Miracle-dz/UniFAR-FTUs huggingface dataset:
From waves to bullets: testing Feynman's idea on the two slit experiment

We test the validity of Feynman's idea that a two-slit experiment performed with classical objects (bullets) does not produce observable interference fringes on the detection screen because the Compton's wavelength of the bullets is so tiny, that no real detector could resolve individual interference fringes, thus producing only an average signal which is the observed smooth curve. To test this idea, we study the two-slit experiment in two different situations using light to simulate both wave-like and particle-like bullets. In the first case, we consider coherent light with short wavelengths and in the second case incoherent light with not-so-short wavelength. While in the former case (simulating Feynman's wave-like bullets) the interference fringes are so dense that they cannot be resolved by a detector, therefore resulting in an averaged smooth signal, in the latter case (simulating Feynman's particle-like bullets), although the detector is fully capable of discriminating each fringe, the observed classical smooth pattern limit is produced because of the lack of spatial coherence of the impinging field.
Numerical Dissipation Effects on Detached Eddy Simulation of Turbomachinery Flows

Detached eddy simulation (DES) is a high-resolution method for predicting complex unsteady flows in turbomachinery. Recent researches have shown that in DES the numerical dissipation from the spatial discretization scheme should be limited to a reasonable extent. Through the test-case of decaying isotropic turbulence, the impact of the Roe scheme is assessed with three reconstruction approaches: the 3rd-order MUSCL, the 5th-order WENO, and the 4th-order minimized dispersion and controllable dissipation (MDCD) scheme. From the results, however, even with the least dissipative 4th-order MDCD scheme, the Roe scheme possesses high numerical damping for the small-scale turbulent structures. To further decrease the dissipation, the Roe scheme is modified via an adaptive factor. This adaptive scheme has small dissipation in the LES region to capture multiscale turbulent structures and returns to the original Roe scheme near shock waves to suppress numerical oscillations. The scheme with adaptive dissipation is also used to calculate flows in a centrifugal compressor. The resolution of small vortex structures, such as the tip leakage vortices and the wake vortices, is well improved.
Non-Stationary Spatial Modeling

Standard geostatistical models assume stationarity and rely on a variogram model to account for the spatial dependence in the observed data. In some instances, this assumption that the spatial dependence structure is constant throughout the sampling region is clearly violated. We present a spatial model which allows the spatial dependence structure to vary as a function of location. Unlike previous formulations which do not account for uncertainty in the speciﬁcation of this non-stationarity (eg. Sampson and Guttorp (1992)), we develop a hierarchical model which can incorporate this uncertainty in the resulting inference. The non-stationary spatial dependence is explained through a constructive ”process-convolution” approach, which ensures that the resulting covariance structure is valid. We apply this method to an example in toxic waste remediation.
Fluorescence lifetime imaging for biomedicine and spectroscopy

Summary form only. The determination of fluorescence lifetime requires only relative intensity measurements and so is especially useful for biomedical samples in which the heterogeneous nature of tissue and autofluorescence present significant problems. Since fluorescence lifetime is dependent upon both radiative and nonradiative decay rates, it may be used to distinguish between different fluorophore molecules (with different radiative decay rates) and to monitor local environmental perturbations that affect the nonradiative decay rate. Fluorescence lifetime imaging (FLIM) can be applied to almost any optical imaging modality, including microscopy and potentially to noninvasive optical biopsy. Fluorescence lifetime data may be acquired in the time or frequency domains. The recent development of user-friendly and relatively portable ultrafast laser technology and the availability of ultrafast gated optical image intensifiers (GOI) enable the development of relatively inexpensive time domain FLIM instruments that may be deployed outside specialist laser laboratories. We report a time domain FLIM system that incorporates an all-solid-state diode-pumped Cr:LiSGAF oscillator-amplifier system combined with a GOI. This laser system requires only four 500 mW pump diodes and will run off a domestic power outlet with minimal water-cooling requirements.
Antioxidant, Antimicrobial and Physicochemical Properties of Beef Sausages Enriched with an Aqueous Extract of Senduduk (Melastoma malabathricum L.) Leaf

The use of natural products in sausages has become a new trend for health reasons. A natural product that could be incorporated into sausages is an extract of the senduduk (Melastoma malabathricum L.) leaf. Senduduk is an abundant shrub herb in Indonesia. This kind of plant is mostly used as a traditional medical remedy and as an ingredient in some culinary recipes. This study was carried out to investigate the effect of an aqueous extract of senduduk leaf (SLE) on the antioxidant, antimicrobial and physicochemical properties of beef sausage. Four treatments were used: ingredients consisting of beef, vegetable oil, skim milk, tapioca, salt, phosphate, ice cubic, garlic, pepper, dan nutmeg as a Control; the Control ingredients plus 0.01% of butylated hydroxytoluene (BHT); the Control ingredients plus 0.83% of SLE (SLE-1), and the Control ingredients plus 1.1% of SLE (SLE-2). All ingredients of each formula were homogenously blended and the sausage mix was cooked. The addition of BHT and SLE affected the proximate composition, with the moisture content decreasing as the duration of chilled storage increased. The addition of SLE lowered the pH and aw value and both tended to increase during chilled storage. SLE also enhanced the WHC of the sausages which increased in value during chilled storage. The addition of BHT and SLE could increase the antioxidant activity of the sausages as indicated by scavenging DPPH free radicals. SLE in sausages could inhibit microbial growth during chilled storage. It can be summarized that the addition of an aqueous extract of senduduk could improve the physicochemical, antioxidant and antimicrobial properties of beef sausages.
A novel hybrid resampling for semiconductor wafer defect bin classification

Generally, defective dies on semiconductor wafer maps tend to form spatial clusters in distinguishable patterns which contain crucial information on specific problems of equipment or process, thus it is highly important to identify and classify diverse defect patterns accurately. However, in practice, there exists a serious class imbalance problem, that is, the number of the defective dies on semiconductor wafer maps is usually much smaller than that of the non‐defective dies. In various machine learning applications, a typical classification algorithm is, however, developed under the assumption that the number of instances for each class is nearly balanced. If the conventional classification algorithm is applied to a class imbalanced dataset, it may lead to incorrect classification results and degrade the reliability of the classification algorithm. In this research, we consider the semiconductor wafer defect bin data combined with wafer warpage information and propose a new hybrid resampling algorithm to improve performance of classifiers. From the experimental analysis, we show that the proposed algorithm provides better classification performance compared to other data preprocessing methods regardless of classification models.
Propagation of hybrid transverse magnetic-transverse electric plasmons on magnetically biased graphene sheets

The propagation of plasmons on magnetically biased graphene sheets is addressed. The analysis is based on the transverse resonance method extended to handle the graphene conductivity tensor and allows easily accounting for substrate effects. A transcendental equation is obtained for the propagation constant of the resulting hybrid transverse magnetic-transverse electric mode. A closed-form approximate expression for a graphene layer sandwitched between two different media is also provided. Application of the method shows that the presence of a magnetic field leads to extreme field localization, namely, very small guided wavelength, as compared with usual plasmons in graphene or noble metals. The extent of field localization and its frequency can be dynamically controlled by modifying the applied magnetostatic and electrostatic bias field, respectively. These features could enable extreme device miniaturization and enhanced resolution in sensing applications.
Category Induction and Representation

ABSTRACT: A provisional model is presented in which categorical perception (CP) provides our basic or elementary categories. In acquiring a category we learn to label or identify positive and negative instances from a sample of confusable alternatives. Two kinds of internal representation are built up in this learning by "acquaintance": (1) an iconic representation that subserves our similarity judgments and (2) an analog/digital feature-filter that picks out the invariant information allowing us to categorize the instances correctly. This second, categorical representation is associated with the category name. Category names then serve as the atomic symbols for a third representational system, the (3) symbolic representations that underlie language and that make it possible for us to learn by "description. " Connectionism is one possible mechainsm for learning the sensory invariants underlying categorization and naming. Among the implications of the model are (a) the "cognitive identity of (current) indiscriminables": Categories and their representations can only be provisional and approximate, relative to the alternatives encountered to date, rather than "exact. " There is also (b) no such thing as an absolute "feature," only those features that are invariant within a particular context of confusable alternatives. Contrary to prevailing "prototype" views, however, (c) such provisionally invariant features must underlie successful categorization, and must be "sufficient" (at least in the "satisficing" sense) to subserve reliable performance with all-or-none, bounded categories, as in CP. Finally, the model brings out some basic limitations of the "symbol-manipulative" approach to modeling cognition, showing how (d) symbol meanings must be functionally grounded in nonsymbolic, "shape-preserving" representations -- iconic and categorical ones. Otherwise, all symbol interpretations are ungrounded and indeterminate. This amounts to a principled call for a psychophysical (rather than a neural) "bottom-up" approach to cognition.
Digital Image Analysis Using Deep Learning Convolutional Neural Networks for Color Matching of Knitted Cotton Fabric

ABSTRACT The customers make use of Pantone color cards as quality control for their reference to know the color consistency of the dyed cloth. The three different color shades like blue, red and pastel violet were selected from the pantone color shades. The three-color hues of the manufactured knitted cotton fabric were captured by the high-resolution optical device and these images were considered for the training and test purpose. A simple backpropagation based artificial neural network and a deep learning convolution network was considered for the training and test purpose. The pantone color hues of three images were offered to the network as training samples. A backpropagation algorithm trained artificial neural network (ANN) and deep learning neural network trained with support vector machine were employed in training phase. The back propagation trained ANN predicted 82.37%, 83.16% and 89.25% correct classification on three color hues. A deep learning convolution network trained with support vector machine method forecasted 100%, 100% and 100% on three color hues. The better performance result was secured by the second method. The second network reduces the features from the color images in training and test phase.
THE EFFECT OF BAKING TIME ON PHYSICOCHEMICAL AND SENSORY CHARACTERISTICS OF CRUNCHY POTATO DONUT PRODUCTS PROCESSED FROM POTATO DONUT WASTE

ABSTRACT: Donuts are one of the most popular food products in the Horeka industry. One of the donut variants sold in the market is potato donuts, which is a donut made from potato tuber substitution. The weakness of this product is that it has a short shelf life, so if it is not sold it will become industrial waste. One way of handling food processing industrial waste that can still be utilized is by reprocessing it into products of economic value. This study aims to examine the effect of baking time on the physicochemical and sensory characteristics of crunchy potato donut snacks produced from potato donut industry waste. The treatments used in this study were differences in baking time, namely P1 (2 minutes), P2 (4 minutes), and P3 (6 minutes). The results showed that the baking time significantly affected the crispness, color, protein content, carbohydrate content, fat content, ash content, moisture content, and dietary fiber as well as hedonic testing which included color, aroma, taste, texture, and overall. The optimum baking time that can produce a product with good and acceptable quality is 4 minutes (P2).
Vorticity evolution in a rigid pipe of circular cross-section

In this paper, we show that the spatio-temporal evolution of incompressible flows in a long circular pipe can be described by vorticity dynamics. The principal techniques to obtain solution are similar to those used for flows in the whole space. As the consideration of the Navier-Stokes equations is given in a cylindrical co-ordinates, two aspects of complication arise. One is the interaction of the velocity components in the radial and azimuthal directions due to the fictitious centrifugal force in the equations of motion. The rate of the vorticity production at pipe wall depends on the initial data at entry and hence is unknown a priori; it must be determined as part of the solution. The vorticity solution obtained defines an intricate flow field of multitudinous degrees of freedom. As the Reynolds number increases, the analytical solution predicts vorticity-scale proliferations in succession. For sufficient large initial data, pipe flows are of turbulent nature. The solution of the governing equations is globally regular and does not bifurcate in space or in time. It is asserted that laminar-turbulent transition is a dynamic process inbred in the non-linearity. The presence of exogenous disturbances, due to imperfect test environments or purpose-made artificial forcing, distorts the course of the intrinsic transition. The flow structures observed by Reynolds (1883) and others can be synthesised and elucidated in light of the current theory.
Agencias sociales comunitarias femeninas y su aporte en la conservación y lucha del territorio en Sumapaz

Los procesos de organización y participación al cuidado del páramo más grande del mundo, ubicado en el territorio de Sumapaz, son históricos y han permitido posicionar la resistencia a megaproyectos económicos y ecoturísticos que afectarían las condiciones naturales de dicho ecosistema. El presente artículo tiene como finalidad reflexionar sobre la relevancia que han tenido las agencias comunitarias femeninas en el Sumapaz en la preservación del medio ambiente, como garante del bienestar de la comunidad y del territorio. Las narrativas fueron la estrategia que permitió conocer desde algunas mujeres los alcances de su agencia y su participación en estos procesos de lucha y protección territorial.
Machine-Learning-Based Methods for Acoustic Emission Testing: A Review

Acoustic emission is a nondestructive control technique as it does not involve any input of energy into the materials. It is based on the acquisition of ultrasonic signals spontaneously emitted by a material under stress due to irreversible phenomena such as damage, microcracking, degradation, and corrosion. It is a dynamic and passive-receptive technique that analyzes the ultrasonic pulses emitted by a crack when it is generated. This technique allows for an early diagnosis of incipient structural damage by capturing the precursor signals of the fracture. Recently, the scientific community is making extensive use of methodologies based on machine learning: the use of machine learning makes a machine capable of receiving a series of data, modifying the algorithms as they receive information on what they are processing. In this way, the machine can learn without being explicitly programmed, and this implies a huge use of data and an efficient algorithm to adapt. This review described the methodologies for the implementation of the acoustic emission (AE) technique in the evaluation of the conditions and in the monitoring of materials and structures. The latest research products were also analyzed in the development of new methodologies based on machine learning for the detection and localization of damage for the characterization of the fracture and the prediction of the failure mode. The work carried out highlighted the strong use of these methods, which confirms the extreme usefulness of these techniques in identifying structural damage in scenarios heavily contaminated by residual noise.
Optimization of the Random Forest Hyperparameters for Power Industrial Control Systems Intrusion Detection Using an Improved Grid Search Algorithm

The intrusion detection method of power industrial control systems is a crucial aspect of assuring power security. However, traditional intrusion detection methods have two drawbacks: first, they are mainly used for defending information systems and lack the ability to detect attacks against power industrial control systems; and second, although machine learning-based intrusion detection methods perform well with the default hyperparameters, optimizing the hyperparameters can significantly improve its performance. In response to these limitations, a random forest (RF)-based intrusion detection model for power industrial control systems is proposed. Simultaneously, this paper proposes an improved grid search algorithm (IGSA) for optimizing the hyperparameters of the RF intrusion detection model to improve its efficiency and effectiveness. The proposed IGSA boosts the speed of calculation from O(nm) to O(n × m). The suggested model is evaluated based on the public power industrial control system dataset after hyperparameter optimization. The experiment results show that our method achieves a superior detection performance with the accuracy of 98% and has more outstanding performance than the same type of work.
Production of Cellulose Nanocrystals Suspension with High Yields from Water Hyacinth

ABSTRACT Acid hydrolysis is commonly used to extract cellulose nanocrystals (CNC) from natural fibers. This research thus investigates the effect of hydrolysis temperatures and time on the yields of CNC from water hyacinth. The aim is to determine the optimal acid hydrolysis condition for water hyacinth fiber that effectively enhances the CNC yield. The acid hydrolysis temperatures were varied between 50 and 60°C and the hydrolysis time between 15, 30, 60, and 120 min. Prior to the hydrolysis, raw water hyacinth fiber was treated with alkaline and bleached by hydrogen peroxide. The results showed that the optimal hydrolysis condition was 60°C and 30 min, with the crystallinity index of 80%, the crystalline size of 3.91 nm, and the CNC yield of 71.5%. The transmission electron microscopy morphology of CNC from water hyacinth was of whisker shape, with the diameter and length of 10 nm and 200–500 nm. The results also indicated that, unlike the hydrolysis time, the hydrolysis temperature had a negligible effect on the crystallinity index. Besides, under the optimal hydrolysis condition, CNC was of stable aqueous suspension with a zeta potential of −43.21 mV, indicating high physical colloidal stability.
Effect of hyperbaric oxygen therapy on cognitive impairment after aneurysm subarachnoid hemorrhage

Purpose: To evaluate the effect of hyperbaric oxygen therapy (HBOT) on cognitive impairment after aneurysm subarachnoid hemorrhage (aSAH). Methods: The current study was carried out in a regional neurosurgical center in Taiyuan, Shanxi Province of China from January 2019 to September 2020. A total of 150 patients with persistent cognitive dysfunction at 3 months after aSAH onset were enrolled, which were randomly classified into group A (HBOT) and group B (control) via the random number table method. The outcome was evaluated by Montreal cognitive assessment (MoCA). Results: There were no significant differences between group A and group B with regard to MoCA score and proportions of normal MoCA patients at 3 months after HBOT (p > 0.05). Both groups showed no significant differences in proportions of normal MoCA patients at 6 months after HBOT (p > 0.05). However, there were significant differences between group A and group B with MoCA score of patients at 6 months after HBOT (p < 0.05). There were also significant differences in MoCA score and proportions of normal MoCA patients at 9 months after HBOT. Conclusion: HBOT alleviates cognitive impairment after aSAH, and thus may be used to manage cognitive impairment in patients after aSAH. However, further clinical trials are required prior to application in clinical practice.
“Waiting out the day, not living, not fun”: a qualitative investigation of children’s experiences of school closures due to COVID-19

Abstract Early school leaving (ESL) is considered a significant societal issue globally due to the negative effect on young people’s health, affect, and quality of life. Continued absenteeism is a risk factor for ESL. This study aimed to explore Irish children’s health and wellbeing experiences during school closures due to the COVID-19 pandemic. These children were all considered at risk of ESL by their schools. Semi-structured interviews, adopting a “write, draw or tell” method, were conducted with ten children ages 9 − 13 years old. The study used an Interpretative Phenomenological Analysis (IPA) methodology. The loss of routine, social ties, and sense of success and belonging experienced in schools had a detrimental impact on the health and wellbeing of the children. They experienced adverse mental health effects, including anxiety, depression, anger, and loneliness. Their physical health and wellbeing were impacted by changes to sleep behavior, physical activity levels, boredom, and gaming usage. Children remain very vulnerable to the impact of school closures, contact restrictions, and living with the pandemic. Recommendations include improving online resources for young people and developing in-school programs to improve sleep hygiene.