Miracle-dz/UniFAR-FTUs huggingface dataset:
The Problem of Electron and Superluminal Signals

The modern means of communication (radio communication, television, radiolocation) are based on the use of electromagnetic waves, and consequently at the moment the velocity of transfer is limited in that of light in vacuum, which is considered as the greatest possible velocity of transfer of a signal existing in nature. A radically different way of transfer of a signal is shown by our research to be possible-by means of its own field of electrically charged particles. This allows it to transfer information faster than the velocity of light in a vacuum. This can lead to complications or problems that are addressed and explained in this book.
THE EFFECT OF TECHNOSTRESS ON ONLINE LEARNING BEHAVIOUR AMONG UNDERGRADUATES

Purpose – Students’ incompetence to deal with the intricacies of technology during the teaching and learning process may have negatively affected their online learning process particularly throughout the COVID-19 pandemic. It is critical to research technostress influence on online learning behaviours among undergraduates. Thus, this research intends to examine the predicting factors that contribute to technostress and the correlation between undergraduates’ technostress and the behavioural intention to use online learning. Methodology – This study employed an online survey and cross-sectional data towards 212 respondents, all of whom were Diploma students from Universiti Teknologi MARA (UiTM) Pahang. This study added three new variables to the existing Unified Theory of Acceptance and Use of Technology (UTAUT) namely teaching-related aspects, price value, and technostress. Structural equation modelling (SEM) was performed to analyse the measurement model and evaluate the developed hypotheses. Findings – The findings established a significant correlation between technostress and behavioural intention to use online learning. Facilitating conditions also demonstrate a significant relationship to technostress. Significance - The study’s findings are likely to increase researchers’ understanding of the present condition of technostress in higher education as a consequence of the implementation of online learning regulations, as well as the scale of the impacts on higher education.
Institutionalizing Continuous Improvement Plan in an Engineering Technology Department- Closing the Loop

Continuous improvement is a corner stone of a quality engineering or engineering technology program. Accreditation Board of Engineering and Technology requires that a well-planned and implemented continuous improvement plan should be in place. The ABET 2015-16 Criterion 4 Continuous Improvement1 states: “The program must regularly use appropriate, documented processes for assessing and evaluating the extent to which the student outcomes are being attained. The results of these evaluations must be systematically utilized as input for the continuous improvement of the program. Other available information may also be used to assist in the continuous improvement of the program. ” A successful continuous improvement plan that is institutionalized is self-driven and does not require external stimuli. For example, if an outcome assessment goal is not reached in an academic term, a sequence of events/actions are set in motion to address the deficiency. Evidence of existence of an institutionalized continuous improvement plan include but not limited to: A timeline of repeated activities related to the assessment and evaluation of student outcomes, agreed upon performance indicators to assess learning outcomes, systematic data collection focusing on direct evidence of student performance related to the student outcomes. Various data streams feeding into the assessment plan may include, course assessment data, senior exit survey, alumni and employer survey, internship reports and feedback from industrial advisory boards.
The The Effect of Preliminary Generational Leadership on The Interest of Continuing Family Business With Family Harmony as a Mediating Variable

Succession is one of the problems in the family business, where there are not many children who should be the next generation who are interested in continuing the family business. Researchers conducted a pre-survey of future generations who have an interest in continuing the family business and it turns out that leadership and family harmony are the two highest factors. The purpose of this study was to determine the relationship between leadership and interest in continuing the family business with family harmony as a mediation. Respondents of this study were 61 students of the family business curriculum who had reached their final semester. The results of the study indicate that leadership has an effect on family harmony, leadership does not affect the interest in continuing the family business, family harmony affects the interest in continuing the family business and is a variable that fully mediates the relationship between leadership and interest in continuing the family business.
Maximum Queue Length of a Fluid Model with an Aggregated Fractional Brownian Input

: A fractional Brownian queueing model, that is, a ﬂuid queue with an input of a fractional Brownian motion, has been applied in network modeling since the self-similarity and long-range dependence were observed in Internet traﬃc. In this paper, a ﬂuid queue with an aggregated fractional Brownian input, which is a generalization of a fractional Brownian queueing model, is considered and the maximum queue length over a time interval [0 ,t ] is studied. The impact of an aggregated fractional Brownian input on the queue length process is analyzed and the main results on the maximum queue length are compared with some related known results in the literature.
Strategi Penipuan pada Teks Fake News Berbahasa Prancis Terkait Emmanuel Macron di Media Sosial

Social media has made it easier for people to access information, especially related to viral news. However, this convenience has not only had a positive impact but has provided space for the transmission of false information (fake news). This study examines how the characteristics of fake news texts are to trick social media users into false information. The object of the research is in the form of fake news text in French related to Emmanuel Macron which has been verified as fake news by the fact-checking platform. Through qualitative descriptive methods and Fairclough textual analysis, five fake news text fraud strategies were found, namely (1) loading provocative invitations, (2) manipulating data through images, (3) manipulating information through grammar, (4) manipulating facts through media sources, (5) includes clickbait and provocative titles. Furthermore, Emmanuel Macron in the fake news text is represented as a leader who has a negative image with the aim of provoking the public.
Effectiveness of Co-Administration of Camelina Oil and Caloric Restriction on Cardiometabolic Risk Factors, Liver Function and Mental Health in Patients with Non-Alcoholic Fatty Liver Disease: A Blinded Randomized Controlled Trial Protocol

Background: Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease (CLD). Omega-3 fatty acids and antioxidants co-supplementation have been considered as an alternative treatment in NAFLD. This trial will evaluate camelina sativa oil (CSO) effects as a rich source of omega-3 fatty acids and antioxidants on cardiometabolic risk factors, metabolic endotoxemia, liver enzymes, hepatic steatosis, and mental health in NAFLD patients. Methods: Forty-six patients with NAFLD will be randomly assigned to either a CSO supplementation or placebo for 12 weeks. Both groups will receive a weight-loss diet too. Insulin resistance, oxidative stress, anti/inflammatory biomarkers, lipid profile, adiponectin, lipopolysaccharide (LPS), anthropometric indices, liver enzymes, hepatic steatosis, and cortisol will be assessed all patients at baseline and post-intervention. DASS and GHQ questionnaires will be completed for all patients at baseline and post-intervention. Results: The CSO is expected to reduce hepatic steatosis and improve cardiometabolic risk factors, liver function, and mental health compared to the placebo group after 12 weeks. Conclusion: The CSO as a phytopharmaceutical drug may improve cardiometabolic risk factors, metabolic endotoxemia, liver enzymes, hepatic steatosis, and mental health in patients with NAFLD.
Prediction of operating dynamics in floating-zone crystal growth using Gaussian mixture model

ABSTRACT We have applied a Gaussian mixture regression to the prediction of operation dynamics in floating zone crystal growth as an example of a materials process. From only five demonstration trajectories, we successfully predicted the operating dynamics using the Gaussian mixture model with better precision than obtained by using linear regression or neural networks. The current results indicate that the Gaussian mixture regression is suitable for predicting the operation dynamics of materials processes in which it is preferable to avoid large changes from stable operating conditions. Furthermore, precise prediction by the Gaussian mixture regression will lead to the optimization of operation trajectories and automatic control of materials processes. GRAPHICAL ABSTRACT
Synergistic effects of nab-PTX and anti-PD-1 antibody combination against lung cancer by regulating the Pi3K/AKT pathway through the Serpinc1 gene

Lung cancer is a type of cancer with higher morbidity and mortality. In spite of the impressive response rates of nab-paclitaxel (nab-PTX) or programmed cell death-1 (PD-1) and its ligand inhibitors, the effective treatment remains limited. Currently, alternative strategies aim at drug combination of nab-PTX and PD-1/PD-L1 inhibitors. Even as the clinical impact of the combined agents continues to increase, basic research studies are still limited and the mechanisms underlying this synergy are not well studied. In this study, we evaluated the antitumor efficacy and the molecular mechanisms of action of nab-PTX in combination with anti-PD-1 antibody, using Lewis lung carcinoma (LLC) cell and subcutaneously transplanted tumor models. The combination of nab-PTX and anti-PD-1 antibody displayed stronger antitumor effects, manifested at tumor volume, proliferation and apoptosis through Ki67 and TUNEL staining. In-vivo experiments showed significant increases in CD4+ T cells, CD8+ T cells, IFN-γ, TNF-α, IL-2, PF, and Gzms-B, exerting antitumor effects with reductions in MDSCs and IL-10 after the treatments. Furthermore, transcriptomic analysis indicated 20 overlapped differentially expressed genes, and Serpin peptidase inhibitor clade C Member 1 (Serpinc1) was downregulated during treatment in vivo, whose expression level was markedly related to metastasis and overall survival of lung cancer patients. Functional enrichment analysis of the target gene revealed primary GO terms related to tumor, which warrants further investigation. We also found that Serpinc1 overexpression promoted cell proliferation, migration, and invasion and inhibited cell apoptosis of LLC cells in vitro, possibly regulating the associated factors via the Pi3K/AKT pathway. In summary, our results reveal the synergistic antitumor responses of nab-PTX combined with anti-PD-1 antibody, in which Serpinc1 may play an important role, providing a target gene for combination treatment strategy.
Image Recognition for Garbage Classification Based on Transfer Learning and Model Fusion

Garbage is an underutilized resource, and garbage classification is one of the effective ways to make full use of these resources. In order to realize the automation of garbage classification, some deep learning models are used for garbage images recognition. A novel garbage image recognition model Garbage Classification Net (GCNet) based on transfer learning and model fusion is proposed in this paper. After extracting garbage image features, EfficientNetv2, Vision Transformer, and DenseNet, respectively, are combined to construct the neural network model of GCNet. Data augmentation is used to expand the dataset and 41,650 garbage images are contained in the new dataset. Compared with other models through experiments, the results show that the proposed model has good convergence, high recall rate and accuracy, and short recognition time.
Dynamical characteristics of the surface plasmon-polariton wave supported by a thin metal film

We study the energy and momentum of the surface plasmon-polariton (SPP) excited in a symmetric three-layer ‘insulator–metal–insulator’ structure, which is known to support the symmetric (S) mode (which, under certain conditions, possesses the negative group velocity) as well as the antisymmetric (AS) mode with always positive energy flow. The electric and magnetic field vectors are calculated via both the phenomenological and the microscopic approach; the latter involves the hydrodynamic model accounting for the quantum statistical effects for the electron gas in metal. Explicit representation for the energy and momentum constituents in the dielectric and in the metal film are obtained, and the wavenumber dependences of the energy and momentum contributions for the whole SPP are analyzed numerically. The various energy and momentum constituents are classified with respect to their origin (‘field’ or ‘material’), and the physical nature (orbital (canonical) and spin (Belinfante) momentum contributions). The pictures characteristic for the S and AS modes are systematically compared. The results can be useful for the studies and applications of the SPP-induced thin-film effects, in particular, for the charge and spin dynamics in thin-film plasmonic systems.
Advances in the study of the relationship between HPV infection and colorectal cancer

Colorectal cancer, as a malignant tumour that seriously endangers human health, has shown an increasing trend in incidence and mortality worldwide. It poses a threat to the health of the population and a heavy economic burden to both society and individuals, and is an important public health problem worldwide, especially in developing countries. Risk factors for colorectal cancer include many. A growing number of studies have shown that human papilloma virus (HPV) infection is closely associated with the development of colorectal cancer, especially high-risk HPV16 and HPV18, but much controversy still exists. Therefore, this article will briefly review the recent studies on the relationship between HPV infection and the development of colorectal cancer.
Formation of NOON states from Fock-state Bose-Einstein condensates

States of the form |N>{sub a}|0>{sub b}+|0>{sub a}|N>{sub b}, where a and b are single-particle states--i.e., NOON states--have been used for predicting violations of local realism (Greenberger-Horne-Zeilinger violations) and are valuable in metrology for precision measurements of phase at the Heisenberg limit. We show theoretically how the use of two Fock-state Bose-Einstein condensates as sources in a modified Mach-Zehnder interferometer can lead to creation of the NOON state in which a and b refer to arms of the interferometer and N is a subset of the total number of particles in the two condensates. The modification of the interferometer involves making ''side'' measurements of a few particles near the sources. These measurements put the remaining particles in a superposition of two phase states, which are converted into NOON states by a beam splitter if the phase states are orthogonal. When they are not orthogonal, a ''feed-forward'' correction circuit is shown to convert them into proper form so that a NOON results. We apply the NOON to the measurement of phase. Here the NOON experiment is equivalent to one in which a large molecule passes through two slits. The NOON components can be recombined in a final beam splitter to showmore » interference. « less
On the stability of a viscoelastic Timoshenko system with Maxwell-Cattaneo heat conduction

This paper discusses a thermoelastic Timoshenko system with viscoelastic damping acting on the shear force, and heat conduction given via Maxwell-Cattaneo’s law (usually called second sound) on the bending moment. We establish a general decay estimate for the solution energy. The exponential and polynomial decay results are only special cases of the present work. The obtained result shows that the viscoelastic damping on the shear force and the thermal damp- ing on the bending moment are strong enough to stabilize the system without any additional re-strictions like “the equal-wave of speed propagation” or “the stability number” conditions which are usually associated with similar problems.
High-Frequency Ladder Network Synthesis of Transformer Winding for Its Mechanical Condition Assessment

Fault localization and severity evaluation of transformer winding is crucial for its mechanical condition assessment. Ladder network synthesis of winding entity based on its frequency response analysis data is an elegant solution to this problem. This article proposes a generic method for high-frequency (HF) ladder network synthesis of winding in different mechanical conditions for its fault diagnosis. The simplified HF network used in the synthesis process is first built. Afterward, the synthesis algorithms with high precision and efficiency and the mathematical model for normal HF ladder network are elaborated. Further, the synthesis method for defective network is proposed to locate the winding fault positions and evaluate their severities. Finally, the network synthesis method is applied on a distribution transformer, with the fact that the obtained network components comply with all the constraints and the diagnostic results also match well with the actual mechanical conditions of winding, which verifies its feasibility and accuracy.
Compact Modeling of Nanocluster Functionality as a Higher-Order Neuron

Disordered nanoclusters with multielectrode input–output functionality had recently been experimentally realized with energy-efficient and emergent computational capacity, and thus an interconnected network of several such nanoclusters had been proposed to realize artificial neural networks. To aid that end, here we show that nanocluster functionality can be fit to the simplest dendritic neuron model (DNM), where the only form of nonlinearity is due to multiplicative interactions. This work brings into the spotlight higher-order neural networks (known for their efficient encoding of geometric invariances) to serve as an explainable baseline model of nano-networks against which experimentalists can compare more sophisticated models (deep neural networks or physics-based models such as the lin-min network introduced here) and provides ground for designing novel approximate hardware and a statistical mechanics analysis of the learning performance of interconnected nanoclusters versus perceptrons (where neurons output a nonlinear function of the weighted sum of their inputs). A network with just ten higher-order neurons is shown to achieve a classification accuracy of more than 96% on the MNIST benchmark for handwritten digit recognition (which required 100 times more neurons in three-layer perceptrons).
Model averaging and variable selection in VAR models

Bayesian model averaging and model selection is based on the marginal likelihoods of the competing models. This can, however, not be used directly in VAR models when one of the issues is which - and how many - variables to include in the model since the likelihoods will be for different groups of variables and not directly comparable. One possible solution is to consider the marginal likelihood for a core subset of variables that are always included in the model. This is similar in spirit to a recent proposal for forecast combination based on the predictive likelihood. The two approaches are contrasted and their performance is evaluated in a simulation study and a forecasting exercise.
Toward Autonomous Farming—A Novel Scheme Based on Learning to Prediction and Optimization for Smart Greenhouse Environment Control

The greenhouse industry has received great attention and experienced tremendous growth in the recent past across the globe. However, energy consumption and labor cost in greenhouses account for more than 50% of the cost of greenhouse production. This demands an Internet of Things (IoT)-based smart solution for automation of greenhouse environment-related activities to ensure maintenance of the desired climate inside the greenhouse to maximize plant production with optimal resource utilization. To this end, several models are proposed in the literature that is based on a selected artificial intelligence (AI) algorithm which is once trained and then deployed. The drawback of such systems is that the trained models are fixed (locked) and, therefore, unable to adapt to dynamically changing conditions, which results in performance degradation. Second, the existing studies on the subject matter are focused on the individual key component (i.e., prediction, optimization, and control). In this article, a novel scheme is presented based on the integration of the key components, and the performance of prediction and optimization components is further enhanced through the exploitation of artificial neural network (ANN)-based learning modules to support autonomous greenhouse environment monitoring and control. For experimental analysis, the greenhouse environment is emulated through the mathematical formulation of essential greenhouse processes, considering the impact of actuators’ operations and external weather conditions. Real environmental data collected for Jeju Island, South Korea is used for model validation and result analysis. Proposed learning-based optimization scheme results are compared with two other schemes, i.e., baseline scheme and optimization scheme. Comparative analysis of the results shows that the proposed model maintains the desired indoor environment for maximizing plant production with reduced energy consumption, i.e., it achieves 61.97% reduced energy consumption than the baseline scheme, 11.73% better than the optimization scheme without learning modules. Furthermore, the proposed model achieves 67.96% and 12.56% reduction in cost when compared to the baseline scheme and optimization scheme without learning modules, respectively.