Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Segment Fraction for Road-Network Locations

When dealing with locations on the road network, geographic coordinates, com­ monly represented in the form of (latitude, longitude), can often be difficult to use and are hard to interpret. Not only do these coordinates omit any explicit road network information, such as what road segment the location belongs to, but even simple operations such as speed computation cannot be performed with­ out summing road distance over the segment geometry. In certain applications, map projection [Yang et al. 1999] and linear referencing [Scarponcini 2002] have been utilized to make locations easier to analyze or to associate certain attributes without compromising precision. We propose extending the concept of linear ref­ erencing to the entirety of road-based locations and show that we can perform local searches, speed computation, location prediction, and interpolation with simple, linear formulas. Using Segment Fraction for Road-Network Locations
A Multi-Mode Flexible Power Point Tracking Algorithm for Photovoltaic Power Plants

Flexible power point tracking (FPPT) is the control of active power generated by grid-connected photovoltaic power plants (GCPVPPs) to provide grid-support functionality. An FPPT algorithm for the reduction of the extracted power from photovoltaic (PV) strings during voltage sags was previously proposed by the authors. An advantage of this algorithm, compared to conventional FPPT algorithms, was its fast dynamics facilitated by use of a simple PI controller that dynamically modifies the PV voltage reference. The previously proposed scheme could only be employed for the short duration in which the power system experiences a voltage sag. A novel modification to this algorithm with multi-mode operation is introduced in this letter, which provides FPPT capability for continuous operation of GCPVPPs. Unlike the previous algorithm, which was able to only move the operation point to the right-hand side of MPP, the proposed algorithm in this letter is able to move the operation point to both right- and left-hand sides of the MPP that provides the flexibility to operate in the optimum operation region for both single- and two-stage GCPVPPs. Experimental results are provided to demonstrate the performance of the proposed algorithm under dynamic irradiance conditions.
Interpolating Refinable Functions And Wavelets For General Scaling Matrices

This paper introduces a general procedure for constructing interpolating refinable functions for arbitrary dilation matrices. The key ideas are based on the construction presented in [24]. Several families of interpolating refinable functions are computed exlicitly. They originate from convolution products of some simple functions, either generalized Haar functions or Laplace schemes. A suitable correction is added to obtain interpolating solutions. AMS Subject classification: Primary 26B05, 41A05, 41A15, Secondary 41A30, 41A63
Research on GNSS interference recognition based on ROI of correlation peaks

The general Global Navigation Satellite System (GNSS) receiver faces several challenges because of jamming signals, spoofing signals, and multipath signals, which severely influence its safety. In this paper, a receiver scheme with an interference recognition function is designed. In the latter, the correlation peak with different shapes is produced according to different interferences. The machine learning method is then applied to recognize and classify these feature maps. This transforms the interference recognition problem into a machine learning‐based classification problem. In order to reduce the complexity of the machine learning network, only the finite‐length correlation peak region of interest (ROI) is extracted as network input, endowing the shallow neural network with the interference recognition function. Afterward, five data acquisition environments are designed: authentic, spoofing, jamming, non‐line‐of‐sight (NLOS) multipath, and line‐of‐sight (LOS) multipath. Moreover, several experimental data are acquired, followed by the production of the correlation peak maps dataset, that are then learned and tested using two machine learning networks: one‐dimensional convolutional neural network (1D‐CNN) and bidirectional long short‐term memory neural network (BiLSTM‐NN). The results demonstrate that a recognition accuracy rate of over 98% can be reached using the shallow machine learning network.
Numerical estimation of energy efficiency in cryogenic chamber with various methods of ventilation and location of test object

Various industries, including automobiles, wind power, railroads, airlines, and defense, have their own individual low-temperature test specifications. Since the performance of a test object has to be evaluated after cooling in a climate-controlled chamber, the process consumes an enormous amount of energy and a long time is needed to cool the object. Reducing energy consumption and increasing the cooling rate would offer a huge environmental and economic benefit. This study analyzed the cooling rate and energy consumption of a cryogenic chamber in terms of the ventilation methods and locations of test objects through computational fluid dynamics analysis. In order to calculate energy consumption, the study fixed the compressor power of the refrigerator and created an energy model in the form of a user-defined function that considered changes in the outlet mass flow and refrigerator coefficient of performance according to changes in outlet temperatures within the chamber; this model is used for numerical analysis. The chamber used for the upper air supply and lower exhaust method (Case A) demonstrated a faster cooling rate by 0%–32% compared to the upper air supply and lower exhaust on the opposite face method (Case B) and the upper air supply and central exhaust on the opposite face method (Case F). Case A generally consumed less energy during cooling, whereas Case F consumed the least amount of energy for 4–6 out of the 12 test object locations. Cases A, B, and F demonstrated the highest cooling rates and the lowest energy consumptions when the test object was located at P5 or P11 opposite of the air supply. The cooling rate of the test object and energy consumption should be considered in the ventilation design of a cryogenic test chamber. In the case of a pre-installed chamber, a proper location should be selected for cooling the test object for fast cooling according to the chamber ventilation method.
A meta-analysis approach for evaluating the effectiveness of complex multisite programs

The National Institutes of Health (NIH) created the Building Infrastructure Leading to Diversity (BUILD) initiative to incentivize undergraduate institutions to create innovative approaches to increasing diversity in biomedical research, with the ultimate goal of diversifying the NIH‐funded research enterprise. Initiatives such as BUILD involve designing and implementing programs at multiple sites that share common objectives. Evaluation of initiatives like this often includes statistical analyses that combine data across sites to estimate the program's impact on particular outcomes. Meta‐analysis is a statistical technique for combining effect estimates from different studies to obtain a single overall effect estimate and to estimate heterogeneity across studies. However, it has not been commonly applied to evaluate the impact of a program across multiple different sites. In this chapter, we use the BUILD Scholar program—one component of the broader initiative—to demonstrate the application of meta‐analysis to combine effect estimates from different sites of a multisite initiative. We analyze three student outcomes using a typical “single‐stage” modeling approach and a meta‐analysis approach. We show how a meta‐analysis approach can provide more nuanced information about program impacts on student outcomes and thus can help support a robust evaluation.
Improvement of photon transport model by including coupled photon-electron transport and kernel refinement

The first part of this work deals with the inverse problem solution in the X-ray spectroscopy field. An original strategy to solve the inverse problem by using the maximum entropy principle is illustrated. It is built the code UMESTRAT, to apply the described strategy in a semiautomatic way. The application of UMESTRAT is shown with a computational example. The second part of this work deals with the improvement of the X-ray Boltzmann model, by studying two radiative interactions neglected in the current photon models. Firstly it is studied the characteristic line emission due to Compton ionization. It is developed a strategy that allows the evaluation of this contribution for the shells K, L and M of all elements with Z from 11 to 92. It is evaluated the single shell Compton/photoelectric ratio as a function of the primary photon energy. It is derived the energy values at which the Compton interaction becomes the prevailing process to produce ionization for the considered shells. Finally it is introduced a new kernel for the XRF from Compton ionization. In a second place it is characterized the bremsstrahlung radiative contribution due the secondary electrons. The bremsstrahlung radiation is characterized in terms of space, angle and energy, for all elements whit Z=1-92 in the energy range 1–150 keV by using the Monte Carlo code PENELOPE. It is demonstrated that bremsstrahlung radiative contribution can be well approximated with an isotropic point photon source. It is created a data library comprising the energetic distributions of bremsstrahlung. It is developed a new bremsstrahlung kernel which allows the introduction of this contribution in the modified Boltzmann equation. An example of application to the simulation of a synchrotron experiment is shown.
Methodology for Evaluating the Introduction of Traceability in Practice

Research has shown that traceability has various advantages such as helping to understand the system, support maintenance activities and facilitate change impact analysis. For traceability to be established in practice having a tool alone is not enough. The tool also needs to be integrated into the development process and the links created need to be purposed in a sense that they are useful. In this study, we will investigate how traceability can be introduced in a company. We will first identify the objectives of the company with regards to traceability and tailor a solution (tool and process) that will fulfill these objectives. We will introduce this solution and investigate how the objectives of the company have been achieved, focusing on the advantages brought about by traceability.
Detecting JPEG Image Forgery Based on Double Compression

Detecting the forgery parts from a double compressed image is very important and urgent work for blind authentication. A very simple and efficient method for accomplishing the task is proposed. Firstly, the probabilistic model with periodic effects in double quantization is analyzed, and the probability of quantized DCT coefficients in each block is calculated over the entire image. Secondly, the posteriori probability of each block is computed according to Bayesian theory and the results mentioned in first part. Then the mean and variance of the posteriori probability are to be used for judging whether the target block is tampered. Finally, the mathematical morphology operations are performed to reduce the false alarm probability. Experimental results show that the method can exactly locate the doctored part, and through the experiment it is also found that for detecting the tampered regions, the higher the second compression quality is the more exact the detection efficiency is.
International Journal of Advanced Research in Computer Science and Software Engineering

--The improvements in the computational and communication capabilities of mobile devices has made them all pervasive and is leading to Ubiquitous computing in which all computations will fade into the background. Much of research has gone into improving the functionality, computational power, size and power constraints of mobile devices such as PDAs, cell phones etc. Also, there has been considerable work on improving the communication capabilities of mobile devices by increasing bandwidth and the development of protocols that are fault tolerant and resilient to the error prone wireless media. However, data management in such mobile networks will be key to the success of Ubiquitous Computing. In this report we will review some of the existing work in Data Management in Mobile Networks. In the final section, we propose a novel caching strategy for time varying data in Mobile Networks.
SINGULAR PROJECTION NESTS IN UHF

A projection nest in a C*-algebra is said to be singular if the normaliser of its generated C*-algebra is trivial. Uncount- ably many pairwise nonconjugate singular homogeneous nests are identified (in a given UHF C*-algebra) all of which have the same binary relation invariant. The constructions are based upon (ho- mogeneous) direct systems of upper triangular matrix algebras with rapidly increasing multiplicities and irregular erabeddings.
Kobe University Repository : Kernel タイトル Tit le Role of lysophosphat idic acid in proliferat ion and different iat ion of intest inal epithelial cells

Intestinal epithelial cells (IECs) are regenerated continuously from intestinal stem cells (ISCs) near the base of intestinal crypts in order to maintain homeostasis and structural integrity of intestinal epithelium. Epidermal growth factor (EGF) is thought to be important to drive the proliferation and differentiation of IECs from ISCs, it remains unknown whether other growth factors or lipid mediators are also important for such regulation, however. Here we show that lysophosphatidic acid (LPA), instead of EGF, robustly promoted the development of intestinal organoids prepared from the mouse small intestine. Indeed, LPA exhibited the proliferative activity of IECs as well as induction of differentiation of IECs into goblet cells, Paneth cells, and enteroendocrine cells in intestinal organoids. Inhibitors for LPA receptor 1 markedly suppressed the LPA-promoted development of intestinal organoids. LPA also promoted the phosphorylation of extracellular signal-regulated kinase (ERK) 1/2 in intestinal organoids, whereas inhibition of mitogen-activated protein kinase/ERK kinase (MEK) 1/2 significantly suppressed the development of, as well as the proliferative activity and differentiation of, intestinal organoids in response to LPA. Our results thus suggest that LPA is a key factor that drives the proliferation and differentiation of IECs.
Culture, heritage looting, and tourism: A text mining review approach

Tourism scholars have been devoted to exploring the significance of cultural heritage in generating economic, environmental, and social values. However, limited efforts were found to verify potential threats that demolish these values such as looting issue in the global heritage tourism industry. Therefore, this study has reviewed extant publications to demonstrate the potential emerged textual clusters discussed by previous studies. It also summarized the network distribution of articles journals and authors’ affiliations to capture the mobility and diversity with a focus on the business and tourism management field. Hence, the core clusters discovered were related to heritage destruction, public access, world heritage, human rights, cultural heritage preservation, and protection of cultural heritage in the event. The results have established theoretical insights and research agendas for future tourism studies, while it determined critical drawbacks in employing technology tools including virtual reality, augmented reality, and artificial intelligence for cultural heritage preservation/protection.
High-order brain functional network for electroencephalography-based diagnosis of major depressive disorder

Brain functional network (BFN) based on electroencephalography (EEG) has been widely used to diagnose brain diseases, such as major depressive disorder (MDD). However, most existing BFNs only consider the correlation between two channels, ignoring the high-level interaction among multiple channels that contain more rich information for diagnosing brain diseases. In such a sense, the BFN is called low-order BFN (LO-BFN). In order to fully explore the high-level interactive information among multiple channels of the EEG signals, a scheme for constructing a high-order BFN (HO-BFN) based on the “correlation’s correlation” strategy is proposed in this paper. Specifically, the entire EEG time series is firstly divided into multiple epochs by sliding window. For each epoch, the short-term correlation between channels is calculated to construct a LO-BFN. The correlation time series of all channel pairs are formulated by these LO-BFNs obtained from all epochs to describe the dynamic change of short-term correlation along the time. To construct HO-BFN, we cluster all correlation time series to avoid the problems caused by high dimensionality, and the correlation of the average correlation time series from different clusters is calculated to reflect the high-order correlation among multiple channels. Experimental results demonstrate the efficiency of the proposed HO-BFN in MDD identification, and its integration with the LO-BFN can further improve the recognition rate.
Modulation of cortical resting state functional connectivity during a visuospatial attention task in Parkinson's disease

Visual dysfunction is a recognized early symptom of Parkinson's disease (PD) that partly scales motor symptoms, yet its background is heterogeneous. With additional deficits in visuospatial attention, the two systems are hard to disentangle and it is not known whether impaired functional connectivity in the visual cortex is translative in nature or disrupted attentional modulation also contributes. In this study, we investigate functional connectivity modulation during a visuospatial attention task in patients with PD. In total, 15 PD and 16 age-matched healthy controls performed a visuospatial attention task while undergoing fMRI, in addition to a resting-state fMRI scan. Tensorial independent component analysis was used to investigate task-related network activity patterns. Independently, an atlas-based connectivity modulation analysis was performed using the task potency method. Spearman's rank correlation was calculated between task-related network expression, connectivity modulation, and clinical characteristics. Task-related networks including mostly visual, parietal, and prefrontal cortices were expressed to a significantly lesser degree in patients with PD (p < 0.027). Resting-state functional connectivity did not differ between the healthy and diseased cohorts. Connectivity between the precuneus and ventromedial prefrontal cortex was modulated to a higher degree in patients with PD (p < 0.004), while connections between the posterior parietal cortex and primary visual cortex, and also the superior frontal gyrus and opercular cortex were modulated to a lesser degree (p < 0.001 and p < 0.011). Task-related network expression and superior frontal gyrus–opercular cortex connectivity modulation were significantly associated with UPDRSIII motor scores and the Hoehn–Yahr stages (R = −0.72, p < 0.006 and R = −0.90, p < 0.001; R = −0.68, p < 0.01 and R = −0.71, p < 0.007). Task-related networks function differently in patients with PD in association with motor symptoms, whereas impaired modulation of visual and default-mode network connectivity was not correlated with motor function.
Decellularized blood vessel development: Current state-of-the-art and future directions

Vascular diseases contribute to intensive and irreversible damage, and current treatments include medications, rehabilitation, and surgical interventions. Often, these diseases require some form of vascular replacement therapy (VRT) to help patients overcome life-threatening conditions and traumatic injuries annually. Current VRTs rely on harvesting blood vessels from various regions of the body like the arms, legs, chest, and abdomen. However, these procedures also produce further complications like donor site morbidity. Such common comorbidities may lead to substantial pain, infections, decreased function, and additional reconstructive or cosmetic surgeries. Vascular tissue engineering technology promises to reduce or eliminate these issues, and the existing state-of-the-art approach is based on synthetic or natural polymer tubes aiming to mimic various types of blood vessel. Burgeoning decellularization techniques are considered as the most viable tissue engineering strategy to fill these gaps. This review discusses various approaches and the mechanisms behind decellularization techniques and outlines a simplified model for a replacement vascular unit. The current state-of-the-art method used to create decellularized vessel segments is identified. Also, perspectives on future directions to engineer small- (inner diameter >1 mm and <6 mm) to large-caliber (inner diameter >6 mm) vessel substitutes are presented.
An empirical assessment of a modified artificially intelligent device use acceptance model—From the task-oriented perspective

Artificial intelligence (AI) is a cutting-edge technology that has been widely applied in tourism operations. To enhance tourists' experience, many tourism suppliers introduced AI devices to interact with tourists. Previous studies classified AI devices as task- and social- oriented based on their functions; however, current models that explain customers' intention to use AI devices did not reflect the discrepancy between the two different types. Therefore, this paper attempts to fill this gap by proposing a theoretical model for the use of task-oriented AI devices. Based on the multi-stage appraisal framework and the Structural Equation Modeling analysis, this paper presents the following findings: (1) utilitarian motivation, interaction convenience, and task-technology fit are the factors appraised in the first stage; (2) perceived competence and flow experience are the factors appraised in the second stage; (3) utilitarian motivation, interaction convenience, and task-technology fit are positively associated with perceived competence. (4) Perceived competence positively influences flow experience, which further affects customers' switching intention from task-oriented AI devices to human service; (5) the serial mediating effect of perceived competence and flow experience between the stimulus mentioned in the first appraisal stage and the switching intention is confirmed. This study reveals the underlying psychological mechanism when customers use task-oriented AI devices, and it provides a theoretical framework for task-oriented AI device adoption.