Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxytocin and vasopressin levels in maltreated children: A systematic review

Objective: The present study aimed to focus on the neurobiological basis of childhood maltreatment. Method: A systematic review approach was followed by focusing on the possible links among oxytocin, vasopressin, and child maltreatment. The review included research based on data examining the role that being exposed to any kind of maltreatment during childhood plays. The review was not limited to a specific period. However, reviews, systematic reviews, dissertations, congress papers, and studies in languages other than Turkish and English were excluded. The search was concentrated on the Web of Science, PubMed, Science Direct, Scopus, EBSCO Host, and Google Scholar databases including the keyword combinations. Results: The review between December 2021 and April 2022 revealed 49 research studies that reported data from a total of 13979 participants (3981 children and adolescents, 9998 adults). Studies reviewed contained demographically diverse samples from a wide range of Western cultural contexts. Conclusion: The findings are promising in terms of clinical implications as there are considerable links between OXT and child maltreatment. However, more studies are needed to understand the nature of these links.
Mechanical and Thermoanalytical Study of Polypropylene Geomats Exposed in the Field and the Laboratory

A Hydroelectric Power Plant (HPP) presents erosion problems on reservoir slopes and margins. Geomats are a biotechnical composite technology increasingly used to protect soils against erosion. Survivability or durability of geomats is essential for successful application. This work analyses the degradation of geomats exposed in the field for more than six years. These geomats were used as erosion-control treatment in a slope at HPP Simplício in Brazil. The degradation of the geomats in the laboratory was also analysed through exposure in a UV ageing chamber for 500 h and 1000 h. Degradation was quantitatively evaluated by testing the tensile strength of the geomat wires and thermal tests such as thermogravimetry (TG) and differential scanning calorimetry (DSC). The results showed that the geomat wires exposed in the field had a greater decrease in resistance compared to the samples exposed in the laboratory. In the samples collected in the field, it could be observed that the degradation of the virgin sample occurred earlier than in the exposed samples, contrary to what was observed in the TG tests carried out with the samples exposed in the laboratory. The DSC analysis showed that the samples had similar behaviours for the melting peaks. This evaluation of the wires of the geomats was presented as an alternative to analysing the tensile strengths of discontinuous geosynthetic materials such as the geomats.
Pengaruh Live Streaming Shopping Instagram Terhadap Kepercayaan Konsumen Online dan Dampaknya pada Keputusan Pembelian

Studies have found that social media is an effective marketing medium to influence consumers. In the midst of intense business competition, business actors must keep up-to-date with the development of trends in social media. Live Streaming is a new feature on Instagram that can be used by business people as a marketing strategy. Live Streaming Shopping (LSS) is a new trend that allows business people to interact in real-time directly with consumers. This study aims to analyze the influence of LSS on online consumer trust, to analyze the influence of trust on purchasing decisions, and to analyze the influence of LSS on purchasing decisions either directly or indirectly through trust in social media Instagram. This research was conducted on 109 respondents who have joined and made purchasing decisions through Live Streaming Shopping on Instagram social media. This type of research is carried out using explanatory research, with quantitative methods. Furthermore, the data were analyzed using the Path Analysis technique or path analysis to determine the direct or indirect effect of the variables studied. The results of the study prove that of the four hypotheses proposed in this study, it is stated that the first, second, third, and fourth hypotheses are accepted. The results of this study found that LSS has an influence on online consumer trust and has a positive impact on purchasing decisions. As for the amount of direct influence between live streaming shopping on purchasing decisions, which is 16.9%, while the indirect influence between live streaming shopping on purchasing decisions through online consumer confidence with a magnitude of influence of 52.7% means that the contribution of indirect influence is greater than the contribution of the direct influence of live streaming shopping on purchasing decisions.
Extracellular Vesicles and Intercellular Communication: Challenges for In Vivo Molecular Imaging and Tracking

Extracellular vesicles (EVs) are a heterogeneous class of cell-derived membrane vesicles released by various cell types that serve as mediators of intercellular signaling. When released into circulation, EVs may convey their cargo and serve as intermediaries for intracellular communication, reaching nearby cells and possibly also distant organs. In cardiovascular biology, EVs released by activated or apoptotic endothelial cells (EC-EVs) disseminate biological information at short and long distances, contributing to the development and progression of cardiovascular disease and related disorders. The significance of EC-EVs as mediators of cell–cell communication has advanced, but a thorough knowledge of the role that intercommunication plays in healthy and vascular disease is still lacking. Most data on EVs derive from in vitro studies, but there are still little reliable data available on biodistribution and specific homing EVs in vivo tissues. Molecular imaging techniques for EVs are crucial to monitoring in vivo biodistribution and the homing of EVs and their communication networks both in basal and pathological circumstances. This narrative review provides an overview of EC–EVs, trying to highlight their role as messengers of cell–cell interaction in vascular homeostasis and disease, and describes emerging applications of various imaging modalities for EVs visualization in vivo.
Image Interpolation using Sparse Matrix Representation

Super Resolution is the process of transforming a low resolution image into a high resolution image. Image Interpolation is a technique which is used in super resolution which is used to find the missing pixels in the given image. Adaptive algorithm is used in this paper to interpolate the missing pixels. In this work an image with noise and blur is taken and processed to achieve high resolution image. Image is represented as sparse matrix. Sparse matrix needs special algorithms and data structures to implement it. In this, work an image is divided into overlapping patches and each patch is processed to remove the noise and blur and the pixels are processed and the optimal pixel value is taken from the dictionary. The dictionary is updated K-SVD (K means clustering singular value decomposition) algorithm. Thus the robustness of the image is achieved by increasing the weights of the pixels which results in better resolution of the image. Keywords—Super Resolution, Sparse Matrix, K-SVD.
Comparison of Methods for Estimating Damage by Wild Ungulates on Field Crops

High numbers of large ungulates are locally accompanied by high levels of damage to field crops, causing economic losses and increased costs for the protection of agricultural fields. Quantifying the levels of damage can be problematic, with the degree of accuracy depending on the method used. The aim of this study was to compare the accuracy, workload and cost of four methods commonly used for estimating damage to wheat fields caused by large ungulates (esp. wild boar) in the Czech Republic. The results suggest that the manual processing of aerial photographs (“Uncrewed Aerial Systems [UAS] with Operator Delineation Method”) was very laborious and the least accurate method, with a high risk of error. In comparison, the automatic evaluation of aerial images (“UAS Crop Height Method”) and the “Ground-Based Assessment” both provided similar results when carefully analyzed and were equally demanding. The “Yield Method”, comparing the net yield from damaged and undamaged areas, provided the same result of assessment and was the least laborious, although it does require the existence of comparable areas and for the conditions to be created in advance before the method is used. Equivalent results were achieved by the UAS Crop Height Method, which we recommend using in cases where the Yield Method cannot be applied.
Correspondence analysis and classification

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Correspondence analysis and classification Gilbert Saporta, Ndeye Niang Keita
Completion Time Minimization for Data Collection in a UAV-enabled IoT Network: A Deep Reinforcement Learning Approach

In this article, we study the completion time minimization in an unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) network, where the UAV tries to collect all the data generated by the ground IoT devices for further processing. To simplify the analysis, the continuous time horizon is discretized into several time slots. The duration of each time slot is set to be less than the pre-defined threshold such that the UAV's location can be considered as unchanged during each time slot. In our work, we aim to minimize the completion time of the UAV by optimizing the association scheme of the IoT devices, the location (i.e., the trajectory) and velocity of the UAV at each time slot. However, the formulated problem is challenging to solve by traditional optimization methods considering the unknown number of time slots (which leads to the unknown number of decision variables) and non-convex functions. We thus reformulate it as a Markov decision process (MDP) and propose a deep deterministic policy gradient (DDPG)-based method to efficiently solve it. The DDPG-based algorithm uses deep function approximators instead of finding the action that maximizes the state-action value, and is therefore well suited to solve high-dimensional, continuous control problems. Extensive numerical results are presented to validate the effectiveness of our proposed algorithm.
Protein Biomarker Discovery Studies on Urinary sEV Fractions Separated with UF-SEC for the First Diagnosis and Detection of Recurrence in Bladder Cancer Patients

Urinary extracellular vesicles (EVs) are an attractive source of bladder cancer biomarkers. Here, a protein biomarker discovery study was performed on the protein content of small urinary EVs (sEVs) to identify possible biomarkers for the primary diagnosis and recurrence of non-muscle-invasive bladder cancer (NMIBC). The sEVs were isolated by ultrafiltration (UF) in combination with size-exclusion chromatography (SEC). The first part of the study compared healthy individuals with NMIBC patients with a primary diagnosis. The second part compared tumor-free patients with patients with a recurrent NMIBC diagnosis. The separated sEVs were in the size range of 40 to 200 nm. Based on manually curated high quality mass spectrometry (MS) data, the statistical analysis revealed 69 proteins that were differentially expressed in these sEV fractions of patients with a first bladder cancer tumor vs. an age- and gender-matched healthy control group. When the discriminating power between healthy individuals and first diagnosis patients is taken into account, the biomarkers with the most potential are MASP2, C3, A2M, CHMP2A and NHE-RF1. Additionally, two proteins (HBB and HBA1) were differentially expressed between bladder cancer patients with a recurrent diagnosis vs. tumor-free samples of bladder cancer patients, but their biological relevance is very limited.
Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification

Active noise cancellation (ANC) is the most important function in an audio device because it removes unwanted ambient noise. As many audio devices are increasingly equipped with digital signal processing (DSP) circuits, the need for low-power and high-performance processors has arisen because of hardware resource restrictions. Low-power design is essential because wireless audio devices have limited batteries. Noise cancellers process the noise in real time, but they have a short secondary path delay in conventional least mean square (LMS) algorithms, which makes implementing high-quality ANC difficult. To solve these problems, we propose a fixed-filter noise cancelling system with a convolutional neural network (CNN) classification algorithm to accommodate short secondary path delay and reduce the noise ratio. The signal-to-noise ratio (SNR) improved by 2.3 dB with CNN noise cancellation compared to the adaptive LMS algorithm. A frequency-domain noise classification and coefficient selection algorithm is introduced to cancel the noise for time-varying systems. Additionally, our proposed ANC architecture includes an even–odd buffer that efficiently computes the fast Fourier transform (FFT) and overlap-save (OLS) convolution. The simulation results demonstrate that the proposed even–odd buffer reduces processing time by 20.3% and dynamic power consumption by 53% compared to the single buffer.
Paleoseismology of a Major Crustal Seismogenic Source Near Mexico City: The Southern Border of the Acambay Graben

The Trans‐Mexican Volcanic Belt is an active continental volcanic arc related to subduction along the Middle America trench. It is characterized by intra‐arc extension resulting into several major arc‐parallel active fault systems and tectonic basins. The Acambay graben, one of the largest of these basins, is located near Mexico City, in the central part of this province. In 1912, a M 6.9 earthquake ruptured the surface along the northern border of the graben together with at least two other faults. In this paper, we analyze the paleoseismic history of the southern border of the Acambay Graben, with new observations made in one natural outcrop and four paleoseismological trenches excavated across branches of the Venta de Bravo Fault at the site where it overlaps with the Pastores Fault. We present evidence of at least two paleo‐earthquakes that occurred between 12,190 ± 175 and 5,822 ± 87 cal year BP and between 647 ± 77 and 250 cal year BP. On one of these branches, we estimate a minimum slip‐rate value between 0.1 and 0.23 mm/year for the last 12 ka and a mean recurrence interval of 8.5 ± 3 ka. By considering several likely rupture lengths along the Venta de Bravo and Pastores faults, we calculated a maximum possible magnitude of Mw 7.01 ± 0.27. Finally, by correlating events recorded along different faults within the Acambay Graben, we discuss several possible rupture coalescent scenarios and related consequences for Mexico City.
Recent Aptamer-Based Biosensors for Cd2+ Detection

Cd2+, a major environmental pollutant, is heavily toxic to human health. Many traditional techniques are high-cost and complicated; thus, developing a simple, sensitive, convenient, and cheap monitoring approach is necessary. The aptamer can be obtained from a novel method called SELEX, which is widely used as a DNA biosensor for its easy acquisition and high affinity of the target, especially for heavy metal ions detection, such as Cd2+. In recent years, highly stable Cd2+ aptamer oligonucleotides (CAOs) were observed, and electrochemical, fluorescent, and colorimetric biosensors based on aptamers have been designed to monitor Cd2+. In addition, the monitoring sensitivity of aptamer-based biosensors is improved with signal amplification mechanisms such as hybridization chain reactions and enzyme-free methods. This paper reviews approaches to building biosensors for inspecting Cd2+ by electrochemical, fluorescent, and colorimetric methods. Finally, many practical applications of sensors and their implications for humans and the environment are discussed.
Penerapan Model UTAUT untuk Mengetahui Minat Perilaku Konsumen dalam Penggunaan QRIS

Technological advances in the payment system have replaced the role of cash into a more efficient and economical form of non-cash payment. Currently, there are more and more technologies that affect the payment system with a variety of applications that people use as a means of non-cash payment. Therefore, on 17 August 2019 Bank Indonesia released a Quick Response (QR) Code standard for payments through server-based electronic money applications, electronic wallets, or mobile banking called the QR Code Indonesian Standard (QRIS). This study aims to determine the factors that influence behavioral intention to use QRIS by using theoryModel Unified Theory of Acceptance and Use Technology (UTAUT). The research method used is descriptive quantitative. Sampling technique is done by using the technique nonprobability sampling that is purposive sampling with a sample of 100 respondents from QRIS users. The results of this study show performance expectancy, effort expectancy, and social influence has a positive and significant influence on behavioral intention the use of QRIS. Behavioral intention has a positive and significant influence on use behavior. Whereas, facilitating conditions has a positive and not significant effect on use behavior the use of QRIS.
Development and validation of the steady state centrifuge experiment for the moisture retention curve of porous building materials

With the development of more efficient hygrothermal computer models, simulation studies have become increasingly important in the design of building components. To obtain trustworthy results from these studies, accurate hygric properties are required. The existing methods for moisture storage properties, however, are not very well suited to accurately measure moisture retention curves within a compact timeframe. To improve on this front, this paper introduces the steady state centrifuge technique, a common experiment in soil physics, for use on porous building materials. The laboratory centrifuge, used for the validation of this technique, is self-made to limit its cost and account for specific design choices. In the first part of the paper, the design of the laboratory centrifuge is described and all problems encountered during the development are explained and resolved. The two main problems are excessive heat generation by the motor and unwanted evaporation from the sample’s surfaces. The excessive heat generation is solved by extraction of heat both at the source, by using a ventilator, and at the rotor, by adding carefully positioned air extraction holes. The unwanted evaporation is eliminated by incorporating sample holders to shield the sample from the surrounding air. In the second part of the paper, the steady state centrifuge experiment is used to measure the desorption moisture retention curves of a ceramic brick starting from both saturated and capillary moisture content. The results are validated by their similarity to the curves obtained by mercury intrusion porosimetry. Besides providing accurate results, the determination of the full moisture retention curve requires only 1–2 weeks, which is significantly quicker than other common protocols, such as the pressure plates, which take about 2 months. Additionally, the ability to measure the desorption moisture retention curve from capillary moisture content as well as the limited cost of the centrifuge design (€6000) provide major advantages.
Image Restoration Using A PDE-Based Approach

Image restoration is an essential preprocessing step for many image analysis applications. In any image restoration techniques, keeping structure of the image unchanged is very important. Such structure in an image often corresponds to the region discontinuities and edges. The techniques based on partial differential equations, such as the heat equations, are receiving considerable attention in image restoration. In this paper, a new algorithm is proposed for image restoration using partial differential equations applied on neighbors longer than one pixel. To better preserve edges in this technique, slant edges are considered in addition to vertical and horizontal edges. A number of experiments have been preformed to evaluate performance of the proposed method and to compare its performance with the existing algorithms, wiener and median filters. The results express the considerable superiority of the proposed methods.
Projected explicit lawson methods for the integration of Schrödinger equation

In this article, it is proved that explicit Lawson methods, when projected onto one of the invariants of nonlinear Schrödinger equation (norm) are also automatically projected onto another invariant (momentum) for many solutions. As this procedure is very cheap and geometric because two invariants are conserved, it offers an efficient tool to integrate some solutions of this equation till long times. On the other hand, we show a detailed study on the numerical performance of these methods against splitting ones, with fixed and variable stepsize implementation. © 2014 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq31: 78–104, 2015
Genetic approach for a localisation problem based upon Particle Filters

Abstract Localisation, i.e., estimating a robot pose relative to a map of an environment, is one of the most important problems in mobile robotics. The literature offers several possible approaches to deal with such task, and optimal algorithms can be devised relying on particular constraints (linear state equations, Gaussian posterior density, etc.). Here, we propose a preliminary study for an algorithm able to approximate a large number of probability distributions when a map of the environment is available. This work improves the particle filters strategies presented in literature, reducing the number of particles needed to solve the localisation problem. The algorithm relies upon a suitable clustering of the particle set and a genetic approach for the resampling step.