Miracle-dz/UniFAR-FTUs huggingface dataset:
Space-time discontinuous Galerkin method for Maxwell's equations

A fully discrete discontinuous Galerkin method is introduced for solving time-dependent Maxwell’s equations. Distinguished from the Runge-Kutta discontinuous Galerkin method (RKDG) and the finite element time domain method (FETD), in our scheme, discontinuous Galerkin methods are used to discretize not only the spatial domain but also the temporal domain. The proposed numerical scheme is proved to be unconditionally stable, and a convergent rate is established under the L 2 -norm when polynomials of degree atmost r and k are used for temporal and spatial approximation, respectively. Numerical results in both 2-D and 3-D are provided to validate the theoretical prediction. An ultra-convergence of order in time step is observed numerically for the numerical fluxes w.r. t. temporal variable at the grid points.
Vedic Methodology That Highlights the Benefits of Sunlight between Twilight and Sunrise/Sunset

Memory destroying diseases such as dementia do not have effective medication. Various approaches including Infrared (IR) and Near-Infrared (NIR) therapies have been partially successful with potential side effects. Laser or LED based Photobiomodulation (PBM) has been relatively better in treating various diseases including Traumatic Brain Injury (TBI). However, finding the appropriate wavelengths and intensities for the therapies continue to create challenges. We have calculated the intensity, energy, and power delivered by Sunrays in the region [IR + Red] during the Twilight to Sunrise and Sunset to Twilight timings (time of interest) to the retinae of closed eyes, opened eyes, and to the skin of humans. Based on the intensity, energy, and power these Sunrays in the region [IR + Red] during the “time of interest”, we propose that these Sunrays in the region [IR + Red] can help humans use NIR therapy to mitigate TBI, mitigate wide range of neurological and psychological conditions, improve alpha rhythm, treat visual diseases, be good for skin, reduce aging, eliminate wrinkles on face, promote tissue regeneration, reduce swelling, reduce inflammation, relieve pain, non‐thermally induce cytocidal effects in cancer cells, induce cell killing of cancer cells. Our methodology is extracted from Vedic Mantras.
Prediction of Outcomes After Heart Transplantation in Pediatric Patients Using National Registry Data: Evaluation of Machine Learning Approaches

Background The prediction of posttransplant health outcomes for pediatric heart transplantation is critical for risk stratification and high-quality posttransplant care. Objective The purpose of this study was to examine the use of machine learning (ML) models to predict rejection and mortality for pediatric heart transplant recipients. Methods Various ML models were used to predict rejection and mortality at 1, 3, and 5 years after transplantation in pediatric heart transplant recipients using United Network for Organ Sharing data from 1987 to 2019. The variables used for predicting posttransplant outcomes included donor and recipient as well as medical and social factors. We evaluated 7 ML models—extreme gradient boosting (XGBoost), logistic regression, support vector machine, random forest (RF), stochastic gradient descent, multilayer perceptron, and adaptive boosting (AdaBoost)—as well as a deep learning model with 2 hidden layers with 100 neurons and a rectified linear unit (ReLU) activation function followed by batch normalization for each and a classification head with a softmax activation function. We used 10-fold cross-validation to evaluate model performance. Shapley additive explanations (SHAP) values were calculated to estimate the importance of each variable for prediction. Results RF and AdaBoost models were the best-performing algorithms for different prediction windows across outcomes. RF outperformed other ML algorithms in predicting 5 of the 6 outcomes (area under the receiver operating characteristic curve [AUROC] 0.664 and 0.706 for 1-year and 3-year rejection, respectively, and AUROC 0.697, 0.758, and 0.763 for 1-year, 3-year, and 5-year mortality, respectively). AdaBoost achieved the best performance for prediction of 5-year rejection (AUROC 0.705). Conclusions This study demonstrates the comparative utility of ML approaches for modeling posttransplant health outcomes using registry data. ML approaches can identify unique risk factors and their complex relationship with outcomes, thereby identifying patients considered to be at risk and informing the transplant community about the potential of these innovative approaches to improve pediatric care after heart transplantation. Future studies are required to translate the information derived from prediction models to optimize counseling, clinical care, and decision-making within pediatric organ transplant centers.
Isolation, Genomic, and Proteomic Characterization of a Novel Neotropical Strain of Bacillus thuringiensis with Mosquitocidal Activities

The combination of genomic and proteomic analyses is a useful tool for the study of novel Bacillus thuringiensis (Bt) strains, as these approaches allow the accurate identification of pesticidal proteins and virulence factors produced. Here, we isolated and evaluated the potential of a novel Neotropical Bt strain (TOD651) for controlling larvae of Aedes aegypti and Culex quinquefasciatus mosquitoes. Aiming for the full comprehension of the TOD651 larvicidal potential, we further evaluated the whole TOD651 genome and conducted the proteomic analysis of the TOD651 spore–crystal mixtures. Our results showed that Bt TOD651 similarly killed both A. aegypti (0.011 µg/mL) and C. quinquefasciatus (0.023 µg/mL) larvae, exhibiting similar potency to the commercial Bt strain. The genome sequence revealed that Bt TOD651 harbors cry11Aa3, cry10Aa4, cry4Aa4, cry4Ba5, cyt1Aa5, cyt1Ca1, cyt2Ba13, mpp60Aa3, and mpp60Ba3. The proteomic analysis revealed no expression of Mpp60Aa3, while all the other pesticidal proteins were expressed (Cry4Ba5 was more abundant than Cyt1Aa5). The expression of the Mppe showed the major proportions between proteases. The virulent factor neutral protease B and spore coat proteins were also expressed. The expression of relevant pesticidal proteins (e.g., Cry, Cyt, Mpp, and other pathogenic factors), whose actions can occur in a synergic relation, indicates that the biocontrol using Bt TOD651 may contribute to delaying the selection of resistant individuals.
Revisiting a model of customer engagement cycle: a systematic review

ABSTRACT The purpose of this paper is to consolidate published research on customer engagement (CE), and building on that, to further develop a set of integrated CE elements that fit the online context, based on Sashi’s [Customer engagement, buyer-seller relationships, and social media. Management Decision, 50(2), 253–272]. CE cycle model. Thus, we develop a systematic alignment of CE concepts and propose additional themes – including Experience, Organizational and Environmental Context, Customer Participation, Employee Participation, and Trust – and solidify the model with theoretical propositions for each theme. This paper’s originality comes from being the pioneer study to systematically examine each CE activity through the lens of an online-dominant context, while contributing to the CE literature. Recommendations for future research are provided for researchers of the CE field, calling for deeper investigation of existing theory, more attention to the areas in which e-commerce is developing rapidly, and additional prospective propositions for the CE cycle model to adapt to a post-pandemic world.
The Hazard of Electric Field Increment in the Damaged Silicon-Rubber and Porcelain Insulators

In power transmission lines, it is necessary to isolate the inherent voltage parts from each other and from zero potential. An insulator is defined as having a high degree of electrical insulation between the conductors and the brackets. Insulators also allow for mechanical conductivity of the conductor and ground. The presence of a broken insulator can alter the stress distribution. In this paper, the effect of mechanical insulation cracking (chattering) on electric field in two common types of insulators (porcelain and silicone rubber) and electric potential distribution models are analyzed. First, the electric field and electric potential patterns in the solid insulator are examined. In the following, the insulator is assumed to be broken, and the electric field and electric potential patterns for a broken insulator relative to the basic insulator shape are illustrated and compared. The results show that the presence of insulation cracking under dry and environmentally benign conditions affects the stress distribution pattern within and around the insulator. Finally, a comparison between the effect of fracture on the electric field and the potential distribution patterns in different parts of the insulator is discussed in detail.
FB-CCNN: A Filter Bank Complex Spectrum Convolutional Neural Network with Artificial Gradient Descent Optimization

The brain–computer interface (BCI) provides direct communication between human brains and machines, including robots, drones and wheelchairs, without the involvement of peripheral systems. BCI based on electroencephalography (EEG) has been applied in many fields, including aiding people with physical disabilities, rehabilitation, education and entertainment. Among the different EEG-based BCI paradigms, steady-state visual evoked potential (SSVEP)-based BCIs are known for their lower training requirements, high classification accuracy and high information transfer rate (ITR). In this article, a filter bank complex spectrum convolutional neural network (FB-CCNN) was proposed, and it achieved leading classification accuracies of 94.85 ± 6.18% and 80.58 ± 14.43%, respectively, on two open SSVEP datasets. An optimization algorithm named artificial gradient descent (AGD) was also proposed to generate and optimize the hyperparameters of the FB-CCNN. AGD also revealed correlations between different hyperparameters and their corresponding performances. It was experimentally demonstrated that FB-CCNN performed better when the hyperparameters were fixed values rather than channel number-based. In conclusion, a deep learning model named FB-CCNN and a hyperparameter-optimizing algorithm named AGD were proposed and demonstrated to be effective in classifying SSVEP through experiments. The hyperparameter design process and analysis were carried out using AGD, and advice on choosing hyperparameters for deep learning models in classifying SSVEP was provided.
Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson’s Disease

Although the multifactorial nature of falls in Parkinson’s disease (PD) is well described, optimal assessment for the identification of fallers remains unclear. Thus, we aimed to identify clinical and objective gait measures that best discriminate fallers from non-fallers in PD, with suggestions of optimal cutoff scores. METHODS: Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months’ falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner). RESULTS: Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85). CONCLUSION: Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD.
Efficient laser performance of Yb:Y3Al5O12/Cr4+:Y3Al5O12 composite crystals

Highly efficient passively Q-switched lasers of Yb:Y3Al5O12/Cr4+:Y3Al5O12 (Yb:YAG/Cr4+:YAG) composite crystals have been demonstrated with an optical-to-optical efficiency of 36%. A slope efficiency of 44% was achieved with respect to the absorbed pump power. An average output power of over 1.75 W was obtained at an absorbed pump power of 4.8 W. Laser pulses with a pulse energy of over 180 μJ, a pulse width of 3 ns and a peak power of 60 kW were achieved. Near-diffraction-limited laser beams with M2 less than 1.3 were obtained. The effects of the absorbed pump power and the transmission of the output coupler on the laser performance of Yb:YAG/Cr4+:YAG composite crystals were addressed.
Fiber-Shaped Cu-Ion Diffusive Memristor for Neuromorphic Computing

Fiber-shaped memristors have attracted enormous attention as potential wearable electronics. Here, a Cu-ion diffusive memristor with fiber shape was proposed for artificial synapse and neuromorphic computing. The fiber-shaped diffusive memristor exhibits gradual conductance modulation characteristics under consecutive voltage sweeps. Typical synaptic plasticity including EPSC, PPF, PPD, LTP/LTD and learning behaviors were all successfully achieved by the memristor. The active Cu<inline-formula> <tex-math notation="LaTeX">$^{{2}+}$ </tex-math></inline-formula> of diffusive memristor was similar as Ca<inline-formula> <tex-math notation="LaTeX">$^{{2}+}$ </tex-math></inline-formula> diffusion in biological synapse, which is the basis of realizing the functions of synaptic plasticity. The fiber-shaped Cu<inline-formula> <tex-math notation="LaTeX">$^{{2}+}$ </tex-math></inline-formula> diffusive memristor acting as artificial synapse paves the way for next-generation wearable neuromorphic computing system.
Trustworthiness and Trust as Drivers of Value Perception and Its Implications for Positive Word of Mouth Intention (Study on the Context of Private Campuses in Jabodetabek)

This research to understand for trustworthiness and trust in implication for perception value of implemen-tation for positive word of mouth intention. Study from private universities in Jabodetabek. Object research this is a student of private universities those who live in jabodetabek. To answer problems in this research and the testing of hypotheses research, data analysis using Structural Equation Model (sem) - amos to know how strong the influence of every variable the treatment, this study found that the variable to connect for trust, perceived value and student loyalty have dimension operational competence, operational benevo-lence, and problem solving orientation as benchmarks of Trust in the institution personnel and Trust in the institutions management to create Perceived Value to the campus and generate student loyalty is expected to be able to have a positive influence on increasing student trust.
Optimal control of flood using finite element method.

This paper presents a method for optimal control of flood propagating through a reservior of river channel using the combination of the finite element method and optimal control theory. The numerical model of the flood propagation can be expressed by the linear two dimensional shallow water equation and the equation can be solved by the two step explicit schemes. For the control theory, the method presented by Sakawa and Shindo is effectively used. It is shown that the water elevation can be controlled as flat as possible by adjusting the discharge of the dam gate. This method is also adaptable to the control constraining the control outflow within the limit of inflow into a reservoir.
Efficient data dimensionality reduction method for improving road crack classification algorithms

Automatic crack classification plays an essential role in road maintenance. Using many features for the classification is inefficient for implementing embedded systems with low computational resources makes it difficult. Therefore, this work proposes a new data dimensionality reduction (DDR) for crack classification algorithms (DDR4CC). DDR4CC reduces the required information about the cracks to only four features. Using these features, the images can be classified into longitudinal, transverse, and alligator cracks or healthy pavement. DDR4CC is compared with eight DDR methods, and the reduced set of features is analyzed using five different classification algorithms. Besides, five different datasets, generated by a combination of several public datasets, are used. We are proposing a simple DDR method with high interpretability of the data, obtaining very fast computation and high accuracy. Experiments show that DDR4CC enhances the results of the classification algorithms, providing almost perfect classifiers with a minimum computation time.
Attention-Based Deep Learning System for Classification of Breast Lesions—Multimodal, Weakly Supervised Approach

Simple Summary Breast cancer affects millions of women worldwide. We aim to provide radiologists with automatic support for mammography review. We propose deploying deep learning models with Multiple Instance Learning algorithms for breast cancer diagnosis (cancer versus non-cancer classification) based on digital mammography images, taking advantage of data annotated at an image level without indicating where the lesion is. We employed algorithms to analyse original, high-resolution images with minimal reduction in size. We used graphical suggestions of attentional maps to verify the correctness of the algorithm and to indicate the areas of the images where cancer lesions are. Finally, we performed comparative and validation studies on external datasets differing in the number of images, pixel intensity levels, and subtypes of existing lesions, showing high accuracy and potential for generalisability of the algorithms. Abstract Breast cancer is the most frequent female cancer, with a considerable disease burden and high mortality. Early diagnosis with screening mammography might be facilitated by automated systems supported by deep learning artificial intelligence. We propose a model based on a weakly supervised Clustering-constrained Attention Multiple Instance Learning (CLAM) classifier able to train under data scarcity effectively. We used a private dataset with 1174 non-cancer and 794 cancer images labelled at the image level with pathological ground truth confirmation. We used feature extractors (ResNet-18, ResNet-34, ResNet-50 and EfficientNet-B0) pre-trained on ImageNet. The best results were achieved with multimodal-view classification using both CC and MLO images simultaneously, resized by half, with a patch size of 224 px and an overlap of 0.25. It resulted in AUC-ROC = 0.896 ± 0.017, F1-score 81.8 ± 3.2, accuracy 81.6 ± 3.2, precision 82.4 ± 3.3, and recall 81.6 ± 3.2. Evaluation with the Chinese Mammography Database, with 5-fold cross-validation, patient-wise breakdowns, and transfer learning, resulted in AUC-ROC 0.848 ± 0.015, F1-score 78.6 ± 2.0, accuracy 78.4 ± 1.9, precision 78.8 ± 2.0, and recall 78.4 ± 1.9. The CLAM algorithm’s attentional maps indicate the features most relevant to the algorithm in the images. Our approach was more effective than in many other studies, allowing for some explainability and identifying erroneous predictions based on the wrong premises.
Synthesis of graphene-based Ag-doped CuFe2O4 composite for improved photocatalytic activity against industrial effluents

Copper ferrite (CuFe2O4, CF) and silver-doped copper ferrite (Ag-CuFe2O4, ACF) were prepared via co-precipitation method. While nano-composite of ACF with rGO was prepared by ultra-sonication approach. Degradation of crystal violet (CV) and benzoic acid (BA) by CF, ACF, and ACF@rGO was conducted under sunlight. Because of tremendous stability and immense surface area of rGO, it shows good ability to transfer electrons during photocatalysis.Band gap for CF and ACF were tuned having values 1.36 and 1.67 eV. Ag-CuFe2O4@rGO has exhibited greater photo-catalytic activity than a bare and doped sample. 82.7% and 48.4% were degradation % of CV and BA by Ag-CuFe2O4@rGO respectively. Ag-CuFe2O4@rGO showed 1.3 times higher efficiency than bare CuFe2O4. ACF was sonicated with rGO and composite was formed which was not reported before and I was the novelty of this paper. The photodegradation ability and cyclic stability etc. was increased after the formation of composite.
Direct-Acting Oral Anticoagulant Therapy in Cancer Patients—A Review

Simple Summary Direct-acting oral anticoagulants are becoming popular therapeutic option in patients with cancer. We investigated the available literature in order to try to sum up the information’s about them. In this article we present both, advantages and disadvantages of direct-acting oral anticoagulants. We found out that vitamin K antagonists are being slowly replaced by direct-acting oral anticoagulants, whereas compared to low-molecular-weight heparins they are mostly considered as an alternative option. Main concerns when using direct-acting oral anticoagulants are: a higher risk of major bleeding and drug-to-drug interactions with antineoplastic agents. We noticed that current guidelines of different scientific associations are not unanimous. We presented clinical trials on direct-acting oral anticoagulants in regard to cancer patients. Overall there are still a lot of discrepancies in field of by direct-acting oral anticoagulants therapy. In conclusion, this review supports the use of DOACs in various clinical situations. Abstract Venous thromboembolism (VTE) is an important aspect in cancer patients. There are various pharmacological methods used for thrombotic event treatment. DOACs (direct-acting oral anticoagulants) are gaining popularity among both physicians and researchers and are slowly starting to replace VKAs (vitamin K antagonists), thus becoming a substitute or alternative option for LMWHs (low-molecular-weight heparins). In this article, we present DOACs’ main therapeutic advantages and disadvantages in patients with cancer. The only major concern with using DOACs is the higher risk of bleeding; however, there are discrepancies in this matter. There are still some types of cancer for which DOACs are not recommended. Specific cancer types may influence the efficacy of DOAC therapy. Additionally, race and ethnicity may affect therapy in cancer patients with DOACs. A sizeable number of clinical trials are focused on comparing DOACs with other anticoagulants. The current guidelines of different scientific associations are not unanimous in their DOAC assessments. There is still a need for more evidence of DOACs’ potential advantages over other methods of anticoagulation in cancer patients to facilitate their position in this recommendation. This literature review presents the current state of knowledge about the use of DOACs in patients with neoplastic growth.