Miracle-dz/UniFAR-FTUs huggingface dataset:
The inter-ELM tungsten erosion profile in DIII-D H-mode discharges and benchmarking with ERO+OEDGE modeling

It is important to develop a predictive capability for the tungsten source rate near the strike points during H-mode operation in ITER and beyond. H-mode deuterium plasma exposures were performed on W-coated graphite and molybdenum substrates in the DIII-D divertor using DiMES. The W-I 400.9 nm spectral line was monitored by fast filtered diagnostics cross calibrated via a high-resolution spectrometer to resolve inter-ELM W erosion. The effective ionization/photon (S/XB) was calibrated using a unique method developed on DIII-D based on surface analysis. Inferred S/XB values agree with an existing empirical scaling at low electron density (ne) but diverge at higher densities, consistent with recent ADAS atomic physics modeling results. Edge modeling of the inter-ELM phase is conducted via OEDGE utilizing the new capability for charge-state resolved carbon impurity fluxes. ERO modeling is performed with the calculated main ion and impurity plasma background from OEDGE. ERO results demonstrate the importance a mixed-material surface model in the interpretation of W sourcing measurements. It is demonstrated that measured inter-ELM W erosion rates can be well explained by C→W sputtering only if a realistic mixed material model is incorporated.
Increasing stability by two-state lasing in quantum-dot lasers with optical injection

A quantum dot (QD) laser capable of two-state lasing in certain parameter regions is studied under external optical injection. Our modeling approach is based on microscopically based rate equations, which include carrier dependent index shifts obtained from a full Bloch-equation approach. Our results show an increase of the dynamical stability of the two-state QD laser under optical perturbations if compared to a single-state QD laser, e.g. to a QD laser with high losses in the excited state. Chaotic dynamics predicted for single-state QD lasers under strong and detuned injection completely vanishes if excited state lasing is possible. This suggests a general trend for semiconductor lasers to yield an improved stability against optical perturbations with more dynamical degrees of freedom.
Accelerated Gradient Flow for Directed Graphs With Event-Based Broadcasting

Accelerated gradients algorithms are currently at the receiving end of widespread interest in optimization theory, both under discrete- and continuous-time (CT) frameworks. In light of recent developments, in the first part of our work, we design a CT accelerated gradient algorithm for strongly connected directed graphs. We show that the convergence is exponential and the convergence rate is proportional to the gradient gain which is chosen arbitrarily. To facilitate implementation of the algorithm over communication networks, in the second part of our work, we design an event-based broadcasting protocol that intermittently checks for events by evaluating an event-triggering condition and accordingly makes decision on broadcasting. The distributed system, with CT dynamics and discrete-time (event-based) broadcasts, is reformulated as a hybrid dynamical system which is devoid of Zeno solutions. Finally, we provide a numerical example to demonstrate our results.
Assessment of Effect of Different Packaging Materials on Microbial Quality of Locust Beans during Storage

This study assesses the effect of different packaging materials on the microbial quality of Africa Locust beans during storage. Locust beans (Dawadawa) also known as iru, among the Yorubas in South-west Nigeria, is a popular condiment used as a taste and flavour enhancer in soup and dishes in Africa. Locust bean is traditionally produced from locust beans seed (Parkia biglobosa) and preserved using different packaging materials before use in order to prolong its shelf life. The Microbiological examination of the produced locust beans was carried out on samples wrapped with different packaging materials such as plastic containers, nylon, and dry banana leaves. The suspected organisms isolated include Salmonella spp, Staphylococcus aureus, Streptococcus lactis, Pseudomonas aeruginosa, Lactobacillus plantarium, Bacillus cereus, Bacillus subtilis, Leuconostoc sp, Escherichia coli and Micrococcus sp. It was observed from this study that locust beans packaged with banana leaves had the highest microbial load, few of which are of public health concern while plastic containers had the least microbial load. It is therefore recommended that locust beans should be produced and packaged with sealed plastic containers to avoid contamination by pathogenic organisms.
A Weighted Fuzzy Time Series Forecasting Model

In this paper we describe a new automatic partitioning method and a first order weighted fuzzy time series forecast ing model. First, we show that our automatic fuzzy partitioning method provides an accurate approximation to the original time series. The fuzzy sets extracted from our partitioning are grouped to create a rule-base that will be used in forecasting. We found that the accuracy of our first order model is improved when an ordered weight ing averaging operator is applied. The model presented in this paper does not attempt to produce the most accurate forecasting results, when compared with other more complex higher order models. Our goal is to show that there is still space for improvement when simple first order forecasting models are used. Our results show that the combination of a simple partitioning method, a first order model, and an averaging operator is still capable of out performing not only the first order models that have proposed in the literature but also other higher order models.
There’s plenty of fractional at the bottom, I: Brownian motors and swimming microrobots

Abstract The only purpose of this paper is to show how fractional calculus can contribute to the development of the very interesting and promising field of micro and nano machines, from the deeper understanding of the world in which they work, to their design and control. The essentials of Brownian motors and untethered swimming microrobots are briefly presented, and two naive examples on how fractional calculus can contribute to open new perspectives are given.
Factors Affecting the Inland and Orographic Enhancement of Lake-Effect Precipitation over the Tug Hill Plateau

The factors affecting the inland and orographic enhancement of lake-effect precipitation are poorly understood, yet critical for operational forecasting. Here we use nine cool seasons (16 November–15 April) of radar data from the Montague/Ft. Drum, New York (KTYX), WSR-88D, the North American Regional Reanalysis (NARR), and observations from the Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine variations in lake-effect precipitation enhancement east of Lake Ontario and over the Tug Hill Plateau (hereafter Tug Hill). Key factors affecting the inland and orographic enhancement in this region include the strength of the incident boundary layer flow, the intensity of the lake-induced convective available potential energy (LCAPE), and the mode of the lake-effect system. Stronger flow favors higher precipitation rates, a precipitation maximum displaced farther downwind, and greater inland and orographic enhancement. The effects of LCAPE depend upon the strength of the flow. During periods of weak flow, higher LCAPE favors lower precipitation rates, a maximum closer to the shoreline, and lesser inland and orographic enhancement. During periods of strong flow, higher LCAPE favors higher precipitation rates, a maximum displaced farther downwind, and greater inland and orographic enhancement. Banded (nonbanded) modes favor higher (lower) precipitation rates, lesser (greater) inland and orographic enhancement, and a maximum closer to the shoreline (over Tug Hill). These results, for both manually measured and radar-estimated precipitation, are robust when many lake-effect events are considered, but substantial variability exists during individual events.
Design and Analysis of a Wireless Ultrasonic Motor Drive System

In this letter, a novel wireless motor system, namely, the wireless ultrasonic motor (WUM) system, is proposed and analyzed. A unique wireless drive topology is designed to solve the problems of the existing wireless drives, such as complex controller and converter at both the transmitting and receiving sides, regular maintenance, and output ripple resulted from motor type and control scheme. The key is to combine the merits of the wireless power transfer and ultrasonic motor to wirelessly energize the ultrasonic motor with high-frequency voltage directly. Meanwhile, a matching inductor is designed for the proposed wireless drive to improve power quality and to filter harmonic voltages at the motor side. Furthermore, laminated coil structure is optimized with electromagnetic decoupled ability and less ferrite core. Two-mode motor control method is designed to extend the speed range of the proposed WUM drive. Finally, operating principles and experimental results are provided to verify the feasibility and correctness of the proposed WUM drive system.
New perspectives on cost partitioning for optimal classical planning

Admissible heuristics are the main ingredient when solving classical planning tasks optimally with heuristic search. There are many such heuristics, and each has its own strengths and weaknesses. As higher admissible heuristic values are more accurate, the maximum over several admissible heuristics dominates each individual one. Operator cost partitioning is a well-known technique to combine admissible heuristics in a way that dominates their maximum and remains admissible. But are there better options to combine the heuristics? We make three main contributions towards this question: Extensions to the cost partitioning framework can produce higher estimates from the same set of heuristics. Cost partitioning traditionally uses non-negative cost functions. We prove that this restriction is not necessary, and that allowing negative values as well makes the framework more powerful: the resulting heuristic values can be exponentially higher, and unsolvability can be detected even if all component heuristics have a finite value. We also generalize operator cost partitioning to transition cost partitioning, which can differentiate between different contexts in which an operator is used. Operator-counting heuristics reason about the number of times each operator is used in a plan. Many existing heuristics can be expressed in this framework, which gives new theoretical insight into their relationship. Different operator-counting heuristics can be easily combined within the framework in a way that dominates their maximum. Potential heuristics compute a heuristic value as a weighted sum over state features and are a fast alternative to operator-counting heuristics. Admissible and consistent potential heuristics for certain feature sets can be described in a compact way which means that the best heuristic from this class can be extracted in polynomial time. Both operator-counting and potential heuristics are closely related to cost partitioning. They offer a new look on cost-partitioned heuristics and already sparked research beyond their use as classical planning heuristics.
Adaptive Event-Triggered Fuzzy Control for Uncertain Active Suspension Systems

This paper studies the adaptive event-triggered fuzzy control issue for active vehicle suspension systems with uncertainties. Takagi–Sugeno fuzzy model is applied for considered systems. In the process of designing controller, a crucial problem, actuator failure, is taken into account. An adaptive event-triggered mechanism is adopted to economize limited communication resource. Compared with the traditional event-triggered scheme with a constant threshold, the adaptive event-triggered mechanism can save more resource effectively. Based on Lyapunov stability theory, an adaptive event-triggered fuzzy control approach is proposed to guarantee the desired performance. Meanwhile, suspension constrained requirements are also ensured. Finally, simulation examples are presented to testify the feasibility of the approach proposed in this paper.
Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection

The prevalence of diabetes is rapidly increasing worldwide and can lead to a range of severe health complications that have the potential to be life-threatening. Patients need to monitor and control blood glucose levels as it has no cure. The development of non-invasive techniques for the measurement of blood glucose based on photoacoustic spectroscopy (PAS) has advanced tremendously in the last couple of years. Among them, PAS in the mid-infrared (MIR) region shows great promise as it shows the distinct fingerprint region for glucose. However, two problems are generally encountered when it is applied to monitor real samples for in vivo measurements in this MIR spectral range: (i) low penetration depth of MIR light into the human skin, and (ii) the effect of other interfering components in blood, which affects the selectivity of the detection system. This review paper systematically describes the basics of PAS in the MIR region, along with recent developments, technical challenges, and data analysis strategies, and proposes improvements for the detection sensitivity of glucose concentration in human bodies. It also highlights the recent trends of incorporating machine learning (ML) to enhance the detection sensitivity of the overall system. With further optimization of the experimental setup and incorporation of ML, this PAS in the MIR spectral region could be a viable solution for the non-invasive measurement of blood glucose in the near future.
Quality of life and its correlates in pretreatment patients with locally advanced head and neck cancer: A cross-sectional study in Thailand

Background Locally advanced head and neck cancer (LAHNC) can significantly impact the quality of life of patients in various ways. However, several factors can contribute to the decrease in quality of life. In Thailand, there is limited knowledge about the factors that affect the quality of life of patients with LAHNC before they receive treatment. Objective This study aimed to examine the correlations between Palliative Performance Scale (PPS), family income, body mass index (BMI), age, comorbidity index, and the quality of life of patients with LAHNC before they undergo treatment. Methods A correlational cross-sectional study was conducted, and data were collected from 94 pretreatment patients with LAHNC who were admitted to a cancer center in central Thailand using purposive sampling. The data collection instruments included a demographic data form, a medical record form, the Charlson Comorbidity Index (CCI), the Palliative Performance Scale (PPS), and the Functional Assessment of Cancer Therapy-Head and Neck (FACT-H&N) version 4. Descriptive statistics, Pearson’s correlation, and Spearman’s rank correlation were used to analyze the data. Results All study participants completed the questionnaire. The results showed that the overall quality of life of the patients was moderate. PPS, family income, and body mass index were moderately positively correlated with quality of life (r = 0.494, p <0.01; r = 0.420, p <0.01; r = 0.339, p <0.01, respectively). Age had a moderate negative correlation with quality of life (r = -0.596, p <0.01), while comorbidity was not significantly associated with quality of life. Conclusion The quality of life of patients with LAHNC before treatment was associated with various factors, including PPS, family income, body mass index, and age. These findings highlight the importance of nutritional support before treatment and the need for social support, especially for older adult patients, to improve their quality of life. The results of this study can be valuable for nurses in developing care programs that enhance the quality of life for patients with LAHNC during the pretreatment phase.
The Effect of Dayak Onion Extract on Lactic Acid Levels in Mice with FST Model

Intense physical exercise can increase lactic acid levels in the blood as a result of the anaerobic metabolism that occurs at that time. The impact of increased levels of lactic acid includes the occurrence of acidosis, damage to body cells, and fatigue which reduces muscle performance. Antioxidants can play a role in preventing body fatigue caused by oxidative stress. Onion Dayak (Eleutherine palmifolia) is a type of medicinal plant that is useful for health. The high content of antioxidants in Dayak onions can be used as an alternative anti-fatigue ingredient. This study aimed to look at the effect of the dose and duration of Dayak onion extract on lactic acid levels and physical activity in mice forced swimming test models. Type of Research Randomized Pre and Post-Test Control Group Design. Male White Mice (Sprague Dawley) 2 months old (weight 150-200g). Divided into 5 groups K- (the experimental group was not given Dayak onion extract, they were still given FST), K+ (the experimental group was given xanthine and FST), P1 (given Dayak onion extract at a dose of 50mg/200gBW), P2 (given the extract onion Dayak dose of 100mg/200gBB), P3 (given onion extract dose of 200mg/200gBW). Dayak onion extract was given for 28 days and given the forced swimming test, then the changes in lactic acid and physical activity endurance in swimming were observed.
Applying One-Class Algorithms for Data Stream-Based Insider Threat Detection

An insider threat is anyone who has legitimate access to a particular organization’s network and uses that access to harm that organization. Insider threats may act with or without intent, but when they have an intention, they usually also have some specific motivation. This motivation can vary, including but not limited to personal discontent, financial issues, and coercion. It is hard to face insider threats with traditional security solutions because those solutions are limited to the signature detection paradigm. To overcome this restriction, researchers have proposed using Machine Learning which can address Insider Threat issues more comprehensively. Some of them have used batch learning, and others have used stream learning. Batch approaches are simpler to implement, but the problem is how to apply them in the real world. That is because real insider threat scenarios have complex characteristics to address by batch learning. Although more complex, stream approaches are more comprehensive and feasible to implement. Some studies have also used unsupervised and supervised Machine Learning techniques, but obtaining labeled samples makes it hard to implement fully supervised solutions. This study proposes a framework that combines different data science techniques to address insider threat detection. Among them are using semi-supervised and supervised machine learning, data stream analysis, and periodic retraining procedures. The algorithms used in the implementation were Isolation Forest, Elliptic Envelop, and Local Outlier Factor. This study evaluated the results according to the values obtained by the precision, recall, and F1-Score metrics. The best results were obtained by the ISOF algorithm, with 0.78 for the positive class (malign) recall and 0.80 for the negative class (benign) recall.
Refractive Error and Ocular Biometric Changes in the Treatment of Diabetes Mellitus

Background and Objectives: Evaluation of changes in refractive errors and biometric parameters in the process of glycemic control in people with type 2 diabetes during three-month treatment. Methods: Patients with the first diagnosis of type 2 diabetes or a history of poor glycemic control (hemoglobin glycate more than 7.5%) and without any systemic disease other than diabetes were included. Hemoglobin glycate, refractive error, and biometric parameters were evaluated before treatment and one and a half and three months after treatment, and their changes were examined by generalized estimating equation (GEE) analysis. Results: A total of 60 eyes of 30 patients with a mean age of 51.63±6.79 years were evaluated. Hemoglobin glycate decreased by an average of 1.028% compared to the baseline measurement in the third month (P<0.001). Mean spherical (P=0.554), spherical equivalent (P=0.340), axial length (P=0.147), and anterior chamber depth (P=0.336) did not show a significant difference between the three examinations. In contrast, the lens thickness showed a significant decrease during treatment (P=0.001). Finally, generalized estimating equation (GEE) analysis showed that a 1% decrease in hemoglobin glycate increased by 0.226 mm. (P=0.002) in the axial length. It should be mentioned in tables FU1means Follow-up 1.5 months and FU2 means Follow-up 3 months. Conclusion: The present study shows that refractive errors and most ocular biometric parameters do not change significantly compared to the baseline levels in the period of one and a half and three months after the start of glycemic control.
Topological data analysis to improve exemplar-based inpainting

Image inpainting is the process of filling in the missing region to preserve continuity of its overall content and semantic. In this paper, we present a novel approach to improve an existing scheme, called exemplar-based inpainting algorithm, using Topological Data Analysis (TDA). TDA is a mathematical approach concern studying shapes or objects to gain information about connectivity and closeness property of those objects. The challenge in using exemplar-based inpainting is that missing regions neighborhood area needs to have a relatively simple texture and structure. We studied the topological properties (e.g. number of connected components) of missing regions surrounding the missing area by building a sequence of simplicial complexes (known as persistent homology) based on a selected group of uniform Local binary Pattern LBP. Connected components of image regions generated by certain landmark pixels, at different thresholds, automatically quantify the texture nature of the missing regions surrounding areas. Such quantification help determine the appropriate size of patch propagation. We have modified the patch propagation priority function using geometrical properties of curvature of isophote and improved the matching criteria of patches by calculating the correlation coefficients from spatial, gradient and Laplacian domain. We use several image quality measures to illustrate the performance of our approach in comparison to similar inpainting algorithms. In particular, we shall illustrate that our proposed scheme outperforms the state-of-the-art exemplar-based inpainting algorithms.
Contesting the Black Flâneur: The rhetoric of disruption and Ubah Cristina Ali Farah's urban literature

This article investigates the spatial poetics of disruption in Ubah Cristina Ali Farah's second novel Il comandante del fiume. The book sets a unique stage around fragmented flashbacks to resignify the configuration of Afro-Italian urban identity and community. These flashbacks are rendered through a complicated recoding of Rome's urbanscape by its protagonist, Yabar, a young Somali immigrant who experiences a mysterious disfigurement of his body and narrates his past as he recuperates in the hospital. The Black body thus becomes the primary shifting site through which new paradigms of spatial arrangements against power and authority are articulated. This article argues that, by having Yabar fantasize, observe, and live through Rome's urban physicality, Ali Farah delineates a poetics of disruption that functions to redraw the conceptual boundaries of Blackness and postcoloniality in contemporary Italy. Ali Farah's experiment with the city as an actual character de-romanticizes it as a historical fixed site, and instead displays the possibility of plurality in the shape of Black corporeal relationality in determined stances. In this novel, not as observed objects, the protagonists remap Rome as a Black city and carry forward a Benjaminian mode of flâneur to observe the city's fragmented and chaotic arrangements, demystifying its undercurrent construction of racial exclusion.