Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cyclic Job Shop Problem with uncertain processing times

Most models for scheduling problems assume deterministic parameters. In contrast, real world scheduling problems are often subject to many sources of uncertainty, for example activities duration can decrease or increase, machines can break down, new activities can be incorporated, etc. In this paper, we focus on scheduling problems that are cyclic and where activity durations are affected by uncertainty. Indeed, the best solution for a deterministic problem can quickly become the worst one in the presence of uncertainties. In this paper, we consider the Cyclic Job Shop Problem (CJSP) where processing times are affected by uncertainty. Several studies were conducted on the deterministic CJSP. The CJSP with identical parts is studied in (Roundy, R. 1992). The author shows that the problem is NP-hard and designs a branch and bound algorithm to solve the problem. Hanen (1994) investigates the general CJSP and presents a branch and bound procedure to tackle the problem. A general framework for modeling and solving cyclic scheduling problems is presented in (Brucker, P. and Kampmeyer, T. 2008). The authors present different models for cyclic versions of the job shop problem. However, a few works consider cyclic scheduling problems under uncertainty. Che, A. et. al. (2015) investigate the cyclic hoist scheduling problem with processing time window constraints where the hoist transportation times are uncertain. The authors define a robustness measure for cyclic hoist schedule and a bi-objective mixed integer linear program to optimize the cycle time and the robustness. In order to deal with uncertainty, we use a robust optimization approach. We model the uncertain parameters by using the idea of uncertainty set proposed by Bertsimas and Sim (2004). Each task duration belongs to an interval, and the number of parameters that can deviate from their nominal values is bounded by a positive number called budget of uncertainty. This parameter allows us to control the degree of conservatism of the resulting schedule. Finally, we propose a branch and bound procedure that computes the minimum cycle time for the robust CJSP such that, for each scenario in the uncertainty set, there exists a feasible cyclic schedule.
The effect of the pulse repetition rate on the fast ionization wave discharge

The effect of the pulse repetition rate (PRR) on the generation of high energy electrons in a fast ionization wave (FIW) discharge is investigated by both experiment and modelling. The FIW discharge is driven by nanosecond high voltage pulses and is generated in helium with a pressure of 30 mbar. The axial electric field (Ez), as the driven force of high energy electron generation, is strongly influenced by PRR. Both the measurement and the model show that, during the breakdown, the peak value of Ez decreases with the PRR, while after the breakdown, the value of Ez increases with the PRR. The electron energy distribution function (EEDF) is calculated with a model similar to Boeuf and Pitchford (1995 Phys. Rev. E 51 1376). It is found that, with a low value of PRR, the EEDF during the breakdown is strongly non-Maxwellian with an elevated high energy tail, while the EEDF after the breakdown is also non-Maxwellian but with a much depleted population of high energy electrons. However, with a high value of PRR, the EEDF is Maxwellian-like without much temporal variation both during and after the breakdown. With the calculated EEDF, the temporal evolution of the population of helium excited species given by the model is in good agreement with the measured optical emission, which also depends critically on the shape of the EEDF.
Text2Scene: Text-driven Indoor Scene Stylization with Part-Aware Details

We propose Text2Scene, a method to automatically create realistic textures for virtual scenes composed of multiple objects. Guided by a reference image and text descriptions, our pipeline adds detailed texture on labeled 3D geometries in the room such that the generated colors respect the hierarchical structure or semantic parts that are often composed of similar materials. Instead of applying flat stylization on the entire scene at a single step, we obtain weak semantic cues from geometric segmentation, which are further clarified by assigning initial colors to segmented parts. Then we add texture details for individual objects such that their projections on image space exhibit feature embedding aligned with the embedding of the input. The decomposition makes the entire pipeline tractable to a moderate amount of computation resources and memory. As our framework utilizes the existing resources of image and text embedding, it does not require dedicated datasets with high-quality textures designed by skillful artists. To the best of our knowledge, it is the first practical and scalable approach that can create detailed and realistic textures of the desired style that maintain structural context for scenes with multiple objects.
Investigation of cellulose filaments as filler in rigid insulating polyurethane foam

Cellulose is a biopolymer that has broad potential applications including in building insulation, and it was studied for its potential as a filler material. A closed-cell polyurethane foam insulation formulation was developed, and cellulose filaments (CFs) were introduced at varying percentages. The viscosity and morphology of the formulations were studied, as were different foam properties, such as water vapor permeability, reaction kinetics, density, porosity, thermal conductivity, and compressive strength foams as a function of cellulose filaments content. A commercial foam was also tested as a reference. The cellulose filaments impacted the formulations’ viscosity, and all the properties of the resulting insulating material. For example, samples containing 5% of cellulose filaments were found to perform differently than samples containing 0%, 1% and 2.5% mainly due to agglomerate formation, which impacted cell size (about 0.1 mm2 at 0%, 1% and 2.5% versus a mean of over 0.4 mm2 at 5%), and differential vapor sorption (with a mass change of 2%wt at 0 parts per hundred of polyol versus 2.5%wt at 5% from 0% to 95% relative humidity). However, the required performances by the standards of polyurethane foam insulation material were always fulfilled regardless of the amount of cellulose filaments present.
Efficient location, imaging and recognition of faces by single-pixel camera

Face recognition is a problem with many practical applications. Modern image analysis methods such as object tracking, feature extraction, classification or verification that explore advanced techniques of machine learning and compressive sensing have been used for this purpose. Many of these methods, which are usually applied in image post-processing, are adaptable to fast intelligent viewing with a single-pixel camera. We study such a camera in the context of face recognition with limited information. We seek the optimal basis of patterns for the camera as well as to resolve practical issues related to face localisation and alignment. We compare the use of the Hadamard and eigenface bases for imaging and verification of faces. For the latter task, we develop a simple algorithm based on compressive sensing.
WISE Causal Models: Wisdom Infused Semantics Enhanced WISE Causal Models: Wisdom Infused Semantics Enhanced Causal Models - A Study in Suicidality Diagnosis Causal Models - A Study in Suicidality Diagnosis

The COVID-19 Pandemic has highlighted the gap between the number of mental 1 health care seekers and care providers. Netizens have taken to internet-based 2 platforms such as Reddit to express their experiences. Mental illness diagnosis 3 processes have clinically accepted causal interpretations and semantics. Curiously, 4 mental illness diagnosis accuracy is low relative to similar well-studied illnesses. 5 Motivated by this discrepancy, we propose Wisdom Infused Semantics Enhanced 6 (WISE) causal models, inspired by the wisdom of the crowd idea that learns from 7 a collective agreement among causal models and their semantics for mental illness 8 diagnoses. We use suicidality diagnosis task descriptions, datasets, and baseline 9 methods to evaluate the effectiveness of WISE causal models. Our experiments 10 show that learning WISE causal models improve performance on these tasks. 11
Defect Engineering of BTe Ovonic Threshold Switch (OTS) With Nitrogen Doping for Improved Electrical and Reliability Performance

This study investigates the effect of N-doping on the nanoscale (d = 30 nm) BTe ovonic threshold switch (OTS) device to achieve ideal selector characteristics in terms of leakage current, cycling endurance, and variation. Based on our findings, N-doping significantly improves device performance. In particular, the N-doped BTe OTS exhibits an ultra-low leakage current (<inline-formula> <tex-math notation="LaTeX">$\text{I}_{\textit {off}}$ </tex-math></inline-formula> @ 1/2 V <inline-formula> <tex-math notation="LaTeX">$_{\textit {th}}$ </tex-math></inline-formula> = 750 pA), low threshold voltage (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textit {th}}$ </tex-math></inline-formula>) variation (<inline-formula> <tex-math notation="LaTeX">$\sigma < 30$ </tex-math></inline-formula> mV), excellent cycling endurance (> 1011), and low <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\textit {th}}$ </tex-math></inline-formula> drift (<inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> = 30 mV/dec.) characteristics. From density functional theory (DFT) calculation, we found that an increase in Te-Te lifetime after the N-doping process can improve the reliability characteristics of the OTS device. This study contributes importantly to the development of high-performance OTS devices, providing a fundamental understanding of the role of N-doping in enhancing device properties.
Pulse characterization by cascading nonlinearity inside a spectrometer (CaNIS)

A complete ultrashort pulse diagnostic requires a linear spectrum associated with a nonlinear autocorrelation or interferometric measurement. A new method is presented requiring only a grating spectrometer and two second harmonic crystals. Spectra of fundamental, second harmonic, and two cascaded spectra are used to reconstruct the spectral phase. A few proof-of-concept examples with simple phases are demonstrated using Nelder-Mead algorithm. A differential evolution genetic algorithm is introduced when the pulse has a more complicated shape or phase.
Experimental study of air-water two-phase jet: bubble size distribution and velocity measurements

An experimental investigation on a horizontal two-phase air-water jet has been conducted in order to develop a technique that can provide information on the interaction between the air bubbles and turbulent flow. In order to allow a measurement of the liquid phase flow field and of the bubbles size, the Particle Image Velocimetry (PIV) has been used, with the liquid phase seeded with a fluorescent tracer consisting of Rhodamine WT mixed with a commercial polyester resin. A phase discrimination algorithm has been developed and successfully used for the detection and separation of tracers and bubbles in distinct images. Three data sets have been recorded: one for a one-phase water jet and two for a two-phase water jet. The obtained results allow a valid characterization of the flow field and an estimation of the effect of the gaseous phase in the air-water jet flow. An analysis of the bubbles size and number has been also conducted and here reported.
Development of Teaching Materials on PLSV Using Scientific and Contextual Approach to Improve Mathematics Communication Ability and Self Confidence

Development of teaching materials on PLSV material using combination of scientific and contextual approach to improve mathematical communication ability and self confidence to seventh grade junior high school students. The purpose of this research and development is to obtain a design for developingÂ mathematical teaching materials in the form of lesson plans and worksheet with combination of scientific and contextual approaches to PLSV material for smpn 3 cipeundeuythis study also aims to test the level of validity, practicality, and effectiveness of teaching materials with a combination of scientific and contextual approaches to PLSV material. This research is a development research. The results showed that based on the assessment of material experts and mathematics teachers, the teaching materials developed had very valid criteria with an average total score of 190.33 for lesson plans and 297.5 for worksheets. Based on the results of filling out questions on communication skills and student responses questionnaires about self-confidence, there has been a significant increase. So it can be concluded that with the development of teaching materials by combining scientific and contextual approaches with the results of filling out observations of the implementation of learning and the results of evaluating learning outcomes, it can be concluded that the teaching materials developed have a very good level of effectiveness with the percentage of learning implementation of 94.87% and the percentage of completeness students to 95%.
Hematological Indices Are Useful in Predicting Complications of Liver Cirrhosis

Background: Liver cirrhosis is the final stage of chronic liver disease. We aimed to evaluate non-invasive scores as predictors of complications and outcome in cirrhotic patients. Methods: A total of 150 cirrhotic patients were included. Models for end-stage liver disease (MELD), albumin-bilirubin (ALBI) score, neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MoLR), and neutrophil-lymphocyte-albumin ratio (NLA) scores were tested in relation to the development of complications and mortality using receiver operating characteristic (ROC) curves. Results: The ROC curve analysis showed (area under the curve) AUC values of NLR, NLA, ALBI, and MELD of 0.711, 0.730, 0.627, and 0.684, respectively, for short-term mortality. MELD, ALBI, and NLA scores showed a statistically significant correlation with hepatic encephalopathy (p = 0.000 vs. 0.014 vs. 0.040, respectively), and the MELD cut-off value of 16 had a sensitivity of 70% and a specificity of 52% (AUC: 0.671, 95% CI (0.577–0.765)). For the assessment of the presence of ascites, the AUC values for NLA and MoLR were 0.583 and 0.658, respectively, with cut-offs of 11.38 and 0.44. Conclusions: MELD, ALBI, and NLA are reliable predictors of hepatic encephalopathy. NLA and MoLR showed a significant correlation with the presence of ascites, and MELD, ALBI, NLR, and NLA have prognostic value to predict 30-day mortality in cirrhotic patients.
PATTERN CLASSIFICATION BY CONVEXITY DEPENDANT DERICHE FILTERING SCHEME

The present paper presents a new alternative for mode detection of the underlying probability density function (pdf), preliminary to unsupervised statistical clustering. The method is based on the global convexity pdf analysis instead the local convexity testing used in some works. Indeed, the test area of sampling points may straddle a boundary between a convex region and a concave one, so that the assumptions for the test of the local convexity can be violated. Furthermore, this local test of convexity is very sensitive to details in the data structure and would rapidly become impracticable as the dimensionality of the data increases. Based on the characteristic theorem of convexity, the proposed technique assigns the concave label to modal regions and the convex label to valleys of the pdf according to an adequate Deriche filtering scheme, generalized to a multidimensional space. This filter is with a reliability criterion allowing to model as well the pdf variations as the noise attached to the density function.
Identifying Novel Data-Driven Dietary Patterns via Dimensionality Reduction and Associations with Socioeconomic Profile and Health Outcomes in Ireland

Dietary patterns and body mass index (BMI) play a significant role in the development of noncommunicable diseases (NCDs), which are the leading cause of mortality worldwide, including Ireland. A cross-sectional survey was conducted across Ireland to collate respondents’ socioeconomic profiles, health status, and dietary patterns with a representative sample size of 957 adult respondents. Principal component analysis (PCA) and statistical analyses were subsequently employed. To the author’s knowledge, this is the first study to use recent (2021) nationally representative data to characterise dietary patterns in Ireland via dimensionality reduction. Five distinct dietary patterns (“meat-focused”, “dairy/ovo-focused”, “vegetable-focused”, “seafood-focused”, and “potato-focused”) were identified and statistically characterised. The “potato-focused” group exhibited the highest mean BMI (26.88 kg/m2), while the “vegetable-focused” group had the lowest (24.68 kg/m2). “Vegetable-focused” respondents were more likely to be associated with a categorically healthy BMI (OR = 1.90) and urban residency (OR = 2.03). Conversely, “meat-focused” respondents were more likely to have obesity (OR = 1.46) and rural residency (OR = 1.72) along with the “potato-focused” group (OR = 2.15). Results show that data-derived dietary patterns may better predict health outcomes than self-reported dietary patterns, and transitioning to diets focusing on vegetables, seafood, and lower meat consumption may improve health.
Approximate Analytical Solution for Time-Fractional Nonlinear Telegraph Equations with Source Term

In this study, we considered time-fractional nonlinear telegraph equations (TFNLTEs). For solving the TFNLTEs, we deployed a method known as the Multistep Modified Reduced Differential Transform Method (MMRDTM). Prior to the multistep technique, the nonlinear term in TFNLTEs is replaced with corresponding Adomian polynomials. It can be observed that the MMRDTM is much simpler and more straightforward. On top of that, it works exceptionally where the obtained solutions are more accurately approximated over time. To demonstrate the performance of the MMRDTM in terms of its capabilities and accuracies, we provided two numerical examples of solving TFNLTEs by using MMRDTM and Modified Reduced Differential Transform Method (MRDTM). By comparing the absolute errors of the obtained solutions by both methods, we demonstrated that the solutions provided by the MMRDTM much closer to the exact solutions compared to the corresponding solutions yielded by the MRDTM. This justified that the MMRDTM provides highly accurate and precise solutions for the TFNLTEs.
Performance of the Vectrino Profiler at the sediment–water interface

ABSTRACT The Vectrino Profiler is increasingly recognized as a promising instrument for characterizing near-bed velocities and turbulence due to its high spatio-temporal resolution and velocity profiling range. However, its measurement performance in the vicinity of the bed (<10 mm) is not well documented. This study reports on the comparisons of mean velocity, turbulent kinetic energy, and power spectral density above various bed materials between the Vectrino Profiler and particle image velocimetry in a laboratory flume. Our analyses demonstrate that the bed interference adversely influences the measurements as close as 1.7–5 mm above the bed, depending on the bed material. We provide a criterion to identify the vertical extent of the interference region. Outside of the interference region, best agreement between the Vectrino Profiler and particle image velocimetry was found around the sweet-spot of the profiler where the observed differences were <6% for mean velocities and <10% for turbulent kinetic energy.
Multi objective fuzzy inventory model with demand dependent unit cost and lead time constraints – A Karush Kuhn Tucker conditions approach

In this paper a multi-objective inventory model with demand dependent unit cost and leading time has been formulated with number of orders and production cost as constraints . In most of the real world situations the cost parameters, the objective function and constraints of the decision makers are imprecise in nature. A demand dependent unit cost is assumed and solved using Karush Kuhn Tucker conditions. Here the unit production cost is considered under fuzzy environment. The model has been solved with demand, lot size and leading time as decision variables.
Comparison of dental anesthetic efficacy between the periodontal intraligamentary anesthesia and other infiltration anesthesia: a systematic review and meta-analysis

Background Uncertainty exists regarding the pain scores and the success rate of intraligamentary anesthesia compared to other infiltration anesthesia. Based on the conditions of clinical anesthesia techniques, we conducted a systematic review and meta-analysis to compare the efficacy of intraligamentary anesthesia with other infiltration anesthesia. Methods The search was carried out in PubMed Central, Cochrane Central Register of Controlled Trials, MEDLINE (via OVID), Embase (via OVID), and Scopus from the inception to March 26, 2023. Results Seven eligible randomized controlled trials were included in the meta-analysis. The results indicated no significant difference in the success rate (RR = 0.96; 95% CI [0.81–1.14]; p = 0.65; I2= 73%) and visual analog scale (VAS) during dental procedures (MD = 3.81; 95% CI [−0.54–8.16]; p = 0.09; I2= 97%) between intraligamentary anesthesia and other infiltration anesthesia. However, intraligamentary anesthesia exhibited a higher VAS score during injection than other infiltration anesthesia (MD = 8.83; 95% CI [4.86–12.79]; p < 0.0001; I2= 90%). A subgroup analysis according to infiltration techniques showed that supraperiosteal anesthesia had a lower VAS score during dental procedures than intraligamentary anesthesia. Conclusions Intraligamentary anesthesia and other infiltration anesthesias have the same success rate and pain during dental procedures. However, the pain during injection of intraligamentary anesthesia is heavier than that of other infiltration anesthesia.
Accurate Step Length Estimation for Pedestrian Dead Reckoning Localization Using Stacked Autoencoders

Pedestrian dead reckoning (PDR) is a popular indoor localization method due to its independence of additional infrastructures and the wide availability of smart devices. Step length estimation is a key component of PDR, which has an important influence on the performance of PDR. Existing step length estimation models suffer from various limitations such as requiring knowledge of user’s height, lack of consideration of varying phone carrying ways, and dependence on spatial constraints. To solve these problems, we propose a deep learning-based step length estimation model, which can adapt to different phone carrying ways and does not require individual stature information and spatial constraints. Experimental results show that the proposed method outperforms existing popular step length estimation methods.