Miracle-dz/UniFAR-FTUs huggingface dataset:
Coherent forecasting for over-dispersed time series of count data

In the context of an over-dispersed count time series data on disease incidences, we consider the Geometric integer-valued autoregressive process of order 1 or GINAR(1), which was ﬁrst introduced by McKenzie ( Adv. Appl. Probab. 18 (1986) 679–705) as an analogue of continuous AR(1) process with exponential margin ( Adv. Appl. Probab. 12 (1980) 727–745) on the positive support ( R + ). A strong enthusiasm still persists as it is appar-ent from Risti´c et al. ( J. Stat. Plann. Inf. 139 (2009) 2218–2226). Coherent forecasting of Poisson INAR(1) process due to Al-Osh and Alzaid ( J. Time Ser. Anal. 8 (1987) 261–275) was studied by Freeland and McCabe ( Int. J. Forecast. 20 (2004) 427–434). Here, we study the h -step ahead forecasting distribution corresponding to GINAR(1) process in details using probability generating function. Large sample distributions of the conditional least squares estimates of the model parameters are derived. Some numerical study is performed to illustrate the theoretical results.
The Character of the Stress‐Strain Function for Highly Filled Elastomers

The uniaxial stress‐strain behavior of highly filled elastomers is examined in terms of a simplified thermodynamic model. Experimental data are presented showing the stress‐strain‐dilatational behavior at a series of hydrostatic pressures from which the behavior at constant volume can be obtained. Deviations from this constant volume stress‐strain state are shown to be dependent upon the rate at which pressure‐volume and surface energies are being expended by the material due to the formation and growth of vacuoles. Mathematically and experimentally the behavior is considered to be essentially reversible as experimental data indicates. Therefore, the only forms of energy considered are mechanical, and energy balances are made using only force‐deflection, pressure‐volume, and surface energies. This method of approach accounts for the energy sinks which reduce the rate at which the material accumulates mechanical energy and indicates what factors govern the stress‐strain response.
PRICING STOCK OPTIONS USING BLACK-SCHOLES AND FUZZY SETS

We use the basic Black-Scholes equation for pricing European stock options but we allow some of the parameters in the model to be uncertain and we model this uncertainty using fuzzy numbers. We compute the fuzzy number for the call value of option with and without uncertain dividends. This fuzzy set displays the uncertainty in the option's value due to the uncertainty in the input values to the model. We also correct an error in a recent paper which also fuzzified the Black-Scholes equation.
Investigation of the wave spectrum gap in the JT-60 LHCD plasma

To understand the physics of lower hybrid current drive (LHCD), an investigation into the wave spectrum gap was carried out in JT-60, using two lower hybrid wave launchers. From one launcher a wave with high phase velocity was injected; this wave can interact with high energy electrons to drive the current. Simultaneously, from the other launcher a wave with slower phase velocity was injected; this wave can interact with the bulk electrons. A high energy neutral beam was also injected in order to investigate the interaction of lower hybrid waves with ions. The results show that a fraction of the high phase velocity wave which is upshifted to fill the spectrum gap in the case where only one wave is injected is replaced by a slower wave when two waves are injected. As a result, the tail electron parameters (tail electron density and/or average tail electron energy) were improved while the interaction of the waves with ions decreased, because the frequency of the slower wave was chosen so that the wave did not interact with ions
Zusammenhang zwischen ebenen und rotationssymmetrischen Grenzschichten in kompressiblen Flüssigkeiten

Verfasser zeigt, das die Berechnung der Geschwindigkeits-, Temperatur- und Dichteverteilung in der laminaren Grenzschicht eines symmetrisch angestromten Rotationskorpers durch eine einfache Transformation auf die der entsprechenden Grosen einer ebenen, laminaren Grenzschicht zuruckgefuhrt werden kann. Subject of the treatise is the computation of the distribution of velocity, temperature, and density in the boundary layer of a rotatory body symmetrically flown round by a laminar flow. By a simple transformation, this computation can be reduced to that of the distribution of the corresponding values appearing in a two dimensional boundary layer in the case of a laminar flow.
Flatness-based boundary control of a class of quasilinear parabolic distributed parameter systems

Motion planning and control of a boundary controlled quasilinear parabolic partial differential equation in one spatial variable is considered. The approach relies on a flatness property of the system; namely, that the system solution can be differentially parameterized in terms of a flat output which, in the case considered, is a boundary value. Such a parameterization allows straightforward motion planning and computation of a control law. The approach is based on power series in the spatial variable, and the convergence of these series is ensured by choosing the flat output to be a nonanalytic, smooth function of appropriate Gevrey class.
Combined Gradient and Newton Projection Quadratic Programming Solver for MPC

Abstract The objective of this paper is to present an effective on-line solver for simple constrained quadratic programming (QP) which arises in linear model predictive control (MPC) framework. In MPC, the QP is solved at each sampling time, thus a fast solver must be used for short sampling times in real-time applications. The multi-parametric quadratic programming (mp-QP) approach (explicit solution) is impossible to use for larger systems due to the memory limitation. On the other hand, the presented approach is well suitable even for medium scale systems with short sampling time, since it is based on combination of gradient and Newton projection algorithm which is very close to optimum in a very few iterations and the computation of the Newton step is not involved at each iteration.
Progressive integration network for single‐image rain removal

Due to the inherent overlap between the background and rain streaks, most deraining methods have suffered greatly from the over‐smoothing effect, which restrains the deraining models’ performance to a great extent. To address this issue, a progressive integration network (PIN) is presented to reduce this effect. Specifically, in our network, the high‐level representations will be used to enhance the low‐level feature maps through our proposed global integration and local integration blocks. Moreover, based on the difficulty of prediction, a novel curriculum‐learning mechanism is explored to ease the procedure of training. Extensive experimental results demonstrate that the proposed method achieves considerable performance in contrast to other deraining methods.
The Development and Validation of Jihad Academic Scale (JAS)

The purpose of this study was to construct and validate the jihad academic scale. Because jihad is broadly divided into major (non-physical) and minor (physical) jihad, this research focuses on the construction of the major (non-physical) jihad, which is carried out in the academic or how they strive to study in their education. The study consisted of a pilot study (n=61), a first study (n=183), and a second study (n=183), using a convenience sampling technique. A quantitative method with exploratory factor analysis (EFA) design followed by confirmatory factor analysis (CFA). Validity uses to construct convergent and discriminant validity—analysis using the JASP program. The results of the EFA study formed three dimensions. The CFA results stated that the proposed model was fit based on the CFI=0.94 (> 0.90) SRMR=0.05 (< 0.08) GFI=0.91 (> 0.90). But still not fit in accordance with p-value 2=0.01 (> 0.05) and RMSEA=0.06 (< 0.05). Then it has been reliably seen from α = 0.88 (> 0.70). Then the convergent validity is seen from the academic resilience scale with the correlation value of Pearson's (r = 0.41) and Spearman's (rho = 0.35) with each masing p-value < . 001. Then, for discriminant validity, it can be seen from the school burnout inventory scale with the correlation value of Pearsons (r = - .11) with p-value=. 012 and spearman's (rho = - .21) with p-value= . 00438. The normality assumption of Shapiro-Wilk (W= 0.87) with p-value < . 001. Overall, this scale can be used in a university environment and can then be developed at other levels of education with a larger number of samples to confirm each factor.
Criminal careers behind foreign bars: A latent class analysis of Dutch prisoners

While at least 1900 Dutch prisoners are imprisoned abroad annually, existing knowledge on this group of prisoners is often limited to descriptions about detention conditions. The current study explores latent classes of criminal careers before entrance in foreign prisons and whether reoffending behavior differed between the distinct typologies of Dutch prisoners. Using registration and reconviction data, the results show four typologies: (a) high persistent offenders, (b) low-rate offenders, (c) sporadic offenders, and (d) long-term incarcerated offenders. Furthermore, the results indicate that high persistent offenders are more likely to get reconvicted after release from foreign prisons than other typologies.
The Impact of Z-Score Transformation Scaling on the Validity, Reliability, and Measurement Error of Instrument SATS-36

The Likert scale is a psychometric scale commonly used for response measurement. This measurement scale includes techniques for designing and administering surveys as well as coding and analyzing data. However, Likert scaling has various limitations that can affect the resulting data. This study aims to reprove the number of dimensions of the SATS-36 instrument, prove the validity, and estimate the reliability of the statistical attitude instrument (SATS-36) on students at religious universities in Indonesia using Z-Score Transformation Scaling. The latent constructs of cognitive competence, value, difficulty, effect, and effort were constructed using a Likert scale according to the pattern of statements on each item. This study uses confirmatory research with a quantitative approach. For students at religious universities in Indonesia, 243 respondents were selected using a stratified one-stage cluster random sampling technique. Proof of validity and estimation of reliability was done using confirmatory factor analysis. The results of this study show that the rescaling method can improve the validity of the factors but cannot increase Cronbach's coefficient of internal consistency and cannot reduce the standard error of measurement for each item. This research implies that it is not enough to rescale or transform the data to improve the validity and reliability of a measuring instrument. However, it is necessary to calibrate the statement sentence or item question so that the item measures its construct. Further research also needs to test the effectiveness of rescaling in addition to the Z-Score in improving the validity and reliability of measuring instruments.
Development of a water Cherenkov neutron detector for the active rotation method and demonstration of nuclear material detection

ABSTRACT The active rotation method is an innovative, transportable, active neutron method suitable for on-site detection of concealed nuclear material. Its assay system consists mainly of a neutron detector and a rotation device for rotating a neutron source such as californium-252. Although we previously demonstrated a low-cost rotation device, the neutron detector bank comprising helium-3 proportional counters was extremely expensive and had low detection efficiency, leaving the challenges of reducing cost and measurement time. In the present study, we developed a water Cherenkov neutron detector as a novel neutron detector for the active rotation method and demonstrated its suitability and effectiveness for nuclear material detection, meeting the cost, efficiency, and transportability criteria for nuclear security. GRAPHICAL ABSTRACT
MCNPX Model/Table Comparison

MCNPX is a Monte Carlo N-Particle radiation transport code extending the capabilities of MCNP4C. As with MCNP, MCNPX uses nuclear data tables to transport neutrons, photons, and electrons. Unlike MCNP, MCNPX also uses (1) nuclear data tables to transport protons; (2) physics models to transport 30 additional particle types (deuterons, tritons, alphas, pions, muons, etc.); and (3) physics models to transport neutrons and protons when no tabular data are available or when the data are above the energy range (20 to 150 MeV) where the data tables end. MCNPX can mix and match data tables and physics models throughout a problem. For example, MCNPX can model neutron transport in a bismuth germinate (BGO) particle detector by using data tables for bismuth and oxygen and using physics models for germanium. Also, MCNPX can model neutron transport in UO{sub 2}, making the best use of physics models and data tables: below 20 MeV, data tables are used; above 150 MeV, physics models are used; between 20 and 150 MeV, data tables are used for oxygen and models are used for uranium. The mix-and-match capability became available with MCNPX2.5. b (November 2002). For the first time, we present here comparisons that calculate radiation transport in materials with various combinations of data charts and model physics. The physics models are poor at low energies (<150 MeV); thus, data tables should be used when available. Our comparisons demonstrate the importance of the mix-and-match capability and indicate how well physics models work in the absence of data tables.
A catalogue of ultra-luminous X-ray source coincidences with FIRST radio sources

Aims. We search for ultra luminous X-ray source (ULXs) radio counterparts located in nearby galaxies in order to constrain their physical nature. Methods. Our work is based on a systematic cross-identification of the most recent and extensive available ULX catalogues and archival radio data. Results. A catalogue of 70 positional coincidences is reported. Most of them are located within the galaxy nucleus. Among them, we find 11 new cases of non-nuclear ULX sources with possibly associated radio emission.
Micro-objective manipulated with optical tweezers

A microscope is described that uses a μm-sized ball lens, which is here termed micro-objective, manipulated with optical tweezers to image the side view of the arbitrary region of a sample. Since this micro-objective is small in size, it can go into a concave region to produce a local image of the inside which the conventional microscope cannot observe. Preliminary results show good lens performance from the micro-objective when combined with optical tweezers.
Infiltration holes enhance the deep soil water replenishment from a level ditch on the Loess Plateau of China

The introduction of exotic plants with high‐water consumption and high‐density planting have greatly changed the deep soil water environment on the Loess Plateau of China. Slope measures can intercept runoff to change slope soil water redistribution, however, the existing measures are not enough to improve deep soil water replenishment. Infiltration holes were used as a supplement of the existing slope measures. In this study, the effects of three different measures (the control without any measures, the level ditch, and the combination of level ditch and infiltration hole) on slope runoff and deep soil water redistribution were evaluated under field conditions. The results showed that the combined measure reduced the runoff by 44.1% and the sediments by 78.6% under an intense rainfall event compared with the control. After two rainy seasons, compared with the control and the level ditch, the combined measures significantly increased the soil water storage below 50 cm in the soil layer. Combining infiltration hole with level ditch improved the deep soil water replenishment within 30 cm from infiltration hole in a horizontal plane. Overall, the single‐level ditch was insufficient to improve deep soil water replenishment, and the combined measure can more effectively intercept runoff to increase the water replenishment of deep soils. Our findings provide a new thinking in regulating and utilizing slope runoff to effectively improve deep soil water replenishment in arid and semiarid areas.
Development of a Novel Deep Learning-Based Prediction Model for the Prognosis of Operable Cervical Cancer

Background Cervical cancer ranks as the 4th most common female cancer worldwide. Early stage cervical cancer patients can be treated with operation, but clinical staging system is not a good predictor of patients' survival. We aimed to develop a novel prognostic model to predict the prognosis for operable cervical cancer patients with better accuracy than clinical staging system. Methods A total of 13,952 operable cervical cancer patients were retrospectively enrolled in this study. The whole dataset was randomly split into a training set (n = 9,068, 65%), validation set (n = 2,442, 17.5%), and testing set (n = 2,442, 17.5%). Cox proportional hazard (CPH) model and random survival forest (RSF) model were used as baseline models for the prediction of overall survival (OS). Then, a deep survival learning model (DSLM) was developed for OS prediction. Finally, a novel prognostic model was explored based on this DSLM. Results The C-indexes for the CPH and RSF model were 0.731 and 0.753, respectively. DSLM, which had four layers that had 50 neurons in each layer, achieved a C-index of 0.782 in the validation set and a C-index of 0.758 in the testing set. The novel prognostic model based on DSLM showed better performances than the conventional clinical staging system (area under receiver operating curves were 0.826 and 0.689, respectively). Personalized survival curves for individual patient using this novel model also showed notably different survival slopes. Conclusions Our study developed a novel, practical, personalized prognostic model for operable cervical cancer patients. This novel prognostic model may have the potential to provide a more prognostic information to oncologists.
Input-output parametric models for non-linear systems Part II: stochastic non-linear systems

Recursive input-output models for non-linear multivariate discrete-time systems are derived, and sufficient conditions for their existence are defined. The paper is divided into two parts. The first part introduces and defines concepts such as Nerode realization, multistructural forms and results from differential geometry which are then used to derive a recursive input-output model for multivariable deterministic non-linear systems. The second part introduces several examples, compares the derived model with other representations and extends the results to create prediction error or innovation input-output models for non-linear stochastic systems. These latter models are the generalization of the multivariable ARM AX models for linear systems and are referred to as NARMAX or Non-linear AutoRegressive Moving Average models with exogenous inputs.
Are People Hesitating—Or Just Postponing—to Get the Covid-19 Vaccine? Vaccine Outreach in Marginalized Urban Communities

Across Philadelphia, approximately 80% of adults are fully vaccinated against Covid-19. However, many zip codes in the city remain far below the city-wide vaccination rate. These zip codes correspond to marginalized sections of the city and to neighborhoods with a high proportion of residents of color and high levels of poverty. In-depth interviews were conducted with representatives from 15 community-based organizations (CBOs) that serve such communities in the city to (1) learn why people are not yet vaccinated and (2) evaluate methods for encouraging vaccination. A qualitative thematic analysis of interview transcripts was conducted to evaluate why people are not getting vaccinated. Together, the findings suggest that distrust toward the vaccine, the government, and the healthcare system, combined with a host of matters considered by residents to be more urgent—such as missing work, cost concerns, and concerns around presenting identification—result in what might be better described as vaccine postponement rather than vaccine refusal. For many, vaccination is simply not a priority. The findings from this analysis illuminate some of the lesser discussed reasons for vaccination delay and provide insights into how to promote vaccinations both for the current Covid pandemic and for future vaccination efforts.
Sustainability challenge on pollution and air quality inside heavy-duty vehicle cabins

Air quality in transportation vehicle cabins is becoming a major concern due to increasing exposure of drivers to harmful gases and aerosol, especially if having in mind that that safety and pollution are positively correlated and adverse events. In order to reduce exposure to air pollution, there is ongoing development of air filters in modern heavy-duty vehicles and increase in protective regulations throughout the world. The aim of this study is to gain insight into extent of pollutants in heavy-duty vehicle cabins, in which drivers spend a large amount of their working (and spare) time, and the necessity for use of air filtration systems. Both experimental results and modelling approach applied in this manuscript reveal the importance of filtration of cabin air, and indicate good correlation between experimental and mathematical results from field measurements conducted in a highly polluted city. Namely, this paper reveals excellent results with the HECA filter which have potential to significantly reduce pollution in the cabin.
Hybrid Deep Learning Approaches for sEMG Signal-Based Lower Limb Activity Recognition

Lower limb activity recognition utilizing body sensor data has attracted researchers due to its practical applications, such as neuromuscular disease detection and kinesiological investigations. The employment of wearable sensors including accelerometers, gyroscopes, and surface electromyography has grown due to their low cost and broad applicability. Electromyography (EMG) sensors are preferable for automated control of a lower limb exoskeleton or prosthesis since they detect the signal beforehand and allow faster movement detection. The study presents hybrid deep learning models for lower limb activity recognition. Noise is suppressed using discrete wavelet transform, and then the signal is segmented using overlapping windowing. Convolutional neural network is used for temporal learning, whereas long short-term memory or gated recurrent unit is used for sequence learning. After that, performance indices of the models such as accuracy, sensitivity, specificity, and F-score are calculated. The findings indicate that the suggested hybrid model outperforms the individual models.