Miracle-dz/UniFAR-FTUs huggingface dataset:
Near thermal-electric field controlled electrohydrodynamic 3D printing of high aspect ratio microstructures

Electrohydrodynamic (EHD) printing is an effective method for high-resolution two-dimensional patterning because of its high material compatibility. The latest research on three-dimensional (3D) structure fabrication has been reported for metal nanoparticles, small molecular materials, and phase change materials by process regulation. One of the main challenges in conventional EHD 3D printing of polymers is the low accuracy of filament deposition and stacking at the microscale. It is difficult to achieve a high aspect ratio (AR) for printed structures. This study develops an external field-assisted EHD printing process for polymer materials, where the filament formation can be improved by the near thermal field and the stacking accuracy of filaments is further promoted by applying an alternating voltage between layers. The feasibility of this method is demonstrated by printing polystyrene, a typical polymer material, on the silicon substrate with. The regulatory effect of the process parameters on the filament width is explored. The influence of the external thermal field on filament formation and deposition is discussed, and a promotion mechanism is revealed of alternating voltage for accurate deposition. Based on these combinative effects, micro-scale 3D structures with a high AR are successfully printed on the insulating substrate with three kinds of polymers.
Light trapped in a photonic dot: Microspheres act as a cavity for quantum dot emission

Optical microcavities that confine the propagation of light in all three dimensions (3D) are fascinating research objects to study 3D-confined photon states, low-threshold microlasers, or cavity quantum electrodynamics of quantum dots in 3D microcavities. A challenge is the combination of complete electronic confinement with photon confinement, e.g., by linking a single quantum dot to a single photonic dot. Here we report on the interplay of 3D-confined cavity modes of single microspheres (the photonic dot states) with photons emitted from quantized electronic levels of single semiconductor nanocrystals (the quantum dot states). We show how cavity modes of high cavity finesse are switched by single, blinking quantum dots. A concept for a quantum-dot microlaser operating at room temperature in the visible spectral range is demonstrated. We observe an enhancement in the spontaneous emission rate; i.e., the Purcell effect is found for quantum dots inside a photonic dot.
Epigenetic signatures in gastric cancer: current knowledge and future perspectives

ABSTRACT Introduction Gastric cancer (GC) is the fifth most common malignancy in the world and accounts for 7.7% of all cancer-related deaths. Early diagnosis of GC is critical in terms of prognosis, and aberrations at the molecular level, especially epigenetic alterations, manifest much earlier than histological findings. In recent years, there has been a great deal of research on the epigenomic profile of GC, and epigenetic alterations seem to play a more important role than genetic factors. With the introduction of epigenetic drugs into clinical use in the last decade, the importance of the epigenetic background of GC has increased considerably. Areas Covered In this review, we summarize the role of methylation changes, histone modifications, and non-coding RNAs in the pathogenesis of GC and how these signatures can be used as diagnostic and therapeutic targets in clinical management. Expert Opinion Epigenetic alterations take place before most genetic aberrations observed in GC and may have an initiating role in the pathogenesis of GC. They can be used as biomarkers in risk calculation, early diagnosis, and evaluation of prognosis of GC, as well as treatment targets.
Hyperspectral Compressive Image Reconstruction With Deep Tucker Decomposition and Spatial–Spectral Learning Network

Hyperspectral compressive imaging has taken advantage of compressive sensing theory to capture spectral information of the dynamic world in recent decades of years, where an optical encoder is employed to compress high dimensional signals into a single 2-D measurement. The core issue is how to reconstruct the underlying hyperspectral image (HSI), although deep neural network methods have achieved much success in compressed sensing image reconstruction in recent years, they still have some unsolved issues, such as tradeoffs between performance and efficiency, and accurate exploitation of cubic structure information. In this article, we propose a deep Tucker decomposition and spatial–spectral learning network (DS-net) to learn the tensor low-lank structure features and spatial–spectral correlation of HSI for reconstruction quality promotion. Inspired by tensor decomposition, we first construct a deep Tucker decomposition module to learn the principal components from different modes of the image features. Then, we cascade a series of decomposition modules to learn multihierarchical features. Furthermore, to jointly capture the spatial–spectral correlation of HSI, we propose a spatial–spectral correlation learning module in a U-net structure for more robust reconstruction performance. Finally, experimental results on both synthetic and real datasets demonstrate the superiority of the proposed method compared to several state-of-the-art methods in quantitative assessment and visual effects.
A comment on the computational complexity of sliding FFT

The sliding fast Fourier transform (FFT) is reviewed and is shown to have the computational complexity of N complex multiplications per sample, as opposed to the well-cited assumption of (N/2) log/sub 2/ N complex multiplication per sample reported in a book by L.R. Rabiner and B. Gold (1975). >
Studies of accretion flows around rotating black holes - III. Shock oscillations and an estimation of the spin parameter from QPO frequencies

In the present communication of our series of papers dealing with the accretion flows in the pseudo-Kerr geometry, we discuss the effects of viscosity on the accretion flow around a rotating black hole. We find the solution topologies and give special attention to the solutions containing shocks. We draw the parameter space where standing shocks are possible and where the shocks could be oscillating and could produce quasi-periodic oscillations (QPOs) of X-rays observed from black hole candidates. In this model, the extreme locations of the shocks give the upper limits of the QPO frequencies (νQPO) which could be observed. We show that both the viscosity of the flow and the spin of the black hole a increase the QPO frequency while, as expected, the black hole mass reduces the QPO frequencies. Our major conclusion is that the highest observed frequency gives a strict lower limit of the spin. For instance, a black hole exhibiting νQPO ∼ 400 and 700 Hz must have the spin parameters of a> 0.25 and >0.75, respectively, provided viscosity of the flow is small. We discuss the implications of our results in the light of observations of QPOs from black hole candidates.
Comparison between Clinical Utility of CXCL-8 and Clinical Practice Tumor Markers for Colorectal Cancer Diagnosis

Owing to the high incidence and mortality rates of colorectal cancer (CRC), novel biomarkers for CRC diagnosis are critically needed. Therefore, this study is aimed at exploring the clinical utility of serum C-X-C motif chemokine 8 (CXCL-8) for CRC diagnosis and progression compared to the routinely used biomarkers, carcinoembryonic antigen (CEA), and carbohydrate antigen-19-9 (CA19-9). This study included 227 patients with CRC, 110 patients with colorectal adenoma (CA), and 123 healthy participants, who were recruited from the Fujian Medical University Union Hospital from July 1, 2019 to October 31, 2020. Serum concentrations of CXCL-8, CEA, and CA19-9 were detected using enzyme-linked immunosorbent assay and chemiluminescent microparticle immunoassay. Clinicopathological features of patients with CRC were collected and analyzed. The diagnostic efficacy of CXCL-8, CEA, and CA19-9 for CRC was evaluated using receiver operating characteristic (ROC) curves. We found that the serum concentrations of CXCL-8, CEA, and CA19-9 were significantly higher in patients with CRC than those in patients with CA and healthy controls. The diagnostic sensitivity of CXCL-8 alone was higher than those of CEA and CA19-9 both and when combined; thus, CXCL-8 may be better at discriminating patients with CRC from healthy controls and patients with CA. Moreover, combining CXCL-8 with CEA or CA19-9 improved their respective diagnostic performances in distinguishing patients with CRC from CA patients and healthy participants. Notably, we also found that serum concentrations of CXCL-8 were positively correlated with metastases and tumor size. Therefore, our study suggests that serum CXCL-8 may serve as an improved biomarker for CRC diagnosis compared to the traditional tumor markers CEA and CA19-9. Moreover, our findings indicate the potential efficacy of serum CXCL-8 levels as a CRC prognostic biomarker.
Doublecortin immunolabeling in canine gliomas with distinct degrees of tumor infiltration

Increased doublecortin (DCX) immunolabeling at the tumor margins has been associated with tumor infiltration in human glioma and canine anaplastic meningioma. No association between DCX immunolabeling and glioma infiltration has been reported in dogs, to our knowledge. Here we compare the DCX immunolabeling in 14 diffusely infiltrating gliomas (gliomatosis cerebri) and 14 nodular gliomas with distinct degrees of tumor infiltration. Cytoplasmic DCX immunolabeling was classified according to intensity (weak, moderate, strong), distribution (1 = <30% immunolabeling, 2 = 30–70% immunolabeling, 3 = >70% immunolabeling), and location within the neoplasm (random or at tumor margins). Immunolabeling was detected in 6 of 14 (43%) diffusely infiltrating gliomas and 8 of 14 (57%) nodular gliomas. Diffusely infiltrating gliomas had moderate and random immunolabeling, with distribution scores of 1 (4 cases) or 2 (2 cases). Nodular gliomas had strong (6 cases) or moderate (2 cases) immunolabeling, with distribution scores of 1 (3 cases), 2 (3 cases), and 3 (2 cases), and random (6 cases) and/or marginal (3 cases) immunolabeling. Increased DCX immunolabeling within neoplastic cells palisading around necrosis occurred in 4 nodular gliomas. DCX immunolabeling was not increased at the margins of diffusely infiltrating gliomas, indicating that DCX should not be used as an immunomarker for glioma infiltration in dogs.
Wavelet encoding and variable resolution progressive transmission

Progressive transmission is a method of transmitting and displaying imagery in stages of successively improving quality. The subsampled lowpass image representations generated by a wavelet transformation suit this purpose well, but for best results the order of presentation is critical. Candidate data for transmission are best selected using dynamic prioritization criteria generated from image contents and viewer guidance. We show that wavelets are not only suitable but superior when used to encode data for progressive transmission at non-uniform resolutions. This application does not preclude additional compression using quantization of highpass coefficients, which to the contrary results in superior image approximations at low data rates.
Music listening interventions for physical activity: a systematic review and meta-analysis of randomised controlled trials

Abstract Purpose Music listening is widely used during exercise, but effects on physical activity are unknown. This review aimed to examine the effectiveness of music listening on physical activity of adults. Materials and methods Searches were conducted until September 2022. Eligible randomised controlled trials examined intentional music listening to enhance physical activity in adults. Trials were assessed using the Revised Cochrane risk-of-bias tool. The GRADE approach assessed certainty of evidence. Results One high and two low risk-of-bias trials (113 participants, 63–68 years) from cardiac and pulmonary rehabilitation programs were included. Moderate certainty evidence favoured music listening for total amounts of physical activity (n = 112, δ = 0.32, 95% CI = −0.06–0.70) and weekly moderate intensity physical activity (n = 88, MD = 52 min, 95% CI = −8–113). Low certainty evidence favoured music listening for exercise capacity (6-min walk test) (n = 79, MD = 101 metres, 95% CI = 2.32–199.88). Conclusions There is imprecise, moderate certainty evidence that music listening increases physical activity by a small amount in older adults. Future research including adults of all ages with and without health conditions might investigate music listening with focussed behaviour change interventions. IMPLICATIONS FOR REHABILITATION There is uncertainty in the current evidence but listening to music may lead to small increases physical activity in older adults with health conditions. Listening to music while walking may lead to improved exercise capacity. Rehabilitation professionals might consider using music listening as an adjunct with focussed behaviour change interventions aiming to increase physical activity.
Numerical analysis of spectral methods : theory and applications

Spectral Methods Survey of Approximation Theory Review of Convergence Theory Algebraic Stability Spectral Methods Using Fourier Series Applications of Algebraic Stability Analysis Constant Coefficient Hyperbolic Equations Time Differencing Efficient Implementation of Spectral Methods Numerical Results for Hyperbolic Problems Advection-Diffusion Equation Models of Incompressible Fluid Dynamics Miscellaneous Applications of Spectral Methods Survey of Spectral Methods and Applications Properties of Chebyshev and Legendre Polynomial Expansions.
Delay-dependent robust stabilization of uncertain state-delayed systems

This paper concerns a problem of robust stabilization of uncertain state-delayed systems. A new delay-dependent stabilization condition using a memoryless controller is formulated in terms of matrix inequalities. An algorithm involving convex optimization is proposed to design a controller guaranteeing a suboptimal maximal delay such that the system can be stabilized for all admissible uncertainties.
A Survey of FPGA-Based Vision Systems for Autonomous Cars

On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges.
IMPLEMENTASI PENDIDIKAN AGAMA ISLAM PADA ANAK YATIM PIATU DAN ANAK JALANAN

This article aims to analyze and describe the implementation and implications of Islamic Religious Education (IRE) for orphans and street children at the Berkas Bersinar Abadi Lamongan foundation. This article uses qualitative research with a case study approach. The subjects in this study were the head of the foundation, members of the Islamic religious education supervisor, orphans and street children. The results showed that: (1) Islamic religious education for orphans and street children at the Berkas Bersinar Abadi Lamongan foundation was divided into three trainings; Akidah Education, Moral Education, and Moral Education, (2) the implementation of Islamic religious education for orphans and street children at the Berkas Bersinar Abadi Lamongan foundation which is applied to objects of development such as religious values ​​or religious values ​​with good morals or noble character, congregational prayers, recitations and several other forms of religious activities as exemplified by the Prophet Muhammad and his companions. (3) Positive implications; can interpret the two sentences of the Shahadah. Forming children's mental and character, and always being grateful for every gift He has given in this life.
Characterization of the Composition Variation of Healthy Human Gut Microbiome in Correlation with Antibiotic Usage and Yogurt Consumption

Antibiotic usage and yogurt consumption are the major interventions for gut microbiota, yet their shared characteristics and disparities in healthy human gut microbiome remain unclear. This study aimed to decipher the composition changes among healthy humans, comparing antibiotic usage and yogurt consumption. The relative bacterial abundances of 1113 fecal samples were collected from an ongoing, population-based longitudinal cohort study in China that covered lifestyle, diet, disease status and physical measurements, and biological indicators of participants were obtained by the sequencing of 16S rRNA. The samples were divided into three groups, which were antibiotic users (122), yogurt consumers (497) and controls (494), where data visualization, alpha diversity, beta diversity and LEfSe analysis were conducted. At the family level, the relative abundances of Streptococcaceae, Enterobacteriaceae and Enterococcaceae families in antibiotic users increased almost 50%, 70% and 200%, respectively, while yogurt consumption also increased relative abundances of Streptococcaceae and Enterococcaceae, but not Enterobacteriaceae. Alpha diversity analyses suggested that the microbiome of the antibiotic usage and yogurt consumption groups exhibited an alpha diversity lower than that of the control. LEfSe analysis showed that, at the family level, the number of biomarkers in the yogurt consumption and antibiotic usage group were respectively 5 and 7, lower than that of the control (13). This study demonstrated the importance in considering the potential assistance of yogurt consumption on ARG gene transfer from commensal bacteria to pathogens in the human gut, which may pose a risk for human health. Antibiotic usage and yogurt consumption share more identical changes on healthy human gut flora than disparities. Therefore, in order to understand the potential risks of antibiotic usage and yogurt consumption on antibiotic resistance transmission in human gut microbiota, further research needs to be undertaken.
Machine learning techniques for emotion detection and sentiment analysis: current state, challenges, and future directions

ABSTRACT Emotion detection and Sentiment analysis techniques are used to understand polarity or emotions expressed by people in many cases, especially during interactive systems use. Recognizing users’ emotions is an important topic for human–computer interaction. Computers that recognize emotions would provide more natural interactions. Also, emotion detection helps design human-centred systems that provide adaptable behaviour change interventions based on users’ emotions. The growing capability of machine learning to analyze big data and extract emotions therein has led to a surge in research in this domain. With this increased attention, it becomes essential to investigate this research area and provide a comprehensive review of the current state. In this paper, we conduct a systematic review of 123 papers on machine learning-based emotion detection to investigate research trends along many themes, including machine learning approaches, application domain, data, evaluation, and outcome. The results demonstrate: 1) increasing interest in this domain, 2) supervised machine learning (namely, SVM and Naïve Bayes) are the most popular algorithms, 3) Text datasets in the English language are the most common data source, and 4) most research use Accuracy to evaluate performance. Based on the findings, we suggest future directions and recommendations for developing human-centred systems.
1.0‐GHz thin‐film bulk acoustic wave resonators on GaAs

This letter reports on a new fabrication technique and experimental results obtained on bulk acoustic wave resonators using thin piezoelectric composite films of A1N on GaAs insulating substrates. The fabrication involves only a wafer top side planar processing compatible with integrated circuit technology. Resonators have been made in the frequency range UHF to 1 GHz in order to demonstrate the fabrication technique and evaluate material performance in resonator devices. Both longitudinal and shear wave resonators have been measured with temperature coefficients of −24 and −26.5 ppm/°C, respectively. Shear wave results were obtained from tilted c‐axis films grown in a dc planar magnetron sputtering system.
Point process models for presence‐only analysis

Presence‐only data are widely used for species distribution modelling, and point process regression models are a flexible tool that has considerable potential for this problem, when data arise as point events. In this paper, we review point process models, some of their advantages and some common methods of fitting them to presence‐only data. Advantages include (and are not limited to) clarification of what the response variable is that is modelled; a framework for choosing the number and location of quadrature points (commonly referred to as pseudo‐absences or ‘background points’) objectively; clarity of model assumptions and tools for checking them; models to handle spatial dependence between points when it is present; and ways forward regarding difficult issues such as accounting for sampling bias. Point process models are related to some common approaches to presence‐only species distribution modelling, which means that a variety of different software tools can be used to fit these models, including maxent or generalised linear modelling software.
Edge Computing and Blockchain in Smart Agriculture Systems

The advancement of Internet-based technologies has made huge progress toward improving the accessibility of "smart agriculture. " With the advent of unmanned and automatic management, smart agriculture is now able to accomplish monitoring, supervision, and real-time picture monitoring. It is not possible to know for sure that the data in a smart agriculture system is complete and secure from intrusion. This article investigates and assesses the potential of edge computing and blockchain for use in smart agriculture. We combine the advantages of blockchain technology and the edge computing framework to create a smart agriculture framework system that is based on a very straightforward analysis of the evolution of smart agriculture. The study proposes a thorough method for emphasizing the significance of agriculture and edge computing, as well as the advantages of incorporating blockchain technology in this context. This paper also proposes an intelligent agricultural product traceability system design: edge computing with blockchain for smart agriculture. The study concludes with a discussion of outstanding problems and difficulties that can arise during the creation of a blockchain-based edge computing system for smart agriculture systems.