Miracle-dz/UniFAR-FTUs huggingface dataset:
Neurostimulation in People with Oropharyngeal Dysphagia: A Systematic Review and Meta-Analyses of Randomised Controlled Trials—Part I: Pharyngeal and Neuromuscular Electrical Stimulation

Objective. To assess the effects of neurostimulation (i.e., neuromuscular electrical stimulation (NMES) and pharyngeal electrical stimulation (PES)) in people with oropharyngeal dysphagia (OD). Methods. Systematic literature searches were conducted to retrieve randomised controlled trials in four electronic databases (CINAHL, Embase, PsycINFO, and PubMed). The methodological quality of included studies was assessed using the Revised Cochrane risk-of-bias tool for randomised trials (RoB 2). Results. In total, 42 studies reporting on peripheral neurostimulation were included: 30 studies on NMES, eight studies on PES, and four studies on combined neurostimulation interventions. When conducting meta analyses, significant, large and significant, moderate pre-post treatment effects were found for NMES (11 studies) and PES (five studies), respectively. Between-group analyses showed small effect sizes in favour of NMES, but no significant effects for PES. Conclusions. NMES may have more promising effects compared to PES. However, NMES studies showed high heterogeneity in protocols and experimental variables, the presence of potential moderators, and inconsistent reporting of methodology. Therefore, only conservative generalisations and interpretation of meta-analyses could be made. To facilitate comparisons of studies and determine intervention effects, there is a need for more randomised controlled trials with larger population sizes, and greater standardisation of protocols and guidelines for reporting.
Uncertainty Analysis Based on Kriging Meta-Model for Acoustic-Structural Problems

This paper consists of evaluating the performance of a vibro-acoustic model in the presence of uncertainties in the geometric and material parameters of the model using Monte Carlo simulations (MCS). The purpose of using a meta-model is to reduce the computational cost of finite element simulations. Uncertainty analysis requires a large sample of MCS to predict the effect of uncertain parameters on the system response. So, if this study is done through the finite element method (FEM), then the computational cost will be very important. Furthermore, for that, we use meta-models to be able to conduct an efficient uncertainty analysis more quickly. In the present contribution, the approximated meta-model is verified and validated using error measures and cross-validation (CV). Then, the uncertainty analysis is performed by Monte Carlo simulations using the computed Kriging meta-model. The developed methodology has been applied in two vibro-acoustic models. In these two models, the covariance of uncertainty of geometric and physical (elasticity and density) parameters are equal to 2% and 5% respectively. The obtained results prove that the suggested methodology of uncertainty propagation based on the Kriging meta-model can be considered as a very efficient and sufficiently accurate approach for the quantification of uncertainties in acoustic-structural systems.
Unveiling the Formation of Political Identities of the Filipino Youth in Tacurong City, The Philippines

The purpose of this study was to investigate the Filipino youth’s participation in politics regarding how their political identities were formed. The novelties in this study are (1) political identities of the Filipino youth, and (2) factors affecting the political identity formation of the Filipino youth. A qualitative case study approach was used to analyze the different statements of the Filipino youth towards the factors that made them be involved in politics, their political identities, and their political identity. Six Filipino youths residing in Tacurong City participants were purposively chosen and interviewed via Google Meet due to COVID-19 restrictions. As revealed by the data gathered, issues in the society, the availability of information to the public, their relationship circle, and their respective school’s Influence was found to play an essential role in making the Filipino youth participate in politics. The same factors such as the issues in the society, availability of information to the public, relationship circle, and the influence of school were also responsible for the formation of their current political identity. It is concluded that the Filipino youth’s political participation and identity were the results of the significant influence of the society. This study has a major impact in contributing to the knowledge on how the youth behave and why they have different perspectives and preferences of sides specifically in politics.
Unique Gate Bias Dependence of Dynamic ON-Resistance in MIS-Gated AlGaN/GaN HEMTs and Its Dependence on Gate Control Over the 2-DEG

This work reports a unique gate bias dependence of dynamic ON-resistance in metal–insulator–semiconductor (MIS)-gated AlGaN/GaN high-electron-mobility transistors (HEMTs). The absence of such dependence in Schottky HEMTs confirms that the phenomenon is unique to MISHEMTs. Based on the observations, the weakening of gate control over GaN channel by the insertion of the gate insulator is proposed as the phenomenon responsible for the observed behavior. The proposal is verified by incorporating high-<inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> (25) <inline-formula> <tex-math notation="LaTeX">$\text {Al}_{{0.5}}\text {Ti}_{{0.5}}\text {O}_{y}$ </tex-math></inline-formula> as the gate oxide, which successfully mitigated the gate bias dependence of dynamic ON-resistance by improving the gate control.
AutoConf: New Algorithm for Reconfiguration of Cyber-Physical Production Systems

The increasing size and complexity of cyber-physical production systems (CPPS) lead to an increasing number of faults, such as broken components or interrupted connections. Nowadays, faults are handled manually, which is time-consuming because for most operators mapping from symptoms (i.e., warnings) to repair instructions is rather difficult. To enable CPPS to adapt to faults autonomously, reconfiguration, i.e., the identification of a new configuration that allows either reestablishing production or a safe shutdown, is necessary. This article addresses the reconfiguration problem of CPPS and presents a novel algorithm called AutoConf. AutoConf operates on a hybrid automaton that models the CPPS and a specification of the controller to construct a QSM. This QSM is based on propositional logic and represents the CPPS in the reconfiguration context. Evaluations on an industrial use case and simulations from process engineering illustrate the effectiveness and examine the scalability of AutoConf.
Semantic Shilling Attack against Heterogeneous Information Network Based Recommend Systems

SUMMARY The recommend system has been widely used in many web application areas such as e-commerce services. With the development of the recommend system, the HIN modeling method replaces the traditional bipartite graph modeling method to represent the recommend system. But several studies have already showed that recommend system is vulnerable to shilling attack (injecting attack). However, the e ﬀ ectiveness of how traditional shilling attack has rarely been studied directly in the HIN model. Moreover, no study has focused on how to enhance shilling attacks against HIN recommend system by using the high-level semantic information. This work analyzes the relationship between the high-level semantic information and the attacking e ﬀ ects in HIN recommend system. This work proves that attack results are proportional to the high-level semantic information. Therefore, we propose a heuristic attack method based on high-level semantic information, named Semantic Shilling Attack (SSA) on a HIN recommend system (HERec). This method injects a speciﬁc score into each selected item related to the target in semantics. It ensures transmitting the misleading information towards target items and normal users, and attempts to interfere with the e ﬀ ect of the recommend system. The experiment is dependent on two real-world datasets, and proves that the at-tacking e ﬀ ect is positively correlate with the number of meta-paths. The result shows that our method is more e ﬀ ective when compared with existing baseline algorithms.
Secure Blockchain Interworking Using Extended Smart Contract

SUMMARY Blockchain is a distributed ledger technology used for trading digital assets, such as cryptocurrency, and trail records that need to be audited by third parties. The use cases of blockchain are expanding beyond cryptocurrency management. In particular, the token economy, in which tokenized assets are exchanged across di ﬀ erent blockchain ledgers, is gaining popularity. Cross-chain technologies such as atomic swap have emerged as security technologies to realize this new use case of blockchain. However, existing approaches of cross-chain technology have unresolved issues, such as application limitations on di ﬀ erent blockchain platforms owing to the incompatibility of the communication interface and crypto al-gorithm and inability to handle a complex business logic such as the escrow trade. In this study, the ConnectionChain is proposed, which enables the execution of an extended smart contract using abstracted operation on in-terworking ledgers. Moreover, ﬁeld experimental results using the system prototype are presented and explained.
Patient’s Experiences of Violence as Perpetrator: A Qualitative Study from Patients with Schizophrenia in Indonesia

BACKGROUND: Individuals with schizophrenia are at an increased risk for violence – limited study on exploring the experience of violence in patients with schizophrenia as a perpetrator. AIM: This research aimed to investigate the perception of violence as a perpetrator in Indonesia in patients with schizophrenia. METHODS: This study used phenomenology using a purposive sampling technique. A total of 40 patients were from the psychiatric ward of significant mental hospitals in West Java, Indonesia. The inclusion criteria for patients were the following: Age over 18 years old confirmed diagnosis with schizophrenia. Focus group discussion of patients comprised four groups. Each group consisted of tens of patients. RESULTS: The patient’s experiences as a perpetrator were categorized into six subthemes: Committing physical violence to family, quarreling with family, verbal abuse to family, a perpetrator of verbal violence to neighbors, expression of anger to object, and physical violence to nurse. CONCLUSION: The patient’s experiences of violence were not linear, but rather it was a complex experience of being a perpetrator, yet at the same time as of perpetrator violence. A staff training program to reduce patient violence is needed.
Centrality-Based Eventual Leader Election in Dynamic Networks

This paper presents CEL, a new distributed eventual leader election algorithm for dynamic networks, which exploits topological information to improve the choice of a central leader and reduce message exchanges. The algorithm has a cross-layer neighbors detection, with a neighbor-aware mechanism, to improve the sharing of topological knowledge and elect a central leader faster. It uses a self-pruning mechanism based on topological knowledge, combined with probabilistic gossip, to improve the performance of broadcast propagation. Evaluations were conducted on the OMNeT++ environment, simulating re-alistic MANET with interference, collision, and messages loss. Using different parameters values, we have compared CEL to Gomez-Calzado et al. algorithm [1], on the Random Walk and the Truncated Levy Walk mobility models. The results show better performances than [1], including fewer messages sent, shortest paths to the leader, and a more stable algorithm.
Intelligent Robust Cross-Domain Fault Diagnostic Method for Rotating Machines Using Noisy Condition Labels

Cross-domain fault diagnosis methods have been successfully and widely developed in the past years, which focus on practical industrial scenarios with training and testing data from numerous machinery working regimes. Due to the remarkable effectiveness in such problems, deep learning-based domain adaptation approaches have been attracting increasing attention. However, the existing methods in the literature are generally lower compared to environmental noise and data availability, and it is difficult to achieve promising performance under harsh practical conditions. This paper proposes a new cross-domain fault diagnosis method with enhanced robustness. Noisy labels are introduced to significantly increase the generalization ability of the data-driven model. Promising diagnosis performance can be obtained with strong noise interference in testing, as well as in practical cases with low-quality data. Experiments on two rotating machinery datasets are carried out for validation. The results indicate that the proposed algorithm is well suited to be applied in real industrial environments to achieve promising performance with variations of working conditions.
Balancing Statistical Power and Risk in HIV Cure Clinical Trial Design

Abstract Background Analytical treatment interruptions (ATI) are pauses of antiretroviral therapy (ART) in the context of human immunodeficiency virus (HIV) cure trials. They are the gold standard in determining if interventions being tested can achieve sustained virological control in the absence of ART. However, withholding ART comes with risks and discomforts to trial participant. We used mathematical models to explore how ATI study design can be improved to maximize statistical power, while minimizing risks to participants. Methods Using previously observed dynamics of time to viral rebound (TVR) post-ATI, we modelled estimates for optimal sample size, frequency, and ATI duration required to detect a significant difference in the TVR between control and intervention groups. Groups were compared using a log-rank test, and analytical and stochastic techniques. Results In placebo-controlled TVR studies, 120 participants are required in each arm to detect 30% difference in frequency of viral reactivation at 80% power. There was little statistical advantage to measuring viral load more frequently than weekly, or interrupting ART beyond 5 weeks in a TVR study. Conclusions Current TVR HIV cure studies are underpowered to detect statistically significant changes in frequency of viral reactivation. Alternate study designs can improve the statistical power of ATI trials.
The Development Training Model MPICC to Increase Achievement Motivation on Water Polo Athlete

This study aims to produce a practical training program to improve the achievements of the Yogyakarta Water Polo regional training athletes during the COVID-19 outbreak and to find out the properness of mental training model. The research method used is research and development using the five-step ADIIE model. The development of mental training models includes motivation, positive thinking, imagery, concentration, and cohesion (MPICC). The sample in this study consisted of six athletes in a small group trial and 13 female athletes from the regional training PON DIY on a large scale. Collecting data with quantitative and qualitative approaches. The instruments using interview and questionnaires. The development of this training model was validated by five material experts consisting of three academics and two trainers. The results obtained are that the development of the MPICC mental training model is effective in increasing the achievements of the Yogyakarta water polo regional training athletes during the COVID-19 outbreak. Results Based on the analysis, the pre-test and post-test data increased by 46.86% with a t-count value of -4.320 and a significance value of 0.000. The MPICC training model is suitable to be used as an exercise program to improve the achievement of Yogyakarta water polo regional training athletes.
Microscopies Enabled by Photonic Metamaterials

In recent years, the biosensor research community has made rapid progress in the development of nanostructured materials capable of amplifying the interaction between light and biological matter. A common objective is to concentrate the electromagnetic energy associated with light into nanometer-scale volumes that, in many cases, can extend below the conventional Abbé diffraction limit. Dating back to the first application of surface plasmon resonance (SPR) for label-free detection of biomolecular interactions, resonant optical structures, including waveguides, ring resonators, and photonic crystals, have proven to be effective conduits for a wide range of optical enhancement effects that include enhanced excitation of photon emitters (such as quantum dots, organic dyes, and fluorescent proteins), enhanced extraction from photon emitters, enhanced optical absorption, and enhanced optical scattering (such as from Raman-scatterers and nanoparticles). The application of photonic metamaterials as a means for enhancing contrast in microscopy is a recent technological development. Through their ability to generate surface-localized and resonantly enhanced electromagnetic fields, photonic metamaterials are an effective surface for magnifying absorption, photon emission, and scattering associated with biological materials while an imaging system records spatial and temporal patterns. By replacing the conventional glass microscope slide with a photonic metamaterial, new forms of contrast and enhanced signal-to-noise are obtained for applications that include cancer diagnostics, infectious disease diagnostics, cell membrane imaging, biomolecular interaction analysis, and drug discovery. This paper will review the current state of the art in which photonic metamaterial surfaces are utilized in the context of microscopy.
The Effects of Chewing Gum on Reducing Anxiety and Stress: A Meta-Analysis of Randomized Controlled Trials

There was currently no consensus on whether chewing gum should be widely instituted as a means to help reduce anxiety and stress. Chewing gum was also not included in guidelines for alleviating anxiety and stress. The purpose of this study was of two aspects: (1) to review the research progress of the relationship between gum chewing and anxiety and stress in recent years and (2) to make a meta-analysis of the effects of mastication on anxiety and stress. We conducted a meta-analysis of studies extracted from PubMed, the Cochrane Library, and Embase to identify randomized controlled trials (RCTs) evaluating the efficacy of chewing gum on anxiety, and stress was evaluated through screening, inclusion, data extraction, and quality assessment. The meta-analysis we performed was using Review Manager 5.3 software. We included a total of 8 RCTs, involving more than 400 adults over 18 years old. Compared with no chewing gum, chewing gum resulted in anxiety (MD = −0.26, 95% CI (−0.48, −0.04), p=0.02, I2 = 11%), where the heterogeneity was low and statistically significant. While in stress (MD = −0.27, 95% CI (−0.79, −0.25), p=0.31, I2 = 48%), the heterogeneity was high, and there was of no statistical significance. Based on current evidence, chewing gum is an inexpensive, well-tolerated, safe, and effective way to relieve anxiety and stress. To confirm the conclusion, we still need to conduct more randomized trials.
Egg-free low-fat mayonnaise from virgin coconut oil

Introduction. Mayonnaise is a widely consumed product all over the world. Nowadays, the number of vegetarians, egg allergy cases, and heart diseases are increasing. This makes manufacturers develop alternatives. The research objective was to select the optimal concentration of emulsifiers for egg-free mayonnaise made from virgin coconut oil. Study objects and methods. We produced 20 egg-free mayonnaise samples with different amounts of emulsifiers. We also determined physicochemical properties of the samples, as well as performed proximate and statistical analyses. Results and discussion. The response surface methodology made it possible to define such parameters as viscosity, stability, and firmness as affected by the following concentrations: cashew nut protein isolates – 5–15%, xanthan gum – 0–1%, and modified starch – 0–0.5%. The optimal values of emulsifiers were obtained as follows: cashew nut protein isolates – 13 g, xanthan gum – 1.0 g, and modified starch – 0.4 g. The optimized mayonnaise had the following parameters: viscosity – 120.2 mPa·s, stability – 98.7%, and firmness – 25 g. The study revealed no significant differences (P > 0.05) between the actual and predicted data, which confirmed the efficiency of the suggested models. Conclusion. The obtained low-fat egg-free mayonnaise was relatively similar to the traditional commercial products. However, virgin coconut oil should be emulsified with a combination of cashew nut protein isolates, modified starch, and xanthan gum.
Review of spike-based neuromorphic computing for brain-inspired vision: biology, algorithms, and hardware

Abstract. Neuromorphic computing is becoming a popular approach for implementations of brain-inspired machine learning tasks. As a paradigm for both hardware and algorithm design, neuromorphic computing aims to emulate several aspects related to the structure and function of the biological nervous system to achieve artificial intelligence with efficiencies that are orders of magnitude better than those exhibited by general-purpose computing hardware. We provide a holistic treatment of spike-based neuromorphic computing (i.e., based on spiking neural networks), detailing biological motivation, key aspects of neuromorphic algorithms, and a survey of state-of-the-art neuromorphic hardware. In particular, we focus on these aspects within the context of brain-inspired vision applications. Our aim is to serve as a complement to several of the existing reviews on neuromorphic computing while also providing a unique perspective.
Complex Methods for Bounds on the Number of Periodic Solutions with an Application to a Neural Model

Abstract The qualitative behavior of the differential equation , where x is real and f is periodic in t, is determined by the attracting periodic solutions. This article gives an exposition of a method of Ilyashenko that uses complex analysis to determine an upper bound on the number of periodic solutions. The method is then applied to a well-known model of a single neuron or pool of neurons.
Awareness of Pain Assessment for Elderly Patients with Dementia among Geriatric Care Hospital Nurses

: The purpose of this study was to examine the experiences of nurses as case managers in the pain assessment of elderly patients with dementia in geriatric care hospitals by conducting a qualitative case study. Data were collected through in-depth interviews with five nurses from two elderly care hospitals in Seoul and Gyeonggi Province. The collected data were qualitatively analyzed to examine experiences, strategies, and issues between the research cases. As a result of this study, five subcategories were derived: understanding of pain perception in dementia patients, difficulty in accurate self-report, assessment of pain behavioral indicators, identification of abnormal symptoms of pain through vital signs, and specific situation recognition. In addition, to verify the validity of the study, participant and peer reviews of the research results and interpretations were conducted. The results of the present study are intended to lay the basis for developing a pain assessment tool that can be used by nurses caring for elderly patients with dementia in geriatric care hospitals and pain management strategies. And, given the increasing importance of pain assessment and management through behavioral observation in the elderly with dementia in the clinical nursing practice for the elderly, a management plan for nurses must be prepared for pain assessment in elderly patients with dementia in geriatric care hospitals. The significance of this study is that it presents its results with supporting data.