Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Adding Silver-Doped Carbon Nanotube Fillers to Heat-Cured Acrylic Denture Base on Impact Strength, Microhardness, and Antimicrobial Activity: A Preliminary Study

Poly (methyl methacrylate) (PMMA), is an acrylic polymer substance that is mostly used for denture base applications. The purpose of this laboratory study was to investigate the effect of adding 0.05 wt. % Ag-doped carbon nanotubes (CNT) to PMMA-based (PMMA and MMA) denture base material on the impact strength, microhardness, and antimicrobial activity. A total of 60 heat-cured acrylic resin specimens were prepared. The specimens were randomly divided into two main groups (n = 30/group), according to the powder used: (a) control group, using heat-cured PMMA; (b) treatment group, using a powder prepared by blending 0.05 wt. % silver-doped CNT nanoparticles with heat-cured PMMA. The impact strength, microhardness and anticandidal activity for each group were assessed via the Charpy, Vickers and agar diffusion tests, respectively (n = 10/test for each subgroup). Data were analyzed using independent-sample t-tests (p ≤ 0.05). The results of the impact strength test revealed that the treated heat-cured PMMA-MMA with Ag-doped CNT (2.2 kJ/mm2) was significantly higher than that of the control heat-cured PMMA (1.6 kJ/mm2). Similarly, the Vickers microhardness of the treatment group (52.7 VHN) was significantly higher than that of the control group (19.4 VHN). Regarding the agar diffusion test, after 24 h of incubation, the treated heat-cured PMMA with the Ag-doped CNT exhibited significantly higher anticandidal activity than that of the control group. Therefore, Ag-doped carbon nanotubes could be considered as promising fillers for the dental heat-cured acrylic resin to improve the resistance of the resultant denture against sudden fractures, scratching, and candida invasion.
Occupational Therapist-Led Mindfulness Training Program for Older Adults Living with Early Cognitive Decline in Primary Care: A Pilot Randomized Controlled Trial

Background: Community-dwelling older adults with early cognitive deficits experience less efficiency in performing everyday life tasks, resulting in decreased satisfaction and other adverse psychological outcomes. Mindfulness training has been linked to cognitive and psychological improvements and, most recently, has been identified as a potential intervention supporting performance of everyday life activities. Objective: This study aimed to evaluate whether mindfulness practice can improve perceived performance and satisfaction with everyday life activity and secondary psychological outcomes. Methods: This study is a pilot randomized controlled trial (RCT) in an interprofessional primary care team practice in Toronto, Ontario, Canada. The participants were 27 older adults aged 60 years of age or older living with early cognitive deficits. Participants were randomized into an 8-Week mindfulness training program (n = 14) group or a Wait-List Control (WLC; n = 13) group compared at baseline, post-intervention and 4-weeks follow-up. MANOVAs with post-hoc independent t-tests were used to compare between groups at different time points. Results: There was a significant improvement in anxiety for the intervention group compared to the WLC group at post-intervention; Time-2 (mean difference = 3.90; CI = 0.04-7.75; p = 0.04) with large effect size (d = 0.80). Conclusion: Mindfulness training significantly improved anxiety scores for patients with early cognitive deficits post-intervention. Further work is required to test the sustainability of reduced anxiety over time, but this study demonstrated that MBSR is a promising primary care intervention for those living with early cognitive deficits. This study warrants the pursuit of a future study in exploring how long the reduced anxiety effects would be sustained.
Analysis of mixed correlated bivariate zero-inflated count and (k, l)-inflated beta responses with application to social network datasets

ABSTRACT This paper presents a new model that monitors the basic network formation mechanisms via the attributes through time. It considers the issue of joint modeling of longitudinal inflated (0, 1)-support continuous and inflated count response variables. For joint model of mentioned response variables, a correlated generalized linear mixed model is studied. The fraction response is inflated in two points k and l (k < l) and a k and l inflated beta distribution is introduced to use as its distribution. Also, the count response is inflated in zero and we use some members of zero-inflated power series distributions, hurdle-at-zero, members of zero-inflated double power series distributions and zero-inflated generalized Poisson distribution as our count response distribution. A full likelihood-based approach is used to yield maximum likelihood estimates of the model parameters and the model is applied to a real social network obtained from an observational study where the rate of the ith node’s responsiveness to the jth node and the number of arrows or edges with some specific characteristics from the ith node to the jth node are the correlated inflated (0, 1)-support continuous and inflated count response variables, respectively. The effect of the sender and receiver positions in an office environment on the responses are investigated simultaneously.
Modification of the Vertical Axis with Variations in the Number of Blades of the Savonius Wind Turbine

The potential wind speed in Indonesia is generally low at 3 m/s to 7 m/s so that this type of vertical axis turbine is considered very suitable for use in wind conditions in Indonesia. There are several types of vertical axis wind turbines. One type of vertical axis wind turbine is the Savonius wind turbine. Many advantages that this type of wind turbine has, such as being able to receive wind from all directions, easy and cheap to manufacture, and can rotate at a fairly low angular speed. This test was carried out to determine the performance of conventional and modified savonius turbines with 2 and 3 blades variables in each turbine blade shape. The form of modification made is by changing the shape of the blade twisting by 45o. The turbine is carried out on a laboratory scale with a wind source using a fan that is directly opposite the turbine. The results showed that the highest turbine power occurred in a modified 2-blade turbine, namely 1.88 Watt with a torque value of 0.04 Nm and a shaft rotation of 450 rpm. The highest rotation value is also obtained by 2 modified blades at a wind speed of 6 m/s producing 896 rpm. However, the highest torque value is obtained by a conventional 2-blade turbine with a value of 0.136 Nm. The highest turbine efficiency in each turbine is obtained by a modified 2 blade turbine with an efficiency value of 36.92% while the highest turbine efficiency for a modified 3 blade turbine reaches 11.69% which is considered less efficient than other turbines.
Kinematic synthesis of planar four bar and geared five bar mechanisms with structural constraints

KINEMATIC SYNTHESIS OF PLANAR FOUR BAR AND GEARED FIVE BAR MECHANISMS WITH STRUCTURAL CONSTRAINTS by Yahia Mohammad Saleh Al-Smadi In motion generation, the objective is to calculate the mechanism parameters required to achieve or approximate a set of prescribed rigid-body positions. This doctoral dissertation study is aimed to integrate the classical kinematic analysis of a planar four-bar and geared five-bar motion generation with three structural design constraints. These constraints consider driving link static torque, deflection of the crank and buckling of the follower for a given rigid-body load or constant external load. This kineto-elastostatic analysis is based on the following assumptions to be considered during the analysis; the crank and the follower are elastic members and the coupler is rigid member, friction in the joints is neglected, link weights are neglected compared to a given rigid-body load or constant external load, the cross sectional properties of a link do not vary, and finally the mechanism is moving in quasi static condition. By incorporating these constraints into conventional planar four-bar and five-bar motion generation models, mechanisms are synthesized to achieve-not only prescribed rigid-body positions-but also satisfy the above mentioned structural constraints for a given rigid-body load or constant external load. KINEMATIC SYNTHESIS OF PLANAR FOUR BAR AND GEARED FIVE BAR MECHANISMS WITH STRUCTURAL CONSTRAINTS by Yahia Mohammad Saleh Al-Smadi A Dissertation Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Mechanical Engineering Department of Mechanical Engineering
Variation of spin–orbit coupling and related properties in skyrmionic system Mn 1 – x FexGe

We report a systematic study of the magnetic and magnetotransport properties in skyrmionic compounds Mn 1 − x FexGe. Helical-spin or skyrmion orders are formed in all the compositions, which allows us to study electron band-filling dependence of those periodically-winding spin textures. Magnetization characteristics reflect the variation in Dzyaloshinskii–Moriya interaction or spin–orbit coupling with chemical composition, typically as a change in the critical magnetic field. This tunable spin–orbit coupling enables control of skyrmion formation, such as its size, helicity and topology. Anomalous Hall effect in Mn 1 − x FexGe can also be a good measure of the strength of spin–orbit coupling. Anomalous Hall conductivity σ xy A = S H M is moderately temperature-dependent, obeying the magnetization variation, except for those at low temperatures in FeGe accompanied by skew contribution. The constancy of anomalous Hall coefficient SH against temperature manifests that the contribution from reciprocal space Berry phase induced by spin–orbit coupling is dominant in anomalous Hall conductivity. Strength of Dzyaloshinskii–Moriya interaction and anomalous Hall coefficient show a similar tendency in their band-filling dependence. Spin–orbit coupling plays a fundamental role behind both of the chiral magnetism and spin-dependent transport phenomena in this system.
3D-Printed Microfluidic One-Way Valves and Pumps

New microfluidic lab-on-a-chip capabilities are enabled by broadening the toolkit of devices that can be created using microfabrication processes. For example, complex geometries made possible by 3D printing can be used to approach microfluidic design and application in new or enhanced ways. In this paper, we demonstrate three distinct designs for microfluidic one-way (check) valves that can be fabricated using digital light processing stereolithography (DLP-SLA) with a poly(ethylene glycol) diacrylate (PEGDA) resin, each with an internal volume of 5–10 nL. By mapping flow rate to pressure in both the forward and reverse directions, we compare the different designs and their operating characteristics. We also demonstrate pumps for each one-way valve design comprised of two one-way valves with a membrane valve displacement chamber between them. An advantage of such pumps is that they require a single pneumatic input instead of three as for conventional 3D-printed pumps. We also characterize the achievable flow rate as a function of the pneumatic control signal period. We show that such pumps can be used to create a single-stage diffusion mixer with significantly reduced pneumatic drive complexity.
Model-based motion planning in POMDPs with temporal logic specifications

Partially observable Markov decision processes (POMDPs) have been used as mathematical models for sequential decision-making under uncertain and incomplete information. Since the state space is partially observable in a POMDP, the agent has to make a decision based on the integrated information over the past experiences of actions and observations. This study aims to solve probabilistic motion planning problems in which the agent is assigned a complex task under a partially observable environment. We employ linear temporal logic (LTL) to formulate the complex task and then convert it to a limit-deterministic generalized Büchi automaton (LDGBA). We reformulate the problem as finding an optimal policy on the product of POMDP and LDGBA based on model-checking techniques. This paper adopts and modifies two reinforcement learning (RL) approaches: value iteration and deep Q-learning. Both are model-based because the optimal policy is a function of belief states that need transition and observation probabilities to be updated. We illustrate the applicability of the proposed methods by addressing two simulations, including a grid-world problem with various sizes and a TurtleBot office path planning problem. GRAPHICAL ABSTRACT
Resilient Decentralized Cooperative Localization for Multisource Multirobot System

Although cooperative localization (CL) is fundamental to multirobot systems, current algorithms suffer from the tracking of interdependencies, information fusion from multiple sources, and restriction to specific measurement models. To improve the accuracy of localization algorithms for multirobot systems and reduce the impact of uncertainty in multisource measurement information, this article proposes a resilient decentralized CL (RDCL) algorithm. We modify the measurement update procedure of the traditional decentralized CL (DCL) algorithm to track inter-robot correlations and ensure the independence of the measurement update procedure of the elemental filters. We use optimal information fusion algorithms to fuse multisource information, and determine the overall estimate of every robot through a weighted sum of multisource estimates, thereby achieving accurate localization. To enhance the robustness of the multirobot localization system, an online validation module is added to validate the multisource estimates. The proposed CL framework is decentralized and not restricted to specific models. Simulations results show that the proposed algorithm improves localization accuracy and resilience of the multirobot system compared to existing CL algorithms. Experimental results using real-world dataset demonstrate that our proposed algorithm can achieve a localization accuracy with an average root mean square error (ARMSE) of 0.68 m, and it is 34% better than that of the traditional DCL algorithm.
Asymptotics of eigenvalues and eigenvectors of Toeplitz matrices

Analysis of the asymptotic behaviour of the spectral characteristics of Toeplitz matrices as the dimension of the matrix tends to infinity has a history of over 100 years. For instance, quite a number of versions of Szegő's theorem on the asymptotic behaviour of eigenvalues and of the so-called strong Szegő theorem on the asymptotic behaviour of the determinants of Toeplitz matrices are known. Starting in the 1950s, the asymptotics of the maximum and minimum eigenvalues were actively investigated. However, investigation of the individual asymptotics of all the eigenvalues and eigenvectors of Toeplitz matrices started only quite recently: the first papers on this subject were published in 2009–2010. A survey of this new field is presented here. Bibliography: 55 titles.
Refraction law and Fermat principle: a project using the ant colony optimization algorithm for undergraduate students in physics

A programming project for undergraduate students in physics is proposed in this work. Its goal is to check the Snell–Descartes law of refraction using the Fermat principle and the ant colony optimization algorithm. The project involves basic mathematics and physics and is adapted to students with basic programming skills. More advanced tools can be used (but are not mandatory) as parallelization or object-oriented programming, which makes the project also suitable for more experienced students. We propose two tests to validate the program. Our algorithm is able to find solutions which are close to the theoretical predictions. Two quantities are defined to study its convergence and the quality of the solutions. It is also shown that the choice of the values of the simulation parameters is important to efficiently obtain precise results.
Robust finite-time consensus tracking for second-order multi-agent systems with input saturation under general directed communication graphs

ABSTRACT In this paper, we study the global finite-time consensus tracking problem for uncertain second-order multi-agent systems subject to input saturation. The communication graphs are allowed to be general directed graphs. Sliding-mode observer-based distributed controllers are proposed such that global finite-time consensus tracking is achieved with bounded control inputs. Only relative state or output measurements are used in the proposed controller which reduces the communication requirement on the agents. Simulation examples are given to illustrate the effectiveness of the proposed control strategies.
Innovative Family-Based Genetically Informed Series of Analyses of Whole-Exome Data Supports Likely Inheritance for Grammar in Children with Specific Language Impairment

Individuals with specific language impairment (SLI) struggle with language acquisition despite average non-verbal intelligence and otherwise typical development. One SLI account focuses on grammar acquisition delay. The current study aimed to detect novel rare genetic variants associated with performance on a grammar assessment, the Test of Early Grammatical Impairment (TEGI), in English-speaking children. The TEGI was selected due to its sensitivity and specificity, consistently high heritability estimates, and its absence from all but one molecular genetic study. We performed whole exome sequencing (WES) in eight families with SLI (n = 74 total) and follow-up Sanger sequencing in additional unrelated probands (n = 146). We prioritized rare exonic variants shared by individuals with low TEGI performance (n = 34) from at least two families under two filtering workflows: (1) novel and (2) previously reported candidate genes. Candidate variants were observed on six new genes (PDHA2, PCDHB3, FURIN, NOL6, IQGAP3, and BAHCC1), and two genes previously reported for overall language ability (GLI3 and FLNB). We specifically suggest PCDHB3, a protocadherin gene, and NOL6 are critical for ribosome synthesis, as they are important targets of SLI investigation. The proposed SLI candidate genes associated with TEGI performance emphasize the utility of precise phenotyping and family-based genetic study.
Evaluation of the Involvement of Heme Oxygenase-1 Expression in Discoid Lupus Erythematosus Lesions

Discoid lupus erythematosus (DLE) is a chronic autoimmune disease that primarily affects the skin, causing red, scaly patches that may be disfiguring and can cause permanent scarring. This study aimed to investigate the potential clinical and therapeutic applications of heme oxygenase-1 (HMOX1) in the context of DLE. Immunohistochemical staining and bioinformatics analysis were performed on skin biopsy samples from DLE patients to examine the levels of HMOX1 and to correlate with markers of inflammation. Our study revealed a negative correlation between HMOX1 levels and the inflammatory status of DLE lesions, as well as an inverse correlation between HMOX1 levels and the infiltration of M1 macrophages and activated mastocytes. These findings suggest that HMOX1 plays a crucial role in the regulation of inflammation in DLE and could be a potential therapeutic target and biomarker for DLE.
Hierarchical Contrastive Learning for Temporal Point Processes

As an important sequential model, the temporal point process (TPP) plays a central role in real-world sequence modeling and analysis, whose learning is often based on the maximum likelihood estimation (MLE). However, due to imperfect observations, such as incomplete and sparse sequences that are common in practice, the MLE of TPP models often suffers from overfitting and leads to unsatisfactory generalization power. In this work, we develop a novel hierarchical contrastive (HCL) learning method for temporal point processes, which provides a new regularizer of MLE. In principle, our HCL considers the noise contrastive estimation (NCE) problem at the event-level and at the sequence-level jointly. Given a sequence, the event-level NCE maximizes the probability of each observed event given its history while penalizing the conditional probabilities of the unobserved events. At the same time, we generate positive and negative event sequences from the observed sequence and maximize the discrepancy between their likelihoods through the sequence-level NCE. Instead of using time-consuming simulation methods, we generate the positive and negative sequences via a simple but efficient model-guided thinning process. Experimental results show that the MLE method assisted by the HCL regularizer outperforms classic MLE and other contrastive learning methods in learning various TPP models consistently. The code is available at https://github.com/qingmeiwangdaily/HCL_TPP.
Leveraging Gene Redundancy to Find New Histone Drivers in Cancer

Simple Summary Histones are a group of proteins that are essential for chromatin function. Histone coding genes have been found to be mutated in a number of cancers but how these mutations arise and whether they contribute to cancer biology remains largely unknown. Answering these questions is difficult because histone proteins are encoded by large families of genes that are thought to be redundant. Here, we overcome these limitations using a large dataset of whole exome sequencing data from cancer patients representing 68 different cancer types. We find that specific cancer types exhibit disproportionate susceptibilities to accruing mutations in histone genes versus other genes. We delineate factors influencing the probability of mutations accumulation in histone genes and detect new histone gene drivers. Abstract Histones play a critical role in chromatin function but are susceptible to mutagenesis. In fact, numerous mutations have been observed in several cancer types, and a few of them have been associated with carcinogenesis. Histones are peculiar, as they are encoded by a large number of genes, and the majority of them are clustered in three regions of the human genome. In addition, their replication and expression are tightly regulated in a cell. Understanding the etiology of cancer mutations in histone genes is impeded by their functional and sequence redundancy, their unusual genomic organization, and the necessity to be rapidly produced during cell division. Here, we collected a large data set of histone gene mutations in cancer and used it to investigate their distribution over 96 human histone genes and 68 different cancer types. This analysis allowed us to delineate the factors influencing the probability of mutation accumulation in histone genes and to detect new histone gene drivers. Although no significant difference in observed mutation rates between different histone types was detected for the majority of cancer types, several cancers demonstrated an excess or depletion of mutations in histone genes. As a consequence, we identified seven new histone genes as potential cancer-specific drivers. Interestingly, mutations were found to be distributed unevenly in several histone genes encoding the same protein, pointing to different factors at play, which are specific to histone function and genomic organization. Our study also elucidated mutational processes operating in genomic regions harboring histone genes, highlighting POLE as a factor of potential interest.