Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Short-term Time Series Forecasting Method Based on Feature Fusion of Electric Power

In the power industry, time series forecasting has many application scenarios, such as accurate electricity consumption forecasting and load forecasting, which can provide a reference for power grid staff and reduce decision-making costs. To improve the accuracy of short-term load forecasting, this paper proposes a hybrid forecasting model based on an improved encoder-decoder architecture model and a Temporal Convolutional Network. Based on the gate recurrent unit, the model in this paper introduces the encoder-decoder architecture, adds the attention mechanism for improving the prediction accuracy, and performs feature fusion with the Temporal Convolutional Network, building a dual-feature fusion model finally. Experiments show that the method proposed in this paper reduces the evaluation metrics RMSE, MAPE and MAE by 7.7%, 15.2%, and 13.9% respectively compared to the Long Short-Term Memory Networks. Compared with the Temporal Convolutional Network, it is reduced by 2.6%, 4.8%, and 4%.
A vowel synthesizer based on F0‐modulated formant sinusoids

A vowel synthesis algorithm was developed resembling parallel formant synthesizers. However, formants are computed as amplitude‐ and phase‐modulated sinusoids instead of being represented by recursive filters or resonators. The fundamental frequency is imposed on the signal in the following way: Each pitch period starts with a short initial rise of a duration of pi/2 of the first formant frequency; at the end of this ramp phase all formants start as sinusoids at pre‐defined phase angles and amplitudes, and successively undergo linear attenuation toward zero amplitude at the end of each pitch period. Adjacent pitch periods, thus, do not influence each other. In principle, the signals produced by this algorithm resemble the output of parallel formant synthesizers [e.g., D. H. Klatt, J. Acoust. Soc. Am. 67, 971–995 (1980)]. However, by incremental sample‐by‐sample computation of the formants’ phase angles, formant frequencies are allowed to continuously change within single pitch periods. This algorithm prod.
The Extra-Islet Pancreas Supports Autoimmunity in Human Type 1 Diabetes

In autoimmune Type 1 diabetes (T1D), immune cells progressively infiltrate and destroy the islets of Langerhans, islands of endocrine tissue dispersed throughout the pancreas. However, it is unclear how this process, called insulitis, develops and progresses within this organ. Here, using highly multiplexed CO-Detection by indEXing (CODEX) tissue imaging and cadaveric pancreas samples from pre-T1D, T1D, and non-T1D donors, we examine pseudotemporalspatial patterns of insulitis and exocrine inflammation within large pancreatic tissue sections. We identify four sub-states of insulitis characterized by CD8+T cells at different stages of activation. We further find that exocrine compartments of pancreatic lobules affected by insulitis have distinct cellularity, suggesting that extra-islet factors may make particular lobules permissive to disease. Finally, we identify staging areas, immature tertiary lymphoid structures away from islets where CD8+T cells appear to assemble before they navigate to islets. Together, these data implicate the extra-islet pancreas in autoimmune insulitis, greatly expanding the boundaries of T1D pathogenesis.
Quality of life by dysmenorrhea severity in young and adult Japanese females: A web-based cross-sectional study

Dysmenorrhea is a monthly menstrual pain that can limit a woman’s quality of life (QOL). The relationship between dysmenorrhea severity and QOL has been reported in several countries; however, the results cannot be generalized because lifestyle and cultural background affect menstrual pain. This study sought to uncover whether 1) different factors, such as emotions and ways of coping with symptoms, vary with the severity of dysmenorrhea and 2) the severity of dysmenorrhea ultimately affects QOL in Japan. A web-based cross-sectional survey was sent to 1000 Japanese females aged 16–30 years. The respondents were divided into two groups: those without dysmenorrhea (n = 24) and those with dysmenorrhea (n = 471). The severity of dysmenorrhea was classified using the Numerical Rating Scale as either mild (1–3), moderate (4–7), or severe (8–10). In total, 156 respondents reported mild dysmenorrhea, 249 reported moderate dysmenorrhea, and 66 reported severe dysmenorrhea. QOL was measured using the 26-item World Health Organization Quality of Life scale. One-way ANOVA and Kruskal-Wallis tests were used to compare QOL across different levels of dysmenorrhea severity, depending on normality. Ultimately, significant differences in QOL scores (p<0.001) were observed based on dysmenorrhea severity, with respondents with severe dysmenorrhea reporting the lowest QOL scores. Meanwhile, significant differences were observed in the physical, psychological, and environmental subscales (p<0.001, p<0.001, p = 0.019) across respondents with different levels of dysmenorrhea severity; notably, respondents with severe dysmenorrhea demonstrated a negative spiral of chronic pain, which may significantly impact QOL, and, relatedly, a relatively low psychological QOL. This study is the first to show the relationship between dysmenorrhea severity and QOL in Japanese females, who are more likely to experience negative feelings during menstruation.
Clinical evaluation of total hip arthroplasty for osteoarthropathy secondary to septic hip arthritis

To investigate the clinical efficacy and prognosis of total hip arthroplasty (THA) in patients with osteoarthropathy secondary to septic hip arthritis. A total of 100 patients with osteoarthropathy secondary to septic hip arthritis who underwent surgery in our hospital from December 2010 to December 2021 were selected. Operative data were summarized. Moreover, preoperative and postoperative differences in Harris score, Western Ontario and McMaster Universities Osteoarthritis Index score, Visual Analogue Scale score, hip mobility were compared. Due to hospital transfer, the epidemic of COVID-2019 and other reasons, 10 patients were lost to follow-up and finally 90 patients were recorded. According to the results, the mean operation time (OT) was 1.78 ± 0.32 (hour), the mean intraoperative blood loss (BL) was 586.62 ± 31.66 (mL), the mean red blood cell (RBC) transfusion was 3.84 ± 0.75 (U), and 2 cases were positive, and 88 cases were negative for Trendelenburg sign. Postoperatively, the unequal length of the lower limbs and the Visual Analogue Scale score were significantly lower than that of the preoperative, while the Western Ontario and McMaster Universities Osteoarthritis Index score, Harris score, and hip joint range of motion were significantly higher than those of the preoperative (P < .05). At the last follow-up, all the incisions healed with no deep infections, 1 case suffered from transient sciatic nerve palsy, 2 cases had intermuscular venous thrombosis, and 2 cases who occurred hip dislocation recovered after treatment. THA surgery for osteoarthropathy secondary to septic hip arthritis has a good effect, relieving the patient’s pain effectively, improving prognosis and joint mobility. For osteoarthropathy secondary to septic hip arthritis, THA may be meaningful in terms of prognosis.
Association between thyroid dysfunction and venous thromboembolism: A systematic review and meta-analysis

Background: Thyroid dysfunction plays an important role in the development of cardiovascular disease. However, its relationship with venous thromboembolism (VTE) remains unclear. We performed a meta-analysis of published cohort and case-control studies to investigate the association between thyroid dysfunction and VTE comprehensively. Methods: Three reviewers independently searched EMbase, PubMed, China national knowledge infrastructure, and Cochrane Library databases for relevant articles from the time of database establishment to 01 October 2022 and identified all studies on thyroid dysfunction and VTE as studies of interest. Of the 2418 publications retrieved, we identified 10 articles with 15 studies that met our selection criteria. Pooled ORs and 95% confidence intervals were calculated using fixed- or random-effect models. Results: We pooled 8 studies by a fixed-effect model, which suggested an increased risk of VTE in patients with (subclinical) hyperthyroidism (OR 1.33, 95% CI: 1.29–1.38). In the other 7 studies on patients with (subclinical) hypothyroidism, the risk was similarly increased when pooled by a random-effect model (OR 1.52, 95% CI: 1.23–1.89). After sensitivity analysis and risk of bias analysis, the risk of VTE was still increased in both (subclinical) hyperthyroidism (OR 1.322, 95% CI: 1.278–1.368) and (subclinical) hypothyroidism (OR 1.74, 95% CI: 1.41–2.16). Conclusion: Patients with thyroid dysfunction have an increased risk of VTE. Therefore, it is recommended to perform thyroid function screening routinely in patients at high risk of VTE.
A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications

Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.
Repeated divergence of amphibians and reptiles across an elevational gradient in northern Madagascar

Abstract How environmental factors shape patterns of biotic diversity in tropical ecosystems is an active field of research, but studies examining the possibility of ecological speciation in terrestrial tropical ecosystems are scarce. We use the isolated rainforest herpetofauna on the Montagne d'Ambre (Amber Mountain) massif in northern Madagascar as a model to explore elevational divergence at the level of populations and communities. Based on intensive sampling and DNA barcoding of amphibians and reptiles along a transect ranging from ca. 470–1470 m above sea level (a.s.l.), we assessed a main peak in species richness at an elevation of ca. 1000 m a.s. l. with 41 species. The proportion of local endemics was highest (about 1/3) at elevations >1100 m a.s. l. Two species of chameleons (Brookesia tuberculata, Calumma linotum) and two species of frogs (Mantidactylus bellyi, M. ambony) studied in depth by newly developed microsatellite markers showed genetic divergence up the slope of the mountain, some quite strong, others very weak, but in each case with genetic breaks between 1100 and 1270 m a.s. l. Genetic clusters were found in transect sections significantly differing in bioclimate and herpetological community composition. A decrease in body size was detected in several species with increasing elevation. The studied rainforest amphibians and reptiles show concordant population genetic differentiation across elevation along with morphological and niche differentiation. Whether this parapatric or microallopatric differentiation will suffice for the completion of speciation is, however, unclear, and available phylogeographic evidence rather suggests that a complex interplay between ecological and allopatric divergence processes is involved in generating the extraordinary species diversity of Madagascar's biota. Our study reveals concordant patterns of diversification among main elevational bands, but suggests that these adaptational processes are only part of the complex of processes leading to species formation, among which geographical isolation is probably also important.
Tinnea gombea (Lamiaceae), a new species from the Sudanian savanna region, Nigeria based on integrative evidence

Tinnea gombea, endemic to the Sudan savanna grasslands in northern Nigeria, is described and illustrated. We used integrative evidence from morphological characters, ecology and molecular phylogenetic data. The new species is morphologically and ecologically similar to T. barteri and T. aethiopica, but can be readily delimited from these taxa by unique characters including a subshrub growth habit, leaves alternate to subopposite, blades lanceolate, apically acuminate, inflorescences raceme, bearing solitary flowers in upper leaf and bract axils, lilac to purplish dusky flowers and the inflated fruits dehiscent. The distribution and habitat of T. gombea are also distinctive, being restricted to the Sudan savanna, while the two most similar species are widespread in tropical Africa. Additionally, molecular phylogenetic assessments using nrITS and chloroplast trnL-F, matK and rbcL support the placement of T. gombea as a distinct species. Tinnea gombea is here assessed as Critically Endangered due to its small population size and restriction to a small area lacking conservation prioritization.
Diurnal Variation in the Troposphere's Energy Balance

Abstract The tropospheric energy balance is examined in an effort to ascertain the diurnal variation of its components. The mean local diurnal temperature change in the 850–300 mb layer was observationally determined utilizing upper air data collected at 142 Northern Hemisphere radiosonde stations. Temperature data from the GATE, ATEX, BOMEX and LIE experiments, and Operations Redwing and Hardtack in the tropical west Pacific were also analyzed. Diurnal radiative cooling values are computed for specific geographical regions, and the magnitude of the required diurnal warming for each region is determined. Required warming is composed of a variety of atmospheric processes such as condensation energy release, surface to air sensible heat transfer, horizontal advection and vertical motion. A large morning maximum and late afternoon-early evening minimum of tropospheric required warming is found for all data sets, at all latitudes, in all seasons, and for both oceanic and land areas. These morning versus late .
Maximum entropy density estimation and modeling geographic distributions of species

Maximum entropy (maxent) approach, formally equivalent to maximum likelihood, is a widely used density-estimation method. When input datasets are small, maxent is likely to overfit. Overfitting can be eliminated by various smoothing techniques, such as regularization and constraint relaxation, but theory explaining their properties is often missing or needs to be derived for each case separately. In this dissertation, we propose a unified treatment for a large and general class of smoothing techniques. We provide fully general guarantees on their statistical performance and propose optimization algorithms with complete convergence proofs. As special cases, we can easily derive performance guarantees for many known regularization types including L1 and L2-squared regularization. Furthermore, our general approach enables us to derive entirely new regularization functions with superior statistical guarantees. The new regularization functions use information about the structure of the feature space, incorporate information about sample selection bias, and combine information across several related density-estimation tasks. We propose algorithms solving a large and general subclass of generalized maxent problems, including all discussed in the dissertation, and prove their convergence. Our convergence proofs generalize techniques based on information geometry and Bregman divergences as well as those based more directly on compactness. As an application of maxent, we discuss an important problem in ecology and conservation: the problem of modeling geographic distributions of species. Here, small sample sizes hinder accurate modeling of rare and endangered species. Generalized maxent offers several advantages over previous techniques. In particular, generalized maxent addresses the problem in a statistically sound manner and allows principled extensions to situations when data collection is biased or when we have access to data on many related species. The utility of our unified approach is demonstrated in comprehensive experiments on large real-world datasets. We find that generalized maxent is among the best-performing species-distribution modeling techniques. Our experiments also show that the contributions of this dissertation, i.e., regularization strategies, bias-removal approaches, and multiple-estimation techniques, all significantly improve the predictive performance of maxent.
A qualitative study exploring how patient engagement activities were sustained or adapted in Canadian healthcare organizations during the COVID-19 pandemic

Background The COVID-19 pandemic caused disruptions across healthcare systems globally exposing the precarious state of patient engagement across all levels of healthcare. While evidence is emerging to describe how engagement was affected across various settings, insights about how some organizations at the policy and practice level of healthcare were able to sustain or adapt patient engagement activities is lacking. Objective This paper addresses the following research question: “How were healthcare, government, and patient partner organizations able to sustain or adapt patient engagement activities during the COVID-19 pandemic? ” Methods A qualitative descriptive study was conducted to understand how patient engagement activities were maintained or adapted in a variety of healthcare, government, and patient partner organizations in Canada throughout the pandemic. This analysis was part of a larger qualitative, multiple case study where one-to-one interviews were conducted with organizational leaders, managers and patient partners. Results The following themes were identified as key aspects of maintaining or adapting patient engagement activities: 1) having an embedded organizational culture of patient engagement; 2) adapting patient engagement activities to focus on COVID-19 response efforts; 3) having patient partners who exercised leadership and advocacy to support patient care and experiences during the pandemic; and 4) leveraging virtual technology as a communication tool to engage patient partners. Conclusion This paper highlights important insights that may be useful to other health care organizations on how to sustain or adapt patient engagement activities during a healthcare crisis. Having patient engagement embedded within an organization’s culture supported by, but not limited to, infrastructure, resources, investments in dedicated staff and patient partner leadership, and communication strategies and tools enabled continued patient engagement activities during the pandemic.
Comparative analysis of fresh food e-commerce brand attitudes based on STM theme model

The circulation of fresh agricultural products is related to the quality of consumption and agricultural development. This article takes the online reviews of fresh products as the research object and studies the comparative differences of consumer brand perception under the different sentiment and brand source classifications. The study was carried out with the aim to explore the influence mechanism of consumers’ different brand attitudes. Structural Topic Modeling (STM) method was used to classify online reviews for brand perception topics, and Gephi network visualization was used to analyze the influence relationship between different brand perception topics. The study also conducts comparative research on the differences in perceived attitudes between positive and negative emotion classifications, as well as self-operated and non-self-operated brands, and analyzes the moderating effect of sentiment scores on the perceived theme intensity of different brands.
A predictive model for understanding the role of emotion for the formation of presence in virtual reality

Users’ emotions may influence the formation of presence in virtual reality (VR). Users’ expectations, state of arousal and personality may also moderate the relationship between emotions and presence. An interoceptive predictive coding model of conscious presence (IPCM) considers presence as a product of the match between predictions of interoceptive emotional states and the actual states evoked by an experience (Seth et al. 2012). The present paper aims to test this model’s applicability to VR for both high-arousal and low-arousal emotions. The moderating effect of personality traits on the creation of presence is also investigated. Results show that user expectations about emotional states in VR have an impact on presence, however, expression of this relationship is moderated by the intensity of an emotion, with only high-arousal emotions showing an effect. Additionally, users’ personality traits moderated the relationship between emotions and presence. A refined model is proposed that predicts presence in VR by weighting emotions according to their level of arousal and by considering the impact of personality traits.
A GENERAL SOFTWARE TESTING MODEL INVOLVING OPERATIONAL PROFILES

An operational profile describes, in a probabilistic way, how a software is utilized by its users. It makes the testing procedure more realistic and efficient. We consider a model where the software is tested sequentially in all of the operations that it is designed to perform. The stochastic and deterministic model parameters involving costs and failures all depend on the operations. In particular, the failure process generated by each fault is quite general and debugging is not necessarily perfect. Our aim is to find the optimal testing durations in all of the operations in order to minimize the total expected cost. This problem leads to an interesting nonlinear programming formulation that can be solved using well-known procedures in convex optimization.