Miracle-dz/UniFAR-FTUs huggingface dataset:
Partisipasi Politik Masyarakat dalam Pemilihan Kepala Daerah Kota Baubau Tahun 2018

The purpose of this study is to explain how the level of public political participation in the 2018 Regional Elections in BauBau City. In addition, this study identifies the factors that influence the participation of BauBau City community and any efforts made by the BauBau City Election Commission in increasing community participation. Data was collected by analyzing documents, observing, interviewing, and analyzing with qualitative and quantitative descriptive techniques. The results of this study show that the level of community participation in BauBau City is relatively high because if it is accumulated at 69.87%. The factors that influence political participation in society are political incentives, personal characteristics factors, social characteristics factors, and political circumstances. To increase community participation, the BauBau City Regional Election Commission has conducted socialization on regional head elections, formed Democratic Volunteers, conducted voter education, and mass media involvement. Tujuan penelitian ini untuk (1) untuk menjelaskan bagaimana tingkat partisipasi politik masyarakat dalam Pemilihan Kepala Daerah tahun 2018 di Kota BauBau (2) untuk mengidentifikasi faktor-faktor apa saja yang mempengaruhi partisipasi masyarakat Kota BauBau (3) untuk mengidentifikasi upaya-upaya apa saja yang dilakukan pihak KPUD Kota BauBau dalam meningkatkan partisipasi masyarakat. Data dikumpulkan dengan analisis dokumen, pengamatan, wawancara, dan dianalisis dengan tehnik deskriptif kualitatif dan Kuantitatif. Hasil dari penelitian ini adalah: (1) Tingkat partispasi masyarakat di Kota BauBau tergolong tinggi dikarenakan jika diakumulasikan sebesar 69,87% %. (2) Faktor – faktor yang mempengaruhi partisipasi politik masyarakat adalah (a) perangsang politik (b) Faktor karakteristik pribadi (c) faktor karakteristik sosial (d) keadaan politik. (3) Upaya yang dilakukan KPUD untuk meningkatkan partisipasi masyarakat adalah (a) sosialisasi pemilihan kepala daerah (b) membentuk Relawan Demokrasi (c) melaksanakan pendidikan pemilih (d) peran media massa.
One-stop stroke management platform reduces workflow times in patients receiving mechanical thrombectomy

Background and purpose Clinical outcome in patients who received thrombectomy treatment is time-dependent. The purpose of this study was to evaluate the efficacy of the one-stop stroke management (OSSM) platform in reducing in-hospital workflow times in patients receiving thrombectomy compared with the traditional model. Methods The data of patients who received thrombectomy treatment through the OSSM platform and traditional protocol transshipment pathway were retrospectively analyzed and compared. The treatment-related time interval and the clinical outcome of the two groups were also assessed and compared. The primary efficacy endpoint was the time from door to groin puncture (DPT). Results There were 196 patients in the OSSM group and 210 patients in the control group, in which they were treated by the traditional approach. The mean DPT was significantly shorter in the OSSM group than in the control group (76 vs. 122 min; P < 0.001). The percentages of good clinical outcomes at the 90-day time point of the two groups were comparable (P = 0.110). A total of 121 patients in the OSSM group and 124 patients in the control group arrived at the hospital within 360 min from symptom onset. The mean DPT and time from symptom onset to recanalization (ORT) were significantly shorter in the OSSM group than in the control group. Finally, a higher rate of good functional outcomes was achieved in the OSSM group than in the control group (53.71 vs. 40.32%; P = 0.036). Conclusion Compared to the traditional transfer model, the OSSM transfer model significantly reduced the in-hospital delay in patients with acute stroke receiving thrombectomy treatment. This novel model significantly improved the clinical outcomes of patients presenting within the first 6 h after symptom onset.
Human-Behavior and QoE-Aware Dynamic Channel Allocation for 5G Networks: A Latent Contextual Bandit Learning Approach

With the rapid advance of smart wireless technologies, a plethora of human behavioral data are generated in 5G networks, which is reported capable to improve network performance by leveraging intelligent channel resource allocation through big data analytics. However, what information can be extracted for the network mobility management, how to exploit the knowledge for resource allocation and to meet the user-centric quality of experience (QoE) are not well understood and fully explored. To address this problem, we propose an online learning algorithm for dynamic channel allocation based on contextual multi-armed bandit (CMAB) theory. Especially, we divide the stochastic human behavioral data into two categories: the user location and the QoE-driven context. Noticing that the distributions of CSI vary spatially, we define a set of user’s geographic locations that shares the same set of CSI distributions as a cluster, and the stochastic channel distributions vary across clusters. The problem is formulated as a novel latent SCB problem, where the proposed agnostic SCB algorithm could automatically find the underlying clusters and significantly improve the learning performance. We then extend our online learning algorithm into the practical multi-user random access scenario. We conduct experiments on a real dataset collected from China Mobile, which indicate that our algorithms outperform existing approaches tremendously and perform extremely well in large-scale and high-mobility networks.
Research on the impact of exchange rates and interest rates on carbon price changes in the context of sustainable development

Carbon emissions are closely linked to a company’s production activities. The amount of carbon allowances a company holds determines its carbon emissions. Changes in carbon prices interact with firms’ production activities, while changes in exchange rates and interest rates have a direct impact on firms’ production structure, willingness to produce and scale of production. Under the existing literature, most scholars have selected only one market to be used to study its influence on the carbon market. In this paper, the exchange rate market and the interest rate market are selected for the study of the impact of both on the carbon market, using the following empirical analysis methods: DCC-GARCH, TVP-VAR and GA-BP neural network. The empirical results show that interest rates are positively linked to carbon prices, while exchange rates are more negatively linked to carbon prices; exchange rates are less affected by macro factors and external shocks, while interest rates are the opposite and very sensitive to macro shocks; under the training and simulation of the neural network, carbon prices can fluctuate drastically under the combined influence of exchange rate and interest rate movements, which can provide appropriate early warning of future price fluctuations in carbon trading. This shows that adjustments to exchange rates and interest rates need to be treated with caution, and appropriate adjustments should be made to keep carbon prices stable; the government should build a mechanism to transform the green development of enterprises, pushing them to save energy and reduce emissions to achieve low-carbon transformational development; the government should not only introduce policies to support low-carbon enterprises, but also improve the national carbon market laws and regulations and price regulation mechanisms.
Comparing the Effects of Eight-Week Zinc Supplementation and Yoga Exercise on Serum Apelin Level and Kidney Function Among Women with Type II Diabetes Mellitus

Background: Diabetes mellitus is a metabolic disorder associated with impaired serum lipid and glucose levels. Objectives: The present study aimed to compare the effects of zinc supplementation, yoga exercise, and zinc supplementation plus yoga exercise on serum apelin level and kidney function among women with type II diabetes mellitus. Methods: This was a four-group pretest-posttest quasi-experimental study. Thirty two women were selected and randomly allocated to either of the following four groups: control; yoga exercise (thrice weekly for eight weeks); zinc supplementation; and zinc supplementation plus yoga exercise. Body composition, insulin resistance, and serum levels of glucose, insulin, creatinine, urea nitrogen, and apelin were measured before and after the study intervention. Data were analyzed through the one-way analysis of variance and the least significant difference post hoc test at a significance level of less than 0.05. Results: There were significant differences among the study groups concerning the serum levels of glucose (P = 0.001), insulin (P = 0.012), apelin (P = 0.001), and urea nitrogen (P = 0.02) as well as insulin resistance (P = 0.016) after the intervention. However, groups did not significantly differ from each other concerning the serum level of creatinine (P = 0.264). Conclusions: Eight-week yoga exercise and zinc supplementation are useful to women with type II diabetes mellitus.
Simulation of electrical fields in cardiac resynchronization therapy and temperature spread in HF ablation

Background: The electrical field (E-field) of the biventricular (BV) stimulation is important for the success of cardiac resynchronization therapy (CRT) in patients with cardiac insufficiency and widened QRS complex. The 3D modeling allows the simulation of CRT and high frequency (HF) ablation. Purpose: The aim of the study was to model different pacing and ablation electrodes and to integrate them into a heart model for the static and dynamic simulation of atrial and BV stimulation and high frequency (HF) ablation in atrial fibrillation (AF). Methods: The modeling and simulation was carried out using the electromagnetic simulation software CST (CST Darmstadt). Five multipolar left ventricular (LV) electrodes, one epicardial LV electrode, four bipolar right atrial (RA) electrodes, two right ventricular (RV) electrodes and one HF ablation catheter were modeled. Selected electrodes were integrated into the Offenburg heart rhythm model for the electrical field simulation. The simulation of an AV node ablation at CRT was performed with RA, RV and LV electrodes and integrated ablation catheter with an 8 mm gold tip. Results: The right atrial stimulation was performed with an amplitude of 1.5 V with a pulse width of 0.5. The far-field potentials generated by the atrial stimulation were perceived by the right and left ventricular electrode. The far-field potential at a distance of 1 mm from the right ventricular electrode tip was 36.1 mV. The far-field potential at a distance of 1 mm from the left ventricular electrode tip was measured with 37.1 mV. The RV and LV stimulation were performed simultaneously at amplitude of 3 V at the LV electrode and 1 V at the RV electrode with a pulse width of 0.5 ms each. The far-field potentials generated by the BV stimulations could be perceived by the RA electrode. The far-field potential at the RA electrode tip was 32.86 mV. AV node ablation was simulated with an applied power of 5 W at 420 kHz and 10 W at 500 kHz at the distal 8 mm ablation electrode. Conclusions: Virtual heart and electrode models as well as the simulations of electrical fields and temperature profiles allow the static and dynamic simulation of atrial synchronous BV stimulation and HF ablation at AF. The 3D simulation of the electrical field and temperature profile may be used to optimize the CRT and AF ablation.
Construction of Bid Evaluation Index System in Government Public Project Green Procurement in China Based on D-S Evidence Theory

It is of great theoretical and practical significance to construct a bid evaluation index system characterized by green for government public project green procurement. With the combination of sustainable development theory and system theory, and with the goal of government public project procurement as the guide, this paper uses the documentary research method, questionnaire survey method, and expert consultation method to analyze the influential factors of bid evaluation for government public project procurement with the perspective of green development from the aspects of qualification, economy, technology, management, and public welfare and to construct the bid evaluation index system for government public project green procurement; based on Dempster-Shafer evidence theory, using the D-S evidence synthesis method to construct a comprehensive judgment matrix for group decision-making (which effectively solves the problem of synthesizing expert opinions), and combined with the analytic hierarchy process, the index weights are determined. Ultimately, a bid evaluation index system for government public project green procurement is obtained, including 5 first-level indexes, 18 secondary indexes, and 56 tertiary indexes, providing theoretical guidance for the government to implement green procurement in the field of public projects.
Distinct phenotype of neutrophil, monocyte, and eosinophil populations indicates altered myelopoiesis in a subset of patients with multiple myeloma

Hematologic malignancies, including multiple myeloma (MM), promote systemic immune dysregulation resulting in an alteration and increased plasticity of myeloid cell subsets. To determine the heterogeneity of the myeloid cell compartment in the peripheral blood of patients with MM, we performed a detailed investigation of the phenotype and function of myeloid subpopulations. We report that a subset of MM patients exhibits a specific myeloid cell phenotype indicative of altered myelopoiesis characterized by significant changes in the properties of circulating granulocytic, monocytic, and eosinophilic populations. The subset, referred to as MM2, is defined by a markedly elevated level of CD64 (FcγRI) on the surface of circulating neutrophils. Compared to healthy controls or MM1 patients displaying intermediate levels of CD64, neutrophils from MM2 patients exhibit a less differentiated phenotype, low levels of CD10 and CXC chemokine receptor 2 (CXCR2), increased capacity for the production of mitochondrial reactive oxygen species, and an expansion of CD16neg immature neutrophil subset. Classical and patrolling monocytes from MM2 patients express elevated levels of CD64 and activation markers. MM2 eosinophils display lower levels of C-C Chemokine receptor 3 (CCR3), Toll-like receptor 4 (TLR4, CD284), and tissue factor (TF, CD142). The MM2 (CD64high) phenotype is independent of age, race, sex, and treatment type. Characteristic features of the MM2 (CD64high) phenotype are associated with myeloma-defining events including elevated involved/uninvolved immunoglobulin free light chain (FLC) ratio at diagnosis. Detailed characterization of the altered myeloid phenotype in multiple myeloma will likely facilitate the identification of patients with an increased risk of disease progression and open new avenues for the rational design of novel therapeutic approaches.
Evidence of Overtone Interference in Fundamental‐Mode Rayleigh Wave Phase and Amplitude Measurements

We present evidence that measurements of minor‐arc fundamental‐mode (FM) Rayleigh waves experience interference from major‐arc overtones, resulting in travel‐time and amplitude measurements oscillating along a ray path. The oscillations are present in synthetic seismograms generated in a 3‐D Earth model via SPECFEM3D_GLOBE and in a 1‐D Earth model by mode summation. The absence of oscillations in synthetics containing only the FM indicates that the oscillations originate from higher‐mode interference. This interference is present across multiple measurement techniques, including multi‐channel cross correlation, phase‐matched filtering, and cluster analysis. Experiments with 1‐D synthetics suggest that contamination from interference is largest at epicentral distances greater than around 120 degrees, where record sections of seismograms show the major‐arc overtones intersecting the minor‐arc FM Rayleigh wave. The short wavelength of the interference pattern means it is only observable with dense station spacing and high data quality, which may explain why it has not, to our knowledge, been previously recognized. We show the interference is visible in real data. Its overall impact on phase‐velocity maps is probably minor due to many measurements from shorter path lengths less prone to interference bias. However, phase‐velocity maps constructed only from measurements at epicentral distances prone to interference exhibit significant noise and poor agreement with maps from measurements that include all path lengths; the issue is especially problematic for approaches that use differential travel times between nearby stations. Accounting for interference may diminish noise in measurements and improve the accuracy of images of the upper mantle.
Sparse Representation Neural Networks for Online Reinforcement Learning

In this thesis, we investigate sparse representations in reinforcement learning. We begin by discussing catastrophic interference in reinforcement learning with function approximation, and empirically investigating difficulties of online reinforcement learning in both policy evaluation and control. We then demonstrate that learning a control policy incrementally with a representation from a standard neural network fails in classic control domains, whereas learning with a sparse representation is effective. We provide evidence that the reason for this is that the sparse representation avoids catastrophic interference. Lastly, we discuss how to learn such sparse representations. We explore the idea of Distributional Regularizers, where the activation of hidden nodes is encouraged to match a particular distribution that results in sparse activation across time. We identify a simple but effective way to obtain sparse representations, not afforded by previously proposed strategies, making it more practical for further investigation into sparse representations for reinforcement learning.
Comparative transcriptome analysis of Locusta migratoria tibetensis Chen (Orthoptera: Oedipodidae) under high‐ and low‐temperature stress

The endemic locust species on the Tibetan plateau, Locusta migratoria tibetensis Chen can grow and breed successfully under the plateau temperature environment. This study explored the molecular response process of Tibetan locusts to high and low temperatures by comparing its transcriptome under different temperature conditions. A total of 66,256 unigenes were obtained through RNA‐Seq, among which 30,051 were successfully annotated. In detail, there were 778 differentially expressed genes (DEGs) at high temperatures and 1,787 DEGs at low temperatures. Under high temperatures, the metabolic pathways of saturated and unsaturated fatty acids and amino acids were up‐regulated, producing a significant amount of fatty acids and amino acids. However, under low temperatures, the chitinase activity was enhanced, causing chitin to decompose into small sugar molecules. Meanwhile, various sugar metabolism pathways were up‐regulated when a significant amount of small sugar molecules were produced. Then, large quantities of alpha‐linolenic acid and arachidonic acid were accumulated to maintain the normal physiological functions of cells. The analysis of annotated genes revealed that heat shock proteins (Hsp) were associated with the response to temperature stress, and mainly involved in the synthesis and transportation of proteins, preventing protein damage and the decomposition of damaged proteins. These results provide a basis for the further understanding of the adaptation mechanism of Tibetan locusts to high and low temperatures, laying a foundation for further understanding the adaptability of Tibetan locusts on Tibetan plateaus.
Aprendizado de métrica de distância sobre o desempenho de um classificador Máquina de Vetor de Suporte (SVM).

: The Metric Learning has been of great use in the development of algorithms machine learning, standing out for the solution of problems related to the performance of classiﬁers. An eﬃcient classiﬁcation tool is the Support Vector Machine ( SVM ), designed to achieve good generalization capability. This study aims to propose the use of distance metric learning on the performance of an SVM classiﬁer. This method uses the Mean Square Error (MSE) to ﬁnd the best Kernel in the training base, and thus apply it to the test base. In the realized experiments, it was veriﬁed that the adjustment of the Kernel parameters through a metric learning approach can considerably improve the performance of an SVM classiﬁer. M´edio (MSE) para encontrar o melhor Kernel na base de treinamento, e assim aplic´a-lo `a base de teste. Nos experimentos realizados, foi constatado que o ajuste dos parˆametros do Kernel por meio de uma abordagem de aprendizagem m´etrica pode melhorar consideravelmente o desempenho de um classiﬁcador SVM .
Effect of temperature, population density, and sex on feeding damage caused by adult pea leaf weevil (Coleoptera: Curculionidae)

Abstract Sitona lineatus (Linnaeus) (Coleoptera: Curculionidae) is an important pest of Pisum sativum Linnaeus (Fabaceae) and Vicia faba Linnaeus (Fabaceae). Managing S. lineatus is difficult. There is currently no forecast that can be used to predict S. lineatus population densities between growing seasons (when management decisions are made). Sitona lineatus populations are monitored in spring by assessing adult-induced feeding damage. We assume the quantity of feeding damage is indicative of population density. However, the effects of abiotic factors, including temperature, on feeding rate are not known. We assessed the effects of temperature, population density, and sex on S. lineatus feeding rate by counting feeding notches on host plants exposed to adult weevils for 24 hours at four densities and under five temperature regimes. Individual females consumed more than male weevils. As expected, feeding damage increased with weevil density. The influence of temperature increased as weevil densities increased. Feeding rate did not differ between constant and fluctuating temperature regimes of equal mean daily temperature. Thus, a model based on mean daily temperature may predict weevil density if temperature and damage levels are known. Historical survey data for S. lineatus could be used to develop and validate new models.
A Framework for the Construction of a Historical Dictionary for Arabic

Arabic is one of the oldest Semitic languages in the world. But despite its rich historical heritage, Arabic is still bereft of a historical dictionary which traces the ﬁrst use of its words and the evolution of their meanings and structures. Therefore, creating such a dictionary is of a great importance for the Arab world as it bridges the gap between its present and its past. This task should undergo several stages and requires a lot of eﬀort. In this paper, we present our framework to help the linguists create a historical dictionary for Arabic. For this aim, we propose a platform which helps to trace the evolution of the meanings of a given word throughout time. The developed system allows the user to extract the meaning of an Arabic word according to the historical period in which it appeared. It also provides information about the oldest date of use of the word with a textual example in which it ﬁrst appeared, and the ﬁrst place where the word was used.