Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Component Model of Abuse in Interpersonal Communication

Introduction. One of the most relevant issues in practical psychology is the topic of abuse – manipulation, violence, and violation of personal boundaries of any kind. The purpose of this study is to provide a concise, structured view of communication abuse, expressed in a three-component model. The communication of abuse contains three components: asymmetry, variability, and purpose. The following hypotheses were verified: 1) the detection of abuse by the person in situations of interpersonal communication occurs with the simultaneous presence of all three components; 2) abuse communication can be identified more frequently (successfully) in case of a higher level of psychological competence. Methods. To verify the hypotheses of the study, we created a questionnaire entitled Experimental Test for the Detection of Abuse. The sample of the study was comprised of 74 respondents aged 20 to 58 years, 34 of whom were professional psychologists (the group of ‘psychologists’) and 40 of whom were specialists of other professions (the group of ‘non-psychologists’). The gender composition was as follows: 24 males and 50 females. Results. The hypotheses verification showed the following results: 1) the empirical distribution of answers for the whole sample, as well as for the “psychologists” and “non-psychologists”, does not differ from the theoretical one; 2) the results for the group of “psychologists” are significantly different from those obtained in the group of “non-psychologists”. Discussion. Both hypotheses of the study were confirmed: 1) the detection of abuse takes place in accordance with the proposed three-component abuse model; 2) psychological competence and other kinds of psychological activities reliably contribute to a more successful recognition of abuse. Conclusion. In the article we formulated the conclusions about the correctness of the threecomponent abuse model and the influence of the level of psychological competence on the ability to determine abuse in social contacts. The presented model may become a method of analyzing communications of abuse and be used to correct them.
Left Atrium Hemodynamic in Atrial Fibrillation and Normal Subjects

Atrial Fibrillation (AF) is associated with a five-fold increase in the risk of cerebrovascular events. Recent studies suggest that a computational fluid-dynamics (CFD) approach could provide insights on AF mechanisms thus potentially allowing a quantitative assessment of cardioembolic risk. The goal of this study was to use a previously developed patient specific CFD model of the left atrium (LA) to enhance differences in blood flow in AF patients and normal subjects. In this study we computed LA blood flow and derived parameters in normal subjects (NL), patients affected by paroxysmal AF (PAR-AF) and patients affected by persistent AF (PER-AF). Results showed mean peak velocities continuously decreasing from NL to PER-AF groups. In agreement, a lower number of vortex structures was observed in PER-AF with respect to PAR-AF and NL, thus limiting an effective washout of the LA and the left atrial appendage (LAA). Velocities at the LAA ostium and inside the LAA were also strongly reduced showing a limited washout effect as confirmed by blood stasis in terms of number of particles still present after five cardiac cycles (NL: 5±2, PAR-AF: 18±3, PER-AF: 41±10). The developed approach quantifies differences in LA hemodynamic between AF patients and NL.
Assessment of tree species diversity and above-ground biomass in two disturbed tropical dry evergreen forests of Coromandel coast of India

The tree diversity and carbon stock of all woody plants were investigated in two-hectare square plots (100 m x 100 m) were established in Suryanpet (SP) and Velleripet (VP) which is tropical dry evergreen forest (TDEF) sites on the Coromandel Coast of peninsular India. All trees ? 10 cm girth at breast height measured at 1.3 m from the rooting point were enumerated. A total of 35 tree species (? 10 cm gbh) belonging to 34 genera and 23 families were recorded in tropical dry evergreen forests. Tree species richness in 27 (dominant species Dimorphocalyx glabellus Thw.) and 18 (dominant species Strychnos nux-vomica L.) in SP and VP respectively. A total density of woody plants 671 and 1154 individuals in SP and VP respectively. The basal area of trees in the two study sites SP (40.70 m2 ha-1) and VP (45.46 m2 ha-1). Most abundant families are Loganiaceae, Euphorbiaceae and Rutaceae and its family index value (FIV) is 56.14, 39.12 and 21.40 respectively. The aboveground biomass (AGB) of trees totaled in site SP (405.3 Mg ha-1) and VP (721.3 Mg ha-1). The extent of tree species diversity and estimated carbon stock of the TDEF sites, which provides the baseline data on the floristic structure and diversity of this forest for better management and conservation.
Cold Atmospheric Plasma Cancer Treatment, a Critical Review

Cold atmospheric plasma (CAP) is an ionized gas, the product of a non-equilibrium discharge at atmospheric conditions. Both chemical and physical factors in CAP have been demonstrated to have unique biological impacts in cancer treatment. From a chemical-based perspective, the anti-cancer efficacy is determined by the cellular sensitivity to reactive species. CAP may also be used as a powerful anti-cancer modality based on its physical factors, mainly EM emission. Here, we delve into three CAP cancer treatment approaches, chemically based direct/indirect treatment and physical-based treatment by discussing their basic principles, features, advantages, and drawbacks. This review does not focus on the molecular mechanisms, which have been widely introduced in previous reviews. Based on these approaches and novel adaptive plasma concepts, we discuss the potential clinical application of CAP cancer treatment using a critical evaluation and forward-looking perspectives.
Obstacle-Aware and Energy-Efficient Multi-Drone Coordination and Networking for Disaster Response

Unmanned aerial vehicles or drones provide new capabilities for disaster response management (DRM). In a DRM scenario, multiple heterogeneous drones collaboratively work together forming a flying ad-hoc network (FANET) instantiated by a ground control station. However, FANET air-to-air and air-to-ground links that serve critical application expectations can be impacted by: (i) environmental obstacles, and (ii) limited battery capacities. In this paper, we present a novel obstacle-aware and energy-efficient multi-drone coordination and networking scheme that features a Reinforcement Learning (RL) based location prediction algorithm coupled with a packet forwarding algorithm for drone-to-ground network establishment. We specifically present two novel drone location-based solutions (i.e., heuristic greedy, and learning-based) in our packet forwarding approach to support heterogeneous drone operation as per application requirements. These requirements involve improving connectivity (i.e., optimize packet delivery ratio and end-to-end delay) despite environmental obstacles, and improving efficiency (i.e., by lower energy use and time consumption) despite energy constraints. We evaluate our scheme by comparing it with state-of-the-art networking algorithms in a trace-based DRM FANET simulation testbed. Results show that our strategy overcomes obstacles and can achieve between 81-90% of network connectivity performance observed under no obstacle conditions. With obstacles, our scheme improves network connectivity performance by 14-38 % while also providing 23-54% of energy savings.
Reasoning about Independence in Open Universe Probabilistic Logic Programs

In the 1980’s J. Pearl and T. Verma [1] developed a graphical criterion to reason about probabilistic independence in Bayesian networks, which is famously known as d-separation. As J. Vennekens and S. Verbaeten [2, §5.2] pointed out in 2003, ground acyclic probabilistic logic programs correspond to Bayesian networks. Hence, we can pass from an acyclic ground program to the associated Bayesian network in order to derive independence statements from d-separation. In the present paper we lift this procedure so as to reason about independence in open universe probabilistic logic programs.
PRBM_A_244397 267..278

Beata A Basinska 1 Ewa Gruszczynska 2 1Faculty of Management and Economics, Gdansk University of Technology, Gdansk, Poland; 2Faculty of Psychology, SWPS University of Social Sciences and Humanities, Warsaw, Poland Background: Burnout has been traditionally seen as a chronic and stable state in response to prolonged stress. However, measures of momentary burnout are not well established, even though the within-person approach suggests that the symptoms of burnout may vary from day to day for the same employee. The aim of this study is to examine the daily interand intra-personal variability of the symptoms of burnout and the cross-lagged relationship between two components of burnout, exhaustion and disengagement. Methods: An online diary study over 10 consecutive workdays was conducted among 235 civil servants (75% women, average tenure of 15 years). Daily burnout was measured with the eight-item Oldenburg Burnout Inventory. Results: The intra-class correlation coefficients indicate that, although significant betweenperson variability exists, most of the burnout variance is within persons. Using the random intercept cross-lagged panel (RI-CLP) model to control for these between-person differences, mainly insignificant “pure” within-person cross-lagged relationships between exhaustion and disengagement were revealed. Moreover, day-to-day autoregressive effects were weaker than same-day residual correlations. Conclusion: This is one of the first studies to use daily diaries and the RI-CLP model to study burnout, including the separation of the more stable and more dynamic parts of each component. When stable parts were controlled for, the same-day relationships between exhaustion and disengagement were more pronounced than day-to-day effects. This might suggest stronger situational influences than carryover mechanism. Thus, conceptualizing burnout in terms of daily symptoms may shed promising insights into how it develops and add implications for pro-healthy changes in the workplace.
Karst spring discharge modeling based on deep learning using spatially distributed input data

Abstract. Despite many existing approaches, modeling karst water resources remains challenging and often requires solid system knowledge. Artificial Neural Network approaches offer a convenient solution by establishing a simple input-output relationship on their own. However, in this context, temporal and especially spatial data availability is often an important constraint, as usually no or few climate stations within a karst spring catchment are available. Hence spatial coverage is often unsatisfying and can introduce severe uncertainties. To avoid these problems, we use 2D-Convolutional Neural Networks (CNN) to directly process gridded meteorological data followed by a 1D-CNN to perform karst spring discharge simulation. We investigate three karst spring catchments in the Alpine and Mediterranean region with different meteorologic-hydrological characteristics and hydrodynamic system properties. We compare our 2D-models both to existing modeling studies in these regions and to 1D-models, which use climate station data, as it is common practice. Our results show that our models are excellently suited to model karst spring discharge and rival the simulation results of existing approaches in the respective areas. The 2D-models learn relevant parts of the input data and by performing a spatial input sensitivity analysis we can further show their potential for karst catchment localization and delineation.
Polaris Scheduler: Edge Sensitive and SLO Aware Workload Scheduling in Cloud-Edge-IoT Clusters

Application workload scheduling in hybrid Cloud-Edge-IoT infrastructures has been extensively researched over the last years. The recent trend of containerizing application workloads, both in the cloud and on the edge, has further fueled the need for more advanced scheduling solutions in these hybrid infrastructures. Unfortunately, most of the current approaches are not fully sensitive to the edge properties and also lack adequate support for Service Level Objective (SLO) awareness. Previously, we introduced software defined gateways (SDGs), which enable managing novel edge resources at scale. At the same time Kubernetes was initially released. In spite of not being specifically developed for the edge, Kubernetes implements many of the design principles introduced by our SDGs, making it suitable for building SDG extensions on top of it. In this paper we present Polaris Scheduler - a novel scheduling framework, which enables edge sensitive and SLO aware scheduling in the Cloud-Edge-IoT Continuum. Polaris Scheduler is being developed as a part of Linux Foundation's Centaurus project. We discuss the main research challenges, the approach, and the vision of SLO aware edge sensitive scheduling.
XÁC ĐỊNH TƯƠNG ĐỒNG XUYÊN NGỮ ANH-VIỆT

TÓM TẮT: Bài toán xác định tương đồng ngữ nghĩa văn bản là một trong những bài toán đóng vai trò rất quan trọng, ảnh hưởng đến chất lượng của nhiều bài toán xử lý ngôn ngữ tự nhiên như truy vấn thông tin, tóm tắt văn bản, phát hiện đạo văn,. Đặc biệt trong thời đại hiện nay, với sự phát triển của các công cụ dịch tự động, thì bài toán xác định tương đồng ngữ nghĩa văn bản còn phải xem xét đến cả các trường hợp các cặp văn bản thuộc các ngôn ngữ khác nhau. Trong bài báo này, chúng tôi đề xuất sử dụng mô hình đồ thị để xác định tương đồng ngữ nghĩa xuyên ngữ AnhViệt. Bên cạnh đó, chúng tôi cũng áp dụng bổ sung các phương pháp như điều chỉnh gán nhãn từ loại giữa văn bản tiếng Việt và văn bản tiếng Anh, bổ sung danh sách từ tiếng Việt đồng nghĩa, kết hợp các lớp đồ thị khác nhau. Kết quả thực nghiệm cho thấy việc sử dụng các phương pháp trên giúp nâng độ chính xác của mô hình từ 71,9% lên 76,3%.
Flexible inorganic membranes used as a high thermal safety separator for the lithium-ion battery

A flexible SiO2 porous fiber membrane (SF) is prepared by electrospinning followed by calcination in this work. Compared with an organic substrate separator, the SF used as a separator will be an absolute guarantee of the battery thermal safety. The porosity of the SF is 88.6%, which is more than twice that of a regular PP separator. Hydrophilic SF shows better electrolyte wetting ability and its high porosity enables the SF to absorb 633% liquid electrolyte on average, while the lithium-ion conductivity reaches 1.53 mS cm−1. The linear sweep voltammogram testing of PP and SF suggested that SF, with great electrochemical stability, can meet the requirements of lithium-ion batteries. The cyclic and rate performances of batteries prepared with SF are improved significantly. Such advantages of the SF, together with its potential in mass production, make the SF a promising membrane for practical applications in secondary lithium-ion batteries.
Automatic Classiﬁcation of 12-, 6, 4, 3-, and 2-Lead Electrocardiograms Using Morphological Feature Extraction

Cardiovascular diseases are the global leading cause of death. Automated electrocardiogram (ECG) analysis can support clinicians to identify abnormal excitation of the heart and prevent premature cardiovascular death. An ex-plainable classiﬁcation is particularly important for support systems. Our contribution to the PhysioNet/CinC Challenge 2021 (team name: ibmtPeakyFinders) therefore pursues an approach that is based on interpretable features to be as explainable as possible. To meet the challenge goal of developing an algorithm that works for both 12-lead and reduced lead ECGs, we processed each lead separately. We focused on signal processing techniques based on template delineation that yield the template’s ﬁducial points to take the ECG wave-form morphology into account. In addition to beat intervals and amplitudes obtained from the template, various heart rate variability and QT interval variability features were extracted and supplemented by signal quality indices. Our classiﬁcation approach utilized a decision tree ensemble in a one-vs-rest approach. The model parameters were determined using an extensive grid search. Our approach achieved challenge scores of 0.47, 0.47, 0.40, 0.43, and 0.45 on 12-, 6, 3-, 4, and 2-lead validation sets, respectively.
The Endocannabinoidome: Expanding the Approach to Treating Traumatic Brain Injury Using the Endocannabinoid System

This paper explores the potential use of endocannabinoidome molecules as a therapeutic approach to treating traumatic brain injury (TBI). Google Scholar was used to obtain the primary research literature analyzed for this review. Studies which manipulate the endocannabinoid system through methods such as administration of 2-AG or AEA ligands, inhibiting breakdown enzymes, and using CB1 and CB2 agonists or antagonists have shown promising results in treating TBI; however, no pragmatic clinical therapy has been found so far. The discovery of similar molecules and receptors has resulted in the expansion of the endogenous system and bred the term endocannabinoidome, which incorporates the newly discovered molecules and receptors. Ligands of the endocannabinoidome produce similar therapeutic benefits for TBI but act by different receptor pathways, which may allow one to overcome current existing problems of manipulating the endocannabinoid system for TBI treatment. Currently, therapies used to treat TBI have many unwanted side effects, establishing the need for alternative research options. This paper examines three of these endocannabinoidome molecules that have been previously researched for treating TBI and illuminates their specific receptor pathways and how these receptor pathways operate differently from the ordinary pathways of the endocannabinoid system. Gaining an understanding of the receptor pathways used by endocannabinoidome molecules will open a new field of research for therapeutics to treat TBI.
A Fuzzy Logic Control System for a Robotic Hand Driven by Shape Memory Alloy Wires

This paper presents a robotic hand using wires with shape memory (NiTi) as non-conventional actuators. The mechanical structure of the robot hand was first designed by means of a CAD computer program and afterwards 3D printed using ABS polymer. The robotic hand was designed according to the physiological characteristics of the human hand, with particular attention to the angles formed by the phalanges of the fingers. A mechanical system accommodates the thin NiTi wires compactly, thus forming an artificial muscle. A fuzzy logic based control system allows an accurate positioning of each phalanx. The contribution of the present work to science lies in the practical implementation of known techniques and materials.
Anaerobic digestion and agronomic applications of microalgae for its sustainable valorization

Microalgae are considered potential candidates in biorefinery processes, and due to their biochemical properties, they can be used in the production of biofuels such as biogas, as well as for bioremediation of liquid effluents. The objective of this review is to study the current status of microalgae anaerobic digestion and agricultural uses (as bio-stimulants and biofertilizers), starting from microalgae cultivation. Indeed, the efficiency of these processes necessarily depends on the evaluation of different biotic and abiotic factors that affect the growth of microalgae. However, the adaptation and the optimization of process parameters on a large scale is also limited by energy and economic constraints. Moreover, the integration of biogas production processes with microalgae cultivation allows a nutrients and CO2 virtuous loop, thus promoting the sustainability of the process. Finally, this paper provides a general overview of biogas and biofertilizers production combination, as well as the related challenges and recommended future research perspectives to complement the gap in the literature.
Design, characterization, and first field deployment of a novel aircraft-based aerosol mass spectrometer combining the laser ablation and flash vaporization techniques

Abstract. In this paper, we present the design, development, and characteristics of the novel aerosol mass spectrometer ERICA (ERC Instrument for Chemical composition of Aerosols) and selected results from the first aircraft-borne field deployment. The instrument combines two well-established methods of real-time in-situ measurements of fine particle chemical composition. The first method is the single particle laser ablation technique (here with a frequency-quadrupled Nd:YAG laser at λ = 266 nm). The other method is a combination of flash vaporization and electron impact ionization (like the Aerodyne aerosol mass spectrometer). The aerosol sample can be analyzed with both methods, each using time-of-flight mass spectrometry. By means of the laser ablation, single particles are qualitatively analyzed (including the refractory components) while the flash vaporization and electron impact ionization technique provides quantitative information on the non-refractory components (i.e., particulate sulfate, nitrate, ammonia, organics, and chloride) of small particle ensembles. These techniques are implemented in two consecutive instrument stages within a common sample inlet and a common vacuum chamber. At its front end, the sample air containing the aerosol particles is continuously injected via an aerodynamic lens (ADL). All particles which are not ablated by the Nd:YAG laser in the first instrument stage continue their flight until they reach the second instrument stage and impact on the vaporizer surface (operated at 600 °C). The ERICA is capable of detecting single particles with vacuum aerodynamic diameters (dva) between ~ 180 nm and 3170 nm (d50 cut-off). The chemical characterization of single particles is achieved by recording cations and anions with a bipolar time-of-flight mass spectrometer (B-ToF-MS). For the measurement of non-refractory components, the particle size range extends from approximately 120 nm to 3.5 µm (d50 cut-off; dva), and the cations are detected with a C-ToF-MS (compact time-of-flight mass spectrometer). The compact dimensions of the instrument are such that the ERICA can be deployed on aircraft, ground stations, or mobile laboratories . During its first deployments the instrument operated fully automated during 11 research flights on the Russian high-altitude research aircraft M-55 Geophysica from ground pressure and temperature up to 20 km altitude at 55 hPa and ambient temperatures as low as −86 °C.
Conveyor Belt Longitudinal Rip Detection Implementation with Edge AI

: Failures in the detection of longitudinal rips on conveyor belts are events considered catastrophic in mining environments due to the ﬁnancial losses caused and the exposure to safety risks of the maintenance teams. The longitudinal rip detection technologies used today have limitations, being the most reliable systems expensive and complex and the simplest and cheapest systems unreliable. In view of this scenario, we studied the implementation of a longitudinal rip detection solution based on images of the conveyor belt. The images will be collected in real time and inference, rip detection, will be carried out locally using a deep neural network model executed on device edge. The results obtained with the prototype, in controlled ﬁeld tests, were satisfactory and showed the feasibility of using deep neural network algorithms executed on device edge. These results encourage the development of a complete solution for the detection of defects in conveyor belts considering all the operational conditions found in the mining environment.