Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance of TiO2-Based Tubular Membranes in the Photocatalytic Degradation of Organic Compounds

This work presents the photocatalytic degradation of organic pollutants in water with TiO2 and TiO2/Ag membranes prepared by immobilising photocatalysts on ceramic porous tubular supports. The permeation capacity of TiO2 and TiO2/Ag membranes was checked before the photocatalytic application, showing high water fluxes (≈758 and 690 L m−2 h−1 bar−1, respectively) and <2% rejection against the model pollutants sodium dodecylbenzene sulfonate (DBS) and dichloroacetic acid (DCA). When the membranes were submerged in the aqueous solutions and irradiated with UV-A LEDs, the photocatalytic performance factors for the degradation of DCA were similar to those obtained with suspended TiO2 particles (1.1-fold and 1.2-fold increase, respectively). However, when the aqueous solution permeated through the pores of the photocatalytic membrane, the performance factors and kinetics were two-fold higher than for the submerged membranes, mostly due to the enhanced contact between the pollutants and the membranes photocatalytic sites where reactive species were generated. These results confirm the advantages of working in a flow-through mode with submerged photocatalytic membranes for the treatment of water polluted with persistent organic molecules, thanks to the reduction in the mass transfer limitations.
The Novel Role of Noncoding RNAs in Modulating Platelet Function: Implications in Activation and Aggregation

It is currently believed that plaque complication, with the consequent superimposed thrombosis, is a key factor in the clinical occurrence of acute coronary syndromes (ACSs). Platelets are major players in this process. Despite the considerable progress made by the new antithrombotic strategies (P2Y12 receptor inhibitors, new oral anticoagulants, thrombin direct inhibitors, etc.) in terms of a reduction in major cardiovascular events, a significant number of patients with previous ACSs treated with these drugs continue to experience events, indicating that the mechanisms of platelet remain largely unknown. In the last decade, our knowledge of platelet pathophysiology has improved. It has been reported that, in response to physiological and pathological stimuli, platelet activation is accompanied by de novo protein synthesis, through a rapid and particularly well-regulated translation of resident mRNAs of megakaryocytic derivation. Although the platelets are anucleate, they indeed contain an important fraction of mRNAs that can be quickly used for protein synthesis following their activation. A better understanding of the pathophysiology of platelet activation and the interaction with the main cellular components of the vascular wall will open up new perspectives in the treatment of the majority of thrombotic disorders, such as ACSs, stroke, and peripheral artery diseases before and after the acute event. In the present review, we will discuss the novel role of noncoding RNAs in modulating platelet function, highlighting the possible implications in activation and aggregation.
Moving Targets Detection Based on Regional Optical Flow of Discontinuous Frame

Combining with the high speed of frame-difference method and the high accuracy of the optical flow method,a moving target detection algorithm based on regional optical flow of discontinuous frame-difference is proposed.After acquiring the region consisting of moving targets by using the discontinuous frame-difference method,only its optical flow is calculated.The calculated region is reduced,therefore it improved the detection speed.The optical flow of the larger gradient points is calculated with the basic equation,while the smaller ones are calculated with the smooth constraint equation,which can improve the accuracy of the moving targets detection.In addition,through importing inertia factor,the iterative numbers of the optical flow equation is reduced while the detection speed is improved.Experimental results prove the effectiveness of the proposed algorithm.
Concurrent method of assembly sequence based on directed constraint graph

A concurrent method of assembly sequence based on directed constraint graph is proposed in order to improve the efficiency of product assembly and realize the concurrent assembly. In the full paper,we explain in detail our new method;in this abstract,we just add some pertinent remarks to listing the three topics of explanation. (1)The mathematical model of clashes states of components;assembly constraint matrix and component constraint degree are defined to describe the clashes states of components in the process of assembly;(2)Assembly sequence planning based on the directed constraint graph decomposition;a directed constraint graph is put forward to describe the assembly constrain relations among components,and the assembly sequence is produced using the directed constraint graph decomposition algorithm based on the correlative release;(3)The concurrent strategy of assembly sequence;the concurrent strategy is used to generate concurrent assembly sequence based on the multi-subtrees. The Feasibility of the method is illustrated by an example of the assembly of machine vice.
Speech Enhancement Based on Array-Processing-Assisted Distributed Fiber Acoustic Sensing

Distributed fiber acoustic sensing (DAS) based on phase-sensitive optical time-domain reflectometry (<inline-formula> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula>-OTDR) has attracted lots of attention due to its unique advantages. In security monitoring, speech recognition, and other relevant fields, DAS’s ability to acquire speech signals is a key indicator for practical applications. However, the speech signal has broadband and nonstationary characteristics, so the conventional filtering is not sufficient, and the speech enhancement algorithm based on the characteristics of the speech signal is needed; in addition, the acoustic sensitivity of conventional fiber layout is not high enough, resulting in poor signal quality. In this article, speech enhancement based on array-processing-assisted DAS system is proposed to recover speech signal with high fidelity. Pulse compression <inline-formula> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula>-OTDR (PC <inline-formula> <tex-math notation="LaTeX">$\Phi $ </tex-math></inline-formula>-OTDR), combined with a multichannel speech enhancement algorithm based on optimally modified log-spectral amplitude (OM-LSA) is used. As far as we know, this is the first time that the multichannel OM-LSA speech enhancement is proposed for DAS. Compared with the signal before processing, the speech signal processed by this method has a great improvement in signal distortion, background noise intrusiveness, and signal overall quality. In addition, other signal enhancement methods, such as wavelet denoising (WD) and beamforming (BF) are also analyzed and compared. Our scheme outperforms in the above three aspects. This scheme does not need to obtain a large number of datasets in advance and can be transplanted to other DAS systems, which has great significance for realizing high-performance speech acquisition.
LLDSNet: Limited Labeled Data Segmentation Network for COVID-19 CT lesion segmentation

With the COVID-19 outbreak in 2019, the world is facing a major crisis and people's health is at serious risk. Accurate segmentation of lesions in CT images can help doctors understand disease infections, prescribe the right medicine and control patients' conditions. Fast and accurate diagnosis not only can make the limited medical resources get reasonable allocation, but also can control the spread of disease, and computer-aided diagnosis can achieve this purpose, so this paper proposes a deep learning segmentation network LLDSNet based on a small amount of data, which is divided into two modules: contextual feature-aware module (CFAM) and shape edge detection module (SEDM). Due to the different morphology of lesions in different CT, lesions with dispersion, small lesion area and background area imbalance, lesion area and normal area boundary blurred, etc. The problem of lesion segmentation in COVID-19 poses a major challenge. The CFAM can effectively extract the overall and local features, and the SEDM can accurately find the edges of the lesion area to segment the lesions in this area. The hybrid loss function is used to avoid the class imbalance problem and improve the overall network performance. It is demonstrated that LLDSNet dice achieves 0.696 for a small number of data sets, and the best performance compared to five currently popular segmentation networks.
Task-oriented arm training for stroke patients based on remote handling technology concepts: A feasibility study

BACKGROUND: Improving arm-hand skill performance is a major therapeutic target in stroke rehabilitation. Arm-hand rehabilitation may be enriched in content and variation by using technology-assisted training. Especially for people with a severely affected arm, technology-assisted training offers more challenging training possibilities. OBJECTIVE: The aim of this study was to explore the feasibility of ReHab-TOAT, a “Remote Handling Based Task-Oriented Arm Training” approach featuring enriched haptic feedback aimed at improving daily activities and participation. METHODS: Five subacute or chronic stroke patients suffering moderate to severe arm-hand impairments and five rehabilitation therapists participated. All participants received 2 ReHab-TOAT sessions. Outcome measure was a bespoke feasibility questionnaire on user experiences and satisfaction regarding ‘motivation’, ‘individualization of training’, ‘potential training effects’, and ‘implementation in rehabilitation’ of patients and therapists. RESULTS: Both patients and therapists experienced ReHab-TOAT as being feasible. They found ReHab-TOAT very motivating and challenging. All patients perceived an added value of ReHab-TOAT and would continue the training. Small improvements regarding exercise variability were suggested. CONCLUSION: ReHab-TOAT seems to be a feasible and very promising training approach for arm-hand rehabilitation of stroke patients with a moderately or severely affected arm. Further research is necessary to investigate potential training effects of ReHab-TOAT.
Review of Osteoradionecrosis of the Jaw: Radiotherapy Modality, Technique, and Dose as Risk Factors

Radiotherapy (RT) or concurrent chemoradiotherapy (CCRT) is the cornerstone of organ-sparing or adjuvant therapy for nearly all head and neck cancers. Unfortunately, aggressive RT or CCRT can result in severe late toxicities, such as osteoradionecrosis of the jaws (ORNJ). The incidence of ORNJ is currently less than 5–6% due to advances in dental preventive care programs, RT planning systems, and RT techniques. Although numerous patient-, tumor-, and treatment-related factors may influence the incidence rates of ORNJ, RT modality (equipment), technique, and dose-volume-related factors are three of the most influential factors. This is mainly because different RT equipment and techniques have different levels of success at delivering the prescribed dose to the focal volume of the treatment while keeping the “organ at risk” safe. ORNJ risk is ultimately determined by mandibular dose, despite the RT technique and method being known predictors. Regardless of the photon delivery method, the radiobiological effects will be identical if the total dose, dose per fraction, and dose distribution within the tissue remain constant. Therefore, contemporary RT procedures mitigate this risk by reducing mandibular dosages rather than altering the ionizing radiation behavior in irradiated tissues. In light of the paucity of studies that have examined the impact of RT modality, technique, and dose-volume-related parameters, as well as their radiobiological bases, the present review aims to provide a comprehensive overview of the published literature on these specific issues to establish a common language among related disciplines and provide a more reliable comparison of research results.
Mesh independent convergence of modified inexact Newton methods for second order nonlinear problems

Mesh Independent Convergence of Modified Inexact Newton Methods for Second Order Nonlinear Problems. (May 2006) Taejong Kim, B.S., Yonsei University, Korea; M.S., Yonsei University, Korea Chair of Advisory Committee: Dr. Joseph E. Pasciak In this dissertation, we consider modified inexact Newton methods applied to second order nonlinear problems. In the implementation of Newton’s method applied to problems with a large number of degrees of freedom, it is often necessary to solve the linear Jacobian system iteratively. Although a general theory for the convergence of modified inexact Newton’s methods has been developed, its application to nonlinear problems from nonlinear PDE’s is far from complete. The case where the nonlinear operator is a zeroth order perturbation of a fixed linear operator was considered in the paper written by Brown et al. The goal of this dissertation is to show that one can develop modified inexact Newton’s methods which converge at a rate independent of the number of unknowns for problems with higher order nonlinearities. To do this, we are required to first, set up the problem on a scale of Hilbert spaces, and second, to devise a special iterative technique which converges in a higher order Sobolev norm, i.e., H(Ω) ∩ H 0 (Ω) with 0 < α < 1/2. We show that the linear system solved in Newton’s method can be replaced with one iterative step provided that the initial iterate is close enough. The closeness criteria can be taken independent of the mesh size. In addition, we have the same convergence rates of the method in the norm of H 0 (Ω) using the discrete Sobolev inequalities.
Motion and Deformation of Elastic Capsules and Vesicles in Flow

This article reviews the mechanical behavior of a capsule under the influence of viscous deforming forces due to a flowing fluid. It focuses on artificial capsules and vesicles with an internal liquid core enclosed by a very thin membrane with different constitutive laws. The recent modeling strategies are outlined together with their respective advantages and limitations. I then consider the motion and deformation of a single, initially spherical capsule freely suspended in a simple shear or plane hyperbolic flow and discuss the effect of the membrane constitutive law, initial prestress, membrane buckling, and bulk or membrane viscosity. Finally, I consider the flow of spherical capsules in small pores and show how numerical models can be used to evaluate the mechanical properties of the membrane.
Evaluation of Respondent-Driven Sampling Prevalence Estimators Using Real-World Reported Network Degree

Respondent-driven sampling (RDS) is used to measure trait or disease prevalence in populations that are difficult to reach and often marginalized. The authors evaluated the performance of RDS estimators under varying conditions of trait prevalence, homophily, and relative activity. They used large simulated networks (N = 20,000) derived from real-world RDS degree reports and an empirical Facebook network (N = 22,470) to evaluate estimators of binary and categorical trait prevalence. Variability in prevalence estimates is higher when network degree is drawn from real-world samples than from the commonly assumed Poisson distribution, resulting in lower coverage rates. Newer estimators perform well when the sample is a substantive proportion of the population, but bias is present when the population size is unknown. The choice of preferred RDS estimator needs to be study specific, considering both statistical properties and knowledge of the population under study.
Anomaly Detection of Hyperspectral Image With Hierarchical Antinoise Mutual-Incoherence- Induced Low-Rank Representation

Hyperspectral image (HSI) anomaly detection (AD) generally considers background pixels as low-rank distribution and anomaly pixels as sparse distribution. However, it is usually difficult to construct an accurate background dictionary for the background pixels composed of different land covers, and completely separate sparse anomaly targets from various complicated background pixels with complex mixed noise interference. To address these challenges, we propose an antinoise hierarchical mutual-incoherence-induced discriminative learning (AHMID) method for the AD of HSI. A structural incoherence constraint is designed to constrain the inherent dissimilarity and incoherence between the background and anomalies for improving their separability. Then, a first-order statistic constraint is conducted on targets to enhance the anomaly representation, and a decentralization constraint is used on the background to suppress the background representation. Meanwhile, a mixed noise model is constructed by $\ell _{1,1}$ -norm and Frobenius norm to improve the antinoise performance. Finally, a hierarchical alternative strategy is developed to gradually optimize the background and anomalies. Experiments on six HSI AD datasets show that the proposed method outperforms a few state-of-the-art AD algorithms. Code: https://github.com/HalongL/HAD-AHMID
Algebraic solution for the forward displacement analysis of the general 6-6 stewart mechanism

The solution for the forward displacement analysis(FDA) of the general 6-6 Stewart mechanism(i.e., the connection points of the moving and fixed platforms are not restricted to lying in a plane) has been extensively studied, but the efficiency of the solution remains to be effectively addressed. To this end, an algebraic elimination method is proposed for the FDA of the general 6-6 Stewart mechanism. The kinematic constraint equations are built using conformal geometric algebra(CGA). The kinematic constraint equations are transformed by a substitution of variables into seven equations with seven unknown variables. According to the characteristic of anti-symmetric matrices, the aforementioned seven equations can be further transformed into seven equations with four unknown variables by a substitution of variables using the Gröbner basis. Its elimination weight is increased through changing the degree of one variable, and sixteen equations with four unknown variables can be obtained using the Gröbner basis. A 40th-degree univariate polynomial equation is derived by constructing a relatively small-sized 9´9 Sylvester resultant matrix. Finally, two numerical examples are employed to verify the proposed method. The results indicate that the proposed method can effectively improve the efficiency of solution and reduce the computational burden because of the small-sized resultant matrix.
The Role of AI in Breast Cancer Lymph Node Classification: A Comprehensive Review

Simple Summary Breast cancer affects countless women worldwide, and detecting the spread of cancer to the lymph nodes is crucial for determining the best course of treatment. Traditional diagnostic methods have their drawbacks, but artificial intelligence techniques, such as machine learning and deep learning, offer the potential for more accurate and efficient detection. Researchers have developed cutting-edge deep learning models to classify breast cancer lymph node metastasis from medical images, with promising results. Combining radiological data and patient information can further improve the accuracy of these models. This review gathers information on the latest AI models for detecting breast cancer lymph node metastasis, discusses the best ways to validate them, and addresses potential challenges and limitations. Ultimately, these AI models could significantly improve cancer care, particularly in areas with limited medical resources. Abstract Breast cancer is a significant health issue affecting women worldwide, and accurately detecting lymph node metastasis is critical in determining treatment and prognosis. While traditional diagnostic methods have limitations and complications, artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) offer promising solutions for improving and supplementing diagnostic procedures. Current research has explored state-of-the-art DL models for breast cancer lymph node classification from radiological images, achieving high performances (AUC: 0.71–0.99). AI models trained on clinicopathological features also show promise in predicting metastasis status (AUC: 0.74–0.77), whereas multimodal (radiomics + clinicopathological features) models combine the best from both approaches and also achieve good results (AUC: 0.82–0.94). Once properly validated, such models could greatly improve cancer care, especially in areas with limited medical resources. This comprehensive review aims to compile knowledge about state-of-the-art AI models used for breast cancer lymph node metastasis detection, discusses proper validation techniques and potential pitfalls and limitations, and presents future directions and best practices to achieve high usability in real-world clinical settings.
Differential Early Mechanistic Frontal Lobe Responses to Choline Chloride and Soy Isoflavones in an Experimental Model of Fetal Alcohol Spectrum Disorder

Fetal alcohol spectrum disorder (FASD) is the most common preventable cause of neurodevelopmental defects, and white matter is a major target of ethanol neurotoxicity. Therapeutic interventions with choline or dietary soy could potentially supplement public health preventive measures. However, since soy contains abundant choline, it would be important to know if its benefits are mediated by choline or isoflavones. We compared early mechanistic responses to choline and the Daidzein+Genistein (D+G) soy isoflavones in an FASD model using frontal lobe tissue to assess oligodendrocyte function and Akt-mTOR signaling. Long Evans rat pups were binge administered 2 g/Kg of ethanol or saline (control) on postnatal days P3 and P5. P7 frontal lobe slice cultures were treated with vehicle (Veh), Choline chloride (Chol; 75 µM), or D+G (1 µM each) for 72 h without further ethanol exposures. The expression levels of myelin oligodendrocyte proteins and stress-related molecules were measured by duplex enzyme-linked immunosorbent assays (ELISAs), and mTOR signaling proteins and phosphoproteins were assessed using 11-plex magnetic bead-based ELISAs. Ethanol’s main short-term effects in Veh-treated cultures were to increase GFAP and relative PTEN phosphorylation and reduce Akt phosphorylation. Chol and D+G significantly modulated the expression of oligodendrocyte myelin proteins and mediators of insulin/IGF-1-Akt-mTOR signaling in both control and ethanol-exposed cultures. In general, the responses were more robust with D+G; the main exception was that RPS6 phosphorylation was significantly increased by Chol and not D+G. The findings suggest that dietary soy, with the benefits of providing complete nutrition together with Choline, could be used to help optimize neurodevelopment in humans at risk for FASD.