Miracle-dz/UniFAR-FTUs huggingface dataset:
Cutter-path generation using multiple cutting-tool sizes for 2-1/2D pocket machining

Existing commercial CAD/CAM systems allow the user to generate the cutter-path for machining a 2-1/2D pocket by using only a single cutting-tool size. Therefore, to avoid potential problems, such as gouging and unmachined areas, the user tends to choose the size of the cutting-tool in a conservative manner. This can result in longer processing time and higher production cost than those that can be achieved by using multiple cutting-tool sizes to machine a given pocket. The key to cutter-path generation using multiple cutting-tool sizes lies in having an efficient mechanism for representing the material volumes that can be removed by a specified cutting-tool size and the unmachined material volumes that will remain after its use. In this paper, we develop a novel concept called the Voronoi mountain and describe its application to cutter-path generation using multiple cutting-tool sizes. The theoretical significance of this approach stems from its ability to: (i) to create a Voronoi mountain representation of unmachined material volumes without having to construct the corresponding Voronoi diagram; and (ii) to accommodate generalized pocket geometries.
On Correlation and Linearity

idea of correlation arises naturally for two random variables that have a joint distribution that is bivariate normal. For each individual variable, two parameters a mean and standard deviation are sufficient to fully describe its probability distribution. For the joint distribution, a single additional parameter is required the correlation. If X and Y have a bivariate normal distribution, the relationship between them is linear: the mean of Y, given X, is a linear function of X ie: The slope is determined by the correlation , and the standard deviations and : The correlation between Y and X is zero if and only if the slope is zero. Also note that, when Y and X have a bivariate normal distribution, the conditional variance of Y, given X, is constant ie not a function of X: This is why, in the usual linear regression model Y = the variance of the "error" term does not depend on X. However, not all variables are linearly related. Suppose we have two random variables related by the equation S = T where T is normally distributed with mean zero and variance 1. Then the linear correlation between S and T is zero, even though one is a function of the other. 2 Linear correlation is a measure of how close two random variables are to being related. In fact, if we know that the linear correlation is +1 or-1, then there must be a deterministic linear relationship between Y and X (and vice versa). linearly If Y and X are linearly related, and f and g are functions, the relationship between f(Y) and g(X) is not necessarily linear, so we should not expect the linear correlation between f(Y) and g(X) to be the same as between Y and X. For a study of correlations between two lognormals see the paper " A common misconception about correlations". click here
Kernel Poisson regression for mixed input variables

An estimating procedure is introduced for kernel Poisson regression when the input variables consist of numerical and categorical variables, which is based on the penalized negative log-likelihood and the component-wise product of two different types of kernel functions. The proposed procedure provides the estimates of the mean function of the response variables, where the canonical parameter is linearly and/or nonlinearly related to the input variables. Experimental results are then presented which indicate the performance of the proposed kernel Poisson regression.
A Multi-Band Common-Aperture Phased Array Antenna For Satellite Communication

This paper designs a multi-band common-aperture phased array antenna for satellite communication. It contains three different types of antennas. A dual-band circularly polarized patch antenna in L frequency band, A dual-layer patch phased array antenna in S frequency band and a cross dipole phased array antenna in S frequency band. It has the advantages of small volume, low profile and high space utilization rate. The antenna has various modes satisfied the anti-interference requirements in satellite communication.
Vaccines against Group B Coxsackieviruses and Their Importance

The group B coxsackieviruses (CVBs) exist in six serotypes (CVB1 to CVB6). Disease associations have been reported for most serotypes, and multiple serotypes can cause similar diseases. For example, CVB1, CVB3, and CVB5 are generally implicated in the causation of myocarditis, whereas CVB1 and CVB4 could accelerate the development of type 1 diabetes (T1D). Yet, no vaccines against these viruses are currently available. In this review, we have analyzed the attributes of experimentally tested vaccines and discussed their merits and demerits or limitations, as well as their impact in preventing infections, most importantly myocarditis and T1D.
Ground layer adaptive optics: analysis of the wavefront sensing issue

Here are presented the basis of an analytical development whose purpose is to give arguments for the evaluation of wavefront sensing concepts for Ground Layer Adaptive Optics. Simple hypothesis make possible the derivation of analytical expressions for the phase measurement error and reveal consequent differences between Star Oriented and Layer Oriented concepts. Influence of key parameters such as guide star statistics or strength of the turbulence in altitude are then studied. In the Layer Oriented case, necessity of reducing the guide stars flux dispersion to achieve a uniform correction in the field of interest is demonstrated.
A New Method of Time Series Piecewise Linear Representation

To meet requirements of time data dynamic growth , and reflect the different effect to the different segment of time series over time, a new method of piecewise linear representation, called tangent piecewise aggregate approximation (TPAA) is proposed based on hyperbolic tangent function. The method can not only meet requirements of time data dynamic growth, but also reflect time property of the time series. Compared with the existing methods, TPAA method can effectively query time series online.
Corrections to virial estimates of molecular cloud masses

The application of the virial theorem to the determination of giant molecular cloud masses is reconsidered. It is argued that it is necessary to transform the results to equivalent velocity widths for the optically thin line of (C-13)O rather than that of (C-12)O. The results lead to a substantial lowering of H2 mass and values of X closer to that of the gamma-ray analysis of Bhat et al. (1986). The total mass of H2 in the Inner Galaxy is significantly less than that of atomic hydrogen. 27 references.
PEC-TDS-Based Fast Characteristic Modes Analysis for Repetitive Array

The characteristic mode formula based on the composite integral equation of perfect electric conductor and thin dielectric sheet (PEC-TDS) is presented in this paper. Then the resultant Characteristic Modes(CM) vectors are utilized as the global basis functions to reduce the dimension of the matrix equation discretized by the method of moments (MoM). As the numerical example, a frequency selective surface (FSS) array is analyzed to demonstrate the accuracy and efficiency of the proposed scheme.
Improvement of sep-CMA-ES for Optimization of High-Dimensional Functions with Low Effective Dimensionality

High-dimensional black-box optimization problems often involve a property referred to as low effective dimensionality (LED), in which design variables contain many redundant elements that scarcely affect the objective function value. The covariance matrix adaptation evolution strategy (CMA-ES) suffers a performance deterioration on objective functions with LED because the redundant dimensions lead to modest hyperparameter settings and slow down the adaptation of the step-size. In this study, we focus on the separable CMA-ES (sep-CMA-ES), a variant of CMA-ES that restricts the covariance matrix to be diagonal, and propose a method to estimate the effectiveness of each dimension using the element-wise signal-to-noise ratios in the mean vector update and rank-µ update. Using the estimated effectiveness, we construct two countermeasures for LED, including an adaptation of hyperparameters and a refinement of the update rule in the cumulative step-size adaptation and two-point step-size adaptation, proposing sep-CMA-ES-LED. We experimentally showed that sep-CMA-ES-LED performed well on several benchmark functions with LED compared to the original sep-CMA-ES.
A Deep Reinforcement Learning Approach for Inventory Control under Stochastic Lead Time and Demand

In the last few years, the deep learning paradigm has experienced huge success in various machine learning research areas like computer vision, drug discoveries, natural language processing, and combinatorial optimizations. Moreover, the world has witnessed remarkable achievements when combining deep learning with reinforcement learning (now known as Deep Reinforcement Learning) in the areas like robotics, video games, business, and healthcare. One of the strongest parts of Deep Reinforcement Learning (DRL) is the ability to solve sequential decision-making problems. The inventory control problem is one such field where DRL can be applied to learn the optimal ordering policy to minimize the total inventory cost. In this paper, a linear supply chain model is considered with stochastic lead time and demand. The problem is then modeled into Markov Decision Processes (MDP). We then designed three different agents: Q-learning agent, Deep Q-network (DQN, also known as Deep Q-Learning), and (R, S) policy-based agent. The Q-learning and DQN agents were trained and evaluated. The (R, S) policy is used as a baseline as it is one of the most popular policies in business organizations. In comparison to traditional reinforcement learning (i.e Q-learning) and rule-based learning (i.e. (R, S) policy), the DQN model performs better in making the optimal ordering decision so that the total cost is minimized.
Empirical Evidence to Support a Nudge Intervention for Increasing Online Engagement in Higher Education

Student engagement is recognised as being a critical factor linked to student success and learning outcomes. The same holds true for online learning and engagement in higher education, where the appetite for this mode of learning has escalated worldwide over several decades, and as a result of COVID 19. At the same time teachers in higher education are increasingly able to access and utilise tools to identify and analyse student online behaviours, such as tracking evidence of engagement and non-engagement. However, even with significant headway being made in fields such as learning analytics, ways in which to make sense of this data, and to utilise data to inform interventions and refine teaching approaches, continue to be areas that would benefit from further insights and exploration. This paper reports on a project that sought to investigate whether low levels of student online engagement could be enhanced through a course specific intervention strategy designed to address student engagement with online materials in a regional university. The intervention used course learning analytics data (CLAD) in combination with the behavioral science concept of nudging as a strategy for increasing student engagement with online content. The study gathered qualitative and quantitative data to explore the impact of nudging on student engagement with 187 students across two disciplines, Education and Regional/Town Planning. The results not only revealed that the use of the nudge intervention was successful in increasing the levels of engagement in online courses but also revealed that the prerequisites for nudging were needed in order to increase success rates. The paper points to the value for the broader awareness, update, and use of learning analytics as well as nudging at a course, program, and institutional level to support student online engagement.
A Non-Invasive Tumor Hyperthermia Method Based on Microwave Field Shaping

In the microwave hyperthermia field, maximizing the energy absorption over irregular-shaped tumor regions within the human body and avoiding hot spots in the surrounding normal tissues are challenging tasks. In this paper, we propose a non-invasive tumor hyperthermia method, which is based on the angular spectrum projection field shaping theory, to effectively control the magnitude distribution of the electric field in the observation plane and focus the energy absorbed by the target tissue. The proposed method could also effectively deposit the microwave energy to the irregularly shaped tumors. The effectiveness of the method is primarily verified by several full-wave simulations with a simplified human tissue model consisting of planar stratified layers.
A Compromise Between Simplicity and Accuracy of Nonlinear Reduced Order Systems

Abstract This paper considers the problem of complexity in reduced nonlinear models derived by System Matrices Optimization method. The solution suggested here is to add a class of secondary conditions that impose a simpler structure on the reduced model. Distinct choices of these conditions have different impacts on the accuracy of the reduced model, therefore a pioneered search based on genetic algorithms is proposed that finds the optimal choice of conditions. By means of two examples, it is illustrated that this method can effectively compromise between simplicity and accuracy of the reduced model. In order to improve the numerical efficiency of the solution and to speed up the pace of convergence some enhancements are presented.
Tidal control and mangrove dieback impact on methane emissions from a subtropical mangrove estuary

Mangrove ecosystems with high sediment deposition and active carbon cycling are a source of methane (CH4) to the coastal atmosphere. We investigated diurnal and seasonal variations in CH4 emissions from a subtropical mangrove estuary in southern Texas, northwest Gulf of Mexico. Tidal processes, amplitude (spring vs. neap tides) and topographic characteristics are crucial factors controlling CH4 cycling in mangrove creeks. Higher CH4 concentrations were observed during the ebb in spring tides due to the combination of processive export of CH4 along the creeks during ebb tides and the addition of porewater CH4 in upper intertidal sediment under water inundation in spring tides. The annual CH4 emissions offset approximately 0.15% of the carbon stock in normal years, indicating that these mangrove creeks are a weak CH4 source. However, significantly elevated CH4 emissions were observed from mangrove dieback after the extreme cold‐freezing event in February 2021. The average CH4 flux from the mangrove creeks (126.1 ± 128.3 μmol [m2·d]−1) increased 45% in 3 months after mangrove die‐off in comparison with the overall average in normal years (87.0 ± 64.4 μmol [m2·d]−1). It is obvious that the previous small CH4 offset of the healthy mangrove forest was enlarged by the dieback event. Because the mangrove forests in this study live close to the limit of their survival range, our study highlights the important management considerations for blue carbon projects in vulnerable areas.
Optimizing Dacarbazine Therapy: Design of a Laser-Triggered Delivery System Based on β-Cyclodextrin and Plasmonic Gold Nanoparticles

Dacarbazine (DB) is an antineoplastic drug extensively used in cancer therapy. However, present limitations on its performance are related to its low solubility, instability, and non-specificity. To overcome these drawbacks, DB was included in β-cyclodextrin (βCD), which increased its aqueous solubility and stability. This new βCD@DB complex has been associated with plasmonic gold nanoparticles (AuNPs), and polyethylene glycol (PEG) has been added in the process to increase the colloidal stability and biocompatibility. Different techniques revealed that DB allows for a dynamic inclusion into βCD, with an association constant of 80 M−1 and a degree of solubilization of 0.023, where βCD showed a loading capacity of 16%. The partial exposure of the NH2 group in the included DB allows its interaction with AuNPs, with a loading efficiency of 99%. The PEG-AuNPs-βCD@DB nanosystem exhibits an optical plasmonic absorption at 525 nm, a surface charge of −29 mV, and an average size of 12 nm. Finally, laser irradiation assays showed that DB can be released from this platform in a controlled manner over time, reaching a concentration of 56 μg/mL (43% of the initially loaded amount), which, added to the previous data, validates its potential for drug delivery applications. Therefore, the novel nanosystem based on βCD, AuNPs, and PEG is a promising candidate as a new nanocarrier for DB.
A Local Hubble Bubble from Type Ia Supernovae?

We analyze the monopole in the peculiar velocities of 44 Type Ia supernovae (SNe Ia) to test for a local void. The sample extends from 20 to 300 h-1 Mpc, with distances, deduced from light-curve shapes, accurate to ~6%. Assuming Ωm = 1 and ΩΛ = 0, the most significant deviation we find from the Hubble law is an outward flow of 6.5% ± 2.2% inside a sphere of radius 70 h-1 Mpc, as would be produced by a void of ~20% underdensity surrounded by a dense shell. This shell roughly coincides with the local great walls. Monte Carlo analyses, using Gaussian errors or bootstrap resampling, show the probability for chance occurrence of this result out of a pure Hubble flow to be ~2%. The monopole could be contaminated by higher moments of the velocity field, especially a quadrupole, which are not properly probed by the current limited sky coverage. The void would be less significant if Ωm is low and ΩΛ is high. It would be more significant if one outlier is removed from the sample, or if the size of the void is constrained a priori. This putative void is not in significant conflict with any of the standard cosmological scenarios. It suggests that the Hubble constant as determined within 70 h-1 Mpc could be overestimated by ~6%, and the local value of Ω may be underestimated by ~20%. While the present evidence for a local void is marginal in this data set, the analysis shows that the accumulation of SN Ia distances will soon provide useful constraints on elusive and important aspects of regional cosmic dynamics.
Depressive symptoms in diabetic patients; prevalence, correlates, and moderating effect in Taif, Saudi Arabia

Background: Diabetes is associated with a range of psychosocial stressors that could lead to considerable distress and increased risk of depressive symptoms. There is a pressing need to understand the underpinnings of diabetes-related distress and how it evolves in connection with depressive moods, and fears related to hypoglycaemia. Our current study attempts to fill this knowledge gap and further explore the interconnections between distress, fear, and depression among Saudi diabetic patients. Methods: Descriptive questionnaire-based cross-sectional study of type II diabetes patients in a specialist diabetes clinic in Taif, Saudi Arabia. We carried out Poisson regression modelling to evaluate the correlates of depressive and distress symptoms. Results: The study included (n = 365) patients living with type II diabetes. Cronbach's alpha for the DDS-17 was 0.93, and for HABS was 0.84, indicative of excellent internal consistency. Diabetes-related distress affected (n = 114, 22.8%) patients, whereas depressive symptoms affected (n = 190, 52.1%) patients. The mean HABS score was 32.7 points (out of 70 points) (SD = 9.8 points). High physical activity levels were found only in (n = 23, 6.3%) patients and moderate physical activity in (n = 65, 17.8%), whereas patients with low physical activity were (n = 277, 75.9%). Diabetes-related distress was associated with increased HbA1c, presence of eye disease, comorbid mental illness, heart disease, stroke, and low physical activity levels. Depressive symptoms were associated with increased HbA1c, longer diabetes duration, presence of eye disease, comorbid mental illness, comorbid neuropathy, heart disease, and low physical activity levels. Conclusions: Distress and depression levels are worryingly higher than previous estimates from Saudi Arabia among patients with type II diabetes, indicative of an upward trend and/or a pandemic-related jump. One significant finding from our results is the substantial effect of glycaemic control on increased distress, and depression among our type II diabetes patients. This interaction is likely due to effects on self-care and medication adherence. We also confirmed the association between depressive symptoms and the duration of diabetes. Our results indicated a connection between comorbid medical illness with depressive and distress symptoms.
Childhood Brain Tumors: A Review of Strategies to Translate CNS Drug Delivery to Clinical Trials

Simple Summary Brain tumors account for over 20% of childhood cancers and are the biggest cancer killer in children and young adults. Several initiatives over the past 40 years have tried to identify more effective drug treatments, but with very limited success. This is largely due to the blood–brain barrier, which restricts the entry of many drugs into the brain. In this review, we describe the main techniques that are being developed to enhance brain tumor drug delivery and explore the preclinical brain tumor models that are essential for translational development of these techniques. We also identify existing approved drugs that, if coupled with an efficient delivery method, could have potential as brain tumor treatments. Bringing this information together is part of a funded initiative to highlight drug delivery as a research strategy to overcome the current challenges for children diagnosed with brain tumors. Abstract Brain and spinal tumors affect 1 in 1000 people by 25 years of age, and have diverse histological, biological, anatomical and dissemination characteristics. A mortality of 30–40% means the majority are cured, although two-thirds have life-long disability, linked to accumulated brain injury that is acquired prior to diagnosis, and after surgery or chemo-radiotherapy. Only four drugs have been licensed globally for brain tumors in 40 years and only one for children. Most new cancer drugs in clinical trials do not cross the blood–brain barrier (BBB). Techniques to enhance brain tumor drug delivery are explored in this review, and cover those that augment penetration of the BBB, and those that bypass the BBB. Developing appropriate delivery techniques could improve patient outcomes by ensuring efficacious drug exposure to tumors (including those that are drug-resistant), reducing systemic toxicities and targeting leptomeningeal metastases. Together, this drug delivery strategy seeks to enhance the efficacy of new drugs and enable re-evaluation of existing drugs that might have previously failed because of inadequate delivery. A literature review of repurposed drugs is reported, and a range of preclinical brain tumor models available for translational development are explored.
Spontaneous reasoning on the propagation of visible mechanical signals

This paper analyses the difficulties encountered by students while learning the propagation of a signal on a string. Two tendencies are shown to recur: a mechanistic reasoning, and a reduction in the number of variables. For students, the medium is a passive support and the travelling ‘bump’ is a material object created and set in motion by the source. Consequently, the signal velocity depends on the source and can decrease with time. The signal cannot move before being entirely formed and its length does not depend on the medium. Students reduce the number of variables in two ways. They tend to combine different physical quantities into a single notion. They also reason on the basis of one variable at a time. Some pedagogical implications are discussed.