Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparing Workplaces for Digital Transformation: An Integrative Review and Framework of Multi-Level Factors

The rapid advancement of new digital technologies, such as smart technology, artificial intelligence (AI) and automation, robotics, cloud computing, and the Internet of Things (IoT), is fundamentally changing the nature of work and increasing concerns about the future of jobs and organizations. To keep pace with rapid disruption, companies need to update and transform business models to remain competitive. Meanwhile, the growth of advanced technologies is changing the types of skills and competencies needed in the workplace and demanded a shift in mindset among individuals, teams and organizations. The recent COVID-19 pandemic has accelerated digitalization trends, while heightening the importance of employee resilience and well-being in adapting to widespread job and technological disruption. Although digital transformation is a new and urgent imperative, there is a long trajectory of rigorous research that can readily be applied to grasp these emerging trends. Recent studies and reviews of digital transformation have primarily focused on the business and strategic levels, with only modest integration of employee-related factors. Our review article seeks to fill these critical gaps by identifying and consolidating key factors important for an organization’s overarching digital transformation. We reviewed studies across multiple disciplines and integrated the findings into a multi-level framework. At the individual level, we propose five overarching factors related to effective digital transformation among employees: technology adoption; perceptions and attitudes toward technological change; skills and training; workplace resilience and adaptability, and work-related wellbeing. At the group-level, we identified three factors necessary for digital transformation: team communication and collaboration; workplace relationships and team identification, and team adaptability and resilience. Finally, at the organizational-level, we proposed three factors for digital transformation: leadership; human resources, and organizational culture/climate. Our review of the literature confirms that multi-level factors are important when planning for and embarking on digital transformation, thereby providing a framework for future research and practice.
A Survey to Evaluate the Association of COVID-19 Restrictions on Perceived Mood and Coping in Australian Community Level Athletes

Highlights: No differences in coping or well-being in Australian community athletes based on the level of support received during COVID-19 restrictions. Community level athletes had better coping when a training program was provided. No difference between individual or team community athletes for well-being or coping scores. Australian community level athletes faced unprecedented changes to their training and competition options as the global COVID-19 pandemic took a stronghold. This disruption was predicted to have a negative impact on emotional well-being as communities braced through periods of social isolation and physical distancing requirements. This study provides an Australian perspective on the emotional well-being of community level athletes and the extent to which they coped during the COVID-19 pandemic. Emotional well-being and coping were measured using the Brief Emotional Experience Scale and the 28-item Brief Cope Scale. Both instruments were administered along with other questions pertaining to participant demographics and training status via an online survey between April and June 2020. The survey was disseminated to community athletes through word-of-mouth and social media platforms. No significant differences in emotional well-being were observed between athlete groups as a result of COVID-19 and its associated restrictions. Coping scores also appeared to be preserved in Australian community athletes, which contrasts the impact expected as a result of the COVID-19 pandemic. While tentative, the observed preservation in coping may have buffered potential declines in emotional well-being, which has been documented in professional and semi-professional athletes and the general population. These unexpected findings and tentative suppositions warrant further investigation and highlight the importance of conducting a country- or region-specific approach to examining the impact of COVID-19 on community athletes, as responses to COVID-19 are undoubtedly not consistent throughout the world.
Fractionation of sediment phosphorus revisited. I: Fractionation steps and their biogeochemical basis

The aim of this work was to assess the validity of a phosphorus fractionation procedure, introduced by Psenner and co‐workers and modified by Jensen and Thamdrup, in chemical characterization of sediment P. In this procedure, P is separated into 6 pools: loosely bound (and pore water) P, redox‐sensitive P (bound to iron and manganese), P bound to oxides of aluminum and nonreducible Fe, calcium‐bound P, and mobile and immobile pools of organic P. The procedure was slightly modified further, and every step of the work is described in detail. Reproducibility of the method and variation within the extracts obtained at each step were investigated with a commercial reference material. The validity of the results considered against the theoretical basis of the P fractionation procedure was evaluated in terms of the elemental composition of the separate extracts. The results showed good reproducibility of the method; variation in amounts of the different P forms in the separate extractions was small (coefficient of variation <15%). In addition, the analysis of selected elements extracted along with P were reliable enough to deduce the origin of the various P forms. The analytical results for P and the other elements were in accordance with the general theoretical basis of the fractionation procedure. Despite some shortcomings, the tested procedure, in combination with the laboratory practices and analytical methods described, gives reliable and valuable results for distinguishing between potentially mobile and immobile P in sediment.
Relative Age Effect in 14- to 18-Year-Old Athletes and Their Initial Approach to This Effect—Has Anything Changed Over the Past 10 Years?

One of the environmental variables associated with early talent development and the achievement of a high level of proficiency in sport is the relative age effect (RAE). The purpose of our study was threefold: (a) to calculate the RAE in young Israeli athletes (ages 14–18 years); (b) to examine how the athletes perceived this effect, if the effect indeed exists; and (c) to compare the RAE findings of this study with those of two previous studies on elite male (Lidor et al., 2010) and female (Lidor et al., 2014) Israeli ballplayers. Participants in the current study were 1,397 athletes (390 females and 1,007 males) who competed in five individual (gymnastics, judo, swimming, tennis, and track and field) and five team (basketball, soccer, team handball, volleyball, and water polo) sports. Data on the RAE, as well as on a number of aspects associated with this effect as perceived by the athletes, were collected via two closed questions. Data analyses showed that the RAE was found to be significant among the male athletes in four sports—swimming, basketball, soccer, and team handball; those who were born early in the year had a higher representation in these sport programs. However, this effect was not found to be significant in the female athletes. Most of the female and male athletes did not think that their birth date influenced their athletic success. However, a large portion of those who were born in the first quarter of the year (Q1) and the second quarter of the year (Q2) among the male athletes felt that they exhibited stronger abilities in the sports program compared to their peers who were born in the third and fourth quarters of the year (Q3 and Q4, respectively). The data of the current study provide additional support for the use of an “open door” approach to accepting children to sport programs by policymakers and coaches in Israel.
Genesis of the Master Circadian Pacemaker in Mice

The suprachiasmatic nucleus (SCN) of the hypothalamus is the central circadian clock of mammals. It is responsible for communicating temporal information to peripheral oscillators via humoral and endocrine signaling, ultimately controlling overt rhythms such as sleep-wake cycles, body temperature, and locomotor activity. Given the heterogeneity and complexity of the SCN, its genesis is tightly regulated by countless intrinsic and extrinsic factors. Here, we provide a brief overview of the development of the SCN, with special emphasis on the murine system.
Fostering Spatial Efficiency in the Marine Space, in a Socially Sustainable Way: Lessons Learnt From a Soft Multi-Use Assessment in the Mediterranean

In a context of growing claim for marine space and in the pursuit of maritime “spatial efficiency,” Multi-use (MU) becomes necessary to assemble more or less compatible sea uses. In this paper, the potential of the soft MU involving small-scale fisheries (SSF), tourism, and nature conservation related to marine protected areas (MPAs), widely encountered in the Mediterranean Sea basin, is being assessed in Greece. Despite the fact that the MU concept is not yet included neither in maritime spatial planning (MSP) laws nor in strategic policy documents due mainly to the dominance of terrestrial spatial plans that favor exclusive rights of highly competitive and expansive maritime activities (e.g., aquaculture), the above MU is increasingly being practiced by local communities as a socio-economic instrument (fishing tourism), able to be also occasionally oriented to nature conservation. Following the Drivers, Added Values, Barriers and Negative Impacts (DABI) analysis, a spectrum of challenges/constraints and opportunities for the application of the MU under study was revealed, grouped in socio-economic, environmental, political–regulatory, and technological factors that can enable or undermine this MU in the Greek seas. The paper concludes that there is a huge potential for the said MU development in areas dependent on fisheries, consistently to the longstanding SSF tradition that despite its decline, continues to be one of the most important among those practiced in the coastal zone and in remote and insular communities, essentially defining their particular social and cultural identity. Besides, SSF have low environmental impact, and also tourists and the local communities are gradually becoming more conservation-oriented. Hence, the MU is highlighted as a tool for sustainable use of marine space supporting the Blue Growth Agenda and reconnecting natural and cultural capital at sea, thus redefining also the role of fishers that under equitable conditions may become defenders of marine biodiversity and key actors for the sustainable management of fish stocks and ecosystems in the protected areas.
Commentary: Urinary Neopterin, a New Marker of the Neuroinflammatory Status in Amyotrophic Lateral Sclerosis

1 Laboratório de Bioenergética e Estresse Oxidativo, Departamento de Bioquímica, Centro de Ciências Biológicas, Universidade Federal de Santa Catarina, Florianópolis, Brazil, 2 Ambulatório de Doenças Neuromusculares e Neurogenéticas Hospital Universitário Polydoro Ernani de São Thiago, Universidade Federal de Santa Catarina, Florianópolis, Brazil, 3 Programa de Pós-Graduação em Ciências Médicas, Universidade Federal de Santa Catarina, Florianópolis, Brazil
Post-stroke Neurogenesis: Friend or Foe?

The substantial clinical burden and disability after stroke injury urges the need to explore therapeutic solutions. Recent compelling evidence supports that neurogenesis persists in the adult mammalian brain and is amenable to regulation in both physiological and pathological situations. Its ability to generate new neurons implies a potential to contribute to recovery after brain injury. However, post-stroke neurogenic response may have different functional consequences. On the one hand, the capacity of newborn neurons to replenish the damaged tissue may be limited. In addition, aberrant forms of neurogenesis have been identified in several insult settings. All these data suggest that adult neurogenesis is at a crossroads between the physiological and the pathological regulation of the neurological function in the injured central nervous system (CNS). Given the complexity of the CNS together with its interaction with the periphery, we ultimately lack in-depth understanding of the key cell types, cell–cell interactions, and molecular pathways involved in the neurogenic response after brain damage and their positive or otherwise deleterious impact. Here we will review the evidence on the stroke-induced neurogenic response and on its potential repercussions on functional outcome. First, we will briefly describe subventricular zone (SVZ) neurogenesis after stroke beside the main evidence supporting its positive role on functional restoration after stroke. Then, we will focus on hippocampal subgranular zone (SGZ) neurogenesis due to the relevance of hippocampus in cognitive functions; we will outline compelling evidence that supports that, after stroke, SGZ neurogenesis may adopt a maladaptive plasticity response further contributing to the development of post-stroke cognitive impairment and dementia. Finally, we will discuss the therapeutic potential of specific steps in the neurogenic cascade that might ameliorate brain malfunctioning and the development of post-stroke cognitive impairment in the chronic phase.
The Effects of Physically Embodied Multiple Conversation Robots on the Elderly

In recent years, communication robots aiming to offer mental support to the elderly have attracted increasing attention. Dialogue systems consisting of two robots could provide the elderly with opportunities to hold longer conversations in care homes. In this study, we conducted an experiment to compare two types of scenario-based dialogue systems with different types of bodies—physical and virtual robots—to investigate the effects of embodying such dialogue systems. Forty elderly people aged from 65 to 84 interacted with either an embodied desktop-sized humanoid robot or computer graphic agent displayed on a monitor. The elderly participants were divided into groups depending on the success of the interactions. The results revealed that (i) in the group where the robots responded more successfully with the expected conversation flow, the elderly are more engaged in the conversation with the physical robots than the virtual robots, and (ii) the elderly in the group in which robots responded successfully are more engaged in the conversation with the physical robots than those in the group in which the robots responded with ambiguous responses owing to unexpected utterances from the elderly. These results suggest that having a physical body is advantageous in promoting high engagement, and the potential advantage appears depending on whether the system can handle the conversation flow. These findings provide new insight into the development of dialogue systems assisting elderly in maintaining a better mental health.
SIMPLE: A Novel Scoring System for Predicting Hemodynamically Significant Patent Ductus Arteriosus Without Echocardiographic Evaluation in Extremely Low Birth Weight Infants

Aim: To develop a novel clinical scoring system for predicting hemodynamically significant patent ductus arteriosus (hsPDA) in extremely low birth weight (ELBW) infants. Methods: A prospective observational study was conducted among ELBW infants born in the study center during a 6-month period. Fourteen items were selected on a literature review basis and weighed by severity on an arbitrary 1–4 scale, the sum of which represented the Scoring preterm Infants for PDA cLinically without Echocardiographic evaluation (SIMPLE) score. The SIMPLE scores were compared at several time points during the first 3 days of life between two groups of patients: those with an hsPDA at echocardiography and those without. Results: A total of 48 ELBW infants were enrolled, of which 30 infants developed hsPDA. The SIMPLE scores of the infants with hsPDA were significantly greater than those of the infants who did not develop hsPDA. Cut-off SIMPLE scores that were significantly associated with detection of symptomatic hsPDA at each evaluation time point were identified. Conclusions: SIMPLE is the first scoring system that depends on the risk factors and clinical findings of ELBW infants for early prediction of hsPDA. It is simple, objective and easy to perform, and it does not require any additional tests and/or echocardiographic evaluation. We suggest that SIMPLE can be used as a screening tool for determining the need for echocardiographic evaluation in ELBW infants in order to minimize the number of unnecessary pediatric cardiology consultations.
SSN_NLP@FIRE2020 : Automatic Extraction of Causal Relations Using Deep Learning and Machine Translation Approaches

Causality can be understood as the relationship between two events such that the occurrence of one event results in the occurrence of the other event either directly or indirectly. This paper aims to identify whether the given sentences have a causality effect present in them and to classify the cause and effect words/phrases if present. The approach used for classification uses deep learning algorithms and the annotation task uses machine translation. These models are applied to the dataset provided by CEREX@FIRE2020. The best result for the causality identification part was obtained from BiLSTM with an F1 score of 0.60 and for the second task of annotation as cause and effect, NMT with Bahdanau attention mechanism with an F1 score of 0.44.
Thermodynamics of Some Amphiphilic Drugs in Presence of Additives

In the present paper, we report the thermodynamics of the four amphiphilic drugs (two antidepressants, amitriptyline hydrochloride and imipramine hydrochloride, and two phenothiazines, chlorpromazine hydrochloride and promethazine hydrochloride) in the presence of additives [NaCl, cetyltrimethylammonium bromide (CTAB), polyethylene glycol t-octylphenyl ether (TX-100)] and evaluated Gibbs energies [at the air/water interface (Gmin(s)), the standard Gibbs energy change of micellization (ΔmicG0), the standard Gibbs energy change of adsorption (ΔadsG0), and the excess free energy change of micellization (ΔGex)].
Reconocimiento facial basado en Least-Squares Support Vector Machine y características LNDP

3D face recognition has become very popular among computer vision reasearchers due to its advantajes against traditional 2D face recognition, this paper presents a 3D face recognition LNDP+LSSVM-based system wich keeps the beneﬁts of a traditional SVM without having to solve a quadratic-programming problem but a linear-programming problem. Experiments conducted on the Texas 3DFR Database show that LNDP-LSSVM method can achieve veriﬁcation and recognition rate over 99 %.
Automated Characterization of Yardangs Using Deep Convolutional Neural Networks

The morphological characteristics of yardangs are the direct evidence that reveals the wind and fluvial erosion for lacustrine sediments in arid areas. These features can be critical indicators in reconstructing local wind directions and environment conditions. Thus, the fast and accurate extraction of yardangs is key to studying their regional distribution and evolution process. However, the existing automated methods to characterize yardangs are of limited generalization that may only be feasible for specific types of yardangs in certain areas. Deep learning methods, which are superior in representation learning, provide potential solutions for mapping yardangs with complex and variable features. In this study, we apply Mask region-based convolutional neural networks (Mask R-CNN) to automatically delineate and classify yardangs using very high spatial resolution images from Google Earth. The yardang field in the Qaidam Basin, northwestern China is selected to conduct the experiments and the method yields mean average precisions of 0.869 and 0.671 for intersection of union (IoU) thresholds of 0.5 and 0.75, respectively. The manual validation results on images of additional study sites show an overall detection accuracy of 74%, while more than 90% of the detected yardangs can be correctly classified and delineated. We then conclude that Mask R-CNN is a robust model to characterize multi-scale yardangs of various types and allows for the research of the morphological and evolutionary aspects of aeolian landform.
Wetland Mapping Using HJ-1A/B Hyperspectral Images and an Adaptive Sparse Constrained Least Squares Linear Spectral Mixture Model

In this study, we proposed an adaptive sparse constrained least squares linear spectral mixture model (SCLS-LSMM) to map wetlands in a sophisticated scene. It includes three procedures: (1) estimating the abundance based on sparse constrained least squares method with all endmembers in the spectral library, (2) selecting “active” endmember combinations for each pixel based on the estimated abundances and (3) estimating abundances based on the linear spectral unmixing algorithm only with the adaptively selected endmember combinations. The performances of the proposed SCLS-LSMM on wetland vegetation communities mapping were compared with the traditional full constrained least squares linear spectral mixture model (FCLS-LSMM) using HJ-1A/B hyperspectral images. The accuracy assessment results showed that the proposed SCLS-LSMM obtained a significantly better performance with a systematic error (SE) of –0.014 and a root-mean-square error (RMSE) of 0.087 for Reed marsh, and a SE of 0.004 and a RMSE of 0.059 for Weedy meadow, compared with the traditional FCLS-LSMM. The proposed methods improved the unmixing accuracies of wetlands’ vegetation communities and have the potential to understand the process of wetlands’ degradation under the impacts of climate changes and permafrost degradation.
Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.