Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Particle Swarm Optimization Based Maximum Power Point Tracking in Grid Connected PV System

Maximum power point tracking (MPPT) approaches are exploited as a piece of Photovoltaic (PV) outlines to augment the PV array produce power through following reliably the maximum power point (MPP) which relies upon board’s temperature and on irradiance circumstances. This paper deals with the improvement of MPPT approach by expanding the yield of the PV cluster under fractional shading circumstances. In our proposed strategy, the maximal power of the PV cluster will be tracked during partial shading conditions by using a novel procedure called Look-up table strategy and the optimization will be done by using the adaptive particle swarm optimization (APSO) algorithm. The resultant curves (I-V and P-V characteristic curves) are gotten because of using look-up table technique. The power produced by the traditional PSO algorithm and the suggested APSO based MPPT are especially analyzed under the partial shading states. The outcomes demonstrate that the APSO based MPPT can encourage the PV cluster to attain the global MPP and additionally aid the PV array to generate more stable yield control contrasted to the ordinary look-up technique. Asignificant benefit of the suggested methodology, measured up to the usual PSO algorithm is, it shouldn’t get caught in the local optima and doesn’t requires a higher number of function evaluations.
Powerful Situation Awareness Using High Resolution Optical Satellites 'ASNARO-1' and Radar Satellites 'ASNARO-2'

NEXTAR" has been developed as a flexible standard satellite platform, which meets diverse mission requirements such as a science satellite and an earth observation satellite. Regarding a satellite bus for ASNARO series, "NEXTAR" allows us to develop a small, affordable, and sophisticated satellite which has performance comparable to a large satellite.So far, the ASNARO series have two satellites in operation: an optical satellite ASNARO-1 and a SAR satellite ASNARO-2. This paper presents some examples for the purpose of obtaining detail observation with the ASNARO series.
Structural optimization of 273nm deep ultraviolet laser in wave guide

The full width at half maxima (FWHM) of the LDs is very narrow, with small threshold current and high luminous power. Through simulation, it can be seen that the radiation recombination rate and wave intensity of different lateral positions of LDs are different. The waveguide layer of the high Al composition has a lower refractive index and can reduce the divergence angle of light. In this paper, the device structure is optimized by gradually increasing the Al component toward the p-cladding layer and decreasing the Al component toward p-side. Compared with the original structure, the grading wave guide proves that it can have better electrical characteristics and the FWHM is smaller.
Design of a Compact Low-Loss Phase Shifter Based on Optical Phase Change Material

We propose the design of a nonvolatile, low-loss optical phase shifter based on optical phase change material (O-PCM). The optical phase change material Ge2Sb2Se4Te1 (GSST), which exhibits low loss at telecommunication wavelength $1.55~\mu \text{m}$ as compared to other commonly used O-PCMs, is used in this work as the active material. Instead of direct interaction of the waveguide mode with the O-PCM, the design utilizes coupling between the primary SiN strip waveguide and a waveguide formed by O-PCM, in its amorphous state. The phase matching in the amorphous state inhibits the interaction of the waveguide mode with GSST in its highly lossy crystalline state resulting in low loss operation. Due to a high differential refractive index between the two states of GSST, the design requires a very small length of the phase shifter to accumulate the desired phase difference. The overall response of the Mach-Zehnder Interferometer (MZI) configuration using the designed phase shifter shows that the design can be used to obtain optical switching with a very small insertion loss and crosstalk over the entire C-band.
Phenolic Composition and Antimicrobial Activity of Different Emirati Date (Phoenix dactylifera L.) Pits: A Comparative Study

The biochemical composition, secondary metabolites (phenolic compounds, flavonoids) and antimicrobial potential of different varieties of Emirati date (Phoenix dactylifera L.) pits were investigated. Total phenolic acids (TPC) and total flavonoid contents (TFC) of the different date pits were measured using a Folin–Ciocalteau reagent. Different organic solvents [(n-hexane; H2O: EtOH (1:1); ethyl acetate; acetone: Water (1:1); and methanol: Chloroform (1:1)] were compared to evaluate the phytotoxicity of Ajwa, Fard, Khalas, Khodari, Abu Maan, Lulu, and Mabroom date pits. The antimicrobial activity of the date pit extracts were evaluated by means of agar-well diffusion assay on Staphylococcus aureus (ATCC 29123), Escherichia coli (ATCC 25922) and Candida albicans (ATCC 66027). Minimum inhibitory concentrations (MICs) were measured following clinical laboratory standardization institute (CLSI) protocol. The biochemical analyses of date pits indicate that TPC were ranged from 7.80 mg of equivalent gallic acid⁄100 g dry weight in Ajwa to 4.65 mg in Mabroom. The TFC were ranged between 1.6–4.54 mg of equivalent catechin⁄100 g dry weight. Ajwa pit extract showed good quality traits (higher protein, lower ash content, and intermediate dietary fiber). The results indicate that the ethyl acetate extract of Khalas and Khodari inhibited S. aureus with an inhibition zone diameter of 20 mm and MIC of 10 mg/mL. Abu Mann pit extract inhibited the S. aureus and also decreased the population of E. coli. The diameter of inhibition zone was 15, 16, and 18 mm after treatment with Ajwa extracts, while the MICs were 7.5 and 5 mg/mL. The MeOH: CFM extract of Abu Mann and Ajwa was more potent against E. coli bacteria than any other extract. This work demonstrates that the Emirati date pits extract has antimicrobial (antibacterial, antifungal) potential and can be used as phytotoxic natural compounds.
A 3.6 GHZ X-Band Wideband Experimental Airborne Sar System

This paper presents a 3.6 GHz X-band wideband airborne SAR system, featured by full-bandwidth transmitting and receiving. After the general introduction, the architecture, inter-connect design and system composition are discussed in detailed. To avoid the main-lobe distortion and asymmetrical side-lobe, the pre-distortion signal is constructed in time domain to compensate the system errors. The results of the experiment were accomplished successfully and validate the effectiveness and applicability of this airborne SAR system.
Crowdchain: A Location Preserve Anonymous Payment System Based on Permissioned Blockchain

Participants can complete tasks on crowdsensing platform with smart mobile devices. They go to specific locations to collect data and upload them. Then, the platform pays them some rewards. However, most of the existing crowdsensing platforms are deployed on centralized servers, which are vulnerable to attack, intrusion, and manipulation. In addition, the location information in task records increases the potential risk of privacy leakage. To reach a balance between privacy protection and functional availability of the system. We design and implement Crowdchain, which is a location preserve anonymous payment system based on a permissioned blockchain. In order to disassociate participants identity information from task records, Crowdchain contains a novel protocol that makes participants transfer wallet address to payment system bypass the crowdsensing platform and receive task rewards correctly. We prove the security of our anonymous payment system by zero-knowledge proof and give the analysis result of system stability.
Panchromatic Sharpening of Multispectral Satellite Imagery Via an Explicitly Defined Convex Self-Similarity Regularization

In satellite imaging remote sensing, injecting spatial details extracted from a panchromatic image into a multispectral image is referred to as pansharpening, which is ill-posed and requires regularization. Self-similarity, a critical prior knowledge yielding great success in regularizing various imaging inverse problems, has been widely observed in natural images; its formalization is not, however, straightforward. Very recently, we mathematically described the self-similarity pattern as a weighted graph, which can then be transformed into an explicit convex regularizer, that is adopted in our pansharpening criterion design. Most importantly, such convexity allows the adoption of convex optimization theory in solving self-similarity regularized inverse problems with convergence guarantee. One step of our pansharpening algorithm is exactly the proximal operator induced by our new self-similarity regularizer, which is solved by another customized algorithm that is interesting in its own right as could be used as a denoiser. Experiments show promising performance of the proposed method.
Online Topology Learning by a Gaussian Membership-Based Self-Organizing Incremental Neural Network

In order to extract useful information from data streams, incremental learning has been introduced in more and more data mining algorithms. For instance, a self-organizing incremental neural network (SOINN) has been proposed to extract a topological structure that consists of one or more neural networks to closely reflect the data distribution of data streams. However, SOINN has the tradeoff between deleting previously learned nodes and inserting new nodes, i.e., the stability–plasticity dilemma. Therefore, it is not guaranteed that the topological structure obtained by the SOINN will closely represent data distribution. For solving the stability–plasticity dilemma, we propose a Gaussian membership-based SOINN (Gm-SOINN). Unlike other SOINN-based methods that allow only one node to be identified as a “winner” (the nearest node), the Gm-SOINN uses a Gaussian membership to indicate to which degree the node is a winner. Hence, the Gm-SOINN avoids the topological structure that cannot represent the data distribution because previously learned nodes overly deleted or noisy nodes inserted. In addition, an evolving Gaussian mixture model is integrated into the Gm-SOINN to estimate the density distribution of nodes, thereby avoiding the wrong connection between two nodes. Experiments involving both artificial and real-world data sets indicate that our proposed Gm-SOINN achieves better performance than other topology learning methods.
A Direct Compression Matrix Made from Xanthan Gum and Low Molecular Weight Chitosan Designed to Improve Compressibility in Controlled Release Tablets

The subject of our research is the optimization of direct compression (DC), controlled release drug matrices comprising chitosan/xanthan gum. The foregoing is considered from two main perspectives; the use of low molecular weight chitosan (LCS) with xanthan gum (XG) and the determination of important attributes for direct compression of the mixtures of the two polymers. Powder flow, deformation behaviour, and work of compression parameters were used to characterize powder and tableting properties. Compression pressure and LCS content within the matrix were investigated for their influence on the crushing strength of the tablets produced. Response surface methodology (RSM) was applied to determine the optimum parameters required for DC of the matrices investigated. Results confirm the positive contribution of LCS in enhancing powder compressibility and crushing strength of the resultant compacts. Compactibility of the XG/LCS mixtures was found to be more sensitive to applied compression pressure than LCS content. LCS can be added at concentrations as low as 15% w/w to achieve hard compacts, as indicated by the RSM results. The introduction of the plasticity factor, using LCS, to the fragmenting material XG was the main reason for the high volume reduction and reduced porosity of the polymer mixture. Combinations of XG with other commonly utilized polymers in controlled release studies such as glucosamine, hydroxypropyl methylcellulose (HPMC), Na alginate (ALG), guar gum, lactose and high molecular weight (HMW) chitosan were also used; all the foregoing polymers failed to reduce the matrix porosity beyond a certain compression pressure. Application of the LCS/XG mixture, at its optimum composition, for the controlled release of two model drugs (metoprolol succinate and dyphylline) was examined. The XG/LCS matrix at 15% w/w LCS content was found to control the release of metoprolol succinate and dyphylline. The former preparation confirmed the strong influence of compression pressure on changing the drug release profile. The latter preparation showed the ability of XG/LCS to extend the drug release at a fixed rate for 12 h of dissolution time after which the release became slightly slower.
Temporal Downscaling of TRMM Precipitation Products Using AMSR2 Soil Moisture Data

Accurate spatialized daily precipitation data plays an important role in meteorology, hydrology and ecology. Tropical Rainfall Measuring Mission (TRMM) precipitation data has been widely used in recent years for the relatively high resolution and large spatial coverage. Among them, two TRMM precipitation products are most commonly used: 3-hour scale (4B42) and monthly scale (3B43). The 3B42 product with a high temporal resolution but low accuracy, while the 3B43 product is the opposite. For hydrological modeling and water resource analysis, the acquisition of daily precipitation data is very important. In most cases, daily precipitation data is obtained by accumulating 3B42 product directly. However, this method ignores the change of precipitation rate. In the case of heavy rainfall, the daily precipitation data from 3B42 data shows a large deviation compared with the daily rainfall observed from rain gauges. Based on the analysis of ground measured daily precipitation and soil moisture data, this paper proposes a temporal disaggregation algorithm of TRMM monthly precipitation products using AMSR2 daily soil moisture data. The results show that this method is simple and feasible, which provide a new reference for the study of temporal downscaling of satellite-based rainfall dataset.
The evolution of resistance and tolerance as cancer defences

Abstract Although there is a plethora of cancer associated-factors that can ultimately culminate in death (cachexia, organ impairment, metastases, opportunistic infections, etc.), the focal element of every terminal malignancy is the failure of our natural defences to control unlimited cell proliferation. The reasons why our defences apparently lack efficiency is a complex question, potentially indicating that, under Darwinian terms, solutions other than preventing cancer progression are also important contributors. In analogy with host-parasite systems, we propose to call this latter option ‘tolerance’ to cancer. Here, we argue that the ubiquity of oncogenic processes among metazoans is at least partially attributable to both the limitations of resistance mechanisms and to the evolution of tolerance to cancer. Deciphering the ecological contexts of alternative responses to the cancer burden is not a semantic question, but rather a focal point in understanding the evolutionary ecology of host-tumour relationships, the evolution of our defences, as well as why and when certain cancers are likely to be detrimental for survival.
Optimal Adaptive Designs for Dose Finding in Early Phase Clinical Trials

A method of designing early clinical trials is developed for nding an optimum dose level of a new drug to be recommended for use in later phases. During the trial, the e cacious doses are allocated to the patients more often and those with a high probability of toxicity are less likely to be chosen. The method proposed is adaptive in the sense that the statistical models are updated after the data from each cohort of patients are collected and the dose level is adjusted at each stage based on the current data. Two classes of designs are presented. Although both are for e cacy and toxicity responses, one of them also considers pharmacokinetic information. The dose optimisation criteria are based on the probability of success and on the determinant of the Fisher information matrix for estimation of the dose-response parameters. They can be constrained by both acceptable levels of the probability of toxicity and desirable levels of the area under the concentration curve or the maximum concentration. The method presented is general and can be applied to various dose-response and pharmacokinetic models. To illustrate the methodology, it is applied to two di erent classes of models. In both cases, the pharmacokinetic model incorporates the population variability by making appropriate assumptions about the model parameters, while the dose responses are assumed to be either trinomial or bivariate binomial. Various design properties of the method are examined by simulation studies. E ciency measures and the sensitivity of the designs to the assumed prior parameter values are presented. All of the computations are conducted in R, where the Dv optimal sampling time points are obtained by using the package PFIM. The results show that the proposed adaptive method works well and could be appropriate as a seamless phase IB/IIA trial design.
Cross-lingual semantic annotation of biomedical literature: experiments in Spanish and English

MOTIVATION Biomedical literature is one of the most relevant sources of information for knowledge mining in the field of Bioinformatics. In spite of English being the most widely addressed language in the field, in recent years there has been a growing interest from the natural language processing community in dealing with languages other than English. However, the availability of language resources and tools for appropriate treatment of non-English texts is lacking behind. Our research is concerned with the semantic annotation of biomedical texts in the Spanish language, which can be considered an under-resourced language where biomedical text processing is concerned. RESULTS We have carried out experiments to assess the effectiveness of several methods for the automatic annotation of biomedical texts in Spanish. One approach is based on the linguistic analysis of Spanish texts and their annotation using an information retrieval and concept disambiguation approach. A second method takes advantage of a Spanish-English machine translation process to annotate English documents and transfer annotations back to Spanish. A third method takes advantage of the combination of both procedures. Our evaluation shows that a combined system has competitive advantages over the two individual procedures. AVAILABILITY UMLSmapper (https://snlt.vicomtech.org/umlsmapper) and the annotation transfer tool (http://scientmin.taln.upf.edu/anntransfer) are freely available for research purposes as web services and/or demos. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collaborative Classification of Hyperspectral and Lidar Data With Information Fusion and Deep Nets

Convolutional neural network (CNN) receives extensive attention in hyperspectral image classification. While hyper-spectral images contain abundant spectral information but lack spatial information, which usually contributes to poor classification results. In this paper, a novel classification framework called information fusion based CNN (IF-CNN) is proposed to compensate for the shortcomings of hyper-spectral images. The proposed method merges hyperspectral images with abundant spectral information and LiDAR images with rich spatial information as the input of classification framework. Furthermore, the framework consists of two convolutional neural networks: one-dimensional CNN for extracting spectral features, and two-dimensional CNN for extracting spatial correlation features. Experimental results demonstrate that the proposed method achieves excellent performance compared with some existing methods.
A Discriminatively Learned CNN Embedding For Remote Sensing Image Scene Classification

In this work, a discriminatively learned CNN embedding is proposed for remote sensing image scene classification. Our proposed siamese network simultaneously computes the classification loss function and the metric learning loss function of the two input images. Specifically, for the classification loss, we use the standard cross-entropy loss function to predict the classes of the images. For the metric learning loss, our siamese network learns to map the intra-class and inter-class input pairs to a feature space where intra-class inputs are close and inter-class inputs are separated by a margin. Concretely, for remote sensing image scene classification, we would like to map images from the same scene to feature vectors that are close, and map images from different scenes to feature vectors that are widely separated. Experiments are conducted on three different remote sensing image datasets to evaluate the effectiveness of our proposed approach. The results demonstrate that the proposed method achieves an excellent classification performance.
Spiking Neuron Model with Gamma-distributed Synaptic Weights for Different Thresholds

In an attempt to propose a closer model of a biological neuron, various artificial neural models have been reported in the literature. Very few reported articles are available which consider the time-varying synaptic weights of the model. Hence there is further scope to develop and investigate alternative improved spiking neural models which will better represent the activities of a biological neuron. With this motivation, the synaptic weight of the conventional integrate and fire (CIF) model is considered as gamma distributed time-varying nature. Further, for spike generation at the output of the model, different thresholds are employed. The gamma distribution in weight is assumed to take into account the temporal behavior of the synapse. To assess the performance of the proposed model, statistical properties such as similarity indices of the output sequence, mean and variance of normalized similarity indices (NSI) are obtained from simulation-based experiments and are compared.
Hyperspectral Image Classification With Background

Background (BKG) is an integral part of an image and has significant effect and impact on hyperspectral image classification (HSIC). Unfortunately, how to address the BKG issue has not received much attention over the past years. This paper investigates this issue by developing a mixed pixel classifier, iterative constrained energy minimization (ICEM) and a posteriori classification measure, called precision (PR).
Stability Analysis and Variable Frequency Control of Zero Voltage Switching Buck Converter

This manuscript describes the implementation of a closed loop variable frequency control of zero voltage switching buck converter, which diminishes the losses during switching period and improves the efficiency of the converter as compared to the typical PWM buck converter. In the presented converter, the zero voltage switching takes place for a very small duration and for rest of the time period, it acts as a conventional PWM buck converter. An ac analysis for the ZVS buck converter is implemented, that gives the transfer function of the converter. Furthermore, stability analysis of the converter has proved that the system is stable. The presented system is simulated and validated in MATLAB/SIMULINK platform.