Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Removal of Planar Clutter from 3D Point Clouds for Improving Industrial Object Recognition

The industrial construction industry makes use of prefabrication, preassembly, modularization and offsite fabrication (PPMOF) for project execution because they offer a superior level of control as compared to on-site operations. This control is enabled by systematic and thorough performance feedback loops. Improvement of the feedback systems within these facilities will require a transition away from suboptimal manual data collection to more reliable automated data collection and processing. Laser scanners are an effective tool for automatically gathering dimensional data but extraction of useful information from point clouds remains a challenge. The speed of 3D object recognition methods depends on the size of the search space. Methods for reducing this search space are needed in order to improve the performance of 3D object recognition and subsequent information extraction. Large planar objects (e.g. floors and walls) constitute a large portion of the search space in fabrication facilities, yet are rarely the objects of interest for analysis. In this paper, an automated framework for detecting and removing large planes in point clouds is presented to speed up object recognition. The raw point cloud is first Guassian mapped to normal vector space by calculating normal vectors at each point. The Gaussian sphere is clustered using a density-based clustering algorithm and major parallel planes are segmented from the rest of the point cloud. The major planes are removed and the remaining objects in the scene continue on to 3D object recognition. Results show the algorithm for automatic plane removal can reduce the search space for object recognition by as much as 60% or 70%.
Qualification of the FSM technology for corrosion monitoring of a subsea pipeline at the Froey field

This paper presents the Field Signature Method (FSM) technology which will be used as part of the monitoring system of a new pipeline in the North Sea. FSM has been installed on site subsea in order to monitor the internal corrosion of the pipeline. Readings will be transferred to the surface giving relevant information on corrosion rates and on remaining wall thickness to the corrosion engineer. This is the first subsea installation of the FSM.
Recent Development in Machining Chatter Control and Predication

On-line predication and control for the machining chatter is a key technique for the stability improvement of machine tools. This paper discusses some recent advances according to the research work at home and abroad. Furthermore, a new on-line chatter control method is brought forwards, which is to actively adjust the machining dynamics in process based on the application of smart materials in manufacturing systems.
Artonin F Induces the Ubiquitin-Proteasomal Degradation of c-Met and Decreases Akt-mTOR Signaling

Targeted therapies that selectively inhibit certain molecules in cancer cells have been considered promising for cancer treatment. In lung cancer, evidence has suggested that mesenchymal-epithelial transition factor (c-Met) oncoprotein drives cancer progression through its signaling transduction pathway. In this paper, we report the downregulation of c-Met by artonin F, a flavonoid isolated from Artocarpus gomezianus. Artonin F was found to be dominantly toxic to lung cancer cells by mediating apoptosis. With regard to its mechanism of action, artonin F downregulated c-Met expression, consequently suppressed the phosphatidylinositol-3 kinase/Akt/mammalian target of rapamycin signaling, increased Bax expression, decreased Bcl-2 expression, and activated caspase-3. The depletion of c-Met was mediated by ubiquitin-proteasomal degradation following co-treatment with artonin F, with the proteasome inhibitor MG132 reversing its c-Met-targeting effect. The immunoprecipitation analysis revealed that artonin F significantly promoted the formation of the c-Met–ubiquitin complex. Given that ubiquitin-specific protease 8 (USP8) prevents c-Met degradation by deubiquitination, we performed a preliminary in silico molecular docking and observed that artonin F blocked the catalytic site of USP8. In addition, artonin F interacted with the catalytic residues of palmitoylating enzymes. By acting as a competitive inhibitor, artonin F could reduce the degree of palmitoylation of c-Met, which affected its stability and activity. In conclusion, c-Met is critical for cancer cell survival and the failure of chemotherapeutic regimens. This novel information on the c-Met downregulating effect of artonin F will be beneficial for the development of efficient anticancer strategies or targeted therapies.
The Effects of Selenium on Rumen Fermentation Parameters and Microbial Metagenome in Goats

This study evaluated the effects of selenium yeast (SY) on rumen fermentation parameters, rumen bacterial diversity, and expression pathways in goats. A total of 18 Qianbei-pockmarked weather goats from Guizhou (body weight, 25.75 ± 1.75 kg; mean ± standard deviation) were assigned to three groups according to a completely randomized design. Control group (CON, n = 6) kids were fed a basal diet, while treatment 1 (LS, n = 6) and treatment 2 (HS, n = 6) kids were fed a basal diet with 2.4 and 4.8 mg/kg SY, respectively. The feeding trial lasted for 74 days. The results indicated that the ruminal fluid of LS goats had significantly higher levels of propionic, caproic, isobutyric, and isovaleric acids than that of the CON. The levels of butyric and valeric acids were higher in the HS group than in the CON. The acetate:propionate ratio was significantly higher in the CON than in the two treatments. In addition, the inclusion of 2.4 mg/kg SY can lead to a significant decrease in the relative abundances of Euryarchaeota, and Proteobacteria at the phylum level compared to the CON and the HS groups. At the genus level, the LS group had a significant decrease in the relative abundance of Methanobrevibacter and Sarcina, whereas it could lead to a significant increase in the relative abundance of Clostridium in the ruminal fluid relative of the other two groups. At the species level, the LS group had a significant decrease in the relative abundance of bacterium_P3, bacterium_P201, and Sarcina_sp. _DSM_11001 compared to the other groups. Moreover, the CON group had a significant decrease in the relative abundance of bacterium_P201 compared to the other two treatments. Compared to the CON, the addition of 2.4 mg/kg SY significantly enriched carbohydrate metabolism pathways in the ruminal fluid for gene encoding. Additionally, goats receiving SY showed a significant upregulation of glycosyl transferase and carbohydrate binding module pathways. These results suggest that dietary supplementation with SY modulates fermentation parameters, and it affects microbial diversity and microbial metagenome in the rumen of Qianbei-pockmarked goats.
Ultra-Low Power SAR-ADC in 28nm CMOS Technology

This thesis presents an improved ultra-low power 10-bit 1 kS/s successive approximation register (SAR ) analog to digital converter (ADC ) building on the work performed by Simon Josepshen in 2013. The improved ADC has a supply of 450mV and operates at almost half the power consumption of previous work; achieving a consumption of 691.5 pW in post-layout simulation. This is possibly the best power consumption found in available literature. Higher resolution and lower power consumption is achieved with incremental changes to the original design and by exploiting the benefits of new technology. The design differs from the previous in some key ways; having a different comparator topology, different switching procedure and asynchronous operation. The effective resoultion is found to be around 9.4-bit, giving a figure-of-merit of 1.0 fJ/conversion-step.
Regional Ionospheric Corrections for High Accuracy GNSS Positioning

Centimetre-level accurate ionospheric corrections are required for a high accuracy and rapid convergence of Precise Point Positioning (PPP) GNSS positioning solutions. This research aims to evaluate the accuracy of a local/regional ionospheric delay model using a linear interpolation method across Australia. The accuracy of the ionospheric corrections is assessed as a function of both different latitudinal regions and the number and spatial density of GNSS Continuously Operating Reference Stations (CORSs). Our research shows that, for a local region of 5° latitude ×10° longitude in mid-latitude regions of Australia (~30° to 40° S) with approximately 15 CORS stations, ionospheric corrections with an accuracy of 5 cm can be obtained. In Victoria and New South Wales, where dense CORS networks exist (nominal spacing of ~100 km), the average ionospheric corrections accuracy can reach 2 cm. For sparse networks (nominal spacing of >200 km) at lower latitudes, the average accuracy of the ionospheric corrections is within the range of 8 to 15 cm; significant variations in the ionospheric errors of some specific satellite observations during certain periods were also found. In some regions such as Central Australia, where there are a limited number of CORSs, this model was impossible to use. On average, centimetre-level accurate ionospheric corrections can be achieved if there are sufficiently dense (i.e., nominal spacing of approximately 200 km) GNSS CORS networks in the region of interest. Based on the current availability of GNSS stations across Australia, we propose a set of 15 regions of different ionospheric delay accuracies with extents of 5° latitude ×10° longitude covering continental Australia.
High genetic correlation for milk yield across Manech and Latxa dairy sheep from France and Spain

Graphical Abstract Summary: Latxa (from Spain) and Manech (from France) are dairy sheep breeds which split into 2 strains: Blond (Latxa Cara Rubia and Manech Tête Rousse) and Black (Latxa Cara Negra of Navarre and Manech Tête Noire). There are exchanges of genetic material and pedigree connections within strains that could make a joint evaluation and selection within strains viable. In this work we estimate the genetic correlation (rg) across countries for milk yield for the Blond and Black strains. In both cases the estimated genetic correlation for milk yield was 0.7. This value encourages future joint work of Latxa and Manech breeders.
Spatial patterns of northeast monsoon rainfall over sub-regions of southern peninsular India and Sri Lanka as revealed through empirical orthogonal function analysis

The spatial variability of northeast monsoon (NEM) rainfall of peninsular India and Sri Lanka is studied using Empirical Orthogonal Function (EOF) analysis based on monthly / seasonal rainfall data for the months of October, November, December, January and for the season October-December (OND) for the 107 year period of 1900-2006 over nine sub-regions defined for the study based on climatology and geography. Monthly / seasonal rainfall series over these nine sub-regions are subjected to EOF analysis and 2-3 significant Principal Components (PCs) are identified for each case. Each PC is then linked to physical modes known to be associated with NEM using correlation and compositing techniques. For the OND season and for all the four individual months, the first PC explaining maximum variance of 49-64% in the spatial rainfall distribution is identified with the overall NEM strength. The second and the third PCs are identified with rainfall due to passage of synoptic scale systems such as cyclones and depressions (explaining 11-20% variation) and southwest monsoon (SWM) rainfall prior to onset of NEM in October (15% variance explained). PCs representing NEM strength and SWM contribution exhibit contrasting nature of relation with the major climate index Southern Oscillation Index. Relation between the PCs and important regional circulation features, viz., the subtropical ridge at 200 hPa level and the equatorial trough at 850 hPa are used to delineate the PCs associated with SWM and NEM rainfall during October. The study also reveals that the sub-region of north coastal Andhra Pradesh is not benefitted by the over NEM strength but, receives rainfall due to passage of cyclonic disturbances and SWM prior to NEM onset.
Structure‐based alteration of tryptophan residues of the multidrug transporter CmABCB1 to assess substrate binding using fluorescence spectroscopy

ABCB1, also known as P‐glycoprotein, is an essential component of many physiological barriers and extrudes a variety of hydrophobic chemicals out of the cell. Structures of ABCB1 provided insights into the structural changes that occur upon ATP binding and the characteristic architecture of the substrate binding site. Yet, the structure–function relationship between substrate binding and transporting still remains largely obscured because there is no robust method for accurately measuring substrate binding constants. The methods currently used cannot identify whether the bound substrates are located in the inner chamber of the molecule in the transmembrane region or not because of the low spatial resolution. Here, we report a system for measuring the affinity of substrate binding to the Cyanidioschyzon merolae ABCB1 (CmABCB1) using site‐specific tryptophan (Trp) fluorescence quenching. We designed a CmABCB1 mutant with an extrinsic Trp residue introduced into the inner chamber. Trp fluorescence was quenched by three substrates and one inhibitor, including rhodamine 6G, in a saturable fashion, allowing for accurate estimation of the dissociation constant (KD) for each molecule. The KD for rhodamine 6G is similar to that determined using a reciprocal fluorescence quenching assay using rhodamine 6G fluorescence, suggesting that Trp fluorescence of the mutant was quenched by the interaction between the extrinsic Trp and substrates bound in the inner chamber. Structural comparison of the ABCB1 structures suggests that the system presented in this study could be ideal method of choice to determine the substrate binding affinities of compounds bound to the chamber of mammalian ABCB1.
Energy-level alignment and open-circuit voltage at graphene/polymer interfaces: theory and experiment

Functionalized graphene promises to become a key component of novel solar cell architectures, owing to its versatile ability to act either as transparent conductor, electron acceptor, or buffer layer. In spite of this promise, the solar energy conversion efficiency of graphene-based devices falls short of the performance of competing solution-processable photovoltaic technologies. Here we address the question of the maximum achievable open-circuit voltage of all-organic graphene: polymer solar cells using a combined theoretical/experimental approach, going from the atomic scale level to the device level. Our calculations on very large atomistic models of the graphene/polymer interface indicate that the ideal open-circuit voltage approaches one volt, and that epoxide functional groups can have a dramatic effect on the photovoltage. Our predictions are confirmed by direct measurements on complete devices where we control the concentration of functional groups via chemical reduction. Our findings indicate that the selective removal of epoxide groups and the use of ultradisperse polymers are key to achieving graphene solar cells with improved energy conversion efficiency.
Cross-Border E-Commerce Platform Logistics and Supply Chain Network Optimization Based on Deep Learning

E-commerce and logistics are symbioses with each other, but cross-border e-commerce (CBEC) still cannot break away from cross-border logistics. With the progress of economic internationalization, economic and trade ties around the world have become closer and closer, and the level of international business exchanges has been improved. The rise of multinational e-commerce has also caused unprecedented difficulties to multinational logistics and supply chain management. The application of deep neural networks in various fields provides opportunities for cross-border e-commerce platforms to solve these problems. The existing logistics distribution model cannot keep up with the development of CBEC and has become a constraint and bottleneck for the development of CBEC. Therefore, this article introduces deep learning neural network to cross-border logistics and supply chain based on the analysis of the existing cross-border logistics model and supply chain model and the status quo of e-commerce development. It optimizes the existing cross-border logistics and supply chain network in order to break through the current bottleneck in the development of CBEC. This paper shows through research that introducing deep learning neural networks into CBEC logistics and supply chain can improve the efficiency of logistics and supply chain. Compared with the previous efficiency, the efficiency of network optimization can be increased to about 50%, reducing the cost of cross-border logistics and supply chain. The research in this article has great theoretical and guiding significance for the development of CBEC.
Voting Behavior on Carbon Pollution from Power Plants

Environmental regulation is a polarizing issue. In 2014, a bill came to a vote in the U.S. House of Representatives that would limit the powers of the Environmental Protection Agency. This empirical note identifies the characteristics that influenced the voting behavior of House Representatives on this bill. Political party, educational background, the location quotient of the mining industry in the representativeâ€™s state, and the amount of emissions in the Representativeâ€™s state are considered. A memberâ€™s political party is the primary factor influencing voting behavior but the location quotient of the mining industry also plays an important role.
Application Research of the 
 K
 -Means Clustering Algorithm in the Construction of Ideological and Political Education Resource Database in Colleges and Universities Based on 5G

With the advance in the social economy, people’s life has undergone great changes, which brings additional enrichment to people’s lives and also greatly enhances their productivity. The changes brought about by this technology also affect people’s thinking. The ideological and political education in colleges and universities has also ushered in an opportunity for change. The goal of this paper is to investigate the implementation of the K -means clustering algorithm applied in a 5G-based intellectual and technical teaching database in high schools. The results of the demonstration indicate that 29 students in the laboratory group identified more with the work carried out by the school in administrative and practical teaching than the 16 students in the traditional control group, which met everyone’s needs for growth. The superiority of the K -means agglomerative categorization algorithm used for the institution’s educational resource base was verified.
Comparative Whole Genome Analysis of an Anaplasma phagocytophilum Strain Isolated from Norwegian Sheep

Anaplasma phagocytophilum is a Gram-negative obligate intracellular tick-borne alphaproteobacteria (family Anaplasmatacea, order Rickettsiales) with a worldwide distribution. In Norway, tick borne fever (TBF), caused by A. phagocytophilum, presents a major challenge in sheep farming. Despite the abundance of its tick vector, Ixodes ricinus, and A. phagocytophilum infections in wild and domestic animals, reports of infections in humans are low compared with cases in the U.S. Although A. phagocytophilum is genetically diverse and complex infections (co-infection and superinfection) in ruminants and other animals are common, the underlying genetic basis of intra-species interactions and host-specificity remains unexplored. Here, we performed whole genome comparative analysis of a newly cultured Norwegian A. phagocytophilum isolate from sheep (ApSheep_NorV1) with 27 other A. phagocytophilum genome sequences derived from human and animal infections worldwide. Although the compared strains are syntenic, there is remarkable genetic diversity between different genomic loci including the pfam01617 superfamily that encodes the major, neutralization-sensitive, surface antigen Msp2/p44. Blast comparisons between the msp2/p44 pseudogene repertoires from all the strains showed high divergence between U. S. and European strains and even between two Norwegian strains. Based on these comparisons, we concluded that in ruminants, complex infections can be attributed to infection with strains that differ in their msp2/p44 repertoires, which has important implications for pathogen evolution and vaccine development. We also present evidence for integration of rickettsial DNA into the genome of ISE6 tick cells.
IMPACT OF USING DIFFERENT STABILIZERS ON THE PHYSIOCHEMICAL AND SENSORY PROPERTIES OF FUNCTIONAL POMEGRANATE YOGHURT DRINK

The use of different stabilizers to improve the rheological properties of functional pomegranate low fat yoghurt drinks was investigated. Standardized milk (1.5% fat) was supplemented with 2.2% skim milk powder to make yoghurt drink. Modified starch (MS), xanthan gum (XA), sodium carboxy methyl cellulose (CMC) and their mixtures were used as stabilizers. The different formulations for yoghurt drink was prepared from 55% yoghurt, 15% pomegranate juice and 30% cooled sweetened solutions of different stabilizers to give 8% sugar in the final product. The different formulations were heat treated to 75±1° C for 5 min. followed by cooling to 5±1° C. Changes in serum separation, phenolic compounds content (PC), antioxidant activity (AA) as well as chemical, rheological and organoleptic characteristics of the different products were followed during cold storage for 12 days. Generally the different stabilizers had no significant effect (p≤0.05) on the protein, fat contents, PC, AA and pH values of the obtained products. Low fat pomegranate yoghurt drinks with optimum rheological characteristics and minimum serum separation and acceptable sensory properties were obtained by using 1.5% MS or mixture of 0.75% MS and 0.15% CMC. ------------Key
Effects of Smartphone Overdependence and the Quality of Friendship on Depression among High School Students

This study analyzes the effects of smartphone overdependence and the quality of friendship on depression among high school students in Korea. The study subjects were 121 high school students. Smartphone overdependence was assessed using the smartphone overdependence scale for adolescents, depression was assessed using the Korean version of the patient health questionnaire-9, and the quality of friendship was assessed using the friendship quality questionnaire. The results of the correlation analysis showed statistically significant differences among all the variables. The results of the multiple linear regression analysis showed that smartphone overdependence increases the depression level, whereas the quality of friendship lowers it. To decrease depression among high school students, smartphone overdependence and the quality of friendship should be managed appropriately. Moreover, the causal relationships and mediating effects between smartphone overdependence and the quality of friendship should be analyzed.