Miracle-dz/UniFAR-FTUs huggingface dataset:
BUCKLING ANALYSES OF CROSS LAMINATED TIMBER PANELS

: This paper investigates the column buckling behaviour of three-layer Cross Laminated Timber (CLT) panels under compression, from both the experimental and numerical point of view. The main aim of present study is hence to define the expected load-bearing capacity for these composite CLT solutions, and to assess the typical fracture mechanism for two different series of specimens of possible technical interest for construction applications. To this aim, a total of 14 column buckling experiments is carried out. First, a set of 7 homogeneous specimens (“HO” series), which are entirely made of beech, are investigated. Their load-bearing capacity is compared with the column buckling performance of 7 hybrid specimens (“HB” series), whose inner layers are made of Corsican pine. Overall, the experimental analysis gives evidence of a rather stable column buckling capacity for CLT panels, with evidence of major failure mode due to out-of-plane bending phenomena, but also rolling shear and delamination. Finally, further assessment of experimental evidence is provided by extended analytical calculations (based on existing formulations, including the Eurocode 5 approach). Comparative results are discussed in terms of structural performance, capacity, weakness of analytical models for CLT solutions.
Dynamic pricing and replenishment: Optimality, bounds, and asymptotics

In many applications, managers face the problem of replenishing and selling products during a finite time horizon. We investigate the problem of making dynamic and joint decisions on product replenishment and selling in order to improve profit. We consider a backlog scenario in which penalty cost (resulting from fulfillment delay) and accommodation cost (resulting from shortage at the end of the selling horizon) are incurred. Based on continuous‐time and discrete‐state dynamic programming, we study the optimal joint decisions and characterize their structural properties. We establish an upper bound for the optimal expected profit and develop a fluid policy by resorting to the deterministic version of the problem (ie, the fluid problem). The fluid policy is shown to be asymptotically optimal for the original stochastic problem when the problem size is sufficiently large. The static nature of the fluid policy and its lack of flexibility in matching supply with demand motivate us to develop a “target‐inventory” heuristic, which is shown, numerically, to be a significant improvement over the fluid policy. Scenarios with discrete feasible sets and lost‐sales are also discussed in this article.
Sensorless Control and Inductances Estimation of IPMSMs Using FPGA and High Bandwidth Current Measurement

This paper presents a sensorless control method and a very fast on-line inductances identification of an IPMSM. The current slopes (derivatives) at one active-voltage vector and one zero-voltage vector are measured every PWM cycle to estimate the rotor speed and position with the aim of increasing the estimation accuracy and reducing total harmonic distortion of the phase currents. In addition to these current slopes, the DC bus voltage of the inverter is measured to estimate the machine inductances on-line. The proposed on-line parameter identification method can overcome the drawback of the existing off-line methods, such as: the requirement of a robust mechanical clamping system and test signal generators. The proposed on-line method also addresses the limit of the existing on-line methods, such as slow update and the possibility of incorrect convergence of the estimated parameters. Extensive experimental studies were conducted to verify the effectiveness and robustness of the proposed sensorless control and parameters identification of the IPMSM.
On the Cauchy problem for the XFEL Schrödinger equation

In this paper, we consider the Cauchy problem for the nonlinear Schrodinger equation with a time-dependent electromagnetic field and a Coulomb potential, which arises as an effective single particle model in X-ray free electron lasers(XFEL). We firstly show the local and global well-posedness for the Cauchy problem under the assumption that the magnetic potential is unbounded and time-dependent, and then obtain the regularity by a fixed point argument.
Czech Adaptation of Test 13 Visual-Auditory Learning Included in Woodcock-Johnson IV Tests of Cognitive Abilities

The main goal of this article is to describe the process of adaptation of Test 13: Visual-Auditory Learning, part of the Woodcock-Johnson IV Tests of Cognitive Abilities, to Czech. Specific attention is given to explaining how the authors of the adaptation dealt with the differences of the two languages between which the adaptation occurred. Test 13 is a controlled learning task and consists of Test Stories, in which symbols represent the words. Some of the differences between English and Czech did not allow simple translation of the words, deeper changes were required. The analysis of standardization data set showed an excellent reliability, however, to confirm the validity of the Czech version, further empirical studies are needed. The uniqueness of this article lies on the description of adaptation of the test to the language from the Slavic language group. It can be useful for other researchers from Slavic languages speaking countries.
Schooling for multi-agent systems via impulsive containment control algorithms with quantized information

In the paper, schooling problems based on containment control in multi-agent systems that have static or dynamic leaders under directed and undirected communication topologies are investigated. We propose a periodic impulsive containment control algorithm to realize schooling in multi-agent systems. Both ideal and quantized relative state measurements are considered under this framework. Some necessary and sufficient conditions, which depend on the eigenvalues of the Laplacian matrix that is associated with the communication graph, the impulsive period as well as the gain parameters, are obtained to realize the containment control of schooling. Finally, some numerical simulations are illustrated to verify the theoretical results.
On Comparing Survival Curves with Right-Censored Data According to the Events Occur at the Beginning, in the Middle and at the End of Study Period

In clinical practice the event of interest does not always occur equally across the study time period. Depending on the disease being investigated, the event that is of interest can occur intensively in different periods of the follow-up time. In such cases, choosing the correct survival comparison test has importance. This study aims to examine and discuss the results of survival comparison tests under some certain circumstances. A simulation study was conducted. We discussed the result of different tests such as Logrank, Gehan-Wilcoxon, Tarone-Ware, Peto-Peto, Modified Peto-Peto tests and tests belonging to Fleming-Harrington test family with (p, q) values; (1, 0), (0.5, 0.5), (1, 1), (0, 1) ve (0.5, 2) by means of Type I error rate that obtained from simulation study, when the event of interest occurred intensively at the beginning of the study, in the middle of the study and at the end of the study time period. As a result of simulation study, Type I error rate of tests is generally lower or higher than the nominal value. In the light of the results, it is proposed to re-examine the tests for cases where events are observed intensively at the beginning, middle and late periods, to carry out new simulation studies and to develop new tests if necessary.
An iterative algorithm for optimal variable weighting in K-means clustering

ABSTRACT The K-means clustering method is a widely adopted clustering algorithm in data mining and pattern recognition, where the partitions are made by minimizing the total within group sum of squares based on a given set of variables. Weighted K-means clustering is an extension of the K-means method by assigning nonnegative weights to the set of variables. In this paper, we aim to obtain more meaningful and interpretable clusters by deriving the optimal variable weights for weighted K-means clustering. Specifically, we improve the weighted k-means clustering method by introducing a new algorithm to obtain the globally optimal variable weights based on the Karush-Kuhn-Tucker conditions. We present the mathematical formulation for the clustering problem, derive the structural properties of the optimal weights, and implement an recursive algorithm to calculate the optimal weights. Numerical examples on simulated and real data indicate that our method is superior in both clustering accuracy and computational efficiency.
The Impact that the Necessary Changes due to COVID-19 had on the Motivation of Allied Health Professionals within a Public Hospital

The objective of this research focuses on the impact that the necessary changes due to the COVID-19 pandemic had on the motivation of a specific cohort of healthcare workers within a public hospital in Malta (Mater Dei Hospital). The changes introduced by the local public healthcare system, which were necessary to cope with the increased stress caused by the pandemic, were: staff relocations, changes in work practices, and changes in working hours. The study explored in depth the impact of these changes on allied health professionals (AHP) motivation, acknowledging the importance of the self-determination theory. During analysis, autonomy, relatedness, and competence were the three domains of motivations explored.The hypothesis focuses on the effect of the specified changes on these three domains of motivation. The study will therefore validate whether there was an impact, or not, on each of these motivational aspect of allied health professionals by the necessary changes due to the COVID-19 pandemic within a public hospital. The ultimate purpose is to discover relations and variableswhich are scientifically measuredbetween these implemented changes and affected motivation aspects. The aim is that healthcare management takes preventive measures in future pandemic recurrences to reduce negative impacts on motivation.
Early Fault Detection in Particle Accelerator Power Electronics Using Ensemble Learning

Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 early fault detection experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemble trees, convolutional neural networks, support vector machines, and hierarchical voting ensembles are proposed to detect the fault precursors. Although all 9 models have shown a perfect and identical performance during the training and testing phase, the performance of most models has decreased in the next test phase once they got exposed to realworld data from the 21 experiments. The hierarchical voting ensemble, which features multiple layers of diverse models, maintains a distinguished performance in early detection of the fault precursors with 95% success rate (20/21 tests), followed by adaboost and extremely randomized trees with 52% and 48% success rates, respectively. The support vector machine models were the worst with only 24% success rate (5/21 tests). The study concluded that a successful implementation of machine learning in the SNS or particle accelerator power systems would require a major upgrade in the controller and the data acquisition system to facilitate streaming and handling big data for the machine learning models. In addition, this study shows that the best performing models were diverse and based on the ensemble concept to reduce the bias and hyperparameter sensitivity of individual models.
Business network and balanced scorecard: an analysis of small and medium enterprises in Malaysia

PurposeThe aim of this paper is to characterise the association between business network and the balanced scorecard used by Malaysian small and medium enterprises (SMEs) as a method for assessing firm efficiency. The business network takes into account both the dimensions of stability and efficiency. The business network can help SMEs, with fewer resources to remain competitive. By having a secure business network, the performance of SMEs in Malaysia can be further improved. A business network can facilitate swift coordination amongst distant geographies to create new competitive advantages by accessing market segments, resources as well as building strategic business alliances.Design/methodology/approachA total of 404 sets of data collected by using stratified random sampling and structured questionnaire as an instrument. The list of SMEs collected from the Malaysia Foreign Trade Growth Corporate Directory (MATRADE) directories. Structural equation modelling (SEM) was utilised to analyse the data.FindingsThe findings show that the business network plays a role in the balanced scorecard (BSC) outcomes of Malaysian SMEs.Originality/valueThis article provides the owners and managers with an awareness to rapidly achieve the company's efficiency. Finally, the new article often has some consequences for decision-makers and regulators.
Wide E-Plane Beamwidth Microstrip Patch Antenna Using H-Shaped Gap-Coupling with Three Parasitic Patches for the K-Band

This article presents a new wide E-plane beamwidth rectangular microstrip patch antenna adopting H-shaped gap-coupling with three parasitic patches for the K-band vehicle parking monitoring system, while keeping single substrate. Both side and upper parasitic elements serve as λ and λ/2 resonators and are excited primarily by magnetic and electric couplings, respectively. The synthesis of radiation patterns from main and auxiliary elements results in the E-plane wide-beamwidth property. The proposed gap-coupling antenna provided the peak gain of 4.82 dBi, E-plane half-power beamwidth (HPBW) of 143.4°, and co-to-cross-pol ratios of 22.7 and 20.2 dB within the HPBW for the E- and H-planes, respectively. The presented geometry showed excellent figure of merits compared with others for the K-band.
Diffraction of pulse sound signals on elastic spheroidal shell, put in plane waveguide

At the basis of the method of the imaginary sources and imaginary scatterers is solved the problem of scattering of the pulse signals of the elastic spheroidal shell, accommodated in the plane waveguide with the ideal boundary conditions. The impulse signals put the energy, therefore they are propagating with the group velocity, which lie in the principles of the method of the imaginary sources and imaginary scatterers.
Investigating Acoustic Similarities of Auditory Elephant Deterrents to Optimize Current Techniques

One of the primary reasons elephants are endangered is human-elephant conflict (HEC), the opposition that occurs between elephants and the humans living nearby. The violence that erupts in settings of HEC, such as crop fields, often results in both human and elephant deaths as both species struggle to coexist. Many methods are being researched to mitigate HEC, including playing audio playbacks that trigger flight responses in elephants near crop fields and reduce chance of contact and destruction. Habituation to these stimuli creates the demand for a greater number and more types of auditory deterrents, but it would be unethical and inefficient to immediately jump to tests with crop fields without first verifying these playbacks are at least somewhat effective. Thus, this paper aims to analyze currently used auditory deterrents to determine if any acoustic similarities exist between them, and create a generalization for what characteristics make up an effective auditory deterrent. The results will help optimize current playbacks and help create a threshold of characteristics to use before future testing, to reduce habituation and human-elephant conflict.
Leveraging metaheuristics with artificial intelligence for customer churn prediction in telecom industries

Customer churn prediction (CCP) is among the greatest challenges faced in the telecommunication sector. With progress in the fields of machine learning (ML) and artificial intelligence (AI), the possibility of CCP has dramatically increased. Therefore, this study presents an artificial intelligence with Jaya optimization algorithm based churn prediction for data exploration (AIJOA-CPDE) technique for human-computer interaction (HCI) application. The major aim of the AIJOA-CPDE technique is the determination of churned and non-churned customers. In the AIJOA-CPDE technique, an initial stage of feature selection using the JOA named the JOA-FS technique is presented to choose feature subsets. For churn prediction, the AIJOA-CPDE technique employs a bidirectional long short-term memory (BDLSTM) model. Lastly, the chicken swarm optimization (CSO) algorithm is enforced as a hyperparameter optimizer of the BDLSTM model. A detailed experimental validation of the AIJOA-CPDE technique ensured its superior performance over other existing approaches.
Physics–Dynamics Coupling with Element-Based High-Order Galerkin Methods: Quasi-Equal-Area Physics Grid

Atmospheric modeling with element-based high-order Galerkin methods presents a unique challenge to the conventional physics–dynamics coupling paradigm, due to the highly irregular distribution of nodes within an element and the distinct numerical characteristics of the Galerkin method. The conventional coupling procedure is to evaluate the physical parameterizations ( physics) on the dynamical core grid. Evaluating the physics at the nodal points exacerbates numerical noise from the Galerkin method, enabling and amplifying local extrema at element boundaries. Grid imprinting may be substantially reduced through the introduction of an entirely separate, approximately isotropic finite-volume grid for evaluating the physics forcing. Integration of the spectral basis over the control volumes provides an area-average state to the physics, which is more representative of the state in the vicinity of the nodal points rather than the nodal point itself and is more consistent with the notion of a “large-scale state” required by conventional physics packages. This study documents the implementation of a quasi-equal-area physics grid into NCAR’s Community Atmosphere Model Spectral Element and is shown to be effective at mitigating grid imprinting in the solution. The physics grid is also appropriate for coupling to other components within the Community Earth System Model, since the coupler requires component fluxes to be defined on a finite-volume grid, and one can be certain that the fluxes on the physics grid are, indeed, volume averaged.
Contrastive analysis in China and abroad on the evolution of hot topics in the field of digital library based on LDA model

Revealing� and� comparing� the� evolution� process� of� hot� topics� in� the� field� of� Digital� Library� in� China� and� abroad. � [Methods]:� Taking� data� in� the� field� of� Digital� Library� from� core� journals� in� CKNI�and�Web�of�Science�from�1990s�to�2020,�topics�are�extracted�by�LDA�model�and�hot�topics� are�selected�based�on�life�cycle�theory. �Topic�evolution�paths�are�generated�to�contrast�evolution� of�hot�topics�between�home�and�abroad�which�are�grouped�into�dimensions�of�technology�and� application. �It�fails�to�analyze�the�lagging�performance�and�reasons�of�research�hot�topics�in�the� field� of� Digital� Library� at� home� and� abroad. � In� technological� dimension� of� Digital� Library,� the� research�content�in�China�lags�behind�that�at�abroad. �In�terms�of�application�dimension,�Chinese� application� tends� to� focus� on� social� sciences,� while� application� at� abroad� tends� to� focus� on� natural� sciences. � The� evolution� of� overall� research� focus� is� U-shaped,� which� gradually� shifted� from� technological� research� to� application� research,� and� now� turn� back� to� technological� dimension. �Nowadays,�there�are�also�many�emerging�topics�combined�with�big�data�technology.
Urban vs Rural: The Young Generation's Understanding of the Lexicon of Subak (Balinese Traditional Irrigation System)

This study aimed at exploring the lexicon of Subak which defines as the traditional Balinese Irrigation System written in the Balinese texts and at analyzing the Balinese young generation's understanding of the lexicon. This research was conducted using a mixed-method research model with an exploratory sequential design. Qualitative data were collected through documentation and interview techniques. The researcher collected eleven ancient manuscripts and interviewed five informants, two ancient manuscript experts, and three Subak leaders, who were selected using a purposive sampling technique. Quantitative data was collected using a test on the Subak lexicon to 100 high school students in cities and 100 students in villages. These students were selected using simple random sampling. The collected qualitative data was analyzed using an interactive data analysis model. While the qualitative data from the test results were analyzed using descriptive statistics and t-tests. This study found that there were 110 lexicons which were grouped into five categories, namely: management of subak, rituals, tools and techniques for planting rice, animals and plants in rice fields, and traditional Balinese rice field infrastructure. In terms of the young generation’s understanding, it was found that there was a significant difference between the understanding of students in the village and the city, which means that the location where they live affects the understanding. Even though the different levels of understanding differed significantly, in general, their understanding was still relatively low. From these findings, it can be concluded that Subak has various special lexicons, but the younger generation of Bali does not understand these lexicons well.
Adjustable Potential Evaluation of Massive Flexible Load Resources from the Perspective of CPS

With the demand-side flexible resources attracting widespread attention, the potential evaluation of these resources becomes the primary issue in their use. However, the efficient use of massive resources is not only determined by its physical adjustment capabilities but also affected by information systems. Therefore, it is necessary to comprehensively consider the potential evaluation of these resources from the perspective of cyber-physical systems. An adjustable potential evaluation method of massive flexible load resources is proposed here under the framework of the cloud-edge collaboration cyber system (CECS). Firstly, the CECS framework of regulating massive demand-side resources is constructed, followed by the modeling of communication latency and reliability. Then, the maximum adjustable potential evaluation model is established based on a fixed time-delay search. Finally, a deep reinforcement learning method combined with the k shortest path algorithm is proposed to solve the optimization model based on proximal policy optimization. The numerical result shows the feasibility and rationality of the proposed method.