Miracle-dz/UniFAR-FTUs huggingface dataset:
TAMU‐RPL: Thompson sampling‐based multichannel RPL

For the success of critical applications in the IoT, there is a need to counteract the effects of external interference, especially when the unlicensed spectrum is used. One way of improving the performance is with a routing protocol that quickly reacts to changes in the environment and avoids path and/or frequencies with higher interference. In this paper, we propose an optimization to the RPL protocol, called TAMU‐RPL, that can keep a more accurate estimation of link quality to the neighbors and quickly react to degradation on the links. TAMU‐RPL also uses the quality of links at different frequencies and opportunistically avoids the ones with bad quality. It is evaluated through simulations with connectivity traces from a 40‐node test bed and in a real 5‐node deployment. We compare TAMU‐RPL with a baseline RPL protocol and a Dijkstra‐based shortest‐path algorithm. Results show that TAMU‐RPL can successfully explore the neighbors and obtain ETX values much closer to the ones obtained with a shortest‐path tree, even when link quality changes over time. In the simulation evaluation, TAMU‐RPL was able to double the number of packets received at the sink compared to RPL and reduced the average delay of packets (in time slots) by more than 10%. In the real deployment, the number of packets received at the sink was increased by more than 33%.
EasiTrack: Decimeter-Level Indoor Tracking With Graph-Based Particle Filtering

Despite decades of efforts, existing indoor location systems do not easily scale with low cost while maintaining high accuracy. We present EasiTrack, an indoor tracking system that achieves decimeter accuracy using a single commodity WiFi access point (AP) under non-line-of-sight (NLOS) conditions and can deploy at scale with almost zero cost. EasiTrack makes two key technical contributions. First, it incorporates RF-based inertial measurement algorithms that can accurately infer a target’s moving distance purely using the RF signals received by itself. Second, EasiTrack devises a map-augmented tracking algorithm that outputs fine-grained locations by jointly leveraging the distance estimates and an indoor map that is ubiquitously available nowadays. We build a fully functional real-time system centering around a satellite-like architecture, which enables EasiTrack to support an unlimited number of clients. We have deployed EasiTrack in seven different scenarios (including offices, hotels, museums, and manufacturing facilities) to track both humans and machines. The results reveal that EasiTrack achieves a median 0.25 m and 90%tile 0.69-m accuracy in distance measurement, a median 0.58 m and 90%tile 1.33-m location accuracy for tracking objects, and a median 0.70 m and 90%tile 1.97-m accuracy for tracking humans in both line-of-sight and NLOS scenarios and supports a broad coverage of 50 $\text{m}{\,\times \,}60$ m using a single AP. It is also verified that EasiTrack can be easily deployed in massive buildings with little cost, promising a practical solution for ubiquitous indoor tracking.
THE HOME MARKET EFFECT IN SPANISH INDUSTRY: AN EMPIRICAL ANALYSIS, 1965-1995

This study was intended to identify empirically the existence of the "Home Market Effect" in Spanish manufacturing industry, i.e., we have attempted to analyse the presence and importance of these effects in production location in Spain. In order to do this we have used the theoretical model of Krugman (1980) and the subsequent empirical analyses of Davis and Weinstein (1996, 1997, 1998, 1999 and 2003). The empirical test carried out for 17 autonomous regions and 9 sectors of activity between 1965 and 1995 lend support to the hypothesis of the existence of the Home Market Effect. This evidence exists in a significant number of sectors of production, specifically, HME is detected in 5 of the 9 sectors analysed: Food, Drink and Tobacco (S1), Paper and Graphics (S4), Products of Various Industries (S5), Non-Metallic Mineral Products (S6) and Metal Products (S7). These results were obtained using a new specification proposed in the present paper, more intuitive as a definition of HME as a mechanism of agglomerating productive activity and which also presents conceptual advantages.
X-Ray Diffraction of the Calcified Tissues in Polypterus

Pyrolyzed scales, fin spines, and bone from the ray-finned bony fish Polyp t e rus (Actinopterygii) showed two mineral phases on X-ray diffraction: hydroxyapat i te (HA), Cas(PO03OH, and whitlockite, Ca:~(PO4)~. The ratio of HA/whitlockite varied with the structure (scale. spine, bone) within each individual fish. The relative proport ions of HA to whitlockite in pyrolyzed samples reflected the Ca/P ratio of the sample. Whitlockite appears after pyrolysis when the Ca/P is lower than 1.67. Among the five fish investigated, for each structure a general trend was noted. The proport ion of HA relative to whitlockite increased with size (age) of the fish. Thus the smallest fish, a juvenile, exhibited a low Ca/P mineral in its calcified tissues, whereas the larger fish had progressively more HA and less whitlockite.
In vitro biological activities of silver nanoparticles synthesized from Scedosporium sp. isolated from soil

One of the important fields in nanotechnology is the development of an environment friendly method for the synthesis of nanoparticles. Many approaches show that microorganisms are the most reliable tools for biosynthesis of nanoparticles compared to physical and chemical methods. In our study, fungi have been exploited for extracellular production of metal nanoparticles. It was observed that in Scedosporium, silver ions are reduced to silver nanoparticles, which was confirmed by UV-visible spectrophotometry and AFM. Optimization studies showed that as the concentration of AgNO3 used for synthesis increased, particles’ size also increased. Size of the particles at different concentrations of AgNO3 was observed to be 79-107 nm with particles being ellipsoidal to spherical in shape. Silver nanoparticles synthesized from 2.0 mM silver nitrate, showed maximum antimicrobial activity compared to all antibiotics tested including synergistic effects. In vitro cytotoxicity of silver nanoparticles against MCF 7 and PC 3 showed that as the concentration of silver nanoparticles increased, a decrease in the percentage cell viability was observed with IC50 values being 60.09 and 57.43 μg/ml respectively. Therefore, through this study, it could be said that extracellular synthesis of silver nanoparticles from Scedosporium was simple, ecofriendly, proving excellent antimicrobial and anticancer agents.
Neurodevelopmental alterations in a mouse model of maternal immune activation

Les etudes epidemiologiques ont demontre un risque accru d’autisme chez les enfants nes d’une mere hospitalisee pour infection au cours de la grossesse. L’imitation d’une infection virale dans le but de declencher une Activation Immunitaire Maternelle (MIA) a ete realise avec succes dans des modeles animaux. Ceci a demontre qu’une MIA conduit a des alterations physiologiques et comportementales sur le long terme. Notre but consistait a etudier la presence de sequelles neonatales chez des souris nees de mere MIA. Nous avons observe que la MIA altere l’activite et la morphologie des neurones des la naissance, et que ces modifications restaient presentes dans les animaux âges de deux semaines. La MIA subie au cours de la grossesse altere donc les neurones des la naissance.
Speech recognition method based on DNN-LSTM combined with Wiener filtering algorithm

Speech activity detection (VAD) algorithms based on deep neural networks (DNNs) ignore the temporal correlation of acoustic features between speech frames, which greatly reduces the performance in noisy environments. To solve this problem, this paper proposes a hybrid network structure based on deep neural network (DNN) and long short-term memory (LSTM), combining the nonlinear learning ability and long sequence node analysis ability of both to learn the dynamic changes of speech signals over time, and optimizing them with wavelet transform and BPTT algorithms. Meanwhile, the signal processing framework is combined with the Wiener filtering algorithm to cope with the untrained noise types in deep learning. Compared with the separate deep learning network and speech signal processing system, the DNN-LSTM-Wiener model has better acoustic modeling ability and speech recognition ability in realistic environments. The study uses the TIMIT corpus for experiments to compare with traditional acoustic models. The experimental results show that the utterance error rate of DNN-LSTM model combined with Wiener filtering algorithm decreases to 21.68%, which is more advantageous in recognition accuracy and still has accurate detection ability at lower signal-to-noise ratio.
CMAR_A_204864 7473..7484

1Department of Central Laboratory, Shenyang Tenth People’s Hospital, Shenyang Chest Hospital, Shenyang, Liaoning, People’s Republic of China; 2Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, Liaoning, People’s Republic of China; 3Department of Pharmacy, Shenyang Tenth People’s Hospital, Shenyang Chest Hospital, Shenyang, Liaoning, People’s Republic of China; 4Department of Thoracic Surgery, Shenyang Tenth People’s Hospital, Shenyang Chest Hospital, Shenyang, Liaoning, People’s Republic of China Purpose: Hepatocellular carcinoma (HCC) is the sixth most lethal neoplasm worldwide. Traditional biomarkers often exploit the relationship between a certain gene and cancer progression, but they cannot predict patient survival or prognosis accurately. We aim to construct a new DNA repair-related gene signature that combines several genes to improve prognosis prediction in HCC. Methods: We selected an HCC mRNA sequencing (mRNA-seq) dataset (n=365) from The Cancer Genome Atlas (TCGA), and gene set enrichment analysis (GSEA) was used to explore bioinformatics information and further screen genes. We then built a gene signature based on the Cox proportional hazards regression model. Results: GSEA revealed that the hallmark DNA repair gene set was significantly upregulated in the tumor phenotype. A set of seven genes, namely, ADA, FEN1, POLR2G, SAC3D1, SEC61A1, SF3A3, and UPF3B, were significantly associated with overall survival (OS) and used to form a gene signature. The signature risk score was calculated and used to divide patients into highand low-risk groups. The high-risk group showed worse prognosis (log-rank test p<0.0001). Univariate and multivariate Cox regression analysis showed that the prognostic performance of this risk score signature was robust in different subgroups based on clinicopathological features, with p-values <0.05 (HR=2.38, 95%CI (confidence interval) =1.355–4.184), indicating that it can serve as an independent prognostic indicator. Conclusion: We developed and identified a seven-gene signature related to the DNA repair process that can predict survival in HCC. It can be used as an effective classification tool and to guide clinical treatment.
Future Directions for Parallel and Distributed Computing

ions may be designed with different goals in mind, including performance, scalability, ease of use, flexibility, compositionality, re-usability and verifiability (assurance). These often trade off against one another, and systems that allow users to manage the details of data movement, energy consumption, and resilience may be difficult to use, whereas a system that abstracts these considerations from users and automatically manages them may incur significant time or storage
The Effect of Carbon Nanofibers Surface Properties in Hydrogenation and Dehydrogenation Reactions

In this study, carbon nanofiber-supported Pd nanoparticles were used in the hydrogenation of cinnamaldehyde and in the dehydrogenation of cinnamyl alcohol. The different graphitisation of the surface of the nanofibers and the amount of oxygen functionalisation significantly affected both activity and selectivity to the various reaction products. In particular, a decrease in nanoparticle dimensions and oxygen content resulted in an increase in overall activity for both of the studied reactions. Moreover, the selectivity to hydrocinnamaldehyde enhanced with increasing surface oxygen content in the cinnamaldehyde hydrogenation, while the selectivity to cinnamaldehyde was higher with low-functionalised nanofibers in the cinnamyl alcohol dehydrogenation. Finally, the most active catalyst proved also to be stable in consecutive runs.
Biological Radiation

For a long time, the scientific community has been discussing whether living beings have specific biological radiation that is different from the standard set of electromagnetic and acoustic waves occur in the process of vital activity of an organism as a whole and its parts. In this paper, the author presents the results of experiments confirming the presence of such specific biological radiation and its material nature.
Asset Pricing with Heterogeneous Recursive Preferences

We introduce heterogeneity in agents’ risk aversion into a general equilibrium asset pricing framework with recursive preferences. Agents trade in a stock, whose dividend is the only source of consumption, and in a short-term bond in zero net supply. In equilibrium the less risk averse agents are leveraged in the stock, and their share in the economy’s wealth is positively correlated with the dividend shock. Loosely speaking, ”average risk aversion” declines when dividend growth is strong, which implies lower expected excess returns. At the same time the price-dividend ratio rises. Thus, in line with the data, a high price dividend ratio predicts low future excess returns. Moreover, predictability of excess returns displays the empirically observed pattern of Rs rising with horizon. We manage to generate Rs of similar magnitudes as in the data at all horizons.
Distributed Human 3D Pose Estimation and Action Recognition*

In this paper, we propose a distributed solution for 3D human pose estimation using a RGBD camera network. The key feature of our method is a dynamic hybrid consensus filter (DHCF) is introduced to fuse the multiple view information of cameras. In contrast to the centralized fusion solution, the DHCF algorithm can be used in a distributed network, which requires no central information fusion center. Therefore, the DHCF based fusion algorithm can benefit from many advantages of distributed network. We also show that the proposed fusion algorithm can handle the occlusion problems effectively, and achieve higher action recognition rate compared to the ones using only single view information.
Title Validity Checker Utilizing Vector Space Model and Topics Model

Many efforts has been made to discover ways to understand topics that lies in texts. Especially, scientific papers are one of important targets of understanding topics by analyzing texts because they contain many technical terms and follow the academic writing. In this paper, we apply text analysis methods that includes topics modelling to build a system that could check whether scientific paper title suits its abstract. We utilized two term weighting methods (TF-IDF and BM25), and terms-topics probability model by utilizing Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA). We evaluated the models in 3 different domains of dataset. We found out that our model performed quite well despite of some drawbacks. We conclude that our method in title checking could provide robust and consistent performance. Keywords— title checker, text analysis.
Numerical analysis of progressive collapse for reinforced concrete frames

This research investigates numerically the potential failure of different framed structures after removal of a corner column. This study is directed towards the role of the joint above the removed column in terms of framed structures stability and internal forces redistribution utilizing linear static, nonlinear static and nonlinear dynamic analysis procedures. First, the preliminary linear static analysis as a part of more detailed nonlinear static and dynamic analysis to model reinforced concrete frames is performed. For nonlinear numerical models, three different moment-rotation capacity curves are considered for the joint above the removed column. These curves are taken according to FEMA 356 regulation according to the reinforcement condition at the joint location to represent normal joint, anchorage deficient joint and shear deficient joint. In linear static models, the results confirmed that, the more moment the joint can resist at the removed column location, the less moment is generated at the beam’s other end. In nonlinear static analysis, the model with normal plastic hinge properties recorded a higher carrying capacity than the model with both shear and anchorage plastic hinges. Nonlinear dynamic analysis with less assumptions offer the more representative modeling for the considered case study which is obvious from its closer perdition of internal forces and deflection compared to the reference findings.
ESTIMATION OF INCOME CHARACTERISTICS FOR REGIONS IN POLAND USING SPATIO-TEMPORAL SMALL AREA MODELS

Abstract The paper presents the comparison of estimation results for spatial and spatiotemporal small area models. The study was carried out for income-related variables drawn from the Polish Household Budget Survey and explanatory variables from the Polish Local Data Bank for the years 2003-2013. The properties of EBLUPs (Empirical Best Linear Unbiased Predictors) based on spatiotemporal models, which utilize spatial correlation between neighbouring areas as well as historical data, were compared and contrasted with EBLUPs based on spatial models obtained separately for each year and with EBLUPs based on the Rao-Yu model. The computations were performed using sae, sae2 and spdep packages for R-project environment. In the case of sae package, the eblupFH, eblupSFH and the eblupSTFH functions were used for point estimation along with the mseFH, mseSFH and the pbmseSTFH functions for the MSE estimation, whereas the eblupRY function was applied for the purposes of sae2 package. The precision of direct estimators was guaranteed by the adoption of the Balanced Repeated Replication method. The results of the analysis demonstrate that a visible reduction of the estimation error was achieved for the implemented spatiotemporal small-area models, especially when significant spatial and time autocorrelations were observed. These results are even more valuable than those achieved by the means of the Rao-Yu model. In the computations, three author-defined functions were adopted, which not only enabled the author to perform the extract of random effects for spatial, spatiotemporal and Rao-Yu models, but also made it possible to obtain their decomposition with respect to spatial and temporal parts, thus creating a novel solution. The comparison was carried out using choropleth maps for spatial effects and distributions of temporal random effects for the considered years.
NBER WORKING PAPER SERIES WHAT DETERMINES IMMIGRATION’S IMPACT? COMPARING TWO GLOBALCENTURIES

Can history shed light on the modern debate about immigration’s labor market impact in high wage economies? This paper examines the relationship between migration and capital flows in the age of mass migration before 1914, the so-called first global century. It then assesses the effects of immigration on wages and employment with and without international capital mobility in first global century and today, that is, the second global century. The paper then explores the links between these economic relationships and immigration policy. It concludes with an explanation for the apparent difference in immigration’s impact in the two global centuries, and thus on policy. Timothy J. Hatton Department of Economics University of Essex Wivenhoe Park Colchester CO4 3SQ UNITED KINGDOM hatton@essex.ac. uk Jeffrey G. Williamson Department of Economics Harvard University Cambridge, MA 02138 and NBER jwilliam@fas.harvard. edu
Service Restoration Algorithm of Distribution Power System

When a fault occurs in power distribution system, Distribution Management System (DMS) isolates the fault immediately. Then the load outside the faulty point has to be restored. As the distribution lines and devices have load limits, a simple restoration by closing the normally open switch can not restore all the loads when the distribution system is in a heavy load condition. Normally heuristic methods are used to solve the problem, but they can not guarantee the optimal solution and are complex and time consuming. A restoration switching algorithm which utilizes front root traversal can search the feeder tree to find the transfer routes which can maximum the restore load, then merge the transfer routes and get the solution that have the minimum operations. This method can make the service restoration through load transfer as much as possible with minimum operation by a fast and simple way and can be utilized in real-time application.
Analysing and optimizing the electrolysis efficiency of a lithium cell based on the electrochemical and multiphase model

Based on an electrochemical multiphysical simulation, a method for analysing electrolysis efficiency has been presented that considers the energy consumption required to produce a single kilogram of lithium and for the production of lithium, rather than the voltage in various parts. By adopting them as the criteria for analysing electrolysis efficiency in the lithium cell, several structural parameters have been optimized, such as the anode radius and anode–cathode distance. These parameters strongly affect the cell voltage and the velocity field distribution, which has a significant impact on the concentration distribution. By integrating the concentration distribution, the lithium production and energy consumption per kilogram, lithium is computed. By appointing the minimum of the chlorine and lithium concentration as the secondary reaction intensity, it is clear where the secondary reaction intensity is strong in the cell. The structure of a lithium electrolysis cell has been optimized by applying an orthogonal design approach, with the energy consumption notably decreasing from 35.0 to 28.3 kWh (kg Li)−1 and the lithium production successfully increasing by 0.17 mol.
Multimodality and the origin of a novel communication system in face-to-face interaction

Face-to-face communication is multimodal at its core: it consists of a combination of vocal and visual signalling. However, current evidence suggests that, in the absence of an established communication system, visual signalling, especially in the form of visible gesture, is a more powerful form of communication than vocalization and therefore likely to have played a primary role in the emergence of human language. This argument is based on experimental evidence of how vocal and visual modalities (i.e. gesture) are employed to communicate about familiar concepts when participants cannot use their existing languages. To investigate this further, we introduce an experiment where pairs of participants performed a referential communication task in which they described unfamiliar stimuli in order to reduce reliance on conventional signals. Visual and auditory stimuli were described in three conditions: using visible gestures only, using non-linguistic vocalizations only and given the option to use both (multimodal communication). The results suggest that even in the absence of conventional signals, gesture is a more powerful mode of communication compared with vocalization, but that there are also advantages to multimodality compared to using gesture alone. Participants with an option to produce multimodal signals had comparable accuracy to those using only gesture, but gained an efficiency advantage. The analysis of the interactions between participants showed that interactants developed novel communication systems for unfamiliar stimuli by deploying different modalities flexibly to suit their needs and by taking advantage of multimodality when required.