Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Audio-Video based Defect Detection through Deep Learning in Railway Maintenance

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.
Understanding the Perseverance of the Muscular Dystrophy Community One-Year into the COVID-19 Pandemic.

INTRODUCTION In this study, we examined the long-term social and health impacts of the coronavirus disease 2019 (COVID-19) pandemic on people with muscular dystrophy. METHODS We modified our prior COVID-19 Impact Survey to assess impacts from the continuing pandemic using feedback from muscular dystrophy experts, patients, and advocacy group/registry representatives. The survey assessed COVID-19 medical history, and the effects of the pandemic on social aspects, muscle disease, and medical care. We also used the validated 10-item Perceived Stress Scale. The de-identified, electronic survey was distributed to adults with muscular dystrophy via international patient registries and advocacy group websites from February 8, 2021 to March 22, 2021. RESULTS Respondents (n = 1243 : 49% Facioscapulohumeral Muscular Dystrophy (FSHD); 43% Myotonic Dystrophy (DM), and 8% Limb-Girdle Muscular Dystrophy (LGMD)) were mostly women and middle-aged (range 18-90 years). Rates of COVID-19 infections were low at 8% with zero deaths. Reported recovery times were also short with only 9% reporting a recovery period greater than eight weeks, and 7% requiring hospitalization with one individual requiring a ventilator. Major challenges reported during the pandemic included: stress management, particularly for those with LGMD, (27%) and wearing a mask (24%). The majority reported a slight worsening in their disease state. Respondents reported moderate stress levels (stress score = 16.4; range = 0-39), with higher stress levels reported by women and those under age 30 years. Seventy-percent of participants who had telemedicine visits were satisfied with the encounters; however, most reported a preference for in-person visits. CONCLUSIONS People with MD found ways to manage their stress and overcome obstacles during the COVID-19 pandemic. COVID-19 infection rates and medical complications were similar to a general population. Telemedicine visits may have a more permanent role in care.
Sources of the Levantine Intermediate Water in winter 2019

Climatic changes and interannual variability in the Mediterranean overturning circulation are crucially linked to dense water formation in the Levantine Sea, namely the Levantine Intermediate Water whose formation zone, comprising multiple and intermittent sources, extends over fluctuating pathways. To probe into the variability of this water formation and spreading, a unique dataset was collected during the winter of 2019 in the western Levantine Sea
Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images

Chest X-ray (CXR) is widely used to diagnose conditions affecting the chest, its contents, and its nearby structures. In this study, we used a private data set containing 1630 CXR images with disease labels; most of the images were disease-free, but the others contained multiple sites of abnormalities. Here, we used deep convolutional neural network (CNN) models to extract feature representations and to identify possible diseases in these images. We also used transfer learning combined with large open-source image data sets to resolve the problems of insufficient training data and optimize the classification model. The effects of different approaches of reusing pretrained weights (model finetuning and layer transfer), source data sets of different sizes and similarity levels to the target data (ImageNet, ChestX-ray, and CheXpert), methods integrating source data sets into transfer learning (initiating, concatenating, and co-training), and backbone CNN models (ResNet50 and DenseNet121) on transfer learning were also assessed. The results demonstrated that transfer learning applied with the model finetuning approach typically afforded better prediction models. When only one source data set was adopted, ChestX-ray performed better than CheXpert; however, after ImageNet initials were attached, CheXpert performed better. ResNet50 performed better in initiating transfer learning, whereas DenseNet121 performed better in concatenating and co-training transfer learning. Transfer learning with multiple source data sets was preferable to that with a source data set. Overall, transfer learning can further enhance prediction capabilities and reduce computing costs for CXR images.
Prevalence and Risk Factors of Zoonotic Dermatophyte Infection in Pet Rabbits in Northern Taiwan

Dermatophytes are the group of keratinophilic fungi that cause superficial cutaneous infection, which traditionally belong to the genera Trichophyton, Microsporum, and Epidermophyton. Dermatophyte infection is not only a threat to the health of small animals, but also an important zoonotic and public health issue because of the potential transmission from animals to humans. Rabbit dermatophytosis is often clinically identified; however, limited information was found in Asia. The aims of this study are to investigate the prevalence and to evaluate the risk factors of dermatophytosis in pet rabbits in Northern Taiwan. Between March 2016 and October 2018, dander samples of pet rabbits were collected for fungal infection examination by Wood’s lamp, microscopic examination (KOH preparation), fungal culture, and PCR assay (molecular identification). Z test and Fisher’s exact test were performed to evaluate the potential risk factors, and logistic regression analysis was then performed to build the model of risk factors related to dermatophyte infection. Of the collected 250 dander samples of pet rabbits, 29 (11.6%) samples were positive for dermatophytes by molecular identification. In those samples, 28 samples were identified as the T. mentagrophytes complex and 1 sample was identified as M. canis. Based on the results of the Firth’s bias reduction logistic analyses, animal source (rabbits purchased from pet shops) and number of rearing rabbits (three rabbits or more) were shown as the main risks for dermatophyte infection in the pet rabbits in Taiwan. The results of the present study elucidate the prevalence of rabbit dermatophyte infection, pathogens, and risk factors in Taiwan, and provide an important reference for the prevention and control of rabbit dermatophytosis.
A Review on Security Issues and Solutions of the Internet of Drones

The Internet of Drones (IoD) has attracted increasing attention in recent years because of its portability and automation, and is being deployed in a wide range of fields (e.g., military, rescue and entertainment). Nevertheless, as a result of the inherently open nature of radio transmission paths in the IoD, data collected, generated or handled by drones is plagued by many security concerns. Since security and privacy are among the foremost challenges for the IoD, in this paper we conduct a comprehensive review on security issues and solutions for IoD security, discussing IoD-related security requirements and identifying the latest advancement in IoD security research. This review analyzes a host of important security technologies with emphases on authentication techniques and blockchain-powered schemes. Based on a detailed analysis, we present the challenges faced by current methodologies and recommend future IoD security research directions. This review shows that appropriate security measures are needed to address IoD security issues, and that newly designed security solutions should particularly consider the balance between the level of security and cost efficiency.
Coordinated Control for Performance Enhancement of an Islanded AC/DC Microgrid With Uninterrupted Power and Seamless Transition Capabilities

This article presents a simplistic control approach and multimode operation of a wind-solar-based ac/dc microgrid. The islanded microgrid consists of a doubly-fed induction generator (DFIG) for wind energy conversion, and a solar photovoltaic array for solar energy conversion. In this configuration, the stator terminals of DFIG are connected at the point of common intersection (PCI) of ac loads via a stator-side solid-state transition switch (SSSTS). The developed control strategy is designed to seamlessly disconnect and reconnect the DFIG stator from the PCI based on the SSSTS operation, without interrupting power to ac and dc loads in the microgrid. The disconnection of stator terminals is required in the event of a prolonged period of no wind generation from the DFIG. The controller is synthesized to ensure no or low transient effect during the transition between the two operating modes, i.e., DFIG stator connected mode (SCM) and stator disconnected mode (SDM). Further, the power quality enhancement of stator voltages and currents amidst nonideal ac loads is ensured without need for computationally exhaustive approaches involving sequence decompositions, notch filters, resonant regulators, or load current sensors. Moreover, the power quality enhancement at the DFIG stator is achieved without injecting any undue harmonics in the DFIG rotor currents. The effectiveness of this islanded microgrid is verified through obtained results from a developed laboratory prototype.
A Bidirectional Isolated Dual-Phase-Shift Variable-Frequency Series Resonant Dual-Active-Bridge GaN AC–DC Converter

This article presents a novel single-stage series resonant dual-active-bridge (SR-DAB) for grid-con- nected bidirectional ac–dc energy storage applications. The single-phase SR-DAB is connected to the grid with an unfolding bridge on the ac side. Three SR-DABs are utilized to connect to a three-phase grid. A control scheme that combines dual-phase shifts and variable frequency is proposed to achieve zero voltage switching and power factor correction over a wide range of ac and dc voltages and loads. A series of comparisons in terms of inductor root mean square current, turn-off current of all transistors are conducted at different loads to prove the advantage of the proposed converter over the traditional dual-active-bridge-based ac/dc converter. A GaN-based hardware prototype connecting the 480Y/240/208$\Delta$ three-phase grid to a 48-V battery was developed with a peak efficiency of 97.2%.
Logic and the comprehension of language

This thesis examines what is necessary to formally model a hearer’s comprehension of a natural language sentence. Our theory of comprehension should at least explain how different words within the same grammatical class make different contributions to the meaning of a sentence. And, our theory should explain how the “full propositional form” that a speaker communicates is recovered from the relatively semantically underspecified acoustic signal. A model is provided which achieves this. A speaker is said to understand an utterance by, first, choosing the maximally “relevant” full propositional semantic enrichment of the underspecified acoustic signal, measured according to a formally defined comparison operator, and, then, computing the inferences that follow from that chosen propositional form in conjunction with their individual word-/world-knowledge. This model of comprehension apparently makes comprehension relative to an individual’s idiosyncratic knowledge. So, I also discuss how conventionalized word-meanings co-ordinate individuals’ knowledges to allow successful interpersonal communication.
Source apportionment and risk assessment of trace element pollution in Yamuna river water in Delhi: a probability based approach

ABSTRACT This study investigate the sources, seasonal variation, and health risks of ten elements in water collected at ten sites in Yamuna, Delhi. Mean concentration of the elements ranged from 0.8 ± 0.8 µg/L for Co to 9455 ± 6379 µg/L for Fe, and followed in the order Co>Cd >As>Zn>Ni>Cu>Cr>Pb>Mn>Fe. Mean Cr and Pb levels exceed the WHO prescribed limit. Spatial variation was insignificant except for Mn. Seasonal variation of Pb, Cr, Fe, Zn, and As was significant, and in the order summer≥monsoon>winter. Cluster analysis identified five groups of elements. Principal component analysis indicates industrial, agricultural, and domestic wastewater effluents as the chief sources of the observed level of elements. Probability of non-carcinogenic health risk was moderate for Cr (69%), Mn (62%), As (44%), and Fe (29%) and high for Pb (96%). Cr, Ni, Cd, As and Pb pose carcinogenic risk with high probability (84–100%) except for Pb (4.8%).
Enhancing Soluble Expression of Phospholipase B for Efficient Catalytic Synthesis of L-Alpha-Glycerylphosphorylcholine

Phospholipase B (PLB) harbors three distinct activities with broad substrate specificities and application fields. Its hydrolyzing of sn-1 and sn-2 acyl ester bonds enables it to catalyze the production of L-alpha-glycerylphosphorylcholine (L-α-GPC) from phosphatidylcholine (PC) without speed-limiting acyl migration. This work was intended to obtain high-level active PLB and apply it to establish an efficient system for L-α-GPC synthesis. PLB from Pseudomonas fluorescens was co-expressed with five different molecular chaperones, including trigger factor (Tf), GroEL-GroES (GroELS), DnaK-DnaJ-GrpE (DnaKJE), GroELS and DnaKJE, or GroELS and Tf or fused with maltose binding protein (MBP) in Escherichia coli BL21(DE3) to improve PLB expression. PLB with DnaKJE-assisted expression exhibited the highest catalytic activity. Further optimization of the expression conditions identified an optimal induction OD600 of 0.8, IPTG concentration of 0.3 mmol/L, induction time of 9 h, and temperature of 25 °C. The PLB activity reached a maximum of 524.64 ± 3.28 U/mg under optimal conditions. Subsequently, to establish an efficient PLB-catalyzed system for L-α-GPC synthesis, a series of organic-aqueous mixed systems and surfactant-supplemented aqueous systems were designed and constructed. Furthermore, the factors of temperature, reaction pH, metal ions, and substrate concentration were further systematically identified. Finally, a high yield of 90.50 ± 2.21% was obtained in a Span 60-supplemented aqueous system at 40 °C and pH 6.0 with 0.1 mmol/L of Mg2+. The proposed cost-effective PLB production and an environmentally friendly PLB-catalyzed system offer a candidate strategy for the industrial production of L-α-GPC.
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

Abstract. Remotely sensed Earth observations have many missing values. The abundance and often complex patterns of these missing values can be a barrier for combining different observational datasets and may cause biased estimates of derived statistics. To overcome this, missing values in geoscientific data are regularly infilled with estimates through univariate gap-filling techniques such as spatial or temporal interpolation or by upscaling approaches in which complete donor variables are used to infer missing values. However, these approaches typically do not account for information that may be present in other observed variables that also have missing values. Here we propose CLIMFILL (CLIMate data gap-FILL), a multivariate gap-filling procedure that combines kriging interpolation with a statistical gap-filling method designed to account for the dependence across multiple gappy variables. In a first stage, an initial gap fill is constructed for each variable separately using state-of-the-art spatial interpolation. Subsequently, the initial gap fill for each variable is updated to recover the dependence across variables using an iterative procedure. Estimates for missing values are thus informed by knowledge of neighbouring observations, temporal processes, and dependent observations of other relevant variables. CLIMFILL is tested using gap-free ERA-5 reanalysis data of ground temperature, surface-layer soil moisture, precipitation, and terrestrial water storage to represent central interactions between soil moisture and climate. These variables were matched with corresponding remote sensing observations and masked where the observations have missing values. In this “perfect dataset approach” CLIMFILL can be evaluated against the original, usually not observed part of the data. We show that CLIMFILL successfully recovers the dependence structure among the variables across all land cover types and altitudes, thereby enabling subsequent mechanistic interpretations in the gap-filled dataset. Correlation between original ERA-5 data and gap-filled ERA-5 data is high in many regions, although it shows artefacts of the interpolation procedure in large gaps in high-latitude regions during winter. Bias and noise in gappy satellite-observable data is reduced in most regions. A case study of the European 2003 heatwave shows how CLIMFILL reduces biases in ground temperature and surface-layer soil moisture induced by the missing values. Furthermore, in idealized experiments we see the impact of fraction of missing values and the complexity of missing value patterns to the performance of CLIMFILL, showing that CLIMFILL for most variables operates at the upper limit of what is possible given the high fraction of missing values and the complexity of missingness patterns. Thus, the framework can be a tool for gap filling a large range of remote sensing observations commonly used in climate and environmental research.
Morpho-functional restoration of an element in esthetic area through modified intentional replantation: A case report with 16 months follow-up

This study aims to evaluate the success of the morpho-functional restoration of a tooth with subgingival carious lesion using the modified replantation technique, which entails the rotation of the element of 180° on its axis. This study has a follow-up of 16 months. A 68-year-old man comes to rehabilitate the esthetic and the functionality of element 1.2, affected by destructive carious process extended below the gingival margin. At the clinical and radiological examination, the possibility of a conservative approach is excluded due to the total lack of healthy dental tissue to construct the ferula. It is executed the technique of modified intentional replantation, followed by a rehabilitative phase. After 16 months of follow-up, it can be appreciated the stability of the hard and soft tissue, the lack of pathological probing or symptomatology, and radiographically there is no evidence of bone or root resorption. The modified intentional replantation can be a valid alternative to the surgical lengthening of the clinical crown, to the orthodontic extrusion or to the surgical extrusion. This variation allows obtaining an apical stop during the replanting phase, which contributes to the stabilization of the element. Due to the short follow-up examination from us, further studies are necessary.
Reconceptualizing a Secondary Content Area Reading Course From a Phenomenological Perspective Using Reflections of Preservice Teachers.

This study is the reconceptualization of a secondary education six credit hour content area reading course. Classroom and field experiences were modified to promote preservice teachers' reflections concerning what teaching is, who teachers are, and self-as-teacher. The purpose of the revised multifaceted course was to provide opportunities for students' rethinking of teaching as they were taught or teaching as they experienced such in student teaching. In addition, prior lived experiences and students' resistance to a course in content area reading were examined from a hermeneutic phenomenological perspective. The existing course classroom experience was modified to include reading autobiographies and construction of personal teaching metaphors. The field experience was modified to include mentor/intern journals and participant observations recorded while observing and teaching in a large urban southern high school. Three pairs of preservice teachers/practicing teachers were selected for study. Student reading autobiographies, personal teaching metaphors, mentor/intern journals, and participant observations were described and interpreted. Ultimately, the reflections by students and their mentors exhibited strengthening of the attitudes, philosophies, and methodologies the preservice teachers used to understand what teaching is, who teachers are, and selfas-teacher. By gaining a thoughtfulness of themselves as
Scalar/Vector potential formulation for compressible viscous unsteady flows

A scalar/vector potential formulation for unsteady viscous compressible flows is presented. The scalar/vector potential formulation is based on the classical Helmholtz decomposition of any vector field into the sum of an irrotational and a solenoidal field. The formulation is derived from fundamental principles of mechanics and thermodynamics. The governing equations for the scalar potential and vector potential are obtained, without restrictive assumptions on either the equation of state or the constitutive relations or the stress tensor and the heat flux vector.
Comparison of various models to compute the vibro-acoustic response of large CMUT arrays

Capacitive Micromachined Ultrasound Transducers (CMUT) is a promising alternative to piezo-electric transducers for medical imaging. CMUT generally comprises more than 10 thousand unitary cells. When immersed in a fluid the cells are strongly coupled by the fluid, leading to cross talk effects. Accurate simulation tools that take into account properly all these coupling are very much CPU time consuming. From an engineering point of view it will be interesting to use simplified models to conduct parametric studies. We propose in this paper to evaluate various simplified models in term of accuracy and CPU time. As a test case, we chose a typical 1D CMUT array in which groups of several hundreds of cells, called elements, are electrically connected. The reference model takes into account explicitly the contribution of all the cells to compute, in the harmonic domain, the displacement field on the transducer surface and the radiated pressure. A first class of simplified models is built by introducing constraints on the cells displacements of the reference model. A second class of simplified models considers infinite array for which periodic conditions can be defined.