Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Cluster Size for Wireless Sensor Networks

: Since energy consumption is one of the main challenges in wireless sensors networks performance and efficiency, this paper discusses and proposes a mathematical model that compromises these two crucial parameters. In addition, number of clusters, cluster size, and number of cluster heads all contribute in network robustness; they have been studied and manipulated in details. This paper suggests a mathematical model that calculates and finds the optimal cluster size and number of sensors for Low Energy Adaptive Cluster Hierarchy (LEACH) protocol. Wireless Sensor Network (WSN) is a network includes devices (sensors) for collecting data by sensing the field. Clustering is one of the simplest strategies used to minimize the power lost in WSNs. Its performance is affected by network area and number of sensors. The cluster head is elected based on traditional Low Energy Adaptive Cluster Hierarchy protocol. The optimal cluster number is detected based on the condition of the LEACH delivery ratio. NS2.35 and MannaSim framework are applied for validating the proposed model. The results show that the proposed model is approved at different areas and nodes density. The optimal cluster size obtained by the proposed model is equivalent to optimal clusters number for 100 % delivery ratio of LEACH.
Adaptive Neural Network Command Filtered Backstepping Control for the Underactuated TORA System

An adaptive neural network command-filtered backstepping design algorithm is proposed for the underactuated translational oscillator with a rotating actuator (TORA). The system dynamics are transformed into a nonlinear cascade system through a global change of coordinates. Considering the weak sinusoid-type nonlinear interaction and affine-free appearance in the cascade model, the TORA is looked at as a pure feedback system. Two neural networks are used to approximate the unknown functions and a command filter is used to produce the virtual control and its first and second-order derivatives to overcome the explosion of complexity problems in backstepping. A filter error compensation dynamic system is designed to overcome the error influence on control performance. Taking full account of the cascade model structure of the underactuated TORA, each backstepping step is designed for a second-order subsystem to reduce the design steps and simplify the design process. The system stability is proved through the Lyapunov stability theorem and verified by numerical simulations.
Exploring Care Home Workers’ Views on Augmented Reality and Virtual Reality Hand Hygiene Training: A Realist Interview Study

Augmented reality (AR) and virtual reality (VR) hand hygiene training has been developed for healthcare workers, but has not yet been applied to care homes. This qualitative realist study seeks to capture care home workers’ theories concerning how and in what contexts AR or VR training delivered via smartphone apps might work in promoting their hand hygiene practice. Semistructured interviews were conducted with 25 care home workers across three residential and three nursing care homes in Northern England. The interviews explored hand hygiene practices and training in care homes. Participants also provided feedback on an existing programme theory that explains how, for whom, and in which contexts AR/VR might be used to upskill health and care workers. Data were analysed on NVivo using a combined deductive and inductive approach. Participants had varying experiences with hand hygiene training, highlighting an opportunity to refresh skills. Repeated practice, interactive learning, feedback, reminding, and perceived task realism were considered important for hand hygiene and could be trigged by AR/VR. There was less support for other mechanisms including deep immersion, practice in a safe environment, visualisation of personal anatomy (hands) and mistakes, and perceived realism of the imagery and environment. Expected outcomes were effective learning, learner satisfaction, enhanced hand hygiene skills, and infection control. Three themes explained how context matters when implementing AR/VR training: learners (e.g., comfort and preference), care homes (e.g., policies, staffing, and infrastructure), and technology (e.g., cost and using a VR headset). Overall, participants theorised that AR/VR could refresh hand hygiene and trigger repeated practice, interactive learning, feedback and reminding, and task realism. Future work should test the mechanisms and technology in care homes. Hand hygiene training in care homes should consider implementation factors by offering alternative technologies, providing technical support, and working with managers to determine the most appropriate choice of technology.
On the competitive facility location problem with a Bayesian spatial interaction model

The competitive facility location problem arises when businesses plan to enter a new market or expand their presence. We introduce a Bayesian spatial interaction model which provides probabilistic estimates on location-specific revenues and then formulate a mathematical framework to simultaneously identify the location and design of new facilities that maximise revenue. To solve the allocation optimisation problem, we develop a hierarchical search algorithm and associated sampling techniques that explore geographic regions of varying spatial resolution. We demonstrate the approach by producing optimal facility locations and corresponding designs for two large-scale applications in the supermarket and pub sectors of Greater London.
Investigation of the Underlying Mechanism of Huangqi-Dangshen for Myasthenia Gravis Treatment via Molecular Docking and Network Pharmacology

The herbal pairing of Huangqi and Dangshen (HD) is traditional Chinese herbal medicine and has been widely used in China, especially to treat myasthenia gravis (MG). However, the mechanism of HD on MG is unclear. Aim of the Study. This study aims to investigate HD's possible role in MG treatment. Materials and Methods. The TCMSP database was used to identify the active chemicals and their targets. The GeneCards, DisGeNET, and OMIM databases were used to search for MG-related targets. The STRING database was employed in order to identify the common PPI network targets. We next utilised Cytoscape 3.8. 2 for target identification and the DAVID database for gene ontology (GO) function analysis as well as Encyclopaedia of Genomes (KEGG) pathway enrichment analysis on the selected targets. The AutoDock Vina software was used to test the affinity of essential components with the hub gene before concluding that the primary targets were corrected through molecular docking. Results. 41 active compounds were screened from HD, and the number of putative-identified target genes screened from HD was 112. There were 21 target genes that overlapped with the targets of MG, which were postulated to be potential treatment targets. Through further analysis, the results showed that the active compounds from HD (such as 7-methoxy-2-methylisoflavone, quercetin, luteolin, Kaempferol, and isorhamnetin) may achieve the purpose of treating MG by acting on some core targets and related pathways (such as EGFR, FOS, ESR2, MYC, ESR1, CASP3, and IL-6). Molecular docking findings demonstrated that these active molecules have a near-perfect ability to attach to the primary targets. Conclusion. Through network pharmacology, the findings in this study provide light on the coordinated action of several HD formula components, targets, and pathways. It provided a theoretical basis for further study of HD pharmacological action.
A Genetic Algorithm based Method for Face Localization and pose Estimation

This paper introduces a new method using genetic algorithms (GA's) for face localization. Face images often have a background that can affect on the face localization algorithm. So, finding the exact location of face after localization algorithm is crucial. In our algorithm, input image is first enhanced by means of histogram equalization. Then connected components are determined by applying a region growing algorithm (coarse segmentation), followed by computing the fit ellipse for face area and at least exact location of face is found by genetic algorithms method. To check the utility of our proposed algorithm, we have define d tilt error and translation error that are the difference between obtained and real tilts and centers of localized face respectively. Simulation results on the ORL database show the effectiveness of this method. As, mentioned errors are obtained 4.2 degree and 5.4, respectively.
Screw Dislocations in Light Wavefronts

Abstract We discuss screw dislocations of a phase surface as the one type of wavefront of a monochromatic wave. The simple method for construction of the optical wavefronts with an isolated screw dislocation is reported. Laser beams with the dislocations of different orders were experimentally achieved by using diffraction on computer-synthesized gratings.
The impact of organizational alignment at the purchasing & logistics interface

ABSTRACT Companies continue to report a deep divide between purchasing and logistics, resulting in dysfunctional supply chain operations. This research seeks to add to growing research stream that has begun to look specifically at purchasing-logistics alignment. In particular, this research investigates the impact that both strategic alignment with top management priorities and operational alignment between purchasing and logistics have on the performance of each function. Analysis suggested that purchasing performance was strongly influenced by operational alignment between purchasing and logistics, but that strategic alignment with top management had little appreciable effect. The reverse was found for logistics: strategic alignment had a strong impact on logistics performance while operational alignment between the functions had little impact. While not definitive, the results are thought provoking in that they run counter to established narratives in the literature about which dimension of alignment is expected to be most relevant for driving purchasing and logistics performance, respectively.
On multi-input second order sliding mode control of nonlinear systems with uncertainty

Some works presented in the literature have shown the effectiveness of second order sliding modes (i.e. a sliding regime on a set s(x(t))=s/spl dot/(x(t))=0 enforced by a control signal directly acting on s/spl uml/(t)) in controlling uncertain systems. Such an approach, in its native formulation, only applies to single-input nonlinear systems with particular types of uncertainties. In this paper the validity of a second order sliding mode control algorithm, previously presented by the authors (1997, 1999), is extended to some classes of multi-input nonlinear uncertain systems, covering a wide class of real processes. The chattering avoidance feature of the proposed second order sliding mode control scheme is presented as an example for a class of multi-input systems.
Model Reduction of Bilinear System using Genetic Algorithm

In designing control system, it is important to minimize the order of controller since the high order controller is impractical. In this paper we propose a method to obtain the minimum order of bilinear system by using the genetic algorithm. The process of optimization is to minimize the 2 H -norm from the transfer function from input to output which implies suppressing the magnitude peaks of the frequency response. This problem is solved by using the genetic algorithm. To verify the proposed method, the simulation and numerical calculations is carried out on a nonlinear RC circuit and the results show that proposed method can able to approximate a high order bilinear model from 200 th order to the 1 st order better than the existing method.
Overflow analysis of a fixed-point implementation of the Goertzel algorithm

It is shown that the second-order Goertzel algorithm, though favored over the first-order Goertzel algorithm for its reduced computational complexity, is in fact prone to overflows when implemented in hardware with only fixed-point arithmetic. An overflow analysis reveals that the second-order Goertzel algorithm requires a different scaling factor at each frequency sample to systematically eliminate the possibilities of overflow. >
Emotion Classification from Multi-Band Electroencephalogram Data Using Dynamic Simplifying Graph Convolutional Network and Channel Style Recalibration Module

Because of its ability to objectively reflect people’s emotional states, electroencephalogram (EEG) has been attracting increasing research attention for emotion classification. The classification method based on spatial-domain analysis is one of the research hotspots. However, most previous studies ignored the complementarity of information between different frequency bands, and the information in a single frequency band is not fully mined, which increases the computational time and the difficulty of improving classification accuracy. To address the above problems, this study proposes an emotion classification method based on dynamic simplifying graph convolutional (SGC) networks and a style recalibration module (SRM) for channels, termed SGC-SRM, with multi-band EEG data as input. Specifically, first, the graph structure is constructed using the differential entropy characteristics of each sub-band and the internal relationship between different channels is dynamically learned through SGC networks. Second, a convolution layer based on the SRM is introduced to recalibrate channel features to extract more emotion-related features. Third, the extracted sub-band features are fused at the feature level and classified. In addition, to reduce the redundant information between EEG channels and the computational time, (1) we adopt only 12 channels that are suitable for emotion classification to optimize the recognition algorithm, which can save approximately 90.5% of the time cost compared with using all channels; (2) we adopt information in the θ, α, β, and γ bands, consequently saving 23.3% of the time consumed compared with that in the full bands while maintaining almost the same level of classification accuracy. Finally, a subject-independent experiment is conducted on the public SEED dataset using the leave-one-subject-out cross-validation strategy. According to experimental results, SGC-SRM improves classification accuracy by 5.51–15.43% compared with existing methods.
The Combined Effect of the Trapezius Muscle Strengthening and Pectoralis Minor Muscle Stretching on Correcting the Rounded Shoulder Posture and Shoulder Flexion Range of Motion among Young Saudi Females: A Randomized Comparative Study

Background: The shortening of the pectoralis minor muscle (PMi-M) and weakening of the lower trapezius muscle (LTr-M) affect scapular movement, resulting in the development of a rounded shoulder posture and reduction in the shoulder flexion range of motion (SFROM). Objective: This study evaluated the combined effect of LTr-M strengthening and PMi-M stretching on correcting the rounded shoulder postures and SFROM among young Saudi females. Methods: This study was based on a two-arm parallel-group repeated measures randomized comparative design. A total of sixty female participants with rounded shoulder postures were recruited and randomly allocated into groups 1 and 2 (n = 30/group). Each group performed supervised PMi-M stretching; however, group 2 performed a combination of LTr-M strengthening and PMi-M stretching. The outcomes, including rounded shoulder posture and SFROM, were assessed using the pectoralis minor length test (PMLT) and universal goniometer. A repeated measure ANOVA was used to compare the differences within-group and between-group for the outcomes measures at one-week (baseline) pre-intervention, two weeks, and three -weeks post-intervention. The significance level was set at q > 2.00 and p < 0.05 for all respective statistical analyses. Results: The within-group comparison revealed significant improvements (q > 2.00) in the outcomes of PMLT and SFROM when comparing their post-intervention scores to the baseline scores. The between-group comparison revealed a significant and an insignificant (q < 2.00) difference in the outcomes of PMLT and SFROM, respectively when comparing their scores at the second- and third-week post-intervention. Furthermore, the effect size of the intervention suggests an advantage of group 2 over group 1 in increasing the resting length of the PMi-M only among young Saudi females. Conclusions: The combined effect of LTr-M strengthening and PMi-M stretching was more beneficial than PMi-M stretching alone in correcting the rounded shoulder posture among young Saudi females by increasing PMi-M resting length. However, it could not yield a differential improvement in the SFROM outcome among them.
Time- and Sex-Dependent Effects of Fingolimod Treatment in a Mouse Model of Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common cause of dementia. Fingolimod has previously shown beneficial effects in different animal models of AD. However, it has shown contradictory effects when it has been applied at early disease stages. Our objective was to evaluate fingolimod in two different treatment paradigms. To address this aim, we treated male and female APP-transgenic mice for 50 days, starting either before plaque deposition at 50 days of age (early) or at 125 days of age (late). To evaluate the effects, we investigated the neuroinflammatory and glial markers, the Aβ load, and the concentration of the brain-derived neurotrophic factor (BDNF). We found a reduced Aβ load only in male animals in the late treatment paradigm. These animals also showed reduced microglia activation and reduced IL-1β. No other treatment group showed any difference in comparison to the controls. On the other hand, we detected a linear correlation between BDNF and the brain Aβ concentrations. The fingolimod treatment has shown beneficial effects in AD models, but the outcome depends on the neuroinflammatory state at the start of the treatment. Thus, according to our data, a fingolimod treatment would be effective after the onset of the first AD symptoms, mainly affecting the neuroinflammatory reaction to the ongoing Aβ deposition.
Modeling the Rheology of Polymer Melts and Solutions

We review constitutive modeling of solutions and melts of linear polymers, focusing on changes in rheological behavior in shear and extensional flow as the concentration increases from unentangled dilute, to entangled, to dense melt. The rheological changes are captured by constitutive equations, prototypes of which are the FENE-P model for unentangled solutions and the DEMG model for entangled solutions and melts. From these equations, and supporting experimental data, for dilute solutions, the extensional viscosity increases with the strain rate from the low–strain rate to the high–strain rate asymptote, but in the densely entangled state, the high–strain rate viscosity is lower than the low–shear rate value, especially when orientation-dependent friction is accounted for. In shearing flow, shear thinning increases dramatically as the entanglement density increases, which can eventually lead to a shear-banding inhomogeneity. Recent improvements in constitutive modeling are paving the way for robust and .
Analysis of Single Event Transients in Arbitrary Waveforms Using Statistical Window Analysis

Window or taper functions are commonly used in data processing to detect transient events or for time-averaging of frequency spectra. A generalized window function is demonstrated using the ionizing radiation effects spectroscopy (IRES) technique to enhance the measurement of transient anomalies within arbitrary waveforms. The IRES filter is used to convolve time data with a sliding window consisting of a moment-generating function. The resulting time-dependent statistical moments can be used to eliminate any steady-state signatures, including noise, and extract transient behaviors. The IRES filter is used to analyze data from heavy-ion exposures of commercial off-the-shelf (COTS) operational amplifiers (Op-Amps), laser-induced transients in CMOS phase-locked loops (PLLs), and simulated transients in digital and analog circuits. The performance of the IRES filter in noisy environments shows that transients can be measured with higher fidelity than standard amplitude thresholding. This statistical window analysis technique may remove the need for complex triggering mechanisms on instrumentation and does not require a priori knowledge of transient characteristics. Potential applications of IRES include real-time measurement, in situ data analysis, and machine learning (ML).
Multipole expansions and plane wave representations of the electromagnetic field

A new and conceptually simple derivation is presented of the multipole expansion of an electromagnetic field that is generated by a localized, monochromatic charge‐current distribution. The derivation is obtained with the help of a generalized plane wave representation (known also as the angular spectrum representation) of the field. This representation contains both ordinary plane waves, and plane waves that decay exponentially in amplitude as the wave is propagated. The analysis reveals an intimate relationship between the generalized plane wave representation and the multipole expansion of the field and leads to a number of new results. In particular, new expressions are obtained for the electric and magnetic multipole moments in terms of certain components of the spatial Fourier transform of the transverse part of the current distribution. It is shown further that the electromagnetic field at all points outside a sphere that contains the charge‐current distribution is completely specified by the radiation pattern (i.e., by the field in the far zone). Explicit formulas are obtained for all the multipole moments in terms of the radiation pattern.
Item response theory : parameter estimation techniques

The Item Characteristic Curve: Dichotomous Response Estimating the Parameters of an Item Characteristic Curve Maximum Likelihood Estimation of Examinee Ability Maximum Likelihood Procedures for Estimating Both Ability and Item Parameters The Rasch Model Marginal Maximum Likelihood Estimation and an EM Algorithm Bayesian Parameter Estimation Procedures The Graded Item Response Nominally Scored Items Markov Chain Monte Carlo Methods Parameter Estimation with Multiple Groups Parameter Estimation for a Test with Mixed Item Types
Periodic and event‐based impulse control for linear stochastic systems with multiplicative noise

This paper studies the performance comparison of periodic and event‐based sampling for a class of linear stochastic systems with multiplicative noise, where the impulse control is adopted. By solving boundary value problems, we obtain the analytic expressions of the mean sampling time and the average state variance under the event‐based sampling. It is shown that the event‐based impulse control has substantially smaller average state variance than the periodic control under the same sampling frequency. Particularly, for the integrator case, the performance ratio of the two sampling methods is given explicitly. By simulation, it is demonstrated that the advantage of event‐based sampling over periodic sampling is most obvious for unstable systems, followed by critical stable systems, and least obvious for stable systems.