Miracle-dz/UniFAR-FTUs huggingface dataset:
Working Load Analysis of a 42 kW Class Agricultural Tractor According to Tillage Type and Gear Selection during Rotary Tillage Operation

The objective of this study was to analyze the effect of tillage type (i.e., primary and secondary tillage) and gear selection (P1L2 to P1L4) on the working load of tractor–implement systems during rotary tillage. Soil properties change with depth, and differences in properties along the depth distribution, such as the location of formation of the hardpan layer, internal friction angle, and moisture content, affect the load of rotary tillage operations. Therefore, the physical properties of soil along the field depth distribution were measured to analyze the effect of tillage type and gear selection on workload in rotary tillage. In addition, a load measurement system equipped with PTO torque meter, axle torque meter, proximity sensor, and RTK-GPS were configured on the 42 kW agricultural tractor. The experimental results show that the combination of tillage type and gear selection has a wide-ranging effect on the tractor’s workload and performance when the rotavator operated at the same tilling depth. Overall working load was higher by up to 14% (engine) and 29.1% (PTO shaft) in primary tillage compared to secondary tillage when the gear selection was the same. When the tillage type is the same, it was analyzed that the overall average torque increased by up to 35.9% (engine) and 33.9% (PTO shaft) in P1L4 compared to P1L2 according to gear selection. Based on load analysis results, it was found that the effect of gear selection (Engine: 4–14%, PTO: 12.1–28.6%) on engine and PTO loads was higher than that of tillage type (Engine: 31.6–35.1%, PTO: 31.9–32.8%), and the power requirement tended to decrease in secondary tillage. Therefore, working load should be considered according to the soil environment and tillage type when designing agricultural machinery system.
Long‐term and short‐term analysis of shoreline change and cliff retreat on Brazilian equatorial coast

Assessing historical changes in the shoreline and retreat of cliffs is essential for understanding the evolution and behaviour of specific coastal systems. The present study focused on shoreline changes and cliff base retreat trends at different time scales (annual/decadal) along a 680‐m stretch of Pacheco Beach (Caucaia, Northeast Brazil), which are characterized by coastal erosion and mass movements that have posed risks to residents and visitors to this beach. Shoreline position data were obtained through images collected by satellites and field campaigns for precision mapping through unmanned aerial vehicle (UAV) photogrammetry. The spatiotemporal variations of the shoreline were acquired through an extension of the ArcGIS Digital Shoreline Analysis System (DSAS) and the linear regression rate (LRR) statistical method that reflects the variations that have occurred in the coastal segment. The long‐term results show constant erosion patterns in the study area, with a shoreline retreat of −31 m in the last 17 years at an average rate of −1.8 m/year. In the short term, the average rate of retreat between May 2021 and Jan 2022 was −2.9 m, indicating that 88.78% of the evaluated section was in a continuous process of erosion. Several factors and processes cause the short‐term rates to be more severe than historical rates, demonstrating the stochastic nature of the Pacheco Beach cliff system. This research provides essential data for understanding and predicting shoreline changes on different time scales, thereby contributing to the identification and zoning of unstable cliffs and allowing for the mapping of risk areas to direct coastal management strategies.
Multi-Branch Cascade Receptive Field Residual Network

Deep convolutional neural networks (CNNs) have significantly enhanced image classification in the past decade. This paper proposes Multi-branch Cascade Receptive Field Residual Networks (MCRF-ResNets) based on the original Residual Network (ResNet) architecture for classification and object detection. MCRF-ResNets incorporate multiple branches with different receptive field (RF) sizes to improve image classification performance. Each MCRF residual block contains a <inline-formula> <tex-math notation="LaTeX">$5\times5$ </tex-math></inline-formula> and a <inline-formula> <tex-math notation="LaTeX">$3\times3$ </tex-math></inline-formula> RF-sized convolution block. The <inline-formula> <tex-math notation="LaTeX">$5\times5$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$3\times3$ </tex-math></inline-formula> RF-sized blocks recognize larger and smaller objects, respectively. Group convolutions are used to reduce redundancy and balance feature extraction and parameter usage in the multiple branches. The proposed model approach demonstrates significant improvement in model performance on CIFAR-10 and CIFAR-100 datasets and a subset of the ImageNet 2012 dataset. The model achieves a 2.8% increase in top-1 accuracy compared with the baseline ResNet-50 model with a similar number of parameters on the subset of the ImageNet 2012 dataset. In addition, the proposed method shows good object detection on Pascal VOC and MS COCO datasets.
Prospects of non-Cartesian 3D-SPARKLING encoding for functional MRI: A preliminary case study for retinotopic mapping

For the first time, non-Cartesian 3D-SPARKLING (Spreading Projection Algorithm for Rapid K-space sampLING) encoding scheme was used for task-based fMRI (retinotopic mapping) at 7T. Additionally, this new acquisition technique was compared with 3D-EPI, considered as one of the reference acquisition schemes in high-resolution fMRI. The experiment was performed on a single participant at 1mm isotropic and for a temporal resolution of 2.4s. We found that both techniques yield similar statistically significant activation patterns in the visual cortex.
Microbiota of Dental Caries in Primary Teeth of a Costa Rican Child Population

The objective of this research was to identify bacteria present in the microbiota of dentinal carious lesions in primary molars of some Costa Rican pediatric patients. Data were collected from 15 children aged between 4 and 8 years old who attended the Pediatric Dentistry Clinic at the Faculty of Dentistry from the University of Costa Rica (UCR). The inclusion criteria were: infants between 4 and 8 years old who presented cavitated carious lesions in primary teeth, who were actively attended by students at the Faculty of Dentistry from the UCR, and whose parents or legal guardians signed the informed consent to participate in this research. Samples were taken using a sterile spoon, placed in storage vials, and subjected to various conventional and molecular microbial identification techniques, such as Gram stain identification, catalase tests, oxidase, TSI, API 20E, API STAPH, and VITEK 2. Of the 60 bacterial strains subjected to Gram staining, the following was obtained: 28 Gram-positive bacteria and 32 Gram-negative bacteria. The main isolated organisms were species of Staphylococcus epidermidis, Pasteurella pneumotropica/Mannheimia haemolytica, Pantoea spp, and Streptococcus mutans.
Examination of Factors Affecting Site-Directed RNA Editing by the MS2-ADAR1 Deaminase System

Adenosine deaminases acting on RNA (ADARs) have double-stranded RNA binding domains and a deaminase domain (DD). We used the MS2 system and specific guide RNAs to direct ADAR1-DD to target adenosines in the mRNA encoding-enhanced green fluorescence protein. Using this system in transfected HEK-293 cells, we evaluated the effects of changing the length and position of the guide RNA on the efficiency of conversion of amber (TAG) and ochre (TAA) stop codons to tryptophan (TGG) in the target. Guide RNAs of 19, 21 and 23 nt were positioned upstream and downstream of the MS2-RNA, providing a total of six guide RNAs. The upstream guide RNAs were more functionally effective than the downstream guide RNAs, with the following hierarchy of efficiency: 21 nt > 23 nt > 19 nt. The highest editing efficiency was 16.6%. Off-target editing was not detected in the guide RNA complementary region but was detected 50 nt downstream of the target. The editing efficiency was proportional to the amount of transfected deaminase but inversely proportional to the amount of the transfected guide RNA. Our results suggest that specific RNA editing requires precise optimization of the ratio of enzyme, guide RNA, and target RNA.
A Deep Learning Approach for Network Intrusion Detection Using a Small Features Vector

With the growth in network usage, there has been a corresponding growth in the nefarious exploitation of this technology. A wide array of techniques is now available that can be used to deal with cyberattacks, and one of them is network intrusion detection. Artificial Intelligence (AI) and Machine Learning (ML) techniques have extensively been employed to identify network anomalies. This paper provides an effective technique to evaluate the classification performance of a deep-learning-based Feedforward Neural Network (FFNN) classifier. A small feature vector is used to detect network traffic anomalies in the UNSW-NB15 and NSL-KDD datasets. The results show that a large feature set can have redundant and unuseful features, and it requires high computation power. The proposed technique exploits a small feature vector and achieves better classification accuracy.
On the Assessment of Interactive Detection of Code Smells in Practice: A Controlled Experiment

Code smells are structures in a program that often indicate the presence of deeper maintainability problems. Code smells should be detected as soon as they are introduced, enabling refactoring actions with less effort and time. Non-Interactive Detection (NID) techniques traditionally support code smells detection, enabling developers to reveal smells in later software program versions. NID techniques do not support developers’ progressive interaction with smelly code, revealing smells in the entire source code upon an explicit developer request, which might lead to the accumulation of code smells and, consequently, the degradation of software quality. Interactive Detection (ID) has emerged as a solution to overcome NID’s limitations. By revealing code smell as soon as they are introduced, developers can detect smell instances earlier, resulting in more effective refactoring actions and improved code quality. However, despite its promising potential, there is a lack of evidence regarding the ID impact on code smell detection and refactoring actions during coding analysis. Our research focused on evaluating the effectiveness of an ID technique in code smell detection. Besides, we analyzed the aid of an ID technique in performing effective refactoring actions during coding analysis. To this end, we conducted a controlled experiment with 16 subjects that underwent tasks related to detecting code smells and judging refactoring actions. The experimental tasks revealed that using the ID technique led to an increase of 60% in recall and up to 13% in precision when detecting code smells. Additionally, developers have effectively identified about 55% more code smells instances using the ID technique. Our study results revealed that using ID can improve the effectiveness of code smells detection, as developers can identify opportunities for refactoring actions earlier when compared to NID.
Pre-Trained Model-Based NFR Classification: Overcoming Limited Data Challenges

Machine learning techniques have shown promising results in classifying non-functional requirements (NFR). However, the lack of annotated training data in the domain of requirement engineering poses challenges to the accuracy, generalization, and reliability of ML-based methods, including overfitting, poor performance, biased models, and out-of-vocabulary issues. This study presents an approach for the classification of NFR in software requirements specification documents by extracting features from word embedding pre-trained models. The novel algorithms are specifically designed to extract relevant representative features from pre-trained word embedding models. In addition, each pre-trained model is paired with the four tailored neural network architectures for NFR classification including RPCNN, RPBiLSTM, RPLSTM, and RPANN. This combination results in the creation of twelve unique models, each with its unique configuration and characteristics. The results show that the integration of pre-trained GloVe models with RPBiLSTM demonstrates the highest performance, achieving an impressive average Area Under the Curve (AUC) score of 96%, a precision of 85%, and recall of 82%, highlighting its strong ability to accurately classify NFRs. Furthermore, among the integration of pre-trained Word2Vec models, RPLSTM achieved notable results, with an AUC score of 95%, precision of 86%, and recall of 80%. Similarly, integrated fastText-based pre-trained models the RPBiLSTM yield competitive performance, with an AUC score of 95%, precision of 85%, and recall of 80%. This comprehensive and integrated approach provides a practical solution for effectively analyzing and classifying NFR, thereby facilitating improved software development practices.
Cyanobacterial Variability in Lichen Cephalodia

The ecological success of lichens is related to both myco- and photobionts which condition the physiological limits of the lichen symbioses and thus affect their ecological niches and geographic ranges. A particular type of lichen, called cephalolichen, is characterized by housing both green algal and cyanobacterial symbionts—the latter is restricted to special structures called cephalodia. In this type of lichen, questions related to specialization within species or within individuals are still unsolved as different patterns have previously been observed. In order to study the variability at the intrathalline, intraspecific, and interspecific level, cyanobionts from different cephalodia within the same thalli and from different thalli were genetically analysed in three cephalolichen species at two different forests (18 thalli, 90 cephalodia). The results showed variability in the cephalodial Nostoc OTUs in all the studied species, both at the intrathalline and intraspecific levels. The variability of Nostoc OTUs found in different cephalodia of the same thallus suggests low specialization in this relationship. Additionally, differences in OTU diversity in the three studied species and in the two forests were found. The variability observed may confer an increased ecological plasticity and an advantage to colonize or persist under additional or novel habitats or conditions.
Post-procedural and long-term functional outcomes of jailed side branches in stented coronary bifurcation lesions assessed with side branch Murray law–based quantitative flow ratio

Introduction In coronary bifurcation lesions treated with percutaneous coronary intervention (PCI) using a 1-stent strategy, the occurrence of side branch (SB) compromise may lead to long-term myocardial ischemia in the SB territory. Murray law–based quantitative flow ratio (μQFR) is a novel angiography-based approach estimating fractional flow reserve from a single angiographic view, and thus is more feasible to assess SB compromise in routine practice. However, its association with long-term SB coronary blood flow remains unknown. Methods A total of 146 patients with 313 non-left main bifurcation lesions receiving 1-stent strategy with drug-eluting stents was included in this retrospective study. These lesions had post-procedural Thrombolysis in Myocardial Infarction (TIMI) flow grade 3 in SBs, and documented angiographic images of index procedure and 6- to 24-month angiographic follow-up. Post-procedural SB μQFR was calculated. Long-term SB coronary blood flow was quantified with the TIMI grading system using angiograms acquired at angiographic follow-up. Results At follow-up, 8 (2.6%), 16 (5.1%), 61 (19.5%), and 228 (72.8%) SBs had a TIMI flow grade of 0, 1, 2, and 3, respectively. The incidences of long-term SB TIMI flow grade ≤1 and ≤2 both tended to decrease across the tertiles of post-procedural SB μQFR. The receiver operating characteristic curve analyses indicated the post-procedural SB μQFR ≤0.77 was the optimal cut-off value to identify long-term SB TIMI flow grade ≤1 (specificity, 37.50%; sensitivity, 87.20%; area under the curve, 0.6673; P = 0.0064), and it was independently associated with 2.57-fold increased risk (adjusted OR, 2.57; 95% CI, 1.02–7.25; P = 0.045) in long-term SB TIMI flow grade ≤1 after adjustment. Discussion Post-procedural SB μQFR was independently associated with increased risk in impaired SB TIMI flow at long-term follow-up. Further investigations should focus on whether PCI optimization based on μQFR may contribute to improve SB flow in the long term.
Validation steps and parameters of bioanalytical methods using in clinical studies: A narrative review

The bioanalytical method certifications (validation) are very essential for therapeutic drug monitoring. It is critical to use well-defined and completely validated procedures to provide reliable results which can be properly interpreted. This review provides practical steps and procedures with bioanalytical methods validation and highlights chromatographic method validations that have been used in the bioanalysis. It also attempts to clarify the nomenclature of certain stages of bioanalytical validation and to describe the overall process used for the drugs measurement and their metabolites in biological matrices and fluids. The stages of this procedure are discussed and correlated with data of scientific literature. A consistent description of key bioanalytical parameters is discussed and investigated with a view to improve scientific standards of bioanalytical methods used in clinical studies.
Evaluation of nutritional status in pediatric intensive care unit patients: the results of a multicenter, prospective study in Turkey

Introduction Malnutrition is defined as a pathological condition arising from deficient or imbalanced intake of nutritional elements. Factors such as increasing metabolic demands during the disease course in the hospitalized patients and inadequate calorie intake increase the risk of malnutrition. The aim of the present study is to evaluate nutritional status of patients admitted to pediatric intensive care units (PICU) in Turkey, examine the effect of nutrition on the treatment process and draw attention to the need for regulating nutritional support of patients while continuing existing therapies. Material and Method In this prospective multicenter study, the data was collected over a period of one month from PICUs participating in the PICU Nutrition Study Group in Turkey. Anthropometric data of the patients, calorie intake, 90-day mortality, need for mechanical ventilation, length of hospital stay and length of stay in intensive care unit were recorded and the relationship between these parameters was examined. Results Of the 614 patients included in the study, malnutrition was detected in 45.4% of the patients. Enteral feeding was initiated in 40.6% (n = 249) of the patients at day one upon admission to the intensive care unit. In the first 48 h, 86.82% (n = 533) of the patients achieved the target calorie intake, and 81.65% (n = 307) of the 376 patients remaining in the intensive care unit achieved the target calorie intake at the end of one week. The risk of mortality decreased with increasing upper mid-arm circumference and triceps skin fold thickness Z-score (OR = 0.871/0.894; p = 0.027/0.024). The risk of mortality was 2.723 times higher in patients who did not achieve the target calorie intake at first 48 h (p = 0.006) and the risk was 3.829 times higher in patients who did not achieve the target calorie intake at the end of one week (p = 0.001). The risk of mortality decreased with increasing triceps skin fold thickness Z-score (OR = 0.894; p = 0.024). Conclusion Timely and appropriate nutritional support in critically ill patients favorably affects the clinical course. The results of the present study suggest that mortality rate is higher in patients who fail to achieve the target calorie intake at first 48 h and day seven of admission to the intensive care unit. The risk of mortality decreases with increasing triceps skin fold thickness Z-score.
Free-Breathing Liver QSM with High Isotropic Resolution Using Respiratory Motion-Resolved 3D Multi-Echo Cones MRI

Liver quantitative susceptibility mapping (QSM) can provide a direct measurement of liver iron concentration (LIC). However, respiratory motion and rapid T2* decay are the major challenges in reliable liver QSM. To address these issues a motion-resolved multi-echo 3D cones MRI method with pseudo-random view ordering was implemented, which was then compared with a multi-echo Cartesian GRE sequence in a phantom and healthy volunteer. The proposed motion-resolved cones QSM presented strong motion-robustness in terms of image quality and ROI-based measurements in our phantom and in-vivo studies, demonstrating the feasibility of free-breathing liver QSM.
Exploring the Impact of Labour Mobility on the Mental Health and Wellbeing of Skilled Trades Workers in Ontario, Canada

Labour mobility and subsequent workers migration is an increasing trend worldwide and can be a force that counteracts Canada’s shortage of skilled labour. Supercommuting allows workers facing economic challenges to pursue more financially advantageous work opportunities in other regions. This study aimed to evaluate the “supercommuting” labour mobility model and its impact on long-distance mobile workers’ mental health and wellbeing. We utilized a non-experimental research design using convenience sampling from workers who participated in Blue Branch Inc.’s (Hamilton, Canada) supercommuting labour mobility model. An online questionnaire collected demographic data, work-related data, occupational stress measures related to burnout, and job-related stress data. Data collection was started on 1 April 2021, and of the total 58 participants, the majority (44, 76%) were male, born outside Canada, and had an average age of 32.8 years. Workplace Safety (95%), full-time employment opportunity (95%), career advancement possibility (95%), and income and benefits (94.9%) were found to be the most crucial factors to keep study participants working in their current position. Of the 47 participants who experienced burnout, only one showed severe burnout in each domain (personal, work-related, and colleague-related). There is a great need for preventative burnout programs and supportive employer resources for those who engage in long-distance labour commuting. The study emphasizes the need to encourage policymakers to develop solutions for training future Ontario workers to support mobile employment and long-distance labour commuting.