Miracle-dz/UniFAR-FTUs huggingface dataset:
Dental Materials Applied to 3D and 4D Printing Technologies: A Review

As computer-aided design and computer-aided manufacturing (CAD/CAM) technologies have matured, three-dimensional (3D) printing materials suitable for dentistry have attracted considerable research interest, owing to their high efficiency and low cost for clinical treatment. Three-dimensional printing technology, also known as additive manufacturing, has developed rapidly over the last forty years, with gradual application in various fields from industry to dental sciences. Four-dimensional (4D) printing, defined as the fabrication of complex spontaneous structures that change over time in response to external stimuli in expected ways, includes the increasingly popular bioprinting. Existing 3D printing materials have varied characteristics and scopes of application; therefore, categorization is required. This review aims to classify, summarize, and discuss dental materials for 3D printing and 4D printing from a clinical perspective. Based on these, this review describes four major materials, i.e., polymers, metals, ceramics, and biomaterials. The manufacturing process of 3D printing and 4D printing materials, their characteristics, applicable printing technologies, and clinical application scope are described in detail. Furthermore, the development of composite materials for 3D printing is the main focus of future research, as combining multiple materials can improve the materials’ properties. Updates in material sciences play important roles in dentistry; hence, the emergence of newer materials are expected to promote further innovations in dentistry.
Differential Expression of ABC Transporter Genes in Brain Vessels vs. Peripheral Tissues and Vessels from Human, Mouse and Rat

Background: ATP-binding cassette (ABC) transporters comprise a superfamily of genes encoding membrane proteins with nucleotide-binding domains (NBD). These transporters, including drug efflux across the blood–brain barrier (BBB), carry a variety of substrates through plasma membranes against substrate gradients, fueled by hydrolyzing ATP. The expression patterns/enrichment of ABC transporter genes in brain microvessels compared to peripheral vessels and tissues are largely uncharacterized. Methods: In this study, the expression patterns of ABC transporter genes in brain microvessels, peripheral tissues (lung, liver and spleen) and lung vessels were investigated using RNA-seq and WesTM analyses in three species: human, mouse and rat. Results: The study demonstrated that ABC drug efflux transporter genes (including ABCB1, ABCG2, ABCC4 and ABCC5) were highly expressed in isolated brain microvessels in all three species studied; the expression of ABCB1, ABCG2, ABCC1, ABCC4 and ABCC5 was generally higher in rodent brain microvessels compared to those of humans. In contrast, ABCC2 and ABCC3 expression was low in brain microvessels, but high in rodent liver and lung vessels. Overall, most ABC transporters (with the exception of drug efflux transporters) were enriched in peripheral tissues compared to brain microvessels in humans, while in rodent species, additional ABC transporters were found to be enriched in brain microvessels. Conclusions: This study furthers the understanding of species similarities and differences in the expression patterns of ABC transporter genes; this is important for translational studies in drug development. In particular, CNS drug delivery and toxicity may vary among species depending on their unique profiles of ABC transporter expression in brain microvessels and BBB.
Can green human resource management promote employee green advocacy? The mediating role of green passion and the moderating role of supervisory support for the environment

Abstract As an emerging concept, employee green advocacy has received increasing attention in the research of marketing and business circles. However, few studies have examined the influence of green human resource management (GHRM) on employee green advocacy. Therefore, this study aims to explore the influence of GHRM on employee green advocacy and investigates the mediation role of green passion and the moderator role of supervisory support for the environment. Data collected from 334 employees from 15 companies in China at three time points. The results suggest that GHRM positively impacts green passion and employee green advocacy, green passion positively impacts employee green advocacy, green passion partially mediates the relationship between GHRM and employee green advocacy. Additionally, supervisory support for the environment moderates the positive relationship between GHRM and green passion and employee green advocacy.
The Role of Corticosteroid Nasal Irrigations in the Management of Chronic Rhinosinusitis: A State-of-the-Art Systematic Review

Chronic rhinosinusitis (CRS) is a highly prevalent condition. CRS is usually managed with intranasal corticosteroids, useful both before as well as after endoscopic sinus surgery (ESS). However, the greatest drawback of these low-volume sprays is the inadequate delivery into the paranasal sinuses, even after ESS. Recent studies have shown that high-volume steroid nasal rinse (HSNR) has a significantly better penetration of the paranasal sinuses. The purpose of this state-of-the-art review is to systematically overview the current literature about the role of nasal rinses with steroids in CRS. Four authors examined four databases (Embase, Pubmed, Scielo, Cochrane). This review identified 23 studies answering 5 research questions. It included 1182 participants, 722 cases, and 460 controls. Available evidence suggests a potential positive effect of HSNR, which seems to be higher in CRS with nasal polyps. More well-designed studies are needed in order to obtain solid conclusions. The evidence is solid regarding the safety of this treatment modality in the short and long-term. We expect that this lack of severe negative effects will facilitate the acceptance of this treatment modality and the development of future studies.
New Inference for a Multiclass Gaussian Process Classification Model using a Variational Bayesian EM Algorithm and Laplace Approximation

In this study, we propose a new inference algorithm for a multiclass Gaussian process classification model using a variational EM framework and the Laplace approximation (LA) technique. This is performed in two steps, called expectation and maximization. First, in the expectation step (E-step), using Bayes’ theorem and the LA technique, we derive the approximate posterior distribution of the latent function, indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. In the maximization step, we compute the maximum likelihood estimators for hyper-parameters of a covariance matrix necessary to define the prior distribution of the latent function by using the posterior distribution derived in the E-step. These steps iteratively repeat until a convergence condition is satisfied. Moreover, we conducted the experiments by using synthetic data and Iris data in order to verify the performance of the proposed algorithm. Experimental results reveal that the proposed algorithm shows good performance on these datasets.
The Inflammatory Puzzle: Piecing together the Links between Neuroinflammation and Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that has a complex genetic basis. Through advancements in genetic screening, researchers have identified more than 40 mutant genes associated with ALS, some of which impact immune function. Neuroinflammation, with abnormal activation of immune cells and excessive production of inflammatory cytokines in the central nervous system, significantly contributes to the pathophysiology of ALS. In this review, we examine recent evidence on the involvement of ALS-associated mutant genes in immune dysregulation, with a specific focus on the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway and N6-methyladenosine (m6A)-mediated immune regulation in the context of neurodegeneration. We also discuss the perturbation of immune cell homeostasis in both the central nervous system and peripheral tissues in ALS. Furthermore, we explore the advancements made in the emerging genetic and cell-based therapies for ALS. This review underscores the complex relationship between ALS and neuroinflammation, highlighting the potential to identify modifiable factors for therapeutic intervention. A deeper understanding of the connection between neuroinflammation and the risk of ALS is crucial for advancing effective treatments for this debilitating disorder.
Resonances in skew and reducible quasi-periodic Hopf bifurcations

Two types of quasi-periodic Hopf bifurcations are known, in which a Whitney smooth family of quasi-periodic attractors loses its hyperbolicity. One is the reducible case, where the normal linear dynamics are trivial. Another is the skew case, where the normal dynamics are topologically non-trivial. There, the dynamics can involve periodicity, quasi-periodicity and chaos, including chaos with a mixed spectrum. This paper investigates a model system where the bifurcating circle supports Morse-Smale (also called resonant) dynamics. First this is done under the assumption of rotational symmetry (in the normal direction) of the system; a local bifurcation analysis is given for this. Then the effects of generic (non-symmetric) perturbations are discussed. It turns out that the bifurcation diagram is organized by several codimension-two bifurcations: saddle-Hopf and degenerate Hopf bifurcations for diffeomorphisms, and Bogdanov-Takens bifurcations of invariant circles. The (local) bifurcation diagram is computed analytically for the model system. We believe that this model is representative for the minimal dynamical complexity which generically occurs whenever the dynamics on the bifurcating circle is resonant.
Comparison of High Ligation Versus Low Ligation of the Inferior Mesenteric Artery (IMA) on Short-Term and Long-Term Outcomes in Sigmoid Colon and Rectal Cancer Surgery: A Meta-analysis

This study was done to compare the perioperative outcomes and long-term outcomes between low ligation and high ligation of the inferior mesentric artery (IMA) in sigmoid colon and rectal cancer surgery. This study was conducted following the recommendations of the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). A literature search was performed in electronic databases including PubMed, CINAHIL, EMBASE, and Web of Science to identify studies published between January 1, 2015, and April 30, 2023. The outcomes assessed in this meta-analysis included postoperative complications (anastomotic leakage, surgical site infection, and postoperative ileus), intraoperative outcomes (duration of surgery in minutes, total intraoperative blood loss in milliliters, total lymph nodes harvested, and total number of metastatic lymph nodes), recovery outcomes (time to first flatus and length of hospital stay), and long-term outcomes (five-year mortality rate and disease-free survival rate). A total of 17 studies were included in this meta-analysis. Of these, six were randomized control trials (RCTs) and 11 were retrospective cohort studies. This meta-analysis suggests that lower ligation may be associated with a lower risk of anastomotic leakage compared to higher ligation in patients undergoing colon cancer surgery. However, there was no significant difference between the two techniques in terms of surgical site infection, postoperative ileus, total lymph nodes harvested, number of metastatic lymph nodes, duration of surgery, intraoperative blood loss, and length of hospital stay. Time to first flatus was significantly shorter in patients who underwent lower ligation. Additionally, there were no significant differences in the five-year mortality rate and disease-free survival rate between the two techniques. The results of this study indicate that both techniques are comparable in most aspects and suggest that the choice of technique should be based on individual patient factors and surgeon preference.
Evaluation of Intra-Abdominal Hypertension Parameters in Patients with Acute Pancreatitis

Background: Patients with acute pancreatitis develop numerous complications and organ damage due to increased intra-abdominal pressure (IAP). These extrapancreatic complications determine the clinical outcome of the disease. Materials and methods: A total of 100 patients with acute pancreatitis were included in the prospective cohort study. Observed patients were divided into two groups according to their mean values of IAP (normal IAP values and elevated IAP values), which were compared with examined variables. Patients with intra-abdominal hypertension (IAH) were divided into four groups by IAP values, and those groups of patients were also compared with the examined variables. Results: Differences between body mass index (BMI) (p = 0.001), lactates (p = 0.006), and the Sequential Organ Failure Assessment (SOFA) score (p = 0.001) were statistically significant within all examined IAH groups. Differences between the mean arterial pressure (MAP) (p = 0.012) and filtration gradient (FG) (p < 0.001) were statistically significant between the first and second IAH groups in relation to the fourth. Differences in diuresis per hour (p = 0.022) showed statistical significance in relation to the first and third groups of IAH patients. Conclusions: Changes in IAP values lead to changes in basic vital parameters MAP, APP, FG, diuresis per hour, and lactate levels in patients with acute pancreatitis. Early recognition of changes in the SOFA score accompanying an increase in the IAP value is essential.
VIRTUAL REALITY TRAVERSE: A NOVEL WAY TO TEACH TRAVERSE SURVEYING

Abstract. With the advancement of technology, lowered cost in hardware and widely available development tools, more and more educators across different disciplines have used virtual reality as a mean to enhance their teaching. The hit of the pandemic has also speeded up the adoption of virtual reality in education where face-to-face classes and practicals are suspended/cancelled. Virtual Reality as a tool to facilitate teaching and learning has created a new way for learners to experience the materials instead of receiving information passively through books and videos. Traverse survey is one of the main topics in surveying, it is a method used for control survey through establishing a series of points that are linked together by straight lines to form a framework. Traversing involves setting a total station and prism targets at points along the traverse line to do observations. Measurements of angles and distances are taken for the calculation of the points’ coordinates using trigonometry. This paper illustrates using freely available tools to develop a virtual reality program for doing traverse survey, the program is used in an assignment as a virtual fieldwork tool for students to learn traverse surveying at home and anywhere with a computer.
Spectral Characteristics of CN Radical (B -> X) and Its Application in Determination of Rotational and Vibrational Temperatures of Plasma

The aim is to resolve the difficulties of measurement of temperature at several thousands of Celsius degrees for some unstable non-equilibrium gas flows. Based on the molecular spectroscopy theory and inherent molecular structure characteristics of the CN radical, the dependence of the spectral profile on the rotational temperature (RT), vibrational temperature (VT) and optical apparatus function are numerically explored within some certain ranges. Meanwhile, by comparing the numerically calculated spectra with the experimental spectra of the CN radical, the corresponding RT and VT of the plasma induced by the interaction of the laser pulse from an oscillated Nd:YAG laser with the coal target are determined, respectively. In addition, a short discussion on the thermodynamic state and the energy transfer process of the CN radical is also given.
Soft Wearable Piezoresistive Sensors Based on Natural Rubber Fabricated with a Customized Vat-Based Additive Manufacturing Process

Piezoresistive sensors for monitoring human motions are essential for the prevention and treatment of injury. Natural rubber is a material of renewable origin that can be used for the development of soft wearable sensors. In this study, natural rubber was combined with acetylene black to develop a soft piezoresistive sensing composite for monitoring the motion of human joints. An additive manufacturing technique based on stereolithography was used, and it was seen that the sensors produced with the method could detect even small strains (<10%) successfully. With the same sensor composite fabricated by mold casting, it was not possible to detect low strains reliably. TEM microscopy revealed that the distribution of the filler was not homogeneous for the cast samples, suggesting a directionality of the conductive filler network. For the sensors fabricated through the stereolithography-based method, a homogeneous distribution could be achieved. Based on mechano-electrical characterization, it was seen that the samples produced with AM combined the ability to endure large elongations with a monotonic sensor response. Under dynamic conditions, the sensor response of the samples produced by 3D printing showed lower drift and lower signal relaxation. The piezoresistive sensors were examined for monitoring the motion of the human finger joints. By increasing the bending angle of the sensor, it was possible to increase the sensitivity of the response. With the renewable origin of natural rubber and manufacturing method, the featured sensors can expand the applicability of soft flexible electronics in biomedical applications and devices.
Reduced Sigma Point Filters and Covariances Through

AbstmctThe Unscented Transform (UT) approximates the result of applying a specifled nonlinear transformation to a given mean and covariance estimate. The UT works by constructing a set of points, referred to a8 sigma points, which has the same known statistics, e.g., Rrst and second and possibly higher moments, as the given estimate. The given nonlinear transformation is applied to the set, and the unscented estimate is obtained by computing the statistics of the transformed set of sigma points. For example, the mean and covariance of the transformed set approximates the nonlinear transformation of the original mean and covariance estimate. The computational efficiency of the UT therefore depends on the number of sigma points required to c a p ture the known statistics of the original estimate. In this paper we examine methods for minimizing the number of sigma points for real-time control, estimation, and filtering applications. We demonstrate results in a 3D localization example.
University of Birmingham Improvement of a stochastic backscatter model and application to large-eddy simulation of street canyon flow

A stochastic backscatter (SB) subgrid-scale (SGS) model is applied, for the first time, to large-eddy simulation (LES) of street canyon flow. We model a ‘skimming flow’ regime under a neutrally stratified atmosphere, in which the approaching wind is perpendicular to the along-street axis of a street canyon of unity aspect ratio. Previous LESs of this type have shown an under-prediction the intensity of the primary eddy (PE) that forms within the street canyon, indicating a lack of momentum transfer across the roof-level shear layer. The SB model, however, acts to increase this momentum transfer, bringing the simulated PE intensity significantly closer towards that observed in a corresponding wind-tunnel experiment . A metric for the PE intensity, 𝜔 PE , based on the two-dimensional vorticity field, is increased from around 70% of the wind-tunnel 𝜔 PE value (with the Smagorinsky SGS model) to as much as 90% (with the SB model). Calculation of the air exchange rate at roof-level confirms that the rate of entrainment into the street canyon is increased with the inclusion of backscatter. We also outline an improvement to the SB model prior to its application. In its previous version, a constraint on the magnitude of the backscatter acceleration variances ensured a
Probabilistic Assessment of Power Systems Resilience Under Natural Disasters

Extreme weather can have a substantial in ﬂ uence on a power system ' s operational resilience since they are high-impact, low-probability (HILP) events. Therefore, power systems must be resilient to HILP incidents in addition to being reliable against widely spread and credible threats. Despite the rarity of such events, the severity of their potential impact necessitates the development of appropriate resilience assessment tools to capture their implications and enhance the resilience of energy infrastructure systems. In this paper, a probabilistic strategy is proposed to assess and evaluate the operational resilience of power distribution networks against the impacts of HILP events depending on value-at-risk and conditionally value-at-risk quantitative risk-based assessments. With several scenarios built on sequentially Monte Carlo simulations, the consequence of a windstorm on a distribution network can be assessed using a probability-based resilience assessment methodology. The presented method is examined on an IEEE 37-bus system. Different case studies based on detailed data are presented and analyzed to demonstrate the proposed method ' s usefulness.
Identification of Malicious Encrypted Traffic Through Feature Fusion

The popularity of encrypted communication has grown due to increased security awareness and rapid internet development. End-to-end encryption can prevent data attacks but also poses new cybersecurity threats. Thus, identifying malicious encrypted traffic is a focus of research in network behavior analysis and anomaly detection. Recently, deep learning has brought new directions for the development of traffic classification and anomaly detection. Based on deep learning technology, this paper starts from the two directions of data preprocessing and model selection, studies and analyzes multi-dimensional traffic characteristics and multi-granularity carrier characteristics, and proposes corresponding solutions, and finally designs and proposes a malware-oriented Identification scheme for encrypted traffic. This paper first proposes a malicious encrypted traffic identification scheme MET-FMF based on fine-grained multi-feature fusion. Two sets of comparative experiments were designed to compare the multi-dimensional traffic features and the multi-branch network model on the recognition results. The results show that the combination of three-dimensional traffic characteristics and three-branch network model has the best results, with an average accuracy rate of 95.08%. Finally, compared with other schemes, it is found that this scheme has multi-dimensional traffic feature extraction, multi-granularity carrier feature Features such as early session detection and end-to-end transferable learning.