Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Perception on Social Robot’s Face and Color Expression Using Computational Emotion Model

Researchers have explored the effects of expressing emotions using various modalities in the field of social robots. Prior studies have demonstrated that the use of color in emotional expressions can enhance user acceptance, relationship building, and communication effectiveness. This study aims to validate the effectiveness of different modalities in expressing Ekman’s six basic emotions. Specifically, four modalities were compared: face expression (F), face and LED color expression (FL), face and LED color expression with blinking (FLB), and face and eye color expression (FE). To accomplish this, we developed a small social robot prototype and used a computational emotion model to improve robot dynamics and interactivity. The findings revealed that, although the F modality effectively expressed emotions, ambiguous emotions were better perceived when color or blinking was incorporated. Emotions such as anger, sadness, disgust, surprise, and fear were better conveyed to the participants when the FL, FLB, and FE modalities were utilized. For happiness, F alone was sufficient for recognition. This study provides empirical evidence on the effectiveness of different modalities for expressing emotions in social robots and offers valuable insights gathered from participants’ feedback and reflections.
Global Demographic Change and Japanese Macroeconomic Performance

The world is in the midst of a significant demographic transition with important implications for the macroeconomic performance of the global economy. This paper summarizes the key features of the current and projected future demographic change that are likely to have macroeconomic effects. It then develops and applies a new ten region DSGE model (the MSG3 model) incorporating demographic dynamics, to examine the impacts of projected global demographic change on the world economy from 2005 to 2100. The focus in this paper is on Japan and the effects of demographic change on recent Japanese macroeconomic performance as well as projected performance over the remainder of this century. A distinction is made between the effects on Japan of demographic change that occurs in Japan and the effects on Japan of the equally large demographic changes occurring in the rest of the world.
On the Benefits of Listen before Talk Scheme for NB-Fi Networks

NB-Fi (Narrow Band Fidelity) is a promising protocol for low-power wide-area networks. NB-Fi networks use license-exempt Industrial, Scientific, and Medical (ISM) bands and, thus, NB-Fi devices can work in two modes: with and without Listen Before Talk (LBT). This paper compares these modes with different implementations of LBT in terms of packet loss rate (PLR), delay, energy consumption, and throughput. Interestingly, in some scenarios, the results contradict expectations from the classic papers on channel access because of the peculiarities of the NB-Fi technology. These contradictions are explained in the paper. The results show that LBT can significantly improve all the considered performance indicators when the network load exceeds 40 packets per second. With extensive simulation, we show that in a small NB-Fi network, the optimal PLR, delay, and energy consumption are obtained with the implementation of LBT that corresponds to non-persistent CSMA. In a large NB-Fi network, where some devices can be hidden from others, the best strategy to improve PLR, delay, throughput, and energy consumption is to use the implementation of LBT that corresponds to p-persistent CSMA.
Identification of a Novel Oxidative Stress- and Anoikis-Related Prognostic Signature and Its Immune Landscape Analysis in Non-Small Cell Lung Cancer

The objective of this study was to identify a kind of prognostic signature based on oxidative stress- and anoikis-related genes (OARGs) for predicting the prognosis and immune landscape of NSCLC. Initially, We identified 47 differentially expressed OARGs that primarily regulate oxidative stress and epithelial cell infiltration through the PI3K-Akt pathway. Subsequently, 10 OARGs related to prognosis determined two potential clusters. A cluster was associated with a shorter survival level, lower immune infiltration, higher stemness index and tumor mutation burden. Next, The best risk score model constructed by prognostic OARGs was the Random Survival Forest model, and it included SLC2A1, LDHA and PLAU. The high-risk group was associated with cluster A and poor prognosis, with a higher tumor mutation burden, stemness index and proportion of M0-type macrophages, and a lower immune checkpoint expression level, immune function score and IPS score. The calibration curve and decision-making curve showed that the risk score combined with clinical pathological characteristics could be used to construct a nomogram for guiding the clinical treatment strategies. Finally, We found that all three hub genes were highly expressed in tumor tissues, and LDHA expression was mainly regulated by has-miR-338-3p, has-miR-330-5p and has-miR-34c-5p. Altogether, We constructed an OARG-related prognostic signature to reveal potential relationships between the signature and clinical characteristics, TME, stemness, tumor mutational burden, drug sensitivity and immune landscape in NSCLC patients.
Targeting PD-1/PD-L1 inhibits rejection in a heterotopic tracheal allograft model of lung transplantation

Immune checkpoint molecules such as programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1) have revolutionized the field of lung cancer treatment. As part of our study, we examined the role of these proteins in acute rejection in a mouse model of heterotopic tracheal transplantation. Recipient mice were untreated (Allo group) or treated with anti-PD-L1 (aPDL1 group) or PD-L1 Fc recombinant protein (PD-L1 Fc group). A further group of C57BL/6 mice received isografts (Iso group). The occlusion rate was significantly higher in the Allo group than in the Iso group (p = 0.0075), and also higher in the aPD-L1 group (p = 0.0066) and lower in the PD-L1 Fc group (p = 0.030) than in the Allo group. PD-L1 Fc recombinant protein treatment significantly decreased interleukin-6 and interferon-γ levels and reduced the CD4+/CD8+ T cell ratio, without increasing PD-1 and T-cell immunoglobulin mucin 3 expression in CD4+ T cells. These data suggest that PD-L1 Fc recombinant protein decreases the levels of inflammatory cytokines and the proportion of CD4+ T cells without exhaustion. The PD-L1-mediated immune checkpoint mechanism was associated with rejection in the murine tracheal transplant model, suggesting a potential novel target for immunotherapy in lung transplantation.
Emotional Intelligence among Nurses and Its Relationship with Their Performance and Work Engagement: A Cross-Sectional Study

Background. Several studies identified that emotional intelligence skills are important indicators for nurse engagement and performance. Issues related to nursing performance in healthcare organizations have been gaining greater attention because they influence the effectiveness of improvement approaches to maintain high-quality care. This study aimed to investigate the relationships between emotional intelligence and nurses’ work performance and work engagement. Methods. A quantitative, descriptive, correlational design was used to evaluate the relationships between the study variables. Data were gathered from 150 nurses working at Madinah Cardiac Center, Saudi Arabia. Three scales were used to measure the study variables which were Emotional Intelligence Scale, Job Performance Scale, and Utrecht Work Engagement Scale in addition to demographics. SPSS was used to analyze data. Results. The results of this study showed that emotional intelligence has a total mean of 3.77 (SD = 0.598), nurses’ performance 3.65 (SD = 0.503), and work engagement 4.29 (SD = 1.04). The results also showed that there is a positive and significant relationship between emotional intelligence and nurses’ work performance (R2 = 0.657, p < 0.001 ). Also, it was found that emotional intelligence has a positive and significant relationship with nurses’ work engagement (R2 = 0.621, p < 0.001 ). Conclusions. This paper highlights the influence of emotional intelligence in nurses’ improved performance and engagement in work. The field of nursing is associated with care and compassion; thus, it needs a high level of emotional intelligence. Nurses need to enhance their emotional intelligence skills by attending workshops. Nurse leaders also have a role in that by building a culture for nurses that is driven by applying emotional intelligence in the workplaces.
Kinematic Parameter Identification for Parallel Robots With Passive Limbs

Parallel robots have passive joints and even passive limbs. Current calibration methods seldom consider the resulted identification and compensation properties and thus accuracy improvement is affected. This letter adopts finite and instantaneous screw (FIS) theory as mathematical tool. A generalized error model is built based on the reciprocal property between twist and wrench. Parameter identifiability is proved by discussing linear dependency of identification mapping matrix among limbs and within limb. Maximum independent geometric errors number of any parallel robot is 4r+4h+2p+6, where r, h and p are the numbers of revolute, helical and prismatic joints. It is found that joint twist errors of passive limbs are not compensable for the indirect compensation. Simulations and experiment show that the presented FIS-based calibration method with identifiable and compensable parameters significantly improves accuracy of the parallel robot. In addition, errors of passive limbs have no contribution to the accuracy improvement and can be ignored from the modeling phase.
Modeling heterogeneous networks for information ranking, enrichment and resolution on microblogs

Microblogging, a new type of online information sharing platform through short messages of up to 140 characters, has grown up quickly and received increasing attentions in recent years. A microblogging platform (e.g., Twitter) enables both individuals and organizations to disseminate information, from current affairs to breaking news in a timely fashion, which makes it a valuable knowledge source with super-fresh information. For example, during Hurricane Irene in 2011, updates from users living in New York City and transportation/evacuation posts from the government are very useful information for people to keep track of the disaster. Therefore, conducting related Natural Language Processing (NLP) research on this new genre is demanded to assist knowledge mining and discovery. Different from the semi-structured knowledge bases (e.g., Wikipedia) and the traditional news, the informal microblogs tend to be noisy, short, and informal. And the phenomenon of information implicitness is more prominent and pervasive in microblogging. These characteristics bring unique challenges to people’s reading and understanding of the informal microblogs, as well as many knowledge mining and discovery tasks. Thus, in order to alleviate these problems, in this thesis we propose to filter noisy and uninformative information, enrich the short microblogs with background knowledge from knowledge bases such as Wikipedia, and resolve the informal and implicit information to their regular referents. To achieve our goals, we propose to leverage and model heterogeneous information networks (HINs), in contrast to most existing NLP approaches on traditional genres (e.g., news) that only explored single type of information (e.g., texts). Microblogging contains heterogeneous types of information from social network structures to cross-genre linkages, forming rich HINs. By designing effective approaches to model both unstructured texts and structured HINs, we can incorporate additional evidence from HIN structures beyond texts. In this thesis, we present different approaches to construct HINs from crossgenre, cross-source, and cross-type information by incorporating the existing clean social relations, as well as performing deep content analysis with some of the well-developed
Proteome response of winter-hardy wheat to cold acclimation

The proteome analysis of winter crops during cold acclimation and overwintering can provide important information for designing breeding processes. The current experiment was carried out to investigate the proteome changes in frost-tolerant winter wheat (cv. Norstar) during different cold acclimation (CA) periods under field conditions in a cold and high-altitude region by two-dimensional gel-based proteomic techniques. The results showed that frost tolerance significantly increased by CA and the lethal freezing temperatures (LT50) 10, 14, and 18 weeks after seed sowing were -28? C, -22? C, and -10? C, respectively. By the beginning of the reproductive stage (double ridge stage), the LT50 values had decreased significantly. Around 1000 protein spots were distinguished by Coomassie staining on the gels. The changes in the proteins during the CA often occurred in those with a functional role in photosynthesis, energy production (glycolysis), transcription, chaperone-like activities, membrane and cytoskeleton reorganisation, transport, redox adjustments, and signalling. The results revealed that changes in chloroplast proteins, certain transcription factors such as MADS-box transcription factor 26, and antioxidant enzymes (ascorbate peroxidase) show a similar trend to freezing tolerance, and their expression decreases with the onset of reproductive growth and the loss of freezing tolerance. During the acclimation period, most of the changes were focused on defence systems and cytoskeleton rearrangement, while, photosynthesis, and energy production became the main priority at the beginning of reproductive growth.
Some statistical remarks on GRBs jointly detected by Fermi and Swift satellites

We made statistical analysis of the Fermi GBM and Swift BAT observational material, accumulated over 15 years. We studied how GRB parameters (T90 duration, fluence, peak flux) that were observed by only one satellite differ from those observed by both. In the latter case, it was possible to directly compare the values of the parameters that both satellites measured. The GRBs measured by both satellites were identified using the k-nearest neighbours algorithm in Euclidean distance. In the parameter space we determined the direction in which the jointly detected GRBs differ most from those detected by only one of the instruments using linear discriminant analyses. To get the strength of the relationship between the parameters obtained from the GBM and BAT, a canonical correlation was performed. The GBM and BAT T90 distributions were fitted with a linear combination of lognormal functions. The optimal number of such functions required for fit is two for GBM and three for BAT. Contrary to the widely accepted view, we found that the number of lognormal functions required for fitting the observed distribution of GRB durations does not allow us to deduce the number of central engine types responsible for GRBs.
An Artificial Spiking Afferent Nerve Based on Synaptic Transistor for Thermal Perception

The emulation of biological perception system enhances electronics via superior environmental interpretation accuracy. This research presents an artificial thermosensitive afferent nerve replicating key biological functions associated with cutaneous thermal perception, information processing, and reflex responses. The neuromorphic device integrates a thermosensitive sensor, a spiking encoder module, an ion gel-gated artificial synapse, and a spiking convolutional neural network (SCNN). This device allows precise temperature detection and initiates reflex-like responses to noxious thermal stimuli. It demonstrates high accuracy in regional temperature recognition and identification of handwritten digits (98.7%). The fundings indicate a viable strategy for paving the way toward future advancements in artificial nerves, neural prosthetics, and humanoid robotics.
Comparing the Use of STATSGO and SSURGO Soils Data in Water Quality Modeling: A Literature Review

The U.S. Department of Agriculture’s Natural Resources Conservation Service (NRCS) leads the National Cooperative Soil Survey and is responsible for collecting, storing, maintaining, and distributing soil survey information. As part of this work, the NRCS maintains three soil geographic databases representing the kinds of soil maps produced from different intensities and scales of mapping: the Soil Survey Geographic database (SSURGO), State Soil Geographic database (STATSGO), and National Soil Geographic database (NATSGO). Soil data comprise a basic input for most hydrologic models. A number of authors have noted that models for predicting runoff, sediment, agricultural chemical yields, and stream flow using STATSGO as opposed to SSURGO can produce different simulation results [118]. Additionally, several studies have evaluated the effect of using these different databases on non waterrelated indicators, such as carbon sequestration [19-22]. To date, however, no comprehensive review has been published on the relative effects of using STATSGO versus SSURGO. Here, we summarize findings from all known studies that have assessed the effects of using STATSGO versus SSURGO as inputs for different water quality-related models. Because the NATSGO database is used primarily for national and multi-state regional analyses, we do not consider its use in this report.
Amelioration of acidity in fresh waters: Individual to ecosystem level responses

The chemical recovery of fresh waters across Europe and North America from anthropogenic acidification has not yet led to a consistent biological recovery. One possible explanation is that interactions between individuals and populations of the acid-tolerant and acid-sensitive species may cause hystereses or lags at higher organisational levels (e.g. the food web). This could result in inertia, whereby the reassembly of an acid-sensitive community does not simply follow the reverse trajectory of the response to acidification as chemical conditions ameliorate. Here, I present the results of experiments on predator-prey interactions, using combinations of stream invertebrates with differing pH optima and across a range of prey densities and temperatures. I fitted a variety of functional response models to these data, hypothesising that Type III functional responses were widespread, inferring a density-dependent stabilising effect of predators on prey at low prey density. The largest of the acid-tolerant predators used (nymphs of the dragonfly, Cordulegaster boltonii) caused high prey mortality, most particularly on acid-sensitive mayflies. The large invertebrate apex predators of acidified streams might delay the re-establishment of an acid-sensitive fauna, despite the amelioration of water chemistry, due to the ‘damping’ of population fluctuations of acid-sensitive prey. The population-level manifestation of these interactions could potentially increase the resistance of acidified systems, which are already
Public policy instruments to promote freight modal shift in Europe: evidence from evaluations

A modal shift of freight from road to rail and waterborne transport can contribute to reduced negative externalities (mainly air pollution) from freight transport. The purpose of this paper is to identify modal shift public policy instruments in Europe and analyse their performance based on existing ex post evaluations. This analysis identi ﬁ es 93 public policy instruments, in which 20 ex post evaluations were found. The evaluations mainly consider subsidies/ grants at the national level or regulations and directives at the European Union (EU) level. The results suggest that evaluations of subsidies and grants at the national level most commonly describe a positive performance, while several evaluations at the EU level describe a poor or mixed performance. Well-de ﬁ ned targets and simpler application processes are mentioned in several ex post evaluations as suggestions for improving the performance of modal shift policy instruments in Europe.
Influence of climate variability on human leptospirosis cases in Jamaica

A retrospective ecological study was conducted using time-series and wavelet analyses to evaluate the impact of weather variables and climatic indicators on the incidence of lepto spirosis in Jamaica between 1992 and 2007. Disease incidence was statistically linked to heavier rainfall and declining temperatures, with reported cases of leptospirosis peaking late in the year following a peak in rainfall. There was also some indication that disease spikes may be linked to the El Niño phenomenon. The climatic associations were used as the basis for the creation of statistical models for predicting disease incidence late in the year around the time it peaks. The models showed reasonable skill, explaining up to 72% of the variability in the disease record. The data also showed 1, 2, and 4 yr periodicities in the wavelet coherency spectrum. The results are signifi cant for surveillance and prediction of the disease.
Maximizing Bioethanol Production from Eucalyptus globulus Using Steam Explosion Pretreatment: A Multifactorial Design and Fermenter Development for High Solid Loads

Steam explosion pretreatment is suitable for bioethanol production from Eucalyptus globulus wood. Multifactorial experiment designs were used to find the optimal temperature and residence time required to obtain the best glucose yield from the enzymatic hydrolysis of pretreated materials. The chemical composition, crystallinity index, morphology and polymerization degree of the pretreated materials were correlated with enzymatic accessibility. Simultaneous saccharification and fermentation (SSF) using a fed-batch strategy was applied to three different laboratory-scale fermenters. The optimization of the pretreatment was obtained at 208 °C and 11 min. However, the enzymatic hydrolysis performance did not show significant differences from the material obtained at 196 °C and 9.5 min, which was determined to be the real optimum, owing to its lower energy requirement. The vertical fermenter with type “G” blades and the horizontal fermenter with helical blades were both highly efficient for reaching ethanol yields close to 90% based on dry wood, and ethanol concentrations close to 9.0% v/v.
The profile of the sports manager between training misunderstandings and impact on internal stakeholders

Purpose: This study aims to understand internal stakeholders’ perceptions of the new legislative provisions introduced in Italy regarding the professional figure of the sports manager, and to evaluate their understanding and the concrete application of these provisions. Methods: A questionnaire was administered to 60 presidents and managers of sports associations and clubs in the province of Salerno. The questions seek to probe stakeholders’ perceptions on the liking, appropriateness, and usefulness of sports manager. The validity and reliability of the questions were calculated. A chi-square (χ2) analysis was performed to check for significant differences in perception. Results: From the results, it was possible to appreciate a discordance of opinions between the stakeholders. Differences in perception emerged concerning the conviction that this professional figure should be integrated within the sports organization chart (χ2 = 7.84; P = .001), for the contribution that the manager can make in terms of improvement to the sports company (χ2 = 7.84; P = .001) and for the most appropriate training path for exercising this profession (χ2 = 7.76; P =.001). Conclusions: The perceptual contradictions found in the stakeholders’ answers show how the complexity of the recent regulatory provisions and the assumed equivalences to the title of sports manager have generated additional confusion compared to the ordinary one. Keywords: exercise and sport sciences, manager, sports law, professional sport profile