Miracle-dz/UniFAR-FTUs huggingface dataset:
Slowness-time mapping of near offset seismic reflection data

Summary. The transformation of a set of seismograms to the delay timeslowness, ~-p, domain is presented as a sequence of Fourier and Bessel transforms, For a horizontally layered medium, this sequence gives an exact cylindrical wave decomposition of the response to a point source; correctly compensating for the phase shifting and geometrical spreading associated with transmission through the Earth. The resultant 7-p map or ‘slant stack’ contains true amplitude and phase information. The spatial aliasing properties of the transformation, when applied to a dataset, are greatly improved by the use of only outgoing waves in the Bessel transform. This is equivalent to using Hankel functions rather than Bessel functions, and is justified by the absence of incoming waves from most datasets. The WKBJ approximation to the medium response enables predictions to be made about the shape and amplitude variation with slowness of truncation effects. Theoretically the 7-p transformation is reversible, thus the 7-p domain is a suitable one in which to perform filtering operations before seismogram reconstruction.
Computer field-matching solution of waveguide transverse discontinuities

A computational method for solving a wide range of transverse waveguide discontinuity problems is described. Results are obtained by the simultaneous solution of matrix equations, generated by Fourier analysis, which relate the complex amplitudes of orthogonal electric and magnetic field components. In some cases, the solution is found to be sensitive to the way in which infinite series of field functions are truncated, and it is shown how the optimum form of truncation can be determined for many configurations of practical importance. Several examples showing the application of the method are given, and comparison of results with those obtained by experiment, and by other analytical techniques, confirms its accuracy.
Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.
Fermat and the solution of x3 - 2 = y2

Using the modular ring Z4, simple algebra is used to study diophantine equations of the form (x 3 − a = y 2). Fermat challenged his contemporaries to solve this equation when a = 2. They were unable to do so, although Fermat had devised a rather complicated proof himself.
Taste your way across the globe: a systematic review of gastronomy tourism literature (2000-2021)

ABSTRACT Gastronomy tourism is a critical driver of regional economic development and socio-cultural renaissance. It has been advocated as part of the tourism recovery initiatives in regards to boosting the worldwide economy in the wake of the COVID-19 pandemic. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, 397 empirical articles from leading hospitality and tourism journals were systematically examined. This review brought attention to significant developments by using a combination of descriptive analysis, conceptual framework, and topic modeling. Several salient literature gaps and future research directions were amalgamated by utilizing the theory, context, characteristics, and methodology (TCCM) framework.
Patient Education Improves Pain and Health-Related Quality of Life in Patients with Established Spinal Osteoporosis in Primary Care—A Pilot Study of Short- and Long-Term Effects

Fragility fractures, in particular vertebral fractures, are associated with high morbidity, including chronic pain and reduced health-related quality of life. We aimed to investigate the short- and long-term effects of patient education, including interdisciplinary themes, with or without physical training or mindfulness/medical yoga for patients with established spinal osteoporosis in primary care. Osteoporotic persons aged sixty years or older with one or more vertebral fractures were randomized to theory only, theory and physical exercise, or theory and mindfulness/medical yoga and were scheduled to once a week for ten weeks. Participants were followed up by clinical tests and questionnaires. Twenty-one participants completed the interventions and the one-year follow-up. Adherence to interventions was 90%. Pooled data from all participants showed significant improvements after intervention on pain during the last week and worst pain, and reduced painkiller use (any painkillers at baseline 70% [opioids 25%] vs. post-intervention 52% [opioids 14%]). Significant improvements were seen regarding RAND-36 social function, Qualeffo-41 social function, balance, tandem walking backwards, and theoretical knowledge. These changes were maintained at the 1-year follow-up. Patient group education combined with supervised training seems to have positive effects on pain, and physical function in persons with established spinal osteoporosis. The improved quality of life was maintained at the 1-year follow-up.
On the Ekman Equations for Ocean Currents Driven by a Stochastic Wind

The basic assumptions for the depth-dependent Ekman equations are presented. An analysis of three wind stress time series, from different geographical locations, is performed: The results lead to interpret the wind stress as a stochastic process, with components fluctuating with deterministic frequencies. The stochastic equations coupling wind stress and ocean currents are formulated; their solutions are stochastically bounded. Results of numerical simulations are given to show the main behaviors of the system.
Metabolic correlates to critical speed in murine models of sickle cell disease

Introduction: Exercise intolerance is a common clinical manifestation in patients with sickle cell disease (SCD), though the mechanisms are incompletely understood. Methods: Here we leverage a murine mouse model of sickle cell disease, the Berkeley mouse, to characterize response to exercise via determination of critical speed (CS), a functional measurement of mouse running speed upon exerting to exhaustion. Results: Upon observing a wide distribution in critical speed phenotypes, we systematically determined metabolic aberrations in plasma and organs—including heart, kidney, liver, lung, and spleen—from mice ranked based on critical speed performances (top vs. bottom 25%). Results indicated clear signatures of systemic and organ-specific alterations in carboxylic acids, sphingosine 1-phosphate and acylcarnitine metabolism. Metabolites in these pathways showed significant correlations with critical speed across all matrices. Findings from murine models were thus further validated in 433 sickle cell disease patients (SS genotype). Metabolomics analyses of plasma from 281 subjects in this cohort (with HbA < 10% to decrease confounding effects of recent transfusion events) were used to identify metabolic correlates to sub-maximal exercise test performances, as measure by 6 min walking test in this clinical cohort. Results confirmed strong correlation between test performances and dysregulated levels of circulating carboxylic acids (especially succinate) and sphingosine 1-phosphate. Discussion: We identified novel circulating metabolic markers of exercise intolerance in mouse models of sickle cell disease and sickle cell patients.
Defect Detection Algorithm of Photovoltaic Module EL Image Based on Two-stage YOLOts

Electroluminescence imaging can obtain high-resolution images of photovoltaic modules, and it is of great significance to obtain EL images of photovoltaic modules through drones for intelligent and refined defect detection. The EL images have a complex texture background with high resolution and non-uniformity, and at the same time, defects such as cracks, breakages, and black spots have various shapes, which bring great challenges to the precise defect detection task. Therefore, this paper proposes a two-stage YOLOts detection model with fused attention. On the one hand, the feature pyramid network of fused attention CBAM is used to fuse the high-level semantic information in the multi-layer feature map, and the feature map output of a single size is obtained to detect the defective cell blocks in the photovoltaic module image and segment the defective cell blocks from it. On the other hand, based on the segmented defective battery block images, a backbone network with added variability convolution is used for feature extraction, and then the high-level semantic feature map output of three scales is obtained through a multi-scale feature fusion network that fuses attention and uses The YOLOX detection head detects the output feature map to improve the model's ability to detect multi-scale defects. The experimental results show that the algorithm proposed in this paper has high accuracy in the detection of black spots, cracks, breakage, and grid line corrosion defects in high-resolution photovoltaic module EL images.
Memory-Enhanced Gated Graph Attention Network for Next Point-of-Interest Recommendation

The recommendation of the next Point-of-Interest (POI) has become an essential component of location-based social networks (LBSNs). Its purpose is to advise users' undiscovered POIs based on their check-in record. However, the existing work mainly regards the long-term interest of users as a periodic and stable change, but ignores the dynamic change. Besides, sparse POI-POI transformation limits the ability of the model to effectively learn contextual information in local views. We present a memory-enhanced gated graph attention network (MEGGA) in this paper. In order to effectively capture context in a local view, a gated graph attention network (GGAN) is designed, where category information is introduced to alleviate sparsity. In the long-term module, combined with the advantages of RNN and memory network, users' interests are dynamically captured by constantly updating user sequence while processing sequence information. Moreover, we use the attention layer to integrate multiple interests and learn the degree of users depending1 on different interests. Two public datasets were used in experiments to confirm MEGGA efficacy.
A Case-Finding Protocol for High Cardiovascular Risk in a Primary Care Dental School—Model with Integrated Care

Background: National Health Service (NHS) strategies in the United Kingdom (UK) have highlighted the need to maximise case-finding opportunities by improving coverage in non-traditional settings with the aim of reducing delayed diagnosis of non-communicable diseases. Primary care dental settings may also help to identify patients. Methods: Case-finding appointments took place in a primary care dental school. Measurements of blood pressure, body mass index (BMI), cholesterol, glucose and QRisk were taken along with a social/medical history. Participants with high cardiometabolic risk were referred to their primary care medical general practitioner (GP) and/or to local community health self-referral services, and followed up afterwards to record diagnosis outcome. Results: A total of 182 patients agreed to participate in the study over a 14-month period. Of these, 123 (67.5%) attended their appointment and two participants were excluded for age. High blood pressure (hypertension) was detected in 33 participants, 22 of whom had not been previous diagnosed, and 11 of whom had uncontrolled hypertension. Of the hypertensive individuals with no previous history, four were confirmed by their GP. Regarding cholesterol, 16 participants were referred to their GP for hypercholesterolaemia: 15 for untreated hypercholesterolaemia and one for uncontrolled hypercholesterolaemia. Conclusions: Case-finding for hypertension and identifying cardiovascular risk factors has high acceptability in a primary dental care setting and supported by confirmational diagnoses by the GP.
Efficient exposure computation by risk factor decomposition

The focus of this paper is the efficient computation of counterparty credit risk exposure on portfolio level. Here, the large number of risk factors rules out traditional PDE-based techniques and allows only a relatively small number of paths for nested Monte Carlo simulations, resulting in large variances of estimators in practice. We propose a novel approach based on Kolmogorov forward and backward PDEs, where we counter the high dimensionality by a generalization of anchored-ANOVA decompositions. By computing only the most significant terms in the decomposition, the dimensionality is reduced effectively, such that a significant computational speed-up arises from the high accuracy of PDE schemes in low dimensions compared to Monte Carlo estimation. Moreover, we show how this truncated decomposition can be used as control variate for the full high-dimensional model, such that any approximation errors can be corrected while a substantial variance reduction is achieved compared to the standard simulation approach. We investigate the accuracy for a realistic portfolio of exchange options, interest rate and cross-currency swaps under a fully calibrated 10-factor model.
On the Structure of Commutative Rings with p 1 k 1 p n k n (1≤ k i ≤ 7) Zero-Divisors II

In this paper, we determine the structure of nonlocal commutative rings with p 6 zero- divisors and characterize the structure of nonlocal commutative rings with p 7 zero-divisors. Also, the structure and classification up to isomorphism all commutative rings with p1 k1 . pn kn zero-divisors, where n is a positive integer, pi , s are distinct prime number and 1≤ ki≤ 4, are determined. 2000 Mathematics Subject Classifications: 16B99; 13A99; 68R10
Design of a wideband isolated out-of-phase filtering power divider/combiner with sharp selectivity

Abstract A new isolated out-of-phase filtering power divider/combiner with high selectivity is presented. Based on a single-layer microstrip, required coupling strength and phase difference are achieved by two pairs of three-line coupled feeding structures. To realize desired bandpass responses, a specific resonator is located between the input and output coupled structures. By connecting a half-wavelength transmission line which is centrally loaded with a grounded resistor between two output ports, high port-to-port isolation is attained. Based on the impedance matrix of coupled three-line structure, theoretical filtering responses are predicted with the specified bandwidth and return loss. For demonstration, a prototype is designed, fabricated, and tested. Both simulated and measured results are displayed to verify the design mechanism.
Multi-channel signal synchronous processing for bearing early fault diagnosis based on TRPCA

The multi-channel signal contains more complete fault information than single channel signal, which can effectively improve the fault diagnosis accuracy. Tensor decomposition based techniques are verified to have unique advantages in multi-channel signal processing. How to remove the noise interference and retain the fault characteristics information from multi-channel signal is an important problem to be solved in multi-channel synchronous diagnosis based on tensor decomposition. To effectively extract weak fault information under strong background noise, this paper studies a multi-channel signal synchronous processing method based on TRPCA for early fault diagnosis. Simulation data shows that the studied method can accurately extract weak fault characteristics in the signal under strong background noise and achieve early weak fault diagnosis.
Deflection Test of Wire-Integrated Steel Deck Plates with Various End Details

This experimental study was conducted to evaluate the deflection performance of wire-integrated steel deck plates with various end details subjected to cumulative gravity loads. In general, when deck plates are installed in the Republic of Korea, vertical bars are mounted at the ends of the wire-integrated deck plates. However, this process can extend the construction time, thus incurring additional costs. Consequently, this study aimed to examine the structural performance of a deck plate when a lattice foot—rather than a vertical bar—is installed at the end of it. A total of nine specimens were prepared; the experimental variables included the end details, height of the lattice truss girder, and structure type. To evaluate the deflection performance, the cumulative gravity load (as a construction load) and a concrete self-weight were applied to the specimens, and the deflections of each specimen were measured. In the experimental results, the deflection values of the specimen with vertical bars were 0.9~6.1 mm, while those for the specimen without vertical bars were 0.8~5.0 mm. This means that a lattice foot exhibits better deflection performance than conventional end details. Additionally, the deflection of the specimens satisfied the deflection limits required in the relevant standards.
The Distribution of Subject Properties in Multiple Subject Constructions

Grammatical relations (GR) such as subject and object have long been recognized to be indispensable in understanding how grammars of natural languages work, though there seems to be no logical necessity for arguments to be filtered through a layer of grammatical relations (GRs) as they receive morphosyntactic expression. Despite the pervasiveness of GRs, pinning down the properties of GRs has met with repeated challenges.
Exact solutions to the transportation problem on the line

Given distributions ;w of production and v of consumption on the line, the Monge–Kantorovich problem is to decide which producer should supply each consumer in order to minimize the total transportation costs. Here cost will be assumed to be a strictly concave function of the distance, which translates into an economy of scale for longer trips and may encourage cross–hauling. The resulting solutions display a hierarchical structure that reflects a striking separation into local and global scales also found in the real world. Moreover, this structure can be exploited to reduce the infinite–dimensional linear problem to a convex minimization in m variables, where 2m + 2 counts the number of times that ;w–v changes sign. A combinatorial algorithm is then derived which yields exact solutions by optimizing a certain finite sequence of convex, separable network flows.
On Minimal Energies of Unicyclic Graphs with Perfect Matching 1

The energy of a graph is the sum of the absolute values of the eigenvalues of its adjacency matrix. We denote by U(2n) the set of all unicyclic graphs of order 2n with a perfect matching. Let B(2n )= {G ∈ U(2n)| the length of the unique cycle of G is divisible by 4} and A(2n )= U(2n) \ B(2n). W. Wang, in the paper “Ordering of unicyclic graphs with perfect matchings by minimal energies”, MATCH Commun. Math. Comput. Chem. 66 (2011) 927–942, [1], posed a conjecture about ordering of graphs in A(2n) by minimal energies. We now characterize the graphs in U(2n) with the first seven minimal energies and offer an answer to the conjecture.
Deep Reinforcement Learning-Based Explainable Pricing Policy for Virtual Storage Rental Service

The shared community energy storage system (CESS) can reduce energy storage costs by exploiting the complementarity of end-users and economies of scale. To further improve the economic feasibility of the CESS, we propose a novel business model and pricing method for the virtual storage rental service (VSRS). In this model, the rental users aim to minimize the electricity bill by renting the virtual capacity and optimizing its operation, while the CESS operator seeks to maximize the revenue from the combination of energy arbitrage and the VSRS. The pricing problem and the optimal operation problems of the CESS and users’ virtual batteries are modeled as a bi-level optimization problem. Next, the proposed problem is solved through transformer-based deep deterministic policy gradient (TDDPG) method and mixed-integer linear programming (MILP) due to the non-convexity and non-continuity of the original problem. The post-hoc interpretability of the policy network is provided based on the Shapley value to reveal the importance of different input features for decision-making. Numerical simulations suggest that the proposed VSRS could benefit the CESS operator and users. Moreover, the explanation based on the Shapley value could effectively generate an implicit solution for understanding the policy network.
A two-step framework for the registration of HE stained and FTIR images

FTIR spectroscopy is an emerging technology with high potential for cancer diagnosis but with particular physical phenomena that require special processing. Little work has been done in the field with the aim of registering hyperspectral Fourier-Transform Infrared (FTIR) spectroscopic images and Hematoxilin and Eosin (HE) stained histological images of contiguous slices of tissue. This registration is necessary to transfer the location of relevant structures that the pathologist may identify in the gold standard HE images. A two-step registration framework is presented where a representative gray image extracted from the FTIR hypercube is used as an input. This representative image, which must have a spatial contrast as similar as possible to a gray image obtained from the HE image, is calculated through the spectrum variation in the fingerprint region. In the first step of the registration algorithm a similarity transformation is estimated from interest points, which are automatically detected by the popular SURF algorithm. In the second stage, a variational registration framework defined in the frequency domain compensates for local anatomical variations between both images. After a proper tuning of some parameters the proposed registration framework works in an automated way. The method was tested on 7 samples of colon tissue in different stages of cancer. Very promising qualitative and quantitative results were obtained (a mean correlation ratio of 92.16% with a standard deviation of 3.10%).