Miracle-dz/UniFAR-FTUs huggingface dataset:
Simultaneously Monitoring Humidity and Temperature Based on Waveguide Bragg Grating Sensor Chip

Herein, we proposed and demonstrated a novel relative humidity (RH) sensing chip with temperature compensation, which is based on waveguide Bragg grating (WBG) assisted with polyvinyl alcohol (PVA) and polydimethylsiloxane (PDMS) composite film. Half of the cladding of the WBG region was corroded with hydrofluoric acid, which results in a split of the Bragg peak into two peaks. The PVA-PDMS composite film coated on the surface of the corroded region shows a high sensitivity to RH due to the hydrophilic hydroxyl groups of PVA film. PDMS not only enhances the stability of the composite film but also reduces the refractive index (RI) of the PVA film, which restricts the energy loss of the light passing through. In our experiment, average RH sensitivities of −12.2 pm/RH% and 0 pm/RH% in the RH range of 50%–90% and temperature sensitivities of 9.6 pm/°C and 9.8 pm/°C were obtained by the two peaks shift, respectively. We testified that the simultaneous measurement of RH and temperature can be achieved by the sensitivity matrix method. Furthermore, the proposed sensor possesses remarkable stability and excellent repeatability. Such a cost-effective, integrated, easily fabricated, and reflection power detection-based humidity sensor promises great application prospects in the fields of agriculture, food industry, biological field, and other production activities.
High sensitivity C reactive protein levels and atrial fibrillation recurrence after catheter ablation for atrial fibrillation: A systematic review and meta‐analysis

Atrial fibrillation (AF) recurrence after AF ablation is not uncommon. High sensitivity C reactive protein (hs‐CRP) is a widely used inflammatory marker with a potential property to predict AF recurrence. We conducted a systematic review and a meta‐analysis to find an association between hs‐CRP levels and AF recurrence after ablation.
EFFECTIVENESS OF ZINC SUPPLEMENTATION FOR TYPE II DIABETES PREVENTION: A SYSTEMATIC REVIEW

Background: The incidence of prediabetes is predicted to continue to increase. Approximately 25% of prediabetes patients develop type 2 diabetes mellitus (T2DM) within 3–5 years. Zinc is known to have a role in the synthesis, storage, secretion, and action of the insulin hormone. Purpose: The aim of the study is to know about the impact of zinc supplementation in type 2 diabetes mellitus (DM) prevention. Methods: A systematic literature search was performed using the PubMed, Cochrane, and EBSCOHost search engines. The inclusion study includes systematic review and metaanalysis of randomized clinical trials study and randomized clinical study, and we only include the research of the last 10 years. Results: Three RCTs were selected from 175 articles to undergo a critical review with a total of 353 patients with a duration of 6-12 months (level of evidence II-III). The incidence of T2DM in the intervention group decreased significantly (OR: 0.37 [95% CI 0.7-0.8]; RR: 0.44; ARR: 14% [95% CI 3.5-24.5%]; NNT: 7.14 [4-29]) in one study. Zinc supplementation significantly reduced Fasting Blood Sugar (FBS) in two studies (mean difference -17.3 and -5.76) and oral glucose tolerance test (OGTT) in one study (MD -22.6). Conclusion: Administration of zinc in prediabetes patients to prevent the development of T2DM still requires further study with a larger sample, longer duration, and control variables by design and statistics.
Feasibility and Preliminary Effectiveness of Behavioral Activation for Patients with Cancer and Depression in Japan

Background: Though the effectiveness of behavioral activation (BA) for patients with cancer and depression were reported, there is no evidence in Japan. Objectives: This study aimed at examining the feasibility and preliminary effectiveness of BA for patients with cancer and depression in Japan. Methods: This pre–post study without a control group was conducted in patients with cancer and depression in Japan. The program completion rate was compared with those of previous studies to examine feasibility. To examine the preliminary effectiveness, outcomes were evaluated four times: before and immediately after the program, and two weeks and three months after the program ended. The primary outcome was the remission rate of depression using the 17-item version of the GRID Hamilton Rating Scale for Depression (HAMD17). Secondary outcomes were self-reported depression, anxiety, quality of life, changes in behavior, values, and perceived reward of activity and environmental factors. Pre- and post-program data were compared using paired-samples t-tests, and data obtained at four time points were analyzed using one-way repeated-measures analysis of variance. Results: Of the 68 patients recruited from February 2018 to January 2022, 32 were registered. The completion rate was 75% (24/32), which was similar to previous studies. The total HAMD17 score significantly improved after the program. The remission rate of depression was 62.5% (20/32), which was above the defined threshold value (30%). All but two secondary outcomes significantly improved after the program (p < 0.05). Conclusions: The feasibility and preliminary effectiveness of BA for patients with cancer and depression in Japan were suggested. The Clinical Trial Registration number: UMIN 000036104.
PROD: A New Algorithm of DeepWalk Based On Probability

Network representation learning is a method of expressing vertexes in a graph in the form of vectors, which facilitates further clustering and classification. Attributed graph is a kind of graph. In the attributed graph, each vertex has some text attribute. Through the network structure of these text attribute and attributed graph itself, it can be well clustered and classified. With the development of deep learning, the network representation learning field combined with deep learning has also produced a series of excellent algorithms, such as DeepWalk and Node2vec, etc. However, the randomly generated sequence will lose the text attribute of the vertex. This paper proposes a new algorithm PROD which enable the deepwalk algorithm to combine the probability to generate the corresponding sequence. Later experiments show that in the case of a large number of edges, this algorithm generates low-dimensional representations of vertices that are better for clustering with the kmeans algorithm than for deepwalk.
An Optimal Wind Farm Operation Strategy for the Provision of Frequency Containment Reserve Incorporating Active Wake Control

This study proposes a novel operation strategy for wind farms' optimal Frequency Containment Reserve (FCR) provision that simultaneously distributes FCR and optimally controls wake formation. The power reserve allocation is dynamically decided at the wind farm supervisory control level, considering the intermittent wind power and direction, grid frequency stochasticity, and the aerodynamic complexity of the wake. A two-stage stochastic programming approach supports decision-making for an optimal contribution to day-ahead energy/FCR markets considering sub-hourly wind power and grid frequency uncertainty. Moreover, a novel method is used to reduce the computational complexity by employing a data-driven surrogate model of wake formation in the optimizer. This surrogate model consists of a neural network trained on the Gauss-Curl-Hybrid wake model in FLORIS. This deep learning approach allows fast estimation of the wake control parameters, i.e., the optimal yaw angles and axial induction factors. Then, a coevolutionary-based multi-objective particle swarm optimization searches for the optimal deloading of the WTs and maximizes the total power production and kinetic energy while minimizing wake. The performance of the proposed algorithm is evaluated on the C-Power wind farm layout in the North Sea. Simulation results demonstrate its effectiveness in improving the wind farm's overall performance for different operational conditions.
Age estimation and body size of the Parsley Frog, Pelodytes caucasicus Boulenger, 1896 from Lake Borçka Karagöl, Turkey

In this study, we described age structure, body size, body mass and the relationships among these parameters for a population of P. caucasicus from Lake Borçka Karagöl, Artvin, Turkey. The mean SVL with standard error was 45.87 mm ± 0.55 (range: 39.98-50.28 mm) and the mean weight with standard error 8.81 g ± 0.39 (range: 6.10-11.47 g) in females whereas 48.16 mm ± 0.45 (range: 43.64-54.78 mm) and 11.32 ± 0.25 (range: 9.56-14.80 g) in males, respectively. We found a significant male-biased difference reflecting sexual dimorphism and statistically significant positive relationships between these variables. According to the results, the age ranged between 2-5 years in females and 2-6 years in males. The mean age distributions significantly differed between the sexes (females: 3.28 years ± 0.19; males: 3.94 years ± 0.20). The mean ages and maximal ages were found identical to the previously reported results from Turkey, but the mean ages were higher than in Georgian populations. Von Bertalanffy growth models demonstrated similar curves, and the growth rate was faster up to 3 years in both sexes. To conclude, this study was the first to determine age structure and growth patterns in Borçka Karagöl population and weight data for P. caucasicus was presented for the first time in the literature.
Item Response Modeling of Multivariate Count Data With Zero Inflation, Maximum Inflation, and Heaping

Questionnaires that include items eliciting count responses are becoming increasingly common in psychology. This study proposes methodological techniques to overcome some of the challenges associated with analyzing multivariate item response data that exhibit zero inflation, maximum inflation, and heaping at preferred digits. The modeling framework combines approaches from three literatures: item response theory (IRT) models for multivariate count data, latent variable models for heaping and extreme responding, and mixture IRT models. Data from the Behavioral Risk Factor Surveillance System are used as a motivating example. Practical implications are discussed, and recommendations are provided for researchers who may wish to use count items on questionnaires.
Exact Solutions of the Richards Equation With Nonlinear Plant‐Root Extraction

The Richards equation, commonly used to model water flow in unsaturated soils, is highly nonlinear, thus making it very challenging to solve analytically for situations meaningful in practical applications. The inclusion of realistic forms of root‐water uptake rates in this equation adds complications in deriving exact solutions. This study provides for the first time analytical solutions of the Richards equation with a sink term nonlinearly dependent on soil water content. These solutions are applied to irrigation furrows, using Cartesian coordinates, and irrigation from a circular plate, in cylindrical coordinates.
Gas leak localization and detection method based on a multi-point ultrasonic sensor array with TDOA algorithm

To resolve the measured target position to determine and locate leak problems with current gas leak detection and localization systems based on ultrasonic technology, this paper presents an improved multi-array ultrasonic gas leak TDOA (time difference of arrival) localization and detection method. This method involves arranging ultrasonic transducers at equal intervals in a high-sensitivity detector array, using small differences in ultrasonic sound intensity to determine the scope of the leak and generate a rough localization, and then using an array TDOA localization algorithm to determine the precise leak location. This method is then implemented in an ultrasonic leak detection and localization system. Experimental results showed that the TDOA localization method, using auxiliary sound intensity factors to avoid dependence on a single sound intensity to determine the leak size and location, achieved a localization error of less than 2 mm. The validity and correctness of this approach were thus verified.
Humidity and air temperature predict post count on Twitter in 10 countries: Weather changes & LIWC psychological categories

There are many indications that weather conditions influence human life and well-being. Some of these indicators, such as the influence of weather on human health, have been explored in detail. On the other hand, the influence of daily fluctuations of different meteorological variables on the human psychological state still remains unknown. We apply combined methods from statistics, psychology, machine learning, and complex networks theory to explore the influence of weather parameters on different psychological categories of Twitter users in ten different countries. Our results show that the temperature, pressure, and humidity are highly correlated with Twitter users' activity, sense, and affect. Our comparative analysis for different countries shows that the strongest correlation was found for the USA, Italy, and Portugal, indicating differences between countries. However, our results show that the level of activity of Twitter users, described as Post Count, is strongly connected to changes in temperature and humidity in all countries. We use complex networks theory to explore these connections and differences between countries further. Our findings suggest that weather parameters can be used to predict Twitter users' activity and psychological manifestations, which can be beneficial to marketing and advertising.
Search for a Signature of Interaction between Relativistic Jet and Progenitor in Gamma-Ray Bursts

The time variability of prompt emission in gamma-ray bursts (GRBs) is expected to originate from the temporal behavior of the central engine activity and the jet propagation in the massive stellar envelope. Using a pulse search algorithm for bright GRBs, we investigate the time variability of gamma-ray light curves to search a signature of the interaction between the jet and the inner structure of the progenitor. Since this signature might appear in the earlier phase of prompt emission, we divide the light curves into the initial phase and the late phase by referring to the trigger time and the burst duration of each GRB. We also adopt this algorithm for GRBs associated with supernovae/hypernovae that certainly are accompanied by massive stars. However, there is no difference between each pulse interval distribution described by a lognorma distribution in the two phases. We confirm that this result can be explained by the photospheric emission model if the energy injection of the central engine is not steady or completely periodic but episodic and described by the lognormal distribution with a mean of ∼1 s.
Integration of speech information (or not) across electric and acoustic modes in hearing impaired listeners

Many people with hearing loss use two cochlear implants (bilateral CIs), or combine a CI and hearing aid (bimodal). While the addition of a second CI or acoustic input improves speech perception (particularly in noise), it is not well understood how listeners fuse these disparate inputs. We addressed this with a duplex perception experiment. Listeners heard the four cardinal vowels from six talkers. In the duplex condition, the first formant was presented to the CI, and the second to the other CI or hearing aid (and vice versa). In normal listeners (N = 14), accuracy under duplex presentation (across ears; M= 0.80) did not differ from combined presentation (both formants, both ears, M = 0.76, p = 0.14), but performance dropped to near chance with isolated formants (M = 0.40, p<0.001). CI users (Bilateral: N = 7; bimodal: N = 11; single-side-of-deafness [SSD]: N = 7) performed well in combined presentation (Bilateral: M = 0.72, Bimodal: M = 0.70, SSD: M = 0.83), and poorly for isolated formants (Bilateral: M = 0.42, Bimodal: M = 0.39, SSD: M = 0.41). Duplex presentation was significantly poorer than combined (Bilateral: M = 0.54, p = 0.0078; Bimodal: M = 0.55, p<0.001; SSD: Duplex: 0.48, p<0.001), though better than isolated formants (p<0.05). Thus, CI users may not fuse inputs well at an auditory level, and the benefits of acoustic + electric hearing may derive from central integration.Many people with hearing loss use two cochlear implants (bilateral CIs), or combine a CI and hearing aid (bimodal). While the addition of a second CI or acoustic input improves speech perception (particularly in noise), it is not well understood how listeners fuse these disparate inputs. We addressed this with a duplex perception experiment. Listeners heard the four cardinal vowels from six talkers. In the duplex condition, the first formant was presented to the CI, and the second to the other CI or hearing aid (and vice versa). In normal listeners (N = 14), accuracy under duplex presentation (across ears; M= 0.80) did not differ from combined presentation (both formants, both ears, M = 0.76, p = 0.14), but performance dropped to near chance with isolated formants (M = 0.40, p<0.001). CI users (Bilateral: N = 7; bimodal: N = 11; single-side-of-deafness [SSD]: N = 7) performed well in combined presentation (Bilateral: M = 0.72, Bimodal: M = 0.70, SSD: M = 0.83), and poorly for isolated formants (Bilateral:.
Enhanced tunable terahertz generation in photonic band-gap structures

We analyze a novel coherent source of millimeter-wavelength waves based on parametric down conversion in a photonic crystal. Our design is based on the band-edge or defect-mode field enhancement phenomena near a photonic band gap. The numerical results show that a wide range of intensities and bandwidths of coherent radiation can be obtained as we vary either the number periods or the index contrast between layers of the crystal array. Calculations demonstrate narrow-band, continuous and tunable THz sources from sub-THz to more than 12 THz.
Research on Modulation Recognition Algorithm Based on Channel and Spatial Self-Attention Mechanism

In the harsh electromagnetic environment with strong interference, the prior information of the received signal can not be fully obtained, and considering the complex and variable modulation modes, the modulation recognition of radio signals has brought great trouble. In this paper, we propose a method for automatic modulation recognition based on deep convolutional neural network for channel and spatial self-attention mechanism by combining the architecture of feature autonomous learning of deep learning. The correlation of input vectors in channel and spatial dimensions is enhanced by a self-attentive mechanism, and the number of layers of network structure, connection method, pooling method, and hyper-parameters are optimized, to enhance the overall fitting ability of the network and improve the accuracy and robustness of modulation recognition. On the RadioML2016.10A dataset, the proposed method is compared with the previous baseline method at different signal-to-noise ratios. The experimental results show that the performance of this paper’s method is better in the identification of 16QAM versus 64QAM.
Diagnostics and prevention of sports-related traumatic brain injury complication

Sports-related traumatic brain injuries (TBI) accounts for up to 20 % of all injuries that are obtained by athletes and its incidence rises annually due to rise in population involving in sports, growing popularity of extreme sports and high level of motivation to achieve record results among young sportsmen. The aim of the review is to present the potential benefits of using microwave radiothermometry and craniocerebral hypothermia technologies in sports-related TBI. The review considers most common form of traumatic brain injury in athletes - mild TBI, which in turn can provoke a wide range of complications and negative consequences in near and delayed periods after the injury. The main shortcomings of programs for complication prevention in treatment and rehabilitation of athletes after TBI are considered, which do not take into account the peculiarities of injury mechanisms, its significant differences from household, road or criminal injuries with brain damage. Lack of objective methods of instrumental diagnosis for injury severity is also described. In addition, pathophysiological component characteristics of sports TBI is accentuated: frequency of repetition, increasement of brain and body temperature, peripheral redistribution of blood flow and hypocapnia, which significantly affects cerebral blood flow. Based on the analysis of the available scientific literature, it is elicited that TBI is an independent cause of cerebral hyperthermia development, which significantly aggravates the consequences of the injury. Conclusions. The authors propose an innovative way to use microwave radiothermometry method as a diagnostic tool for sports-related TBI. In addition, the review highlights the main recommendations for complications prevention by using craniocerebral hypothermia technology, which reduces overall physical and cerebral hyperthermia, and augments the resistance of cerebral cortex neurons to hypoxia and trauma. However, the authors believe that the described approaches in sports medicine are not used purposefully due to lack of awareness of sports team doctors and coaches.
Scholarly Communication and Machine-Generated Text: Is it Finally AI vs AI in Plagiarism Detection?

This study utilizes GPT (Generative Pre-Trained Transformer) language model-based AI writing tools to create a set of 80 academic writing samples based on the eight themes of the experiential sessions of the LTC 2023. These samples, each between 2000 and 2500 words long, are then analyzed using both conventional plagiarism detection tools and selected AI detection tools. The study finds that traditional syntactic similarity-based anti-plagiarism tools struggle to detect AI-generated text due to the differences in syntax and structure between machine-generated and human-written text. However, the researchers discovered that AI detector tools can be used to catch AI-generated content based on specific characteristics that are typical of machine-generated text. The paper concludes by posing the question of whether we are entering an era in which AI detectors will be used to prevent AI-generated content from entering the scholarly communication process. This research sheds light on the challenges associated with AI-generated content in the academic research literature and offers a potential solution for detecting and preventing plagiarism in this context.
Monitoring public electoral sentiment through online comments in the news media: a comparative study of the 2019 and 2022 presidential elections in Kazakhstan

Abstract This study examined how comments about Kazakhstan’s 2019 and 2022 presidential elections posted on Tengrinews, one of the most commonly used news websites in Kazakhstan, reflected public sentiment toward the elections after two political flashpoints: Nazarbayev’s resignation and the January 2022 protests (known as Bloody January - Qandy Qantar). In particular, the study seeks an answer to the question, “How has public sentiment changed between 9 April 2019 and 25 June 2019, as well as between 20 September 2022 and 21 November 2022? ” In this paper, we applied a content analysis of all user-generated comments (n = 3,147) to identify Kazakhstani people’s mood through the lenses of cues given in comments. The method demonstrates that people were most concerned about presidential nominations and candidates, election costs, protests, and social problems, and showed some hope for change. The results also demonstrate that Kazakhstanis tend to use sarcastic language and indicate the performative nature of the country’s elections. Based on comparative analysis, we confirm that political flashpoints significantly affect citizens’ sentiments, even regarding topics such as protests.