Miracle-dz/UniFAR-FTUs huggingface dataset:
Scenedesmus rubescens Heterotrophic Production Strategies for Added Value Biomass

Microalgae attract interest worldwide due to their potential for several applications. Scenedesmus is one of the first in vitro cultured algae due to their rapid growth and handling easiness. Within this genus, cells exhibit a highly resistant wall and propagate both auto- and heterotrophically. The main goal of the present work is to find scalable ways to produce a highly concentrated biomass of Scenedesmus rubescens in heterotrophic conditions. Scenedesmus rubescens growth was improved at the lab-scale by 3.2-fold (from 4.1 to 13 g/L of dry weight) through medium optimization by response surface methodology. Afterwards, scale-up was evaluated in 7 L stirred-tank reactor under fed-batch operation. Then, the optimized medium resulted in an overall productivity of 8.63 g/L/day and a maximum biomass concentration of 69.5 g/L. S. rubescens protein content achieved approximately 31% of dry weight, similar to the protein content of Chlorella vulgaris in heterotrophy.
Statistical inference for competing risks model under progressive interval censored Weibull data

Abstract In this paper, we discussed the estimation problem for the competing risks model when the data are progressive interval censored and follow Weibull distribution with an unknown common shape parameter. The parameters were estimated using both maximum likelihood and Bayesian methods and the Fisher information matrix was derived. The importance sampling method was employed to obtain the Bayes estimates. In the presence of competing risks, it is possible that the causes of failure for some units are not observed and the problem of missing causes of failure arises. In this situation, discussing the consequences of departures from the ignorability assumption on the Bayes estimates of the model parameters is crucial. Therefore, we studied the impact of missing causes of failure on inference in the proposed competing risks model using the Bayesian “index of local sensitivity to non-ignorability”. A simulation study was conducted to compare Bayes estimates under both informative and less informative priors with the corresponding maximum likelihood estimates, and also to assess the sensitivity of the parameter estimation to the non-ignorable missing data in a Bayesian setup. An illustrative example based on the real data is given.
Point-wise Regularity Exponents and Markets Cross-Correlation

The multifractional Brownian motion is a locally dependent Gaussian nonstationary process, whose flexibility in describing complex phenomena justifies its use in financial dynamics modeling. Assuming it as a model of stock indexes, we estimate the pointwise regularity function for the Dow Jones Ind. Avg., the Footsie 100 and the Nikkei 225. We also analyze the pairwise cross-correlation of the functions themselves and compare them with the pairwise cross-correlation of log variations. Field of Research: multifractional Brownian motion, pointwise regularity, cross-correlation.
COMBATING HEAT STRESS IN LAYING HENS A REVIEW

Pakistan is located in the subtropical zone of the world where the ambient temperature remains higher most of the year and causes significant economic losses in commercial laying hens. The ambient temperature of some parts in Pakistan reaches up to 52 °C whilst, the ideal temperature for efficient performance for laying hens is 19-22°C. Yet, ambient temperature particularly on the higher side is very disruptive, therefore; heat stress during summer is a major problem of poultry producers in most regions of Pakistan. Several studies have investigated the biohazards of heat stress on production performance, physiology, immunology, digestibility of nutrients, hematology, serology, welfare, behavior and livability of commercial layers. During summer production at farms decreases drastically, thus adversely affecting the economics of layer farms, which might add to a number of culled birds. The key strategies to alleviate heat stress in laying birds are feeding practices and management practices. New innovative approaches such as exploiting and incorporating genes for heat tolerance and genetic marker assisted selection for genotypes with improved heat tolerance should also be explored. Therefore, this review article intends to discuss the effects of heat stress on layer birds’ health and production and various management and nutritional approaches to coping with it.
G-NETWORKS AND THEIR APPLICATIONS TO MACHINE LEARNING, ENERGY PACKET NETWORKS AND ROUTING: INTRODUCTION TO THE SPECIAL ISSUE

This paper introduces a special issue of this journal (Probability in the Engineering and Informational Sciences) that is devoted to G(elenbe)-Networks and their Applications. The special issue is based on revised versions of some of the papers that were presented at a workshop held in early January 2017 at the Séminaire Saint-Paul in Nice (France). It includes contributions in several research directions that followed from the introduction of the G-Network in the late 1980s. The papers present original theoretical developments, as well as applications of G-Networks to Machine Learning, to the performance optimization of energy systems via the novel Energy Packet Networks formalism for systems that operate with renewable and intermittent energy sources, and to packet network routing and Cloud management over the Internet. We introduce these contributions from the perspective of an overview of recent work based on G-Networks.
Beat Tracking: Is 441 kHz Really Needed?

—Beat tracking is essential in music information retrieval, with applications ranging from music analysis and automatic playlist generation to beat-synchronized effects. In recent years, deep learning methods, usually inspired by well-known architectures, outperformed other beat tracking algorithms. The current state-of-the-art offline beat tracking systems utilize temporal convolutional and recurrent networks. Most systems use an input sampling rate of 44.1 kHz. In this paper, we retrain multiple versions of state-of-the-art temporal convolutional networks with different input sampling rates while keeping the time resolution by changing the frame size parameter. Furthermore, we evaluate all models using standard metrics. As the main contribution, we show that decreasing the input audio recording sampling frequency up to 5 kHz preserves most of the accuracy, and in some cases, even slightly outperforms the standard approach.
Estimating Microvascular Invasion in Patients with Resectable Multinodular Hepatocellular Carcinoma by Using Preoperative Contrast-Enhanced MRI: Establishment and Validation of a Risk Score

Objective To determine the preoperative clinicoradiological factors to predict microvascular invasion (MVI) in patients with resectable multinodular hepatocellular carcinoma (mHCC), and further to establish and validate a stratified risk scoring system. Methods Two hundred and seventy-three patients with pathologically confirmed mHCC (≥2 lesions) without major vascular invasion and biliary tract tumor thrombosis, who underwent preoperative contrast-enhanced MRI and hepatectomy, were consecutively enrolled (training/validation cohort=193/80). Preoperative clinicoradiological variables were collected and analyzed. The multivariable logistic regression was performed to determine the independent predictors of MVI and create a risk score system. The C-index, calibration curve and decision curve were used to evaluate the performance of the risk score. A risk score-based prognostic stratification system was performed in mHCC patients. The risk score system was further verified in the validation cohort. Results AFP > 400 ng/mL, presence of satellite nodule, mosaic architecture and increased total tumor diameter were independent predictors of MVI while fat in mass was an independent protective factor of MVI. The risk score yielded satisfactory C-index values (training/validation cohort: 0.777/0.758) and fitted well in calibration curves. Decision curve analysis further confirmed its clinical utility. Based on the risk score, mHCC patients were stratified into high-/low-MVI-risk subgroups with significantly different recurrence-free survival (both P < 0.001). Conclusion The presented risk score incorporating clinicoradiological parameters could stratify mHCC patients into high-risk and low-risk subgroups and predict prognosis in patients with resectable mHCC.
Tough, adhesive biomimetic hyaluronic acid methacryloyl hydrogels for effective wound healing

The development of cost-effective, biocompatible soft wound dressings is highly desirable; however, conventional dressings are only designed for flat wounds, which creates difficulty with promising healing efficiency in complex practical conditions. Herein, we developed a tough, adhesive biomimetic hyaluronic acid methacryloyl hydrogels composed of chemically crosslinked hyaluronic acid methacryloyl (HAMA) network and poly(N-hydroxyethyl acrylamide) (PHEAA) network rich in multiple hydrogen bonding. Due to the multiple chemical crosslinking sites (acrylamide groups) of HAMA; the bulk HEMA/PHEAA hydrogels presented significant enhancements in mechanical properties (∼0.45 MPa) than common hyaluronic acid hydrogels (<0.1 MPa). The abundant hydrogen bonding also endowed the resultant hydrogels with extremely high adhesiveness on many nonporous substrates, including glass and biological tissues (e.g., heart, liver, lung, kidney, stomach, and muscle), with a considerable interfacial toughness of ∼1432 J m−2. Accordingly, since both natural hyaluronic acid derivative polymers and hydrophilic PHEAA networks are highly biocompatible, the hydrogel matrix possesses good blood compatibility (<5% of hemolysis ratio) and satisfies the general dressing requirements (>99% of cell viability). Based on these physicochemical features, we have demonstrated that this adhesive hydrogel, administered in the form of a designed patch, could be applied to wound tissue healing by promoting epithelialization, angiogenesis, and collagen deposition. We believe that our proposed biomimetic hydrogel design holds great potential for wound repair and our developed HAMA/PHEAA hydrogels are extremely promising for the next-generation tissue healings in emergency situations.
Whistle characterization of long-beaked common dolphin (Delphinus delphis bairdii) in La Paz Bay, Gulf of California

Long-beaked common dolphin (Delphinus delphis bairdii) distribution is limited to the Eastern North Pacific Ocean. Its whistle repertoire is poorly investigated, with no studies in the Gulf of California. The aim of the present study is to characterize the whistles of this species and compare their parameters with different populations. Acoustic monitoring was conducted in La Paz Bay, Gulf of California. Recordings were inspected in spectrogram view in Raven Pro, selecting good quality whistles (n = 270). In the software Luscinia, contours were manually traced to obtain whistle frequencies and duration. Number of steps, inflection points and contour type were visually determined. We calculated the descriptive statistics of the selected whistle parameters and we compared the results with a dolphins population from the Eastern Pacific Ocean. Permutational multivariate analysis of variance (PERMANOVA) was performed to test the intraspecific variation of the whistle parameters among groups. In the present study the mean values (±SD) of the whistle parameters were: maximum frequency = 14.13 ± 3.71 kHz, minimum frequency = 8.44 ± 2.58 kHz and duration = 0.44 ± 0.31 s. Whistles with the upsweep contour were the most common ones (34.44%). The coefficient of variation (CV) values for modulation parameters were high (>100%), in accordance with other studies on dolphins. Whistle parameters showed significant differences among groups. Finally, ending and maximum frequencies, duration and inflection points of the whistles recorded in the present study were lower compared with the parameters of the long-beaked common dolphins from the Eastern Pacific Ocean. This study provides the first whistle characterization of long-beaked common dolphin from the Gulf of California and it will help future passive acoustic monitoring applications in the study area.
Exact Convex Relaxation-Based Sensor Network Localization Using Noisy Distance Measurements

This article studies the problem of distributed localization for a sensor network consisting of target nodes and anchor nodes using only noisy distance measurements, which is challenging when the target nodes are outside the convex hull of the anchor nodes. The concerned localization problem is formulated as a squared-range-based least-squares estimation problem, and a coordinate descent scheme-based distributed localization method is developed where each subproblem can be exactly resolved using the Lagrangian convex relaxation technique and the convergence of the proposed distributed algorithm can be guaranteed. Simulation examples show that the proposed localization method has a high chance to reach the globally optimal solution in the absence of measurement noise and yields reliable localization results in the presence of measurement noise.
Application of Helmholtz/Hodge Decomposition to Finite Element Methods for Two-Dimensional Maxwell's Equations

In this work we apply the two-dimensional Helmholtz/Hodge decomposition to develop new nite element schemes for two-dimensional Maxwell's equations. We begin with the introduction of Maxwell's equations and a brief survey of nite element methods for Maxwell's equations. Then we review the related fundamentals in Chapter 2. In Chapter 3, we discuss the related vector function spaces and the Helmholtz/Hodge decomposition which are used in Chapter 4 and 5. The new results in this dissertation are presented in Chapter 4 and Chapter 5. In Chapter 4, we propose a new numerical approach for two-dimensional Maxwell's equations that is based on the Helmholtz/Hodge decomposition for divergence-free vector elds. In this approach an approximate solution for Maxwell's equations can be obtained by solving standard second order scalar elliptic boundary value problems. This new approach is illustrated by a P1 nite element method. In Chapter 5, we further extend the new approach described in Chapter 4 to the interface problem for Maxwell's equations. We use the extraction formulas and multigrid method to overcome the low regularity of the solution for the Maxwell interface problem. The theoretical results obtained in this dissertation are con rmed by numerical experiments.
Apples and Apple By-Products: Antioxidant Properties and Food Applications

In recent years, there has been a growing interest in utilizing natural antioxidants as alternatives to synthetic additives in food products. Apples and apple by-products have gained attention as a potential source of natural antioxidants due to their rich phenolic content. However, the extraction techniques applied for the recovery of phenolic compounds need to be chosen carefully. Studies show that ultrasound-assisted extraction is the most promising technique. High yields of phenolic compounds with antioxidant properties have been obtained by applying ultrasound on both apples and their by-products. Promising results have also been reported for green technologies such as supercritical fluid extraction, especially when a co-solvent is used. Once extracted, recent studies also indicate the feasibility of using these compounds in food products and packaging materials. The present review aims to provide a comprehensive overview of the antioxidant properties of apples and apple by-products, their extraction techniques, and potential applications in food products because of their antioxidant or nutritional properties. The findings reported here highlight the proper utilization of apples and their by-products in food to reduce the detrimental effect on the environment and provide a positive impact on the economy.
Image Fusion through Deep Convolutional Neural Network and Laplacian Pyramid

— In the technically advanced world image fusion attracts as a considerable assistant for image processing experts. The role of image fusion in processing of images is robust by extracting the best and complementary features from two or more images and integrating that information by using appropriate algorithm in order to provide better recognition characteristics. Image fusion experts have been using images for a long time with machine learning algorithms. It requires very intensive pre-processing steps. Recently experts are very much interested in using long existing deep learning algorithms in processing the image data. This paper presents the deep convolutional neural network based image fusion using Laplacian pyramid method. Firstly the paper concentrates on the existing image fusion techniques and related work. Secondly on convolutional neural networks, deep learning and their features. Thirdly it presented the similarities among Convolutional Neural Network, Gaussian pyramid, Laplacian pyramid models. Lastly our proposed method and discussion on experimental results. It was observed that Deep Convolutional Neural Network and Laplacian pyramid based image fusion method gave better PSNR Values than the existing Laplacian Pyramid fusion methods for various images.
Bibliometric and Visual Analysis in the Field of Dry Needling for Myofascial Pain Syndrome from 2000 to 2022

Background Evidence has shown that dry needling (DN) is effective for myofascial pain syndrome (MPS). However, bibliometrics has rarely been used to analyze the literature related to DN for MPS. The purpose of this study is to provide a systematic overview of global frontiers and research hotspots of DN in the treatment of MPS from 2000 to 2022. Methods A search was conducted on Web of Science Core Collection (WoS CC) for literature on DN for MPS from 2000 to 2022. Based on the basic information provided by WoS CC, CiteSpace software was used to conduct bibliometric analysis of the countries, institutions, categories, journals, authors, references and keywords involved in this topic. Results A total of 458 papers were obtained, with the number of publications increasing over time. Journal of Bodywork and Movement Therapies (31) was the most productive journal based on the number of publications, while Arch Phys Med Rehab (329) was the most co-cited journal. The most productive countries and institutions were USA (112) and Universidad Rey Juan Carlos (39), respectively. Fernandez-de-las-penas, Cesar has the highest number of publications (24) and Simons DG, who was an author with the highest number of citations (250). The article published by Gattie et al (co-citations: 65), and Mejuto-Vazquez et al (centrality: 0.36) were the most representative and symbolic. Based on the co-cited literature and keywords, myofascial trigger point, research methods, and acupuncture were the hot research topics and trends in the field. Conclusion The current status and trends in clinical research of DN for MPS are revealed according to the results of this bibliometric study, which may facilitate researchers to identify hot topics and new directions for future research.
Angular feature extraction and ensemble classification method for 2D, 2.5D and 3D face recognition.

It has been recognised that, within the context of face recognition, angular separation between centred feature vectors is a useful measure of dissimilarity. In this thesis we explore this observation in more detail and compare and contrast angular separation with the Euclidean, Manhattan and Mahalonobis distance metrics. This is applied to 2D, 2.5D and 3D face images and the investigation is done in conjunction with various feature extraction techniques such as local binary patterns (LBP) and linear discriminant analysis (LDA). We also employ error-correcting output code (ECOC) ensembles of support vector machines (SVMs) to project feature vectors non-linearly into a new and more discriminative feature space. It is shown that, for both face verification and face recognition tasks, angular separation is a more discerning dissimilarity measure than the others. It is also shown that the effect of applying the feature extraction algorithms described above is to considerably sharpen and enhance the ability of all metrics, but in particular angular separation, to distinguish inter-personal from extra-personal face image differences. A novel technique, known as angularisation, is introduced by which a data set that is well separated in the angular sense can be mapped into a new feature space in which other metrics are equally discriminative. This operation can be performed separately or it can be incorporated into an SVM kernel. The benefit of angularisation is that it allows strong classification methods to take advantage of angular separation without explicitly incorporating it into their construction. It is shown that the accuracy of ECOC ensembles can be improved in this way. A further aspect of the research is to compare the effectiveness of the ECOC approach to constructing ensembles of SVM base classifiers with that of binary hierarchical classifiers (BHC). Experiments are performed which lead to the conclusion that, for face recognition problems, ECOC yields greater classification accuracy than the BHC method. This is attributed primarily to the fact that the size of the training set decreases along a path from the root node to a leaf node of the BHC tree and this leads to great difficulties in constructing accurate base classifiers at the lower nodes.
Dual-Function Meta-Grating Based on Tunable Fano Resonance for Reflective Filter and Sensor Applications

Localized surface plasmon resonance (LSPR)-based sensors exhibit enormous potential in the areas of medical diagnosis, food safety regulation and environmental monitoring. However, the broadband spectral lineshape of LSPR hampers the observation of wavelength shifts in sensing processes, thus preventing its widespread applications in sensors. Here, we describe an improved plasmonic sensor based on Fano resonances between LSPR and the Rayleigh anomaly (RA) in a metal–insulator–metal (MIM) meta-grating, which is composed of silver nanoshell array, an isolation grating mask and a continuous gold film. The MIM configuration offers more freedom to control the optical properties of LSPR, RA and the Fano resonance between them. Strong couplings between LSPR and RA formed a series of narrowband reflection peaks (with a linewidth of ~20 nm in full width at half maximum (FWHM) and a reflectivity nearing 100%) within an LSPR-based broadband extinction window in the experiment, making the meta-grating promising for applications of high-efficiency reflective filters. A Fano resonance that is well optimized between LSPR and RA by carefully adjusting the angles of incident light can switch such a nano-device to an improved biological/chemical sensor with a figure of merit (FOM) larger than 57 and capability of detecting the local refractive index changes caused by the bonding of target molecules on the surface of the nano-device. The figure of merit of the hybrid sensor in the detection of target molecules is 6 and 15 times higher than that of the simple RA- and LSPR-based sensors, respectively.