Miracle-dz/UniFAR-FTUs huggingface dataset:
Defect Detection on 3D Print Products and in Concrete Structures Using Image Processing and Convolution Neural Network

This paper explores the automated detection of surface defects on 3-D printed products and concrete structures. They are the main factors to evaluate their quality in addition to dimension and roughness. Traditional detection by human inspectors is far from satisfactory. Manual inspection is time-consuming, error-prone and often leads to loss of resources. For this purpose, image processing and deep learning-based object detection adopted by Google Cloud Machine Learning (ML) Engine is used to detect surface defects. In the case of image processing, two approaches are presented in this paper. In both cases, pixels are being considered to differentiate a smooth or rough surface from a picture taken by a USB camera. For the deep learning- based solution, MobileNet -a base convolution neural network treated as an image feature extractor in combination with Single Shot MultiBox Detector (SSD) as an object detector hence MobileNet-SSD. The model was successfully trained on the Google Cloud ML Engine with the dataset of 20000+ images. The review of the results confirms that with the help of MobileNet-SSD can automatically detect surface defects more accurately and rapidly than conventional deep learning methods.
Knowledge, Oligopoly and Labour in Global Value Chains

The article explores knowledge in global value chains (GVCs) and its correspondence with the nature of employment in different GVC segments. It starts with the role of knowledge that is protected through intellectual property rights in creating oligopolies in product markets, which are then re-created as oligopolies in the input markets. Knowledge requirements, transmitted through governance relations and the distribution of power within GVCs, lead to the inter-firm distribution of profits within GVCs, and result in differing qualities of employment corresponding to the level of knowledge required in different production segments.
Genetic Variation in Early Growth and Bud Production among Natural Populations of Fraser Fir

A series of open-pollinated progeny tests of Fraser fir (Abies fraseri (Pursh) Poir. ) was analyzed to determine natural variation among six geographic seed sources and to estimate genetic parameters for traits important in Christmas tree production. Highly significant differences were found among seed sources and families within sources (P # 0.0001) for height after 4 years in the field. The individual-tree within-population heritability values for the traits measured in Year 4 ranged from 0 to 0.44, with height having the highest heritability, overall tree quality having a heritability of 0.14, and bud and branching traits having varying heritabilities. Heritability values for height at age 4 varied greatly among the six sources, from 0.15 for the Black Mountains to 0.67 for the Great Smoky Mountains. Highly significant seed source · site interactions as well as family within source · site interactions existed for height. Stability variance analysis, after removing the environmental heterogeneity, showed significant instability across the test sites for two of the six seed sources for height after 4 years, and some rank changes occurred. The high heritability values for height indicate that economically important genetic gains can be made in Fraser fir for Christmas tree production because of the importance of height in determining Christmas tree value.
Automatic Quality of Experience Management for WLAN Networks using Multi-Armed Bandit

Providing acceptable Quality of Experience (QoE) in Wireless Local Area Network (WLAN) is very difficult: home networks are managed by non-technical people, and the proprietary management solutions of enterprise networks usually do not incorporate QoE mechanisms. Due to these difficulties, automatic QoE management mechanisms are welcome. This paper presents control loops capable of changing the power and transmission channels in the WLAN, based on Software Defined Wireless Networks and reinforcement learning, in order to improve user satisfaction for Web applications. A prototype evaluates our proposal in three case studies with a web browsing application, in which several access points are controlled by a central controller or by independent controllers. Our results show that the control loop can improve the Mean Opinion Score (MOS) by at least 4 % in the worst case, and 167 % in the best case, thus benefiting the user. Further, the control loop also reduced in page load time by 25 % in the worst case, and 233 % in the best case.
Vibration suppression and shape change of thin plate by clusters of actuators

Presented field in this paper is vibration suppression of the plate with heavily distributed grid of the piezo actuators and sensors. Such mechatronic smart materials have potential in many different field of use. For example, morphing wing of the plane [3]. The classic wing of the aircraft uses flaps, which are mechanically positioned. The morphing wing, on the other hand, is a single element that has the ability to change its shape. This eliminates the need for gears, rods and other mechanical components. Its advantage is uniformity, which gives the wing better aerodynamic properties. There are several possibilities how to shape the wing into the desired shape. One possibility is to use smart material with active elements to clad the pliable internal structure of the wing. Another advantage of such material is the possibility of active vibration damping in the wing. One of the vibration cases studied is flutter. Flutter phenomena are seen when vibrations occurring in an aircraft match the natural frequency of the structure. If they aren’t properly damped, the oscillations can increase in amplitude, leading to structural damage or even failure. Work focuses on the research of creating such a smart material that is able to suppress vibration or even change its shape. Studied way is that the base material is equipped with a uniform network of piezo sensors and actuators [4] Fig. 1, which are divided into individual units (clusters Fig. 3). It is then possible to design a single cluster control and apply it to a large structure, such as the wing of aircraft.
Heavy metals concentration in surface soils of sewage irrigated areas at Nathnagar block, Bhagalpur district, Bihar, India

The present investigation was carried out on “Heavy metals concentration in surface soils of sewage irrigated areas at Nathnagar block, Bhagalpur district, Bihar, India”. Soil is considered as a potential sink for pollutants and also an important means of transmittance for many pollutants to the atmosphere, groundwater and plants (Chen et al., 1997). Heavy metals and metalloids affect human health directly and indirectly as they are non-biodegradable in nature (Wang et al., 2001). The DTPA-micronutrients (DTPA-Fe, Mn, Zn and Cu) of surface soils from sewage irrigated areas have been evaluated and results revealed that DTPA-Fe of sewage irrigated surface soils varied from 10.29 to 28.19 mg kg -1 with mean 3.88 mg kg-1. The DTPA-Mn of sewage irrigated surface soils varied from 0.67 mg kg-1 to 15.08 mg kg-1 with mean 5.30 mg kg-1. The DTPA-Zn and Cu content in surface soil varied from 0.62 mg kg-1 to 3.37 mg kg-1 and 1.01 mg kg-1 to 68.58 mg kg-1 respectively, whereas the As content varied from 0.10 to 0.68 mg kg-1 with mean 0.35 mg kg-1and Cd content varied from 0.08 mg kg -1 to 20.55 mg kg -1 with mean 0.49 mg kg -1. The range of Pb content varied from 0.37 mg kg-1 to 6.83 mg kg-1 with mean 4.03 mg kg-1. Over all it can be concluded that selected surface soil samples accumulate high amount of heavy metals (Fe, Mn, Zn, Cu, As, Cd and Pb).
Circulating Proteomic Signatures of Chronological

To elucidate the proteomic features of aging in plasma, the subproteome targeted by the SOMAscan assay was profiled in blood samples from 202 females from the TwinsUK cohort. Findings were replicated in 677 independent individuals from the AddNeuroMed, Alzheimer’s Research UK, and Dementia Case Registry cohorts. Results were further validated using RNAseq data from whole blood in TwinsUK and the most significant proteins were tested for association with aging-related phenotypes after adjustment for age. Eleven proteins were associated with chronological age and were replicated at protein level in an independent population. These were further investigated at gene expression level in 384 females from the TwinsUK cohort. The two most strongly associated proteins were chordin-like protein 1 (meta-analysis β [SE] = 0.013 [0.001], p = 3.66 × 10−46) and pleiotrophin (0.012 [0.005], p = 3.88 × 10−41). Chordin-like protein 1 was also significantly correlated with birthweight (0.06 [0.02], p = 0.005) and with the individual Framingham 10-years cardiovascular risk scores in TwinsUK (0.71 [0.18], p = 9.9 × 10−5). Pleiotrophin is a secreted growth factor with a plethora of functions in multiple tissues and known to be a marker for cardiovascular risk and osteoporosis. Our study highlights the importance of proteomics to identify some molecular mechanisms involved in human health and aging.
Use of Viability PCR in Food Pathogen Diagnostics

Author Title Number of Pages Date Sini-Riikka Katajamäki The Use of Viability PCR in Food Pathogen Diagnostics 47 pages 29 October 2016 Degree Bachelor of Laboratory Services Degree Programme Laboratory Sciences Instructors Mikko Kauppinen, Senior R&D Scientist Tiina Soininen, Senior Lecturer This study was conducted for Thermo Fisher Scientific aiming to the development of a method for the exclusive detection of viable bacteria from food samples, using viability PCR. Real-time PCR (qPCR) is readily used in food diagnostics for its speed, simplicity and specificity. However, the method lacks the ability to differentiate between viable and dead pathogens. To exclude the signals originating from dead cells, dyes that penetrate compromised cell membranes and have a high affinity to bind covalently to nucleic acids are used to inhibit the amplification of DNA modified by the dye. In this study the function of two commercially available viability dyes was evaluated with both genomic DNA and cells. The dye’s specificity and the contribution of different factors on inhibition efficiency were analyzed in order to find the most optimal conditions for efficient dead-cell signal suppression. The effect of dye and sample concentration and composition, incubation and photoactivation conditions, amplicon length and detection chemistry used were evaluated. The experiments showed that the inhibition of dead-cell signals was incomplete with one amplicon routinely used in pathogen detection. The length of target amplicon was observed to significantly affect the efficiency of viability dye and the detection rate of deadcell DNA was also highly dependent on the qPCR detection chemistry used. The results suggested that low reagent concentration was the most effective when suppressing signals from cells. A longer amplicon and an elongated photoactivation period could further enhance the inhibition efficiency. The results obtained show that by increasing the amplicon length, using a low dye concentration and further optimizing the PCR protocol, efficient inhibition of dead-cell DNA could be achieved in food matrices.
Therapeutic risk-taking: A justifiable choice

Summary Taking risks is a fundamental part of the human experience that supports personal growth. Therapeutic risk-taking enables patients to make decisions about their level of safety and to pursue goals. Promoting therapeutic risk-taking can be complex. Professionals can experience tension striving for a balance between the interests of the individual and societal pressures to control risk. This article examines therapeutic risk-taking, recognising the challenges to supporting it in practice and debating how they may be overcome. Learning Objectives • Be able to explain what therapeutic risk-taking is and discuss its application to psychiatric practice • Understand the factors that challenge and enable therapeutic risk-taking in psychiatric practice • Describe the association between therapeutic risk-taking and recovery
Development and nutritional evaluation of low glycemic index recipe from ragi and pulses for diabetes

Refined carbohydrates, such as white rice and white flour, are the mainstay of the modern Asian Indian diet and may contribute to the rising incidence of Type- 2 diabetes and other life style diseases in this population. Low-glycemic index foods and high fibre content might play a significant role as functional foods in reducing the risk of such chronic diseases such as diabetes, cardiovascular diseases, obesity and cancer etc. In this study, two breakfast/ lunch items were targeted to modify and develop a low glycemic index recipe that can be consumed by diabetic individual. The prepared foods were investigated for organoleptic evaluation and proximate composition (moisture, protein, fat, crude fibre and ash content). Both the prepared products were organoleptically fell in the category of “liked very much” and nutritionally have a good potential for use as low glycemic food. The concept of glycemic index of various foods has emerged as a boon to dietary therapy of diabetes mellitus, indicating the beneficial aspect of foods consumed both individually and as mixed meals.
Local Orthogonality Preserving Alignment for Nonlinear Dimensionality Reduction

We present a new manifold learning algorithm called Local Orthogonality Preserving Alignment (LOPA). Our algorithm is inspired by the Local Tangent Space Alignment (LTSA) method that aims to align multiple local neighborhoods into a global coordinate system using affine transformations. However, LTSA often fails to preserve original geometric quantities such as distances and angles. Although an iterative alignment procedure for preserving orthogonality was suggested by the authors of LTSA, neither the corresponding initialization nor the experiments were given. Procrustes Subspaces Alignment (PSA) implements the orthogonality preserving idea by estimating each rotation transformation separately with simulated annealing. However, the optimization in PSA is complicated and multiple separated local rotations may produce globally contradictive results. To address these difficulties, we first use the pseudo-inverse trick of LTSA to represent each local orthogonal transformations with the unified global coordinates. Then the orthogonality constraints are relaxed to be an instance of semidefinite programming (SDP). Finally a two-step iterative procedure is employed to further reduce the errors in orthogonal constraints. Extensive experiments show that LOPA can faithfully preserve distances, angles, inner products, and neighborhoods of the original data sets. In comparison, the embedding performance of LOPA is better than PSA and comparable to stateof-the-art algorithms like MVU and MVE, while the complexity of LOPA is significantly lower than other competing methods.
Incremental Mixture Importance Sampling With Shotgun Optimization

Abstract This article proposes a general optimization strategy, which combines results from different optimization or parameter estimation methods to overcome shortcomings of a single method. Shotgun optimization is developed as a framework which employs different optimization strategies, criteria, or conditional targets to enable wider likelihood exploration. The introduced shotgun optimization approach is embedded into an incremental mixture importance sampling algorithm to produce improved posterior samples for multimodal densities and creates robustness in cases where the likelihood and prior are in disagreement. Despite using different optimization approaches, the samples are combined into samples from a single target posterior. The diversity of the framework is demonstrated on parameter estimation from differential equation models employing diverse strategies including numerical solutions and approximations thereof. Additionally the approach is demonstrated on mixtures of discrete and continuous parameters and is shown to ease estimation from synthetic likelihood models. R code of the implemented examples can be found at https://github.com/BiljanaJSJ/IMIS-ShOpt. Supplementary materials for this article are available online.
Insulating behavior of an amorphous graphene membrane

threefold coordinated networks consisting of hexagonal rings but also includingmany pentagons and heptagons distributed in a random fashion. Using the Kubo transport methodology andthe Lanczos method, the density of states, mean free paths, and semiclassical conductivities of such amorphousgraphene membranes are computed. Despite a large increase in the density of states close to the charge neutralitypoint, all electronic properties are dramatically degraded, evidencing an Anderson insulating state caused bytopological disorder alone. These results are supported by Landauer-Buttiker conductance calculations, which¨show a localization length as short as 5 nm.DOI: 10.1103/PhysRevB.86. 121408 PACS number(s): 72
Impedance hand controllers for increasing efficiency in teleoperations

An impedance hand controller with direct force feedback is examined as an alternative to bilateral force reflection in teleoperations involving force contact. Experimentation revealed an operator preference for direct force feedback which provided a better feel of contact with the environment. The advantages of variable arm impedance were also made clear in tracking tests where subjects preferred the larger hand controller inertias made possible by the acceleration feedback loop in the master arm. The ability to decouple the hand controller impedance from the slave arm dynamics is expected to be even more significant when the inertial properties of various payloads in the slave arm are considered.
Outlier Analysis in Sensor Data Streams: A case study

In this paper, a method to detect outliers is described in a data oriented manner (rather than algorithmoriented manner) . This method consists of application of Class Outliers: Distance Based (CODB) and Hoeffding tree algorithm. Subsequently, machine learning models were built to detect outlier in data streams. The case study outlined in this paper could be used for building effective and real-time methodologies for outlier detection.
Ingredients for Happiness: Modeling constructs via semi-supervised content driven inductive transfer

Modeling aﬀect via understanding the social constructs behind them is an important task in devising robust and accurate systems for socially relevant scenarios. In the CL-Aﬀ Shared Task (part of Aﬀec-tive Content Analysis workshop @ AAAI 2019), the organizers released a dataset of ‘happy’ moments, called the HappyDB corpus. The task is to detect two social constructs: the agency (i.e., whether the author is in control of the happy moment) and the social characteristics (i.e., whether anyone else other than the author was also involved in the happy moment). We employ an inductive transfer learning technique where we utilize a pre-trained language model and ﬁne-tune it on the target task for both the binary classiﬁcation tasks. At ﬁrst, we use a language model pre-trained on the huge WikiText-103 corpus. This step utilizes an AWD-LSTM with three hidden layers for training the language model. In the second step, we ﬁne-tune the pre-trained language model on both the labeled and unlabeled instances from the HappyDB dataset. Finally, we train a classiﬁer on top of the language model for each of the identiﬁcation tasks. Our experiments using 10-fold cross validation on the corpus show that we achieve a high accuracy of ∼ 93% for detection of the social characteristic and ∼ 87% for agency of the author, showing signiﬁcant gains over other baselines. We also show that using the unlabeled dataset for ﬁne-tuning the language model in the second step improves our accuracy by 1-2% across detection of both the constructs.
Changes in the concentration of urea in whey related to udder health status of goat

A total of 60 samples of goat milk from udder halves were analysed. Based on the score of California Mastitis Test (CMT), the samples were equally grouped into Normal, Subclinical 1+, Subclinical 2+, Subclinical 3+ and Clinical. The average of urea concentration in whey was highest (33.96 ± 0.96 mg/dl) in the normal group, followed by Subclinical 1+ (24.25 ± 0.97 mg/dl), Subclinical 2+ (14.55 ± 0.61 mg/dl), Subclinical 3+ (12.96 ± 0.46 mg/dl) and lowest (9.42 ± 0.33 mg/dl) in the Clinical group. The results showed significant decrease in urea concentration with an increase in the severity of mastitis. The urea concentration of normal group, subclinical combined group and clinical group ranged from 32.00 to 39 mg/dl, 10.50 to 30.00 mg/dl and 9.50 to 12.50 mg/dl respectively.
Genetic variation in TGFB 1 gene and risk of idiopathic recurrent pregnancy loss

Transforming growth factor b1 plays a significant role in pregnancy outcome. We investigated the association of TGFB1 exon 1 (rs1800471, rs1800470) and promoter region (rs1800469, rs1800468) polymorphisms with recurrent pregnancy loss (RPL) in 675 Tunisian women: 304 women with a history of three consecutive pregnancy losses of unknown etiology with the same partner and 371 age-matched multiparous control women. TGFB1 genotyping was done by TaqMan assays. Higher minor allele frequency for rs1800471 (P , 0.001), but not for rs1800470, rs1800469 or rs1800468 was found in RPL cases compared with controls. A significant difference in the distribution of rs1800471 genotypes was seen between the RPL cases and control women, irrespective of the genetic model used. Increased RPL risk was seen with rs1800471 allele C in the heterozygous state and to a greater degree in the homozygous state, thus establishing a dose-dependent effect. Haploview analysis revealed differential linkage disequilibrium between the TGFB1 single-nucleotide polymorphisms analyzed. TGFB1 haplotype analysis identified eight common haplotypes (rs1800471/rs1800470/rs1800469/rs1800468) with three (GTTG, Pc 1⁄4 0.02; CCTG, Pc 1⁄4 0.02 and CTCG, Pc 1⁄4 0.02) positively associated with RPL and one (GCCG, Pc 1⁄4 0.009) negatively associated with RPL. This study provides the first evidence that the TGFB1 genotype may influence RPL.
Universitet A-, Band C-type prymnesins are clade specific compounds and chemotaxonomic markers in Prymnesium

Harmful blooms formed by planktonic microalgae (HABs) in both freshwater and coastal waters regularly lead to severe mortalities of fish and invertebrates causing substantial economic losses of marine products worldwide. The mixotrophic haptophyte Prymnesium parvum is one of the most important microalgae associated with fish kills. Here 26 strains of P. parvum with a wide geographical distribution were screened for the production of prymnesins, the suspected causative allelochemical toxins. All investigated strains produced prymnesins, indicating that the toxins play an important role for the organism. The prymnesins can be classified into three types based on the length of the carbon backbone of the compound and each algal strain produced only one of these types. Biogeographical mapping of the prymnesin distribution indicated a global distribution of each type. In addition, phylogenetic analyses based on internal transcribed spacer (ITS) sequences revealed monophyletic origin of all prymnesin types and clades could therefore be defined based on the toxic compound. It might be that evolution of new species within the P. parvum species complex is driven by changes in toxin type or that they are a result of it. Such a correlation between chemotype and phylotype has never been documented before for a harmful microalga. Chemotaxonomy and ITS-type classification may thus be used to further delimit the P. parvum species complex.