Pengembangan Media Pembelajaran Fisika Berupa Buletin Dalam Bentuk Buku Saku Untuk Pembelajaran Fisikakelas VIII Materi Gaya Ditinjau Dari Minat Baca Siswa

Tujuan dari penelitian ini untuk mengembangkan media pembelajaran berupa buletin dalam bentuk buku saku untuk pembelajaran Fisika kelas VIII pada materi Gaya ditinjau dari aspek materi, konstruk, dan bahasa serta minat baca siswa. Penelitian ini termasuk penelitian pengembangan yang menggunakan metode Research and Development (R&D). Penelitian ini menggunakan model pengembangan model prosedural yaitu model yang bersifat deskriptif yang menunjukkan tahapan-tahapan yang harus diikuti untuk menghasilkan produk berupa media pembelajaran.Jenis data yang diperoleh bersifat kualitatif dan kuantitatif yaitu angket dan wawancara. Teknik analisis data yang digunakan adalah analisis deskriptif kualitatif dan kuantitatif. Hasil penelitian menunjukkan bahwa media pembelajaran yang dikembangkan berupa buletin Fisika dalam bentuk buku saku memiliki kriteria sangat baik berdasarkan penilaian dari ahli materi, ahli bahasa Indonesia, dan ahli media memberikan rata-rata penilaian sebesar 86,56%. Media pembelajaran yang dikembangkan juga memiliki kriteria sangat baik bila ditinjau dari peningkatan minat baca siswa. Hal ini terbukti pada hasil angket minat baca awal dan akhir yang diberikan kepada siswa yang memberikan rata-rata peningkatan sebesar 11,13%. Selain itu juga dianalisis dengan menggunakan uji-t berpasangan terhadap data masing-masing kelompok uji coba untuk mengetahui signifikansi dari peningkatan minat baca siswa. Untuk uji coba perorangan diperoleh hasil perhitungan thitung = 6,957 > ttabel = 1,943 dan nilai Sig. = 0,001 < 0,05 yang berarti sangat signifikan. Untuk kelompok kecil didapatkan hasil perhitungan bahwa thitung = 7,848 > ttabel = 1,725 dan nilai Sig. = 0,000 < 0,05 yang berarti sangat signifikan. Untuk kelompok besar juga didapatkan hasil perhitungan bahwa thitung = 20,214 > ttabel = 1,725 dan nilai Sig. = 0,000 < 0,05 yang berarti sangat signifikan. Simpulan dari penelitian ini adalah media pembelajaran berupa buletin dalam bentuk buku saku memiliki kriteria sangat baik bila ditinjau dari aspek materi, konstruk, dan bahasa serta minat baca siswa

The effect of peak serum estradiol level during ovarian stimulation on cumulative live birth and obstetric outcomes in freeze-all cycles

ObjectiveTo determine whether the peak serum estradiol (E2) level during ovarian stimulation affects the cumulative live birth rate (CLBR) and obstetric outcomes in freeze-all cycles.MethodsThis retrospective cohort study involved patients who underwent their first cycle of in vitro fertilization followed by a freeze-all strategy and frozen embryo transfer cycles between January 2014 and June 2019 at a tertiary care center. Patients were categorized into four groups according to quartiles of peak serum E2 levels during ovarian stimulation (Q1-Q4). The primary outcome was CLBR. Secondary outcomes included obstetric and neonatal outcomes of singleton and twin pregnancies. Poisson or logistic regression was applied to control for potential confounders for outcome measures, as appropriate. Generalized estimating equations were used to account for multiple cycles from the same patient for the outcome of CLBR.Result(s)A total of 11237 patients were included in the analysis. Cumulatively, live births occurred in 8410 women (74.8%). The live birth rate (LBR) and CLBR improved as quartiles of peak E2 levels increased (49.7%, 52.1%, 54.9%, and 56.4% for LBR; 65.1%, 74.3%, 78.4%, and 81.6% for CLBR, from the lowest to the highest quartile of estradiol levels, respectively, P<0.001). Such association remained significant for CLBR after accounting for potential confounders in multivariable regression models, whereas the relationship between LBR and peak E2 levels did not reach statistical significance. In addition, no significant differences were noticed in adverse obstetric and neonatal outcomes (gestational diabetes mellitus, pregnancy-induced hypertension, preeclampsia, placental disorders, preterm birth, low birthweight, and small for gestational age) amongst E2 quartiles for either singleton or twin live births, both before and after adjustment.ConclusionIn freeze-all cycles, higher peak serum E2 levels during ovarian stimulation were associated with increased CLBR, without increasing the risks of adverse obstetric and neonatal outcomes

NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition

Dynamic facial expression recognition (DFER) in the wild is an extremely challenging task, due to a large number of noisy frames in the video sequences. Previous works focus on extracting more discriminative features, but ignore distinguishing the key frames from the noisy frames. To tackle this problem, we propose a noise-robust dynamic facial expression recognition network (NR-DFERNet), which can effectively reduce the interference of noisy frames on the DFER task. Specifically, at the spatial stage, we devise a dynamic-static fusion module (DSF) that introduces dynamic features to static features for learning more discriminative spatial features. To suppress the impact of target irrelevant frames, we introduce a novel dynamic class token (DCT) for the transformer at the temporal stage. Moreover, we design a snippet-based filter (SF) at the decision stage to reduce the effect of too many neutral frames on non-neutral sequence classification. Extensive experimental results demonstrate that our NR-DFERNet outperforms the state-of-the-art methods on both the DFEW and AFEW benchmarks.Comment: 10 page

Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild

Compared with the image-based static facial expression recognition (SFER) task, the dynamic facial expression recognition (DFER) task based on video sequences is closer to the natural expression recognition scene. However, DFER is often more challenging. One of the main reasons is that video sequences often contain frames with different expression intensities, especially for the facial expressions in the real-world scenarios, while the images in SFER frequently present uniform and high expression intensities. Nevertheless, if the expressions with different intensities are treated equally, the features learned by the networks will have large intra-class and small inter-class differences, which are harmful to DFER. To tackle this problem, we propose the global convolution-attention block (GCA) to rescale the channels of the feature maps. In addition, we introduce the intensity-aware loss (IAL) in the training process to help the network distinguish the samples with relatively low expression intensities. Experiments on two in-the-wild dynamic facial expression datasets (i.e., DFEW and FERV39k) indicate that our method outperforms the state-of-the-art DFER approaches. The source code will be available at https://github.com/muse1998/IAL-for-Facial-Expression-Recognition

MMNet: Muscle motion-guided network for micro-expression recognition

Facial micro-expressions (MEs) are involuntary facial motions revealing peoples real feelings and play an important role in the early intervention of mental illness, the national security, and many human-computer interaction systems. However, existing micro-expression datasets are limited and usually pose some challenges for training good classifiers. To model the subtle facial muscle motions, we propose a robust micro-expression recognition (MER) framework, namely muscle motion-guided network (MMNet). Specifically, a continuous attention (CA) block is introduced to focus on modeling local subtle muscle motion patterns with little identity information, which is different from most previous methods that directly extract features from complete video frames with much identity information. Besides, we design a position calibration (PC) module based on the vision transformer. By adding the position embeddings of the face generated by PC module at the end of the two branches, the PC module can help to add position information to facial muscle motion pattern features for the MER. Extensive experiments on three public micro-expression datasets demonstrate that our approach outperforms state-of-the-art methods by a large margin.Comment: 8 pages, 4 figure

Evaluation of groundwater quality using improved fuzzy comprehensive assessment based on AHP

Abstract: In this study, an improved fuzzy comprehensive assessment for groundwater quality evaluation based on the analytic hierarchy process (AHP) is proposed. Based on the data collected from the Menkeqing Groundwater Source, a case study was used to demonstrate the applicability of this method to evaluate groundwater quality using routine physiochemical indices. Instead of crisp sets in conventional quality standards, continuous-form standards are used in this paper, and the concentration of physiochemical indices are transformed into membership degrees based on fuzzy theory. At last, defuzzification of these membership degrees determine the classification results. The results are discussed compared with the comprehensive evaluation method (Nemerow index method). The case study demonstrates the practical validity and feasibility of this method. Key words: fuzzy comprehensive assessment, analytic hierarchy process, water quality evaluation, groundwater source, Ordos, Mu Us desert. 1

Intelligent meta-imagers: From compressed to learned sensing

International audienceComputational meta-imagers synergize metamaterial hardware with advanced signal processing approaches such as compressed sensing. Recent advances in artificial intelligence (AI) are gradually reshaping the landscape of meta-imaging. Most recent works use AI for data analysis, but some also use it to program the physical meta-hardware. The role of "intelligence " in the measurement process and its implications for critical metrics like latency are often not immediately clear. Here, we comprehensively review the evolution of computational meta-imaging from the earliest frequency-diverse compressive systems to modern programmable intelligent meta-imagers. We introduce a clear taxonomy in terms of the flow of task-relevant information that has direct links to information theory: compressive meta-imagers indiscriminately acquire all scene information in a task-agnostic measurement process that aims at a near-isometric embedding; intelligent meta-imagers highlight task-relevant information in a task-aware measurement process that is purposefully non-isometric. The measurement process of intelligent meta-imagers is, thus, simultaneously an analog wave processor that implements a first task-specific inference step "over-the-air. " We provide explicit design tutorials for the integration of programmable meta-atoms as trainable physical weights into an intelligent end-to-end sensing pipeline. This merging of the physical world of metamaterial engineering and the digital world of AI enables the remarkable latency gains of intelligent meta-imagers. We further outline emerging opportunities for cognitive meta-imagers with reverberation-enhanced resolution, and we point out how the meta-imaging community can reap recent advances in the vibrant field of metamaterial wave processors to reach the holy grail of low-energy ultra-fast all-analog intelligent meta-sensors