Miracle-dz/UniFAR-FTUs huggingface dataset:
Biomechanical invasive reconstruction of the distal oblique band of the interosseous membrane with extensor carpi radialis longus in radioulnar instability: A new clinical therapy for athletes

Distal Radioulnar Joint (DRUJ) instability is a pathology without a clear reconstructive procedure. Throughout history, numerous techniques have been described whose objectives have been to increase stability as well as relieve pain. The main stabilizing element is the Triangular Fibrocartilage Complex (TFCC) and, there are also secondary stabilizers among where the Distal Oblique Bundle (DOB) is consistently located. We performed a biomechanical study with three cadaveric arms to verify the influence of DOB on joint stability and performing a new reconstruction technique with hemi tendon of carpi radialis longus extensor to get distal radio cubital stability for fiber cartilage repair. There are positive results in the volar and dorsal displacement of the ulna on the radius with the reconstruction technique presented to restore joint stability. The proposed technique is a valid and biomechanically effective option to restore joint stability as a complement to the repair of the TFCC or as a unique technique if the TFCC is irreparable. This technique became important when you cannot repair TFCC in athletes, the recommended alternative can be done and distal oblique soft plasty.
Optimizing Finger Spacing in Multifinger Bipolar Transistors for Minimal Electrothermal Coupling

We present a compact modeling framework to optimize finger spacing for improving the thermal stability in multifinger bipolar transistors with shallow-trench isolation. First, we present an accurate physics-based model for total junction temperature in all the fingers of a transistor. Other than validating the model with 3-D TCAD simulations and measured data, we demonstrate its efficacy to achieve finger spacing optimization with the aid of an iterative algorithm. Since the proposed technique is scalable from the viewpoint of the number of fingers within a transistor and their geometries, the proposed framework is found to work seamlessly for various emitter finger numbers.
Multilayered Microcoils for Microactuators and Characterization of Their Operational Limits in Body-Like Environments

Multilayered microcoils are of great importance for the development of advanced electromagnetic microactuators and especially important to develop high-sensitivity microsensors and magnetic field neural stimulators for medical applications. A clean room-free procedure for manufacturing multilayered micrometric coils is presented in this article. The production of miniaturized multilayered coils from tens to hundreds of micrometres long is demonstrated. The microcoils have outer diameters ranging between 150 and 300 μm, arrangements of up to five consecutive layers, and an average fill factor of 85%. This means three times smaller diameters than the smallest diameter ever achieved by winding techniques while keeping a high fill factor and a large number of layers. Such small and highly performant microcoils have never been demonstrated neither by winding processes nor epitaxial growth techniques. These microcoils were tested inside different human body-like environments. Maximum current density versus temperature was measured in air, fat tissue, muscle tissue, and simulated body fluid at 36 °C. A maximum current density of 3600 A/mm2 has been measured before coil failure. Experiments demonstrated that current densities of up to 610 A/mm2 can safely be supplied to coils without risk of harm to internal tissues. These counterintuitive values are orders of magnitude larger than typical current densities used in macroscale actuators windings.
Smooth Structures on Complex Projective Spaces

We compute the group $\mathcal{C}(\mathbb{C}\textbf{P}^m)$ of concordance classes of smoothings of complex projective space $\mathbb{C}\textbf{P}^m$ for $m=5,6,7,8$. As an application, for $m=7$, $8$, we compute the smooth tangential homotopy structure set of $\mathbb{C}\textbf{P}^m$ and also determine the number of smooth structures on $\mathbb{C}\textbf{P}^m$ with fixed Pontryagin classes.
Facing the Post-Pandemic Challenges: The Role of Leadership Effectiveness in Shaping the Affective Well-Being of Healthcare Providers Working in a Hybrid Work Mode

The COVID-19 pandemic has brought new challenges to the medical industry, including hybrid work, in which specialists can perform some of their duties remotely, in addition to physical contact with patients and their teams. Hybrid work provides opportunities, but also generates difficulties (e.g., accurate long-distance diagnosis); therefore, there is a need to ensure the well-being of healthcare workers, especially in the context of leadership strategies. As there is little research on leadership practices in remote and hybrid medical worker management, this study analyses the relationship between certain behavioural strategies and competencies of leaders and the affective well-being of hybrid employees. The research was conducted among a group of employees (N = 135) from seven countries who provide healthcare in a hybrid model. The correlations between the variables showed the statistical significance of all leadership strategies introduced into the model and focused on building involvement (employee empowerment and team orientation), creating a shared vision, defining clear goals and strategies, promoting adaptability (change management, promotion of organisational learning and patient focus), managing consistency through shared values, agreement and effective coordination, as well as competencies such as communicativeness, credibility, self-development and digital readiness. Despite the significance of all the relationships, the linear regression showed that the variability of affective well-being was explained mainly by the adaptability leadership strategy. The results of the study expand the knowledge on the competencies of healthcare leaders, and shed new light on the management of medical employees performing remote and hybrid work. Because such research into well-being has not been published to date, the analysis begins an important discussion on redefining leadership in the healthcare sector, taking into account the digital transformation.
Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects

Surface defect inspection plays an important role in the process of industrial manufacture and production. Though convolutional neural network (CNN)-based defect inspection methods have made huge leaps, they still confront a lot of challenges such as defect scale variation, complex background, low contrast, and so on. To address these issues, we propose a joint attention-guided feature fusion network (JAFFNet) for saliency detection of surface defects based on the encoder–decoder network. JAFFNet mainly incorporates a joint attention-guided feature fusion (JAFF) module into decoding stages to adaptively fuse low-level and high-level features. The JAFF module learns to emphasize defect features and suppress background noise during feature fusion, which is beneficial for detecting low-contrast defects. In addition, JAFFNet introduces a dense receptive field (DRF) module following the encoder to capture features with rich context information, which helps detect defects of different scales. The JAFF module mainly utilizes a learned joint channel-spatial attention map provided by high-level semantic features to guide feature fusion. The attention map makes the model pay more attention to defect features. The DRF module utilizes a sequence of multireceptive-field (MRF) units with each taking as inputs all the preceding MRF feature maps and the original input. The obtained DRF features capture rich context information with a large range of receptive fields. Extensive experiments conducted on SD-saliency-900, magnetic tile, and German Association for Pattern Recognition (DAGM) 2007 indicate that our method achieves promising performance in comparison with other state-of-the-art methods. Meanwhile, our method reaches a real-time defect detection speed of 66 FPS.
Chamber structure for some equivariant relative Gromov-Witten invariants of $\mathbb{P}^1$ in genus $0$

In this paper, we study genus $0$ equivariant relative Gromov-Witten invariants of $\mathbb{P}^1$ whose corresponding relative stable maps are totally ramified over one point. For fixed number of marked points, we show that such invariants are piecewise polynomials in some parameter space. The parameter space can then be divided into polynomial domains, called chambers. We determine the difference of polynomials between two neighboring chambers. In some special chamber, which we called the totally negative chamber, we show that such a polynomial can be expressed in a simple way. The chamber structure here shares some similarities to that of double Hurwitz numbers.
A Comparison of the Serial Order Strategy and the Associative Cue Strategy for Decision Making in Wayfinding Tasks

It has been proposed that in wayfinding, humans can use multiple strategies to decide which direction to take at intersections. One of them is the serial order strategy, where travelers memorize the order in which those directions should be taken. Another is the associative cue strategy, where travelers memorize associations between conspicuous objects along the way, and the directions to take. We designed tasks in which participants had to base their decisions on the serial order strategy (task S), on the associative cue strategy (task A), or were free to use either of those strategies (task SA). We found that performance errors decreased with practice in all three tasks but were higher in A than in S and SA. We conclude that in our study, the serial order strategy was more efficient than the paired associate strategy. We further conclude that this outcome is likely to depend on task demand, which calls for additional research that varies not only the available strategies, but also the task demand.
Bilingual Automatic Speech Recognition: A Review, Taxonomy and Open Challenges

In this technological era, smart and intelligent systems that are integrated with artificial intelligence (AI) techniques, algorithms, tools, and technologies, have impact on various aspects in our daily life. Communication and interaction between human and machine using speech become increasingly important, since it is an obvious substitute for keyboards and screens in the communication process. Therefore, numerous technologies take advantage of speech such as Automatic Speech Recognition (ASR), where human natural speech for many languages is used as the means to interact with machines. Majority of the related works on ASR concentrate on the development and evaluation of ASR systems that serve a single language only, such as Arabic, English, Chinese, French, and many others. However, research attempts that combine multiple languages (bilingual and multilingual) during the development and evaluation of ASR systems are very limited. This paper aims to provide comprehensive research background and fundamentals of bilingual ASR, and related works that have combined two languages for ASR tasks from 2010 to 2021. It also formulates research taxonomy and discusses open challenges to the bilingual ASR research. Based on our literature investigation, it is clear that bilingual ASR using deep learning approach is highly demanded and is able to provide acceptable performance. In addition, many combinations of two languages such as Arabic-English, Arabic-Malay, and others, are still limited, which can open new research opportunities. Finally, it is clear that ASR research is moving towards not only bilingual ASR, but also multilingual ASR.
Communication and Performance Trade-offs in a Systolic Machine

This paper presents the trade-offs concerning lnput-Output communications at the interface of a SIMD systolic machine, and inside the machine. These problems have been raised by the accelerating board we are currently designing. We discuss about the issues involved by VLSI design: cascadability, scalability, fault tolerance, and feasibility. We present the main features of our architecture, and the performances measured with an actual application which proves that despire of the host computer slowness, the I/O bottleneck is not a penalty for the calculation acceleration.
Skin Cancer Classification and Comparison of Pre-trained Models Performance using Transfer Learning

Background: Skin cancer can quickly become fatal. An examination and biopsy of dermoscopic pictures are required to determine if skin cancer is malignant or benign. However, these examinations can be costly. Objective: In this research, we proposed deep learning (DL)-based approach to identify a melanoma, the most dangerous kind of skin cancer. DL is particularly excellent in learning traits and predicting cancer. However, DL requires a vast number of images. Method: We used image augmentation and transferring learning to categorise images into benign and malignant. We used the public ISIC 2020 database to train and test our models. The ISIC 2020 dataset classifies melanoma as malignant. Along with the categorization, the dataset was examined for variation. The training and validation accuracy of three of the best pre-trained models were compared. To minimise the loss, three optimizers were used: RMSProp, SGD, and ADAM. Results: We attained training accuracy of 98.73%, 99.12%, and 99.76% using ResNet, VGG16, and MobileNetV2, respectively. We achieved a validation accuracy of 98.39% using these three pre-trained models. Conclusion: The validation accuracy of 98.39% outperforms the prior pre-trained model. The findings of this study can be applied in medical science to help physicians diagnose skin cancer early and save lives. Keywords: Deep Learning, ISIC 2020, Pre-trained Model, Skin Cancer, Transfer Learning
An improvement for the sharp Adams inequalities in bounded domains and whole space $\mathbb{R}^n$

We prove an improvement for the sharp Adams inequality in $W^{m,\frac nm}_0(\Omega)$ where $\Omega$ is a bounded domain in $\mathbb{R}^n$ inspired by Lions Concentration--Compactness principle for the sharp Moser--Trudinger inequality. Our method gives an alternative approach to a Concentration--Compactness principle in $W^{m,\frac nm}_0(\Omega)$ recently established by do O and Macedo. Moreover, when $m$ is odd, we obtain an improvement for their result by finding the best exponent in this principle. Our approach also is successfully applied to whole space $\mathbb{R}^n$ to establish an improvement for the sharp Adams inequalities in $W^{m,\frac nm}(\mathbb{R}^n)$ due to Ruf, Sani, Lam, Lu, Fontana and Morpurgo. This type of improvement is still unknown, in general, except the special case $m=1$ due to do O, de Souza, de Medeiros and Severo. Our method is a further development for the method of $\check{\rm C}$erny, Cianchi and Hencl combining with some estimates for the decreasing rearrangement of a function in terms of the one of its higher order derivatives.
The Calderón problem for the fractional
Schrödinger equation

We show global uniqueness in an inverse problem for the fractional Schrodinger equation: an unknown potential in a bounded domain is uniquely determined by exterior measurements of solutions. We also show global uniqueness in the partial data problem where the measurements are taken in arbitrary open, possibly disjoint, subsets of the exterior. The results apply in any dimension $\geq 2$ and are based on a strong approximation property of the fractional equation that extends earlier work. This special feature of the nonlocal equation renders the analysis of related inverse problems radically different from the traditional Calderon problem.
Three-Component Sparse S Transform

In this article, the sparse S transform (ST) is extended to three-component (3C) data and considered in the framework of the sparse inverse theory. The 3C sparse ST is formulated as a constrained optimization where the group sparsity constraint is minimized subject to a data fidelity constraint. Then a fast and efficient algorithm based on the alternative split Bregman technique is employed to solve the optimization. Numerical experiments using synthetic and real seismic data show that the proposed 3C sparse ST automatically generates higher resolution time-frequency (TF) maps compared to single-component sparse decompositions, which has application in phase splitting and earthquake analysis.
Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the classification. In this proof-of-concept preliminary study based on secondary analysis, 20 microstate features were extracted from 14 SZ patients and 14 healthy controls’ EEG signals. These parameters were then ranked as predictors based on their importance, and an optimized machine learning approach was applied to evaluate the performance of the classification. SZ patients had altered microstate features compared to healthy controls. Furthermore, Bayesian optimization outperformed conventional multivariate analyses and showed the highest accuracy (90.93%), AUC (0.90), sensitivity (91.37%), and specificity (90.48%), with reliable results using just six microstate predictors. Altogether, in this proof-of-concept study, we showed that machine learning with Bayesian optimization can be utilized to characterize EEG microstate alterations and contribute to the classification of SZ patients.
Finding groups in Zariski-like structures

We study quasiminimal classes, i.e. abstract elementary classes (AECs) that arise from a quasiminimal pregeometry structure. For these classes, we develop an independence notion, and in particular, a theory of independence in $\M^{eq}$. We then generalize Hrushovski's Group Configuration Theorem to our setting. In an attempt to generalize Zariski geometries to the context of quasiminimal classes, we give the axiomatization for Zariski-like structures, and as an application of our group configuration theorem, show that groups can be found in them assuming that the pregeometry obtained from the bounded closure operator is non-trivial. Finally, we study the cover of the multiplicative group of an algebraically closed field and show that it provides an example of a Zariski-like structure.
On the categorical entropy and the topological entropy

To an exact endofunctor of a triangulated category with a split-generator, the notion of entropy is given by Dimitrov-Haiden-Katzarkov-Kontsevich, which is a (possibly negative infinite) real-valued function of a real variable. In this paper, we propose a conjecture which naturally generalizes the theorem by Gromov-Yomdin, and show that the categorical entropy of a surjective endomorphism of a smooth projective variety is equal to its topological entropy. Moreover, we compute the entropy of autoequivalences of the derived category in the case of the ample canonical or anti-canonical sheaf.
Large deviations and the Lukic conjecture

We use the large deviation approach to sum rules pioneered by Gamboa, Nagel, and Rouault to prove higher-order sum rules for orthogonal polynomials on the unit circle. In particular, we prove one half of a conjectured sum rule of Lukic in the case of two singular points, one simple and one double. This is important because it is known that the conjecture of Simon fails in exactly this case, so this article provides support for the idea that Lukic’s replacement for Simon’s conjecture might be true.
The Bootstrap and von Neumann algebras: The Maximal Intersection Lemma

Given a suitably nested family $Z = \langle Z(m,k,\gamma) \rangle_{m,k \in \mathbb N, \gamma >0}$ of Borel subsets of matrices, and associated Borel measures and rate function, $\mu$, an entropy, $\chi^{\mu}(Z)$, is introduced which generalizes the microstates free entropy in free probability theory. Under weak regularity conditions there exists a finite tuple of operators $X$ in a tracial von Neumann algebra such that \begin{eqnarray*} \chi^{\mu}(X) & \geq & \chi^{\mu}(X \cap Z) & = & \chi^{\mu}(Z)\\ \end{eqnarray*} where $X \cap Z = \langle \Gamma(X;m,k,\gamma) \cap Z(m,k,\gamma) \rangle_{m, k \in \mathbb N, \gamma >0}$. This observation can be used to establish the existence of finite tuples of operators with finite $\chi^{\mu}$-entropy. The intuition and proof come from the bootstrap in statistical inference.
Addressing the Methodological Challenges that Cloaked Profiles Pose to Digital Observations

Although digital ethnographic studies concerned with online misinformation have focused on analyzing the contents shared by “cloaked” profiles (concealed or fake identities), less attention has been given to the epistemological and ontological dilemmas that cloaked profiles pose to digital ethnography. This article deals with these issues by asking: how can digital ethnographers determine who and what we are observing? And how can we conduct online observations when confronted with cloaked profiles? Drawing on field research, this article argues that researchers would benefit from including more critical reflections on the presence of cloaked profiles and learning how to apply digital skills for how to unveil cloaked profiles. Such practices will challenge a commonly accepted ontology that online profiles represent human behavior and enhance researchers’ digital literacy and ability to recognize cloaked profiles. Finally, applying techniques to unveil cloaked profiles will arguably strengthen the hermeneutic process of knowledge production in digital observations.
48-V Input DC-DC High Step-Down Converter in GaN-Based Design

This paper presents a novel high step-down DC-DC converter based on a switched-capacitor resonant topology. It is presented in a 48/8 V version, which was verified by simulations and experiments. The most notable features specific to this converter are an almost non-iductive design and relatively low voltage stress across switching devices. The proposed topology allows for zero-current-switching (ZCS) and zero-voltage-switching (ZVS). Experimental tests confirm that ZVS operation brings a significant increase in the efficiency of the proposed device. Taking into account all the advantages of the suggested converter, it can be concluded that this device is suitable for any application where a high step-down voltage conversion is required. In a prospective application, the proposed converter can be utilized as a part of the power supply unit for data centers.