Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Vagus Nerve Stimulation on Animal Models of Stroke-Induced Injury: A Systematic Review

Simple Summary Ischemic stroke is one of the most common causes of death worldwide, putting a significant strain on society and the healthcare system. In recent years, significant progress has been made in the treatment of ischemic stroke. However, owing to the ever-increasing cost of medical care, there is an urgent need to develop alternative treatments for stroke prevention and treatment. Vagus nerve stimulation, currently approved for the treatment of depression and epilepsy, has emerged as a useful therapeutic approach in recent decades. Although numerous animal studies have been conducted on vagus nerve stimulation, particularly in stroke conditions, little effort has been made to examine them together. In this review, we compiled and compared the findings of VNS studies up to June 2022. Vagus nerve stimulation was found to improve most of the stroke parameters examined. We concluded that, specifically in an animal model, vagus nerve stimulation may be a treatment option that helps alleviate stroke symptoms. These findings may be beneficial for future translational research in animal models and patients with stroke. Abstract Ischemic stroke is one of the leading causes of death worldwide, and poses a great burden to society and the healthcare system. There have been many recent advances in the treatment of ischemic stroke, which usually results from the interruption of blood flow to a particular part of the brain. Current treatments for ischemic stroke mainly focus on revascularization or reperfusion of cerebral blood flow to the infarcted tissue. Nevertheless, reperfusion injury may exacerbate ischemic injury in patients with stroke. In recent decades, vagus nerve stimulation (VNS) has emerged as an optimistic therapeutic intervention. Accumulating evidence has demonstrated that VNS is a promising treatment for ischemic stroke in various rat models through improved neural function, cognition, and neuronal deficit scores. We thoroughly examined previous evidence from stroke-induced animal studies using VNS as an intervention until June 2022. We concluded that VNS yields stroke treatment potential by improving neurological deficit score, infarct volume, forelimb strength, inflammation, apoptosis, and angiogenesis. This review also discusses potential molecular mechanisms underlying VNS-mediated neuroprotection. This review could help researchers conduct additional translational research on patients with stroke.
Characterization of landslide displacements in an active fault zone in Northwest China

Landslides can be caused by natural forcing and anthropogenic activities. Zhouqu County (China) on the eastern margin of Qinghai‐Tibet Plateau is set within the active Pingding‐Huama fault zone with evident fractures on the land surface. Frequent landslides and debris flows have occurred in this region due to river erosion, rainfall and deforestation. Here we quantified the slope movements using time‐series synthetic aperture radar interferometry (InSAR) based on the ascending and descending Sentinel‐1 satellite images acquired between October 2014 and August 2020. We observed distinct displacements in the highly fractured fault zone. The eastward and vertical displacement time series between February 2017 and July 2020 were constrained by the common‐day ascending and descending acquisitions. The eastward rates (461 mm/year) were greater than those in the vertical direction (−185 mm/year). We also note displacement discontinuities across the thrust faults beneath the Suoertou and Zhongpai landslides. Seasonal variations in the displacement time series suggest that the cyclic rainfall is the primary driver for the mass wasting processes rather than the tectonic loading. As a complement to in situ observations, our results demonstrate that InSAR is an effective tool to characterize the spatio‐temporal nature of landslide displacements in complicated geological environments.
Exploring the Extremes of Heat Conduction in Anisotropic Materials

ABSTRACT Anisotropic solids possess thermal conductivities ranging from among the highest found in nature, as in the in-plane thermal conductivity of graphite, to the lowest, as in the cross-plane thermal conductivity of disordered layered crystals. Though these extremes of thermal conductivity make anisotropic materials attractive for diverse applications such as thermal management and thermal insulation, the microscopic physics of heat conduction in these materials remain poorly understood. In this review article, we discuss the recent advances in our understanding of thermal phonon transport in anisotropic solids obtained using new theoretical, computational, and experimental tools.
Flexural Strength, Shear Bond Strength, and Color change of Veneering Dental Porcelain Modified by Silver Nanoparticles on zirconia and metal cores

Aim: Evaluation the effect of modification of veneering porcelain bonded to metallic and zirconia cores by silver nanoparticles (SNP) on flexural strength, shear bond strength (SBS), and color change. Materials and methods: A total of sixty samples were constructed and divided into two groups in accordance with the core material type (n=30). Group I: Zirconia core, while group II: Metal core. Each group was classified into 3 sub-groups (n=10) in accordance with the test performed: flexural strength, SBS, and color change, each subgroup was subdivided into two classes (n=5), according to incorporation of SNP. A control class (class I) and Class II: Modified samples. Fracture strength and SBS were tested utilizing a universal testing machine, while color change was measured using a spectrophotometer, Data were tabulated and analyzed statistically. Results: Flexural strength test results showed a significant increase in modified classes on both metallic and zirconia cores (2904±76.4, 1318±58.1MPa) compared to unmodified classes (2542±71.9, 1066.8±47.2 MPa) respectively. Where SBS mean values showed a significant increase in control classes for both metallic and zirconia cores (42.5±0.5, 27.6±0.4 MPa) compared to modified classes (41.4±0.5, 25.7±0.3) respectively. Color change results showed significantly higher mean values (ΔE=10.75±1.1) in Zirconia core group than those in metal group (1.65±0.6). Conclusions: Incorporation of SNP into the veneering porcelain significantly increased flexural strength in both groups while SBS was significantly decreased. Color change of veneering porcelain on metal core was within the acceptable clinical range, while on zirconia core showed higher mean values & was not clinically accepted.
On a continuous analogue of the stochastic difference equation Xn = ρ X n–1 + Bn

Let Bi, B 2 , ••• be a sequence of independent,_ identically distributed random variables, let X, be a random variable that is independent of B; for n ~ 1, let p be a constant such that 0 < p < 1 and let Xl' X 2 , ••• be another sequence of random variables that are defined recursively by the relationships X, = pXn l + B n • It can be shown that the sequence of random variables Xj, X 2 , ••• converges in law to a random variable X if and only if E[Iog+ IBll]<oo. In this paper we let {B(t): O~t<oo} be a stochastic process with independent, homogeneous increments and define another stochastic process {Xir): 0 ~ t ~ oo} that stands in the same relationship to the stochastic process {B(t): 0 ~ t < oo} as the sequence of random variables Xj, X 2 , ••• stands to Bj, B 2 , •••• It is shown that X(t) converges in law to a random variable X as t~ +00 if and only if E[Iog+ IB(1)\] < 00 in which case X has a distribution function of class L. Several other related results are obtained. A necessary and sufficient condition is given for a discrete-time stochastic process to be embeddable in a continuous-time stochastic process.
A Defensive Strategy Against Beam Training Attack in 5G mmWave Networks for Manufacturing

Millimeter-wave (mmWave) carriers are an essential building block of fifth-generation (5G) systems. Satisfactory performance of the communications over the mmWave spectrum requires an alignment between the signal beam of the transmitter and receiver, achieved via beam training protocols. Nevertheless, beam training is vulnerable to jamming attacks, where the attacker intends to send jamming signals over different spatial directions to confuse legitimate nodes. This paper focuses on defending against this attack in smart factories where a moving Automated Guided Vehicle (AGV) communicates with a base station via a mmWave carrier. We introduce a defensive strategy to cope with jamming attacks, including two stages: jamming detection and jamming mitigation. Developed based on autoencoders, both algorithms can learn the characteristics/features of the received signals at the AGV. They can be employed consecutively before performing the downlink data transmission. In particular, once a jamming attack is identified, the jamming mitigation can be utilized to retrieve the corrupted received signal strength vector, allowing a better decision during the beam training operation. In addition, the proposed algorithm is straightforward and fully compliant with the existing beam training protocols in 5G New Radio. The numerical results show that not only the proposed defensive strategy can capture more than 80% of attack events, but it also improves the average signal-to-interference-plus-noise-ratio significantly, i.e., up to 15 dB.
SYSTEM IDENTIFICATION USING QUANTIZED DATA

Abstract System identification using set-valued output observations is investigated under a stochastic framework. The problem is especially relevant to system identification problems in which data-flow rates are limited due to computer networking, communications, wireless channels. Properties of empirical measures yield a general result on asymptotic analysis of space and time complexities. The result is employed to guide robust, optimal, and adaptive selections of sensor thresholds. It provides a feasible approach for optimal utility of communication bandwidth resources in enhancing identification accuracy.
Assessment of Impact of Human Leukocyte Antigen-Type and Cytokine-Type Responses on Outcomes after Targeted Therapy Currently Used to Treat Chronic Lymphocytic Leukemia

Tumor growth and metastasis are reliant on intricate interactions between the host immune system and various counter-regulatory immune escape mechanisms employed by the tumor. Tumors can resist immune surveillance by modifying the expression of human leukocyte antigen (HLA) molecules, which results in the impaired presentation of tumor-associated antigens, subsequently evading detection and destruction by the immune system. The management of chronic lymphocytic leukemia (CLL) is based on symptom severity and includes various types of targeted therapies, including rituximab, obinutuzumab, ibrutinib, acalabrutinib, zanubrutinib, idelalisib, and venetoclax. These therapies rely on the recognition of specific peptides presented by HLAs on the surface of tumor cells by T cells, leading to an immune response. HLA class I molecules are found in most human cell types and interact with T-cell receptors (TCRs) to activate T cells, which play a vital role in inducing adaptive immune responses. However, tumor cells may evade T-cell attack by downregulating HLA expression, limiting the efficacy of HLA-dependent immunotherapy. The prognosis of CLL largely depends on the presence or absence of genetic abnormalities, such as del(17p), TP53 point mutations, and IGHV somatic hypermutation status. These oral targeted therapies alone or in combination with anti-CD20 antibodies have replaced chemoimmunotherapy as the primary treatment for CLL. In this review, we summarize the current clinical evidence on the impact of HLA- and cytokine-type responses on outcomes after targeted therapies currently used to treat CLL.
A Study of Equity Incentive Schemes in Technology-based Companies: The Case of iFLYTEK

In modern companies, equity incentives have been widely used to improve corporate governance. However, not all equity incentives can achieve the desired effect of the managers, and if the internal factors of the equity incentive scheme are not set properly, the ultimate benefit is often minimal or even negative. As a technology company, iFLYTEK has implemented a number of equity incentive schemes in recent years, but not all of them have achieved satisfactory results. The equity incentive schemes adopted by iFLYTEK have both common typical problems and problems unique to the industry. This paper will take the equity incentive scheme of iFLYTEK as the research object, analyze its scheme, subsequent implementation status and problems, and make suggestions for equity incentive schemes for technology companies.
Modulating Optoelectronic and Elastic Properties of Anatase TiO2 for Photoelectrochemical Water Splitting

Titanium dioxide (TiO2) has been investigated for solar-energy-driven photoelectrical water splitting due to its suitable band gap, abundance, cost savings, environmental friendliness, and chemical stability. However, its poor conductivity, weak light absorption, and large indirect bandgap (3.2 eV) has limited its application in water splitting. In this study, we precisely targeted these limitations using first-principle techniques. TiO2 only absorbs near-ultraviolet radiation; therefore, the substitution (2.1%) of Ag, Fe, and Co in TiO2 significantly altered its physical properties and shifted the bandgap from the ultraviolet to the visible region. Cobalt (Co) substitution in TiO2 resulted in high absorption and photoconductivity and a low bandgap energy suitable for the reduction in water without the need for external energy. The calculated elastic properties of Co-doped TiO2 indicate the ductile nature of the material with a strong average bond strength. Co-doped TiO2 exhibited fewer microcracks with a mechanically stable composition.
A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation

Robotic manipulation challenges, such as grasping and object manipulation, have been tackled successfully with the help of deep reinforcement learning systems. We give an overview of the recent advances in deep reinforcement learning algorithms for robotic manipulation tasks in this review. We begin by outlining the fundamental ideas of reinforcement learning and the parts of a reinforcement learning system. The many deep reinforcement learning algorithms, such as value-based methods, policy-based methods, and actor–critic approaches, that have been suggested for robotic manipulation tasks are then covered. We also examine the numerous issues that have arisen when applying these algorithms to robotics tasks, as well as the various solutions that have been put forth to deal with these issues. Finally, we highlight several unsolved research issues and talk about possible future directions for the subject.
Wearable Smart Bandage-Based Bio-Sensors

Bandage is a well-established industry, whereas wearable electronics is an emerging industry. This review presents the bandage as the base of wearable bioelectronics. It begins with introducing a detailed background to bandages and the development of bandage-based smart sensors, which is followed by a sequential discussion of the technical characteristics of the existing bandages, a more practical methodology for future applications, and manufacturing processes of bandage-based wearable biosensors. The review then elaborates on the advantages of basing the next generation of wearables, such as acceptance by the customers and system approvals, and disposal.
The Ordering of the Connected Graphs with the Smallest Laplacian Spectral Radii

This paper first gives the second to the fifth unicyclic graphs of order n with the smallest Laplacian spectral radii and the bicyclic graph of order n with the smallest Laplacian spectral radii.Then by using the ordering of the trees of order n with the smallest Laplacian spectral radii,the authors obtain the first fourteen connected graphs of order n with the smallest Laplacian spectral radii among all the connected graphs of order n,which reach the nine smallest values of the Laplacian spectral radii among all connected graphs of order n when n is even(where some graphs are juxtaposed)and reach the eight smallest values of the Laplacian spectral radii when n is odd(where some graphs are juxtaposed).
The completeness criterion for some systems containing P-sets of automaton functions

Abstract A class of subsets (P-sets) in the functional system of automaton functions is considered. These P-sets are defined using the Post classes. The paper is devoted to the construction of efficient completeness criteria for infinite automaton function systems M such that N ⊆ M,M \ N is finite and N is a P-set. The criterion should determine whether the system is complete via a finite set M \ N. A number of decidable cases are presented.
Construction of a functional basis with automatic fulfillment of interface conditions at discontinuity points of coefficients of an elliptic operator

Abstract Special basis functions adjusted for solving problems with elliptic operators that have discontinuous coefficients are proposed. A specific property of bases constructed here is the automatic fulfillment of jump conditions for those problems at the points of discontinuity of the coefficients of equations. Another essential property of such basis is high-accuracy approximation of approximate piecewise-smooth solutions to the above problems by a small number of basis functions. The justification of the results follows from two theorems presented here. This paper is a further development of the author’s results [1, 2] concerning the expansions of functions into fast-convergent series composed of bases arranged from trigonometric functions.
Liveability transitioning: results of a pilot study of walking, accessibility, and social connection strengths weaknesses in established suburbs in Adelaide

ABSTRACT Population health is profoundly affected by the liveability of the urban environments where people live. In Australia today most people live in suburbs which fall well short of the form and function required for liveability, which is adversely affecting population health and health equity. We produced the Healthy Urban Neighbourhood Transition Tool (HUNTT) to analyze the existing liveability strengths and weaknesses of neighbourhoods with the objective of assessing their potential for, and pathways required, for a liveability transition. This paper presents a summary of the findings of the application of the HUNTT in 22 suburbs of Adelaide, South Australia, looking at the liveability determinant of walkability. The study showed that there were walkability strengths and weaknesses in all surveyed suburbs, and weaknesses tended to proliferate more in middle and all outer suburbs and those with lower median incomes. It also showed that a walkability transition is possible in all the suburbs surveyed. However, it would require coordination between multiple stakeholders, government regulatory changes and intervention, and significant public funding.
Design of an electronic oscillator for resonant pressure sensor with non-collocated sensor and actuator

This paper presents a simple closed loop circuit (oscillator) design for producing sustained, oscillations required to continuously vibrate the resonance based pressure sensor at its resonant frequency. For each variation in applied input pressure, the sensor’s resonant frequency varies and the circuit makes the sensor to vibrate at its new resonant frequency, thereby enabling the measurement of change in resonant frequency shift due to corresponding pressure. The resonant condition is achieved by automatic tuning of phase angle required to satisfy Barkhausen criteria. The proposed circuit is evaluated analytically and verified experimentally for different pressure sensors fabricated using various grades of Stainless Steel material.
Antibiotic Use in Dental Implant Procedures: A Systematic Review and Meta-Analysis

Background and Objectives: This project was developed from anecdotal evidence of varied practices around antibiotic prescribing in dental procedures. The aim of the study was to ascertain if there is evidence to support whether antibiotic (AB) use can effectively reduce postoperative infections after dental implant placements (DIPs). Materials and Methods: Following PRISMA-P© methodology, a systematic review of randomised controlled clinical trials was designed and registered on the PROSPERO© database. Searches were performed using PubMed®, Science Direct® and the Cochrane© Database, plus the bibliographies of studies identified. The efficacy of prophylactic antibiotics, independent of the regimen used, versus a placebo, control or no therapy based on implant failure due to infection was the primary measured outcome. Secondary outcomes were other post-surgical complications due to infection and AB adverse events. Results: Twelve RCTs were identified and analysed. Antibiotic use was reported to be statistically significant in preventing infection (p < 001). The prevention of complications was not statistically significant (p = 0.96), and the NNT was >5 (14 and 2523 respectively), which indicates that the intervention was not sufficiently effective to justify its use. The occurrence of side effects was not statistically significant (p = 0.63). NNH was 528 indicating that possible harm caused by the use of ABs is very small and does not negate the AB use when indicated. Conclusion: The routine use of prophylactic antibiotics to prevent infection in dental implant placement was found to be not sufficiently effective to justify routine use. Clear clinical assessment pathways, such as those used for medical conditions, based on the patients’ age, dental risk factors, such as oral health and bone health, physical risk factors, such as chronic or long-term conditions and modifiable health determinants, such as smoking, are required to prevent the unnecessary use of antibiotics.
Ensemble polarimetric SAR image classification based on contextual sparse representation

Polarimetric SAR image interpretation has become one of the most interesting topics, in which the construction of the reasonable and effective technique of image classification is of key importance. Sparse representation represents the data using the most succinct sparse atoms of the over-complete dictionary and the advantages of sparse representation also have been confirmed in the field of PolSAR classification. However, it is not perfect, like the ordinary classifier, at different aspects. So ensemble learning is introduced to improve the issue, which makes a plurality of different learners training and obtained the integrated results by combining the individual learner to get more accurate and ideal learning results. Therefore, this paper presents a polarimetric SAR image classification method based on the ensemble learning of sparse representation to achieve the optimal classification.