bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assesment of the Prevalence and Risk Factors of Haemonchus Contortus Infection of Sheep and Goat in Afgooye District, Lower Shabelle, Somalia
Authors: Mohamed Moktar Omar Sheikh , Yasin Abdirahmaan Mohamad . , Jama Naciima Ahmad . 
Published: July 2024

Abstract

Background
Haemonchus contributes considerably to the economic losses incurred in the rearing of small ruminants in developing countries, impacting both small and large-scale operations. This parasitic nematode is widespread in tropical and subtropical regions, resulting in substantial losses in production, impaired growth, and elevated mortality rates among the young animals.
Methods
A cross-sectional investigation was implemented in seven villages situated in the Afgoye district, Lower Shabelle, Somalia, between September 2023 and February 2024. During this period, 384 fecal samples from small ruminants were obtained and examined using direct smear and flotation techniques to determine the occurrence and risk factors of haemonchosis in sheep and goats.
Results
The study's results demonstrated a substantial prevalence of Haemonchus contortus among the animals, with a total of 41.67% being infected. Specifically, the infection rate was 30.73% for sheep (246/384) and 10.94% for goats (138/384), with no statistically significant difference between the two species (χ²=2.235, P=0.135). The higher rate of infection in sheep may be due to their grazing habits, which involve feeding closer to the ground and increased exposure to infective larvae (L3). Additionally, variations in feeding behaviors between sheep and goats were also observed.
Sheep and goats in the Afgooye district of the Lower Shabelle region in Somalia were examined for haemonchosis. In female sheep, 20.09% tested positive for the condition, while in male sheep, 7.29% showed positive results. For goats, 9.11% of females and 5.18% of males were infected, with no observable gender-based difference. Animals aged over four years had a slightly higher infection rate of 18.75%, compared to those aged 1-4 years (11.46%) and under one year (11.46%), indicating a significant age-related correlation.
The influence of body condition on infection rates was not found to be significant. However, animals in middle and poor condition exhibited higher rates of infection at 17.71% and 14.58%, respectively, compared to those in good condition at 9.38%. Additionally, the grazing practices employed had an impact on the prevalence of the infection, with animals that were allowed to free-graze showing a higher prevalence rate of 20.83%, compared to those engaged in mixed grazing at 16.67% and zero grazing at 4.17%.
Conclusions
This research offers essential insights into haemonchosis in sheep and goats in Afgoye, which can be used to develop effective farm management practices. The findings particularly highlight the importance of parasite control and eradication strategies to mitigate the impact of haemonchosis on small ruminant production.
Analysis on Interfering factor of Superhighway Pavement Roughness Based on DEMATEL/ISM/MICMAC Models
Authors: HE Yongming , Song Yuting , XING Wanyu , WU Jiaxuan , KANG Jia 
Published: July 2024

Abstract

To make superhighways safer and more comfortable, the interfering factor of the superhighway roughness were determined through investigation and analysis, and the correlations among the interfering factors and the degree of interfering on the roughness were evaluated by the method of integrating DEMATEL-ISM and MICMAC model. Through the analysis of DEMATEL model, 9 causal factors and 19 result factors that affect roughness were obtained. The mutual interfering correlation among these factors and the interfering degree on roughness are studied. Based on ISM model, a multilayer hierarchical model of all factors affecting roughness was established, and the 28 factors were divided into 14 levels. Through the MICMAC model, 8 dependent factors, 5 independent factors and 15 autonomous factors were obtained, which verified the rationality of the selection of interfering factor. The results show that reasonable design, strict management and strict construction process can effectively improve the roughness of the superhighway.
ZnO-Salen NPs employed as chemosensor for detection of Al 3+ and K + in aqueous medium, developing human cell images
Authors: Huerta-Aguilar Carlos Alberto , Bazany-Rodríguez Iván J. , Hansberg-Pastor Valeria , Camacho-Arroyo Ignacio , Reyes-Dominguez Iván Alejandro , Cervantes-Avilés Pabel Antonio , Thangarasu Pandiyan 
Published: July 2024

Abstract

ZnO nanoparticles (NPs) were prepared and characterized by different analytical methods and then they were used to decorate with N,N´-bis(salicylidene)ethylenediamine (salen) in order to perform as receptor for the metal ions in an aqueous medium. The results show that ZnO-salen selectively detects Al3+ ions in aqueous medium since the intensity of fluorescence has been enhanced significantly. However, the presence of K+ in the medium further intensified the fluorescence emission for the [ZnO-salen-Al3+] system. The above system has been applied to recognize cells by developing the cell images, for which, the fluorescence image is brightened if a human glioblastoma U251 cell contains [ZnO-salen-Al3+] + K+ ions, consisting of the fluorescence titration. The binding global constant for Al3+ and the subsequent recognition of K+ by ZnO-salen resulted in β2(Al3+) = 6.61x103 and β2(K+) = 3.71x103 with a detection limit of 36.51 µM for Al3+ and 17.39 µM for K+. In the cell toxicity analysis, the cell viability was over 85% for the ZnO-salen even in the concentration as high as 100 mM.
Whether pattern memory can be truly realized in deep neural network?
Authors: Xie Zhenping , li tingting , Lyu Ruimin 
Published: July 2024

Abstract

The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is increasingly surpassing human intelligence in handling general intelligent tasks. However, the absence of DNN’s interpretability and recurrent erratic behavior remain incontrovertible facts. Inspired by perceptual characteristics of human vision on optical illusions, we propose a novel working capability analysis framework for DNNs through innovative cognitive response characteristics on visual illusion images, accompanied with fine adjustable sample image construction strategy. Our findings indicate that, although DNNs can infinitely approximate human-provided empirical standards in pattern classification, object detection and semantic segmentation, they are still unable to truly realize independent pattern memorization. All super cognitive abilities of DNNs purely come from their powerful sample classification performance on similar known scenes. Above discovery establishes a new foundation for advancing artificial general intelligence.
Deep Learning based Approaches for Intelligent Industrial Machinery Health Management &amp; Fault Diagnosis in Resource-Constrained Environments
Authors: Khan Ali Saeed , Akram Muhammad Usman , Khattak Muazzam K , Jawed Soyiba 
Published: July 2024

Abstract

Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes.Industrial Machinery Health Management (IMHM) is a crucial element, based on the Industrial Internet of Things (IIoT), which focuses on monitoring the health and condition of industrial machinery. The academic community has focused on various aspects of IMHM, such as prognostic maintenance, condition monitoring, estimation of remaining useful life (RUL), intelligent fault diagnosis (IFD), and architectures based on edge computing. Each of these categories holds its own significance in the context of industrial processes. In this survey, we specifically examine the research on RUL prediction, edge-based architectures, and intelligent fault diagnosis, with a primary focus on the domain of intelligent fault diagnosis. The importance of IFD methods in ensuring the smooth execution of industrial processes has become increasingly evident. However, most methods are formulated under the assumption of complete, balanced, and abundant data, which often does not align with real-world engineering scenarios. The difficulties linked to these classifications of IMHM have received noteworthy attention from the research community, leading to a substantial number of published papers on the topic. While there are existing comprehensive reviews that address major challenges and limitations in this field, there is still a gap in thoroughly investigating research perspectives across RUL prediction, edge-based architectures, and complete intelligent fault diagnosis processes. To fill this gap,we undertake a comprehensive survey that reviews and discusses research achievements in this domain, specifically focusing on IFD. Initially, we classify the existing IFD methods into three distinct perspectives: the method of processing data, which aims to optimize inputs for the intelligent fault diagnosis model and mitigate limitations in the training sample set; the method of constructing the model, which involves designing the structure and features of the model to enhance its resilience to challenges; and the method of optimizing training, which focuses on refining the training process for intelligent fault diagnosis models and emphasizes the importance of ideal data in the training process. Subsequently, the survey covers techniques related to RUL prediction and edge-cloud architectures for resource-constrained environments. Finally, this survey consolidates the outlook on relevant issues in IMHM, explores potential solutions, and offers practical recommendations for further consideration.
Dietary practices and nutritional status of elderly persons in post-conflict: A cross-sectional study of Northern Uganda
Authors: Ocaya Dennis , Ndagire Regina , Namboze Joweria , Kyeyune Jemimah , Atim Fiona 
Published: July 2024

Abstract

Background
Malnutrition remains a problem in older populations globally. Most older persons do not meet the required dietary intake with the majority consuming more of carbohydrate-based foods and vegetables. The current study therefore aimed at assessing the dietary practices, nutritional status and associated factors among elderly persons in the post-war setting of Gulu district.
Methods
This was a cross-sectional study among elderly persons ≥ 60 years residing in Bungatira Sub-County, Aswa County-Gulu District. The study used a multi-stage sampling procedure. Nutritional status was measured using the Mini Nutrition Assessment – Short Form (MNA-SF) Analysis was done in STATA 16 using modified Poisson regression model. Variables with a p-value of 0.05 at multivariable analysis were considered as factors associated with malnutrition.
Results
The study enrolled 141 participants with a median age (IQR) of 71(64,79) years. Majority of the
respondents, 96(68.1%) were female and 72(51.1%) reported to be widows or widowers. Nearly three quarters, 104(73.8%) were involved in agriculture as a source of livelihood and 64(45.4%) had no education background. The prevalence of malnutrition was 53.9% [95%Cl: 45.6 – 62.0]. The study found that 47(33.6%) participants ate Sorghum and its products daily and 64(45.7%) ate it 1-4 times per week. Millet was eaten by 62(45.9%) participants 1-4 times a week while maize and its products was eaten by 56(40.6%) 1-2 times monthly. We found that increasing age (aPR=1.02, 95%Cl =1.00–1.04, p-value=0.022), having primary education (aPR=1.59, 95%Cl=1.06–2.38, p-value=0.026), staying <5km away from the health facility (aPR=1.60, 95%Cl=1.18–2.16, p-value=0.003) and a DDS ≥60 (aPR=0.70, 95%CI=0.53–0.94, p-value=0.018) were significantly associated with having malnutrition.
Conclusion
Majority of the participants were malnourished. Elderly persons were found to have a decline in food intake in the past months; and age, education, dietary diversity and distance from healthcare facility were the factors that influenced nutritional status. There’s need for age-appropriate elderly health education programs. Elderly programs should aim at bringing healthcare services nearer to the elderly through community and home-based approaches.
A Meaningful Machine Learning Model for Predicting Amputation Rate of Patients with Diabetic Foot Ulcer
Authors: Liu Zixuan , Han Siyang , Gao Lei , Wang Jiangning , Yao Qi 
Published: July 2024

Abstract

Background. Diabetic foot (DF) disease, which includes ulcers, infections and gangrene of the feet, is one of the leading causes of disability worldwide. Due to the high disability rate and expensive treatment cost of diabetic foot, doctors and patients all hope to forecast the prognosis in time and give early intervention. With the development of artificial intelligence technology, more and more methods are used in the diagnosis and prognosis prediction of chronic diseases. Machine learning, a type of artificial intelligence, has excellent predictive effects with a certain accuracy.1 The results of diabetic foot are affected by many factors, so it is necessary for the machine learning to reasonably predict the relationship between input variables and output variables, and to correct and tolerate faults.2 Objective. To develop an accurate and applicable predictive model for diabetic foot amputation and use it to guide clinical diagnosis and treatment, indicating the direction for the prevention of diabetic foot amputation.
Methods and Materials. This retrospective study collected the basic data of 150 patients with DFU who met the study criteria in Beijing Shijitan Hospital from January 2019 to December 2022. Above all, We divided them into amputation group and non-amputation group based on prognostic outcome. Then we used Lasso algorithm to screen relevant risk factors, and predictive models were built with support vector mechanism(SVM) to input risk factors and predict amputation. Besides, we divided the test set and training set by 5-fold cross-validation. The area under the receiver operating characteristic (ROC) curves of the model were 0.89. This model’s calibration capability was 19.614 through Hosmer-Lemeshow test (p=0.012).
Conclusion. In summary, our survey data suggested that C-reactive protein (CRP) in the infection index and the Wagner scale of the affected foot might play a vital role in predicting diabetic foot amputation. The predictive model we constructed can accurately estimate the rate of amputation during hospitalization in DFU patients. In addition, the model allows for personalized analysis of patients' risk factors.
Stress wave propagation characteristics and energy dissipation effects in sandy soil
Authors: CUI Peng , SU Tian , LIU Le , WANG Teng , LI Bangxiang , GUAN Xuetao , MEI Xuefeng 
Published: July 2024

Abstract

To investigate the energy attenuation law and propagation velocity of stress waves in sandy soil material, this paper focuses on the dynamic response of stress waves in a specimen under both single impact and repetitive impact conditions. The improved split Hopkinson pressure bar (SHPB) system is utilized for this purpose. The results reveal that the length of the specimen follows an exponential function relationship with the attenuation of peak stress. As the length of the specimen increases from 40 mm to 240 mm, the attenuation rate of peak stress increases from 26.3–99.0%. The velocity of peak stress decreases with the increase in specimen length, indicating a correlation between these two factors. Furthermore, the impact test results show that the number of impacts and the density of the specimen affect the attenuation of the stress wave. As the number of impacts increases, the density of the specimen also increases, resulting in a gradual decrease in the degree of stress wave attenuation. When the density increases from 1.60 g·cm− 3 to 2.29 g·cm− 3, the attenuation of the stress wave decreases by 17.0%, and the energy absorption density increases from 0.241 MJ·m− 3 to 1.172 MJ·m− 3, representing a 386.3% increase. Additionally, the study finds that the energy absorption efficiency decreases with the increase in the number of impacts at the same stress level. However, after two impacts, the energy absorption efficiency approaches a steady state. At the same strain level, the energy absorption efficiency remains consistent, indicating the material's energy dissipation characteristics.
Dynamical Analysis of Neuron-Astrocyte Interactions at the Tripartite Synapse
Authors: Garcia Den Whilrex , Jacquir Sabir 
Published: July 2024

Abstract

Astrocytes are currently gaining attention from the neuroscience community due to their contribution to multitude of functions, which includes but not limited to their ability to change the neuron's spiking frequency, their capacity to alter neuron's firing activity and their involvement in memory formation and synaptic plasticity regulation. To date, many computational models describing the dynamics of astrocytes, together with their complex interaction with neurons, were proposed, however, these could still be improved since the exploration to their functions and mechanisms was way later than neurons. Hence, in this paper, we want to investigate the steady state behavior of the neuron-astrocyte interaction through a presentation of phase plane analysis. We utilize the Adaptive Exponential Integrate-and-Fire model to describe the firing dynamics of the neuron while drawing inspiration from the work of Postnov and collaborators to describe the calcium dynamics of the astrocyte. Our findings unveil that astrocytes can stimulate an inactive neuron and modulate an active neuron's firing pattern. In addition, astrocyte can also inhibit neurons from firing. These highlight the crucial role that the contribution of astrocyte to the synapse plays in regulating neuronal activity and producing a range of neuronal firing behaviors within the neuron-astrocyte ensemble. They may impact neuronal synchronization, an attribute of several illnesses, including epilepsy, and, on the other hand, may enhance brain information processing.
MAQT:Multi-scale Attention and Query-Optimized Transformer for End-to-End Pose Estimation
Authors: Liang Hong , Wang Cuiping , Shao Mingwen , Zhang Qian 
Published: July 2024

Abstract

Researchers are rapidly turning their focus to human pose estimation as a crucial area of computer vision. In light of the shortcomings of existing Transformer-based pose estimate methods when handling localized features, this work presents MAQT, an enhanced end-to-end method aimed at precise multi-human body pose estimation.To improve the localization of keypoints that are sensitive to scale changes, MAQT offers a Asym-Fusion block. Additionally, we design a new query strategy to optimize the initial selection of queries with Uncertainty-minimal Query Selection. This study combines two self-attention mechanisms in the decoding phase to more correctly understand and record the intricate relationships among keypoints. Based on experimental results on MS COCO using the CrowdPose dataset, MAQT performs better than current contemporary methods.
A Study of the Application of Nafamostat Mesylate Anticoagulation in the Treatment of Patients with Hepatic Failure in a Double-Plasma Molecular Adsorption System
Authors: Hu Yao , He Xin , Sun Min , Wu Yuxi , Xiang Yuanbing , Tang Yayun , Wang Ying , Zhong Lihui , Lei Min 
Published: July 2024

Abstract

Background
Hepatic failure is a critical condition that frequently necessitates advanced therapeutic interventions, including extracorporeal treatments. This study aimed to evaluate the efficacy and safety of nafamostat mesylate in a double-plasma molecular adsorption system (DPMAS) compared to enoxaparin.
Methods
We conducted a single-center randomized controlled study. We included adult patients who required DPMAS in our department from 2021 to 2024. Clinical and filter-related data were extracted.
Results
We included 30 patients in the study, with 16 in the MN group and 14 in the Enoxaparin group. The MN group received an average dosage of 92.75 ± 6.28 mg, whereas the Enoxaparin group received 3214.29 ± 892.58 IU. Use of MN was associated with a lower risk of bleeding complications at the puncture site (6.25% vs. 28.57%, P = 0.037). Both groups showed significant reductions in total bilirubin levels post-treatment (MN: 139.12 ± 34.69 µmol/L, Enoxaparin: 135 ± 16.61 µmol/L), with no significant difference between the groups. There were no significant differences between the groups in terms of dialyzer coagulation, hypotension, red blood cell transfusion, or 30- and 90-day survival rates (P > 0.05).
Conclusions
The use of NM as an anticoagulant during DPMAS was associated with a decreased incidence of bleeding complications compared to the use of enoxaparin. NM is a safe and effective anticoagulant for DPMAS treatment in hepatic failure patients.
Pigment production by identified Pseudofusicoccum adansoniae: extract production, Cytotoxicity activity and diketopiperazines identified
Authors: Alves Bianca V. B. , Borges Letícia J. , Hanna Samira A. , Soares Milena B. P. , Bezerra Daniel P. , Moreira Laysa Lanes Pereira Ferreira , Borges Warley S. , Portela Ricardo Wagner Dias , Fernandez Clara Couto , Umsza-Guez Marcelo A. 
Published: July 2024

Abstract

This study aimed to molecularly identify an isolated fungus that produces a burgundy pigment, and to verify the influence of the type of light on the growth of the fungus and on the production of the pigment in submerged fermentation. In addition, a cytotoxicity assay was performed with HepG2, SCC4, BJ and MRC-5 cells, and the identification of the secondary metabolites produced by the fungus was performed by NMR. The isolated fungus was identified as Pseudofusicoccum adansoniae, which demonstrated better growth and pigment production under yellow light. The extract produced did not show cytotoxicity. Among the isolated compounds, cyclo(L-Pro-L-Val) and cyclo(L-Leu-L-Pro) (Diketopiperazines) have already been described in food and are produced by different organisms, presenting several beneficial biological activities. The identified fungus is a promising producer of biopigments, whose crude extract does not show cytotoxicity. The isolated compounds exhibit interesting biological activities in the food area, such as antibacterial, antifungal and antioxidant actions, making it a viable option for application in food as a natural pigment.
Assessment of antioxidant capacity of Cystosphaera jacquinotii (Fucales, Ochrophyta) by two methods: DPPH and CUPRAC.
Authors: Pena-Martín Carolina , Serrano Raquel , Grindlay Guillermo , Crespo Manuel B. 
Published: July 2024

Abstract

The present work was carried out to evaluate the antioxidant activity of Cystosphaera jacquinotii and to quantify its total phenolic content (TPC), compared with other fucal, Ericaria amentacea. Phenolic compounds occur in high quantities in brown algae and they have an essential paper in antioxidative action.
Extracts were prepared with methanol dilution. The quantification methods used for the determination of the antioxidant capacity were CUPRAC and DPPH.
From the obtained results it can be concluded that Cystosphaera jacquinotii presents a high TPC, which directly contributes to the strong antioxidant activity detected, even higher than Ericaria amentacea. Besides, this work evidences that the kinetics of the extracts in DPPH assays is a markedly variable factor that must be considered to assess the antioxidant capacity of seaweeds species. Furthermore, standardized extraction methods and antioxidant activity analyses are needed to properly compare data and get the optimal ones. In the same way, more studies on the role of different compounds in antioxidant activity are wanting, which will help to get the best performance.