Miracle-dz/UniFAR-FTUs huggingface dataset:
Model of Transnational Online Serial Sociocultural Lectures for Overseas Chinese Children and Its Evaluation

Previous lectures targeted at overseas Chinese children have mostly been offline in the form of youth camps sponsored by the Chinese governments before the COVID-19 pandemic and are now usually administered online in a single-topic format transnationally post-pandemic. This form of “one-to-many” singular online lectures comes along with discontinuity, mass audiences, chaos, and being less tailored to the individual needs and context, failing to meet the evaluation metrics in different evaluation levels. This paper proposes a model of transnational online livestreaming serial socio-cultural lectures for overseas Chinese children, which puts forward the synergy of lecturing staff from higher educational institutions (HEIs) in China and students at Chinese language schools (CLSs) worldwide as participants. Lecturing staff from needs and context HEIs can guarantee sufficient keynote speakers for “one-on-one” mode and cope with the above-mentioned problems. After the implementation of five sessions of these serial lectures, evaluation of this model shows that the pre-lecture, during-lecture, and post-lecture stages have are applicable. Furthermore, for sociocultural lectures, the audience may be more interested in literature classics than folk arts from a specific region. Correlation analysis reveals that younger children have a better evaluation of this model and an improved inclination to attend lectures, which sheds light on the improvement of sociocultural lectures as online lectures are preferred among overseas Chinese children.
Hygro-thermo-mechanical analysis of sandwich shallow shells considering the effects of transverse normal strain

Abstract The static response of softcore doubly-curved sandwich shallow shells under hygro-thermo-mechanical loading is presented herein using a fifth-order shell theory which considers the effects of transverse normal strains and stresses at the same time. The present theory accounts for non-linear variations of displacements. The strain-displacement relations take into account only small curvatures following a shallow shell description. The present theory ensured the traction-free boundary conditions at the top and the bottom faces of the shell. The governing equations along with the natural and essential boundary conditions are derived using the principle of virtual work. The governing equations are solved by using Navier’s method. Three layered sandwich shallow shells of double curvature subjected to hygro-thermo-mechanical loading are analyzed. The accuracy and efficacy of the present theory are proved by applying it to the benchmark solutions available in the literature and then the present theory is extended for the analysis of softcore sandwich shells of double curvature. Since an exact 3D elasticity solution is not available for many such problems; this paper can be serving as a benchmark reference solution for future researchers working in this area.
THE INFLUENCE OF SELF-CONFIDENCE ON THE MATHEMATICAL REASONING ABILITY OF JUNIOR HIGH SCHOOL STUDENTS

Self-confidence is an aspect of personality that plays a role in shaping the potential of students, if they master the confidence with slick, it is also expected to pursue their mathematics to produce optimal results, so that this aspect also has an influence as an important role in the ability of mathematical reasoning students. This study has the intention to see the effect of student confidence on the mathematical reasoning ability of Grade IX students of SMPN 11 Bekasi semester 1 of the 2022-2023 academic year. The proposed research method is quantitative research techniques with survey research design. The sampling technique used is Simple Random Sampling. The population of Class IX was 237 students. Samples were taken as many as 149 students using the Slovin formula. The research Data was obtained through testing instruments about mathematical reasoning ability as much as 5 points and instrument questionnaire confidence as much as 20 statements. The results obtained can be concluded in the form of self-confidence affect the reasoning ability of students. Self-confidence had a positive effect of 3.4% on students ' reasoning ability. however, students ' self-confidence is still at a moderate level. So, the reasoning ability of students cannot be said to be optimal. Based on the results found, it is necessary to conduct extended research on this theme by using various other influencing factors.
Database search based on Bayesian alignment Jun Zhu, Roland L/ithy

The size of protein sequence database is getting larger each day. One common challenge is to predict protein structures or functions of the sequences in databases. It is easy when a sequence shares direct similarity to a well-characterized protein. If there is no direct similarity, we have to rely on a third sequence or a model as intermediate to link two proteins together. We developed a new model based method, called Bayesian search, as a means to connect two distantly related proteins. ~Ve compared this Bayesian search model with pairwise and multiple sequence comparison methods on structural databases using structural similarity as the criteria for relationship. The results show that the Bayesian search can link more distantly related sequence pairs than other methods, collectively and consistently over large protein famihes. If each query made one error on average against SCOP database PDB40D-B, Bayesian search found 36.5% of related pairs, PSIBlast. found 32.6%, and Smith-Waterman method found 25%. Examples are presented to show that the alignments predicted by the Bayesian search agree well with structural alignments. Also false positives found by Bayesian search at low cutoff values are analyzed.
Analyzing characteristic of the four-quadrant detector applied to laser alignment

The photoelectric conversion characteristic and detecting principle of the four-quadrant photo detector as position sensitive device are discussed in detail in theory applied to laser alignment. At the same time, the analyzing results are approved by the numerical simulation and experiment. The factors affecting the precision are analyzed detail by detail. The analysis results show that the theoretical basis for analyzing error during the laser alignment with four-quadrant detector.
Patterns of Code-switching in the Amharic Media

This study examines patterns of Amharic-English code-switching in the Amharic media language use. It employed a qualitative descriptive approach, which helped break down the recordings and transcripts into intelligible units that helped explain the various language use forms created by code-switching in the Amharic media. Thus, five Amharic media outlets focusing on sport, medical science, and science and technology were purposively selected to study language use in the Amharic media. Hence, 1090 examples which have both Amharic and English elements were selected. A generic step of data analysis was made so as to classify the different types and patterns of code-switching instances. The findings indicated that the three different types of code-switching, namely tag switching, inter-sentential code-switching, and intra-sentential code-switching, are found in the Amharic media language use. Moreover, it was found that there are different patterns of code-switching from English to Amharic. Thus, it is shown that Amharic is the matrix language and is quite resistant against any change of its basic word order. It can also be seen that the mixed constituents found in the Amharic media confirm that the matrix language frame (MLF) model can satisfactorily explain the patterns of Amharic-English code-switching.
Data and Model Dual-Driven Seismic Deconvolution via Error-Constrained Joint Sparse Representation

Deconvolution is an essential step in seismic data processing. Sparse-spike deconvolution is often used to enhance the resolution of the seismic image by adding a model driven regularization term. However, this method does not consider the features of the data, nor does it exactly describe the relationship between seismic data and the desired attribute (such as seismic reflectivity). We propose a data and model dual-driven seismic deconvolution method based on error-constrained joint sparse representation using borehole measurement and surface seismic data. The combined features of the borehole reflectivity and the surface seismic data can be obtained through joint dictionary learning. With the help of the joint dictionary, the relationship between seismic waveforms and reflectivity is captured by the sparse coefficients. We construct the regularization term of deconvolution by alternately decomposing the error of the synthesized data via sparse reconstruction and the observed seismic data. Unlike model-driven methods, the constraint term of the new method can be established by the error-constrained sparse representation. Based on this sparse representation, the initial model of reflectivity is obtained to realize the sparse deconvolution of seismic data under the constraint of borehole data features. In general, this method is a data and model dual-driven deconvolution. Synthetic and field data tests demonstrate that this method can effectively improve the resolution and accuracy of deconvolution.
Line search globalization of a semismooth Newton method for operator equations in Hilbert spaces with applications in optimal control

We consider the numerical solution of nonlinear and nonsmooth operator equations in Hilbert spaces. A semismooth Newton method is used for search direction generation. The operator equation is solved by a globalized semismooth Newton method that is equipped with an Armijo linesearch using a semismooth merit function. We prove that an accumulation point of the globalized algorithm is a solution and transition to fast local convergence under a directional Hadamard-like continuity assumption on the Newton matrix. In particular, no auxiliary descent directions or smoothing steps are required. Finally, we apply this method to a control-constrained and also to a regularized state-constrained optimal control problem subject to partial differential equations.
Chrome Extension for Misinformation Detection

– Misinformation can be stories, hoaxes, or news deliberately created to spread false news and deceive readers. Fake news has always been a part of our lives. However, it has become a topic of interest only recently. Majorly due to the rise of SOCIAL MEDIA. As stated in news articles the Supreme Court condemns these actions and advice a regulatory mechanism. As the use of social media has increased so, has the number of unreliable sources. During covid, there was a variety of misinformation floating around like, applying cow dung can cure covid. In reality, cow dung can't cure covid but can cause black fungal infection. Some other examples include inciting religious sentiments, causing chaos, and harming someone's reputation. Therefore, detecting fake news and stopping it from spreading is necessary. In this model, I have applied TF-IDF vectorizer and Passive Aggressive Classifiers to train my model. After the training and testing have been completed, a local server using Flask has been set up to assist in the development of the second phase of the project, which is Chrome Extension. The Chrome Extension is called Gossip Checker and sends selected data to the model and returns a predicted score of 0 and 1, where 0 means the data is reliable and 1 is not.
Quality Regularization-Based Semisupervised Adversarial Transfer Model With Unlabeled Data for Industrial Soft Sensing

Traditional soft sensors typically rely only on labeled data to predict key variables, despite the significant amount of unlabeled data that could provide valuable information. To solve this problem, a quality regularization-based semisupervised adversarial transfer model (QR-SATM) is proposed. The idea of transfer learning is used in QR-SATM. QR-SATM comprises a pretraining model and a regression model. The pretraining model is an unsupervised model. And the regression model is a supervised model with a similar structure to the pretraining model, allowing for easy transfer between the two models. First, the pretraining model is trained with unlabeled data to extract features. Then, the trained parameters of pretraining model are transferred to the regression model, and the regression model is fine-tuned with labeled data. During fine-tuning the regression model, an improved quality regularization is introduced in order to select useful features and prevent overfitting. QR-SATM is validated by a real industrial dataset of purified terephthalic acid. The experimental results show the effectiveness of the proposed QR-SATM in accurately predicting key variables.
Involvement of Degenerating 21.5 kDa Isoform of Myelin Basic Protein in the Pathogenesis of the Relapse in Murine Relapsing–Remitting Experimental Autoimmune Encephalomyelitis and MS Autopsied Brain

Multiple sclerosis (MS) is the chronic inflammatory demyelinating disease of the CNS. Relapsing–remitting MS (RRMS) is the most common type of MS. However, the mechanisms of relapse and remission in MS have not been fully understood. While SJL mice immunized with proteolipid protein (PLP) develop relapsing–remitting experimental autoimmune encephalomyelitis (RR-EAE), we have recently observed that some of these mice were resistant to the active induction of relapsing EAE after initial clinical and histological symptoms of EAE with a severity similar to the relapsing EAE mice. To clarify the mechanism of relapsing, we examined myelin morphology during PLP139–151-induced RR-EAE in the SJL mice. While RR-EAE mice showed an increased EAE severity (relapse) with CNS inflammation, demyelination with abnormal myelin morphology in the spinal cord, the resistant mice exhibited a milder EAE phenotype with diminished relapse. Compared with the RR-EAE mice, the resistant mice showed less CNS inflammation, demyelination, and abnormalities of the myelin structure. In addition, scanning electron microscopic (SEM) analysis with the osmium-maceration method displayed ultrastructural abnormalities of the myelin structure in the white matter of the RR-EAE spinal cord, but not in that of the resistant mice. While the intensity of myelin staining was reduced in the relapsing EAE spinal cord, immunohistochemistry and immunoblot analysis revealed that the 21.5 kDa isoform of degenerating myelin basic protein (MBP) was specifically induced in the relapsing EAE spinal cord. Taken together, the neuroinflammation-induced degenerating 21 kDa isoform of MBP sheds light on the development of abnormal myelin on the relapse of MS pathogenesis.
Hybrid Improved Self-adaptive Differential Evolution and Nelder-Mead Simplex Method for Solving Constrained Real-Parameters

In this paper, a new hybrid algorithm based on exploration power of a new improvement self-adaptive strategy for controlling parameters in DE (differential evolution) algorithm and exploitation capability of Nelder-Mead simplex method is presented (HISADE-NMS). The DE has been used in many practical cases and has demonstrated good convergence properties. It has only a few control parameters as number of particles (NP), scaling factor (F) and crossover control (CR), which are kept fixed throughout the entire evolutionary process. However, these control parameters are very sensitive to the setting of the control parameters based on their experiments. The value of control parameters depends on the characteristics of each objective function, therefore, we have to tune their value in each problem that mean it will take too long time to perform. In the new manner, we present a new version of the DE algorithm for obtaining self-adaptive control parameter settings. Some modifications are imposed on DE to improve its capability and efficiency while being hybridized with Nelder-Mead simplex method. To valid the robustness of new hybrid algorithm, we apply it to solve some examples of structural optimization constraints.
Depression Detection of User in Media Social Twitter Using Random Forest

One of the disorders of mental health that often occurs in individuals is depression. Identifying depression in the first place is important for the individual. But in fact, conducting an early examination of depression still has some drawbacks. If it continues to be ignored, this can have an impact on the health of the individual. Therefore, there is a need for other methods that can represent the level of depression in individuals, through other media such as social media such as Twitter. Twitter has become one of the media to tell what users of the application experience or feel. This is encouraging to detect of depression in Twitter users. The data used is data taken from the results of the distribution of forms based on DASS-42 with a total of 159 Twitter users for each username taken 100 tweets. This study uses the Word2Vec extraction feature, to convert data from text to vector by looking at the relationship of each word and Random Forest as a classification method, to maintain the balance of data in different classes, especially very large data sets. Based on the test results, the Random Forest model produces an accuracy of 68.75%.
Phototoxic or Photoprotective?—Advances and Limitations of Titanium (IV) Oxide in Dermal Formulations—A Review

The widespread role of titanium (IV) oxide (TiO2) in many industries makes this substance of broad scientific interest. TiO2 can act as both a photoprotector and photocatalyst, and the potential for its role in both applications increases when present in nanometer-sized crystals. Its sunlight-scattering properties are used extensively in sunscreens. Furthermore, attempts have been made to incorporate TiO2 into dermal formulations of photolabile drugs. However, the propensity to generate reactive oxygen species (ROS) rendering this material potentially cytotoxic limits its role. Therefore, modifications of TiO2 nanoparticles (e.g., its polymorphic form, size, shape, and surface modifications) are used in an effort to reduce its photocatalytic effects. This review provides an overview of the potential risks arising from and opportunities presented by the use of TiO2 in skin care formulations.
Classical optimal planning method based on compacted encodings

Based on Graphplan-encoding,a new encoding called PMA-based encoding by reducing the mutex axioms of actions,is proposed.The soundness and completeness of the new encoding are justified.The preprocessing method for goal states during encoding solving process is also proposed. The corresponding planner called PMA-SP based on the above encoding is designed and implemented,and test with benchmarks adopted by International Planning Competition.Comparing with the planner SATPLAN2006,the experimental results demonstrate that,for both tested domains,the scales of encodings of PMA-SP are compacted and the solver efficiencies are improved.For sequential planning problem Blocks World,the scales of encodings of PMS-SP are compacted nearly 40%and solver efficiencies are increased nearly two times.For parallel planning problem Logistics,the scales of encodings of PMA-SP are compacted more than 80%.
Study of kabul river canal sediments for heavy metals status and its accumulation in wheat plant (triticum aestivum)

Canal cleaning is a routine activity on the canals diverted from the Kabul River. As a result, thousands of tons of sediment are removed as sludge and flushed back into the Kabul River. In Peshawar city, most people use this sediment as a soil conditioner in lawns. But the farmers are hesitant to apply it in agricultural fields. It is perceived that the sediment may be rich in heavy metal contents and, if used as a soil conditioner, may contaminate the food chain. To unveil the facts, this study was conducted with the aim of analyzing sediment samples for selected heavy metals and their uptake and accumulation in different parts of the wheat plant. For this purpose, the sediment collected from Warsak Gravity Canal (WGC) was analyzed for total essential heavy metals including iron (Fe), copper (Cu), cobalt (Co), manganese (Mn), and Zinc (Zn). Wheat crop was grown in four different pots consisting of pure soil, pure sediment, and two amendments i.e. soil mixed with 25% and 50% sediments. In comparison to sediment, the soil samples were high in copper, cobalt, and Zinc concentration. The concentration of iron and manganese was comparatively high in pure sediment but within permissible limits. The heavy metals uptake by the wheat crop was also within the permissible limits. The highest accumulation of copper, iron, and Zinc was observed in the roots of wheat plants. The highest value of cobalt was observed in the seed (0.407 mg Kg-1), which was within the safe range. Therefore, it is concluded that the sediment of the Kabul River canal cannot be considered a source of pollution in terms of heavy metals and can be used as a soil conditioner.
Spectral Embedding-Based Meter-Transformer Mapping (SEMTM)

Distributed energy resources enable efficient power response but may cause transformer overload in distribution grids, calling for recovering meter-transformer mapping to provide situational awareness, i.e., the transformer loading. The challenge lies in recovering meter-transformer (M.T.) mapping for two common scenarios, e.g., large distances between a meter and its parent transformer or high similarity of a meter’s consumption pattern to a non-parent transformer’s meter. Past methods either assume a variety of data as in the transmission grid or ignore the two common scenarios mentioned above. Therefore, we propose to utilize the above observation via spectral embedding by using the property that inter-transformer meter consumptions are not the same and that the data noise is limited so that all the $k$ smallest eigenvalues of the voltage-based Laplacian matrix are smaller than the next smallest eigenvalue of the ideal Laplacian matrix. We also provide a performance guarantee for Spectral Embedding-based M.T. mapping (SEMTM). Furthermore, we partially relax the assumption by utilizing location information to aid voltage information for areas geographically far away, but with similar voltages. Numerical simulations on the IEEE test systems and real feeders from our partner utility show that the proposed method correctly identifies the M.T. mapping.
Distinct Traits of Structural and Regulatory Evolutional Conservation of Human Genes with Specific Focus on Major Cancer Molecular Pathways

The evolution of protein-coding genes has both structural and regulatory components. The first can be assessed by measuring the ratio of non-synonymous to synonymous nucleotide substitutions. The second component can be measured as the normalized proportion of transposable elements that are used as regulatory elements. For the first time, we characterized in parallel the regulatory and structural evolutionary profiles for 10,890 human genes and 2972 molecular pathways. We observed a ~0.1 correlation between the structural and regulatory metrics at the gene level, which appeared much higher (~0.4) at the pathway level. We deposited the data in the publicly available database RetroSpect. We also analyzed the evolutionary dynamics of six cancer pathways of two major axes: Notch/WNT/Hedgehog and AKT/mTOR/EGFR. The Hedgehog pathway had both components slower, whereas the Akt pathway had clearly accelerated structural evolution. In particular, the major hub nodes Akt and beta-catenin showed both components strongly decreased, whereas two major regulators of Akt TCL1 and CTMP had outstandingly high evolutionary rates. We also noticed structural conservation of serine/threonine kinases and the genes related to guanosine metabolism in cancer signaling: GPCRs, G proteins, and small regulatory GTPases (Src, Rac, Ras); however, this was compensated by the accelerated regulatory evolution.