Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound and Microbubbles Mediated Bleomycin Delivery in Feline Oral Squamous Cell Carcinoma—An In Vivo Veterinary Study

To investigate the feasibility and tolerability of ultrasound and microbubbles (USMB)-enhanced chemotherapy delivery for head and neck cancer, we performed a veterinary trial in feline companion animals with oral squamous cell carcinomas. Six cats were treated with a combination of bleomycin and USMB therapy three times, using the Pulse Wave Doppler mode on a clinical ultrasound system and EMA/FDA approved microbubbles. They were evaluated for adverse events, quality of life, tumour response and survival. Furthermore, tumour perfusion was monitored before and after USMB therapy using contrast-enhanced ultrasound (CEUS). USMB treatments were feasible and well tolerated. Among 5 cats treated with optimized US settings, 3 had stable disease at first, but showed disease progression 5 or 11 weeks after first treatment. One cat had progressive disease one week after the first treatment session, maintaining a stable disease thereafter. Eventually, all cats except one showed progressive disease, but each survived longer than the median overall survival time of 44 days reported in literature. CEUS performed immediately before and after USMB therapy suggested an increase in tumour perfusion based on an increase in median area under the curve (AUC) in 6 out of 12 evaluated treatment sessions. In this small hypothesis-generating study, USMB plus chemotherapy was feasible and well-tolerated in a feline companion animal model and showed potential for enhancing tumour perfusion in order to increase drug delivery. This could be a forward step toward clinical translation of USMB therapy to human patients with a clinical need for locally enhanced treatment.
Honda Theory for Formal Groups of Abelian Varieties over Q of GL2-Type

Honda proved that two formal groups attached to an elliptic curve E over Q are strongly isomorphic over Z, where one of them is obtained from the formal completion along the zero section of the Neron model over Z and another is obtained from the L-series attached to the l-adic Galois representations on E. In this paper, we generalize his theorem to abelian varieties over Q of GL2-type. As an application, we give a method to calculate the coefficients of the L-series attached to an algebraic curve over Q with a Jacobian variety of GL2-type.
Duodenal Metastasis of a Pulmonary Adenocarcinoma: Report of a Case and Review of the Literature

Symptomatic gastrointestinal metastases from lung cancer were considered quite rare given their low incidence, especially the duodenal location. In this article, we report a clinical case of a patient followed for a pulmonary adenocarcinoma revealed by a duodenal metastasis. The patient was a 63-year-old female. She was admitted to the oncology department of the Ibn Rochd University Hospital for a low digestive hemorrhage ‘’Melena’’ complicated by acute anemia. Duodenal metastasis from a primary adenocarcinoma of the lung was diagnosed following histopathological examination supplemented by immunohistochemical analysis of biopsies of the duodenal mass taken under gastrointestinal fibroscopy. Post-mortem studies have confirmed that large cell lung carcinoma is significantly associated with gastrointestinal metastases with an incidence ranging from 30% to 75.9%. For this reason, physicians should consider secondary gastrointestinal locations if patients with lung cancer exhibit digestive symptoms and not relate this symptom to side effects of chemotherapy.
The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters

A new implementation of the Discrete Wavelet Transform is presented, suitable for a range of signal and image processing applications. It employs a dual tree of wavelet lters to obtain the real and imaginary parts of complex wavelet coeecients. This introduces limited redundancy (4:1 for 2-dimensional signals) and allows the transform to provide approximate shift in-variance and directionally selective lters (properties lacking in the traditional wavelet transform) while preserving the usual properties of perfect reconstruction and computational eeciency. An application to texture synthesis is presented.
A Practical Rough Image Curve Matching Method Based on Accumulation of Evidence

In this paper, an evidence accumulation based image curve matching method is pro posed. The method can efficiently and robustly find the rough location of a usua lly short extracted image curve to a long reference curve. The main characterist ics of the proposed method are: Firstly, experiments show that the distance betw een two curve points is more reliable than the curve length itself to be used as a matching invariant in the presence of noise, hence in the work, the distance from the start point to the end point of the extracted image curve is primarily used for the selection of matching candidates in the reference curve. Secondly, the evidence accumulation concept is introduced in the matching algorithm, which not only significantly eliminates the incorrect matching of control points, but also substantially decreases the proportion of curve segments to be verified fi nally, a time consuming process. As a result, the computational efficiency and r obustness of the proposed method is largely increased; Thirdly, the statistic Ga ussian model is introduced in the classical Hausdorff distance calculation, whic h makes it more fit for the matching of curves with significant partial deformat ions. Extensive experiments with simulated data and images of satellite and digi tal camera show that the proposed curve matching method is practical, efficient and robust.
Comparison of Antioxidant Properties and Flavonoid of Natural and in vitro Cultivated Nardostachys jatamansi

Background: India has very rich diversity of medicinal plants. Medicinal plants are thought to be a rich source of ingredients that can be used in the development of pharmaceutical or synthetic drugs. Aside from that, these plants play an important role in the development of human drug all over the world. Whether in modern or traditional medicine, medicinal plants are used to maintain health, to treat a specific condition, or both. Nardostachys is one of the most important medicinal plant having several therapeutic properties. It is threatened in its natural habitat due to over exploitation for therapeutic purposes and high demand in the traditional medicine system. Keeping these points in mind, we attempted to investigate alternative uses of in vitro grown plants in place of wild plants without disrupting plant-based therapeutics and market demand. And then compare the root extract of the Nardostachys jatamansi plant’s antioxidant and flavonoid levels under in vitro and natural growth conditions. Methods: Nardostachys jatamansi is a plant that is widely used in traditional medicine systems. Because of its wide spread use in traditional medicine, this plant is considered endemic. In our study, we compare of antioxidant quality and Flavonoid amount of Natural and in vitro propagated Nardostachys jatamansi. Firstly we cultivated in vitro plants from Nardostachys jatamansi nodal explants for comparative analysis. The methanol extract of in vitro grown and wild-type plant root extract was then prepared using the maceration method. The extract was subjected to a comparative DPPH method to determine the presence of antioxidant potential in natural and in vitro grown plants. Furthermore, the HPLC analysis was used to detect and quantify the amount of Quercetin in both natural and in vitro propagated plants. Result: When grown in vitro at a higher concentration, the roots of Nardostachys jatamansi have greater antioxidant potential than when they are grown naturally. They demonstrated antioxidant DPPH radical scavenging activity, with an IC50 value of 29.55 µg/ml for in vitro generated plants and 24.18 µg/ml for naturally grown plants. The concentration of Quercetin (mg/ml) for natural plant species is 1.95 and for in vitro propagate plant is 1.83. The HPLC analysis presents distinct peaks, with the main peaks having retention time for standard Quercetin (10.38) in the natural plant (10.34) and in vitro grown plants (10.32). In the end, natural-type species that had been produced in vitro were used to obtain the potential of micro propagated plants. The DPPH test and flavonoid were tested on the root extract of natural and in vitro plants. Both plants displayed promising antioxidant activity and an HPLC study identified the Quercetin component.
Do Savant Syndrome and Autism Spectrum Disorders Share Sex Differences? A Comprehensive Review

Savant syndrome was described before autism. However, they soon became closely associated, as many of their symptoms (intellectual disability, repetitive behaviors, alterations in social communication, and islets of abilities) overlap. Only a few women with autism have been diagnosed with savant syndrome. The theories or hypotheses that attempt to explain savant syndrome, which are common in autism, present differential treatment according to sex. We postulate that savant syndrome associated with autism as well as autism in general is underdiagnosed in women.
Investigating the Effects of Flipped Classroom on Delta University Students’ Writing Skill and Engagement

The current study's objectives are to ascertain how students' writing abilities are impacted by the flipped classroom approach as well as to examine their engagement with and attitudes about this method. An experimental design with a pretest and posttest was employed to accomplish these goals. There were 60 participants, and they were split into two groups. The first group presents the flipped classroom as the experimental group, whereas the other was assigned to resemble the typical classroom and named the control group. Six sessions of treatment were given to each group to see if there were any notable differences in the overall caliber of the two groups' IELTS task two essays. A pre-test and a post-test were given to both groups at the start and completion of the treatment, respectively, to evaluate the grades of the writing skill. In order to assess students' engagement with and attitudes toward this method, a self-created questionnaire based on the eighteen-item Learner Empowerment Scale (LES) (Weber, Martin, & Cayanus, 2005) was distributed. The results found that, the majority of candidates in FC group echoed positively. These findings in the FC group may be ascribed to both flipped instruction and the practice of actively involving students in their education. Other methods that were used included collaborative writing, uploading videos to online channels, and in-class teacher-student interactions.
Deep Learning Network Traffic Prediction based on Bayesian Algorithm Optimization

In order to solve the problem of low prediction accuracy caused by artificial determination of hyperparameters of network traffic prediction model, this paper applies Bayesian optimization algorithm (BOA) to the determination of hyperparameters of neural network, and proposes a CNN-LSTM network traffic prediction model based on Bayesian optimization algorithm. The local features and long-term development trends of the data are mined by combining the Convolutional Neural Network (CNN) and the Long Short-Term Memory Neural Network (LSTM), and the Bayesian optimization algorithm is used to calibrate the hyperparameters. This paper uses real network traffic data to test and compare with LSTM, GRU and other models. Experimental results demonstrate that the proposed BOA-CNN-LSTM model reduces the RMSE and MAE by 21.17% and 27.52%, compared to the traditional network traffic prediction model. The results presented in this paper show that the Bayesian optimization algorithm can more quickly and accurately calibrate the hyperparameters of the model, which provides a new method for predicting network traffic time series data in the future.
Linear Stochastic Systems Method for Designing Optimum Feedback Controllers

This paper introduced computer as action arithmetic unit of optimum feedback controllers Its structure system, as well as the influence which the system subjects to the stochastic interference, Hence, the stale of every sample moment must be estimated timely, in order to be a evidence of calculating feedback. The quality of the controllers is well related to the formulation of the objective function. But the most difficult problem in the practice is that can not find a suitable objective function soon yet.
Speech Recognition Using Intelligent Piezoresistive Sensor Based on Polystyrene Sphere Microstructures

Rapid advances in wearable sensing technology have demonstrated unprecedented opportunities for artificial intelligence. In comparison with the traditional hand‐held electrolarynx, a wearable and intelligent artificial throat with sound‐sensing ability is a more comfortable and versatile method to assist disabled people with communication. Herein, a piezoresistive sensor with a novel configuration is demonstrated, which consists of polystyrene (PS) spheres as microstructures sandwiched between silver nanowires and reduced graphene oxide layers. In fact, changes in the device's conducting patterns are obtained by spay‐coating the various weight ratios and sizes of the PS microspheres, which is a fast and convenient way to establish microstructures for improving sensitivity. The wearable artificial throat device also exhibits high sensitivity, fast response time, and ultralow intensity level detection. Moreover, the device's excellent mechanical–electrical performance allows it to detect subtle throat vibrations that can be converted into controllable sounds. In this case, an intelligent artificial throat is achieved by combining a deep learning algorithm with a highly flexible piezoresistive sensor to successfully recognize five different words (help, sick, patient, doctor, and COVID) with an accuracy exceeding 96%. Herein, new opportunities in voice control as well as other human‐machine interface applications are opened.
Enhancing Air Quality Prediction Accuracy Using Hybrid Deep Learning

PM2.5 (Particulate Matter) and PM10 are the most common pollutants, and the increasing of concentration in the air will threaten people’s health. The machine learning method has recently been of particular interest to many researchers due to its effectiveness in air quality prediction models. Many solutions employing deep learning-based techniques such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models to enhance air quality prediction accuracy have been developed. This paper proposes a hybrid Encoder STM model for predicting the next day to the next five days’ PM2.5 and PM10 concentrations in Hanoi. Additionally, we proposed five extended features to increase the accuracy of prediction. Then other models, namely the LSTM model and the Bidirectional LSTM model, are also considered for PM2.5 and PM10 concentration prediction. Our results show that the proposed approaches outperform other state-of-the-art deep learning-based methods on both Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) due to low error and the small number of features.
Dynamic Mechanical Properties of PVC Plastics in the Formation of Microstructures with Novel Magnetostrictor

Molding in thermoplastic polymers using ultrasonic hot embossing technology is promising due to its high precision reproducibility. To understand, analyze and apply the formation of polymer microstructures by the ultrasonic hot embossing method, it is necessary to understand dynamic loading conditions. The Standard Linear Solid model (SLS) is a method that allows analyzing the viscoelastic properties of materials by representing them as a combination of springs and dashpots. However, this model is general, and it is challenging to represent a viscoelastic material with multiple relaxations. Therefore, this article aims to use the data obtained from dynamic mechanical analysis for extrapolation in a wide range of cyclic deformations and to use the obtained data in microstructure formation simulations. The formation was replicated using a novel magnetostrictor design that sets a specific temperature and vibration frequency. The changes were analyzed on a diffractometer. After the diffraction efficiency measurement, it was found that the highest quality structures were formed at a temperature of 68 °C, a frequency of 10 kHz, a frequency amplitude of 1.5 µm and a force of 1 kN force. Moreover, the structures could be molded on any thickness of plastic.
Nachhaltige und intelligente additive Fertigung – Frühzeitige Erkennung von Produktionsfehlern im 3D-Druck durch Künstliche Intelligenz

Die additive Fertigung ist eine zunehmend an Bedeutung gewinnende Fertigungstechnologie mit einem großen wirtschaftlichen Potenzial. Ihre Beliebtheit geht jedoch mit hohen Material- und Zeitverlusten einher, da fehlerbehaftete Werkstücke in vielen Fällen erst sehr spät im Fertigungsprozess erkannt werden. Ein Lösungsansatz für eine nachhaltigere und effizientere Produktion ist das automatisierte und frühzeitige Erkennen von Fertigungsfehlern mit Verfahren der Künstlichen Intelligenz. Dieser Beitrag beschreibt die Digitalisierung des Fehlererkennungsprozesses im 3D-Druck mithilfe eines bildbasierten, lernenden Systems. Dabei wird neben den einzelnen Arbeitsschritten zur automatisierten Fehlererkennung auch auf die Leistung des Systems eingegangen.
Malliavin Derivatives of Solutions for BSDE

We discuss second order differentiability of solutions for BSDE under Malliavin Calculus sense. The second order derivative is determined by a linear backward stochastic differential equation. In this paper we employ iterating method to construct a pair of stochastic process sequences (Yn , Zn) and prove that it converges to the solution of a linear BSDE in Sobolev space D2,2. As a result it is second order differentiable under the sense of Malliavin Calculus.
Study on Cut Angle of Direction Finding Location System

This paper studies the cut angle problem of direction finding location system. In the past many researchers study this problem on the assumption that the distance between the target and the baseline is constant, i.e. the target moves along the parallel line of the baseline. In this paper we take no account of this restriction, and give the optimal cut angle of the whole detection area when the circular probable error is minimum. The relationship between the circular probable error and the bearing measurements of passive sensors or the cut angle is shown in simulation figures, and the GDOP distribution figure is also given. Some conclusions are drawn, which can be used for reference in collocating passive sensors to improve location precision.
The impact of epithelial to mesenchymal transition in kidney fibrosis

: Fibrosis is a pathological condition attributed to an inappropriate deposition of extracellular matrix that promotes scarring and dysfunction of the implicated organ or tissue. Moreover, fibrosis encompasses an enormous range of organs and tissues, each with a distinct molecular basis. Yet, two factors are discussed: the critical function of transforming growth factor-beta (TGF-β) and the participation of the inflammatory cascade, which is required to initiate fibrotic destruction. Epithelial to mesenchymal transition (EMT), the most prevalent cellular program underlying fibrosis, is regulated by TGF-β, as well as other cytokines. EMT has been widely investigated, although it has not yet been adequately examined as a potential treatment option for fibrosis. A better knowledge of the linkages between fibrosis and EMT may provide an option for the establishment of a highly useful anti-fibrotic treatment. Thus, we provide a general overview of EMT; summarize the significance of TGF-β/Smad signaling cascade in EMT induction, the correlation between both EMT and renal fibrosis and the promising treatment strategies targeting fibrosis induced by EMT.
Audio-visual feature fusion for improved thoracic disease classification

In this work, we fuse imaging features from Chest X-Ray (CXR) scans and audio features from dictations of a radiologist to improve thoracic disease classification. Recent deep learning-based disease classification methods mostly use imaging modalities. Dictation audio from a radiologist contains rich auxiliary diseaserelated contextual information. The main hypothesis of this proposed work is that leveraging complementary imaging and audio representations improves disease classification. We use shifting window (Swin) transformer architectures as encoders for both visual and audio modalities and finally fuse the feature representations using cross-correlational feature multiplication fusion strategy. This fused feature representation is fed to a classification head for downstream disease classification. We experimentally show that the proposed fused model outperforms the individual modality models for multi-class thoracic disease classification that includes normal, pneumonia, and congestive heart failure cases. We report F1-score of 0.5415 and 0.5353 for shifting window transformer base and small architectures respectively, for fused modalities, while the corresponding baselines are reported at 0.5046 and 0.5076 for the audio modality and 0.4676 and 0.5261 for the imaging modality, respectively.
An Integrated Model for Evaluating the Sustainability of Gamified Mobile Health Apps: An Instrument Development and Validation

Mobile health (mHealth) apps are designed to support health behavior outcomes and improve well-being. The existing body of literature confirms mHealth’s overall efficacy in promoting physical activity; however, more research on its utility in sustaining user engagement is needed. Understanding the determinants of an individual’s willingness to continue using mHealth is vital to improving the intervention’s success. This study developed a unified model and survey instrument adapted from extant literature while introducing new constructs to predict the sustained use of gamified mHealth. A pilot study was conducted to validate the survey instrument using 48 gamified fitness app users in Malaysia. The survey instrument was tested following rigorous guidelines for quantitative research in the information system context. According to the findings, the reliabilities of most measurement items met the criterion, and those items were retained. Overall, this paper contributes by integrating social comparison theory and the self-determination theory for sustaining user engagement with gamified mHealth through an extrinsic and intrinsic motivation perspective.
Adaptive practical synchronisation of Lagrangian networks with a directed graph via pinning control

This study investigates the practical synchronisation of networked Lagrangian systems with a directed graph by pinning adaptive control. Some simple yet general algebraic criteria are proposed based on practical stability theory on dynamical systems, and the proposed criteria ensure that all the robotic agents described by Lagrangian dynamics can achieve desired practical synchronisation. The main features of the present investigation include, an efficient pinning adaptive strategy is implemented by applying local linear feedback injections to a small fraction of agents, which means that the presented control strategy does not require the prerequisite knowledge of system models. Moreover, an allowable attraction region is explicitly given by Lagrangian dynamics parameter and the controlled network structure, and so it is very convenient to application in practice. Besides, this study addresses the fundamental problems in the pinning control of networked Lagrangian systems: what kind of agents and how many agents should be pinned? As a direct application of the theoretical results, practical synchronisation of eight two-link revolute manipulators is discussed in detail. Numerical experiments verify the effectiveness of the proposed control technique.