Pengaruh Pendekatan Pembelajaran Matematika Realistik Terhadap Prestasi Belajar Matematika Ditinjau Dari Kemampuan Numerik Siswa Kelas VIII SMP Negeri 2 Amlapura

Penelitian ini bertujuan untuk mengetahui dan mendeskripsikan pengaruh pendekatan pembelajaran matematika realistik terhadap prestasi belajar matematika ditinjau dari kemampuan numerik siswa. Penelitian ini merupakan eksperimen semu dilaksanakan dengan menggunakan rancangan the post test only control group design. Populasinya adalah seluruh siswa kelas VIII SMP Negeri 2 Amlapura tahun pelajaran 2013-2014. Dari delapan kelas yang ada, empat kelas dipilih sebagai sampel yakni dua kelas sebagai kelas eksperimen dan dua kelas sebagai kelas kontrol yang diambil dengan teknik random. Data penelitian dikumpulkan menggunakan tes, yaitu tes kemampuan numerik dan tes prestasi belajar matematika. Data yang diperoleh dianalisis dengan analisis varians dua jalur dilanjutkan dengan uji Tukey. Berdasarkan hasil analisis data dan pembahasan, dapat disimpulkan, terdapat perbedaan yang signifikan prestasi belajar matematika antara siswa yang mengikuti pendekatan pembelajaran matematika realistik dengan siswa yang mengikuti pendekatan pembelajaran konvensional. Terdapat pengaruh interaksi antara pendekatan pembelajaran matematika realistik dan kemampuan numerik terhadap prestasi belajar matematika. Pada Siswa yang memiliki kemampuan numerik tinggi, prestasi belajar matematika siswa yang mengikuti pendekatan pembelajaran matematika realistik lebih baik daripada pendekatan konvensional. Pada siswa yang memiliki kemampuan numerik rendah, prestasi belajar matematika siswa yang mengikuti pendekatan pembelajaran matematika realistik tetap lebih tinggi dari siswa yang mengikuti pendekatan pembelajaran konvensional.Kata Kunci : pendekatan pembelajaran matematika realistik, kemampuan numerik, dan prestasi belajar matematika The study aimed at finding out and describing the contribution of realistic mathematic instructional approach towards mathematic learning achievement viewed from numeric skills. It was a quasi-experimental research by utilizing the post test only control group design. The study involved all students class VIII SMP Negeri 2 Amlapura in 2013-2014 as the population. Four classes of the students were chosen from eight parallel classes as the samples consisting of two classes as experimental and another two classes as control groups. They were determined based on random technique. The data were collected by testing, involving numeric ability and mathematic achievement tests. They were analysed based on two tailed variant analysis followed by Tukey-test. The results indicated that there was a significant difference between mathematic learning achievement of the students joining realistic mathematic instruction and those joining a conventional approach. There was an interactional contribution of realistic mathematic instructional approach and numeric ability towards mathematic learning achievement. The students having higher numeric skills, when joining realistic mathematic instruction approach their mathematic learning achievement was found better or higher than those joining a conventional approach. The students having lower numeric skills, when joining realistic mathematic instruction approach, their mathematic learning achievement was found better or higher than those joining a conventional approach

Adaptive Robust Actuator Fault Accommodation for a Class of Uncertain Nonlinear Systems with Unknown Control Gains

An adaptive robust fault tolerant control approach is proposed for a class of uncertain nonlinear systems with unknown signs of high-frequency gain and unmeasured states. In the recursive design, neural networks are employed to approximate the unknown nonlinear functions, K-filters are designed to estimate the unmeasured states, and a dynamical signal and Nussbaum gain functions are introduced to handle the unknown sign of the virtual control direction. By incorporating the switching function σ algorithm, the adaptive backstepping scheme developed in this paper does not require the real value of the actuator failure. It is mathematically proved that the proposed adaptive robust fault tolerant control approach can guarantee that all the signals of the closed-loop system are bounded, and the output converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by the simulation examples

Study on the Nonsingular Problem of Fractional-Order Terminal Sliding Mode Control

An improved type of control strategy combining the fractional calculus with nonsingular terminal sliding mode control named non-singular fractional terminal sliding mode control (NFOTSM) is proposed for the nonlinear tire-road friction control system of vehicle in this paper. A fractional-order switching manifold is proposed, and the corresponding control law is formulated based on the Lyapunov stability theory to guarantee the sliding condition. The proposed controller ensures the finite time stability of the closed-loop system. Then, a terminal attractor is introduced to solve the singularity problem of fractional terminal sliding mode control (FOTSM). Finally, the performance of the NFOTSM is fully investigated compared with other related algorithms to find the effectiveness for the tire-road friction system. The results show that the NFOTSM has better performance than other related algorithms.</jats:p

Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework

Structured tabular data is a fundamental data type in numerous fields, and the capacity to reason over tables is crucial for answering questions and validating hypotheses. However, constructing labeled data for complex reasoning tasks is labor intensive, and the quantity of annotated data remains insufficient to support the intricate demands of real-world applications. To address the insufficient annotation challenge, we present a self-training framework for unsupervised complex tabular reasoning (UCTR-ST) by generating diverse synthetic data with complex logic. Specifically, UCTR-ST incorporates several essential techniques: we aggregate diverse programs and execute them on tables based on a Program-Management component, and we bridge the gap between programs and text with a powerful Program-Transformation module that generates natural language sentences with complex logic. Furthermore, we optimize the procedure using a Table-Text Manipulator to handle joint table-text reasoning scenarios. The entire framework utilizes self-training techniques to leverage the unlabeled training data, which results in significant performance improvements when tested on real-world data. Experimental results demonstrate that UCTRST achieves above 90% of the supervised model performance on different tasks and domains, reducing the dependence on manual annotation. Additionally, our approach can serve as a data augmentation technique, significantly boosting the performance of supervised models in low-resourced domains.Submitted to TKDE, preprint versio

FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering

Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users. Unfortunately, the performance of most KBQA models tends to decline significantly in real-world scenarios where high-quality annotated data is insufficient. To mitigate the burden associated with manual annotation, we introduce FlexKBQA by utilizing Large Language Models (LLMs) as program translators for addressing the challenges inherent in the few-shot KBQA task. Specifically, FlexKBQA leverages automated algorithms to sample diverse programs, such as SPARQL queries, from the knowledge base, which are subsequently converted into natural language questions via LLMs. This synthetic dataset facilitates training a specialized lightweight model for the KB. Additionally, to reduce the barriers of distribution shift between synthetic data and real user questions, FlexKBQA introduces an executionguided self-training method to iterative leverage unlabeled user questions. Furthermore, we explore harnessing the inherent reasoning capability of LLMs to enhance the entire framework. Consequently, FlexKBQA delivers substantial flexibility, encompassing data annotation, deployment, and being domain agnostic. Through extensive experiments on GrailQA, WebQSP, and KQA Pro, we observe that under the few-shot even the more challenging zero-shot scenarios, FlexKBQA achieves impressive results with a few annotations, surpassing all previous baselines and even approaching the performance of supervised models, achieving a remarkable 93% performance relative to the fully-supervised models. We posit that FlexKBQA represents a significant advancement towards exploring better integration of large and lightweight models. The code is open-sourced.Comment: Accepted as AAAI-24 Oral paper; Knowledge Base Question Answering; Large Language Model; Data Generation; Few-Shot & Zero-Sho

Effect of an auxiliary acceptor on D–A–π–A sensitizers for highly efficient and stable dye-sensitized solar cells

As one of the promising photovoltaic technologies, high performance metal-free dye-sensitized solar cells (DSSCs) have been explored due to the fact that they can be potentially produced using low-cost materials, their color can be tuned and they exhibit reasonable stability.</p

Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis by integrated bioinformatics analysis and machine learning

BackgroundDermatomyositis (DM) is an autoimmune disease that primarily affects the skin and muscles. It can lead to increased mortality, particularly when patients develop associated malignancies or experience fatal complications such as pulmonary fibrosis. Identifying reliable biomarkers is essential for the early diagnosis and treatment of DM. This study aims to identify and validate pivotal diagnostic biomarker for DM through integrated bioinformatics analysis and clinical sample validation.MethodsGene expression datasets GSE46239 and GSE142807 from the Gene Expression Omnibus (GEO) database were merged for analysis. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Advanced machine learning methods were utilized to further pinpoint hub genes. Weighted gene co‐expression network analysis (WGCNA) was also conducted to discover key gene modules. Subsequently, we derived intersection gene from these methods. The diagnostic performance of the candidate biomarker was evaluated using analysis with dataset GSE128314 and confirmed by immunohistochemistry (IHC) in skin lesion biopsy specimens. The CIBERSORT algorithm was used to analyze immune cell infiltration patterns in DM, then the association between the hub gene and immune cells was investigated. Gene set enrichment analysis (GSEA) was performed to understand the biomarker’s biological functions. Finally, the drug-gene interactions were predicted using the DrugRep server.ResultsInterferon-stimulated gene 15 (ISG15) was identified by intersecting DEGs, advanced machine learning-selected genes and key module genes from WGCNA. ROC analysis showed ISG15 had a high Area under the curve (AUC) of 0.950. IHC findings confirmed uniformly positive expression of ISG15, particularly in perivascular regions and lymphocytes, contrasting with universally negative expression in controls. Further analysis revealed that ISG15 is involved in abnormalities in various immune cells and inflammation-related pathways. We also predicted three drugs targeting ISG15, supported by molecular docking studies.ConclusionOur study identifies ISG15 as a highly specific diagnostic biomarker for DM, ISG15 may be closely related to the pathogenesis of DM, demonstrating promising potential for clinical application

Visualization of Endolymphatic Hydrops in Patients With Unilateral Idiopathic Sudden Sensorineural Hearing Loss With Four Types According to Chinese Criterion

Objective: The aim of this study is to evaluate the possible value of endolymphatic hydrops (EH) in patients with unilateral idiopathic sudden sensorineural hearing loss (UISSNHL) with four types according to audiometry.Methods: Seventy-two patients (40 men and 32 women; age range, 28–78 years; mean age: 50.0 ± 12.9 years) with UISSNHL were admitted retrospectively into this study. Based on the pure tone audiometry before treatment, the hearing loss of all these patients were categorized into four types: low-frequency group (LF-G), high-frequency group (HF-G), flat group (F-G), and total deafness group (TD-G). The average time from symptom onset to the first examination was 6.9 ± 4.4 days (1–20 days). 3D-FLAIR MRI was performed 24 h after intratympanic injection of gadolinium (Gd) within 1 week after the UISSNHL onset. The incidence of EH in the affected ears based on four types of hearing loss were analyzed using the Chi-square test, and the possible relationship with vertigo and prognosis were also assessed.Results: Eleven of 21 patients (52.4%) in LF-G had the highest EH-positive rate, followed by 18.2% in HF-G, 11.8% in F-G, and 17.4% in TD-G. The significant difference was found in the four groups (P = 0.018). The EH rate of LF-G was statistically significantly higher than that of F-G and TD-G (P = 0.009, P =0.014), respectively. After being valued by the volume-referencing grading system (VR scores), the EH level was represented by the sum scores of EH. In LF-G, no statistically significant difference was found in the prognosis of ISSNHL patients between with the EH group and the no EH group (P = 0.586). The symptom “vertigo” did not correlate with EH and prognosis.Conclusions: EH was observed in UISSNHL patients by 3D-FLAIR MRI. EH may be responsible for the pathology of LF-G but not related to prognosis. It might be meaningless to assess EH in other hearing loss types, which might be more related to the blood-labyrinth dysfunction

Comparative Analysis of Patients’ Characteristics, Treatment, and Survival Outcomes in CLL from China and the US

Abstract: Background: Chronic lymphocytic leukaemia (CLL) is considerably more common in Americans compared with Asians. The bases for these differences and implications for therapy outcomes are controversial and mostly unknown. Methods: We compared baseline co-variates, therapies and outcomes from two databases, Flatiron Health database in the United States (N=15,786) and Tianjin CAMS database from China (N=2,996). Results: Chinese subjects had younger age at diagnosis, more advanced Rai stage and an increased prevalence of lymphadenoma, thrombocytopenia and increased β2-microglobulin. Americans had higher rates of unmutated IGHV, TP53 deletion and cytogenetic abnormalities. These differences persisted after adjusting for age, Rai stage and IGHV mutation state. There were also substantial differences in therapy patterns between the cohorts. Median survival in Chinese was 9.7 versus 7.5 years in Americans (P&lt;0.001). In sub-group analyses Chinese CLL had better 5-year survivals with chemotherapy (69% [95% CI, 66, 72%] versus 49% [47, 52%]; P&lt;0.001), immune therapies (67% [63, 72%] versus 65% [64, 66%]; P=0.041) and targeted therapies (85% [81, 88%] versus 65% [64, 67%]; P&lt;0.001). These advantages were pronounced among older patients and those with early stage, mutated IGHV and without TP53 deletion. Conclusion: This cross-sectional study identifies significant clinical and treatment outcome disparities in CLL between Eastern and Western populations, attributed to distinct genetic and molecular profiles

Personal protection and influencing factors of livestock workers in Xinjiang

BackgroundPersonal protection is crucial for reducing the risk of zoonotic pathogen infection among livestock workers. Investigating the current status of its implementation and associated influencing factors can provide empirical evidence for developing more effective intervention measures. ObjectiveTo investigate the current status of personal protection implementation among livestock workers in Xinjiang, China and its influencing factors, providing a reference for formulating targeted intervention measures. MethodsThis study was conducted in Bayingolin Mongol Autonomous Prefecture, Kashgar region, and the First and Eighth Divisions of Xinjiang Production and Construction Corps. We selected large-scale cattle and sheep farms, cooperatives, individual livestock households, livestock trading markets, slaughterhouses, and retail markets. Using cluster sampling, we recruited all livestock workers (1074 participants) at sampled sites. During the period from April to July 2024, we conducted face-to-face surveys with a self-designed questionnaire among livestock workers, collecting general demographic information, occupational exposure history, and information related to the use of personal protective equipment, resulting in 939 valid questionnaires. EpiData 3.1 was used for data entry and SPSS 26.0 for statistical analysis. We employed logistic regression to compare the implementation of personal protection among workers by selected categories. ResultsAmong the study subjects, there were 300 workers from large-scale farms (31.95%) and 583 self-employed livestock farmers (62.09%). The sample included 600 males (63.90%) and 339 females (36.10%). The majority of the study subjects was farmers, breeders, or aged 18-60 years (87.75%), auxiliary workers (87.97%) and had an education level of junior high school or below (81.36%). The proportion of workers who consistently and correctly implemented all five personal protective measures (washing hands after work, wearing gloves, wearing masks, wearing dedicated work clothes, and using rubber boots) was 35.14% (330/939). The implementation rates for these five protective measures were 93.61% (879/939), 58.68% (551/939), 57.93% (544/939), 46.01% (432/939), and 42.81% (402/939), respectively. There were statistically significant differences in the implementation rates of differen personal protective measures among the workers by occupation and education level (P<0.05). Among them, workers with lower education levels had lower implementation rates for various protective measures. The results of logistic regression indicated that, compared to male workers, female workers (OR=2.26, 95%CI: 1.68, 3.04) showed a higher rate of non-standard implementation of personal protection. Using the age group of 18 to less than 36 years as a reference, those aged 36 to less than 60 years (OR=1.82, 95%CI: 1.33, 2.50) and 60 years or older (OR=4.45, 95%CI: 2.56, 7.73) reported higher rates of non-standard implementation of personal protection. Compared to those with an education level of primary school or below, those with a junior high school education (OR=0.64, 95%CI: 0.47, 0.88) and a high school education or above (OR=0.13, 95%CI: 0.19, 0.29) showed lower rates of non-standard implementation of personal protection. Compared to farmers, veterinarians (OR=0.03, 95%CI: 0.02, 0.07) reported the lowest rate of non-standard implementation of personal protection, followed by auxiliary workers (OR=0.08, 95%CI: 0.05, 0.12), breeders (OR=0.10, 95%CI: 0.07, 0.15), and other occupational groups (OR=0.28, 95%CI: 0.15, 0.52). Using large-scale farms as a reference, self-employed farmers (OR=13.5, 95%CI: 9.65, 18.88) reported a higher rate of non-standard implementation of personal protection, followed by workers from other workplaces (OR=3.63, 95%CI: 2.01, 6.54). ConclusionThe implementation of personal protective measures among livestock workers in Xinjiang is generally satisfactory. However, significant disparities in the execution of these measures exist among different types of workers, indicating the need to enhance occupational health promotion to reduce the risk of zoonotic pathogen infection among the workers