Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Antioxidant Activity of Garden Blackberries (Rubus fruticosus L.) Extracts Obtained with Different Extraction Solvents

Blackberries are rich in antioxidants due to their high polyphenol and vitamin content, making them an excellent source of health protection. It is well known that good quality juices and fruit extracts can be obtained only from high quality fruits. The aim of this research is to characterize the antioxidant properties of garden blackberries fruits grown in Turia (Romania). Extracts were made from blackberry fruits with four different solvents, and the antioxidant capacity was studied by applying DPPH and FRAP assay. The total phenolic content (with Folin–Ciocâlteu reagent), total anthocyanin content (with the pH differential method), and total flavonoid content (with aluminum chloride colorimetric method) were also measured. The quercetin and gallic acid content were also determined by HPLC-DAD. As the best results were obtained with 90% v/v acidified acetonitrile, the Hansen parameters analysis was performed for the acetonitrile-water solvent mixture as a solvent and cyanidin-3-O-glucoside as a solute.
HeadStart: Efficiently Verifiable and Low-Latency Participatory Randomness Generation at Scale

—Generating randomness by public participation allows participants to directly contribute randomness and verify the result’s security. Ideally, the difﬁculty of participating in such activities should be as low as possible to reduce the computational burden of being a contributor. However, existing randomness generation protocols are unsuitable for this scenario because of scalability or usability issues. Hence, this paper presents HeadStart, a participatory randomness protocol designed for public participation at scale. HeadStart allows contributors to verify the result on commodity devices efﬁciently and provides a parameter L that can make the result-publication latency L times lower. Additionally, we propose two implementation improvements to speed up the veriﬁcation further and reduce the proof size. The veriﬁcation complexity of HeadStart is only O ( L × polylog ( T ) + log C ) for a contribution phase lasting for time T with C contributions.
A Control Strategy to Avoid Drop and Inrush Currents during Transient Phases in a Multi-Transmitters DIPT System

Electrical Vehicles (EVs) have gained popularity in recent years in the automotive field. They are seen as a way to reduce the CO2 footprint of vehicles. Although EVs have witnessed significant advancement in recent years, they still have two major setbacks: limited autonomy and long recharging time. Dynamic Inductive Power Transfer (DIPT) systems permit charging EVs while driving, provide unlimited autonomy, and eliminate stationary charging time and lower battery dependency. Multiple transmitters are required to achieve DIPT; thus, dealing with transient phases is essential because every time a receiver crosses over from one transmitter to another, it experiences a new transient phase. This article presents a novel control strategy for multi-transmitter DIPT systems that ensures a continuous and stable power transfer to a moving EV. The proposed control strategy eliminates drop and inrush currents during transient phases. The control integrates a soft start feature and a degraded operating mode at a predefined maximum current value. The studied structure is a symmetrical series–series compensation network. Each transmitter coil is driven by a variable frequency inverter (around 85 kHz) to ensure Zero Phase Angle mode. The control strategy was numerically validated using MATLAB Simulink and then tested experimentally. Results show a relatively low power disruption after applying the proposed control during transmitter sequencing.
“Heat waves” experienced during larval life have species-specific consequences on life-history traits and sexual development in anuran amphibians

Extreme temperatures during heat waves can induce mass-mortality events, but can also exert sublethal negative effects by compromising life-history traits and derailing sexual development. Ectothermic animals may, however, also benefit from increased temperatures via enhanced physiological performance and the suppression of cold-adapted pathogens. Therefore, it is crucial to address how the intensity and timing of naturally occurring or human-induced heat waves affect life-history traits and sexual development in amphibians, to predict future effects of climate change and to minimise risks arising from the application of elevated temperature in disease mitigation. We raised agile frog (Rana dalmatina) and common toad (Bufo bufo) tadpoles at 19 °C and exposed them to a simulated heat wave of 28 or 30 °C for six days during one of three ontogenetic periods (early, mid or late larval development). In agile frogs, exposure to 30 °C during early larval development increased mortality. Regardless of timing, all heat-treatments delayed metamorphosis, and exposure to 30 °C decreased body mass at metamorphosis. Furthermore, exposure to 30 °C during any period and to 28 °C late in development caused female-to-male sex reversal, skewing sex ratios strongly towards males. In common toads, high temperature only slightly decreased survival and did not influence phenotypic sex ratio, while it reduced metamorph mass and length of larval development. Juvenile body mass measured two months after metamorphosis was not adversely affected by temperature treatments in either species. Our results indicate that heat waves may have devastating effects on amphibian populations, and the severity of these negative consequences, and sensitivity can vary greatly between species and with the timing and intensity of heat. Finally, thermal treatments against cold-adapted pathogens have to be executed with caution, taking into account the thermo-sensitivity of the species and the life stage of animals to be treated.
Increased Patient Empowerment Is Associated with Improvement in Anxiety and Depression Symptoms in Type 2 Diabetes Mellitus: Findings from the INDICA Study

Introduction. In cross-sectional analyses, higher levels of patient empowerment have been related to lower symptoms of anxiety and depression. The aims of this study are: (1) to assess if patient empowerment predicts anxiety and depression symptoms after 12 and 24 months among patients with type 2 diabetes mellitus, and (2) to analyze whether a change in patient empowerment is associated with a change in anxiety and depression level. Methods. This is a secondary analysis of the INDICA study, a 24 month-long, multi-arm randomized controlled trial. Patient empowerment (DES-SF), depression (BDI-II), and state-anxiety (STAI-S) were assessed at the baseline (pre-intervention) and after 12 and 24 months. Multilevel mixed linear models with a random intercept were performed to correct for our clustered data. Results. The multilevel regression models showed that the baseline empowerment did not significantly predict anxiety and depression after 12 and 24 months. However, a higher increase in patient empowerment was significantly associated with reductions of anxiety (p < 0.001) and depression levels (p < 0.001). This association was not significantly different between the two follow-ups. Conclusion. This study contributes to the knowledge on how to reduce affective symptoms in patients with uncomplicated T2DM through comprehensive patient-centered interventions, and it highlights patient empowerment as a significant contributor.
Event‐triggered H∞$$ {H}_{\infty } $$ state estimation for neural networks with time‐varying delays: The continuous‐time case

This article is concerned with the event‐triggered H∞$$ {H}_{\infty } $$ state estimation problem of delayed neural networks. A new event‐triggered scheme is designed based on the estimation error, which makes a balance between the state estimator performance and network communication bandwidth in accordance with practical requirement. An auxiliary function‐type free‐matrix‐based integral inequality is proposed, which can fully link the relationship among time‐varying delay and system states due to additional delay‐product‐type matrices. Based on these condiments, sufficient condition is established to guarantee the estimation error system to be asymptotically stable and satisfies the H∞$$ {H}_{\infty } $$ performance. Meanwhile, the desired event‐triggered estimator gains are derived in terms of linear matrix inequalities. A numerical example is illustrated to demonstrate the effectiveness of the obtained methods.
Research on Fault-Tolerant Operation Strategy of Permanent Magnet Synchronous Motor with Common DC Bus Open Winding Phase-Breaking Fault

In some key areas, fault-tolerant control is usually needed in order to enable the motor to operate continuously in fault mode. Given that it is difficult to detect the zero-sequence current of the open winding permanent magnet synchronous motor after the phase break fault occurs, the traditional zero-sequence current suppression strategy is no longer applicable after the phase break fault occurs. Therefore, a zero-sequence current suppression strategy for a common DC bus under a phase break fault is proposed in this paper. By establishing the mathematical model between the current component in the synchronous coordinate system and the current component and the zero-sequence current in the static coordinate system, the relationship between the non-fault phase current and the zero-sequence current in the open phase fault is analyzed. A method of suppressing the zero-sequence current by using proportional integral double resonance in a zero-sequence current control loop is proposed. In addition, according to the large number of calculations in traditional space vector modulation (SVPWM)—such as sector judgment and coordinate transformation—a decoupling modulation algorithm is proposed to modulate the reference voltage vector. Finally, the experimental platform for the common DC bus open winding permanent magnet synchronous motor is built, and the zero-sequence current suppression method for the common DC bus OW-PMSM under phase break fault is verified experimentally.
VPNalyzer: Systematic Investigation of the VPN Ecosystem

—Use of Virtual Private Networks (VPNs) has been growing rapidly due to increased public awareness of online risks to privacy and security. This growth has fueled the VPN ecosystem to expand into a multi-billion dollar industry that sees a frequent inﬂux of new VPN providers. Nevertheless, the VPN ecosystem remains severely understudied, and the limited research concerning VPNs has relied on laborious manual processes. There is a need for a solution which empowers researchers and average users to investigate their VPN providers. In this work, we present VPNalyzer , a system that enables systematic, semi-automated investigation into the VPN ecosystem. We develop a cross-platform tool with a comprehensive measurement test suite containing 15 measurements that test for aspects of service, security and privacy essentials, misconﬁgurations, and leakages. Using the VPNalyzer tool, we conduct the largest investigation into 80 desktop VPNs. Our investigation reveals several previously unreported ﬁndings highlighting key issues and implementation shortcomings in the VPN ecosystem. We ﬁnd evidence of trafﬁc leaks during tunnel failure in 26 VPN providers, which seriously risk exposing sensitive user data. We are the ﬁrst to measure and detect DNS leaks during tunnel failure, which we observe in eight providers. Overall, we ﬁnd a majority of providers lack IPv6 support, and ﬁve even leak IPv6 trafﬁc to the user’s ISP. We observe that adoption of practices we consider security and privacy essentials is not uniform across VPN providers. Multiple providers share underlying infrastructure, and 29 providers use third-party, public DNS services. Alarmingly, 10 VPN providers leak trafﬁc even in their most secure conﬁguration, with six leaking data even with a “kill switch” feature enabled. Our results highlight the effectiveness of VPNalyzer in ﬁnding issues even
Pathogenesis of Type 1 Diabetes: Established Facts and New Insights

Type 1 diabetes (T1D) is an autoimmune disease characterized by the T-cell-mediated destruction of insulin-producing β-cells in pancreatic islets. It generally occurs in genetically susceptible individuals, and genetics plays a major role in the development of islet autoimmunity. Furthermore, these processes are heterogeneous among individuals; hence, different endotypes have been proposed. In this review, we highlight the interplay between genetic predisposition and other non-genetic factors, such as viral infections, diet, and gut biome, which all potentially contribute to the aetiology of T1D. We also discuss a possible active role for β-cells in initiating the pathological processes. Another component in T1D predisposition is epigenetic influences, which represent a link between genetic susceptibility and environmental factors and may account for some of the disease heterogeneity. Accordingly, a shift towards personalized therapies may improve the treatment results and, therefore, result in better outcomes for individuals in the long-run. There is also a clear need for a better understanding of the preclinical phases of T1D and finding new predictive biomarkers for earlier diagnosis and therapy, with the final goal of reverting or even preventing the development of the disease.
Inorganic Polyphosphate—Regulator of Cellular Metabolism in Homeostasis and Disease

Inorganic polyphosphate (polyP), a simple anionic polymer consisting of even hundreds of orthophosphate units, is a universal molecule present in both simple and complex organisms. PolyP controls homeostatic processes in animals, such as blood coagulation, tissue regeneration, and energy metabolism. Furthermore, this polymer is a potent regulator of inflammation and influences host immune response in bacterial and viral infections. Disturbed polyP systems have been related to several pathological conditions, including neurodegeneration, cardiovascular disorders, and cancer, but we lack a full understanding of polyP biogenesis and mechanistic insights into the pathways through which polyP may act. This review summarizes recent studies that describe the role of polyP in cell homeostasis and show how disturbances in polyP levels may lead to disease. Based on the collected findings, we highlight the possible usage of this polymer as a promising therapeutic tool in multiple pathologies.
Video-Assisted Intubating Stylet Technique for Difficult Intubation: A Case Series Report

Induction of anesthesia can be challenging for patients with difficult airways and head or neck tumors. Factors that could complicate airway management include poor dentition, limited mouth opening, restricted neck motility, narrowing of oral airway space, restricted laryngeal and pharyngeal space, and obstruction of glottic regions from the tumor. Current difficult airway management guidelines include awake tracheal intubation, anesthetized tracheal intubation, or combined awake and anesthetized intubation. Video laryngoscopy is often chosen over direct laryngoscopy in patients with difficult airways because of an improved laryngeal view, higher frequency of successful intubations, higher frequency of first-attempt intubation, and fewer intubation attempts. In this case series report, we describe the video-assisted intubating stylet technique in five patients with difficult airways. We believe that the intubating stylet is a feasible and safe airway technique for anesthetized tracheal intubation in patients with an anticipated difficult airway.
Density-Based Geometric One-Class Classifier Combined with Genetic Algorithm

One of the most prospective issues in recent machine learning research is one-class classification (OCC), which considers datasets composed of only one class and outlier. It is more reasonable than the traditional multiclass classification in dealing with problematic datasets or special cases. Generally, classification accuracy and interpretability for users are considered to have a trade-off in OCC methods. A classifier based on hyperrectangle (H-RTGL) can alleviate such a trade-off and uses H-RTGL formulated by the conjunction of geometric rules (called an interval). This interval can form a basis for interpretability since it can be easily understood by the user. However, the existing H-RTGL-based OCC classifiers have the following limitations: (i) they cannot reflect the density of the target class, (ii) the density is considered using a primitive interval generation method, and (iii) there exists no systematic procedure for determining the hyperparameter of the H-RTGL-based OCC classifier, which influences its classification performance. Therefore, we suggest a one-class hyperrectangle descriptor based on density 1 − H R D d with a more elaborate interval generation method, including parametric and nonparametric approaches. Specifically, we design a genetic algorithm that comprises a chromosome structure and genetic operators for systematic generation of 1 − H R D d through optimization of the hyperparameter. Our study is validated through a numerical experiment using several actual datasets with different sizes and features, and the result is compared to the existing OCC algorithms along with other H-RTGL-based classifiers.
Pattern Mathematical Model for Fingerprint Security Using Bifurcation Minutiae Extraction and Neural Network Feature Selection

Biometric based access control is becoming increasingly popular in the current era because of its simplicity and user-friendliness. This eliminates identity recognition manual work and enables automated processing. The fingerprint is one of the most important biometrics that can be easily captured in an uncontrolled environment without human cooperation. It is important to reduce the time consumption during the comparison process in automated fingerprint identification systems when dealing with a large database. Fingerprint classification enables this objective to be accomplished by splitting fingerprints into several categories, but it still poses some difficulties because of the wide intraclass variations and the limited interclass variations since most fingerprint datasets are not categories. In this paper, we propose a classification and matching fingerprint model, and the classification classifies fingerprints into three main categories (arch, loop, and whorl) based on a pattern mathematical model using GoogleNet, AlexNet, and ResNet Convolutional Neural Network (CNN) architecture and matching techniques based on bifurcation minutiae extraction. The proposed model was implemented and tested using MATLAB based on the FVC2004 dataset. The obtained result shows that the accuracy for classification is 100%, 75%, and 43.75% for GoogleNet, ResNet, and AlexNet, respectively. The time required to build a model is 262, 55, and 28 seconds for GoogleNet, ResNet, and AlexNet, respectively.
Lactic acid bacteria

Mycotoxins produced from Aspergillus, Penicillium, and Fusarium cause food spoilages during handling and storage, owing to immense economic losses and serious human health concerns including immunosuppression and carcinogenic effects. Furthermore, these species are also known to produce mycotoxins. Aflatoxin B1 (AFB1), zearalenone (ZEA), ochratoxin A (OTA), and deoxynivalenol (DON) are the most commonly occurring mycotoxins. The removal of mycotoxins from the contaminated feed using lactic acid bacterias (LABs) has been proposed as a green, inexpensive, safe, and promising mycotoxin decontamination strategy. LABs can control fungal growth by secreting antimicrobial compounds out of their cell wall, thus employing lactobacilli defense mechanisms to destroy bacterial and fungal cells. This article provides systematic review of LABs as bio-green preservative anti-mycotoxin for sustainable food safety.
Microwave-Absorbing Properties of PbMg1/3Nb2/3O3-PbZrO3-PbTiO3-PbGeO3 (PMN-PZT-PG) Solid Solutions on a Microstrip Line in the Microwave Range

In this paper, a brief review of the current state of research on ferroelectric, ferromagnetic, and multiferroic materials in the microwave range is given, and the main research methods are described. The main areas of application of functional materials in radio electronics are outlined. Multicomponent ferroelectric media based on ferroelectric (PbTiO3), antiferroelectric (PbZrO3), and relaxors (PbMg1/3Nb2/3O3) are considered promising radio-absorbing materials. A method for comparing the electrical parameters of samples using a microstrip line and a network analyzer is described in detail, in which cylindrical samples were placed on a microstrip line. The measurements were carried out in the frequency range 10 MHz–20 GHz. Based on the frequency dependences of the S parameters, the relationships between the resonant frequencies of the samples and their composition were determined. The frequency dependences of the absorption coefficient were calculated for materials of various compositions. For each composition, the effective absorption band was calculated, and a pattern of its distribution over the concentrations of the system components was plotted. For empty areas of the phase diagram, interpolation was performed using the obtained results, which made it possible to obtain a qualitative idea of the radio-absorbing properties of the entire system. It was revealed that the compositions with the greatest variety of different phases had the widest absorption band. The results obtained will help in future research and composition selection for further development of microwave devices such as dielectric resonators, filters, and attenuators.
An FPGA platform for demonstrating embedded vision systems

This thesis presents an FPGA platform that can be used to enable real-time embedded vision systems, specifically object detection. Interfaces are built between the FPGA and a high definition (1980 x 1080) HDMI camera, an off-chip DRAM, an FMC connector, an SD Card, and an HDMI display. The interface processing includes debayering for the camera input, arbitration for DRAM, and object annotation for the display. The platform must also handle the different clock frequencies of various interfaces. Real-time object detection at 30 frames per second is demonstrated by either connecting the platform to an object detection ASIC via the FMC connector, or directly implementing the object detection RTL on the FPGA. Using this platform, ASICs developed in the Energy-Efficient Multimedia Systems lab can be verified and benchmarked on both live video via the HDMI camera as well as pre-recorded media via an SD Card. Finally, a post-processing filter has been implemented on the FPGA to reduce false positives and interpolate missed object detections by leveraging temporal correlations. Thesis Supervisor: Vivienne Sze Title: Emanuel E. Landsman (1958) Career Development Professor
Minimum break relay dependency set approach for coordination of directional relays in multi-loop networks

Opening all of the directional loops in the network is a key step during calculating protection setting values of directional over-current or distance relays. The existing method based on minimum break point set leads to large amounts of broken relay dependency relations. A new method for coordination of directional relays based on minimum break relay dependency set (MBRDS) is proposed. First, a mathematical model for determining MBRDS is established, and a method based on self-adaptive biogeography-based optimisation (SaBBO) is proposed to effectively solve the model. Then case studies on various test systems verify the feasibility and efficiency of the proposed approach. Moreover, comparison with biogeographybased optimisation, particle swarm optimisation, and genetic algorithm confirms that SaBBO has faster convergence speed, stronger robustness, and higher convergence precision in determining the MBRDS.