Miracle-dz/UniFAR-FTUs huggingface dataset:
Whatever happened to group dynamics in the scientific study of groups?

The purpose of this study is to explore whether a group dynamics perspective still exists in the scientific study of groups and what factors may account for the current situation. ,Alongside reflections based on my professional experience, I have analyzed the main academic journals that publish group research. ,A group dynamics perspective is almost totally absent in the scientific study of groups. Contributing factors to this state of affairs are disciplinary developments in psychology (e.g. individualization, experimentalization and specialization), the demise of the status of psychoanalysis, changes in the meaning and manifestation of the “group,” and effects of New Public Management. ,The study offers a critical perspective on current group research practices and considers these in a larger (social and historical) context. It advocates a group dynamics perspective for the study of groups, based on systems-psychodynamic insights.
Application and Analysis of Random Forest Algorithm for Estimating Lawn Grass Lengths in Robotic Lawn Mower

This paper states an estimation method for lawn grass lengths or ground conditions based on random forest algorithm from the observation data obtained by fusion of sensors. This estimation relates to Digital Twin and Virtual Twin of Hybrid Twin approach for the autonomous driving of robotic lawn mowers. The robotic lawn mowers are becoming popular with the advent of efficient sensors and embedded systems and we are now developing a practical autonomous driving and its group control algorithm for large lawn grass areas. However, one of the important functions of robotic lawn mower, that is, the length of lawn grasses or such ground conditions as dirt, gravel, or concrete, etc., are not recognized precisely with the current robotic lawn mower. As a result, the motor for cutting lawn grasses is running with constant rotation speed from the beginning to the end of operation of robotic lawn mower. This leads to the waste of battery and gives a large drawback for the control of robotic lawn mower. In order to precisely control the rotation speed of motor and save the battery, the lawn grass lengths and ground conditions are estimated by using the effective sensor data. The application of random forest algorithm to the fusion of sensors on a commercial robotic lawn mower attained more than 90% correct estimation ratio in several experiments on actual lawn grass areas. Now, the suggested algorithm and the fusion of sensors are evaluated against wide range of lawn and grounds.
Electron transfer in solution : nonadiabatic dynamics and applications to catalysis

A mechanistic understanding of electron transfer in solution will advance our understanding of many chemical processes, including heterogeneous redox catalysis and photochemistry–processes which are fundamental in energy storage and solar energy conversion, among other applications. In this thesis, we first apply density functional theory (DFT) to study the mechanistic intermediates of the oxygen evolution reaction (OER) on metal-oxide redox catalysts. From these thermodynamic calculations, we are able to gain insight into catalytic design principles. Afterwards, we study nonadiabatic electron transfer in solution. After benchmarking various resummations of a fourth-order perturbation theory expansion of a generalized master equation memory kernel for the spin-boson model, we apply our theoretical understanding to study the short-time dynamics of electron transfer beyond the Condon approximation in aqueous iron(II) / iron(III) electron self-exchange. We discuss the application of this method to identify conical intersections in condensed-phase photochemistry. Finally, we examine the range of validity of electron couplings predicted by constrained density functional theory with configuration interaction (CDFT-CI). The nonadiabatic electron transfer methods developed and applied in this work will contribute to a relatively sparse computational toolkit for studying challenging problems in photochemical electron transfer, such as the prediction of nonradiative decay rates from first principle; these, in turn, will contribute to the design of catalytic materials for solar energy conversion. Thesis Supervisor: Troy Van Voorhis Title: Haslam and Dewey Professor of Chemistry
Deep Learning In Wireless Sensor Network

In the era of the development of the Internet of Things, how collecting data from sensors located everywhere in society is an important issue, and in wireless sensor networks, distributed processing technology for deep learning using advanced computing and coordination between peripherals has attracted attention. In this study, a new architecture is proposed for the distributed implementation of deep learning in a wireless sensor network where the distributed application divides the middle layer of deep learning into each sensor with the purpose of reducing traffic and reducing the load, in order to improve data processing speed, but this is done by increasing the number of connections and energy consumption, and in order to evaluate the effectiveness of the proposed method, been compared the amount of data communication and accuracy of learning between learning by using Convolutional Neural Network (CNN) normally and the distributed learning, and this was done using the Raspberry Pi open-source hardware platform, the system that is low-cost and highly scalable in terms of sensor devices type and the number of sensor nodes, which makes them well suited to a wide range of applications related to environmental monitoring, the sensors device have been connected to hardware and software design. Some software components, such as operating systems, sensor/hardware drivers, may need development. In the proposed method, the amount of data is large because the result in the middle of the computation processing is sent and it can be predicted that the total computation processing time will be reduced because the computational processing handled by each node will be reduced by section processing. The concentration of energy consumption can also be suppressed because the proposed method achieves decentralization compared to performing all calculations with a single sensor.
Management of Traumatic Cervical Spondyloptosis

Abstract Traumatic cervical spondyloptosis is a 3-column fracture-dislocation resulting in a highly unstable spine requiring urgent reduction, stabilization, and fixation. Since its occurrence is not that common, there are a lot of controversies concerning its management. A holistic approach has been proposed regarding the management of traumatic cervical spondyloptosis.
A Novel Stop-Gain Mutation in MSH2 Gene Among a Persian Family Fulfilling Classic Amsterdam Criteria for Lynch Syndrome

Purpose Lynch syndrome is the most common hereditary cancer syndromes due to a germline mutation in one of the mismatch-repair (MMR) genes. It results in early-onset colorectal cancer (CRC) and other Lynch-associated cancers in an autosomal dominant pattern. In this article, a new pathogenic variant in a Persian family with familial CRCs and positive Amsterdam II criteria has been described. Methods IHC-MMRs was done on tissue sections from tumor and its adjacent healthy tissue of the proband. Microsatellite instability (MSI) testing was also performed on DNA extracted from tumor and healthy tissue using Promega kit. Next Generation Sequencing (NGS) was finally done on genomic DNA of the proband using a 12-gene-panel including MMR genes. Variant filtering and prioritization were done using bioinformatics tools. Co-segregation analysis was used to evaluate the explored pathogenic variant. Results The proband was a 38-year woman at-diagnosis affected with CRC located in the sigmoid colon. The family history of cancer was observed in three successive generations. IHC was absent for MSH2 and MSH6, and MSI-High was reported from MSI testing. NGS analysis explored a new stop gained codon mutation on the first exon of MSH2 gene as a substitution of A to G MSH2: c.364A > T which was pathogenic according to the variant interpretation guidelines of American College of Medical Genetics and Genomics. Conclusion Revealing of a more obvious molecular feature of Lynch-syndrome among Iranian populations could lead to identification of at-risk people for early care and prevention of cancer.
A Signal Processing Framework for Agile RF Beamforming: From RF-Chain-Free to Hybrid Beamformers

In conventional hybrid beamforming approaches, the number of radio-frequency (RF) chains is the bottleneck on the achievable spatial multiplexing gain. Recent studies have overcome this limitation by increasing the update-rate of the RF beamformer. This paper presents a framework to design and evaluate such approaches, which we refer to as agile RF beamforming, from theoretical and practical points of view. In this context, we consider the impact of the number of RF-chains, phase shifters’ speed, and resolution to design agile RF beamformers. Our analysis and simulations indicate that even an RF-chain-free transmitter, which its beamformer has no RF-chains, can provide a promising performance compared with fully-digital systems and significantly outperform the conventional hybrid beamformers. Then, we show that the phase shifter’s limited switching speed can result in signal aliasing, in-band distortion, and out-of-band emissions. We introduce performance metrics and approaches to measure such effects and compare the performance of the proposed agile beamformers using the Gram-Schmidt orthogonalization process. Although this paper aims to present a generic framework for deploying agile RF beamformers, it also presents extensive performance evaluations in communication systems in terms of adjacent channel leakage ratio, sum-rate, power efficiency, error vector magnitude, and bit-error rates.
The Design and Development of the Destiny-Class CyberCANOE Hybrid Reality Environment

The Destiny-class CyberCANOE (Destiny) is a Hybrid Reality environment that provides 20/20 visual acuity in a 13-foot-wide, 320-degree cylindrical structure comprised of tiled passive stereo-capable organic light emitting diode (OLED) displays. Hybrid Reality systems combine surround-screen virtual reality environments with ultra-high-resolution digital project-rooms. They are intended as collaborative environments that enable multiple users to work minimally encumbered for long periods of time in rooms surrounded by data in the form of visualizations that benefit from being displayed at resolutions matching visual acuity and/or in stereoscopic 3D. Destiny is unique in that it is the first Hybrid Reality system to use OLED displays and it uses a real-time GPU-based approach for minimizing stereoscopic crosstalk. This paper chronicles the non-trivial engineering research and attention-to-detail that is required to develop a production quality hybrid-reality environment by providing details about Destiny’s design and construction process. This detailed account of how a Hybrid Reality system is designed and constructed from the ground up will help VR researchers and developers understand the engineering complexity of developing such systems. This paper also discusses a GPU-based crosstalk mitigation technique and evaluation, and the use of Microsoft’s augmented reality headset, the HoloLens, as a design and training aid during construction.
Prediction of Mortality and Clinical Decisions Using Frailty Scores in Hospitalized Elderly with Covid-19

Aim: Many decisions in clinical care in elderly patients admitted to the hospital with COVID-19 are based upon mortality risk. Screening for frailty is recommended, however its predictive value in hospitalized older adults with COVID-19 infection is unknown. Objective is to compare the prognostic power of frailty scores with other prognostic parameters including age, laboratory biomarkers, comorbidities and imaging findings. In addition, to determine usefulness of clinical frailty scores in clinical decision-making including instalment of Do-Not-Intubate (DNI) order and discharge to institutionalized care. Methods: A single-centre retrospective observational study in hospitalized patients > 65 years with COVID-19. Clinical Frailty Scale (CFS) and/or Groningen Frailty Indicator (GFI), Charlson Comorbidity Index (CCI), inflammation, CT severity scores (Do-Not-Intubate) DNI order, in hospital mortality and discharge destination were determined using chart data as well as data from the ZuydErLand COVID-19 regiStry (ELVIS). Results: 157 patients were included, mean age 77 ± 7 years. 30% was considered frail. The frailty scores failed to predict mortality. However, the frailty scores appeared to be associated with clinical decision-making including instalment of DNI orders and discharge to institutionalized care. Conclusions: Clinical frailty scores in our study showed promise as a tool for decision making in COVID-19, but probably not for prediction of mortality.
N and P Addition Reduce Semiarid Grassland Stability via Increasing the Dominance of Less-Stable Functional Groups

Aims Extraneous fertilization could efficiently improve grassland productivity and promote grassland restoration. Increasing fertilization may profoundly affect community stability, whereas the underlying compensatory dynamics among species and functional groups in regulating grassland stability remain unclear.Methods Three different grasslands, annuals and biennials (AB) community, perennial grass (PG) community and perennial forb (PF) community, on semiarid Loess Plateau were selected. We designed a 3-year split-plot experiment (main-plot: 0, 25, 50, and 100 kgN ha-1 yr-1; subplot: 0, 20, 40 and 80 kgP2O5 ha-1 yr-1) to explore how N and P addition affects community stability and its relationship with species richness, species asynchrony (SA) and functional group stability. Results Temporal stability differed largely between functional groups under N and P addition, perennial forbs or grasses had higher stability than perennial legumes or annuals and biennials. Decreased stability of PG and PF communities was primarily due to increased dominance of perennial grasses or forbs and reduced SA under N addition alone, while it attributed to increased dominance of perennial legumes after P addition alone. 50 and 100 kgN ha-1 yr-1 combined with P addition notably increased dominance of annuals and biennials at the expense of perennial species, but decreased stability of annuals and biennials, which caused declines in stability of the three communities.Conclusions Nutrient-induced shifts of functional groups, particularly increased dominance of less-stable functional groups, is the main driver reducing perennial grassland stability independent of diversity. Whereas N and P addition decreased annual grassland stability via additive effect of increased dominance of annuals and biennials and diversity decline. Targeted N and P combined addition was essential to maintain stability for different grasslands by regulating functional groups on semiarid Loess Plateau.
Sex Difference in the Tolerance of Hepatic Ischemia-reperfusion Injury and Hepatic Estrogen Receptor Expression According to Age and Macrosteatosis in Healthy Living Liver Donors

Supplemental Digital Content is available in the text. Background. Hepatic estrogen signaling, which is important in liver injury/recovery, is determined by the level of systemic estrogen and hepatic estrogen receptor. We aimed to evaluate whether females’ advantage in the tolerance of hepatic ischemia-reperfusion injury decreases according to the age of 40 y (systemic estrogen decrease) and macrosteatosis (hepatic estrogen receptor decrease). Methods. We included 358 living liver donors (128 female and 230 male individuals). The tolerance of hepatic ischemia-reperfusion injury was determined by the slope of the linear regression line modeling the relationship between the duration of intraoperative hepatic ischemia and the peak postoperative transaminase level. Estrogen receptor content was measured in the biopsied liver samples using immunohistochemistry. Results. In the whole cohort, the regression slope for aspartate transaminase was comparable between female and male individuals (P = 0.940). Within the subgroup of donors aged ≤40 y, the regression slope was significantly smaller in female individuals (P = 0.031), whereas it was comparable within donors aged >40 y (P = 0.867). Within the subgroup of nonmacrosteatotic donors aged ≤40 y, the regression slope was significantly smaller in female individuals in univariable (P = 0.002) and multivariable analysis (P = 0.006), whereas the sex difference was not found within macrosteatotic donors aged ≤40 y (P = 0.685). Estrogen receptor content was significantly greater in female individuals within nonmacrosteatotic donors aged ≤40 y (P = 0.021), whereas it was not different in others of age >40 y or with macrosteatosis (P = 0.450). Conclusions. The tolerance of hepatic ischemia-reperfusion injury was greater in female individuals than in male individuals only when they were <40 y and without macrosteatosis. The results were in agreement with the hepatic estrogen receptor immunohistochemistry study.
FINANCIAL AND ECONOMIC ASPECTS OF PROFESSIONAL FOOTBALL INDUSTRY: TRENDS ANALYSIS AND DEVELOPMENT PROSPECTS

The purpose of the study is the analysis of financial and economic aspects and assessment of current trends and prospects for the development of professional football industry as an independent academic field In this article the following general scientific and applied methods were used: analysis and comparison - to determine the main trends in the development of top professional football clubs in the context of globalization;generalization - to establish the influence of institutional factors on the economic results of football clubs activity;expert assessments - to determine the market value of players in the global transfer market;abstract and logical methods - for theoretical generalization of the obtained results, forecasting the prospects for the development of European professional football and formulating conclusions of the study The article deals with the analysis of financial and economic results of European football top clubs functioning in the pre-crisis period (before COVID-19) The author emphasizes the dualistic origins of modern professional football, which combines sports and commercial components It is analyzed the impact of "Bosman case" and the rules of "financial fair play" on the professional football industry development in the context of globalization The sources and structure of football top clubs income are also analyzed The main focus is on European football, where current trends are most clearly manifested The source of information was the annual reports of audit firms, specialized analytical laboratories and FIFA data As a result of globalization processes, the author predicts the creation of full-fledged "SuperLeague" championship of top clubs with the greatest financial opportunities, as well as the appearence of polycentrism phenomenon in the world football industry The forecast scenarios of football industry perspectives, formulated by the author, to substantiate the financial and economic aspects of clubs and players transfer market functioning, can be used in practice to assess the potential and formulate strategies for professional football leagues and clubs
Changing the Conversation on the Science–Practice Gap: An Adherence-Based Approach

The science–practice gap has been recognized as a grand challenge for management scholars in the 21st century. Despite the generation of a considerable amount of knowledge, which is clearly relevant to practice, the science–practice gap continues to persist. We challenge past notions that areas of management have not sufficiently developed to be prescriptive. The Editorial leadership of the Journal of Management commissioned our team to change the conversation on the science–practice gap by changing the question to one which focuses on adherence. We introduce an adherence-based framework from the medical literature to advance the conversation on the science–practice gap in management. This includes a rubric that identifies five criteria to evaluate the prescriptive readiness of research (theoretical grounding, rigor, relevance, practical recommendations, and communication) that produce five levels of research advancement (exploratory, preliminary, option, guidelines, and standards). We identify how scholars and practitioners can overcome structural, social, and psychological barriers to adherence (make recommendations possible, easy, normative, rewarding, and sometimes required). While the science–practice gap may remain a persistent concern, an adherence-based approach can serve to change the nature of the conversation in order to reduce the gap in many areas of management.
Deep Learning-Based Ultrasonic Testing to Evaluate the Porosity of Additively Manufactured Parts with Rough Surfaces

Ultrasonic testing (UT) has been actively studied to evaluate the porosity of additively manufactured parts. Currently, ultrasonic measurements of as-deposited parts with a rough surface remain problematic because the surface lowers the signal-to-noise ratio (SNR) of ultrasonic signals, which degrades the UT performance. In this study, various deep learning (DL) techniques that can effectively extract the features of defects, even from signals with a low SNR, were applied to UT, and their performance in terms of the porosity evaluation of additively manufactured parts with rough surfaces was investigated. Experimentally, the effects of the processing conditions of additive manufacturing on the resulting porosity were first analyzed using both optical and scanning acoustic microscopy. Second, convolutional neural network (CNN), deep neural network, and multi-layer perceptron models were trained using time-domain ultrasonic signals obtained from additively manufactured specimens with various levels of porosity and surface roughness. The experimental results showed that all the models could evaluate porosity accurately, even that of the as-deposited specimens. In particular, the CNN delivered the best performance at 94.5%. However, conventional UT could not be applied because of the low SNR. The generalization performance when using newly manufactured as-deposited specimens was high at 90%.
Mesoporous silica nanoparticles prepared by different methods for biomedical applications: Comparative study

Abstract In the current investigation, mesoporous silica nanoparticles were obtained by various techniques, namely sol–gel (S1), micro‐emulsion (S2) and hydrothermal synthesis (S3). The effect of those methods on the final features of the obtained mesoporous silica nanoparticles was studied. The obtained nanoparticles were investigated by TEM, BET surface area, Zetasizer, XRD and FTIR. The preparation method effect was evaluated on the drug release behaviour using doxycycline hyclate as a model drug. In addition, the degree of their compatibility against Saos‐2 cell line was also determined. The morphology and microstructure of silica nanoparticles were found to be dependent on the utilised method. Those techniques produced particles with particle sizes of 50, 30–20 and 15 nm and also surface areas of 111.04, 164 and 538.72 m2/g, respectively, for S1, S2 and S3. However, different preparation methods showed no remarkable changes for the physical and chemical integrities. The drug release test showed faster release from S2 compared with S1 and S3, which make them more applicable in cases require large doses for short periods. However, the release behaviour of S3 was satisfied for treatments which require long period with relatively highest release rate. The preparation method influenced the cell viability as S1 and S2 showed acceptable cell cytotoxicity compared with S3.