Miracle-dz/UniFAR-FTUs huggingface dataset:
The occupational anxiety of teachers caused by China’s ‘double reduction’ policy—a study based on the grounded theory

Teachers’ occupational anxiety is a kind of negative emotional state of teachers, which is prevalent in Chinese teachers. Unfortunately, in the existing research, teachers’ occupational anxiety caused by China’s ‘double reduction’ policy has not been paid attention to. Based on the grounded theory, this study conducted in-depth interviews with 45 in-service primary and junior high school teachers, and used NVivo 12 to process recording materials. Through a series of steps such as open coding, axial coding and selective coding, we found that the core feature of teachers’ occupational anxiety caused by the ‘double reduction’ policy was that the implementation of the ‘double reduction’ policy was incomplete matching the actual educational ecology. Then we constructed a theoretical model of the formation mechanism of teachers’ occupational anxiety caused by the ‘double reduction’ policy. The study showed that due to the influence of teachers’ own personality characteristics and incomplete match between external factors, although teachers insisted on self-adjustment, it was difficult to fundamentally solve the teachers’ occupational anxiety caused by the ‘double reduction’ policy.
Feature Selection Method of Insulator Infrared Thermal Image Based on Variance Analysis

As for detecting the insulator contamination grades by infrared thermal imaging,it is the key problem to obtain infrared thermal image features with excellent classification performance.For this purpose,the authors propose an infrared thermal image feature selection method based on variance analysis.By means of equalizing the histogram,the contrast of original thermal image is enhanced;then image region of the disc surface of insulator is intercepted manually;through the envelope of the smoothened histogram of thermal image the segmentation threshold is extracted and the morphological filtering is applied to the threshold-segmented binarized image to obtain insulator disc surface image and background image,and then 10 infrared image features of insulator disc surface image and background image,such as the highest temperature,the lowest temperature,mean temperature,variance of temperature distribution of both kinds of images,as well as maximum temperature rise and mean temperature rise of insulator disc surface relative to background,are extracted;finally,the good and bad features are discriminated by single factor variance analysis,thus the feature selection is implemented.The results of artificial pollution test of ceramic insulators show that the proposed selection method for infrared thermal image and the image segmentation algorithm are simple and effective.
Clinical practice guidelines for neonatal hypoxic-ischemic encephalopathy: A systematic review using the appraisal of guidelines for research and evaluation (AGREE) II instrument

Background and Objective To systematically review, critically appraise the quality of recent clinical practice guidelines (CPGs) for neonatal hypoxic ischemic encephalopathy (HIE), and map their recommendations. Data Sources CPG databases (GIN, ECRI, NICE, SIGN, DynaMed), Bibliographic databases (PubMed, Embase, CINAHL), and related specialized professional societies (e.g., AAP, CPS, BAPM, RCPCH, and SNS). Study Selection Original de-novo developed evidence-based CPGs for HIE, group authorship, Arabic or English languages, and international or national scope. The systematic review was drafted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement and Johnston et al methodological guide. Data Extraction Quality assessment of the included HIE CPGs by the Appraisal of Guidelines for REsearch & Evaluation II (AGREE II) Instrument and report their characteristics, AGREE II ratings, and recommendations Data Synthesis Our search retrieved 2,489 citations, of which two recent HIE CPGs were eligible and appraised: Canadian Paediatric Society (CPS) and Queensland Maternity and Neonatal Services (QMN). The overall assessment of the QMN CPG was superior (83%). Domain 1 (Scope & Purpose) scored (47%, 63%), Domain 2 (Stakeholder Involvement) (72%, 39%), Domain 3 (Rigour of Development) (48%, 43%), Domain 4 (Clarity & Presentation) (100%, 96%), Domain 5 (Applicability) (59%, 9%), and Domain 6 (Editorial Independence) (67%, 17%) for the QMN and CPS CPGs respectively. All appraisers recommended the QMN CPG for use in practice. Conclusion The methodological quality of the QMN CPG was superior with the relevant recommendations for its use in neonatal practice. Limitations limited to Arabic and English languages. Systematic Review Registration https://www.crd. york.ac. uk/prospero/display_record. php? RecordID=258291, identifier: CRD42021258291.
Widespread latitudinal asymmetry in the performance of marginal populations: A meta‐analysis

Aim: Range shifts are expected to occur when populations at one range margin perform better than those at the other margin, yet no global trend in population performances at range margins has been demonstrated empirically across a wide range of taxa and biomes. Here we test the prediction that, if impacts of ongoing climate change on performance in marginal populations are widespread, then populations from the high-latitude margin (HLM) should perform as well as or better than central populations, whereas low-latitude margin (LLM) populations should perform worse. Location: Global. Time period: 1995– 2019. Major
Nanoconfined ionic liquids under electric fields

The effect of external electric fields (EEFs) on ionic liquid films confined within a nanogap has been investigated by measuring the film thickness with the thin film interferometry and calculating the effective viscosity. Experimental results indicated that the film thickness of ionic liquids could be increased obviously by the application of EEFs with strengths weaker than the electric interactions between cationic head groups and anions. The effect of EEFs on the confined ionic liquid film with a shorter alkyl side chain is more noticeable. It is thought that the charged anions and headgroups of the cations are structured near electrified walls to form ordered layers and short alkyl side chains at the interfaces are aligned along the EEF direction due to induced dipoles.
Restinga ectomycorrhizae: a work in progress

Background: The Brazilian Atlantic Forest is one of the most biodiverse terrestrial ecoregions of the world. Among its constituents, restinga vegetation makes a particular case, acting as a buffer zone between the oceans and the forest. Covering some 80% of Brazilian coastline (over 7,300 km in length), restinga is a harsh environment where plants and fungi interact in complex ways that just now are beginning to be unveiled. Ectomycorrhizal symbiosis, in particular, plays a so far ungauged and likely underestimated role. We recently described the morpho-anatomical and molecular features of the ectomycorrhizae formed by several basidiomycetous mycobionts on the host plant Guapira opposita, but the mycorrhizal biology of restinga is still largely unexplored. Here, we report new data on the ectomycorrhizal fungal symbionts of G. opposita, based on the collection of sporomata and ectomycorrhizal root tips in restinga stands occurring in southern Brazil. Methods: To obtain a broader view of restinga mycorrhizal and ecological potential, we compiled a comprehensive and up-to-date checklist of fungal species reported or supposed to establish ectomycorrhizae on restinga-inhabiting host plants, mainly on the basis of field observations. Results: Our list comprises some 726 records, 74 of which correspond to putative ectomycorrhizal taxa specifically associated with restinga. These include several members of Boletaceae, Amanita, Tomentella/ Thelephora, Russula/ Lactifluus, and Clavulina, as well as hypogeous fungi, like the recently described Longistriata flava. Conclusions: Our survey reveals a significant diversity of the restinga ectomycorrhizal mycobiota, indicating the importance of this symbiosis for the ecological functioning of a unique yet poorly known and threatened ecosystem.
Altered Intracellular Signaling Associated with Dopamine D2 Receptor in the Prefrontal Cortex in Wistar Kyoto Rats

Wistar-Kyoto rats (WKY), compared to Wistar rats, are a well-validated animal model for drug-resistant depression. Thanks to this, they can provide information on the potential mechanisms of treatment-resistant depression. Since deep brain stimulation in the prefrontal cortex has been shown to produce rapid antidepressant effects in WKY rats, we focused our study on the prefrontal cortex. Using quantitative autoradiography, we observed a decrease in the binding of [3H] methylspiperone to the dopamine D2 receptor, specifically in that brain region—but not in the striatum, nor the nucleus accumbens—in WKY rats. Further, we focused our studies on the expression level of several components associated with canonical (G proteins), as well as non-canonical, D2-receptor-associated intracellular pathways (e.g., βarrestin2, glycogen synthase kinase 3 beta—Gsk-3β, and β-catenin). As a result, we observed an increase in the expression of mRNA encoding the regulator of G protein signaling 2-RGS2 protein, which is responsible, among other things, for internalizing the D2 dopamine receptor. The increase in RGS2 expression may therefore account for the decreased binding of the radioligand to the D2 receptor. In addition, WKY rats are characterized by the altered signaling of genes associated with the dopamine D2 receptor and the βarrestin2/AKT/Gsk-3β/β-catenin pathway, which may account for certain behavioral traits of this strain and for the treatment-resistant phenotype.
A Quasi-Experimental Exploration of Activity-Based Flexible Office Design and Demographic Differences in Employee Absenteeism

This study examines whether transitioning from cellular offices to an activity-based flexible office (A-FO) impacts employee absenteeism over time. Based on privacy theory, we hypothesized that changing from cell offices to an A-FO setting would lead to increased employee absenteeism. We further assumed that longer-tenured and female employees would experience greater difficulty with the transition, leading to more absenteeism among these groups. Using a sample of 2,017 white-collar workers tracked over 8 years, we quasi-experimentally investigated if absenteeism in the group with the office design intervention (1,035 individuals) differed from the control group (982 individuals). In the difference-in-difference (DiD) framework, nested negative binomial regression showed no difference in absenteeism between the intervention and control groups. However, a three-way interaction revealed that long-term employees showed higher absenteeism when switching to an A-FO. We discuss our contributions and the implications for corporate leadership, human resources, and change management.
Microencapsulation of a Pickering Oil/Water Emulsion Loaded with Vitamin D3

The ionotropic gelation technique was chosen to produce vitamin D3-loaded microparticles starting from oil-in-water (O/W) Pickering emulsion stabilized by flaxseed flour: the hydrophobic phase was a solution of vitamin D3 in a blend of vegetable oils (ω6:ω3, 4:1) composed of extra virgin olive oil (90%) and hemp oil (10%); the hydrophilic phase was a sodium alginate aqueous solution. The most adequate emulsion was selected carrying out a preliminary study on five placebo formulations which differed in the qualitative and quantitative polymeric composition (concentration and type of alginate selected). Vitamin D3-loaded microparticles in the dried state had a particle size of about 1 mm, 6% of residual water content and excellent flowability thanks to their rounded shape and smooth surface. The polymeric structure of microparticles demonstrated to preserve the vegetable oil blend from oxidation and the integrity of vitamin D3, confirming this product as an innovative ingredient for pharmaceutical and food/nutraceutical purposes.
Bulk Ionic Dissociation is Crucial for Understanding Chemically-Propelled Swimmers

Water is a polar solvent and hence supports the bulk dissociation of itself and its solutes into ions, and the re-association of these ions into neutral molecules in a dynamic equilibrium, e.g., H2O2 −⇀↽ H + HO – 2. We find, using a continuum theory, that taking the dynamics of these bulk reactions into account has a strong influence on the propulsive behavior of colloids driven by surface chemical reactions (swimmers) in aqueous solution. In particular, such bulk reactions permit charged swimmers to propel electrophoretically even if all species involved in the surface reactions are neutral. The bulk reactions also significantly modify the predicted speed of standard electrophoretic swimmers, by up to an order of magnitude. For swimmers whose surface reactions produce both anions and cations (ionic self-diffusiophoresis), the bulk reactions produce an additional reactive screening length, analogous to the Debye length in electrostatics. This in turn leads to an inverse relationship between swimmer radius and swimming speed, which could provide an alternative explanation for recent experimental observations on Pt-polystyrene Janus swimmers [S. Ebbens et al., Phys. Rev. E 85, 020401 (2012)]. We also use our continuum theory to investigate the effect of the Debye screening length itself, going beyond the infinitely thin limit approximation used by previous analytical theories. We identify significant departures from this limiting behavior even for micron-sized swimmers under typical experimental conditions, and find that the limiting behavior fails entirely for nanoscale swimmers.
Multi-source information diffusion in online social networks

Individual spreading behavior in online social networks is closely related to user activity, tie strength, and other user and network features. The results concentrate on personal spreading decisions; however, whether these features promote the global information diffusion and increase the macroscopic density of infected agents, remains unclear to us. In this paper, we propose a multi-source diffusion model in which agents may create new messages and spread other agents’ messages. Agents receive many messages, and each time they select a certain message preferentially to spread in consideration of different features. Simulation results show the density of infected agents for different messages follows a power-law distribution in both scale-free and small-world networks. Selecting the largest author degree, author activity and tie strength preferentially can advance the overall diffusion process. Weak tie bias is the least effective feature for multiple information diffusion, but it helps to diffuse a single message. Unexpectedly, the bias of interest similarity does not have an apparent effect. Integrated with the influence on individual diffusion behavior, strong tie bias is a significant feature both for local and global diffusion.
Effect of housing factors and surficial uranium on the spatial prediction of residential radon in Iowa

Growing epidemiologic evidence suggests that residential radon is an important risk factor for lung cancer. Consequently, public health professionals have expressed interest in characterizing the spatial distribution of radon concentrations in order to identify geographic regions of high exposure. Ambient radon concentrations are a function of geologic features including soil radium content. Indoor radon concentrations can vary based on building characteristics that affect the entry of radon into the building and movement between rooms therein. We present a geostatistical hierarchical Bayesian model for radon that allows for spatial prediction based on geologic data and housing characteristics. Our model is applied to radon data from an epidemiologic study in Iowa that consist of 136 outdoor measurements and 2590 indoor measurements from 614 residential homes. Housing characteristics collected in the Iowa Study are included as predictors in the model. Geologic data in the form of county‐average surficial uranium concentrations from the USGS National Uranium Resource Evaluation project are also considered. A ‘change of support’ approach is implemented to combine the radon measurements, collected at points in space, and the uranium concentrations, averaged over counties, so that point‐source concentrations for the latter are available for the analysis. Estimates of the effect of select housing factors on radon are provided along with spatial maps of predicted radon concentrations in Iowa. Copyright © 2006 John Wiley & Sons, Ltd.
Mapping fire ignition risk in a complex anthropogenic landscape

The progressing spread of urban development into the surrounding fuel-loaded wildland creates a particular landscape structure where the co-occurrence of human activities and flammable fuel greatly increases the fire ignition risk. In this framework, land use and land cover (LULC) maps derived from remotely sensed images represent a useful tool for assessing fire risk. The aim of this letter is thus to use a LULC map derived from QuickBird satellite imagery for assessing ignition risk at the rural–forest interface in Sardinia (Italy). Our results show that fire risk is strongly positively correlated with anthropogenic land use and negatively correlated with semi-natural and natural vegetation cover, such as shrubland or forest.
Time series analysis in astronomy : an application to quasar variability studies

Many astrophysical objects can be associated with continuous non-linear stochastic systems. Up to now, however, the signals coming from these systems have been analyzed mainly through linear approaches, such as the power spectrum (PS) and the structure function (SF) techniques, frequently with controversial results. Here, it is shown that in the study of astronomical time series the PS (even in the maximum entropy or CLEAN version) and the SF techniques are often of little use or even misleading, since they do not take into account all the information contained in the data
Time dependent magnetically induced variations in optical transmission of magnetite nanoparticle aqueous suspension

We observe time dependent variations in the light intensity transmitted through an aqueous suspension of Fe3O4 nanoparticles caused by applied DC magnetic field. Two types of variations can be distinguished. Fast response takes less than 1ms while slow variations occur at the time interval fromseconds to hundreds of minutes. Possible mechanisms of these variations are discussed. Formation of chain-like structures consisted from iron oxide nanoparticles is responsible for the slow variations. It is also accompanied by a diffraction pattern when the magnetic field is orthogonal to the light beam. Fast variations are due to particle rotation and reorientation of the magnetic moment inside a nanoparticle.
ChatGPT opens a new door for bioinformatics

ChatGPT is an artificial intelligence (AI) system that can perform sophisticated writing and dialogs after learning from vast amounts of linguistic data. The success of ChatGPT is phenomenal. AI-based human-machine language interaction has been at the center of AI competition in recent years. The major players in this game have been Google, Meta, and OpenAI. Google was in the best position from the outset, given its invention of Transformer (the cornerstone of all cutting-edge language models) and its significant edge in reinforcement learning. Yet, Google’s efforts in this area were rather diffusing. It kept generating language model variants with incremental innovations but failed to reach the next level. Meta has a strong AI team, including many top AI researchers in the world. Nevertheless, their faith in self-supervised learning to solve human-machine interaction did not deliver high-impact success. Conversely, OpenAI, with a small team, stayed focused on a single product line (GPT, including its latest release of GPT-4). It moved in the right direction of using human input to “align” the language model based on the Reinforcement Learning from Human Feedback (RLHF) approach. The fact that OpenAI ultimately prevailed in this game shows that the model alignment to human labeling through supervised and reinforcement learning is critical for human-machine interaction. However, a chatbot’s actions rely heavily on cues (prompts) provided by human operators. To properly utilize ChatGPT’s capabilities, prompts to instruct or mentor the chatbot must be carefully designed to get valuable, valid, and robust responses. This process becomes another “alignment” problem of using prompt engineering to best probe ChatGPT’s knowledge graph for best serving users’ needs.
Spherical Fourier-Transform-Based Real-TimeNear-Field Shaping and Focusing in Beyond-5G Networks

For ultra-reliable high-data-rate communication, the beyond fifth generation (B5G) and the sixth generation (6G) wireless networks will heavily rely on beamforming, with mobile users often located in the radiative near-field of large antenna systems. Therefore, a novel approach to shape both the amplitude and phase of the electric near-field of any general antenna array topology is presented. Leveraging on the active element patterns generated by each antenna port, the beam synthesis capabilities of the array are exploited through Fourier analysis and spherical mode expansions. As a proof-of-concept, two different arrays are synthesized from the same active antenna element. These arrays are used to obtain 2D near-field patterns with sharp edges and a 30 dB difference between the fields’ magnitudes inside and outside the target regions. Various validation and application examples demonstrate the full control of the radiation in every direction, yielding optimal performance for the users in the focal zones, while significantly improving the management of the power density outside of them. Moreover, the advocated algorithm is very efficient, allowing for a fast, real-time modification and shaping of the array’s radiative near-field.
Performance Optimization for Energy-Efficient Industrial Internet of Things Based on Ambient Backscatter Communication: An A3C-FL Approach

The recent advent of the Industry 4.0 era has led to the need to transform the industrial Internet of Things towards green, low-carbon and sustainable development. This is due to the fact that traditional industries consume too much energy. It is urgent to make use of digital technology for energy saving and emission reduction. However, there are still some unresolved issues in the transformation process: 1) the inability to use equipment resources thoroughly and efficiently, 2) the waste caused by overly simple resource management. In this paper, based on the above issues, we develop the ambient backscatter system to optimize the overall resource scheduling scheme and combine intelligent algorithms to solve the problem of offloading tasks. The solution optimizes offloading decisions to minimize system energy consumption and latency. Meanwhile, the proposed optimization problem is designed as a Markov decision process by combining the proposed federated learning assigned with asynchronous advantage actor-critic algorithm to obtain the optimal policy. The final evaluation results significantly show that the system performance indicator based on our proposed solution is better than others.