Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Learning of Failure Prevention Policies

In order to ensure safety in robot manipulation, it is essential for robots to experience potential failures that may give rise to unsafe situations. Experiencing numerous potentially unsafe situations in the real world can be both unsafe and inefficient. Hence, experiencing these unsafe situations in a simulated environment is much more convenient. In this work, we propose an adversarial reinforcement learning system that includes adversary components to learn safe manipulations. Adversarial policies that generate failures are learned using reinforcement learning. Then, these adversarial policies are used to increase the risks of failures and to learn a robust protagonist policy that reduces the risk of failures in an adversarial reinforcement learning framework. The proposed system is tested on a humanoid robot that stirs the contents of a bowl. Failures of overturning the bowl, sliding the bowl, and spilling contents from the bowl that occur in this scenario are produced and a protagonist policy that prevents these failures is learned. The results demonstrate that learning the protagonist policy with the proposed system enhances the safety of robot manipulation effectively.
Impact of HR-HPV infection on oncological outcomes in early cervical cancer

Background This study aimed to investigate the differences in long-term oncological outcomes between high-risk human papillomavirus (HR-HPV) negative and HR-HPV positive early-stage cervical cancers. Methods We retrospectively analysed 2061 cases of early-stage cervical cancer from the Chinese cervical cancer clinical diagnosis and treatment database. Kaplan-Meier curves were used to describe the survival outcomes of different HR-HPV infections. Cox proportional hazard regression model was used to analyze and determine independent risk factors. Results K-M analysis revealed no significant difference in 5-year OS between HR-HPV negative and HR-HPV positive groups (OS: 95.0% vs.95.6%, P=0.900). A significant difference was observed in 5-year DFS between the HR-HPV negative and HR-HPV positive groups (DFS: 87.2% vs.91.9%, P=0.025). Cox proportional hazard regression model indicated that HR-HPV infection (negative vs. positive) was an independent factor influencing 5-year DFS after early cervical cancer surgery (DFS: hazard ratio [HR]=1.862, P=0.022). HR-HPV infection (negative vs positive) was not an independent factor influencing 5-year OS after early cervical cancer surgery (OS: P=0.813). After 1:1 PSM pairing, there was no significant difference in 5-year OS and DFS between HR-HPV negative group and HR-HPV positive group (OS: 91.6% vs.95.0%, P=0.297; DFS: 87.2% vs.85.1%, P=0.758). Cox multivariate analysis indicated that HR-HPV infection was not an independent factor influencing 5-year OS and DFS after early cervical cancer surgery (OS: P=0.806, DFS: P=0.251). Conclusions The tumour results of HR-HPV negative group and HR-HPV positive group were similar, after eliminating the differences in known variables that affect the oncological outcomes of cervical cancer. The treatment plan of HR-HPV positive cervical cancer is suitable for HR-HPV negative cervical cancer.
A Meshless Method for Solving Volume Integral Equations with Complex Materials

Meshless or meshfree methods use discrete points to replace meshes in the geometric description of solution domains and this replacement can significantly reduce the cost of discretizing geometries. The meshless methods are also desirable for solving electromagnetic (EM) problems but they have not received a sufficient attention. In this work, we propose a truly-meshless method for solving EM volume integral equations (VIEs) with complex materials which have not been addressed before. The method transforms the volume integrals of VIEs into the boundary or surface integrals based on Green-Gauss theorem so that volume integrals and volumetric discretization can be fully removed. To facilitate the calculation of boundary integrals, the original domain occupied by the object is expanded to a cylinder or cube circumscribing the object and the involved integrands are redefined. The singular integrals are excluded from the transformation and they are individually evaluated by a singularity treatment technique. A numerical example is presented to demonstrate the method and its good performance has been validated.
Orientation and Location Tracking of XR Devices: 5G Carrier Phase-Based Methods

Accurate knowledge of the three-dimensional (3D) orientations and 3D locations of the user devices, such as wearable glasses, is of paramount importance in different extended reality (XR) use cases and applications. In this article, we address the corresponding six degrees-of-freedom (6DoF) tracking challenge of 5G-empowered XR devices. We describe a new uplink (UL) carrier phase measurements based estimation approach, allowing for low-latency 3D orientation and 3D location tracking directly at the 5G network base-stations or gNodeBs (gNBs). extended Kalman filter (EKF) based practical signal processing algorithms are described while also the applicable Cramér-Rao lower-bounds (CRLBs) are derived and presented. Also, the related aspect of over-the-air estimation of the XR headset antenna constellation or antenna geometry is addressed. Additionally, the important practical challenges related to user equipment (UE) clock drifting as well as integer ambiguities in carrier phase based methods are both considered. Finally, an extensive set of numerical results is provided in an example indoor factory like environment, covering both 3.5 GHz and 28 GHz network deployments. The obtained results demonstrate the feasibility of continuous 6DoF tracking through the proposed approach, with root mean squared error (RMSE) accuracies below one degree for the 3D orientation and below one centimeter for the 3D location, respectively. The results also demonstrate that UE clock drifting and carrier phase integer ambiguities can both be efficiently estimated and tracked, as part of the overall proposed concept and methods.
Optimal Tuning of PID Controller for Boost Converter using Meta-Heuristic Algorithm for Renewable Energy Applications

The Dynamic Levy Flight Chimp optimisation (DLFC) method is used in this study to optimise the Proportional- Integral-Derivative (PID) Controller for the Boost converter. As a possible application, the tuned PID controller is utilised to adjust voltages in the use of renewable power sources. The maximum power point tracking control approach based on machine learning (ML) is used to anticipate the reference voltages for the solar system based on the irradiance and the ambient temperature. The tuned PID controller uses this reference signal to regulate the maximum power point (MPP) voltages. To finetune the PID controller, comparisons are done with grey wolf optimiser (GWO), Harris hawk optimisation algorithms (HHO), and particle swarm optimisation (PSO) algorithms. The tuned PID controller has fewer oscillations and requires little tracking time to adapt to changing load and environment conditions. Additionally, statistical analysis, such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) between the reference voltage and the output voltage, is presented. Since the DLFC tuned PID controller performs better than HHO, GWO, and PSO in terms of RMSE and MAE, it may be a promising way for optimising PID controller tuning for boost converters in photovoltaic (PV) system applications.
Classification of Heart Sounds Using Machine Learning

Artificial intelligence has become an important tool in the domain of medicine. This includes providing care to those with limited access or improving the accuracy of diagnosis. In this project a machine learning model was developed for the classification of heart sounds as either normal or abnormal. This model could be used with an electronic stethoscope to analyze a patient’s heart sounds and provide assistance to clinicians. The model was developed using the Random Forest classifier and uses the PhysioNet database. The final model could classify normal and abnormal heart sounds with an F1 score of 0.8773. Important features to the classification included Age, Murmurs, Gender and # Beats.
From Traditional to Reformed: A Review of the Land Use Regulations in the Nation’s 50 Largest Metropolitan Areas

Local land use regulations aid in the definition of the character of towns, cities, counties, and entire regions. Infrastructure control, zoning, urban containment, comprehensive plans, permit caps, and building moratoriums can promote density, drive development outward, or something in between. They can also directly affect the composition of residents by facilitating low-income residents and rental properties, especially when these regulations go along with programs to promote affordable housing. This comprehensive survey of local land use regulations finds a wide variety of regulatory types. It classifies them in four broad typologies, across the nation’s 50 largest metropolitan areas. These types range from restrictive and exclusionary to accommodating and innovative. The regulations produce a variety of effects on metropolitan density and growth, and on the opportunities afforded to the inhabitants of the areas.
Explorer A multistage sequencing strategy pinpoints many novel and candidate disease alleles for orphan Disease Emery-Dreifuss Muscular Dystrophy and Supports Gene Misregulation as its Pathomechanism

A B S T R A C T Background: As genome-wide approaches prove dif ﬁ cult with genetically heterogeneous orphan diseases, we developed a new approach to identify candidate genes. We applied this to Emery-Dreifuss muscular dystrophy (EDMD), characterised by early onset contractures, slowly progressive muscular wasting, and life-threat- ening heart conduction disturbances with wide intra- and inter-familial clinical variability. Roughly half of EDMD patients are linked to six genes encoding nuclear envelope proteins, but the disease mechanism remains unclear because the affected proteins function in both cell mechanics and genome regulation. Methods: A primer library was generated to test for mutations in 301 genes from four categories: (I) all known EDMD-linked genes; (II) genes mutated in related muscular dystrophies; (III) candidates generated by exome sequencing in ﬁ ve families; (IV) functional candidates — other muscle nuclear envelope proteins functioning in mechanical/genome processes affected in EDMD. This was used to sequence 56 unlinked patients with EDMD-like phenotype. Findings: Twenty-one patients could be clearly assigned: 18 with mutations in genes of similar muscular dystrophies; 3 with previously missed mutations in EDMD-linked genes. The other categories yielded novel can- didate genes, most encoding nuclear envelope proteins with functions in gene regulation. Interpretation: Our multi-pronged approach identi ﬁ ed new disease alleles and many new candidate EDMD genes. Their known functions strongly argue the EDMD pathomechanism is from altered gene regulation and mechanotransduction due to connectivity of candidates from the nuclear envelope to the plasma membrane. This approach highlights the value of testing for related diseases using primer libraries and may be applied for other genetically heterogeneous orphan diseases. Funding: The Wellcome Trust, Muscular Dystrophy UK, Medical Research Council, European Community ’ s Seventh Framework Programme “ Integrated European (cid:1) omics research project for diagnosis and therapy in rare neuromuscular and neurodegenerative diseases (NEUROMICS) ” .
Réponse des processus biochimiques d’une tourbière soumise à des fluctuations du niveau d’eau

Les tourbieres remplissent une importante fonction de regulation des cycles biogeochimiques. Elles sont tres sensibles aux modifications du regime hydrologique qui resultent du climat ou de pressions anthropiques. Le travail presente dans cette these applique une approche interdisciplinaire en combinant hydrologie, geophysique, hydrochimie, biologie et modelisation numerique. Le site d'investigation est une prairie tourbeuse situee dans la presqu'ile du Cotentin, en Normandie. L'approche par datation des eaux souterraines a permis d'acquerir une comprehension conceptuelle de l'aquifere. La modelisation met en evidence les interactions entre l'aquifere et la zone humide et predit un impact important d'un accroissement du pompage et du changement climatique sur les niveaux d'eau pouvant provoquer un assechement de la tourbe et sur les debits des cours d'eau. L'etude confirme que la vulnerabilite biochimique de la tourbe aux changements environnementaux, tel que l'etat d'aeration, est tres dependante de la qualite de la tourbe. Une tourbe plus degradee, ou les teneurs en nutriments et biomasse sont plus faibles, est plus sensible aux processus d'oxydation, qui entrainent une acidification importante, un relargage de sulfates et un lessivage de cations. Les resultats confirment que des conditions anaerobies doivent etre favorisees dans la gestion des tourbieres, mais qu'une legere fluctuation des niveaux n'altere pas son fonctionnement.
Prevalence, Risk Factors, Prognosis, and Management of Pericardial Effusion in COVID-19

Background: There is limited data in the literature about the clinical importance and prognosis of pericardial effusion (PE) in patients discharged after recovering from COVID-19, but large-scale studies have yet to be available. This study investigated the prevalence, risk factors, prognosis, late clinical outcomes, and management of PE in COVID-19. Materials and Methods: Between August 2020 and March 2021, 15,689 patients were followed up in our pandemic hospital due to COVID-19. Patients with positive polymerase chain reaction (PCR) test results and PE associated with COVID-19 in computed tomography (CT) were included in the study. The patients were divided into three groups according to PE size (mild, moderate, and large). Transthoracic echocardiography (TTE) records, laboratory data, clinical outcomes, and medical treatments of patients discharged from the hospital were retrospectively reviewed. Results: According to the PE size (mild, moderate, large) of 256 patients with PE at admission or discharge, the mean age was 62.17 ± 16.34, 69.12 ± 12.52, and 72.44 ± 15.26, respectively. The mean follow-up period of the patients was 25.2 ± 5.12 months. Of the patients in the study population, 53.5% were in the mild group, 30.4% in the moderate group, and 16.1% in the large group. PE became chronic in a total of 178 (69.6%) patients at the end of the mean three months, and chronicity increased as PE size increased. Despite the different anti-inflammatory treatments for PE in the acute phase, similar chronicity was observed. In addition, as the PE size increased, the patients’ frequency of hospitalization, complications, and mortality rates showed statistical significance between the groups. Conclusions: The clinical prognosis of patients presenting with PE was quite poor; as PE in size increased, cardiac and noncardiac events and mortality rates were significantly higher. Patients with large PE associated with COVID-19 at discharge should be monitored at close intervals due to the chronicity of PE and the increased risk of tamponade.
Association Of GSTM1, GSTT1 And GSTP1 Polymorphisms With Breast Cancer Among Jordanian Women

Purpose Genetic predisposition to disease has become one of the most investigated risk factors in recent years, and breast cancer (BC) is no exception. In this study, we investigated specific genetic variants of three candidate genes belonging to the glutathione-S-transferase superfamily that have been implicated in increased risk of cancers. Materials and methods This case-control study comprised 241 Jordanian women who were diagnosed with BC in addition to 219 matched controls. Gel electrophoresis of PCR products was used to visualize and genotype both the GSTM1 and GSTT1 genes, while PCR-RFLP was employed to genotype the rs1695 of the GSTP1 gene. Results Our findings did not reveal any correlation between the investigated polymorphisms of GST genes and BC risk among Jordanian women. Otherwise, the combination of GSTM1 entire gene deletion and (GG) genotype of GSTP1 polymorphism (rs1695) was significantly associated with BC with p-value <0.05 (i.e. p-value was not significant after correcting for multiple comparison). Conclusion We suggest that the interaction between GSTM1 polymorphism and rs1695 of GSTP1 may influence BC development and progression among Jordanian women. More epidemiological studies are needed to provide a baseline for the underlying role of GSTs polymorphisms in tumorigenesis.
Machine Learning Controller for Optimised Charging in Solar Power Fed EV Battery Swapping Stations

In this era of electric vehicles, battery swapping stations (BSS) have a significant role as they help EV owners and station operators by providing fast, reliable, and convenient solutions to overcome driving range anxiety, high charging time, and high cost of electric vehicles. If these battery swapping stations are fed with renewable energy sources, and an optimized charging infrastructure is integrated to it, this can be made zero-carbon, reliable, convenient, and economical for EV users and station operators. In this work, the authors designed and analyzed a solar power fed battery swapping station, with pattern recognition network-based predictor and controller for charging its batteries. An open dataset collected from the Georgia Tech University campus is selected for the case study. Simulation analysis is carried out using PVSyst, Homer grid, MATLAB/ Simulink, and the results show 83% accuracy in predicting healthy charging rates of the batteries.
Episodes and Topics in Multivariate Temporal Data

The term ‘episode’ refers to a time interval in the development of a dynamic process or behaviour of an entity. Episode‐based data consist of a set of episodes that are described using time series of multiple attribute values. Our research problem involves analysing episode‐based data in order to understand the distribution of multi‐attribute dynamic characteristics across a set of episodes. To solve this problem, we applied an existing theoretical model and developed a general approach that involves incrementally increasing data abstraction. We instantiated this general approach in an analysis procedure in which the value variation of each attribute within an episode is represented by a combination of symbols treated as a ‘word’. The variation of multiple attributes is thus represented by a combination of ‘words’ treated as a ‘text’. In this way, the the set of episodes is transformed to a collection of text documents. Topic modelling techniques applied to this collection find groups of related (i.e. repeatedly co‐occurring) ‘words’, which are called ‘topics’. Given that the ‘words’ encode variation patterns of individual attributes, the ‘topics’ represent patterns of joint variation of multiple attributes. In the following steps, analysts interpret the topics and examine their distribution across all episodes using interactive visualizations. We test the effectiveness of the procedure by applying it to two types of episode‐based data with distinct properties and introduce a range of generic and data type‐specific visualization techniques that can support the interpretation and exploration of topic distribution.
Analytical Model for HF-Losses Caused by 2D Magnetic Fields in Litz Wire

The 2D magnetic field effect arises when the winding height is lower than the window height due to isolation requirements and mechanical constraints. In this situation, most analytical models for calculating HF-losses in Litz wire fail to predict the winding losses accurately since they are normally based on a 1D magnetic field assumption. Although numerical or semi-numerical methods can accurately calculate the winding losses, they are too time-consuming to be integrated into the converter optimization routines. This article provides a new loss model, which is validated to have a much lower error (<10%) than 1D field loss models (up to −45%) by numerical calculations and experiments. The model is presented for two winding transformers with layered windings and non-gapped cores with high permeability in the frequency range, where the strand diameter to skin depth ratio is equal to 1.2. Moreover, the model can also be extended for interleaved windings by separating the windings to multiple blocks. Furthermore, the computation time is less than 300µs using the algorithm provided in this article. This new model achieves a good balance between accuracy and computational time and has great potential to improve the transformer as well as the converter design.
Estimation of the Leaf Area Index of Winter Rapeseed Based on Hyperspectral and Machine Learning

Leaf area index (LAI) is essential for evaluating crop growth and development. Destructive manual measurement methods mainly achieve traditional crop LAI acquisition. Due to the advantages of being fast and non-destructive, spectroscopy technology provides a feasible method for obtaining crop LAI. In order to achieve efficient acquisition of winter oilseed rape LAI, this study collected hyperspectral data and LAI data at the full-bloom stage of winter oilseed rape. It calculated the spectral indexes related to the LAI of the original spectrum and the first-order differential spectrum, respectively. The index with the highest correlation with the LAI of winter oilseed rape at the flowering stage was selected as the optimal spectral index for input. Subsequently, three machine learning methods, Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF), were used to construct the LAI model of winter oilseed rape, and the model was tested. The results show that the correlation coefficient between the spectral index calculated by the first-order differential processing of the original spectral data and the LAI of winter rapeseed is significantly improved compared with the original data. Among them, the spectral index NDVI with the best correlation coefficient with LAI can be obtained under the first-order differential: the correlation coefficient is 0.734, and the wavelength combination is 716 nm and 724 nm. At the same time, we found that when the input variables are the same, the RF model has higher estimation accuracy than the other models. The best estimation accuracy is obtained when the input variable is the first-order differential spectral index. The R2 of the model validation set is 0.810, RMSE is 0.455 cm2/cm2, MRE is 10.465%, and the model accuracy is high. The results of this study can provide a theoretical basis for crop monitoring based on spectral technology and provide a theoretical basis for crop growth.