Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual−Layer Distributed Optimal Operation Method for Island Microgrid Based on Adaptive Consensus Control and Two−Stage MATD3 Algorithm

Island microgrids play a crucial role in developing and utilizing offshore renewable energy sources. However, high operation costs and limited operational flexibility are significant challenges. To address these problems, this paper proposes a novel dual−layer distributed optimal operation methodology for islanded microgrids. The lower layer is a distributed control layer that manages multiple controllable distributed fuel−based microturbines (MTs) within the island microgrids. A novel adaptive consensus control method is proposed in this layer to ensure uniform operating status for each MT. Moreover, the proposed method can achieve the total output power of MTs to follow the reference signal provided by the upper layer while ensuring plug−and−play capability for MTs. The upper layer is an optimal scheduling layer that manages various forms of controllable distributed power sources and provides control reference signals for the lower layer. Additionally, a two−stage twin−delayed deterministic policy gradient (MATD3) algorithm is utilized in this layer to minimize the operating costs of island microgrids while ensuring their safe operation. Simulation results demonstrate that the proposed methodology can effectively reduce the operating costs of island microgrids, unify the operational status of MTs, and achieve plug−and−play capability for MTs.
Behavioral and Electrocortical Response to a Sensorimotor Conflict in Individuals with Fibromyalgia

People with fibromyalgia have been shown to experience more somatosensory disturbances than pain-free controls during sensorimotor conflicts (i.e., incongruence between visual and somatosensory feedback). Sensorimotor conflicts are known to disturb the integration of sensory information. This study aimed to assess the cerebral response and motor performance during a sensorimotor conflict in people with fibromyalgia. Twenty participants with fibromyalgia and twenty-three pain-free controls performed a drawing task including visual feedback that was either congruent with actual movement (and thus with somatosensory information) or incongruent with actual movement (i.e., conflict). Motor performance was measured according to tracing error, and electrocortical activity was recorded using electroencephalography. Motor performance was degraded during conflict for all participants but did not differ between groups. Time–frequency analysis showed that the conflict was associated with an increase in theta power (4–8 Hz) at conflict onset over the left posterior parietal cortex in participants with fibromyalgia but not in controls. This increase in theta suggests a stronger detection of conflict in participants with fibromyalgia, which was not accompanied by differences in motor performance in comparison to controls. This points to dissociation in individuals with fibromyalgia between an altered perception of action and a seemingly unaltered control of action.
Evaluating the Effect of Semantic Enrichment on Entity Embeddings of IoT Knowledge Graphs

IoT devices in Smart homes generate large amounts of data by sending messages to share measurements at frequent intervals. To make this IoT measurement data interoperable, ontologies such as SAREF are used to represent this data as knowledge graphs (KGs). These resulting KGs contain measurements from IoT devices together with the context about those devices, such as the type of measurements, or in which room it is located. Through the use of embedding methods, we learn embedding representations for entities in KGs that can be used to generate prediction models. In this study, we investigate how the structure of the IoT knowledge graph affects the effectiveness of embedding methods for such tasks. Specifically, we look at the effect on embedding quality of enriching IoT KGs by making implicit information such as temporal sequences and measurement value similarity explicit. We perform experiments on two IoT KGs represented using the SAREF ontology. One is in a basic setting, and the other has this information explicitly added. We use RDF2vec to create embeddings, train a classifier, and compare classifier accuracy. Analysis of our results shows that classifiers trained with embeddings from enriched KGs outperform classifiers trained with embeddings from original KGs. This indicates that the explicit information in the enriched graph improves the quality of the embeddings of entities.
Spats: confidential assets and non-fungible tokens

In privacy-preserving transaction protocols, confidential asset designs permit transfer of quantities of distinct asset types in a way that obscures their types and values. Spark is a protocol that provides flexible privacy properties relating to addressing, transaction sources and recipients, and value transfer; however, it does not natively support the use of multiple confidential asset types or non-fungible tokens. Here we describe Spats, a new design for confidential assets and serialized tokens compatible with Spark that focuses on efficient and mod-ular implementation. It does so by extending coin value commitments to bind and mask an asset type and identifier, and asserting in zero knowledge that they are maintained throughout transactions. We describe the cryptographic components and changes to the Spark protocol necessary for the design of Spats.
Combining (Deep) Reinforcement Learning with Goal-Based Investment

Recent advancements in the efficient implementation of Deep Learning (DL) models provided a new methodology in solving problems in finance. In particular, using Deep Reinforcement Learning (DRL) in portfolio management proved to be a reliable and efficient approach. Goal-Based Investment (GBI) differs from the traditional portfolio management tasks due to the unique investment objective. In this paper, we formulate a GBI problem into a DRL optimization problem and create a custom reward function to be implemented in the Deep Deterministic Policy Gradient (DDPG) model. Finally, we discuss the performance of the model against different benchmarks in Monte Carlo simulation and conclude the GBI model have the highest probability of achieving the investment goal under any market situations.
Spectral property of ultrashort chirped pulsed Gaussian beams diffracted by Gaussian aperture in dispersive media

Based on the Fresnel-Kirchhoff diffraction integral and Fourier transform, the propagation equation and its Fourier spectrum for ultra-short chirped pulsed Gaussian beams diffracted by Gaussian aperture are derived in dispersive medium, and the frequency-domain analytical electric field are presented. The effects of relative aperture, transmission distance and chirp parameter on the axial spectral properties are illustrated with numerical calculation results, and the variations of off-axis power spectrum with relative aperture, transmission distance and off-axis radius are given. It is found that the axial power spectrum of ultra-short chirped pulsed Gaussian increases with increasing relative aperture, the axial spectral blue-shift increases and approaches an asymptotic value associated with chirp parameter and propagation distance. The axial spectra of ultra-short chirped pulsed Gaussian become broadened with increasing the absolute value of the chirp parameter. With increasing off-axis radius, the off-axis power spectrum reduce rapidly, and the distribution of spectra shifts to the left. The off-axis spectral redshift increases with increasing off-axis radius.
Early Detection of Brain Tumor Using Capsule Network

The brain tumor is one of the deadliest diseases in the world nowadays. Only in the United States of America, today the number of people having brain tumor is more than 700,000 [1]. Approximately 16,000 people would die in the process of a brain tumor in the year 2020 [1]. It'll be really grateful for monitoring and identification if the characterization of tumors in the brain can be done at a very pre-mature stage. Numerous researchers have already taken some attempts to use various techniques, such as digital mammography, MRI, CT (Computed Tomography), etc. To detect the exact type of brain tumor from MRI images CapsNets became an improved architecture. Since these networks can operate with fewer training samples. We used a dataset from kaggle to monitor the tumor in the brain at the very initial stage. AT first, in the CNN model, each of the input pictures will move through a set of filter convolution layers (called Kernels), then pooling, then completely related layers (FC) and applying Soft-max function to define a probabilistic meaning object. The outcome from the proposed technique reveals that 92 percent of accuracy can be gained from this technique.
Noncontact cable tension force estimation using an integrated vision and inertial measurement system

In this study, a noncontact cable tension force estimation technique was developed using an integrated vision and inertial measurement system (VIS) installed at a reference point, and the movement of the VIS was explicitly considered. Cable displacement was first estimated by applying a proposed contour-based algorithm to the vision measurements. Thereafter, the movement of the VIS at the reference point was estimated from the inertial measurement system and was used to compensate the error in the previously estimated cable displacement. Finally, the cable tension force was estimated using the compensated cable displacement. The feasibility of the proposed technique was validated through a laboratory test on a full-scale pedestrian bridge cable and a field test on a single-pylon cable-stayed pedestrian bridge. Overall, the proposed technique estimated the cable tension forces reliably with less than 1.3% discrepancy compared to those estimated by accelerometers.
Narrow linewidth, tunable Tm/sup 3+/-doped fluoride fiber laser for optical-based hydrocarbon gas sensing

Operation of an efficient continuous-wave (CW) thulium-doped fiber laser emitting at wavelength, /spl lambda/=2.31 /spl mu/m is reported. The fiber laser parameters are optimized with a view to ultimately producing a compact and efficient laser source for optical absorption based gas sensing. A number of fiber laser configurations are investigated to assess their suitability for narrow linewidth, tunable fiber laser operation emitting around /spl lambda/=2.3 /spl mu/m, which is a wavelength region of significant importance for hydrocarbon gas monitoring. Tuning ranges of 140 nm and linewidths of less than 210 MHz have been demonstrated with lasers with bulk external tuning grating. Preliminary hydrocarbon gas sensing investigation confirm the potential of this source for detection of ppb gas concentrations.
Parallelizing Stream Compression for IoT Applications on Asymmetric Multicores

Data stream compression attracts much attention recently due to the rise of IoT applications. Thanks to the balanced computational power and energy consumption, asymmetric multicores are widely used in IoT devices. This paper introduces CStream, a novel framework for parallelizing stream compression on asymmetric multicores to minimize energy consumption without violating the user-specified compressing latency constraint. Existing works cannot effectively utilize asymmetric multicores for stream compression, primarily due to the non-trivial asymmetric computation and asymmetric communication effects. To this end, CStream is developed with the following two novel designs: 1) fine-grained decomposition, which decomposes a stream compression procedure into multiple fine-grained tasks to better expose the task-core affinities under the asymmetric computation effects; and 2) asymmetry-aware task scheduling, which schedules the decomposed tasks based on a novel cost model to exploit the exposed task-core affinities while considering asymmetric communication effects. To validate our proposal, we evaluate CStream with five competing mechanisms of parallelizing stream compression algorithms on a recent asymmetric multicore processor. We evaluate CStream with five competing mechanisms of parallelizing stream compression algorithms to validate our proposal on a recent asymmetric multicore processor. Our extensive experiments based on a benchmark of three algorithms and four datasets show that CStream outperforms alternative approaches by up to 53% lower energy consumption without compressing latency constraint violation.
EEG Artifacts Removal: A Detailed Review Based Study

: Electroencephalography, or EEG for short, is a technique used to record the electrical activity of the brain. This EEG detects errors that affect how the human brain functions. This method is the most commonly used for recording the brain in laboratory research, clinical investigations, patient health monitoring, diagnostics, and a variety of other applications due to its non-invasiveness and cost-benefit ratio. Most EEG recordings are contaminated by a variety of irregularities, including those caused by electrode displacement, motion, ocular, and muscular activity-related EMG anomalies. These unwanted artifacts may make it difficult to distinguish genuine information from them, in addition to confusing the brain's information processing that supports them. EEG signal artifacts can be removed in a variety of ways. The top and most popular artifact reduction techniques are listed on this page as PCA, pure EEG, and wavelet transform. The study provides a thorough evaluation of current artifact identification and removal methods from scalp EEG for all conceivable EEG-based applications.
Geranylgeraniol Application in Human Osteoblasts and Osteoclasts for Reversal of the Effect of Bisphosphonates

Nitrogen-containing bisphosphonates lead to the depletion of geranylgeranyl pyrophosphate involved in the mevalonate pathway. The effect of geranylgeraniol (GGOH) on human osteoblast and osteoclast activities suppressed by zoledronate was investigated in this study. The effect of GGOH on human osteoblasts and osteoclasts subjected to treatment with zoledronate was analyzed by assessing cell viability, osteoclast differentiation, resorption ability, gene expression, and protein synthesis. Cell viability suppressed by bisphosphonates in osteoblasts and osteoprogenitor cells was restored with GGOH. Osteoclast differentiation was analyzed by vitronectin receptor immunofluorescence staining, and the addition of GGOH to zoledronate significantly increased osteoclast differentiation compared with zoledronate alone. A trend of reversal of osteoclast resorption by GGOH was observed; however, it was not significant in all groups. The expression of ALP, type 1 collagen, and RUNX2 in osteoblasts was recovered by the addition of GGOH. Only CALCR expression in osteoclasts was significantly recovered by GGOH addition in the zoledronate group. Although the activities of osteoblasts and osteoclasts were not entirely restored, the possibility that the topical application of GGOH in MRONJ patients or patients with dental problems and bisphosphonates might lessen the risk of development and recurrence of MRONJ is shown.
Toward the construction of a fourth-order difference scheme for transient EM wave simulation: staggered grid approach

A compact central-difference approximation in conjunction with the Yee (1966) grid is used to compute the spatial derivatives in Maxwell's equations. To advance the semi-discrete equations, the four-stage Runge-Kutta (RK) integrator is invoked. This combination of spatial and temporal differencing leads to a scheme that is fourth-order accurate, conditionally stable, and highly efficient. Moreover, the use of compact differencing allows one to apply the compact operator in the vicinity of a perfect conductor-an attribute not found in other higher order methods. Results are provided that quantify the spectral properties of the method. Simulations are conducted on problem spaces that span one and three dimensions and whose domains are of the open and closed type. Results from these simulations are compared with exact closed-form solutions; the agreement between these results is consistent with numerical analysis.
AggNCE: Asymptotically Identiﬁable Contrastive Learning

Self-supervised contrastive learning has attracted great attention for its promising empirical performances. However, the identiﬁability theory of contrastive learning is still under-exploited. In this paper, we propose the Aggregated InfoNCE (AggNCE) loss function to deal with this problem. The latent variable learned by AggNCE is proved to be asymptotically identiﬁable to the ground truth latent under the original data distribution. In experiments, we verify the theoretical ﬁndings and show the empirical advantage of AggNCE over InfoNCE
Euclidean Constructions and the Geometry of Origami

Although the connection between geometry and origami is quite obvious, and has been researched in a limited way for centuries, very few people active in one field appear to be more than casually aware of the other. Occasionally an example in the problems section of some journal will concern itself with folding paper in some way, but origami is not generally considered a mathematical discipline. Some origamians (notably Kazuo Haga and Kunihiko Kashahara, see [6], and Tomoko Fuse, see [3]) have done quite impressive work on the geometry of origami (especially on Platonic solids and related subjects), but most prefer to stick to the artistic side of the handicraft. In origami circles, excessive pondering of geometry is usually considered to detract in some way from the elegance and harmony of the art. Nevertheless, the connection between geometry and origami is well established. Noted educators, such as the German, Friedrich Froebel, have suggested the use of origami as a tool for the teaching of elementary geometric forms. Some interesting work has been published on geometric aspects of origami, particularly as applied to specific models. Much is known about methods of folding regular polygons and polyhedra, for instance [3,5,6,9]. It is also well known that the classic Delian problem (finding a cube twice the volume of a given cube) as well as the problem of angle trisection can be solved using methods of origami, despite being unsolvable by Euclidean methods. This article attempts to show the connections between origami folds and Euclidean constructions with straight-edge and compass. Somewhat surprisingly, we shall see that parabolas are elementary to origami construction, and play a role similar to that of circles in Euclidean constructions. There are also some aspects of projective geometry that sneak in unexpectedly. Perhaps this paper will help in getting more mathematicians interested in origami from the scientific viewpoint. It is my firm belief that this can be fruitful for both the art of origami and the science of geometry.
The effect of gargling with green tea extract solution (Camelia sinensis) in efforts to prevent caries incidence: A systematic review

Background: One of the main dental and oral health problems that occur throughout the world is dental caries. Cariogenic bacteria such as Streptococcus mutans and Lactobacillus acidophilus are the main microorganisms that cause dental caries. Saliva plays a significant function in maintaining optimal dental health and oral cavity especially because of its components such as pH, flow, and buffering capacity. These three components have a significant role in the initiation and development of dental caries. Gargling is one of the activities that can be done as a prevention of dental caries. Green tea contains the compound Epigallocatechin gallate (EGCG) which is a flavonoid compound that has antioxidant, anti-inflammatory, antifungal, antibacterial, and anticariogenic properties. This systematic review aims to describe the effect of gargling with green tea to prevent dental caries incidents. Method: The method used is a systematic review which is done by searching articles through Google Scholar, PubMed, and ScienceDirect. Discussion: Gargling with green tea regularly can help to increase the pH of saliva. It will help the tooth remineralization process and prevent caries. Green tea contains catechins that inhibit acid formation and reduce the work of the glucosyltransferase enzyme of Streptococcus mutans. The largest catechin content in green tea is EGCG. EGCG can reduce the quantity of Streptococcus mutans. Conclusion: Gargling with green tea is effective in preventing dental caries incidents because it contains catechins, especially EGCG, tannins, and fluoride.
UnfoldIR: Tactile Robotic Unfolding of Cloth

Robotic unfolding of cloth is challenging due to the wide range of textile materials and their ability to deform in unpredictable ways. Previous work has focused almost exclusively on visual feedback to solve this task. We present UnfoldIR (“unfolder”), a dual-arm robotic system relying on infrared (IR) tactile sensing and cloth manipulation heuristics to achieve in-air unfolding of randomly crumpled rectangular textiles by means of edge tracing. The system achieves $> $85% coverage on multiple textiles of different sizes and textures. After unfolding, at least three corners are visible in 83.3 up to 94.7% of cases. Given these strong “tactile-only” results, we argue that the fusion of both tactile and visual sensing can bring cloth unfolding to a new level of performance.
A Mass Flux Convection Scheme with Representation of Cloud Ensemble Characteristics and Stability-Dependent Closure

Abstract The convection scheme used at the UK Meteorological Office in large-scale numerical models is described. The scheme uses a “bulk” cloud model to represent an ensemble of convective clouds and aims to represent shallow, deep and midlevel convection. A simple closure is employed, the initial convective mass flux being related to the stability of the initial convecting layer. The ability of the scheme to represent convective processes in a variety of situations is evaluated using GATE, BOMEX, and ATEX data. In each case realistic heating rates are simulated and although the closure of the scheme does not demand a balance between convective and large-scale forcings as in many other types of convection scheme (for example the Arakawa–Schubert scheme), a quasi-equilibrium is established while retaining realistic atmospheric structure. The performance of the scheme in an 11-layer atmospheric general circulation model used in climate research at the UK Meteorological Office is also evaluated by comparing.
Extreme Points and Linear Isometries of the Banach Space of Lipschitz Functions

Let X be a compact metric space with metric d. A complex-valued function ƒ on X is said to satisfy a Lipschitz condition if, for all points x and y of X, there exists a constant K such that The smallest constant for which the above inequality holds is called the Lipschitz constant for ƒ and is denoted by ||ƒ||d, that is,
Stray light suppression in FY-2 radiometer with shutter

FY-2radiometer is a multi-field Ritchey-Chretien(R-C)optical system.Stray light outside the field grazes the secondary mirror,then goes across the primary mirror,and finally hits the detector,which produces the straight hit path.To eliminate this straight hit,we used Tracepro software package to trace the path and used Zemax software package to find out the generation mechanisms of it,and then calculated the area of the straight hit.Based on the simulation and calculation,a shutter with some laminas was designed,which could allow signal light to transmit but prevent the stray light entering into the detector.Results of the simulation illuminate that the direct stray light in this area can be eliminated completely by using the designed shutter.Also the integrated sphere experiment indicates the shutter can decrease 95.2%of the visible stray light.