Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring-Based Traffic Participant Detection in Urban Mixed Traffic: A Novel Dataset and A Tailored Detector

Monitoring-based traffic participant detection (TPD) is a highly desirable but challenging task. So far, deep learning-based methods have attained significant improvements on the TPD task, but oftentimes fail in urban mixed traffic due to the lack of relevant datasets and suitable detectors. In this study, we propose a large and detailed dataset named SEU_PML specialized for monitoring-based TPD in urban mixed traffic. This dataset contains a total of 270,684 objects annotated with 2D bounding box and covers 13 sub-categories, having (i) high-resolution images (from $1920\times 1080$ to $4096\times 2160$ pixels), (ii) high-quality annotation (annotation accuracy reaches 98%), and (iii) rich traffic scenarios covering diverse traffic scenes as well as different weather and illumination conditions. The mixed traffic along with high-quality annotation bring about a variety of small objects. To further address the issue on small object detection, we propose a novel detector named YOLO SOD, which embeds a super-resolution feature extraction module and uses knowledge distillation to learn the knowledge how the detector with high-resolution inputs perceives small objects. Moreover, a novel loss function named S-IoU is designed to enable YOLO SOD to focus more on small objects. Experimental results show that (1) the YOLO SOD detector has an increased mAP of 1.58% and operates approximately four times faster when compared to a state-of-art detector; (2) the detectors trained on the SEU_PML dataset have a strong transferability and could be well applied to traffic participant detection in urban mixed traffic. Our dataset is now available at https://github.com/vvgoder/SEU_PML_Dataset.
PENGARUH TATA KELOLA PERUSAHAAN, CSR, DAN FAKTOR LAINNYA TERHADAP MANAJEMEN LABA

The purpose of this research was to determine the effect of independence commissioners, managerial ownership, institutional ownership, audit quality, corporate social responsibility, firm size, and environmental uncertainty on earning management. The population of this research consists of non-financial companies listed on the Indonesia Stock Exchange (IDX) from 2019-2021. The number of research samples is 153 companies. The sample selection technique in this research is purposive sampling and uses secondary data obtained from the official website of the Indonesia Stock Exchange. The data are tested and analyzed using multiple regression methods. The results of this study indicate that there is no influence between independence commissioners, managerial ownership, audit quality, corporate social responsibility, firm size, and environmental uncertainty on earnings management, while institutional ownership variables have an influence on earnings management.
Associations between Fatty Acid Intakes and Plasma Phospholipid Fatty Acid Concentrations in the European Prospective Investigation into Cancer and Nutrition

Background: The aim of this study is to determine the correlations between dietary fatty acid (FA) intakes and plasma phospholipid (PL) FA levels in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods: The dietary intake of 60 individual FAs was estimated using centre-specific validated dietary questionnaires. Plasma PL FA concentrations of these FAs were measured in non-fasting venous plasma samples in nested case-control studies within the EPIC cohort (n = 4923, using only non-cases). Spearman rank correlations were calculated to determine associations between FA intakes and plasma PL FA levels. Results: Correlations between FA intakes and circulating levels were low to moderately high (−0.233 and 0.554). Moderate positive correlations were found for total long-chain n-3 poly-unsaturated FA (PUFA) (r = 0.354) with the highest (r = 0.406) for n-3 PUFA docosahexaenoic acid (DHA). Moderate positive correlations were also found for the non-endogenously synthesized trans-FA (r = 0.461 for total trans-FA C16-18; r = 0.479 for industrial trans-FA (elaidic acid)). Conclusions: Our findings indicate that dietary FA intakes might influence the plasma PL FA status to a certain extent for several specific FAs. The stronger positive correlations for health-enhancing long-chain PUFAs and the health-deteriorating trans-FA that are not endogenously produced are valuable for future cancer prevention public health interventions.
Mapping Intrinsic and Extrinsic Muscle Myoelectric Activity During Natural Dynamic Movements Into Finger and Wrist Kinematics Using Deep Learning Prediction Models

Objective: We investigate the use of high-density surface electromyographic (HDsEMG) recordings of intrinsic hand muscles, along with those from extrinsic muscles, on finger and wrist kinematic prediction performance. We incorporate these HDsEMG signals using a framework based on a custom hybrid convolutional-recurrent deep learning model. Methods: Five healthy subjects performed a wide variety of motion tasks activating multiple degrees of freedom of the wrist and fingers. During the tasks, HDsEMG signals were recorded from extrinsic and intrinsic muscles of the hand while motion capture technology tracked the hand/wrist kinematics. A convolutional-recurrent model architecture was designed and trained on the recorded dataset, incorporating both residual connections as well as inception convolutional structures. Results: The proposed model led to greater regression accuracy over the simultaneous prediction of 12 joint angles (correlation coefficient, mean absolute error, and root mean squared error of 0.850, 4.84°, and 11.2°, respectively) than previously proposed mapping models, when incorporating both intrinsic and extrinsic muscle signals. The inclusion of both sets of hand muscles also led to statistically greater performance than the same model trained on only extrinsic muscle data. Conclusion: We show accurate predictions of hand/wrist kinematics from combined extrinsic and intrinsic hand muscle myoelectric activity, using a convolutional-recurrent hybrid deep learning model. This greater performance is replicated over several subjects and across multidegree of freedom motion tasks. Significance: Our developed system (electrode setup and deep neural networks) can be translated into a compact wearable interface in the future for medical as well as consumer applications.
Speech Recognition Algorithm in a Noisy Environment Based on Power Normalized Cepstral Coefficient and Modified Weighted-KNN

Speech recognition is widely used in robot control and automation. Nevertheless, the use of speech recognition in robots is limited due to its susceptibility to background noise. This paper proposes a speech recognition algorithm to control robots in noisy environments. The proposed algorithm is based on Perceptual Linear Predictive Cepstral Coefficients (PNCC), which is a noise-resistant feature extraction technique, and Modified K-Nearest Neighbors (KNN) with Dynamic Time Warping (DTW) as the classifier. A new KNN-DTW classifier is proposed, integrating weighted KNN and DTW. The proposed algorithm results from experiments comparing PNCC and Mel-frequency cepstral coefficients (MFCC) feature extraction techniques with different classifiers, namely KNN-DTW, two types of KNN (weighted KNN and Medium-KNN), and two types of Support Vector Machine SVM (Linear SVM and Quadratic SVM). The database used to investigate the accuracy was the audio-visual data corpus database UOTletters, which includes 30 speakers, 26 English letters, and 1560 utterances. The database is divided into 50% for training and 50% for testing purposes. In a noise-free environment, the accuracy of the proposed algorithm reached 100%. Moreover, the proposed algorithm demonstrates greater noise immunity across all five noise levels, with an average accuracy difference of 13.67% compared to baseline algorithms.
Convolutional Features-Based Broad Learning With LSTM for Multidimensional Facial Emotion Recognition in Human–Robot Interaction

Convolutional feature-based broad learning with long short-term memory (CBLSTM) is proposed to recognize multidimensional facial emotions in human–robot interaction. The CBLSTM model consists of convolution and pooling layers, broad learning (BL), and long- and short-term memory network. It aims to obtain the depth, width, and time scale information of facial emotion through three parts of the model, so as to realize multidimensional facial emotion recognition. CBLSTM adopts the structure of BL after processing was done at the convolution and pooling layer to replace the original random mapping method and extract features with more representation ability, which significantly reduces the computational time of the facial emotion recognition network. Moreover, we adopted incremental learning, which can quickly reconstruct the model without a complete retraining process. Experiments on three databases are developed, including CK+, MMI, and SFEW2.0 databases. The experimental results show that the proposed CBLSTM model using multidimensional information produces higher recognition accuracy than that without time scale information. It is 1.30% higher on the CK+ database and 1.06% higher on the MMI database. The computation time is 9.065 s, which is significantly shorter than the time reported for the convolutional neural network (CNN). In addition, the proposed method obtains improvement compared to the state-of-the-art methods. It improves the recognition rate by 3.97%, 1.77%, and 0.17% compared to that of CNN-SIPS, HOG-TOP, and CMACNN in the CK+ database, 5.17%, 5.14%, and 3.56% compared to TLMOS, ALAW, and DAUGN in the MMI database, and 7.08% and 2.98% compared to CNNVA and QCNN in the SFEW2.0 database.
Seasonal variations in the production of singlet oxygen and organic triplet excited states in aqueous PM2.5 in Hong Kong SAR, South China

Abstract. Photooxidants drive many atmospheric chemical processes. The photoexcitation of light-absorbing organic compounds (i.e., brown carbon, BrC) in atmospheric waters can lead to the generation of reactive organic triplet excited states (3C∗), which can undergo further reactions to produce other photooxidants such as singlet oxygen (1O2∗). To determine the importance of these aqueous photooxidants in secondary organic aerosol (SOA) formation and transformation, we must know their steady-state concentrations and quantum yields. However, there have been limited measurements of aqueous 3C∗ and 1O2∗ in atmospheric samples outside of North America and Europe. In this work, we report the first measurements of the steady-state concentrations and quantum yields of 3C∗ and 1O2∗ produced in aerosols in South China. We quantified the production of 3C∗ and 1O2∗ in illuminated aqueous extracts of PM2.5 collected in different seasons at two urban sites and one coastal semi-rural site during a year-round study conducted in Hong Kong SAR, South China. The mass absorption coefficients at 300 nm for BrC in the aqueous PM2.5 extracts ranged from 0.49 to 2.01 m2 g-C−1 for the three sites. Both 1O2∗ and 3C∗ were produced year-round. The steady-state concentrations of 1O2∗ ([1O2∗]ss) in the illuminated aqueous extracts ranged from 1.56×10-14 to 1.35×10-12 M, with a study average of (4.02±3.52)×10-13 M. At nearly 2 orders of magnitude lower than [1O2∗]ss, the steady-state concentrations of 3C∗ ([3C∗]ss) ranged from 2.93×10-16 to 8.08×10-14 M, with a study average of (1.09±1.39)×10-14 M. The quantum yields of 1O2∗ and 3C∗ also spanned wide ranges across samples, with a range of 1.19 % to 13.74 % and an average of (5.19±2.63) % for 1O2∗ and a range of 0.05 % to 3.24 % and an average of (0.56±0.66) % for 3C∗. The [1O2∗]ss and [3C∗]ss correlated with the concentration and absorbance of BrC, thus implying that the amount of BrC drives the steady-state concentrations of these photooxidants. The locations (urban vs. semi-rural) did not have a significant effect on [3C∗]ss and [1O2∗]ss, which indicated that BrC from local sources did not have a significant influence on the year-round 3C∗ and 1O2∗ production. 3C∗ and 1O2∗ production were found to be the highest in winter and the lowest in summer for all three sites. The observed seasonal trends of 1O2∗ and 3C∗ production could be attributed to the seasonal variations in the long-range air mass transport. Our analysis highlighted the key role that regional sources play in influencing the composition and concentrations of water-soluble BrC in winter PM2.5 in Hong Kong SAR, which contributed to their highest 3C∗ and 1O2∗ production. The current results will be useful for modeling seasonal aqueous organic aerosol photochemistry in the South China region.
Effect of a dynamic axial magnetic field on a preconditioned single-wire Z-pinch

In this study, the effect and mechanism of a dynamic axial magnetic field on a preconditioned single-wire Z-pinch were investigated experimentally and theoretically. Optical diagnostic methods, including shadowgraphy, interferometry, Faraday rotation, and Thomson scattering, have been used to measure the parameters of magnetized plasmas. Compression of the azimuthal and axial magnetic fields was observed, and the suppression of the plasma instability was recorded and analyzed. The results showed that an external axial magnetic field could reduce the plasma instability and non-uniformity, but prolong the implosion time and weaken the compression ratio. In the implosion process with an axial magnetic field, the plasma rotated at a speed similar to that of imploding, which could be regarded as a stabilization method. A simplified model of the diffusion and compression processes of a dynamic axial magnetic field was developed to investigate the conditions for maximizing the amplitude of the axial magnetic field. Subsequently, the snowplow model was used to calculate the effect of axial magnetic fields on the implosion process and energy conversion.
The efficacy and safety of direct factor Xa inhibitors versus vitamin K antagonists for atrial fibrillation in patients on hemodialysis: a meta-analysis of randomized controlled trials

Abstract Background Direct factor Xa inhibitors have been extensively prescribed for multiple indications; however, hemodialysis patients have been excluded from most of the randomized controlled trials (RCTs) of direct factor Xa inhibitors. Therefore, the efficacy and safety of direct factor Xa inhibitors versus vitamin K antagonists (VKAs) in hemodialysis patients is uncertain. Methods A systematic review and meta-analysis of RCTs was conducted by systematically searching PubMed, EMBASE, Web of Science, SCOPUS, and Cochrane through November 25, 2022. We used the fixed-effect model to pool the risk ratio (RR) with a 95% confidence interval (CI). RevMan v5.4 software was used to pool dichotomous outcomes using RR and continuous outcomes using mean difference presented with the corresponding CI. Results Three RCTs with a total of 341 patients were included in our analysis. There was no difference between direct factor Xa inhibitors and VKAs regarding all-cause mortality (RR, 0.99; 95% CI [0.76, 1.30]; P = 0.96), cardiovascular mortality (RR, 1.35; 95% CI [0.71, 2.60]; P = 0.36), noncardiovascular mortality (RR, 0.75; 95% CI [0.53, 1.05]; P = 0.09), sudden mortality (RR, 1.33; 95% CI [0.53, 3.33]; P = 0.54), any cerebrovascular event (RR, 0.52; 95% CI [0.21, 1.29]; P = 0.16), ischemic stroke (RR, 0.51; 95% CI [0.19, 1.37]; P = 0.18), and hemorrhagic stroke (RR, 0.61; 95% CI [0.10, 3.70]; P = 0.59). Conclusion In patients with atrial fibrillation who are on hemodialysis, direct factor Xa inhibitors and VKAs were similar in terms of efficacy and safety outcomes. However, evidence is still sparse, warranting dedicated RCTs.
The Effect of Elderly Gymnastics on Bone Density and Balance in the Elderly at Batara Hati Mulia Foundation

Elderly is the process of slowly disappearing the ability of tissues to repair themselves / replace and maintain their normal structure and function so that they cannot survive the traces (including infection) and repair the damage suffered. Problems that are often experienced by the elderly are decreased bone density and balance problems. In every body activity or movement, the body will always need balance both statically and dynamically so that the position of the human body becomes stable and reduces the risk of falling. This study aims to determine the effect of elderly exercise on bone density and balance in the elderly at the Hati Mulia Foundation, Takalar Regency. This study uses a quasi-experimental research design with a pretest-post-test one group design. The research population is the elderly at the Hati Mulia Foundation, Takalar Regency. The number of samples was 30 people who were divided into 2 groups, namely the control group carried out by the Batara Hati Mulia Gowa Foundation and the control group each 15 people by the Batara Hati Mulia Takalar Foundation. This study used bioelectrical impedance analysis (BIA) to measure bone density and Stork Stand Test to measure balance before and after the Elderly exercise treatment for the treatment group, while the control group was not given any intervention.
Reliable detection of blast disease in rice plant using optimized artificial neural network

Early Plant disease diagnosis is necessary for several purposes, including reducing yield losses, monitoring and predicting infections, detecting host resistance, and studying basic host‐pathogen biological processes. However early detection has been limited a trained workforce and the ability to identify the problem early in the growing season. Artificial intelligence / machine learning algorithms can help fill this gap. Automatic leaf detection using machine learning is proposed in this study. The presented approach of three stages namely, pre‐processing, feature extraction, and classification. Initially, the input image is transformed into the red, green, and blue format, and the noise in the green band is removed using a median filter. Then, important features of the green band are extracted. After feature extraction, the extracted features are fed to the Optimized Artificial Neural Network (OANN) classifier to classify an image as normal or diseased. To improve Artificial Neural Network (ANN) performance, the Artificial Neural Network (ANN) parameters are chosen optimally using the Adaptive Sunflower Optimization (ASFO) algorithm. Then, the infected region is separated using a level set segmentation algorithm. The efficiency of our work is analyzed based on accuracy, sensitivity, and specificity, the proposed method reached the maximum accuracy of 97.94% for plant disease prediction.This article is protected by copyright. All rights reserved
Effects of Different Feeding Systems on Ruminal Fermentation, Digestibility, Methane Emissions, and Microbiota of Hanwoo Steers

This study evaluates how different feeding systems impact ruminal fermentation, methane production, and microbiota of Hanwoo steers native to Korea. In a replicated 2 × 2 crossover design over 29 days per period, eight Hanwoo steers (507.1 ± 67.4 kg) were fed twice daily using a separate feeding (SF) system comprising separate concentrate mix and forage or total mixed rations (TMR) in a 15:85 ratio. The TMR-feeding group exhibited a considerable neutral detergent fiber digestibility increase than the SF group. However, ruminal fermentation parameters and methane production did not differ between two feeding strategies. In addition, TMR-fed steers expressed elevated Prevotellaceae family, Christensenellaceae R-7 group, and an unidentified Veillonellaceae family genus abundance in their rumen, whereas SF-fed steers were rich in the Rikenellaceae RC9 gut group, Erysipelotrichaceae UCG-004, and Succinivibrio . Through linear regression modeling, positive correlations were observed between the Shannon Diversity Index and the SF group’s dry matter intake and methane production. Although feeding systems do not affect methane production, they can alter ruminal microbes. These results may guide future feeding system investigations or ruminal microbiota manipulations as a methane-mitigation practice examining different feed ingredients.
METAL ATTENUATION AND TOXICITY REDUCTION OF CHROMATED COPPER ARSENATE PRESSURE TREATED WOOD LEACHATE BY THREE FLORIDA SOILS

A strap for carrying an article is disclosed and includes a foam element having identations in an outer surface of the foam element. A fluid-filled bladder is positioned within the identations such that a combination of the foam element and the bladder provides cushioning when carrying the article. A resilient cover may be positioned over the bladder to prevent movement of the bladder away from the foam element, and the bladder and foam element may be located within a void formed by a sheath. The foam element may include protrusions located opposite the indentations.
Variable Water Flow Control of Hybrid Geothermal Heat Pump System

Ground heat accumulation caused by imbalanced heating and cooling loads in a building can cause the heat-source temperature to increase as the operating age of a geothermal heat pump (GHP) system increases. An alternative system to improve upon this situation is the hybrid GHP system. This study reviews existing research on GHP systems and hybrid GHP systems, variable water flow (VWF) control, and coefficient of performance (COP) prediction. Generally, constant flow control is applied to the circulating pump to provide a flow rate according to the maximum load. The need for VWF control was identified because the hybrid GHP system is used mainly as a heating and cooling heat source system for partial loads rather than the entire building load. Previous studies on predicting the COPs of GHP systems developed prediction models by selecting input values based on mathematical models, collecting data through multiple measurement points, and utilizing data from production environments. The model can be limited by the field environment, and it is necessary to predict the COP using machine learning based on existing field monitoring data.
A Glass-Fiber-Optic Turbidity Sensor for Real-Time In Situ Water Quality Monitoring

Turbidity is an important water quality parameter, especially for drinking water. The ability to actively monitor the turbidity level of drinking water distribution systems is of critical importance to the safety and wellbeing of the public. Traditional turbidity monitoring methods involve the manual collection of water samples at set locations and times followed by laboratory analysis, which are labor intensive and time consuming. Fiber-optic measurement permits real-time, in situ turbidity monitoring. But the current technology is based on plastic fibers, which suffer from high optical attenuation and hence are unsuitable for large-scale remote monitoring. In this paper, we report the demonstration of a fiber-optic turbidity sensor based on multi-mode glass fibers. The system uses a single fiber to both deliver laser light into the water sample and collect the back-scattered light for detection. A balanced detection scheme is utilized to remove the common-mode noise to enhance the turbidity sensitivity. Highly linear turbidity responses are obtained and a turbidity resolution as low as 0.1 NTU is achieved. The test unit is also shown to have excellent reproducibility against repeated measurements and good stability against temperature changes. Turbidity measurement in real environmental matrices such as tap water and pond water is also reported with an assessment of the impact of flow rate. This work demonstrates the feasibility of future large-scale distributed fiber-optic turbidity monitoring networks.