Miracle-dz/UniFAR-FTUs huggingface dataset:
Using recent baselines as benchmarks for megafauna restoration places an unfair burden on the Global South

The potential for megafauna restoration is unevenly distributed across the world, along with the socio-political capacity of countries to support these restoration initiatives. We show that choosing a recent baseline to identify species’ indigenous range puts a higher burden for megafauna restoration on countries in the Global South, which also have less capacity to support these restoration initiatives. We introduce the Megafauna Index, which considers large mammal’s potential species richness and range area at country-level, to explore how the responsibility for megafauna restoration distributes across the world according to four scenarios using various temporal benchmarks to define species’ indigenous range – current, historical (1500AD), mid-Holocene and Pleistocene. We test how the distribution of restoration burden across the world correlates to indicators of conservation funding, human development, and governance. Using a recent or historical baseline as a benchmark for restoration puts a higher pressure on African and southeast Asian countries while lifting the responsibility from the Global North, where extinctions happened a long time ago. When using a mid-Holocene or Pleistocene baseline, new opportunities arise for megafauna restoration in Europe and North America respectively, where countries have a higher financial and societal capacity to support megafauna restoration. These results contribute to the debate around benchmarks in rewilding initiatives and the ethical implications of using recent baselines to guide restoration efforts. We suggest that countries from the Global North should reflect on their responsibility in supporting global restoration efforts, by increasing their support for capacity building in the South and taking responsibility for restoring lost biodiversity at home.
Mixed Silver/Gold Colloids: A Study of Their Formation, Morphology, and Surface-Enhanced Raman Activity

Ag-coated Au and Au-coated Ag colloidal particles were prepared by deposition of Ag or Au through chemical reduction on Au or Ag colloids, respectively. Different amounts of the depositing metal were employed in order to obtain different compositions of the Au100-xAgx and Ag100-yAuy resulting particles. The obtained colloids were characterized by UV−vis spectroscopy and transmission electron microscopy. A different formation mechanism was deduced for the different mixed colloids obtained by this procedure. Moreover, the activity of these colloids in surface-enhanced Raman spectroscopy (SERS), that is, the SERS enhancement factor, was checked by using pyridine as an adsorbate probe. The SERS excitation profiles for each colloid were investigated in order to obtain information about the activity of these substrates at different excitation wavelengths as well as to estimate the relative exposed area of each metal in every composite colloid.
An extended DNA-free intranuclear compartment organizes centrosomal microtubules in Plasmodium falciparum

Rapid proliferation of Plasmodium falciparum parasites in human red blood cells is the cause of malaria and is underpinned by an unconventional cell division mode, called schizogony. Contrary to model organisms, P. falciparum replicates by multiple rounds of closed and asynchronous nuclear divisions that are not interrupted by cytokinesis. Organization and dynamics of the critical nuclear division factors are, however, poorly understood. Centriolar plaques, the centrosomes of P. falciparum, are important regulators of division and serve as microtubule organizing centers. Early microscopy studies reveal an acentriolar, amorphous structure although its detailed organization remains elusive. Intranuclear microtubules mediate chromosome segregation, but the small size of parasite nuclei has precluded detailed analysis of their arrangement by classical fluorescence microscopy. We apply recently developed STED, expansion microscopy, and live cell imaging protocols to describe the reconfiguration of microtubules during schizogony. Analysis of centrin, nuclear pore, and microtubule positioning reveals a bipartite organization of the centriolar plaque. While centrin is extranuclear, we confirm by correlative light and electron tomography that microtubules are nucleated in a previously unknown and extended intranuclear compartment, which is devoid of chromatin. This study enables us to build a working model of the organization of an unconventional centrosome and better understand the diversity of eukaryotic cell division modes.
Thermo-Physical Properties of Iron-Magnesium Alloys

According to the common phase diagrams, iron and magnesium are almost immiscible at ambient pressure Massalski (1986). In the liquid phase, the solubility of Mg in Fe is of the order of 0.025 atomic percent (at.%). The maximum solid solubility of Fe in Mg is 0.00041 at. % and the Fe content in Mg at the eutectic point is less than 0.008 at. % (Haitani et al., 2003). Below 1273 K the solubility of Mg in α-Fe is below the detection limit and about 0.25 at. % Mg can be solved in δ−Fe at the monotectic temperature. The immiscibility of Fe and Mg at ambient conditions is in line with the well-known Hume-Rothery rules, according to which more than 15% atomic size difference between alloy constituents hinders solid solution formation (Massalski, 1996). In spite of the negligible solubility of Mg in Fe, several Fe-rich metastable Fe-Mg solid solution have been synthesized. According to the pioneering work by Hightower et al. (Hightower et al., 1997), mechanical alloying produced Fe-Mg substitutional solid solutions with up to 20 at. % Mg and having the body centered cubic (bcc) crystallographic phase of α-Fe. Later, using the similar alloying procedure, Dorofeev et al. (Dorofeev et al., 2004; Yelsukov et al., 2005) found that about 5 − 7 at. % Mg in α-Fe forms supersaturated solid solution. These authors suggested that the driving force for the formation of Fe-Mg solid solutions is associated with the excess energy of coherent interfaces in the Fe-Mg nanocomposite, which facilitates incorporation of Mg into α-Fe. Indeed, based on semiempirical thermodynamic calculations, Yelsukov et al. (Yelsukov et al., 2005) obtained 6 kJ/mol for the enthalpy of formation for Fe-Mg solid solutions, compared to 20 kJ/mol calculated for the corresponding Fe-Mg nanocomposites. In addition to the mechanical alloying techniques, pressure was also found to facilitate the solid solution formation between Fe and Mg. Dubrovinskaia et al. (Dubrovinskaia et al., 2004) reported that at pressures around 20 GPa and temperatures up to 2273 K, the solubility of Mg in bcc Fe was increased to 4 at. %. They found that the lattice parameter of the bcc Fe-Mg alloy increased approximately by 0.6 % per at. %Mg. Furthermore, recent experimental measurements in combination with theoretical simulations demonstrated that at the megabar pressure range more than 10 at. % Mg could be dissolved in liquid Fe, which then could be quenched to ambient conditions (Dubrovinskaia et al., 2005). The mechanism behind the 4
SciNeM: A Scalable Data Science Tool for Heterogeneous Network Mining

Heterogeneous Information Networks (HINs) provide a natural way to represent various relationships between entities of different types, thus they are valuable in many domains. Analysing and extracting knowledge from HINs typically relies on the concept of metapaths, which are paths in the network schema denoting relations of different semantics among entities. Moreover, real-world HINs are often extremely large, containing millions of nodes and edges. Therefore, exploring and analysing HINs not only requires interdisciplinary expertise, being able both to interpret and select appropriate metapaths in the network, but also to run the analysis in an efficient and scalable manner. However, there is a lack of tools to facilitate this task. To fill this gap, we present SciNeM, an open source, publicly available, scalable analysis tool for metapath-based knowledge discovery in HINs.
Using Multioutput Learning to Diagnose Plant Disease and Stress Severity

Early diagnosis of leaf diseases is a fundamental tool in precision agriculture, thanks to its high correlation with food safety and environmental sustainability. It is proven that plant diseases are responsible for serious economic losses every year. *e aim of this work is to study an efficient network capable of assisting farmers in recognizing pear leaf symptoms and providing targeted information for rational use of pesticides. *e proposed model consists of a multioutput system based on convolutional neural networks. *e deep learning approach considers five pretrained CNN architectures, namely, VGG-16, VGG-19, ResNet50, InceptionV3, MobileNetV2, and EfficientNetB0, as feature extractors to classify three diseases and six severity levels. Computational experiments are conducted to evaluate the model on the DiaMOS Plant dataset, a self-collected dataset in the field. *e results obtained confirm the robustness of the proposed model in automatically extracting the discriminating features of diseased leaves by adopting the multitasking learning paradigm.
Progressive Mergesort: Merging Batches of Appends into Progressive Indexes

Interactive exploratory data analysis consists of workloads that are composed of filter-aggregate queries with highly selective filters [1]. Hence, their performance is dependent on how much data they can skip during their scans, with indexes being the most efficient technique for aggressive data-skipping. Progressive Indexes are the state-of-the-art on automatic index creation for interactive exploratory data analysis. These indexes are partially constructed during query execution, eventually refining to a full index. However, progressive indexes have been designed for static databases, while in exploratory data analysis updates — usually batch-appends of newly acquired data — are frequent. In this paper, we propose Progressive Mergesort , a novel merging technique to make Progressive Indexes cope with updates. Progressive Mergesort differs from other merging techniques for partial indexes as it incorporates the index budget strategy design from Progressive Indexing. It follows the same three principles as Progressive Indexes: (1) fast query execution, (2) high robustness,(3) guaranteed convergence. Our experimental evaluation demonstrates that Progressive Mergesort is capable of achieving a 2x speedup when merging updates and up to 3 orders of magnitude lower variance than the state of the art.
Command Filtering and Barrier Lyapunov Function-Based Adaptive Control for PMSMs with Core Losses and All-State Restrictions

With the troubles of core losses and all-state confined to certain limitations which are the innate traits of permanent magnet synchronous motors (PMSMs), this article develops a command filtered adaptive backstepping approach to follow the track of PMSM’s desired rotor position. To begin with, the RBF neural network technique is utilized to get close to the uncharted nonlinear terms which existed in PMSM’s mathematical model. Meanwhile, an advanced adaptive command filter control methodology is constructed to avoid the computing explosion during the process of backstepping design. Furthermore, to make sure that all the state variables are confined into certain ranges, we employed the barrier Lyapunov function (BLF) at every step of the controllers construction. In addition, an error compensating mechanism is proposed to neutralize filtering errors and only one adaptive law is required. At last, simulation results bear out the superiority of the aforementioned control scheme.
Deep Learning Approaches for Text-to-SQL Systems

To bridge the gap between users and data, numerous text-to-SQL systems have been developed that allow users to pose natural language questions over relational databases. Recently, novel text-to-SQL systems are adopting deep learning methods with very promising results. At the same time, several challenges remain open making this area an active and flourishing field of research and development. To make real progress in building text-to-SQL systems, we need to de-mystify what has been done, understand how and when each approach can be used, and, finally, identify the research challenges ahead of us. The purpose of this tutorial is to present recent advances of deep learning techniques for text-to-SQL translation, and to highlight open problems and new research opportunities for researchers and practitioners in the fields of database systems, natural language processing and deep learning.
Fixing Wikipedia Interlinks Using Revision History Patterns

Wikipedia, the web-based free content encyclopedia project, is one of the most popular websites on the Web. Its “open-door" policy, allowing anyone to edit, has made Wikipedia the largest and possibly the best encyclopedia in the world. At the same time, the continuously evolving content, constantly updated by a large number of uncoordinated users, renders the maintenance of a clean, consistent encyclopedia an extremely challenging task. The goal of the W I C LEAN (WC) system presented in this paper is to assist Wikipedia editors in this difficult task. Specifically, we focus on the correctness of Wikipedia inter-links that point from one article (entity) to another. Such inter-links form a key component of the structured part of Wikipedia and their correctness is critical for coherent browsing. Given an entity type of interest, our highly parallelizable algorithm identifies relevant edit patterns across revision histories of Wikipedia entities of related types, along with time windows in which partial edits are tolerable. The discovered patterns/windows are then used by WC to alert Wikipedia editors on past edits that appear to be incomplete, as well as to provide users with on-line assistance as they update the encyclopedia. Our experiments with real-life Wikipedia data demonstrate the efficiency and effectiveness of WC in identifying actual errors in a variety of Wikipedia entity types.
BAUHINIA SEED OIL, A NOVEL SUBSTRATE FOR SOPHOROLIPID PRODUCTION

The seeds of Bauhinia racemosa have 80% of C-18 fatty acids present in them, majority of which is linoleic acid followed by oleic acid and a variety of other oils. We have successfully synthesized sophorolipid by using Bauhinia racemosa seed oil as a novel substrate. Upon supplementing the yeast Candida bombicola (ATCC 22214) with 1% extracted seed oil and 10% glucose, 1.1 g/100 mL of sophorolipid was synthesized during fermentation for seven days. Physicochemical and analytical characterization techniques like oil displacement, emulsification index, thin layer chromatography (TLC), matrix assisted laser desorption/ionization-mass spectroscopy (MALDI-MS) revealed the biotransformation of oil to sophorolipid with its molecular mass being in the range of 600-700 Da. Tensiometric studies showed that the surface tension of water is reduced from about 72 mN/m to 33.35mN/m with a very low critical micelle concentration (CMC) of 60 mg/L. These findings are at par with other sophorolipids synthesized using various lipidic substrates. Moreover these sophorolipids are synthesized from natural, unutilized plant sources using green technique contributing towards development of value added products.
The Evolution and Determinants of Interorganizational Coinvention Networks in New Energy Vehicles: Evidence from Shenzhen, China

With the increasing attention to climate change, air pollution, and related public health issues, China’s new energy vehicles (NEVs) industry has developed rapidly. However, few studies investigated the evolution of interorganizational collaborative innovation networks in the sector domain of NEVs and the influence of different drivers on the establishment of innovation relationships. In this context, this paper uses the joint invention patent of Shenzhen, a low-carbon pilot city of China, to investigate the dynamics of network influencing factors. The social network analysis shows that the scale of coinvention network of NEVs is constantly increasing, which is featured with diversified cooperative entities, and collaboration depth (i.e., the intensity of the interactions with these partners) is also expanding. The empirical results from the Exponential Random Graph Model (ERGM) demonstrate that, with the deepening of collaborative innovation, technological upgrading caused by knowledge exchange makes organizations in the network more inclined to cognitive proximity and less dependent on geographical proximity. In addition, organizational proximity and triadic closure contribute positively to the collaborative network, with their relevance remaining nearly the same, while the impeding effect of cultural/language difference is slightly decreasing with time.
The Role of Artificial and Nonartificial Intelligence in the New Product Success with Moderating Role of New Product Innovation: A Case of Manufacturing Companies in China

School of Art and Design, Nanjing Institute of Engineering, Nanjing, Jiangsu, China School of Political Science and Public Administration, Shandong University, Qingdao, Shandong, China Business School, Liaoning University, Shenyang, China Department of Business Administration, Iqra University, Karachi, Pakistan Department of Economics, -e Islamia University of Bahawalpur, Bahawalpur, Pakistan School of Tourism, Hospitality and Event Management, Universiti Utara Malaysia, Sintok, Malaysia
Defect Detection of Pandrol Track Fastener Based on Local Depth Feature Fusion Network

*ere are three main problems in track fastener defect detection based on image: (1) *e number of abnormal fastener pictures is scarce, and supervised learning detectionmodel is difficult to establish. (2)*e potential data features obtained by different feature extraction methods are different. Some methods focus on edge features, and some methods focus on texture features. Different features have different detection capabilities, and these features are not effectively fused and utilized. (3)*e detection of the track fastener clip will be interfered by the track fastener bolt subimage. Aiming at the above three problems, a method for track fastener defects detection based on Local Deep Feature Fusion Network (LDFFN) is proposed. Firstly, the track fastener image segmentationmethod is used to obtain the track fastener clip subimage, which can effectively reduce the interference of bolt subimage features on the track fastener clip detection. Secondly, the edge features and texture features of track fastener clip subimages are extracted by Autoencoder (AE) and Restricted Boltzmann Machine (RBM), and the features are fused. Finally, the similarity measurement method Mahalanobis Distance (MD) is used to detect defects in track fasteners. *e effectiveness of the proposed method is verified by real Pandrol track fastener images.
Forecast Model of TV Show Rating Based on Convolutional Neural Network

The TV show rating analysis and prediction system can collect and transmit information more quickly and quickly upload the information to the database. The convolutional neural network is a multilayer neural network structure that simulates the operating mechanism of biological vision systems. It is a neural network composed of multiple convolutional layers and downsampling layers sequentially connected. It can obtain useful feature descriptions from original data and is an effective method to extract features from data. At present, convolutional neural networks have become a research hotspot in speech recognition, image recognition and classification, natural language processing, and other fields and have been widely and successfully applied in these fields. Therefore, this paper introduces the convolutional neural network structure to predict the TV program rating data. First, it briefly introduces artificial neural networks and deep learning methods and focuses on the algorithm principles of convolutional neural networks and support vector machines. Then, we improve the convolutional neural network to fit the TV program rating data and finally apply the two prediction models to the TV program rating data prediction. We improve the convolutional neural network TV program rating prediction model and combine the advantages of the convolutional neural network to extract effective features and good classification and prediction capabilities to improve the prediction accuracy. Through simulation comparison, we verify the feasibility and effectiveness of the TV program rating prediction model given in this article.
A Deep Learning Architecture for Audience Interest Prediction of News Topic on Social Media

Personalized social media offer communication opportunities that mass media could not afford, yet also raise novel challenges. A prime challenge arising from this shift in digital communication is to detect topics and events of interest. In this paper, we propose and deploy a novel Deep Learning architecture that predicts if a news topic becomes viral by analyzing social media diffusion and audience interest in current news events . The proposed solution: (i) analyzes news articles, (ii) extracts associated topics and events, (iii) matches the topics and events to filter and extract developing topics, (iv) extracts current events from Twitter and matches them to the filtered news topics, and (v) predicts audience interest in news topics using Twitter likes and retweets. We employ several feature engineering techniques to improve prediction by integrating user metadata into the training set. In our experiments, we correlate two datasets collected over several months in the same time period. The first dataset contains news articles collected from different news venues, while the second one contains tweets regarding the news. The experimental results from our real-world deployment prove that the proposed system achieves high accuracy when integrating influencers metadata and the dayof the week. Thus, proving that the news topics virality prediction is improved under the assumptions that spreaders and the day of the week play a huge role in information diffusion.
Exchanging Data under Policy Views

Exchanging data between data sources is a fundamental problem in many data science and data integration tasks. In this paper, we focus on the data exchange problem in the presence of privacy constraints on the source data, which has been disregarded in the literature to date. By leveraging a logical privacy-preservation paradigm, the privacy restrictions are expressed as a set of policy views representing the information that is safe to expose over all instances of the source in order to exchange them with the target. We introduce a protocol that provides formal privacy guarantees and is data-independent , i.e., under certain criteria, it guarantees that the mappings leak no sensitive information independently of the instances lying in the source. Moreover, we design an algorithm for repairing an input mapping w.r. t. a set of policy views, in cases where the input mapping leaks sensitive information. We show that the repairing can build upon hard-coded and learning-based user preference functions and we show the trade-offs. Our empirical evaluation shows that repairing mappings is quite efficient, leading to repairing sets of 300 s-t tgds in an average time of 5s on a commodity machine. It also shows that the repairing based on learning is robust and has comparable runtimes with the hard-coded one.