Miracle-dz/UniFAR-FTUs huggingface dataset:
CRASHWORTHINESS ANALYSIS OF THE STRUCTURE OF METRO VEHICLES CONSTRUCTED FROM TYPICAL MATERIALS AND THE LUMPED PARAMETER MODEL OF FRONTAL IMPACT

This paper establishes a Finite Element (FE) model of a rigid barrier impact of a single vehicle constructed from carbon steel, stainless steel, and aluminum alloy, which are three typical materials used in metro vehicle car body structures. The different responses of the three materials during the collision are compared. According to the energy absorption, velocity, deformation and collision force flow characteristics of each vehicle, the relationship between the energy absorption ratio of the vehicle body and the energy absorption ratio of its key components is proposed. Based on the collision force flow distribution proportion of each component, the causes of the key components’ deformation are analysed in detail. The internal relationship between the deformation, energy absorption and impact force of the key components involved in a car body collision is elucidated. By determining the characteristic parameters describing the vehicle’s dynamic stiffness, a metro vehicle frontal impact model using lumped parameters is established that provides a simple and efficient conceptual design method for railway train safety design. These research results can be used to guide the design of railway trains for structural crashworthiness.
FERMI SYSTEMS IN TWO DIMENSIONS AND FERMI SURFACE FLOWS

A proof of Fermi liquid behaviour of weakly coupled Hubbard–like models in two spatial dimensions at positive temperature, in the sense of ﬁniteness of the quasiparticle interactions and regularity of the selfenergy, is discussed. The proof is by a renormalization group ﬂow in which the Fermi surface gets adjusted dynamically during the ﬂow, so that no counterterms are needed. To show the required regular- ity properties of the selfenergy and the Fermi surface, the technique of improving power counting by single and double overlaps is implemented in a nonperturbative setting.
Usutu Virus Infects Human Placental Explants and Induces Congenital Defects in Mice

Usutu virus (USUV) is a neurotropic mosquito-borne flavivirus that has dispersed quickly in Europe these past years. This arbovirus mainly follows an enzootic cycle involving mosquitoes and birds, but can also infect other mammals, causing notably sporadic cases in humans. Although it is mainly asymptomatic or responsible for mild clinical symptoms, USUV has been associated with neurological disorders, such as encephalitis and meningoencephalitis, highlighting the potential health threat of this virus. Among the different transmission routes described for other flaviviruses, the capacity for some of them to be transmitted vertically has been demonstrated, notably for Zika virus or West Nile virus, which are closely related to USUV. To evaluate the ability of USUV to replicate in the placenta and gain access to the fetus, we combined the use of several trophoblast model cell lines, ex vivo human placental explant cultures from first and third trimester of pregnancy, and in vivo USUV-infected pregnant mice. Our data demonstrate that human placental cells and tissues are permissive to USUV replication, and suggest that viral transmission can occur in mice during gestation. Hence, our observations suggest that USUV could be efficiently transmitted by the vertical route.
Secure Online Payment with Facial Recognition using MTCNN

The recent advancements in technology have led to a surge in online transactions via online shopping, internet banking, payment gateways, etc. Security is the most prevailing issue during these transactions. Due to such issues people are hesitant to use online transactions, so we propose our system which secures online transactions using two-step verification. The first step is OTP verification followed by facial recognition. The system uses an online interface in order to interact with a user. The interface is used to get card details from the user. After the OTP verification, the user is authenticated using facial recognition. The system uses a MTCNN in order to verify the user by comparing the real time captured image of the user against the images associated with the users account.
Recent Advances in Drug Delivery Nanocarriers for Targeting Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is among the primary drivers of cancer-related death globally, owing to the ineffectiveness of treatments in intermediate and terminal stages—an issue that drug delivery therapy techniques are expected to address. Drug delivery that works therapeutic techniques necessitates the use of efficient and safe drug carriers, and recent improvements in drug delivery nanocarriers have had a significant impact on HCC targeted delivery. These nanocarriers, including liposomes, micelles, nanocapsules, polymers, exosomes, and inorganic nanoparticles, are accompanied by favorable unique properties such as small particle size, good biocompatibility and biodegradability, low immunogenicity, sustained and controlled release capacity, unique optical, magnetic and heat properties, high drug loading efficiency, and easy surface modification with targeting ligands. Moreover, studies have shown that these nanocarriers can achieve passive targeting or active targeting effect, which improves the therapeutic efficacy of drugs and decreases their side effects. In this paper, we provide a brief overview of some latest studies about nanocarriers for targeting HCC.
SAFETY BEHAVIORS IN GENERALIZED ANXIETY DISORDER: EFFECTS ON SYMPTOM SEVERITY AND TREATMENT OUTCOME

Safety behaviors are behaviors performed with the intention of reducing and managing threat. Paradoxically, their use leads to increases in anxiety that maintain symptoms across a variety of anxiety disorders. However, the role of safety behaviors in Generalized Anxiety Disorder (GAD) specifically is not well understood. Preliminary research has shown GAD patients engage more frequently in these behaviors, but it is unclear whether safety behaviors are associated with GAD symptoms above the effects of worry and the extent to which they impact treatment outcomes. The current study examined the role of safety behaviors in GAD; we hypothesized a positive correlation between symptom severity and safety behavior usage above the effects of worry, significantly higher levels of safety behavior usage in GAD patients, and a moderating effect of safety behaviors on treatment outcome, with greater usage leading to poorer treatment outcomes. The study was a secondary analysis of a computerized worry reduction treatment study. Participants completed the Penn State Worry Questionnaire, Worry Behaviors Inventory, and DSM-5 GAD symptom severity measure at baseline and follow-up. Safety behavior usage was positively correlated with GAD symptom severity controlling for worry ( r =.335, p =.020), but did not differ in those with and without a GAD diagnosis. Contrary to predictions, heightened safety behaviors at baseline were associated with better treatment outcome. The study lends support to the consideration of behavioral symptoms in GAD. Future studies should further clarify the interaction between cognitive and behavioral avoidant strategies in this condition.
Studies on Bioethanol Production from Rice Stalk using Co-cultures of Aspergillus niger and Saccharomyces cerevisiae

Bioethanol production from lignocellulosics as an alternative automobile and engine fuelhas received a considerable attention from researchers worldwide. In the current work, Bioethanol was produced from sulfuric acid and alkaline hydrogen peroxide treated rice stalk employing simultaneous saccharification and fermentation process using co-cultures of isolated and characterized strains of A. niger and S. cerevisiae. The proximate composition of the substrate was determined following standard procedures described by Association of Official Analytical Chemist. The composition of the substrate treated with sulfuric acid was moisture (4.95%), ash (4.75%), fats (4.50%),protein (5.25%), fibre (50.90%),carbohydrates (80.55%), while that of alkaline peroxide-treatment was moisture (3.65%),ash (5.10%), fats (6.60%),protein (7.00%), fibre (38.65%) andcarbohydrates (77.65%). At optimal fermentation conditions of 35ºC temperature, 5.0 pH, 4% substrate concentration, 300rpm agitation rate and 4 days fermentation period and after determining the quantity of the ethanol produced using specific gravity method, a maximum of 5.06g/100ml and 3.91 g/100ml of ethanol was obtained from sulfuric acid and hydrogen peroxide treated rice stalk respectively. The qualitative analysis using FTIR-Spectrophotometry shows the absorbance peaks of the ethanol functional groups from all the ethanol samples produced and the functional groups had their absorption peaks within their normal ranges of 3100-3600cm-1, 2800-3000cm-1 and 1600-1675cm-1 for hydroxyl, alkane and alkene functional groups respectively.
Energy-Aware Scheduling for Microservice-Based Applications

Despite the improvements in energy-efficiency of hardware, the overall energy consumption in large-scale computing data centers continues to grow. That increasing energy consumption of computing systems has become a limiting factor for further performance growth due to overwhelming electricity bills, complex requirements for the cooling system and power supply infrastructure, high carbon dioxide footprints, and more [1]. Existing work on energy-aware resource management approaches for data centers mostly relies on reallocating virtual machines between physical hosts, thus consolidating the workload into the minimum of physical resources and preventing resource underutilization [2, 3]. However, little attention is given to application running on these machines and to application-level policies that can facilitate further energy savings. One substantial portion of cloud-native workloads nowadays is applications built following microservice architectural principles [4, 5, 6, 7]. A microservicebased application is a large distributed system that consists of small, loosely coupled, mono-functional services communicating with each other over a network. The benefits of decomposing a monolithic application into smaller independently deployable and decentralized services include an independent development and deployment of each service, heterogeneity of used tech stacks, loose coupling, and high cohesion. However, as interrelated parts of the application extensively communicate with each other, deployment of services in nodes can greatly affect the energy consumption of the system. In fact, according to data provided by Intel Labs [8], networking accounts for a large fraction of the power consumed by a machine; as such, deploying services with a high communication volume on separate machines will negatively impact the energy consumption of a cluster. With the growing popularity of microservices, the goal of this project is to propose an energy-aware scheduler for microservice-based applications. The scheduler takes into account information about (a) the traffic patterns between microservices, (b) constraints on resource consumption of each individual microservice, and (c) availability and utilization of nodes in the cluster. It uses this information to adjust the placement of microservices and optimally distribute interrelated microservice workloads. By the end of the project, we intend to measure whether the proposed algorithm improves the utilization of cluster resources and energy-efficiency of the cluster, without sacrificing performance. We perform our experiments in a cluster managed by Kubernetes [9]. In such clusters, microservices run inside pods (typically, one microservice per pod)
Are Speech Perception Scores in Cochlear Implant Recipients Consistent Across Different Tests?

Objective Cochlear implant (CI) candidacy and postoperative outcomes are assessed using sets of speech perception tests that vary from center to center, limiting comparisons across institutions and time periods. The objective of this study was to determine if scores on one speech perception test could be reliably predicted from scores on another test. Study Design Arizona Biomedical (AzBio) Sentence Test, Consonant-Nucleus-Consonant word (CNCw), and Hearing in Noise Test (HINT) scores in quiet for the implanted ear were collected for individuals who received a CI between 1985 and 2019. Scores collected during the same testing session were analyzed using Bland-Altman plots to assess agreement between testing methods. Simple linear regression with logit transformation was used to generate predictive functions and 95% confidence intervals for expected mean and individual scores. Setting Single academic medical center. Patients A total of 1,437 individuals with a median age of 59.9 years (range, 18–95 yr) and 46% (654 of 1,437) male. Interventions N.A. Main Outcome Measures Agreement as a function of test score, mean, variance, and correlation coefficients. Results A total of 2,052 AzBio/CNCw, 525 AzBio/HINT, and 7,187 CNCw/HINT same-session score pairings were identified. Pairwise test comparisons demonstrated limited agreement between different tests performed in the same session, and a score correlation between different speech tests revealed large variances. Conclusion Transformation functions between test batteries were predictive of mean scores but performed poorly for prediction of individual scores. Point-wise comparisons of scores across CI test batteries should be used with caution in clinical and research settings.
Travel Behavior Classification: An Approach with Social Network and Deep Learning

Uncovering human travel behavior is crucial for not only travel demand analysis but also ride-sharing opportunities. To group similar travelers, this paper develops a deep-learning-based approach to classify travelers’ behaviors given their trip characteristics, including time of day and day of week for trips, travel modes, previous trip purposes, personal demographics, and nearby place categories of trip ends. This study first examines the dataset of California Household Travel Survey (CHTS) between the years 2012 and 2013. After preprocessing and exploring the raw data, an activity matrix is constructed for each participant. The Jaccard similarity coefficient is employed to calculate matrix similarities between each pair of individuals. Moreover, given matrix similarity measures, a community social network is constructed for all participants. A community detection algorithm is further implemented to cluster travelers with similar travel behavior into the same groups. There are five clusters detected: non-working people with more shopping activities, non-working people with more recreation activities, normal commute working people, shorter working duration people, later working time people, and individuals needing to attend school. An image of activity map is built from each participant’s activity matrix. Finally, a deep learning approach with convolutional neural network is employed to classify travelers into corresponding groups according to their activity maps. The accuracy of classification reaches up to 97%. The proposed approach offers a new perspective for travel behavior analysis and traveler classification.
Engaging and motivating students with authentic statistical projects in a capstone unit

Students preparing for professional statistical work can benefit from experience with real statistical problems presented by real clients and requiring them to engage with the complete problem-solving process without the usual cues of ‘textbook’ examples. We have used such an approach in a capstone unit in statistical consulting, asking students to write about their learning experiences and to reflect on the ways in which such consultations helped them (or not) to develop their professional statistical skills. A content analysis of students’ comments focusing on engagement and motivation confirmed that students had generally positive views of their experience and found the approach engaging and motivating. On this basis, we recommend utilising real problems from real clients as a means of including authentic practice in a professional statistics degree, with consequent benefits for students’ engagement and motivation.
Modular Design and Decentralized Control of the Recupera Exoskeleton for Stroke Rehabilitation

Robot-assisted therapy has become increasingly popular and useful in post-stroke neurorehabilitation. This paper presents an overview of the design and control of the dual-arm Recupera exoskeleton to provide intense therapist-guided as well as self training for sensorimotor rehabilitation of the upper body. The exoskeleton features a lightweight design, high level of modularity, decentralized computing, and various levels of safety implementation. Due to its modularity, the system can be used as a wheel-chair mounted system or as a full-body system. Both systems enable a wide range of therapies while efficiently grounding the weight of the system and without compromising the patient’s mobility. Furthermore, two rehabilitation therapies implemented on the exoskeleton system, namely teach & replay therapy and mirror therapy, are presented along with experimental results.
Effects of Magnesium Oxide and Zinc Oxide Nanoparticles on Motor Activity, Electrocardiographic Parameters, and Oxidative Stress in an Acute Restraint Stress Model of Rats

Background: Stress can alter behavioral parameters, oxidative stress markers, and electrocardiographic (ECG) records. Also, metal oxide nanoparticles can affect behavioral and oxidative stress parameters in animal models, but their effects on the physiological functions of the body in stress situations and ECG changes are not yet clear. Objectives: In this study, motor activity, ECG records, and oxidant/antioxidant balance changes were investigated following administration of nanoparticles of magnesium oxide (nano-MgO) and zinc oxide (nano-ZnO) in normal and acute restraint stressed rats. Methods: Adult male Wistar rats were divided into 16 groups, including control (saline), restraint stress (90, 180, and 360 minutes + saline), nano-MgO, and nano-ZnO (1, 5, and 10 mg/kg, intraperitoneally) groups, with or without restraint stress of 90 minutes. Motor activities were evaluated by the elevated plus maze (EPM) and open field tests. Electrocardiographic parameters were evaluated in the 6 groups. Catalase (CAT) activity and malondialdehyde (MDA) level were measured in the cerebellum + brain stem and brain hemispheres of animals. Results: Motor activity was decreased by the stress of 90 minutes, nano-MgO 10 mg/kg, and nano-ZnO 10 mg/kg. The PR interval and ST height were decreased by the stress of 90 minutes. The QRS interval was increased by nano-MgO 5 mg/kg, and QRS amplitude, T amplitude, and ST height were decreased by nano-MgO 5 mg/kg. The QT interval and QTc were increased by nano-ZnO 5 mg/kg, and ST height was decreased by nano-ZnO 5 mg/kg. The PR interval, QRS interval, QTc, and QT interval were increased by nano-MgO 5 mg/kg in the stress of 90 minutes, and the heart rate (HR) and ST height were decreased by nano-MgO 5 mg/kg in the stress of 90 minutes. HR, QRS interval, and QTc were increased by nano-ZnO 5 mg/kg in the stress of 90 minutes, and T amplitude and ST height were decreased by nano-ZnO 5 mg/kg in the stress of 90 minutes. The MDA level was increased by nano-MgO and nano-ZnO in the brain hemisphere and cerebellum + brain stem, and CAT activity was decreased by nano-MgO and nano-ZnO in the brain hemisphere and cerebellum + brain stem. The MDA level was increased by the restraint stress of 360 minutes in the cerebellum + brain stem while it was not changed in the brain hemispheres. Conclusions: It seems that the side effects of these nanoparticles on motor activity could be related to the imbalance of oxidant/antioxidant systems in the cerebellum + brain stem. Also, it is possible that nanoparticles increase the effects of acute stress on changes in ECG parameters.
A Microservices Model to Enhance the Availability of Data for Building Energy Efficiency Management Services

In the Internet of Things (IoT)-supported energy data management infrastructure, objects from various energy generation and consumption terminals in buildings produce a tremendous amount of data. However, this data is not useful unless it is available on-time for services that discover meaningful information in order to provide intelligent decisions. The microservices-based data caching, data virtualization, data processing, data analysis, and data ingestion methods can be applied to enhance the data availability for energy efficiency management services provision across buildings. To foster building energy efficiency management services (BEEMS), Web of Objects (WoO) provides data abstraction, aggregation, and ingestion mechanism with virtual objects (VOs) and composite virtual objects (CVOs) by using ontologies and availability and scalability of services with microservices. This article proposes the use of data processing microservices modeling to enhance data availability and expose services capabilities with microservices for BEEMS. We present a semantic web agent based on an ontology for linking, enhancement, reusability, and availability of data-objects, services, and microservices. For the evaluation, we present a use case, which includes heterogeneous data collection and processing and provision of various BEEMS. A prototype for the use case scenario has been built and the results have been evaluated in the laboratory to mimic the enhanced data availability for BEEMS.
A Multi-Domain Co-Simulation Method for Comprehensive Shifted-Frequency Phasor DC-Grid Models and EMT AC-Grid Models

To accurately capture the dynamics of a large-scale ac/dc system as a whole and the interactions between its individual components, a simulation method with high precision and efficiency is in great demand. For this purpose, we develop a multi-domain co-simulation method, in which the target system is partitioned into multiple dc and ac subsystems, represented by our proposed shifted-frequency phasor (SFP) models and the traditional EMT models, respectively. SFP models can be simulated with a much larger time step, leading to a significant improvement in simulation efficiency under a given requirement of precision. Further, a new interface model, namely, hybrid multi-domain transmission-line model (HMD-TLM), is developed to reflect the interactions between SFP models and EMT models, with the additional benefit of producing instantaneous and phasor waveforms simultaneously. Thus, the multi-domain co-simulation is implemented based on the efficient SFP models, the HMD-TLM, and a designed time sequences of simulation. The performance (efficiency and accuracy) of the proposed method has been validated on the well-known CIGRE ac/dc system as well as a practical system integrating large ac grids and modular-multilevel-converter-based multi-terminal dc grids.