Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Mold Prototyping: Creating Complex 3D Castables From 2D Cuts

Designers, makers, and artists prototype physical products by iteratively ideating, modeling, and realizing them in a fast, exploratory manner. A popular method of bringing 3D designs to life is through casting. Casting is the process of pouring a material into a mold, such that once the material sets, the target object is created. Currently, the process of turning a digital design into a tangible product can be a difficult one. One reason for this is that building the mold for example by 3D printing it can take hours, slowing down the prototyping process. This can be particularly true when prototyping molds for casting interactive (sensate and actuated) or geometrically complex (curvy) objects. To this end, we developed two mold-making techniques intended to facilitate different, complementary needs for rapid prototyping. Both rely on computational support: as an intrinsic element of the first, and as an empowering add-on for the second. The first technique we introduce is Silicone I/O, a making method based on Computer Numerical Control (CNC) that enables the molding of sensate, actuated silicone devices. This making method uses stacked laser-cut slices of wood bound together with molten wax in order to create cheap, accessible, one-time-use molds that are quick and easy to assemble. The Silicone I/O devices are pneumatically actuated using air channels created through lostwax casting, and made sensate by mixing carbon fibre with silicone. We demonstrate the performance of these sensors with machine learning through a small user study (n=10). The second technique that we describe is FoldMold, which allows curvy molds to be rapidly built out of paper and wax. This approach is based on “unfolding” a 3D object, cutting the 2D layout, and using papercraft techniques to reassemble the mold. Beyond the physical challenges of rapid mold-making, digitally designing mold patterns from 3D objects poses a bottleneck in the design process. We contribute the FoldMold Blender Add-on, a computational tool that turns 3D positives into CNC-ready papercraft mold patterns. This add-on unwraps the 3D model into flat designs, placing joinery along the edges, adding scores on the pattern where bends or curves occur, and providing patterns for external pieces that prevent the deformation of the paper mold under the weight of the casting material. This thesis contributes within two different broad approaches to increasing increasing speed in mold prototyping. The first method is by creating flat, laser-cuttable mold patterns, significantly speeding up the actual mold creation process. The second method is by automating mold design, off-loading much of the tricky and tedious design work to a computer software that can help a maker design a mold very quickly.
The Effect of Real Effective Exchange Rate on Bilateral Trade between China and the EU

In order to maintain its pace of economic and export growth, China has the ability to influence the development of the domestic currency, supporting its own international trade positions and making significant changes to its structure. Thanks to its absorption capacity, China is safeguarded against such changes and can effectively react to the trade policy of the US. These processes have more severe impact on countries whose foreign trade exposure is high. Our article aims to describe the current trends in the bilateral trade between the PRC and the EU and test how it’s influenced by the real effective exchange rate of China. This analysis utilized ARDL bounds testing procedure and ECM model where the trade balance is a function of exchange rate and domestic and foreign income. We suggest that the decrease in China’s international price competitiveness is a reflection of its rising non-price competitiveness and that the latter currently plays a more dominant role in shaping China’s trade flows.
Dynamic Network Link Prediction by Learning Effective Subgraphs using CNN-LSTM

Predicting the future link between nodes is a significant problem in social network analysis, known as Link Prediction (LP). Recently, dynamic network link prediction has attracted many researchers due to its valuable real-world applications. However, most methods fail to perform satisfying prediction accuracy in various types of networks because the dynamic LP in evolving networks is struggling with spatial and nonlinear transitional patterns. Besides this, existing methods mostly involve the whole network and target link for the LP process. It leads to high computational costs. This paper aims to address these issues by proposing a novel framework named DLP-LES using deep learning methods. DLP-LES uses common neighbors based subgraph of a target link and learns the transitional pattern of it for a given dynamic network. We extract a set of heuristic features of the evolving subgraph to gather additional information about the target link. In this way, we avoid examining the entire network. Additionally, our model introduces new mechanisms to reduce computational costs. DLP- LES generates a lookup table to keep the required information of links of the network and uses a hashing method to store and fetch link information. We propose an algorithm to construct feature matrices of the evolving subgraph to learn transitional link patterns. Our model transforms the dynamic link prediction to a video classification problem, and uses Convolutional Neural Networks with Long Short-Term Memory neural networks. To verify the effectiveness of DLP-LES, extensive experiments are carried out on five real-world dynamic networks. We compare those results against four network embedding methods and basic heuristic methods.
ON SOME NEMATODES FROM MARINE FISHES IN HAINAN ISLAND, CHINA

The specimens for the present study were collected from marine fishes in Sanya and Baimajing of Hainan Island. Four species of nematodes belonging to 4 genera and 3 families were identified. Among them, 3 species, namely, Camallanus marinus Schmidt et Kuntz 1969 from Scomberomorus commersoni, Spirocamallanus pereirai (Annereaux, 1946) Olsen 1952 from Sillago sihama and Sillago maculate, and Cucullanus cirratus Muller 1777 from Pisoodotiophis cancrivorous are for the first time recorded from China, and one species Raphidascaroides hdieutaeae from Halieutaea stellata is described as new to science. Four fishes are found respectively to be new hosts of the 3 former species of nematodes. The description of the new species is given below. All the type specimens are deposited in the Institute of Zoology, Academia Sinica. Raphidascaroides halieutaeae, sp. nov. (fig. 1)Holotype , allotype , paratype 1 , found in intestine of Halieutaea stellata from Baimajing, Hainan Island.Male: Body 64 mm in length and 0.99 mm in diameter. Ventricular appendix 0.54mm in length. Preanal papillae 27 pairs (larger ones 6 pairs and smaller ones 21 pairs), adanal papillae 2 pairs (larger ones 1 pair and smaller ones 1 pair), postanal papillae 8 pairs (larger ones 4 pairs and smaller ones 4 pairs). Spicules unequal in length, the right one 1.617mm and the left 1.386mm. Female: Body 69-117 mm in length and 1.155mm in diameter. Ventricular appendix 0.78-0.804 mm in length. Vulva situated in the anterior half of body.The new species resembles. R. chilontycteri and R. myliobatum in having unequal spicules, but differs from both in the number and arrangement of anal papillae and in the length of ventricular appendix.
Sentiment-Driven Price Prediction of the Bitcoin based on Statistical and Deep Learning Approaches

Nowadays, Bitcoin has become the most popular cryptocurrency, which gains the attention of investors and speculators alike. Asset pricing is a risky and challenging activity that enchants lots of shareholders. Indeed, the difficulty in making predictions lies in understanding the multiple factors that affect the Bitcoin price trend. Modeling the market behavior and thus, the sentiment in the Bitcoin ecosystem provides an insight into the predictions of the Bitcoin price. While there are significant studies that investigate the token economics based on the Bitcoin network, limited research has been performed to analyze the network sentiment on the overall Bitcoin price. In this paper, we investigate the predictive power of network sentiments and explore statistical and deep-learning methods to predict Bitcoin future price. In particular, we analyze financial and sentiment features extracted from economic and crowd-sourced data respectively, and we show how the sentiment is the most significant factor in predicting Bitcoin market stocks. Next, we compare two models used for Bitcoin time-series predictions: the Auto-Regressive Integrated Moving Average with eXogenous input (ARIMAX) and the Recurrent Neural Network (RNN). We demonstrate that both models achieve optimal results on new predictions, with a mean squared error lower than 0.14%, due to the inclusion of the studied sentiment feature. Besides, since the ARIMAX achieves better predictions than the RNN, we also prove that, with just a linear model, we may obtain outstanding market forecasts in the Bitcoin scenario.
European Efforts to Protect Maritime Commerce in the Indian Ocean, 2007-2012

In 1890, an American naval officer Alfred Thayer Mahan, then holding the rank of Captain, published his seminal work The Influence of Sea Power Upon History, 1660-1783 . Although chiefly historical in its nature, his enquiry expressed some broader, universal fundamentals of the use of sea power and its importance to the state. Among the most interesting observations is the relation Mahan notices between the volume of a country’s maritime trade and the necessity to protect the shipping as far as possible throughout the voyage:
Analysis of Proteins

The purification and analysis of proteins is integral to designing oligonucleotide probes for gene cloning, confirming DNA sequence data, and synthesizing peptides for eliciting antipeptide antibodies. Chapter 10 presents protocols for the detection, separation, and purification of proteins. Additional aspects of protein analysis are covered elsewhere in this manual: for example, in Chapter 12 (DNA-protein interactions), Chapter 16 (protein expression), Chapter 17 (protein glycosylation), Chapter 18 (protein phosphorylation), Chapter 19 (bioinformatics and sequence databases), and Chapter 20 (protein-protein interactions). The increasing expansion and sophistication of technologies for protein production and purification have encouraged the editors of Current Protocols to continue to expand the coverage of these issues. Thus, not only do several of the more recent chapters of this manual deal with specific aspects of protein analysis, but a companion volume in this series, Current Protocols in Protein Science (CPPS; Coligan et al., 1998), now exists that is wholly devoted to strategies and protocols for purification and characterization of proteins. CPPS provides specific strategies and recommendations for experimental design of protein purification schemes for both native and recombinant proteins. In addition, it covers in depth and detail such topics as detection and separation of proteins from complex mixtures; computational, structural, and functional analyses; and detection and analysis of posttranslational modifications. Throughout this introduction, reference is made, wherever appropriate, to the relevant chapters or units within Current Protocols in Molecular Biology and CPPS.
CACHE SNIPER : Accurate timing control of cache evictions

Microarchitectural side channel attacks have been very prominent in security research over the last few years. Caches have been an outstanding covert channel, as they provide high resolution and generic cross-core leakage even with simple user-mode code execution privileges. To prevent these generic cross-core attacks, all major cryptographic libraries now provide countermeasures to hinder key extraction via cross-core cache attacks, for instance avoiding secret dependent access patterns and prefetching data. In this paper, we show that implementations protected by 'good-enough' countermeasures aimed at preventing simple cache attacks are still vulnerable. We present a novel attack that uses a special timing technique to determine when an encryption has started and then evict the data precisely at the desired instant. This new attack does not require special privileges nor explicit synchronization between the attacker and the victim. One key improvement of our attack is a method to evict data from the cache with a single memory access and in absence of shared memory by leveraging the transient capabilities of TSX and relying on the recently reverse-engineered L3 replacement policy. We demonstrate the efficiency by performing an asynchronous last level cache attack to extract an RSA key from the latest wolfSSL library, which has been especially adapted to avoid leaky access patterns, and by extracting an AES key from the S-Box implementation included in OpenSSL bypassing the per round prefetch intended as a protection against cache attacks.
Cloning of homologous gene ZmGS5 in maize based on OsGS5

High yield is an important goal of the maize breeding program,grain size is a major determinative factor of cereal grain yield.OsGS5,encoding a serine carboxypeptidase,is a key gene for controlling grain length and 1000-grain weight in rice,which was isolated by the map-based cloning strategy.In this study,the OsGS5sequence was used as a query sequence to search for maize homologous EST sequences,the targeted EST sequences were assembled into a consensus sequence,and designed the gene's specific primers for 3′-RACE.Homology cloning was conducted to isolate the 3′region of orthologous gene from maize genome,the sequencing result revealed that thehomologous fragment was 981bp in length,the result of function annotation indicated that the orthologous gene also encoded a serine carboxypeptidase with high similarity to OsGS5in amino acid sequence,the gene named ZmGS5temporarily.
Recurrent Neural Network-based Base Transceiver Station Power Supply System Failure Prediction

In mobile telecom networks, Base Transceiver Station (BTS) is a key infrastructure that connects customers with the mobile network. BTSs are geographically scattered across the networks service area and thousands of fault indicating alarms are generated by a typical BTS on a daily basis. Thus, proactively maintaining the BTS before faults happen is beneficial to guarantee proper operation of the network and reduce operational costs. In the mobile networks installed in the city of Addis Ababa, Ethiopia, failure in power supply system of BTSs takes the biggest share for mobile services interruption. This work investigates the early prediction of BTS failures due to power system and environmental abnormalities using recurrent neural networks (RNN) with long short term memory (LSTM) and gated recurrent unit (GRU) cells. Eleven power-supply system related features and alarms were collected from a real-time power and environmental monitoring system installed in each BTS. Moreover, the experiments were performed on five BTS sites with sixteen weeks of observations. The experimental results show that GRU using sigmoid activation function with feature reduction achieves better performance than using LSTM with the different configurations investigated. Unlike the prevailing practice in the network operator, which is taking corrective actions in response to alarms, the results in this experimental provide a new insight to the mobile operator to exploit patterns inherent in the daily measured data and predict the well-being of its infrastructure.
The influence of training variables on lingual strength and swallowing in adults with and without dysphagia

Swallowing disorders (dysphagia) occur in a large proportion of individuals over the age of 60. The improvement of tongue strength by resistance exercise is postulated to be directly related to lingual‐palatal pressure generation and bolus propulsion into the pharynx during swallowing. To the best of our knowledge, however, there is no evidence‐based discussion evaluating the strength training variables of the tongue for improving tongue strength maximally.
The April 1999 Sydney Hailstorm

The pattern of the Sydney hailstorm, the patterns of the damage caused by them, the description of the particles of hailstone, and the distribution of the hail falls are presented. Improved risk management, disaster mitigation, and response strategies are expected from the experience and the knowledge gained, providing impetus for the development of measures to reduce losses from such future happenings.
Preface

The first successful experiments in Neutron Radiography were carried out (to our knowledge) in 1935, just a few years after the discovery of the neutron, by H. Kallmann and E. Kuhn using a small neutron generator [1]. Not surprisingly, the field has developed and diversified over the last 70 years so that neutron imaging, in the broad sense, is now routinely used in a wide range of applications. The aim of this book (one of a series on the applications of neutron scattering [2]) is to introduce to the reader, whether novice or experienced researcher, the basic techniques used to image objects using neutron beams and to give a flavor of the vast range of applications where these imaging capabilities provide unique insight (Fig. 1).
Parental migration and children’s psychological and cognitive development in China: differences and mediating mechanisms

Abstract Internal migration has resulted in a large number of left-behind children in China. Despite growing attention to this population, important gaps remain in our understanding of their cognitive development and the factors that mediate the impact of migration on children. The present study draws on a new nationally representative survey of Chinese children to study the psychological and cognitive development of left-behind children. Results show that rural children left behind by both parents (but not by one parent) are worse off in both psychological well-being and cognitive development than rural children living with both parents. The disadvantage of left-behind children is mediated by their caregivers’ emotional well-being, parenting practices, and education. We also find a pronounced rural-urban difference in children’s cognitive development.
Unsupervised Hybrid Algorithm to Detect Anomalies for Predicting Terrorists Attacks

This work presents a hybrid approach for unsupervised algorithms ( UHA ), in order to extract information and patterns from data concerning terrorist attacks. The reference data are those of the Global Terrorism Database. The work presents an approach based on autoencoders and k-modes type clustering. The results obtained are examined through some metrics presented in the article and it is also considered methodologically how to determine a robust threshold for anomaly detection problems.
Experimental Study of Compound Tablets of Bamboo-leaf-flavonoids on Hyperlipidimia of Rats

Objective Bamboo-leaf-flavonoid has attracted more and more attention for its beneficial effect on lipid metablism and heart protection recently.This experiment deals with the synergetic effects between bamboo-leaf-flavonoid and other plant extracts,such as extract of Cassia seed and juice of Citrus grandis,in order to figure out a more efficient compound product.Method We established high blood lipid rat model and made random groups according to TC level.Experimental groups were given the same dose of bamboo-leaf-flavonoid compound(group I: bamboo-leaf-flavonoid,Cassia,juice of Citrus grandis and vitamin C palmitate compound;group Ⅱ: bamboo-leaf-flavonoid,Cassia and juice of Citrus grandis compound) or bamboo-leaf-flavonoid,varies of groups,and control group was given distilled water.The TC,TG,HDL-C,LDL-C levels in serum and the antioxidant activity of whole blood,SOD,GSH-Px,MDA levels in liver were determined.Results After intervention experiment of 6 weeks,TC,TG,LDL-C levels in serum of group I were significantly reduced.Meanwhile,HE stained paraffin sections of liver showed significant improvement of fat deposoition and denaturing conditions.Compared with model group,the serum total antioxidant capacity,SOD activity,GSH-Px activity of group I and Ⅱ were significant increased and superior to bamboo-leaf-flavonoid test group.In addition,the decrease of level of MDA in rat liver was more significantly in group I(P0.01) than that of group Ⅱ(P0.05). Conclusion The compound of bamboo-leaf-flavonoid,Cassia,juice of Citrus grandis and vitamin C palmitate has a better prevention effect on serum lipid level and reduce the atherosclerosis index.
Anaerobic digestion of dairy manure in a fixed bed CSTR: Methane production performance and microbial diversity

To improve the fixed bed and continuous stirred-tank reactor (CSTR) technology relative to the rate of treatment of dairy manure (DM), a continuous stirred tank-treated DM with immobilized carrier biofilm was investigated for 20 days (hydraulic retention time (HRT) was 10 d). Methane productivity, biofilm characteristics, and microbial diversities of the biofilm and digestate were measured. The highest content of extracellular polymeric substances (EPS), proteins, and polysaccharides occurred at 15 days of digestion. An agglomeration phenomenon was observed using a scanning electron microscope on the biofilm. This indicated that the biofilm consisting of EPS was stable during the second HRT. Microbial diversities in digestate and carrier biofilm were characterized using a 16S rRNA sequencing technique. Results showed that the dominant bacterial communities were Pseudomonas (17% to 26%), Clostridium (13% to 21%), and Bacteroidetes (7% to 16%), while the archaea communities were Methanocorpusculum (25% to 37%), Methanosarcina (15% to 33%), Methanoculleus (11% to 15%), and Methanosaeta (13% to 18%). The methane production rate was significantly correlated with bacterial communities (Pseudomonas, Clostridium, Altererythrobacter atlanticus), archaeal communities (Methanosarcina, Methanoculleus, Methanosaeta, and Methanoplanus), and biofilm characteristics (chemical oxygen demand (COD) and EPS). These findings showed that a carrier biofilm could efficiently increase methane production.