleic acid incubation of the emulsions for 19 h based on the neutralization of the DPPH radical (2,2-diphenyl-1-picrylhydrazyl) and the ABTS cation radical (2,2′-azino-bis[3-ethylbenzothiazoline-6-sulfonic acid]) as well as by the ferric reducing antioxidant power (FRAP) assay. The analyses showed that the extract from P. pyraster fruits is characterized by a higher content of phenolic compounds and a higher antioxidative potential compared with that from P. ×myloslavensis. In extracts of both pear species, seven phenolic compounds and four organic acids were identified. The total fiber content in pears of P. pyraster and P. ×myloslavensis was determined at 36.45 g and 24.74 g/100 g d.m. of the pear fruits, of which most comprised the insoluble fraction (32.49 g and 20.86/100 g, respectively). The results of the conducted research are highly significant, as they confirm that pears contain many valuable nutrients and biologically active compounds, including antioxidants and dietary fiber. Adding pear extracts to food products may offer a way to boost their health benefits while also broadening the variety of items that have appealing sensory characteristics. Moreover, research has shown that fruit extracts can help to prolong the shelf life of food products by safeguarding them against lipid oxidation and the decline in their nutritional value.
Design and Testing of a Pleurotus pulmonarius Stick Cutting Machine
Authors: Cai Chunlin , Jing Pengyu , Wang Lihong , Niu Qi , Jiao Haobo , Ma Chen , Li Chengsong
Published: October 2024

Based on the cultivation environment and material parameters of Pleurotus pulmonarius sticks, a P. pulmonarius stick cutting machine is designed to solve the problem of directly realizing the cutting of P. pulmonarius sticks for a net-shaped mushroom stick cultivation layer frame. The machine consists of three parts: a traveling trolley, an XYZ three-axis moving mechanism, and a clamping and cutting mechanism. Based on the force analysis of the clamping and cutting mechanism and the P. pulmonarius sticks, the key components were designed and selected. A prototype was constructed, and several cutting tests were conducted, with the cutting speed, number of cutting circles, and depth of the cut as the main factors. The success rate of cutting, the roundness of the cut, and the degree of loss in terms of the cutout substrate, were used as the performance indicators. An optimized design, based on orthogonal testing, was employed to determine the optimal parameter combinations for the cutting process. The optimal parameters were found to be a cutting speed of 376.3 r/min, 5.4 cutting circles, and a cutting depth of 4 mm. Finally, a validation test was carried out on the machine, and the results show that under the same parameter conditions, the success rate of cutting was 86.6%, the roundness of the cut was 0.235, and the degree of loss in terms of the cutout substrate was 0.851%, which is consistent with the optimization results. This study provides a reference, including a practical reference, for the design and development of a machine to realize the cutting of P. pulmonarius sticks for a cultivation layer frame.
An Effective Yak Behavior Classification Model with Improved YOLO-Pose Network Using Yak Skeleton Key Points Images
Authors: Yang Yuxiang , Deng Yifan , Li Jiazhou , Liu Meiqi , Yao Yao , Peng Zhaoyuan , Gu Luhui , Peng Yingqi
Published: October 2024

Yak behavior is a valuable indicator of their welfare and health. Information about important statuses, including fattening, reproductive health, and diseases, can be reflected and monitored through several indicative behavior patterns. In this study, an improved YOLOv7-pose model was developed to detect six yak behavior patterns in real time using labeled yak key-point images. The model was trained using labeled key-point image data of six behavior patterns including walking, feeding, standing, lying, mounting, and eliminative behaviors collected from seventeen 18-month-old yaks for two weeks. There were another four YOLOv7-pose series models trained as comparison methods for yak behavior pattern detection. The improved YOLOv7-pose model achieved the best detection performance with precision, recall, mAP0.5, and mAP0.5:0.95 of 89.9%, 87.7%, 90.4%, and 76.7%, respectively. The limitation of this study is that the YOLOv7-pose model detected behaviors under complex conditions, such as scene variation, subtle leg postures, and different light conditions, with relatively lower precision, which impacts its detection performance. Future developments in yak behavior pattern detection will amplify the simple size of the dataset and will utilize data streams like optical and video streams for real-time yak monitoring. Additionally, the model will be deployed on edge computing devices for large-scale agricultural applications.
Coupling Image-Fusion Techniques with Machine Learning to Enhance Dynamic Monitoring of Nitrogen Content in Winter Wheat from UAV Multi-Source
Authors: Li Xinwei , Su Xiangxiang , Li Jun , Anwar Sumera , Zhu Xueqing , Ma Qiang , Wang Wenhui , Liu Jikai
Published: October 2024

Plant nitrogen concentration (PNC) is a key indicator reflecting the growth and development status of plants. The timely and accurate monitoring of plant PNC is of great significance for the refined management of crop nutrition in the field. The rapidly developing sensor technology provides a powerful means for monitoring crop PNC. Although RGB images have rich spatial information, they lack the spectral information of the red edge and near infrared bands, which are more sensitive to vegetation. Conversely, multispectral images offer superior spectral resolution but typically lag in spatial detail compared to RGB images. Therefore, the purpose of this study is to improve the accuracy and efficiency of crop PNC monitoring by combining the advantages of RGB images and multispectral images through image-fusion technology. This study was based on the booting, heading, and early-filling stages of winter wheat, synchronously acquiring UAV RGB and MS data, using Gram–Schmidt (GS) and principal component (PC) image-fusion methods to generate fused images and evaluate them with multiple image-quality indicators. Subsequently, models for predicting wheat PNC were constructed using machine-selection algorithms such as RF, GPR, and XGB. The results show that the RGB_B1 image contains richer image information and more image details compared to other bands. The GS image-fusion method is superior to the PC method, and the performance of fusing high-resolution RGB_B1 band images with MS images using the GS method is optimal. After image fusion, the correlation between vegetation indices (VIs) and wheat PNC has been enhanced to varying degrees in different growth periods, significantly enhancing the response ability of spectral information to wheat PNC. To comprehensively assess the potential of fused images in estimating wheat PNC, this study fully compared the performance of PNC models before and after fusion using machine learning algorithms such as Random Forest (RF), Gaussian Process Regression (GPR), and eXtreme Gradient Boosting (XGB). The results show that the model established by the fusion image has high stability and accuracy in a single growth period, multiple growth periods, different varieties, and different nitrogen treatments, making it significantly better than the MS image. The most significant enhancements were during the booting to early-filling stages, particularly with the RF algorithm, which achieved an 18.8% increase in R2, a 26.5% increase in RPD, and a 19.7% decrease in RMSE. This study provides an effective technical means for the dynamic monitoring of crop nutritional status and provides strong technical support for the precise management of crop nutrition.
Object Detection Algorithm for Citrus Fruits Based on Improved YOLOv5 Model
Authors: Yu Yao , Liu Yucheng , Li Yuanjiang , Xu Changsu , Li Yunwu
Published: October 2024

To address the challenges of missed and false detections in citrus fruit detection caused by environmental factors such as leaf occlusion, fruit overlap, and variations in natural light in hilly and mountainous orchards, this paper proposes a citrus detection model based on an improved YOLOv5 algorithm. By introducing receptive field convolutions with full 3D weights (RFCF), the model overcomes the issue of parameter sharing in convolution operations, enhancing detection accuracy. A focused linear attention (FLA) module is incorporated to improve the expressive power of the self-attention mechanism while maintaining computational efficiency. Additionally, anchor boxes were re-clustered based on the shape characteristics of target objects, and the boundary box loss function was improved to Foal-EIoU, boosting the model’s localization ability. Experiments conducted on a citrus fruit dataset labeled using LabelImg, collected from hilly and mountainous areas, showed a detection precision of 95.83% and a mean average precision (mAP) of 79.68%. This research not only significantly improves detection performance in complex environments but also provides crucial data support for precision tasks such as orchard localization and intelligent picking, demonstrating strong potential for practical applications in smart agriculture.
White Paper on User Centred Design to Address Challenges in the Design and Creation of Agricultural Data Visualisation Interfaces
Authors: Gonzalez Paula , Azpiroz Izar , Zabala Mikel , Paolini Giovanni , Maiza Mikel
Published: October 2024

The increase in agricultural data has created a need for developing tools that can manage, share, and visualise this information, while also being accessible to farmers. The use of different technical languages, varying needs, and differing objectives for the tools being developed and used creates a gap between technology developers and farmers, leading to inefficient information transfer. Consequently, interfaces are often poorly adapted to the real needs of end-users and lack essential functionality. This white paper aims to propose a User-Centred Design methodology in order to address the challenges encountered in creating interfaces for shared agricultural Data Space Ecosystems. The proposed methodology, an adaptation of ISO 9241:210-2019, highlights the importance of user participation at various stages of the interface design cycle for the creation of user-adaptive technologies.
Sustainable Poultry Feeding Strategies for Achieving Zero Hunger and Enhancing Food Quality
Authors: Vlaicu Petru Alexandru , Untea Arabela Elena , Oancea Alexandra Gabriela
Published: October 2024

As global demand increases for poultry products, innovative feeding strategies that reduce resource efficiency and improve food safety are urgently needed. This paper explores the potential of alternative sustainable poultry feeding strategies aimed at achieving SDG2 (Zero Hunger) while increasing production performance and food quality, focusing on the potential recycling of by-products, plants, and food waste derived from fruits, vegetables, and seeds, which account for up to 35% annually. The paper provides a review analysis of the nutritional (protein, fat, fiber, and ash) and minerals (i.e., calcium, phosphorus, zinc, manganese, copper, and iron) content as well as the bioactive compounds (polyphenols, antioxidants, carotenoids, fatty acids, and vitamins) of alternative feed ingredients, which can contribute to resource efficiency, reduce dependency on conventional feeds, and lower production costs by 25%. The nutritional benefits of these alternative feed ingredients, including their effects on poultry production and health, and their potential for improving poultry product quality, are presented. Carrot, paprika, rosehip, and some berry waste represent a great source of carotenoids, polyphenols, and vitamins, while the seed meals (flax, rapeseed, and sea buckthorn) have been reported to enhance the essential fatty acid composition in eggs and meat. Numerous plants (basil, sage, rosemary, and lettuce) are natural reservoirs of bioactive compounds with benefits for both animal and food products. Some challenges in implementing these alternative sustainable feeding strategies, including inconsistencies in quality and availability, the presence of anti-nutrients, and regulatory barriers, are also explored. In conclusion, future research directions in sustainable poultry feeding with alternative feed ingredients should be considered to achieve SDG2.
ADL-YOLOv8: A Field Crop Weed Detection Model Based on Improved YOLOv8
Authors: Jia Zhiyu , Zhang Ming , Yuan Chang , Liu Qinghua , Liu Hongrui , Qiu Xiulin , Zhao Weiguo , Shi Jinlong
Published: October 2024

This study presents an improved weed detection model, ADL-YOLOv8, designed to enhance detection accuracy for small targets while achieving model lightweighting. It addresses the challenge of attaining both high accuracy and low memory usage in current intelligent weeding equipment. By overcoming this issue, the research not only reduces the hardware costs of automated impurity removal equipment but also enhances software recognition accuracy, contributing to reduced pesticide use and the promotion of sustainable agriculture. The ADL-YOLOv8 model incorporates a lighter AKConv network for better processing of specific features, an ultra-lightweight DySample upsampling module to improve accuracy and efficiency, and the LSKA-Attention mechanism for enhanced detection, particularly of small targets. On the same dataset, ADL-YOLOv8 demonstrated a 2.2% increase in precision, a 2.45% rise in recall, a 3.07% boost in mAP@0.5, and a 1.9% enhancement in mAP@0.95. The model’s size was cut by 15.77%, and its computational complexity was reduced by 10.98%. These findings indicate that ADL-YOLOv8 not only exceeds the original YOLOv8n model but also surpasses the newer YOLOv9t and YOLOv10n in overall performance. The improved algorithm model makes the hardware cost required for embedded terminals lower.