Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of neural networks systems on FPGA for feature detection

Nowadays Neural Networks are fast developing for a large number of applications. In particular, the detection of features inside images is an extensively investigated task for its many purposes. Inside each image the network can recognize patterns, features and events that may be used to automatically monitor the ﬁeld of view, as well as taking decisions without the intervention of an external human operator. For fast detection purposes, high speed cameras are usually employed, which can generate a huge stream of data in a very short time. The information stored inside these bits may exceed the transmission capabilities of state-of-the-art systems. In order to apply the beneﬁts that a Neural Network can supply to a camera system without having to compress the information, FPGA-based circuits have been developed, which can faster emulate the behavior of neural algorithms requiring less space and power. While the solution to integrate such features on FPGA already exists, each camera system presents speciﬁc hardware implementation which needs to be combined with the existing characteristics of the network. Moreover, the network response to incoming data can be used inside the device to develop new features. The work of this thesis consists in extending the existing FPGA framework in order to include new functionalities related to the Neural Network embedded block to the system. A new technique to feed data to the accelerator has been proposed. With the incoming data, a new block has been designed inside the framework to state whether a signiﬁcant change has occurred inside the ﬁeld of view, with the possibility of using this signal to trigger data acquisition. During the work, a review of the existing sensor interface has resulted necessary in order to proceed further with the hardware on disposal. This part consisted in adapting the project design to a diﬀerent model of FPGA in use, which has been successfully included inside the latest version. As far as it concerns the data processing, much care was spent to respect the preexistent characteristics of the framework, which presented an already functioning apparatus able to capture images using the VIA1M CMOS sensor developed inside CSEM company. In order to extend the functionalities, the thesis has also focused on understanding the previous work done, trying to implemented the new features as compatible as possible with the already included framework.
Capacity of the common Arctic picoeukaryote Micromonas to adapt to a warming ocean

Phytoplankton are sensitive to temperature and other environmental conditions expected to change with warming over the next century. We quantified the capacity of an ecologically dominant Arctic phytoplankton species, Micromonas polaris, to adapt to changes in temperature, increased temperature and irradiance, and increased temperature and periodic nitrogen starvation, over several hundred generations. When originally isolated, this strain of Micromonas had its maximum growth rate at 6°C, and its growth rate declined above 10°C. We find an evolutionary increase in growth rate, with the largest increases associated with the elevated temperature treatments, especially when combined with repeated nitrate starvation. After several hundred generations of exposure, the growth rate of Micromonas under 13°C almost doubled and was higher than under 6°C. This increase in growth rate is consistent with the Arrhenius model of temperature effects on metabolism and suggests a general hypothesis for the evolutionary potential of phytoplankton to respond evolutionarily to temperature change.
The complete chloroplast genome sequence of Altingia yunnanensis

Abstract The first complete chloroplast genome (cpDNA) sequence of Altingia yunnanensis was determined from Illumina HiSeq pair-end sequencing data in this study. The cpDNA is 160,860 bp in length, contains a large single-copy region (LSC) of 89,162 bp and a small single-copy region (SSC) of 19,008 bp, which were separated by a pair of inverted repeat (IR) regions of 26,325 bp each. The genome contains 130 genes, including 85 protein-coding genes, 8 ribosomal RNA genes, and 37 transfer RNA genes. Further, the phylogenomic analysis showed that A. yunnanensis and Altingia excelsa clustered in a clade in Saxifragales order.
Comparative Study for Outlier Detection In Air Quality Data Set

Outlier detection considered as non-trivial task because it can be misclassified as noise, also sometimes it can be misclassified as normal data or otherwise. Difficulties level of detecting outliers arose in time series data, it happens because different context has different amount of outlier data set. One of type data which considered as time series is air quality data set, because level of air quality can be different over time. In order to overcome the difficulties of detects outlier in time series data set, this research compare three different techniques of outlier detection are : statistical, density ratio, and forecasting technique, using the same data set about air quality data set in Madrid in three year period. The result of this research concludes that density ratio has outperformed the other two techniques that obtain the highest balanced accuracy and the lowest percentage of FAD. The lowest percentage of FAD means that those technique have the ability to not misclassified outlier data set as normal data or otherwise.
Understanding the impacts of self-shuffling approach on structure and function of shuffled endoglucanase enzyme via MD simulations

Abstract Objective We identify the impacts of structural differences on functionality of EG3_S2 endoglucanase enzyme with MD studies. The results of previous experimental studies have been explained in details with computational approach. The objective of this study is to explain the functional differences between shuffled enzyme (EG3_S2) and its native counterpart (EG3_nat) from Trichoderma reseei, via Molecular Dynamics approach. Materials and methods For this purpose, we performed MD simulations along 30 ns at three different reaction temperatures collected as NpT ensemble, and then monitored the backbone motion, flexibilities of residues, and intramolecular interactions of EG3_S2 and EG3_nat enzymes. Results According to MD results, we conclude that EG3_S2 and EG3_nat enzymes have unique RMSD patterns, e.g. RMSD pattern of EG3_S2 is more dynamic than that of EG3_nat at all temperatures. In addition to this dynamicity, EG3_S2 establishes more salt bridge interactions than EG3_nat. Conclusion By taking these results into an account with the preservation of catalytic Glu residues in a proper manner, we explain the structural basis of differences between shuffled and native enzyme via molecular dynamic studies.
Molecular confirmation of candidate Hsp70 gene associated with heat tolerance in BC3F2 advanced backcross lines and their phenotypic resemblance with recurrent chilli Kulai

ABSTRACT Domestic production of chilli in Malaysia is insufficient which accounted for 52% self-sufficiency level. Poor performance of local varieties under high temperatures above 42°C is one of the major factors affecting chilli production. This study was conducted with the objective of introgress heat-tolerant gene (Hsps) from AVPP0702 donor into Kulai using marker-assisted backcrossing. A total of 68 SSR markers, including Hsp linked markers that showed polymorphism between the parents were used to assess the generation of backcrossing; BC1F1, BC2F1, BC3F1 and BC3F2 and the average RPG percentage of the recurrent parent was found to be 81, 90, 95 and 97%, respectively. The pattern of Hsp expression in the backcross generations was similar to the donor parent (upregulated with more than 4-fold increase). Twelve improved heat tolerant chilli lines were developed. Most of the morphological and agronomical traits were recovered in the selected improved heat-tolerant genotypes from Kulai such as plant height, number of days to 50% flowering, number of fruits, fruit length and total fruit yield per plant. Improved high-yielding heat-tolerant chilli lines showed tolerance to high temperature as well as did not express any negative effect on agronomic traits in comparison with Kulai variety.
Static and Dynamic Load Balancing Algorithms in Cloud Computing: A Comparative Survey

: Cloud computing is the most reliable, competent and useful in the world of current technology. Cloud computing is a technique that consists of several Web-enabled software and services. Resource programming and resource allocation in cloud computing is the vast area of research today. Therefore, the efficient algorithm for scheduling and allocating resources in the cloud will result in more elastic and efficient cloud computing services. The cloud implementation uses several steps from maintenance to deployment, etc. In this document, we conduct a survey of different load balancing algorithms and classify them based on their dynamics and find out the challenges facing these algorithms. So, we compare these algorithms with an approach to the Markov hidden model technique and with this technique, we will try to clarify the load balancing scenario of the cloud service when programming resources. Markov's hidden model in cloud resource scheduling can help predict the next workload that will arrive on the server, under which the system will allocate resources to a given job. This will contribute to better CPU utilization as well as a faster execution environment in cloud computing.
BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

The vacant licensed spectrum can be employed by the secondary users in cognitive radio networks. Nevertheless, this process of identification is frequently agreed with shadowing, multipath fading and receiver uncertainty problems. The primary contributions of the spectrum sensing techniques adopted in this work mainly concentrate on energy detection for spectrum sensing as well as to recognize the optimal spectral estimation according to the multitaper spectral estimation. The algorithm proposed in this work is based on bivariate Lévy‐stable bat algorithm (BLSBA), energy detector (ED), and with the help of K out of M fusion rule; it is analysed for the single user as well as cooperative multiple users. In the BLSBA algorithm, a modified search equation with more helpful information from the search experiences is brought‐in to create an optimal energy solution and bivariate Lévy‐stable random walk is associated with BLSBA to remove the trapping process into local optima. The energy detection is defined numerically from this optimal detection. At last, a multi‐taper spectral estimator (MSE) is proposed to cognitive radio detection for a huge network. The simulation results in both cases are computed and checked with the help of a BLSBA optimizer. For an individual secondary user scenario, the advancement of MSE to the ED is broadly described. Experimental result indicates that the objective false alarm likelihood is minimized and the demanded signal‐to‐noise ratio is accomplished to the extent.
Effects of Repeated Systemic Administration of Fluoxetine on Offensive Aggresion in Syrian Hamsters (Mesocricetus auratus)

Syrian hamsters are a useful model for offensive aggression because males and females spontaneously engage in agonistic bouts. In hamsters, there is a large sex difference on aggression in the serotonin (5-HT) pathways. Male aggression is inhibited and female aggression increases with injections of a 5-HT agonist into the anterior hypothalamus (AH), but little is known if similar effects are seen in adult hamsters with repeated systemic administration of the selective serotonin reuptake inhibitor (SSRI), fluoxetine (FLX), which is one of the few approved pharmacological treatments for mood disorders in children and adolescents. The goal of this study is to determine if repeated intraperitoneal injections of FLX over 30 days in adolescent male and female hamsters has an effect on offensive aggression similar to site specific alterations of the 5-HT system in the AH. Our data suggest that systemic administration of FLX as adolescents over 30 days does not affect offensive aggression in males or females as adults.
Control for autonomous all-electric ships : Integrating maneuvering, energy management, and power generation control

In the last few years, autonomous shipping has been under extensive consideration by academic and industrial communities as well as governmental organizations due to several potential advantages that it introduces. Furthermore, due to the drastic environmental consequences of transport overwater, international organizations have enforced the shipping industry to reduce its emissions significantly. As a result, the emergence of sustainable autonomous shipping seems inevitable.
Comparing views about evidence in Ontario public health units: a qualitative descriptive study

Background: Ways of perceiving evidence by public health managers, practitioners and policy makers is one of the key determinants of evidence uptake. Recent public policy in Ontario requires programs to be based on evidence. Therefore, understanding views about evidence in both practice and policy contexts is important to bridge the research-policy-practice gap in public health. Objective and Methods: This qualitative descriptive study examined understandings about evidence in Ontario public health units by comparing perspectives from managers and frontline staff across six geographically-diverse units. A secondary qualitative content analysis was used to re-analyze transcripts of focus groups from the Renewal of Public Health Systems (RePHS) research project. Results: Similarities and differences were revealed with respect to how public health managers and frontline staff view evidence. Although both managers and frontline staff understand that multiple forms of evidence exist and that these forms must be integrated when making decisions regarding program development and implementation, frontline staff highlighted the role of practice-based evidence. Both groups named tools and processes that were available to assist their decision-making. Frontline staff indicated capacity building as important for supporting evidence use. Both groups noted that leadership could present a challenge to evidence-based programs if not supportive of the evidence-based solution for public health problems. However, the understanding of leadership differed between frontline staff and managers. Conclusion: Findings from this study provide insight into how use of evidence can be promoted and how to better support policy implementation efforts within practice contexts.
SuRF: Identification of Interesting Data Regions with Surrogate Models

Several data mining tasks focus on repeatedly inspecting multidimensional data regions summarized by a statistic. The value of this statistic (e.g., region-population sizes, order moments) is used to classify the region’s interesting-ness. These regions can be naively extracted from the entire dataspace – however, this is extremely time-consuming and compute-resource demanding. This paper studies the reverse problem: analysts provide a cut-off value for a statistic of interest and in turn our proposed framework efficiently identifies multidimensional regions whose statistic exceeds (or is below) the given cut-off value (according to user’s needs). However, as data dimensions and size increase, such task inevitably becomes laborious and costly. To alleviate this cost, our solution, coined SuRF (SUrrogate Region Finder), leverages historical region evaluations to train surrogate models that learn to approximate the distribution of the statistic of interest. It then makes use of evolutionary multi-modal optimization to effectively and efficiently identify regions of interest regardless of data size and dimensionality. The accuracy, efficiency, and scalability of our approach are demonstrated with experiments using synthetic and real-world datasets and compared with other methods.
Stereolithography 3D-Printed Catalytically Active Devices in Organic Synthesis

This article describes the synthesis of stereolithography (SLA) 3D-printed catalyst-impregnated devices and their evaluation in the organocatalyzed Friedel–Crafts alkylation of N–Me–indole with trans-β-nitrostyrene. Using a low-cost SLA 3D printer and freeware design software, different devices were designed and 3D-printed using a photopolymerizable resin containing a thiourea-based organocatalyst. The architectural control offered by the 3D-printing process allows a straightforward production of devices endowed with different shapes and surface areas, with high reproducibility. The 3D-printed organocatalytic materials promoted the formation of the desired product up to a 79% yield, although with longer reaction times compared to reactions under homogeneous conditions.
A crowdsensing-based framework for urban air quality decision support

Air pollution is considered a major health problem in urban areas. Small sensor technology integrated with smart phones can be widely used to collect air quality information in real time using mobile applications. By applying the concept of crowdsensing, citizens and authorities can be aware of exposure to pollution during their daily activities in urban areas. This paper describes an on-road air quality monitoring and control approach based on the crowdsensing paradigm. In addition to collecting air pollution data, we are exploring the possibility of using this technology to effectively detect critical situations and redistribute all information through a proactive decision support framework. This information can be combined with sensed air quality parameters for displaying, on an interactive map, the detected pollutants' concentrations using sensors attached to smart phones. The proposed framework provides users with real-time traffic and air quality information, traffic recommendations and notifications, and environmental conditions. Moreover, the authorities can use this system to improve urban mobility and traffic regulation. Such behavior and movements related to geographic information can provide a better understanding of the dynamics of a road network. In this work, we propose to combine the benefits of the crowdsensing paradigm with both machine learning and Big Data tools. An artificial neural networks model and the A* algorithm are used for air quality prediction and the least polluted path finding. All data processing tasks are performed over a Hadoop-based framework.
A joint neural network model for combining heterogeneous user data sources: An example of at‐risk student prediction

Information service providers often require evidence from multiple, heterogeneous information sources to better characterize users and offer personalized service. In many cases, statistic information (for example, users' profiles) and sequentially dynamic information (for example, logs of interaction with information systems) are two prominent sources that can be combined to achieve optimized results. Previous attempts in combining these two sources mainly exploited models designed for either static or sequential information, but not both. This study aims to fill the gap by proposing a novel joint neural network model that can naturally fit both static and sequential user data. To evaluate the effectiveness of the proposed method, this study uses the problem of at‐risk student prediction as an example where both static data (personal profiles) and sequential data (event logs) are involved. A thorough evaluation was conducted on an open data set, with comparisons to a range of existing approaches including both static and sequential models. The results reveal superb performances of the proposed method. Implications of the findings on further research and applications of joint models are discussed.
Drivers and barriers to MOOC adoption: perspectives from adopters and non-adopters

Despite the advantages of MOOCs (Massive Online Open Courses), the adoption rate is still low, especially in less developed countries. The present study aims to investigate the drivers and barriers faced by adopters and non-adopters of MOOCs, and examine how these factors influence the intention of MOOC adoption. ,A survey questionnaire was developed to measure how the identified factors may impact adopters and non-adopters' intention of using MOOCs. Through both offline and online channels, 1018 valid questionnaires were collected in China. Multiple regression analysis was applied to test the influential factors for adopters and non-adopters of MOOCs. ,This study found that the drivers (i.e. perceived usefulness, performance-to-cost value, and self-regulation) have similar effects on both adopters and non-adopters on intention to adopt MOOCs. What differs between the two groups are the barriers to MOOC adoption. While adopters considered educational tradition and peers' impact as major barriers, non-adopters regarded lack of publicity and relevant information to be an obstacle. In addition, both adopters and non-adopters were hindered by the lack of accessibility and instructions. ,As all the participants of this study were from one country in Asia, the findings may not be applicable to explain the situation of MOOC adoption in other countries with different social and cultural context. ,In addition to the perceptions of adopters, this study also reveals the perceptions of non-adopters of MOOCs, which has seldom been investigated. In addition, this study focuses on the context of economically disadvantaged regions which have been neglected in previous studies of MOOC adoption.
Exploring Health Benefits through Senior Center Engagement: A Qualitative Investigation of Older Korean Immigrants

ABSTRACT Senior centers serve as an avenue where older adults gain various health, social and psychological resources and information. This study attempted to illuminate the benefits that older Korean immigrants gained at ethno-specific senior centers. Using in-depth interviews among eighteen participants, three major themes were identified as health-related benefits: (a) experiencing self-development, (b) perceiving psychological comfort, and (c) cultivating a healthy lifestyle. This suggests that senior centers not only offered educational and self-learning opportunities but enhanced social and psychological well-being to participants. It also suggests that senior centers create a unique cultural avenue where older Korean immigrants maintain their cultural values as well as embrace new cultural perspectives.
Effects of graphene oxide on white poplar X ASPEN (Populus alba x Populus tremula) hybrid microsprouts at various growth stages

Graphene-like materials are promising in biotechnology as antimicrobial agents and plant growth stimulants. We investigated the effects of graphene oxide nanoparticles in the composition of the MS0 medium on microclonal seedlings of the poplar and aspen hybrid. In the early stages of cultivation, graphene oxide improved microclone survival by 6–20%. However, then, along with some stimulation, negative effects were observed, manifested in a decrease in the development of the root system and a decrease in the thickness of the leaf lamina and the diameter of the stem. Our results indicate the promise of using graphene oxide in the early stages of the development of microclones of white poplar and aspen hybrids.