Miracle-dz/UniFAR-FTUs huggingface dataset:
Agglomeration Effects in the Diffusion of Advanced Manufacturing Technologies

Knowledge spillovers associated with the diffusion of new technologies have long been viewed as an important driver of economic growth. However, this claim has largely resisted econometric scrutiny because patterns of technology adoption are generally not observable. This paper overcomes this problem by exploiting a proprietary panel data set that reports the adoption of 22 advanced manufacturing technologies by 1,902 Canadian plants. The paper starts with documenting the fact that the adoption of these technologies is more highly concentrated geographically (i.e., agglomerated) than other forms of economic activity. Motivated by this fact, I test for knowledge spillovers by investigating how a plant’s probability of adopting a new technology depends on the presence of prior adopters. The results indicate that technology adoption is facilitated by the presence of prior adopters with four characteristics. First, they are adopters of the same technology (as opposed advanced technologies more generally). Second, they reside in the same region. Third, they are similar to the potential adopter in that they purchase a similar set of intermediate goods and services, and finally, they are dissimilar to the potential adopter in that they do not operate in the same product market (i.e., the same 4-digit SIC code). These results are robust to controlling for the effects of regional labour pooling, regional linkages to suppliers and buyers, as well as industry, region, and technology fixed effects. These findings strongly suggest that agglomeration in the adoption of new technologies is driven by knowledge spillovers.
A New Binary Hybrid Cascaded H-Bridge Multi Level Converter for Electric Vehicle Application

Over the years the concept of multilevel inverters has been widely accepted in the zone of high power industrial and commercial applications. The low level inverters create harmonic distortions in the output voltage and high dv/dt stresses leading to electromagnetic compatibility (EMC) issues and higher harmonic distortion results in malfunction of the device or other extra heating losses. A new approach to voltage source multilevel converter has been proposed here which creates a smoother sinusoidal output waveform reducing the THD (Total Harmonic Distortion). As the number of levels increases the number of power semiconducting devices also increased. In this paper, a new inverter is proposed with lower number of switches and sources in order to acquire maximum level. A 15 level inverter which employs three sources and 10 switches has been developed. The new topology is framed out by cascading three half bridge and a single ‘H-bridge configuration. Three isolated dc sources are realized by connecting the battery cells. The voltage level of each source is selected such that it has a binary weighted voltage level. In addition with the reduction of switches, a binary combination of the dc sources is selected for each level to reduce the THD. The performance of proposed converter is evaluated using MATLAB with its Simulink and Simpowersystems. The results verified the performance of the proposed converter.
Data Extraction Techniques for Data on different Environment: A Review

: In the field of computer science, a data structure is a data management, storage and organization format which enables an efficient access and modification. Data Extraction can be defined as retrieving data from source and then further utilizing it as per required necessities. Data Extraction is a process of extracting unstructured, structure semi-structure data from the user requirement based upon data warehouse or web or any type of automation level etc. Structured data is data that has been organized into a formatted repository, typically like a database, so that its elements can be made addressable for more effective processing and analysis. Unstructured data (or say unstructured information) is the information that either does not have a predefined data model or is not organized in a pre-defined manner. This paper will review and give a basic ideal understanding concept overview to existing techniques on data extraction for data on different environment. This review is aimed to discuss different data extraction approaches together with the basic tools algorithms for extracting favour data from different sources mainly such as OCR
LTE-WiFi link Aggregation System Design for LTE-Unlicensed Spectrum utilization in Small-cells

: In order to accommodate the exponentially-growing data traffic, LTE operators are considering the extension of their commercial operations by recognizing the opportunity offered both Wi-Fi technology and unlicensed spectrum for traffic offload. In a recent technical report for the third Generation Partnership Project, LTE-Wi-Fi link Aggregation (LWA) to support the unlicensed spectrum has been introduced. The report and other works in the literature reveal coexistence challenges that are raised when the LTE system coexists with other technologies such as Wi-Fi, with fairness being the primary challenge. In this work, we propose a preliminary design for a semi-distributed LTE-Unlicensed scheme where the LTE Base Station uses Wi-Fi-like carrier sense and Quality of Service facility techniques. Also describes LTE-Wi-Fi Aggregation architecture and operation that LWA can provide comparable performance to other technology innovations for the use of unlicensed bands. Concentrated focus is given to LWA deployment consideration to address operator desire to deploy the technology with minimum impact to the legacy Wi-Fi and LTE small cells infrastructure . ---------------------------------------------------------------------------------------------------------------------------------------
Implicit and Explicit Social Recommendation Using Machine Learning Framework

: Now a day’s recommendation system plays important role. The search for recommendations aims to exploit information to improve the quality of a system of recommendations. It tends to be partitioned into two classes. The explicit recommendation presupposes the existence not only of user ratings in articles, but also explicit social connections between users. The implicit recommendation presupposes the availability only of qualifications, but no social connection between users and attempts to infer implicit social connections between users to promote accuracy of the recommendation. This paper proposes a framework applicable to explicit and implicit recommendation. Author propose an improvement structure to together get familiar with the level of social relationship and the forecast of capability, so these two assignments can commonly upgrade each other's performance. Furthermore, a well-known challenge for the implicit recommendation is it takes a quadratic time to learn the strength of connections. This paper also offers several practical tricks to reduce the complexity of our model must be linear with respect to the observed evaluations. Experiments show that the proposed model, with only two Parameters can significantly exceed state-of-the-art solutions for both explicit and implicit social recommendation systems.
Edge Detection using Distributed Canny Edge Detector

: Edges of an image represents significant local changes of intensity. Edges mostly occur on the boundary between two different regions in an image. Edge detection is the process of segmenting images based on abrupt changes in intensity. Important features can be extracted from the edges of an image. Canny edge detector is a type of edge detector which is used to extract useful structural information from images and dramatically reduces the amount of data to be processed. When compared with other edge detection algorithms, Canny edge detector is computationally more intensive and also has higher latency. To solve this problem, we present Distributed Canny Edge Detection Algorithm. The entire image is classified into different blocks and adaptively computes the edge detection thresholds based on block type. The resulting distributed algorithm has significantly reduced latency and also has better edge detection performance than original Canny edge detector. All this implementation is done using Matlab.
Population Genetics of Boise Basin Bull Trout (Salvelinus confluentus)

We analyzed the population genetic structure of bull trout ( Salvelinus confluentus ) in the Boise River Basin, Idaho. We determined the influence of contemporary (including anthropogenic) and historic factors on genetic structure, taking into account existing data on bull trout habitat patches in this basin. We tested three models of the organization of genetic structure in this system, where genetic structure would: a) parallel the stream hierarchy, b) correspond to habitat patch structure, or c) follow a pattern of isolation by distance. We found strongest support for the isolation by distance model. In addition, we found weak population differentiation within the Boise system ( F ST = 0.064), relative to other similarly scaled systems containing bull trout. Frequent disturbance may be responsible for the strong isolation by distance yet weak overall levels of population subdivision in this system. In addition, we found that the South Fork was a genetic outlier from the remainder of the Boise system and may have been colonized at a separate time than the Middle and North Forks. At least one dam (Kirby Dam) has noticeably reduced levels of gene flow. These results show that distinct patterns of genetic structure may occur in separate portions of a species’ range, especially at the range extremes. Based on these results, we suggest the South Fork should be treated separately from the remainder of the basin. Bull trout in Mores Creek should be protected but our results suggest that this population may be comprised of adults entrained by Arrowrock Dam and unable to return to natal streams to spawn and are thus spawning in the only available habitat. Connectivity should be restored whenever possible in cases where human activities have eliminated migratory corridors.
Physical and psychological health domains of quality of life in type 2 diabetic patients in relation to clinical factors of diabetes mellitus in Egypt

Quality of life is an important aspect in diabetes because the poor quality of life leads to diminished selfcare, which in turn leads to worsened glycemic control, increased risks for complications, and exacerbation of diabetes overwhelming in both the short run and the long run. The aim of the study was to assess physical and psychological health domains of the quality of life in type 2 diabetic patients in relation to clinical factors of diabetes mellitus in Egypt. A cross-sectional analytic study design was utilized in this study. This study was conducted at the Family Medicine Outpatient Clinic of Suez Canal University Hospitals in Ismailia city, Egypt. 143 type 2 diabetic patients were included who agreed to participate in this study and were selected using probability systematic sampling technique. Two tools were used to collect data; structured interviewing and World Health Organization Quality of Life Questionnaire abbreviated version were used to collect data. Revealed that the mean age of the study group was 54.74 (SD = 7.32) years and the majority of them were females. Factors related to lower quality of life in the present study were obesity, cigarette smoking, physical inactivity, and poor glycemic control. In conclusion, type 2 DM has negatively affected all domains of quality of life of the study group.
UvA-DARE (Digital Academic Repository) Training Interaction Skills of Pre-service ECEC Teachers Moving from in-Service to Pre-service Professional Development

Whereas various studies have examined the effects of in-service training, relatively little is known about effective approaches for improving interaction skills among pre-service teachers for early childhood education and care. In this study, the evidence-based in-service Caregiver Interaction Profile training course was implemented in Dutch pre-service teacher training. The pre-service teachers on the program with relatively low scores at the pretest showed a significant growth in relation to four interaction skills: sensitive responsiveness, respect for autonomy, structuring and limit setting, and verbal communication. We discuss these results in the context of effective professional development in early childhood education and care.
A novel microRNA signature for pathological grading in lung adenocarcinoma based on TCGA and GEO data

Lung adenocarcinoma (LUAD) is one of the most common types of lung cancer and its poor prognosis largely depends on the tumor pathological stage. Critical roles of microRNAs (miRNAs) have been reported in the tumorigenesis and progression of lung cancer. However, whether the differential expression pattern of miRNAs could be used to distinguish early-stage (stage I) from mid-late-stage (stages II–IV) LUAD tumors is still unclear. In this study, clinical information and miRNA expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. TCGA-LUAD (n=470) dataset was used for model training and validation, and the GSE62182 (n=94) and GSE83527 (n=36) datasets were used as external independent test datasets. The diagnostic model was created through miRNA feature selection followed by SVM classifier and was confirmed by 5-fold cross-validation. A receiver operating characteristic curve was calculated to evaluate the accuracy and robustness of the model. Using the DX score and LIBSVM tool, a 16-miRNA signature that could distinguish LUAD pathological stages was identified. The area under the curve rates were 0.62 [95% confidence interval (CI): 0.56–0.67], 0.66 (95% CI: 0.54–0.76) and 0.63 (95% CI: 0.43–0.82) in TCGA-LUAD internal validation dataset, the GSE62182 external validation dataset, and the GSE83527 external validation dataset, respectively. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses suggested that the target genes of the 16-miRNA signature were mainly involved in metabolic pathways. The present findings demonstrate that a 16-miRNA signature could serve as a promising diagnostic biomarker for pathological staging in LUAD.
Various Methods of Using Content-Based Filtering Algorithm for Recommender Systems

: Recommender systems play an active role in many applications for generating personalized recommendations to the customers. In that the content-based filtering recommend items based on the content analysis and the user profile and the collaborative filtering recommend items based on the user ratings. This paper presents a survey on various ways in which the content-based filtering algorithms optimized for effective recommendations to the customers. The content-based filtering algorithm predicts the suggestions for the user according to the user’s interest.
Diminishing DC link voltage variation in DVR using load current feed forward

For voltage sensitive industrial applications a momentary disturbance in grid voltage interrupts the production line. Dynamic voltage restorer is used as a solution to ride through such conditions. The paper presents a DVR with fast and accurate dynamic response. The operation and performance of DVR is highly influenced by the control algorithm implemented. The paper presents control architecture for DVR using Proportional Resonant controller. DVR topology with an active shunt converter connected to load side is used in this paper. DC link voltage variation during sudden load changes and sag/swell occurrences is diminished by an active current feed forward algorithm using moving average filter. The performance is verified experimentally at field trial in a textile mill with numerous non-linear loads and sophisticated electronic equipment which are sensitive to the quality of power.
Anonymous Routing in Trust Valued MANETs Prior to the Deployment of Onion Routing

: Invisible property is very important for communication of the confidential data which is usually applied in mobile adhoc networks (MANETS) and it is a tedious task to have unidentifiability and unlinkability in structureless network. In existing system several anonymous routing protocol have been proposed but still there are no satisfactory results that have high packet transmission based on trustworthiness. Therefore this paper proposes an effective trustworthy algorithm which is explained in brief as part of onion routing process which gives effective increment of the packet security exploitation from the middling nodes which is to be forwarded to final destination, which gives efficient anonymicity of the packet carrying the confidential data. It also explains the tor-onion routers, its traffic, hidden services, abuses and attacks in tor network.
Identification of Cuts in Wireless Sensor Networks Scientific Through Spectral Classification

: Wireless sensor networks (WSNs) often suffer from the disrupted connectivity due to unpredictable wireless channels, early depletion of node energy, and physical tampering by hostile users. The existence of a disconnected segment of the network referred to as network cut, leads to data loss, wasted power consumption, and congestion in the WSN. However, existing approaches to network cut detection in the WSN rely on the assumption that a node or a link either works normally or fails, without considering the uncertain and random features of wireless links in the WSN. In this paper, we extend the notion of the network cut based on the realistic wireless channel model. Furthermore, we formulate the problem of minimizing the normalized cut (Ncut) with critical nodes, considering the quality of wireless links, degree weights, and different priorities of sensor nodes. Then, we propose a network cut identification algorithm and dominant eigenvector computation algorithm that efficiently identify multiple network cuts by computing multiple Eigen values and eigenvectors according to a given parameter of eigen value gap. Extensive simulations are conducted to examine the effectiveness and robustness of the proposed approach. The results show that the proposed method strikes a balance between minimizing the Ncut objective and the degree of disconnection of critical nodes and achieves a better performance than existing algorithm.
A Preliminary Assessment into the Use of Composites in Large High-Speed Vessels

Weight is a critical factor to be considered in the marine industry. Redundancy of weight for high-speed light craft results in a reduction in fuel consumption and hence operating costs. Composites are, therefore, seen as a possible future construction material of choice in the marine industry due to the high strength to weight ratio achievable. Carbon fibre reinforced polymer (CFRP) sandwich composite panels provide high stiffness and strength at a reduced weight when compared to aluminium. This project conducts a first principle analysis on the technical viability of large composite vessels through the use of four discrete size vessels; 60m, 80m, 100m and 120m. Aluminium and composite midships for each vessel size have been designed to comply with the longitudinal bending high-speed craft are subjected to during hogging and sagging moments. A weight comparison for the hull and bridge deck was conducted between the aluminium and composite midships at each vessel weight. The comparison concluded that an approximate weight reduction of 50% may be achievable through the use of CFRP composite constructions.
NLRC5, a valuable marker for the diagnosis and prognostic assessment of hepatocellular carcinoma

Background NLR family CARD domain containing 5 (NLRC5) is involved the initiation and progression of several cancers. However, its role in hepatocellular carcinoma (HCC) is still unclear. This study aimed to explore the expression, clinical significance, and regulated gene sets of NLRC5 in HCC. Methods Data related to NLRC5 was extracted from The Cancer Genome Atlas (TCGA) database and analyzed. Quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to verify the NLRC5 mRNA expression in HCC. Immunohistochemistry (IHC) and western blot were performed to detect the NLRC5 protein level in HCC. The clinical significance of NLRC5 was investigated after separating patients into NLRC5-positive and NLRC5-negative groups based on the IHC results. Gene set enrichment analysis (GSEA) was performed to detect gene sets regulated by NLRC5 in HCC. Results Increased NLRC5 mRNA and protein expression were found in HCC tissues compared to paracancerous tissues. Moreover, enhanced NLRC5 protein expression was associated with a higher presence rate of cirrhosis, a higher TNM stage, and a shorter 3-year overall survival (OS) of HCC participants. Finally, gene sets related to cancer metastasis were up-regulated in the NLRC5 high phenotype. Conclusions NLRC5 is a potential marker for the diagnosis and prognostic assessment of HCC.
Maximum Power Point Optimization of Photo-voltaic System using Active Disturbance Rejection Control

Photovoltaic technology based power generation system is the growing form of energy conversion generating system resulting in electrical power output. It is susceptible to the impact of solar radiation, external temperature and seasonal changes. There are many indecisions.Active Disturbance Rejection Control (ADRC) technology if applied to solar PV systems to control of DC-DC Converter can help in performance improvement. In the system model, uncertainties and external disturbances are unified to effect the system with unknown interference. An observer is used for dynamic observation of the disturbances. The nonlinear state error feedback the control strategies are used to compensate the system. So, it doesn’t affect the parameters within the system and unknown disturbance. It will simplify the process of the control system. Simulation results show that the design of ADRC can achieve the desired control effect, and has good stability and robustness.
ORGANIC ANIONS PRODUCTION BY BACILLUS SP. TO ENHANCE MAIZE AND MILLET GROWTH

In this study, four bacterial isolates, YIId (KT764030), YIC (KT764029), S2 (KT764028) and O2 (KT764027) were selected on the basis of their broad range Zinc Oxide and Zinc Phosphate solubilising and antimicrobial activity against indicator bacterial strains. All four isolates were able to solubilise Zinc Phosphate and Zinc Oxide. The solubilisation activity is mostly related to the production of organic anions, so the production of Gluconic acid was confirmed by HPLC. Plant growth experiments were performed and the findings revealed very promising results from selected bacterial isolates YIC and YIId. The Maize seedlings showed 51% increase in seedling length, when treated with bacterial isolate YIC. The maximum increase approximately 39% and 36% in Millet seedling length was recorded as compared to the control when treated with bacterial isolate YIC, Zinc oxide and bacterial isolate YIId, zinc oxide and zinc phosphate, respectively. The result showed an increase in seedling biomass. Millet plants showed maximum soluble protein content (357.7 μg ml-1) when treated with YIC, ZnO and Zn3(PO4)2 as compared to control (99.8 μg ml-1). The free proline content of Maize plants was significantly increased when inoculated with bacterial isolate YIId (562.68 μg ml-1) as compared to control (316.52 μg ml-1). It was concluded that selected PGPR can be used to enhance plant yield and biomass by producing organic anions to solubilize zinc and phosphate salts.