Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Pedestrian Tracking Based on Improved ByteTrack

In order to augment the robustness of pedestrian tracking in video sequences, we offer an enhanced automatic pedestrian tracking method that is based on the ByteTrack framework. The objective of the proposed approach is to tackle the issue of missed detections and trajectory loss in pedestrian tracking due to dense occlusion. The achievement of multi-object pedestrian tracking is realized through the integration of YOLOX-CF, an enhanced iteration of YOLOX, in conjunction with the BYTE tracking approach. In order to improve the ability of the network to detect pedestrians in various places, we have incorporated the coordinate attention (CA) module into the feature extraction network of YOLOX. In addition, we want to tackle the complex issue of crowd occlusion in pedestrian objects by proposing the utilization of focus loss as a confidence loss function. The above function aims to achieve weight balance between positive and negative samples, hence enhancing the network's attention on problematic samples. The experimental results obtained from the MOT17 dataset demonstrate a notable enhancement in both the mean Average Precision (mAP) and Multiple Object Tracking Accuracy (MOTA) as compared to the first approach. We observe a notable enhancement of 3.1 percentage points in mAP and 3.4 percentage points in MOTA. Furthermore, with the transformation of the model into TensorRT, the rate of inference improves to 126 frames per second (FPS) when executed on a single 2080Ti GPU. The proposed methodology offers enhanced efficacy in real-time pedestrian tracking within the context of autonomous driving, beyond the capabilities of the original.
A Critical Overview of the Construct of Supportive Care Need in the Cancer Literature: Definitions, Measures, Interventions and Future Directions for Research

The growing amount of evidence about the role of supportive care in enhancing cancer patients’ outcomes has made healthcare providers more sensitive to the need for support that they experience during cancer’s trajectory. However, the lack of a consensus in the definition of supportive care and lack of uniformity in the theoretical paradigm and measurement tools for unmet needs does not allow for defined guidelines for evidence-based best practices that are universally accepted. Contemporary cancer literature confirms that patients continue to report high levels of unmet supportive care needs and documents the low effectiveness of most of the interventions proposed to date. The aim of this critical review is to consolidate the conceptual understanding of the need for supportive care, providing definitions, areas of expertise and a careful overview of the measurement tools and intervention proposals developed to date. The possible reasons why the currently developed interventions do not seem to be able to meet the needs, and the issues for future research were discussed.
Heart Failure Prediction System Using Various Approaches: A review

The subfield of Artificial Intelligence (AI) is Machine Learning (ML) which involves developing algorithms for computers to learn from data and make decisions with minimal human intervention. It utilizes data analysis, statistical methods, and other techniques to identify patterns and predict outcomes. ML algorithms are used in various applications, such as medical diagnosis, speech recognition, recommender systems, and natural language processing. Cardiovascular diseases (CVD), including Congestive Heart Failure (CHF) and Coronary Artery Disease (CAD), are the leading cause of death globally. Traditional medical procedures for diagnosing heart disease can be expensive and pose serious health risks. To address this, AI-based systems are being developed to detect and diagnose heart conditions using patient data without invasive procedures, potentially reducing costs and risks associated with traditional diagnostic methods. The paper also examines previous approaches and their limitations, as well as makes recommendations for further research on automated heart disease diagnosis using ML and other forms of data.
DSP Filters and Quality Metrics for Image De-noising

Digital image processing (DIP) has a much broader scope of tools, techniques and algorithms than the corresponding analog image processing. This valuable advantage can be invested in processing and restoring informative image out of distorted one, for instance noisy image. The main objective of this paper is to insure accurate quality metrics to measure spatial filtering techniques in estimating an actual image out of noisy data. The image is distorted with Salt & Pepper noise. The proposed restoration techniques include Mean filter, Median filter, Max filter, Min filter and Wiener filter. The filters are investigated versus the grayscale image. Different quality metrics are employed, including, Mean Square Error (MSE), Performance Index (PI), Peak Signal-to-Noise Ratio (PSNR), and Image Enhancement Factor (IEF). Moreover, blind metrics that include BRISQUE and NIQE are studied and their accuracies get assessed. Constantly, the investigated quality metrics show that Median filter demonstrates a greater outcome than all of its counterparts. Study shows also that the accuracies of quality measures are in agreement with each other as well as with the subjective assessment of the restored images.
Multi-unmanned Surface Vehicles Formation Based on DMPC and Improved APF Method

With the widespread application of unmanned surface vehicles in navigation, many unmanned surface vehicle clusters have become a key research direction. Firstly, using Leader-Follower for the unmanned surface vehicle structure formation, designed the unmanned surface vehicle controller based on a dis-tributed model predictive control algorithm to maintain the stability of the unmanned surface vehicle cluster formation. Secondly, to improve the obstacle avoidance performance of multiple unmanned surface vehicles, the structure of the traditional artificial potential field method has been enhanced, and dynamic regulatory factors are introduced to optimize the penetration of unmanned surface vehicles. And establish cost functions from security, gentleness, and power consumption, and comprehensively evaluate the optimization performance of the way to avoid the way. Finally, in response to changing the needs of the formation in the voyage of the unmanned surface vehicles, introduce stable formation functions, and achieve team form transformation in the state of the formation.
Integrating Repeat Listening Patterns for Enhanced Music Recommendation

Music is a form of content that is often repetitively consumed. Determining the frequency at which to recommend previously heard tracks to users is a critical issue. In recent years, sequential recommendation systems employing self-attention mechanisms, that recommend appropriate content based on a user's music listening sequence, have been proposed. On the other hand, it is known that human hedonic-responses to stimuli, including music, follow an inverted U-shaped curve when the same stimulus is repeated. This suggests that a user's interest in, or perception of, a track changes with repeat listening. However, existing music recommendation approaches have not incorporated this change in interest in individual tracks due to repeat listening. This paper introduces ReSANs, which derive embedding representations based on the frequency of track repetitions and correlate them with item embeddings to enhance recommendation quality. ReSANs compute attention scores that account for repeat listening while also considering the music listening sequence. thereby modeling the change in a track's state for the user. For evaluation, experiments were conducted using the listening history of users from the music streaming service Last.fm. The proposed method outperformed other self-attention-based sequential recommendation systems.
Effect of Microbial Carbon, Nitrogen, and Phosphorus Stoichiometry on Soil Carbon Fractions under a Black Locust Forest within the Central Loess Plateau of China

Microbial C/N/P stoichiometry ratios are of interest because of important ecosystem fluxes of C, N, and P, such as mineralization and immobilization, and they can help to characterize the cycling of soil elements, especially regarding soil C sequestration. However, it is not clear how soil microbial C/N/P stoichiometry influences C cycling. In this study, the concentrations of microbial biomass C, microbial biomass N, microbial biomass P, and soil C fractions were measured in 45-, 40-, and 25-yr-old black locust (Robinia pseudoacacia L.) forest soils and a sloped cropland. The results showed that soil organic C (SOC) concentrations or stocks and the percentage increment of the SOC stocks were highest under 45-yr-old black locust (RP45a). The concentrations of each C fraction in the black locust forest soils (45a, 40a, and 25a) were higher than that of sloped cropland in the 0- to 30-cm soil profile. The concentrations of microbial C, N, and P in RP45a were also higher by an average of 29.6 to 232.9, 9.2 to 17.9, and 0.6 to 1.1 mg kg⁻¹, respectively, compared with the same concentrations in sloped cropland at the 0- to 30-cm soil depth. Redundancy analysis indicated that soil C fractions, especially for particulate organic C (53–2000 and >2000 μm), were significantly correlated with the microbial C/P and N/P ratios, and the “best” model selection indicated that the microbial N/P and C/P ratios significantly changed with soil C fractions with regard to stand age and soil depth in the black locust forests. Therefore, the present study demonstrated that C fractions respond to microbial C, N, and P stoichiometry after farmland-to-forest conversion and hence have the potential to affect C storage in the loess hilly region.
A Comparative Study of Transcranial Color-Coded Doppler (TCCD) and Transcranial Doppler (TCD) Ultrasonography Techniques in Assessing the Intracranial Cerebral Arteries Haemodynamics

Cerebrovascular disease (CVD) poses a major public health and socio-economic burden worldwide due to its high morbidity and mortality rates. Accurate assessment of cerebral arteries’ haemodynamic plays a crucial role in the diagnosis and treatment management of CVD. The study compared a non-imaging transcranial Doppler ultrasound (TCD) and transcranial color-coded Doppler ultrasound (with (cTCCD) and without (ncTCCD)) angle correction in quantifying middle cerebral arteries (MCAs) haemodynamic parameters. A cross-sectional study involving 50 healthy adults aged ≥ 18 years was conducted. The bilateral MCAs were insonated via three trans-temporal windows (TTWs—anterior, middle, and posterior) using TCD, cTCCD, and ncTCCD techniques. The MCA peak systolic velocity (PSV) and mean flow velocity (MFV) were recorded at proximal and distal imaging depths that could be visualised on TCCD with a detectable spectral waveform. A total of 152 measurements were recorded in 41 (82%) subjects with at least one-sided open TTW across the three techniques. The mean PSVs measured using TCD, ncTCCD, and cTCCD were 83 ± 18 cm/s, 81 ± 19 cm/s, and 93 ± 21 cm/s, respectively. There was no significant difference in PSV between TCD and ncTCCD (bias = 2 cm/s, p = 1.000), whereas cTCCD yielded a significantly higher PSV than TCD and ncTCCD (bias = −10 cm/s, p < 0.001; bias = −12 cm/s, p ≤ 0.001, respectively). The bias in MFV between TCD and ncTCCD techniques was (bias = −0.5 cm/s; p = 1.000), whereas cTCCD demonstrated a higher MFV compared to TCD and ncTCCD (bias = −8 cm/s, p < 0.001; bias = −8 cm/s, p ≤ 0.001, respectively). TCCD is a practically applicable imaging technique in assessing MCA blood flow velocities. cTCCD is more accurate and tends to give higher MCA blood flow velocities than non-imaging TCD and ncTCCD techniques. ncTCCD is comparable to non-imaging TCD and should be considered in clinical cases where using both TCD and TCCD measurements is needed.
A Dual CNN Method for Speed Sensing of Sonar Robots

To improve the perception accuracy of sonar robots' speed, this paper introduces a dual-CNN approach based on spectrum and time-frequency features. Rough features with high-speed and fine features with low-speed are separately extracted through spectral and time-frequency map. Then, these features are put into two CNN networks for training, and weighted voting method is finally applied to obtain the classification results. Experimental results demonstrate that the dual-CNN’s perceptual accuracy is improved by 1% to 6% compared to the single CNN model. Furthermore, the inclusion of rough and fine features enhances feature diversity, thereby enhancing the speed perception accuracy and stability of sonar robots.
Research on Macroscopical Quantitative Relation Between Wave Velocity and Strength of Rock(Rock Mass)

Research purposes: The vertical wave velocity and compressive strength of the rock(rock mass) are the very important indexes for appraising the rock mass strength,the slope stability and surrounding rock classification.In this paper,based on the tests data,the statistics is made for the average values of the vertical wave velocity and compressive strength of the rocks of all kinds of stratum,the macroscopic relation between the vertical wave velocity and compressive strength of the rocks is analyzed,and the model is established for the macrocopic quantitative relation between them.All these are crucial to appraising the rock mass quality,classification of the surrounding rock of tunnel,saving the test cost,shortening project construction time and checking the reliability of the test result. Research conclusions:(1) When the rock(rock mass) is dense,hardy,complete and fresh,its vertical wave velocity is big,otherwise its vertical wave velocity is low when the rock(rock mass) is weak,serious weathering,watery and dissected. (2) The vertical wave velocity of the rock is directly proportional to the cube root of rock compressive strength,and the vertical wave velocity of the rock mass is directly proportional to the cube root of the compressive strength of rock mass and the square root of the integrity index of rock mass. (3)The practices show the model for the macroscopic quantitative relation between the vertical wave velocity and compressive strength of rock(rock mass) is reliable.
Rapid reinflation following the 2011–2012 rhyodacite eruption at Cordón Caulle volcano (Southern Andes) imaged by InSAR: Evidence for magma reservoir refill

Cordón Caulle is a large fissural volcano that has erupted rhyodacitic magma of the same composition in its past three historical eruptions in 1921, 1960, and 2011–2012. There was significant ground deformation observed before and during the 2011–2012 eruption—here we use C and X band interferometric synthetic aperture radar (InSAR) time series results to document posteruptive uplift up to 0.8 m between March 2012 and May 2015, with line‐of‐sight rates up to 45 cm/yr that have been largely aseismic, along with subsidence in the 2011–2012 lava flow. The 2012 uplift rate is one of the largest for silicic systems and was likely produced by the intrusion of ~0.125 km3 of magma in the same tectonically controlled plumbing system that has been active during the historical eruptions. Nevertheless, the uplift ended before the reservoir refilled with the erupted volume, maybe due to a change in the pressure gradient produced by the 2011–2012 eruption.
Land degradation monitoring in Braila agricultural area using RADARSAT2 data

The estimation of degradation in agricultural lands from fully polarimetric synthetic aperture radar (SAR) data at C-band using differential SAR interferometry is investigated. To this aim, we used a dataset of high resolution SAR images collected in the joint ESA-CSA SOAR Europe-16605 scientific proposal framework that have been processed through the persistent scattering – DInSAR technique. Moreover, to improve PSInSAR analysis, we used polarimetric optimization method on multi-temporal polarimetric SAR data. Optimization is based on the selection of the most stable scattering mechanism over time since the unitary complex column vector is related to the geometric and electromagnetic features of the target. We applied this method on a dataset including 14 compact polarization SAR data (HH/HV/VV) acquired by RADARSAT2 from August 2014 to November 2015 over Braila agricultural area. The area has been affected by land degradation due to salinization and irrigation water overexploitation. The results reveal that the use of an optimum scattering mechanism provides a significant improvement in increasing the PS density and hence the density of the pixels with valid deformation results with respect to single-pol data (about 50% more than single channel datasets).
Multi-Layer Resource Computing Offloading Based on Power Price Difference

Sixth-generation (6G) mobile communication technologies will drive convergence in areas such as cloud computing and multi-access edge computing (MEC) through integrated satellite-terrestrial networks, aiming to provide quality computing services to user equipment (UE). However, it is important to note that the power price may vary among different regions. This paper aims to study a multi-layer computing resource system that takes into account power price differences. For the multi-layer resource computing offloading problem, the optimization objective is defined as minimizing the weighted sum of the time cost and offloading cost. To address this problem, a Q-Learning-based multi-layer resource offloading (Q-MRO) scheme is proposed. Simulation results show that the Q-MRO scheme can obtain a lower total cost compared with other benchmark schemes.
Characteristics of the Pressing Process and Density Profile of MUPF-Bonded Particleboards Produced from Waste Plywood

Waste plywood containing phenol–formaldehyde (PF) resin is one of the materials that are difficult to use in the production of particleboards based on UF resin. Therefore, the aim of this research was to analyze the possibility of using this type of waste in the production of particleboards bonded with melamine-urea-phenol-formaldehyde (MUPF) resin in order to determine their suitability for particleboard production. The pressing process and density profile of three-layer particleboards were presented. The press closing time for mats containing only recovered particles in the core layer (100%), produced with a face layer ratio of 50%, a resin load for a face layer of 12%, and a core layer of 10%, at a unit pressure of 3 MPa, was 29% shorter than for the industrial particle mats. Regardless of the level of variability of independent factors, the heating time of the mats containing recovered particles was 10–20% shorter than the heating time of the mats with industrial particles. The greatest impact on the maximum density of the face layer of particleboards was observed for the content of the recovered particles and then the resin load. The maximum density area of the face layer was located closer to the surface in particleboards produced with a higher (80%, 100%) content of the recovered particles, a higher (i.e., 12% and 10%, respectively, for face and core layers) resin load, a lower (35%) face layer ratio, and a higher (3 MPa) unit pressure.
Left Ventricular Apical Ballooning, Catecholamine Toxicity, and Cardiomyopathy

Case reports and clinical experiences have implicated catecholamine. Excess likely contributes to the pathophysiologic process as a cause of cardiac dysfunction, impaired hemodynamic function, and poor outcomes. Cardiac dysfunction has also been described in many other diseases; there is likely a common underlying pathophysiology. In this review, we will examine the pathophysiology of cardiac dysfunction after catecholamine surge and discuss the evidence surrounding cardiac dysfunction.
Role of TRAK1 variants in epilepsy: genotype–phenotype analysis in a pediatric case of epilepsy with developmental disorder

Purpose The TRAK1 gene is mapped to chromosome 3p22.1 and encodes trafficking protein kinesin binding 1. The aim of this study was to investigate the genotype–phenotype of TRAK1-associated epilepsy. Methods Trio-based whole-exome sequencing was performed on a cohort of 98 patients with epilepsy of unknown etiologies. Protein modeling and the VarCards database were used to predict the damaging effects of the variants. Detailed neurological phenotypes of all patients with epilepsy having TRAK1 variants were analyzed to assess the genotype–phenotype correlations. Results A novel TRAK1 compound heterozygous variant comprising variant c.835C > T, p.Arg279Cys and variant c.2560A > C, p.Lys854Gln was identified in one pediatric patient. Protein modeling and VarCards database analyses revealed that the variants were damaging. The patient received a diagnosis of early infantile epileptic spasms with a developmental disorder; he became seizure-free through valproate and adrenocorticotropic hormone treatment. Further results for six variants in 12 patients with epilepsy indicated that biallelic TRAK1 variants (including homozygous or compound heterozygous variants) were associated with epilepsy with developmental disorders. Among these patients, eight (67%) had epileptic spasms and seven (58%) were intractable to anti-seizure medicines. Moreover, eight patients experienced refractory status epilepticus, of which seven (88%) died in early life. To our knowledge, this is the first reported case of epilepsy caused by TRAK1 compound heterozygous variants. Conclusion Biallelic TRAK1 variants can cause epilepsy and developmental disorders. In these patients, seizures progress to status epilepticus, suggesting a high risk for poor outcomes and the requirement of early treatment.
Effect of Microbial Phytase on Nutrient Availability and Growth of Juvenile Clarias gariepinus Fed Soyabean and Groundnut-based Diets

Several studies have shown the positive effect of phytase on phosphorus utilization by fish and animals, with the use of phytase sources determined for different fish species. Few studies have tested phytase response to different diets, which may affect nutrient availability for optimum growth due to differences in phytate location. The research, therefore, studied the effect of phytase to diets based on soya bean and groundnut meal for Clarias gariepinus on nutrient availability and growth. In trial 1, four groups of soya bean S1, S2, S3 and S4 replaced fish meal at 25%, 50%, 75% and 100% and supplemented with 250, 500, 750 and 1000 FTU/g phytase, respectively. In trial 2, four groups of groundnut meal diet G1, G2, G3, G4, G5 and G6, were similarly supplemented with the same phytase levels used in experiment 1. Fish meal control (S0=G0) was not supplemented with phytase. Result showed that 250 FTU/g phytase showed the highest mean weight gain for both plants. In conclusion, the research has shown that the chemical nature of phytate, rather than its concentration and location, may influence the utilization of phosphorus for optimum growth in the fish by supplementing 250 FTU/g, with a range of available phosphorus requirement of between 0.75% (Y = 0.363 + 4.155X - 2.772X2, R2 = 0.759) and 0.80% (Y = 0.307 + 3.303X - 2.059X2, R2= 0.210)