Miracle-dz/UniFAR-FTUs huggingface dataset:
Individual Locating of Soccer Players from a Single Moving View

Positional data in team sports is key in evaluating the players’ individual and collective performances. When the sole source of data is a broadcast-like video of the game, an efficient video tracking method is required to generate this data. This article describes a framework that extracts individual soccer player positions on the field. It is based on two main components. As in broadcast-like videos of team sport games, the camera view moves to follow the action and a sport field registration method estimates the homography between the pitch and the frame space. Our method estimates the positions of key points sampled on the pitch thanks to an encoder–decoder architecture. The attention mechanisms of the encoder, based on a vision transformer, captures characteristic pitch features globally in the frames. A multiple person tracker generates tracklets in the frame space by associating, with bipartite matching, the player detections between the current and the previous frames thanks to Intersection-Over-Union and distance criteria. Tracklets are then iteratively merged with appearance criteria thanks to a re-identification model. This model is fine-tuned in a self-supervised way on the player thumbnails of the video sample to specifically recognize the fine identification details of each player. The player positions in the frames projected by the homographies allow the obtaining of the real position of the players on the pitch at every moment of the video. We experimentally evaluate our sport field registration method and our 2D player tracker on public datasets. We demonstrate that they both outperform previous works for most metrics. Our 2D player tracker was also awarded first place at the SoccerNet tracking challenge in 2022 and 2023.
Applications of Tumor Cells in an In Vitro 3D Environment

Spherical, multicellular aggregates of tumor cells, or three-dimensional (3D) tumor models, can be grown from established cell lines or dissociated cells from tissues in a serum-free medium containing appropriate growth factors. Air–liquid interfaces (ALIs) represent a 3D approach that mimics and supports the differentiation of respiratory tract and skin 3D models in vitro. Many 3D tumor cell models are cultured in conjunction with supporting cell types, such as fibroblasts, endothelial cells, or immune cells. To further mimic the in vivo situation, several extracellular matrix models are utilized to support tumor cell growth. Scaffolds used for 3D tumor cell culture growth include both natural and synthetic hydrogels. Three-dimensional cell culture experiments in vitro provide more accurate data on cell-to-cell interactions, tumor characteristics, drug discovery, metabolic profiling, stem cell research, and diseases. Moreover, 3D models are important for obtaining reliable precision data on therapeutic candidates in human clinical trials before predicting drug cytotoxicity. This review focuses on the recent literature on three different tissue types of 3D tumor models, i.e., tumors from a colorectal site, prostate, and skin. We will discuss the establishment of 3D tumor cell cultures in vitro and the requirement for additional growth support.
Covid Prediction via X-Ray Images

: The situation of novel corona virus (also known as COVID 19) is already on its peak, impacting more than 216 countries across the world. Infecting nearly 62 million people and killing over 1 million people. The worst struck countries are USA, India, Brazil, Russia and many European countries like France, Spain, UK and Italy. The virus is spreading at a very high rate and increasing its impact day by day. Many countries are facing the Third waves of the virus. It's been more than a year, since the first case of the virus was reported back in China (Wuhan province) in November 2019. The top vaccine/drugs producing and manufacturing companies working hard to develop an effective vaccine against the virus. The companies like Pfizer from US ad Pfizer from Germany have already started the trials and stated that their vaccines are more than 90 and 95 percent effective. The vaccines will surely be available in the upcoming year i.e., 2021.The research will also include the prediction of COVID 19 using X RAY scans. The model will predict, whether a person is infected with the virus or not, using his/her X RAY scan.
Review of Modeling, Modulation, and Control Strategies for the Dual-Active-Bridge DC/DC Converter

This paper provides a comprehensive review of the existing research on the Dual Active Bridge (DAB) DC-DC converter, focusing on modeling methods, modulation strategies, optimization algorithms, and control methods. A comparative analysis of selected methods along with guidelines to assist engineers and researchers in their study of DAB is also presented. Firstly, a comprehensive review of modulation strategies for DAB is provided, ranging from classical phase-shift modulation to the popular asymmetric duty modulation. The intrinsic relationships among different modulation methods are summarized, and a comparison is made based on the difficulty of control and DAB operating characteristics. Secondly, the various modeling methods for DAB are described, including reduced-order modeling, generalized state-space averaging modeling, and discrete-time modeling methods. A comparison is made based on the suitability for different application scenarios, providing recommendations for the adoption of different modeling methods. Furthermore, a survey of optimization algorithms for modulation methods is presented, including classical algorithms, swarm intelligence optimization, and reinforcement learning algorithms. A number of criteria are proposed for different algorithms, and an analysis of the unresolved challenges and future prospects is provided. Finally, the advanced control methods for DAB are summarized based on control effectiveness and applicability. The article concludes with a summary and an outlook on future research directions is also provided.
Multi-temporal Pacific madrone leaf blight assessment with unoccupied aircraft systems

Pacific madrone leaf blight (PMLB) is a contributing agent to the decline of Pacific madrone (Arbutus menziesii) trees. Multiple fungal pathogens cause PMLB, resulting in leaf spotting that can eventually kill leaves, increasing stress in individuals, and leaving them more susceptible to deadly cankers. Spores transmit via air and water droplets, particularly during wet Spring months. Unoccupied aircraft systems (UAS) technologies are in their relative infancy, but UAS are becoming more affordable and accessible. UAS promise increased efficiency in forest health monitoring applications, providing a safer aerial data collection method at a relatively-low cost when compared to occupied aircraft. In this study, we develop and present a UAS methodology to detect PMLB with a multispectral sensor. This methodology combines orthomosaic products derived from high-resolution (~4 cm) multirotor platform UAS multispectral imagery with machine learning and ground assessment of PMLB to classify visual presence of blight at the individual tree-level during multiple site revisits. The resulting model detected PMLB infection status of 29 field surveyed madrone trees with a kappa coefficient of , a balanced accuracy of 0.85, and a true positive rate of 0.92. The method presented here can be readily scaled to efficiently cover a much larger extent with a beyond-line-of-site capable UAS and minimal field sampling. The increased efficiency of this approach may be critical to characterizing PMLB in the near future as it is anticipated that PMLB prevalence will continue to increase as a result of climate change.
Pattern Recognition Spiking Neural Network for Classification of Chinese Characters

The Spiking Neural Networks (SNNs) are biologically more realistic than other types of Artificial Neural Networks (ANNs), but they have been much less utilised in applications. When comparing the two types of NNs, the SNNs are considered to be of lower latency, more hardware-friendly and energy-efficient, and suitable for running on portable devices with weak computing performance. In this paper we aim to use an SNN for the task of classifying Chinese character images, and test its performance. The network utilises inhibitory synapses for the purpose of using unsupervised learning. The learning algorithm is a derivative of the traditional Spike-timing-dependent Plasticity (STDP) learning rule. The input images are first pre-processed by traditional methods (OpenCV). Different hyperparameters configurations are tested reaching an optimal configuration and a classification accuracy rate of 93%.
Comparative performance of high yielding crossbred dairy cows reared at different farms level in Chattogram metropolitan area of Bangladesh

Progress of production in dairy cattle is frequently considered possible by either improving the genetic merit and/or by husbandry practices. The study aims to evaluate of productive and reproductive performance of dairy cows considering genotype at different selected dairy farms in Chattogram metropolitan area. A total of 80 crossbred dairy cows belong to four different genetic groups eg. Friesian × Local, Friesian × Jersey, Friesian × Sahiwal (25%) and Friesian × Sahiwal (50%) were randomly selected and their information regarding milk yield and other reproductive parameters were collected from farm record for a period of last five years (January 2015 to March 2019). The overall age at 1st heat and age at 1st calving differ significantly (p0.05) was found in case of service per conception and calving interval. A significant difference (p
The Effectiveness of Adductor Canal Block Compared to Femoral Nerve Block on Readiness for Discharge in Patients Undergoing Outpatient Anterior Cruciate Ligament Reconstruction: A Multi-Center Randomized Clinical Trial

This study evaluated the effect of adductor canal block (ACB) versus femoral nerve block (FNB) on readiness for discharge in patients undergoing outpatient anterior cruciate ligament (ACL) reconstruction. We hypothesized that ACB would provide sufficient pain relief while maintaining motor strength and safety, thus allowing for earlier discharge. This was a randomized, multi-center, superiority trial. From March 2014 to July 2017, patients undergoing ACL reconstruction were enrolled. The primary outcome was the difference in readiness for discharge, defined as Post-Anesthetic Discharge Scoring System score ≥ 9. Twenty-six patients were allocated to FNB and twenty-seven to ACB. No difference in readiness for discharge was found (FNB median 1.8 (95% CI 1.0 to 3.5) vs. ACB 2.9 (1.5 to 4.7) hours, p = 0.3). Motor blocks and (near) falls were more frequently reported in patients with FNB vs. ACB (20 (76.9%) vs. 1 (3.7%), p < 0.001, and 7 (29.2%) vs. 1 (4.0%), p = 0.023. However, less opioids were consumed in the post-anesthesia care unit for FNB (median 3 [0, 21] vs. 15 [12, 42.5] oral morphine milligram equivalents, p = 0.004) for ACB. Between patients with FNB or ACB, no difference concerning readiness for discharge was found. Despite a slight reduction in opioid consumption immediately after surgery, FNB demonstrates a less favorable safety profile compared to ACB, with more motor blocks and (near) falls.
Periodic Variable Star Classification with Deep Learning: Handling Data Imbalance in an Ensemble Augmentation Way

Time-domain astronomy is progressing rapidly with the ongoing and upcoming large-scale photometric sky surveys led by the Vera C. Rubin Observatory project (LSST). Billions of variable sources call for better automatic classification algorithms for light curves. Among them, periodic variable stars are frequently studied. Different categories of periodic variable stars have a high degree of class imbalance and pose a challenge to algorithms including deep learning methods. We design two kinds of architectures of neural networks for the classification of periodic variable stars in the Catalina Survey’s Data Release 2: a multi-input recurrent neural network (RNN) and a compound network combing the RNN and the convolutional neural network (CNN). To deal with class imbalance, we apply Gaussian Process to generate synthetic light curves with artificial uncertainties for data augmentation. For better performance, we organize the augmentation and training process in a “bagging-like” ensemble learning scheme. The experimental results show that the better approach is the compound network combing RNN and CNN, which reaches the best result of 86.2% on the overall balanced accuracy and 0.75 on the macro F1 score. We develop the ensemble augmentation method to solve the data imbalance when classifying variable stars and prove the effectiveness of combining different representations of light curves in a single model. The proposed methods would help build better classification algorithms of periodic time series data for future sky surveys (e.g., LSST).
Enterobacter Strain IPPBiotE33 Displays a Synergistic Effect with Bacillus thuringiensis Bt185

The discovery and isolation of new non-Bt insecticidal bacteria and genes are significant for the development of new biopesticides against coleopteran pests. In this study, we evaluated the insecticidal activity of non-Bt insecticidal bacteria, PPBiotE33, IPPBiotC41, IPPBiotA42 and IPPBiotC43, isolated from the peanut rhizosphere. All these strains showed insecticidal activity against first- and third-instar larvae of Holotrichia parallela, Holotrichia oblita, Anomala corpulenta and Potosia brevitarsis. IPPBiotE33 showed the highest toxicity among the four strains and exhibited virulence against Colaphellus bowringi. The genome of IPPBiotE33 was sequenced, and a new protein, 03673, with growth inhibition effects on C. bowringi was obtained. In addition, IPPBiotE33 had a synergistic effect with Bacillus thuringiensis Bt185 against H. parallela in bioassays and back-inoculation experiments with peanut seedlings. IPPBiotE33 induced a decrease in hemocytes and an increase in phenol oxidase activity in H. parallela hemolymph, known as the immunosuppressive effect, which mediated synergistic activity with Bt185. This study increased our knowledge of the new insecticidal strain IPPBiotE33 and shed new light on the research on new insecticidal coaction mechanisms and new blended pesticides.
Feature Extraction Methods for Binary Code Similarity Detection Using Neural Machine Translation Models

Binary code similarity detection is an effective analysis technique for vulnerability, bug, and plagiarism detection in software for which the source code cannot be obtained. The recent proliferation of IoT devices has also increased the demand for similarity detection across different architectures. However, there are currently not many examples of feature extraction methods using neural machine translation (NMT) models being applied to similarity detection in basic block units across different architectures. In this research, we propose new methods that extract features at a higher speed and detect similarities across different architectures with higher accuracy than existing methods for basic block feature extraction using neural machine translation models. We assume that the intermediate representation of the NMT model, which learned the translation of basic blocks across different architectures, includes the semantics of the instructions in the basic block. Hence we adopted the intermediate representation as the features of the basic blocks. Then, we applied the linear transformation used in bilingual word embedding to match the embedding space of basic blocks across different architectures. This enables the similarity detection in basic block units across different architectures with higher accuracy than the distance learning method used in existing research to match the embedding space. In the evaluation experiment, we compare the Precision at k (P@k) on the same dataset with existing research methods and our method achieved the highest accuracy of 92%. In addition, We also compare the time required for feature extraction using GPUs, and found that it was up to 16 times faster.
Rebuilding state and nation in Solomon Islands: policy options for the regional assistance mission

DAVID HEGARTY, RON MAY, ANTHONY REGAN, SINCLAIR DINNEN, HANK NELSON & RON DUNCAN This Discussion Paper was written as a contribution to policy deliberations on how the ‘Regional Assistance Mission to Solomon Islands’ might assist the people and government of Solomon Islands rebuild their state and nation after years of divisive and debilitating internal conflict. Issues raised in the paper will have relevance for policy-makers, donor agencies and non-government organisations.
Receptive Vocabulary Size of Omani Post-Basic Education Graduates

This descriptive study examined the receptive vocabulary size of Omani post-basic education graduates. Also, it investigated the relationship between receptive vocabulary size and language proficiency among these students. Finally, it examined differences in receptive vocabulary size with respect to gender. The sample consisted of 294 Omani post-basic education graduates (183 males and 111 females) who were enrolled in foundation programs at Sultan Qaboos University. The study instrument was the Vocabulary Level Test Version B, which was developed by Nation (1983) and revised by Schmitt, Schmitt, and Clapham (2001). The study found that the Omani post-basic graduates had a small receptive vocabulary of approximately 1,725 word families. It also found a statistically significant relationship between students’ receptive vocabulary size and their language proficiency. Finally, it revealed statistically significant differences in receptive vocabulary size with respect to gender in favor of female students. Accordingly, the researcher provides recommendations for practice and further research.
Automatic Trajectory Determination in Automated Robotic Welding Considering Weld Joint Symmetry

The field of inspection for welded structures is currently in a state of rapid transformation driven by a convergence of global technological, regulatory, and economic factors. This evolution is propelled by several key drivers, including the introduction of novel materials and welding processes, continuous advancements in inspection technologies, innovative approaches to weld acceptance code philosophy and certification procedures, growing demands for cost-effectiveness and production quality, and the imperative to extend the lifespan of aging structures. Foremost among the challenges faced by producers today is the imperative to meet customer demands, which entails addressing both their explicit and implicit needs. Furthermore, the integration of emerging materials and technologies necessitates the exploration of fresh solutions. These solutions aim to enhance inspection process efficiency while providing precise quantitative insights into defect identification and location. To this end, our project proposes cutting-edge technologies, some of which have yet to gain approval within the sector. Noteworthy among these innovations is the integration of vision systems into welding robots, among other solutions. This paper introduces a groundbreaking algorithm for tool path selection, leveraging profile scanning and the concept of joint symmetry. The application of symmetry principles for trajectory determination represents a pioneering approach within this expansive field.
New Insights into the Applications of 3D-Printed Biomaterial in Wound Healing and Prosthesis

Recently three-dimensional bioprinting (3D-bioP) has emerged as a revolutionary technique for numerous biomedical applications. 3D-bioP has facilitated the printing of advanced and complex human organs resulting in satisfactory therapeutic practice. One of the important biomedical applications of 3D-bioP is in tissue engineering, wound healing, and prosthetics. 3D-bioP is basically aimed to restore the natural extracellular matrix of human’s damage due to wounds. The relevant search was explored using various scientific database, viz., PubMed, Web of Science, Scopus, and ScienceDirect. The objective of this review is to emphasize interpretations from the pre-executed studies and to assess the worth of employing 3D-bioP in wound healing as well as prosthetics in terms of patient compliance, clinical outcomes, and economic viability. Furthermore, the benefits of applying 3D-bioP in wound healing over traditional methods have been covered along with the biocompatible biomaterials employed as bioinks has been discussion. Additionally, the review expands about the clinical trials in 3D-bioP field, showing promise of biomedical applicability of this technique with growing advancement in recent years.
Efficacy of nonpharmacological interventions targeting social function in children and adults with autism spectrum disorder: A systematic review and meta-analysis

Background and aims This paper aimed to evaluate the use of nonpharmacological interventions for the management of autism spectrum disorder (ASD). The effects of acupuncture and behavioural therapy, two nonpharmalogical interventions, on social function in ASD patients are still controversial. This meta-analysis investigated the impact of these two treatments and compared their effects. Methods Seven electronic databases were systematically searched to identify randomized controlled trials (RCTs) on the use of acupuncture or behavioural therapy for ASD. A meta-analysis was carried out using Review Manager 5.4 software. Continuous data are reported as mean differences (MDs) or standardized mean differences (SMDs) with 95% confidence intervals (CIs). An assessment of methodological quality using the Cochrane risk-of-bias (ROB) tool for trials was carried out. The Grading of Recommendation Assessment, Development, and Evaluation (GRADE) was applied to evaluate the quality (certainty) of evidence for results regarding social function indicators. Results Thirty RCTs on acupuncture and 36 on behavioural therapy were included. Compared with the control condition, body acupuncture (SMD: 0.76, 95% CI: [0.52, 1.01]; low certainty), modern acupuncture technology (SMD: 0.84, 95% CI: [0.32, 1.35]; low certainty), cognitive behavioural therapy (SMD: 0.42, 95% CI: [0.26, 0.58]; high certainty), the Denver model (SMD: 0.61, 95% CI: [0.23, 0.99]; moderate certainty) and social skills training (SMD: 0.56, 95% CI: [0.41, 0.71]; moderate certainty) improved social functioning. Conclusion Behavioural therapies (such as CBT, the Denver model, social skills training), improved the social functioning of patients with ASD in the short and long term, as supported by high- and moderate-quality evidence. Acupuncture (including scalp acupuncture, body acupuncture and use of modern acupuncture technology) also improved social functioning, as supported by low- and very low-quality evidence. More high-quality evidence is needed to confirm the effect of acupoint catgut embedding and Early Intensive Behavioural Intervention (EIBI).