Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-fouling additives for the consolidation of archaeological mortars in underwater environment: Efficacy tests performed on the apsidal fishpond of Castrum Novum (Rome, Italy)

This paper deals with the formulation of innovative mortars for the consolidation of archeological structures in underwater environment. The research was conducted within the frame of the MaTaCoS project (Advanced materials and technologies applied to the conservation of underwater cultural heritage) funded by MISE (Italian Ministry of Economic Development). The project concerns the design of advanced tools and methods for the protection of Underwater Cultural Heritage, through specific cleaning and consolidating procedures to be applied directly in situ. The fishpond of the archaeological site of Castrum Novum (Santa Marinella, Rome, Italy) was chosen as a pilot site for experimentation. It is a structure on the coastline, composed of only one tank, with an average immersion of 0.37 m below the sea level, and developing with an NE/SW orientation. The masonry structures reach the maximum thickness at the apex of the fishpond (4.70 m) and consist of a concrete conglomerate composed of slightly rough stones of medium size bound with non-hydraulic mortar. Selected mortars’ fragments taken from the fishpond were characterized by means of minero-petrographic technique in order to define their main constituents from a compositional and textural point of view and also to identify the various degradation processes, mainly biological colonization. Starting from the acquired information, innovative mortars were formulated with the addition of two different anti-fouling additives aimed to slow down the above-mentioned degradation. The efficacy of the anti-fouling products was tested on specimens settled in laboratory environment (artificial marine environment). This information could be useful both for the definition of restoration interventions and for planning preservation protocols to be applied in this peculiar environment.
EFFECT OF WET CUPPING AGAINST INCREASED ARTERIAL BAROREFLEX SENSITIVITY IN HYPERTENSIVE PATIENTS: RANDOMIZED CONTROLLED TRIAL (RCT)

Wet cupping therapy can remove toxins so that blood flow increases and prevents atherosclerosis. This process will stimulate the sensitivity of arterial baroreflex which will provide stimulus to the autonomic nerve. This stimulus-response will reduce the work of the sympathetic nerves and will inhibit the vasomotor center which causes vasodilation so that blood pressure decreases. The purpose of this study was to determine the effect of wet cupping therapy on increased arterial baroreflex and side effects after therapy in the intervention group. The research method uses a Randomized Controlled Trial (RCT) conducted in Sidrap, South Sulawesi, eastern Indonesia between January and April 2020. In this study, there were two groups, each with 21 respondents. The intervention group used wet cupping therapy by controlling anti-hypertensive drugs and the control group only used anti-hypertensive drugs with blood pressure measurements carried out until 6 weeks after therapy. Wet cupping provides increased sensitivity to arterial baroreflex with an indicator of decreased blood pressure. There was a significant difference between blood pressure measurements (systole; diastole) before and 2 weeks of the follow-up period (P = 0.000; P = 0.001); between 2 weeks and 4 weeks (P = 0,000; P = 0,000); between 4 weeks and 6 weeks there was no significant difference in the intervention group (P = 0.267; P = 0.771). So it can be concluded that wet cupping therapy effectively increases the sensitivity of arterial baroreflex with an indicator of decreased blood pressure in the elderly with hypertension up to a limit of 4 weeks after therapy, without serious side effects experienced by respondents. Wet cupping can be alternative prevention for the elderly who suffer from hypertension and still need further research on the number of blood clots per cupping.
FRED-SD: A Real-Time Database for State-Level Data with Forecasting Applications

We construct a real-time dataset (FRED-SD) with vintage data for the U.S. states that can be used to forecast both state-level and national-level variables. Our dataset includes approximately 28 variables per state, including labor market, production, and housing variables. We conduct two sets of real-time forecasting exercises. The first forecasts state-level labor-market variables using five different models and different levels of industrially-disaggregated data. The second forecasts a national-level variable exploiting the cross-section of state data. The state-forecasting experiments suggest that large models with industrially-disaggregated data tend to have higher predictive ability for industrially-diversified states. For national-level data, we find that forecasting and aggregating state-level data can outperform a random walk but not an autoregression.
Adaptive Payload Distribution in Multiple Images Steganography Based on Image Texture Features

With the coming era of cloud technology, cloud storage is an emerging technology to store massive digital images, which provides steganography a new fashion to embed secret information into massive images. Specifically, a resourceful steganographer could embed a set of secret information into multiple images adaptively, and share these images in cloud storage with the receiver, instead of traditional single image steganography. Nevertheless, it is still an open issue how to allocate embedding payload among a sequence of images for security performance enhancement. This article formulates adaptive payload distribution in multiple images steganography based on image texture features and provides the theoretical security analysis from the steganalyst's point of view. Two payload distribution strategies based on image texture complexity and distortion distribution are designed and discussed, respectively. The proposed strategies can be employed together with these state-of-the-art single image steganographic algorithms. The comparisons of the security performance against the modern universal pooled steganalysis are given. Furthermore, this article compares the per image detectability of these multiple images steganographic schemes against the modern single image steganalyzer. Extensive experimental results show that the proposed payload distribution strategies could obtain better security performance.
Joint Feature and Label Adversarial Network for Wafer Map Defect Recognition

Deep neural networks (DNNs), e.g., convolutional neural network (CNN), are able to learn effective features from wafer maps for dimensional reduction and feature extraction. However, very large image data are needed to train DNNs to obtain high generalization performance. It is still a difficult task due to the lack of sufficient labeled images with various defects. This article proposes a semisupervised deep transfer learning algorithm called joint feature and label adversarial network (JFLAN). JFLAN uses CNNs to extract transferable features of wafer maps and then introduces a multilayer domain adaptation and pseudolabel learning block based on the generative adversarial network (GAN). This effectively reduces the distribution discrepancy and the among-class distance of the transferable features. Finally, JFLAN transfers knowledge from wafer image source data collected offline and then achieves the goal of significantly improved accuracy of wafer defect recognition and realizes online adaptive defect recognition. Note to Practitioners—The defect recognition on wafer maps plays a key role to recognize fault sources of the semiconductor manufacturing processes. Transfer learning is able to use the existing labeled data to assist in the classification of unlabeled data and is very effective to solve the problem of small samples and nonstationary generalization errors. In particular, the infusion of adversarial learning in transfer learning will provide a new idea for deep feature learning. This article provides a novel method based on transfer learning to implement wafer map defect recognition (WMDR) to quickly identify defect root causes for yield enhancement. This article provides a novel way for quality control of semiconductor manufacturing processes based on transfer and adversarial learning.
A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively accurate positron emission tomography (PET) images. PET AC based on computed tomography (CT) frequently results in artifacts in attenuation-corrected PET images, and these artifacts mainly originate from CT artifacts and PET-CT mismatches. The AC in PET combined with a magnetic resonance imaging (MRI) scanner (PET/MRI) is more complex than PET/CT, given that MR images do not provide direct information on high-energy photon attenuation. Deep-learning (DL)-based methods for the improvement of PET AC have received significant research attention as alternatives to conventional AC methods. Many DL studies were focused on the transformation of MR images into synthetic pseudo-CT or attenuation maps. Alternative approaches that are not dependent on the anatomical images (CT or MRI) can overcome the limitations related to current CT- and MRI-based ACs and allow for more accurate PET quantification in stand-alone PET scanners for the realization of low radiation doses. In this article, a review is presented on the limitations of the PET AC in current dual-modality PET/CT and PET/MRI scanners, in addition to the current status and progress of DL-based approaches, for the realization of improved performance of PET AC.
Opening the Door to the Past: Accessing Phylogenetic, Pathogen, and Population Data From Museum Curated Bees

Abstract Tens of thousands of insects are deposited in collections every year as a result of survey-based studies that aim to investigate ecological questions. DNA-based techniques can expand the utility of these collections to explore their demographic and evolutionary history, temporal changes in their abundance, and pathogen dynamics. Using museum collections of the non-model bee species Eucera (Peponapis) pruinosa Say 1837 (Hymenoptera: Apidae: Eucerini), we developed a standard minimally-destructive and budget-friendly protocol to extract DNA and amplify common gene-fragments for barcoding, phylogenetic analysis, and pathogens. We also generated genome-wide single nucleotide polymorphism (SNP) data from DNA sequencing (ddRADseq) libraries for population structure analyses. We systematically studied the effect of specimen age (≤10 years ago) and tissue type (whole bees vs. abdomen) on DNA quality, single gene-fragment amplification, and SNP calling. We found that all analyses were achievable with both tissue types, yet with variable levels of efficiency because of general DNA degradation. Specifically, we found that not all samples yielded satisfactory results for molecular studies; however, we did not find a systematic effect of specimen age on DNA quality which is encouraging for future studies involving historical specimens. We report the first evidence for the presence of the microsporidian pathogen Nosema spp. in squash bees, opening a window for the study of historical changes in disease pressure in this important agricultural pollinator. Our protocols can be used as a template for the design of future experiments that extract multiple pieces of information using DNA-based methods from insect museum stored specimens.
The development of a laboratory system to investigate the interactions of tropospheric aerosol and HOx radicals

This thesis describes the development and application of a laboratory system to investigate the loss of peroxy radicals to aerosol. The laboratory system consisted of an aerosol ﬂow tube coupled to a custom-built PEroxy Radical Chemical Ampliﬁer (PERCA). Aerosol was generated using an atomiser and their distribution measured using a SMPS. New values of the HO2 uptake coeﬃcient, γ , were obtained for wet and dry sodium chloride aerosol and dry ammonium sulphate aerosol. The mass accommodation coeﬃcient, α, was also determined for NaCl. A box model was used to determine the implications of the new uptake coeﬃcient of HO2 loss to NaCl aerosol for tropospheric chemistry in the marine boundary layer.
Mechanism of Electrical Defibrillation: Current Status and Future Perspective

Ventricular fibrillation (VF) is currently a major cause of sudden cardiac death (SCD). To cure VF, electrical defibrillation is the only therapy. However, strong energy is required. Thus, to reduce the energy or develop a new method is desired. The mechanism of how the electric shock sweeps VF is still controversial. In this article, we summarize evidence and remaining problems of this topic. There are three issues in time sequence of VF: how to initiate, how to continue, and how to terminate. Many investigations to achieve VF-free heart have been reported, but there are currently no definite methods to prevent VF. Thus, to terminate VF is one of the big challenges to prevent SCD. There are two strategies to improve electrical defibrillation: elucidate the substantial mechanism and reduce the energy. (1) Substantial mechanism proposed: In a failed defibrillation episode, at the energy level of the near defibrillation threshold, the initial activation site is related to the repolarization phase of the location. However, it is still not clear whether it is part of the continuous VF activity or initiation of re-VF. It is well known that strong field electric shock (including cathodal and anodal stimuli) has many effects on the cardiac tissue, such as electroporation, virtual electrode effects, and electrophysiological responses, which are influenced by tissue geometry (including fiber orientation and bifurcation of tissues). These phenomena should modify the defibrillation effect. Finally, the characteristics of dynamic spiral wave (SW; the sources of continuity of re-entries) influence the continuity of VF. (2) Efforts to reduce the defibrillation energy: To reduce the defibrillation energy, biphasic pulse, regional cooling, modified stimuli programs, and automated local stimuli to SW are proposed. The superiority of biphasic pulse to monophasic pulse was established in the late 20th century; however, the mechanism is still not well understood. Cooling of some region of the heart ventricles widens the route of SW trajectory and terminates SW. Programming high frequency stimulus or double stimuli according to computer simulation of the heart model could reduce the defibrillation threshold. Automated local stimulus to the site between the tail of SW activation and the next activation front could terminate the SW.
Biomarkers in canine renal disorders

Renal problems in canines are of worrying concern to both pet owners and practitioners throughout the globe as death becomes inevitable if appropriate diagnostic and therapeutic intervention is not carried out at the earliest. Creatinine has been used as a gold standard for screening renal disorders for years in canines. Much research has been carried out to develop and standardise novel renal biomarkers which can effectively diagnose renal issues well in advance than the existing renal biomarkers. In this review, we will discuss the various attributes of existing, timetested renal biomarkers and the prospects of various newly developed and emerging renal biomarkers.
OMNI: A Framework for Integrating Hardware and Software Optimizations for Sparse CNNs

Convolution neural networks (CNNs) as one of today’s main flavor of deep learning techniques dominate in various image recognition tasks. As the model size of modern CNNs continues to grow, neural network compression techniques have been proposed to prune the redundant neurons and synapses. However, prior techniques disconnect the software neural networks compression and hardware acceleration, which fail to balance multiple design parameters, including sparsity, performance, hardware area cost, and efficiency. More concretely, prior unstructured pruning techniques achieve high sparsity at the expense of extra performance overhead, while prior structured pruning techniques relying on strict sparse patterns lead to low sparsity and extra hardware cost. In this article, we propose OMNI, a framework for accelerating sparse CNNs on hardware accelerators. The innovation of OMNI stems from that it uses hardware amenable on-chip memory partition patterns to seamlessly engage the software CNN model compression and hardware CNN acceleration. To accelerate the compute-intensive convolution kernel, a promising hardware optimization approach is memory partition, which divides the original weight kernels into several groups so that the different hardware processing elements can simultaneously access the weight. We exploit the memory partition patterns including block, cyclic, or hybrid as a means of CNN compression patterns. Our software CNN model compression balances the sparsity across different groups and our hardware accelerator employs hardware parallelization coordinately with the sparse patterns, leading to a desirable compromise between sparsity and performance. We further develop performance models to help the designers to quickly identify the pattern factors subject to an area constraint. Last, we evaluate our design on application specific integrated circuit (ASIC) and field-programmable gate array (FPGA) platform. Experiments demonstrate that OMNI achieves $3.4\times $ – $6.2\times $ speedup for the modern CNNs, over a comparably ideal dense CNN accelerator. OMNI shows $114.7\times $ energy efficiency improvement compared with GPU platform. OMNI is also evaluated on Xilinx ZC706 and ZCU102 FPGA platforms, achieving 41.5 GOP/s and 125.3 GOP/s, respectively.
Relationship between Aspects of the Social Support Provided by the Spouse and the Blood Pressure in Hypertension Patients Who Referred to Healthcare Centers in Qom, Iran

Article type: Original article Background and Aim: Hypertension is one of the most important risk factors for cardiovascular disease and stroke. Social support is one of the effective factors in the prevention and control of this disease. Therefore, this study aimed to determine the relationship between the aspects of social support received from the spouse and high blood pressure in patients who referred to healthcare centers in Qom, Iran. Materials and Methods: This cross-sectional study was performed on 280 patients with primary hypertension referred to healthcare centers in Qom. Data collection tools were demographic form, social support survey by Sherburne and Stuart, a checklist for checking and recording systolic and diastolic blood pressure, and a standard sphygmomanometer. The data were analyzed in SPSS software (version 20) using independent t-test, one-way ANOVA, Pearson correlation coefficient, and multivariate linear regression tests. Results: According to the results, the social support received from the spouse was good in 82.9% of the patients, while 9.3% and 7.9% of the patients received moderate and low social support from their spouses, respectively. In addition, the results showed that the systolic (P=0.01) and diastolic blood pressures (P=0.003) had a significant relationship with the social support received from the spouse. Moreover, the findings revealed that occupation (P<0.001), gender (P<0.001), and income (P=0.019) had a significant correlation with the received social support (P<0.05). Conclusion: The results of this study indicated that social support received from the spouse should be considered as an influential component on systolic and diastolic blood pressure in hypertension patients. Article History: Received: 28 03 2020 Revised: 22 04 2020 Accepted: 27 04 2020
Structural and Mechanical Properties of Chitosan Doped with Zirconium Oxide

In this work, chitosan (CTS) powder has been doped with zirconium oxide (ZrO 2 ) at different wt. % in order to improve the mechanical strength of the composites for biomedical applications. The composites have been prepared by ball milling method and the sintering has been done at three temperatures 100  C, 150  C and 180  C for 3 hours in air. The crystalline structure of the composites was confirmed by X-ray diffraction (XRD) pattern and the crystallite size of the composites decreases with increasing the percentage of ZrO 2 and sintering temperature. The improvement of mechanical strength of the composites with the addition of ZrO 2 in CTS polymer matrix has been investigated using micro-Vickers hardness tester and observed that the hardness of the prepared composite increased to 536 MPa when 90 wt. % CTS doped with 10 wt. % ZrO 2 at 180  C of sintering temperature, demonstrating that the composites can be a potential candidate
DISTRIBUTED INFORMATION SPACE AS AN ASSOCIATIVE ENVIRONMENT OF COMMUNICATION SETTING

Modeling the structure of a distributed network can be based on the architecture of an associative infocommunication network, which displays the processes and mechanisms of interaction of components in the corresponding nodes of the structure. It is shown that for the description and functional representation of a distributed network, it is appropriate to apply decomposition for the field of software engineering, the purpose of which is to create theories that interact with each other according to well-defined rules. A conceptual representation of the distributed space behavior model is given. The paper discusses the modeling of the evolutionary search
Granger Causality Effect of Taxation on Economic Growth in Nigeria

This study examines the extent to which revenue from taxation has improved the growth of Nigerian economy. An Ordinary Least Square (OLS) model was specified where Company Income Tax (CIT), Petroleum Profit Tax (PPT) and Value Added Tax (VAT) are employed as pre-determined variables for economic growth measured by Real Gross Domestic Product (RGDP). Prior to model fitting, Jarque-Bera test for normality revealed that all variables employed showed normality and therefore suitable for the specified function. However, a logarithmic transformation was required to reduce levels of variability among the observed variables. Different model‟s validity checks such as Coefficient of determination (R 2 ), Multiple Correlation Coefficient(r), Durbin-Watson, Akaike info Criterion (AIC), Bayesian Information Criterion (BIC) and F-statistic were used to validate the model. Hypotheses were formulated and tested using statistical tools to validate theoretical backgrounds on economic growth as it is influenced by taxation. The results indicated that taxation had impacted positively on the growth of Nigerian economy and the study therefore recommended that VAT and CIT collections should be further encouraged and developed while more political efforts should be made towards ensuring stable business environments in the oil producing areas of the nation to improved collection of PPT.