Miracle-dz/UniFAR-FTUs huggingface dataset:
OUTCOMES OF MICROSURGICAL DIGITAL REVASCULARIZATION WITH LOCAL ANESTHESIA: IS IT WORTH IT?

OBJETIVE. Report the outcomes of patients with non-viable incomplete amputations at the level of the phalanges of the hand with evidence of vascular compromise undergoing digital revascularization using local anesthesia without epinephrine – no tourniquet. METHODS The authors technique proposes microsurgical digital revascularization using local anesthesia without the use of vasoconstrictor and without tourniquet following the inclusion criteria of non-viable incomplete amputation, single vascular lesion or associated with nerve, tendon, and bone lesions. Data are obtained from 20 patients from January 2022 to June 2022 and a three-month postoperative follow-up is performed where vitality, functionality and rehabilitation results are evaluated. RESULTS Successful revascularization in 100% of the cases, the most affected area was the distal phalanx in a 75% and second and third phalanges was the most affected fingers. Despite not using vasoconstrictor and no tourniquet, adequate visibility and control of the surgical field was achieved, allowing digital revascularization in a time not exceeding 1 hour with adequate results in terms of digital vitality and functionality. CONCLUSION Digital revascularization performed under local anesthesia without epinephrine and sedation provides the hand surgeon a prudent time to perform the procedure with pain, bleeding, and digital movements control, minimizing the rate of complications and adequate functional results.
Testing System of Phosphor Excited by Blue LED Based on Integral Sphere

A testing system for phosphor luminous characteristics based on integral sphere is proposed so as to show luminous characteristics of phosphor at actual operation circumstances and measure its luminous characteristics accurately.Blue LED is used as an excited source in the system.TEC temperature control system is used to control the temperature of the source effectively.And continuous and stable excited light is provided to excite phosphor illumining.Energy loss is prevented effectively and testing accuracy is enhanced for the direction of source controlled by a light tube and light ray collected by integral sphere.Based on experiments,spectrum power distributions of phosphor excited by blue light with difference light strength and variation rules of parameters such as luminous efficiency,quantum efficiency and light conversion efficiency are got.With the increasing of driving current,a small blue shift appears in blue light peak wavelength for quantum confined Stark effect in blue light chip.Three kinds of efficiency almost show linear decline trend at low current and then approach to constant at high current.Experimental results show that luminous characteristics of phosphor on real LED chip at operation state can be evaluated effectively by the system and method.
DEGREE OF INFESTATION AND PREFERENCES OF HEMIPARASITES IN URBAN ARBORIZATION

ABSTRACT Mistletoe is an important nutritional resource for the frugivorous ornithological fauna in several ecosystems. However, in great intensity, they can reduce the vigor of their host trees, requiring adequate management in urban afforestation, without depleting this source of food for birds. The hypothesis that there are hemiparasites with host specificity was the reason for this study. Thus, a floristic census of the trees was carried out at UFMT, campus Cuiabá; the infestation by genus of hemiparasite present was recorded, in quantity and degree of infestation, in addition to the quantification of seeds of the hemiparasite germinated without the development of the vegetative part, whose cotyledons necrotized and died. A total of 4265 tree individuals were evaluated, distributed in 46 families, in which 16,52% were identified of the genera Phoradendron, Psittacanthus, and Struthanthus, of which about 90,0% were Psittacanthus. The highest infestation occurred in 24 families, highlighting Fabaceae (28,3%) and Combretaceae (25,6%), and the species Terminalia catappa, Anadenanthera peregrina, and Vatairea macrocarpa. The presence of germinated seeds of the hemiparasite with dead cotyledons varies between tree species and between individuals of the same species. The highlight was in Rutaceae, Arecaceae, and Rubiaceae, and in the species Acrocomia aculeata, Caryocar villosum, and Genipa americana L. that present individuals with natural control potential, even with the presence of frugivorous ornithological fauna and the dispersion of the hemiparasite. In general, the intensity and degree of infestation are low, and there is no infestation in the Caryocaceae, Rutaceae, and Rubiaceae families, highlighting the species Caryocar villosum, Murraya paniculata and Alibertia edulis.
Experimental study on phase vortices of speckles and their propagation properties

By recording the interference patterns of the speckle fields and the reference beam with the charge coupled device （CCD）, and by using the digital Fourier transform technique, the amplitude and the phase distribution of the speckle fields are experimentally extracted. We find that at the tangential points of the zero curves of the real and the imaginary parts, a new kind of speckle phase singularities may appear. Different from the conventional singularities at the zero crossings of the real and imaginary parts with the monotonically spiral change of phase, this new kind of singularities has the property that the phase undergoes an increase and then a decrease around the singular point with nearly a symmetric distribution. We introduce the concept of quasi twin phase vortices to explain the formation of the new kind of phase vortices. Based on a theoretical study of the longitudinal autocorrelation function of the speckle intensities, we experimentally observe the propagation of the phase vortices of speckles. It is found that in planes at different propagation distances but within a longitudinal correlation length, the real and the imaginary parts of the complex speckle field vary considerably, but the position of the phase vortices and the angle of the zero crossings of the real and imaginary parts remain unchanged.
Simulation of bended planar waveguides for optical bus-couplers

In our work an optical bus-coupler is proposed, which enables easy bidirectional connection between two waveguides without interrupting the bus using a core-to-core coupling principle. With bended waveguides the coupling ratio can be tuned by adjusting the overlap area of the two cores. In order to ensure large overlap areas at short coupling lengths, the waveguides have rectangular cross sections. To examine the feasibility of this coupling concept a simulation was performed, which is presented in this paper. Due to multimode waveguides, used in short range data communication, a non-sequential ray tracing simulation is reasonable. Simulations revealed that the bending of the waveguide causes a redistribution of the energy within the core. Small radii push the main energy to the outer region of the core increasing the coupling efficiency. On the other hand, at excessive lowered bend radii additional losses occur (due to a coupling into the cladding), which is why an optimum has to be found. Based on the simulation results it is possible to derive requirements and design rules for the coupling element.
A Data-Driven Deep Neural Network for Modeling of Ionospheric Clutter in HFSWR

In the practical application of high-frequency surface wave radar (HFSWR), ionospheric clutter has become the principal obstacle to target detection. Since ionospheric clutter has nonlinear and time-varying characteristics, the current suppression methods still require improvement. In this letter, we proposed a method merging the deep neural networks (DNNs) and linear Koopman operator from the dynamic perspective, combining the powerful learning capacity of neural networks and the interpretability of Koopman theory, and applied linear Koopman mode to represent nonlinear ionospheric clutter in the state space based on time-delayed coordinates to achieve the finite-dimensional linear approximate reconstruction of ionospheric clutter. The validation results of the measured data reveal that the method has satisfactory modeling accuracy, which proves the effectiveness of the approach and lays the foundations for the subsequent breakthrough in new methods for ionospheric clutter suppression.
A Multi-Dictionary Approach to Abstractness/Concreteness-Based Authorship Attribution

We present some early results from a research project aimed at exploring the usefulness of abstractness/concreteness as stylistic features for authorship attribution. We conjecture that authors use abstract/concrete words and phrases in suf- ficiently unique ways, so that machine learning classifiers can learn to distinguish the individual authors’ writing styles. Our approach is based on using the abstractness rat- ings of words and phrases from texts with established au- thorship to generate training vectors for different machine learning classifiers. The combined word/phrase ratings are extracted from two separate abstractness dictionaries – an approach that yields stronger results than using single ab- stractness dictionaries. The paper describes the details of our methodology and compares the results to those obtained using traditional authorship attribution stylistic features. The limitations of our current methodology and directions for further research are outlined at the end of the paper.
Healthy Aging: A Deep Meta-Class Sequence Model to Integrate Intelligence in Digital Twin

Objective: The behavior monitoring of older adults in their own home and enabling daily-life activity analysis to healthcare practitioner is a key challenge. Methods and procedures: Our framework replicates the elderly home in digital space which can provide an unobtrusive way to monitor the resident&ahat;s daily life activities. The learning challenges posed by different performed activities at home are solved by introducing the deep meta-class sequence model. The notion is to group the set of activities into a single meta-class according to the nature of the activities. It helps the learning process, which is based on long short-term memory (LSTM) to learn feature space abstraction. Each meta-class abstraction is further decomposed to an individual activity performed by the elderly at home. Results: The experiments are carried out over the Center for Advanced Studies in Adaptive Systems dataset and proposed model outperforms as compared to baseline models. Clinical impact: Our findings demonstrate a robust framework to digitally monitor the elderly behavior, which is beneficial for healthcare practitioners to understand the level of support the elderly needed to perform the daily tasks or potential risk of an emergency in their own homes.
Improved Energy Spectrum via Spectral Correntropy-Based Coherence-Gram for Bearing Fault Diagnosis

The key steps of gram methods for bearing fault diagnosis are using an indicator to identify the optimal frequency band for demodulation and obtaining the signal’s squared envelope spectrum (SES). Using cyclostationary-based indicators for optimal band determination is one of the most widely used methods. However, the effectiveness of these methods may be compromised when the signal is non-Gaussian distributed. Compared with cyclostationary analysis, correntropy has the potential capability to detect cyclostationarity under non-Gaussian conditions. Therefore, this study proposes a novel tool called the spectral correntropy-based coherence gram (IEESCCgram) as an optimal band determination and fault diagnosis method. First, a novel bi-frequency map, the spectral correntropy-based coherence (SCECoh), is proposed, which is more robust to impulsive noise and can reveal hidden cyclic periodicities. Then, represented in a 1/3 binary tree, the IEESCCgram uses the proposed cyclic band spectral negentropy (SNc) to select the optimal band. Finally, the improved energy spectrum (IEES) is obtained for bearing fault diagnosis. Vibration signals collected from experimental test rigs and real-life applications are used to verify the effectiveness and robustness of the IEESCCgram for bearing fault diagnosis when the signal is contaminated by impulsive noise and cyclostationary interference.
Maximum Likelihood Analysis for Bivariate Exponential Distributions

A generalization of Marshall-Olkin(1967) bivariate exponential model is proposed and the existence, uniqueness and asymptotic distributional properties of the maximum likelihood estimators are studied. The classical Marshall- Olkin model is a mixture of an absolutely continuous and a singular component, that concentrates its mass on the line x = y. In this paper, I generalize Marshall-Olkin s results considering a distribution with positive mass on the line x = \mu y, \mu > 0. Some simulation results to compare the two models are presented. I also derive an extension of Marshall-Olkin (1967) model for any function which is continuous and twice continuously differentiable in some open dense domain. This extension gives class of models some of it have exponential marginals. I derive its asymptotic normalities. I model the first mixed moments of bivariate exponential models whose marginals are also exponential using the method of generalized linear models.
A Hierarchical Feature Extraction Model for Multi-Label Mechanical Patent Classification

Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs) is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM) neural network model is proposed to capture sequential correlations from higher-level sequence representations. Then the HFEM algorithm and its hierarchical feature extraction architecture are detailed. We establish the training, validation and test datasets, containing 72,532, 18,133, and 2679 mechanical patent documents, respectively, and then check the performance of HFEMs. Finally, we compared the results of the proposed HFEM and three other single neural network models, namely CNN, long–short-term memory (LSTM), and BiLSTM. The experimental results indicate that our proposed HFEM outperforms the other compared models in both precision and recall.
Expected Benefits and Challenges of Using Economic Evaluations to Make Decisions About the Content of Newborn Screening Programs in Vietnam: A Scoping Review of the Literature

Screening newborns for genetic and other diseases is one of the most effective ways to improve health and reduce disease in a population. In developed countries, newborn screening has been a cornerstone of public health for decades. In many developing countries, however, newborn screening is still in its infancy. Many countries still lack screening programs. When a program is available, it generally lacks well-defined criteria on which decision-makers can justify the choice of diseases screened for and the methods used. One of the reasons put forward to understand this observation is the fact that little consideration is given by decision-makers to economic evaluations as a pillar of decision-making, as is the case in industrialized countries. This article provides a brief description of the challenges of using economic evaluation of newborn screening in developing countries. This will be illustrated by the example of the national newborn screening program in Vietnam.
Approximation and Control of Curvilinear Shapes via Deployable Mechanisms With Two Degrees of Freedom

This paper proposes a multi-unit mechanism, which can be used to approximate, with two independent degrees of freedom, the shape of the geometric outline of an arbitrarily large area. The new device is a variant of a recently introduced planar deployable mechanism with two uncoupled degrees of freedom, built from identical units, each combining Sarrus and scissor linkages. Similar units, but with varying sizes, are used in the new device, which is able to change its elliptical physical boundary by varying independently the two parameters in the standard ellipse equation. The size and placement of the deployable units and their links are analyzed and selected for getting the expected geometric shape. The relationships between the number of dividing lines and the approximating accuracy and the degree of overconstraint, respectively, are calculated. A similar deployable mechanism controlling a hyperbola is also outlined. Kinematic analysis and simulated models show that the mechanisms can be used to approximate geometric curves, as desired. © 2014 ASME
RISTrack: Learning Response Interference Suppression Correlation Filters for UAV Tracking

With the high computation efficiency and tracking accuracy, discriminative correlation filters (DCFs) have been applied to unmanned aerial vehicle (UAV) tracking. However, in the scenarios (i.e., complex background and temporary occlusion), DCF-based trackers usually generate low credibility response under the influence of background distractors, which contains multiple side peaks and declines the tracking performance. Motivated by the response consistency in adjacent frames and background information penalization, we propose learning a response interference suppression (RIS) CF to tackle this problem. Specifically, we introduce an RIS regularization into the DCF-based framework, which aims to keep the target area response consistent in adjacent frames and repress distractors’ response in the background. Besides, we adopt a response auxiliary strategy (RAS) to smooth the target response, which intends to obtain the precise location and avoid target drift. Furthermore, extensive experiments on three UAV benchmarks demonstrate the excellent performance of the proposed method against other 19 state-of-the-art trackers. Moreover, the tracking speed of the proposed method can reach $\sim $ 42 frames/s (FPS) on a single CPU.
Discrete Wavelet Transform-Based Gaussian Mixture Model for Remote Sensing Image Compression

High-ratio image compression is difficult because remote sensing images have complex backgrounds and rich information, and the correlation between features is weak. An accurate entropy model is an important way to solve the problem by enhancing the representation ability of the compression models. The entropy model is more suited to estimate the probability distributions with the sparse latent representations. This study proposes a novel entropy model [discrete wavelet transform Gaussian mixture model (DWTGMM)] based on discrete wavelet transform (DWT) and Gaussian mixture model (GMM) for remote sensing image compression. The method uses DWT to transform the latent representations into a wavelet domain and obtains four sparse representations, and then uses the proposed DWTGMM to model them separately to estimate the probability distribution of each element. It is noteworthy that the DWT used in our approach does not require learning parameters and can be combined with other entropy models to acquire the distribution of latent representations. To evaluate our method, we construct three remote sensing image datasets, i.e., GoogleMap, GF1, and GF7. We compare our method with several popular learned compression models and traditional codecs. Experimental results show that the proposed method can achieve excellent performance with low complexity. Especially with the same model architecture, the DWTGMM achieves the best compression performance.
A 30 GHz Steerable Patch Array Antenna for Software-Defined Radio Platforms

We present design and simulation of a beam-steering patch antenna array that operates at 30 GHz. The antenna design includes feeding lines needed for connection to a software-defined radio (SDR) platform. Phase differences on the feeding lines are minimal and the impact of the feeding network on the gain is negligible at the operational frequency. Each patch element of the antenna has an associated 5-bit phase shifter allowing fine granularity beam-forming capability out of the array. The simulation results show that the antenna attains very similar gain patterns to ideal ones.
Fog Assisted Tiger Alarming Framework for Saving Endangered Wild Life

Real-time monitoring is necessary for saving endangered wildlife. The camera-trapping technology is used to monitor wild animals like tigers, lions, bears etc. in forests. Due to changes in the forest echo system and the expansion of human civilization near the forest, tigers often enter the villages. As a consequence, the Tiger-Human conflict occurs more frequently. Typically, cloud computing technologies are used for storing and processing the image data generated from trap cameras. A wildlife monitoring system is a time-sensitive application, and processing and decision-making using cloud computing are relatively slow. Timeliness and quick response are essential for these types of applications. Highlighting this issue, this article focuses on the design and development of a fog-assisted tiger alarming framework that detects tigers in the corridor. The application also delivers systematic alerts to the villagers. Therefore, the conflict between humans and tigers will reduce. For comparison, we have deployed the same model in a cloud computing environment. The proposed framework is simulated in the iFogSim simulator. The outcome exhibits that the proposed fog-based model successfully reduces latency and network usage compared to the traditional cloud-based model. The comparative analysis also indicates a significant improvement in the execution time over the cloud system.
Bone Regeneration Using Mesenchymal Stromal Cells and Biocompatible Scaffolds: A Concise Review of the Current Clinical Trials

Bone regenerative medicine is a clinical approach combining live osteoblast progenitors, such as mesenchymal stromal cells (MSCs), with a biocompatible scaffold that can integrate into host bone tissue and restore its structural integrity. Over the last few years, many tissue engineering strategies have been developed and thoroughly investigated; however, limited approaches have been translated to clinical application. Consequently, the development and clinical validation of regenerative approaches remain a centerpiece of investigational efforts towards the clinical translation of advanced bioengineered scaffolds. The aim of this review was to identify the latest clinical trials related to the use of scaffolds with or without MSCs to regenerate bone defects. A revision of the literature was performed in PubMed, Embase, and Clinicaltrials.gov from 2018 up to 2023. Nine clinical trials were analyzed according to the inclusion criteria: six presented in the literature and three reported in Clinicaltrials.gov. Data were extracted covering background trial information. Six of the clinical trials added cells to scaffolds, while three used scaffolds alone. The majority of scaffolds were composed of calcium phosphate ceramic alone, such as β-tricalcium phosphate (TCP) (two clinical trials), biphasic calcium phosphate bioceramic granules (three clinical trials), and anorganic bovine bone (two clinical trials), while bone marrow was the primary source of the MSCs (five clinical trials). The MSC expansion was performed in GMP facilities, using human platelet lysate (PL) as a supplement without osteogenic factors. Only one trial reported minor adverse events. Overall, these findings highlight the importance and efficacy of cell–scaffold constructs in regenerative medicine under different conditions. Despite the encouraging clinical results obtained, further studies are needed to assess their clinical efficacy in treating bone diseases to optimize their application.