Miracle-dz/UniFAR-FTUs huggingface dataset:
On Placental Toxicology Studies and Cerium Dioxide Nanoparticles

The human placenta is a transient organ essential for pregnancy maintenance, fetal development and growth. It has several functions, including that of a selective barrier against pathogens and xenobiotics from maternal blood. However, some pollutants can accumulate in the placenta or pass through with possible repercussions on pregnancy outcomes. Cerium dioxide nanoparticles (CeO2 NPs), also termed nanoceria, are an emerging pollutant whose impact on pregnancy is starting to be defined. CeO2 NPs are already used in different fields for industrial and commercial applications and have even been proposed for some biomedical applications. Since 2010, nanoceria have been subject to priority monitoring by the Organization for Economic Co-operation and Development in order to assess their toxicity. This review aims to summarize the current methods and models used for toxicology studies on the placental barrier, from the basic ones to the very latest, as well as to overview the most recent knowledge of the impact of CeO2 NPs on human health, and more specifically during the sensitive window of pregnancy. Further research is needed to highlight the relationship between environmental exposure to CeO2 and placental dysfunction with its implications for pregnancy outcome.
Pairwise Ranking Network for Affect Recognition

In this work we study the problem of emotion recognition under the prism of preference learning. Affective datasets are typically annotated by assigning a single absolute label, i.e. a numerical value that describes the intensity of an emotional attribute, to each sample. Then, the majority of existing works on affect recognition employ sample-wise classification/regression methods to predict affective states, using those annotations. We take a different approach and use a deep network architecture that performs joint training on the tasks of classification/regression of samples and ordinal ranking between pairs of samples. By treating input samples in a pairwise manner, we leverage the auxiliary task of inferring the ordinal relation between their corresponding affective states. Incorporating the ranking objective allows capturing the inherently ordinal structure of emotions and learning the inter-sample relations, resulting in better generalization. Our method is incorporated into existing affect recognition architectures and evaluated on datasets of electroencephalograms (EEG) and images. We show that the approach proposed in this work leads to consistent performance gains when incorporated in classification/regression networks.
Coming to Terms with Engineering Design as Content

With the publication of standards for teaching, learning, and the inculcation of technological literacy (International Technology Education Association, 2000), technology education in the United States has made a significant leap forward toward greater acceptance as a valid school subject. Standards represent content terrain claimed by a community of practitioners, and once stakes are put down, it is left to adherents to move in seeking title. It is doubtful whether we will witness a rush towards bio-technology or medical technology, new areas in the standards that do not naturally issue from our accustomed traditions. But for design there will be great interest since this is a content area over which the field has long toiled. Design is arguably the single most important content category set forth in the standards, because it is a concept that situates the subject more completely within the domain of engineering. Four of the 20 standards address the question of design directly. Standard 8 deals with the “attributes of design;” Standard 9 with “engineering design;” Standard 10 with “trouble shooting, research and development, invention and innovation, and experiment in problem solving; and Standard 11 with the “design process. ” It is not inconsequential that the foreword heralding the standards is authored by William Wulf, in his capacity as President of the National Academy of Engineering. This is a significant benediction for a subject whose advocates have for the past decade or so been of the view that its acceptance by the public and by the dominant academic culture of schools turned on the degree of rapprochement that could be worked out with the science as well as the engineering communities. The Project 2061 curriculum standards acknowledged the common epistemological ground shared by science and technology as school subjects, embodied in the designed world (American Association for the Advancement of Science, 1993; Johnson, 1989). With ties with science thus formalized, engineering was but a step away. The sentiments expressed by Bensen & Bensen (1993) foreshadowed what appears now to be a significant opportunity for the field of technology education to lay claim to aspects of engineering as part of its curriculum purview. Arguing that the subject should
On Clustering Algorithms: Applications in Word-Embedding Documents

: In this paper, we study the effectiveness of classical literature clustering algorithms applied to free text documents. We analyze the effects of varying the parameters on their performance and which aspects directly influence in the results. We apply a word-embedding-based technique to represent the document's bag-of-words and therefore be able to compare and study how these algorithms performs in the task of clustering these documents. We use two metrics that captures different aspects of the partitions and analyze those algorithms on the light of it. One of the main findings of this work is that some clustering algorithms are able to have a partition that's up to 91% of the real partition, whilst other performs really poor for the same dataset. We also find limitations on these techniques when trying to cluster hard datasets.
Enabling heterogeneous recommendations in OSS development: what's done and what's next in CROSSMINER

Open source software (OSS) forges contain rich data sources that are useful for supporting development activities. Research has been done to promote techniques and tools for providing open source developers with innovative features aiming at obtaining improvements in terms of development effort, cost savings, and developer productivity, just to mention a few. In the context of the EU H2020 CROSSMINER project we are conceiving a set of recommendations to assist software programmers in different phases of the development process. To this end, we defined a graph-based representation to encode in a homogeneous manner different aspects of OSS ecosystems as well as to incorporate various well-founded recommendation techniques. Following the proposed paradigm, we have implemented recommender systems for providing various artifacts, such as third-party libraries and API usage. The preliminary results we achieved so far are promising: our proposed systems are able to suggest highly relevant items with respect to the current development context. In this paper, we describe what has been achieved so far as well as our planned medium and longer-term objectives. As a proof of concept, we present a use case where we built a context-aware recommender system to recommend API function calls and usage patterns.
Deep Learning-Based GNSS Network-Based Real-Time Kinematic Improvement for Autonomous Ground Vehicle Navigation

Much navigation over the last several decades has been aided by the global navigation satellite system (GNSS). In addition, with the advent of the multi-GNSS era, more and more satellites are available for navigation purposes. However, the navigation is generally carried out by point positioning based on the pseudoranges. The real-time kinematic (RTK) and the advanced technology, namely, the network RTK (NRTK), were introduced for better positioning and navigation. Further improved navigation was also investigated by combining other sensors such as the inertial measurement unit (IMU). On the other hand, a deep learning technique has been recently evolving in many fields, including automatic navigation of the vehicles. This is because deep learning combines various sensors without complicated analytical modeling of each individual sensor. In this study, we structured the multilayer recurrent neural networks (RNN) to improve the accuracy and the stability of the GNSS absolute solutions for the autonomous vehicle navigation. Specifically, the long short-term memory (LSTM) is an especially useful algorithm for time series data such as navigation with moderate speed of platforms. From an experiment conducted in a testing area, the LSTM algorithm developed the positioning accuracy by about 40% compared to GNSS-only navigation without any external bias information. Once the bias is taken care of, the accuracy will significantly be improved up to 8 times better than the GNSS absolute positioning results. The bias terms of the solution need to be estimated within the model by optimizing the layers as well as the nodes each layer, which should be done in further research.
Study of Activity Tracking through Bluetooth Low Energy-Based Network

This paper proposes a proof-of-concept, low-cost, and easily deployable Bluetooth low energy- (BLE-) based localization system which actively scans and localizes BLE beacons attached to mobile subjects in a room. Using the received signal strength (RSS) of a BLE signal and the uniqueness of BLE hardware addresses, mobile subjects can be identified and localized within the hospital room. The RSS measurement of the BLE signal from a wearable BLE beacon varies with distance to the wall-anchored BLE scanner. In order to understand and demonstrate the practicality of the relationship between RSS of a BLE beacon and the distance of a beacon from a scanner, the first part of the paper presents the analysis of the experiments conducted in a low-noise and nonreflective environment. Based on the analysis conducted in an ideal environment, the second half of the paper proposes a data-driven localization process for pinpointing the movements of the subject within the experimental room. In order to ensure higher accuracy like fingerprinting techniques and handle the increased number of BLE-anchored scanners like geometric techniques, the proposed algorithm was designed to combine the best aspects of these two techniques for better localization. The paper evaluates the effects of the number of BLE wall-mounted scanners and the number of packets on the performance of the proposed algorithm. The proposed algorithm locates the patient within the room with error less than 1.8 m. It also performs better than other classical approaches used in localization.
Comparing the Effectiveness of Using Design and Code Measures in Software Faultiness Estimation

Background. Early identification of software modules that are likely to be faulty helps practitioners take timely actions to improve these modules' quality and reduce development costs in the remainder of the development process. To this end, module faultiness estimation models can be built at any point during development by using measures collected up to that time. Models available in later phases are expected to be more accurate than those available in earlier phases. However, waiting until late in the development process may reduce the impact of the effectiveness and efficacy of any software quality improvement actions and increase their cost. Aims. Our goal is to investigate to what extent using software code measures along with software design measures helps improve the accuracy of module faultiness estimation with respect to using software design measures alone. Method. We built faultiness estimation models---by using Binary Logistic Regression, Naive Bayes, Support Vector Machines, and Decision Trees---for 54 datasets from the PROMISE repository. These datasets contain design and code measures and faultiness data of software modules of real-life projects. We compared the models built by using the code measures and design measures together against the models built by using design measures alone via a few accuracy indicators. Results. The results indicate that the models built by using code measures and design measures together are only slightly more accurate than the models built by using design measures alone. Conclusions. Our analysis shows that measures that can be obtained during design can provide models that are almost as accurate as models that can be achieved in later development phases. This is good news for practitioners, who can start early ---hence cheaper and more effective---quality improvement initiatives based on fairly reliable models.
Chromatin Evolution-Key Innovations Underpinning Morphological Complexity

The history of life consists of a series of major evolutionary transitions, including emergence and radiation of complex multicellular eukaryotes from unicellular ancestors. The cells of multicellular organisms, with few exceptions, contain the same genome, however, their organs are composed of a variety of cell types that differ in both structure and function. This variation is largely due to the transcriptional activity of different sets of genes in different cell types. This indicates that complex transcriptional regulation played a key role in the evolution of complexity in eukaryotes. In this review, we summarize how gene duplication and subsequent evolutionary innovations, including the structural evolution of nucleosomes and chromatin-related factors, contributed to the complexity of the transcriptional system and provided a basis for morphological diversity.
Low voltage ride through enhancement in DFIG based WECS by hybrid method

ABSTRACT This paper proposes a hybrid technique for the enhancement of Low Voltage Ride Through (LVRT) capacity in a Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion System (WECS). The proposed hybrid technique is the joint execution of S-shaped binary version of Whale Optimisation Algorithm (S-bWOA) and Random Decision Forest (RDF), hence it called S-bWOA-RDF method. The major objective is to ensure the LVRT capacity in the WECS at voltage drop including failure stages. S-bWOA model is used to identify the optimal solutions for offline mode as an available search location under objective function and generates the training dataset. In light of proficient dataset, RDF performs and predicts better feasible machine side with grid side converter control signals. The proposed technique is activated in MATLAB/Simulink site, and the efficiency of the proposed S-bWORDF method is compared with the existing methods, such as Random Decision Forest and Whale Optimisation Algorithm. The IAE and ITAE of the proposed and existing methods are also analysed. The IAE and ITAE of the proposed hybrid technique are identified as 5.0071 and 54.0475. The comparison results demonstrate the efficiency of the S-bWORDF method and confirm its potential for enhancing the LVRT of DFIG based WECS.
Special issue on big data for IoT cloud computing convergence

Big data refers to the data set that cannot be captured, managed and processed by conventional software tools within a certain period of time. It is a massive, high growth rate and diversified information asset that requires new processing patterns to have stronger decision-making power, insight and process optimization capabilities The last decade has seen huge advances in the scale of data we routinely generate and collect in pretty much everything we do, as well as our ability to use technology to analyze and understand it. We have been witnessing a digital revolution associated with developments of various emerging technologies including ubiquitous computing devices, sensors and sensing devices, smart devices, cloud computing and big data analytics tools are dramatically changing the mode and accessibility of science, research and practice in all domains [1,4,8]. The Internet of Things is an important part of the new generation of information technology, and also an important stage of development in the “information” era. Internet of Things (IoT) applications are considered to be a major source of big data obtained from a more connected dynamic and real life world and is evolving at a rapid pace. The realization of the IoT vision brings Information and Communication Technology (ICT) closer to many aspects of the real-world life instead of the virtual life through advanced theories, algorithms and applications. Technology of real-world IoT based on cloud computing has rapidly emerged as a novel industry and life paradigm. These topics will be the most comprehensive field focused on the various aspects of advances in computer engineering technologies, applications, and services. In cloud computing environments that include mobile infrastructures, the most important and final goal is to provide users more secure and richer Internet of Things services. Tremendous advances in algorithms of sensing, processing, communication and actuating core technologies are leading to new intelligent IoT services in our life such as smart cities, smart healthcare, smart grids, and others to improve all aspects of life. There might be many issues to realize it and provide intelligence IoT services based on the advanced applied algorithms and application technologies with much effort and enormous attention. The advanced applied algorithms and application technologies of this research area poses challenges such as context information fusion, security, reliability, autonomous and intelligent connecting, trust application and framework for realworld life. Advanced algorithms and applications for IoT based on the cloud computing research contributions that present new technologies, concepts, or analysis, reports on experiences and experiments of implementation and application of theories, and tutorials on new trends, are required in this research fields [9]. The following is a non-exhaustive list of topics in focus of this special issue:
A Digital Mode-Matching Control System Based on Feedback Calibration for a MEMS Gyroscope

A digital mode-matching control system based on feedback calibration, where two pilot tones are applied to actuate the sense mode by the robust feedback controller, is presented for a MEMS gyroscope in this paper. A dual-mass decoupled MEMS gyroscope with the integrated electrostatic frequency tuning mechanisms, the quadrature correction electrode, and the feedback electrode is adopted to implement mode-matching control. Compared with the previous mode-matching method of forward excitation calibration, the proposed mode-matching scheme based on feedback calibration has better adaptability to the variation in the frequency of calibration pilot tones and the quality factor of the sense mode. The influences of calibration pilot tone frequency and the amplitude ratio on tuning performance are studied in theory and simulation. The simulation results demonstrate that the tuning error due to the amplitude asymmetry of the sense mode increases with a frequency split between pilot tones and the drive mode and is significantly reduced by the amplitude correction technology of pilot tones. In addition, the influence of key parameters on the stability of the mode-matching system is deduced by using the average analysis method. The MATLAB simulation of the mode-matching control system illustrates that simulation results have a good consistency with theoretical analysis, which verifies the effectiveness of the closed-loop mode-matching control system. The entire mode-matching control system based on a FPGA device is implemented combined with a closed-loop self-excitation drive, closed-loop force feedback control, and quadrature error correction control. Experimental results demonstrate that the mode-matching prototype has a bias instability of 0.63°/h and ARW of 0.0056°/h1/2. Compared with the mode-mismatched MEMS gyroscope, the performances of bias instability and ARW are improved by 3.81 times and 4.20 times, respectively.
Internet Service Selection Using Service Association Factor (SAF)

Today, business IT organizations equip themselves with modern regime of online application infrastructure through cloud, fog and edge computing. In the presence of several opportunities, selection criterion for such internet based services becomes vital when there is no supporting information available. Recommender systems provide best services using customer's feedback, applying quality of service parameters, k-mean clustering as well as some other approaches. However, existing schemes typically relay on customer’s feedback and do not provide any information of interrelationship between the services. Feedback may be self-generated or biased and may lead to improper recommendation to service seekers. To address this, we propose an innovative service association factor approach that calculates value of interrelationship among online services appearing together in provider’s package. It enables the users to select best services on their preferences. The proposed agent works on fog environment to recommend user’s desires in all types of service models.
Assessment of The Diagnostic Value of TOX Versus CD3 Immunohistochemical Markers in Detection of Early Mycosis Fungoides Cases

Background: Primary cutaneous lymphoma (PCL) with clonal proliferation of atypical CD4+ skin-homing T lymphocyte cells is called Mycosis Fungoides (MF). TOX staining is observed in subtypes of PCL, as MF & Sézary Syndrome (SS) in comparison to controls. Early MF is difficult to diagnose, & its distinction from inflammatory diseases may be impossible. Objective: This study aimed to evaluate the expression of the TOX versus CD3 as a diagnostic marker for early MF. Patients and methods: retrospective-cross sectional study includes 30 MF and 30 benign cutaneous inflammatory diseases (BCID) cases. All were evaluated using H & E and immunohistochemical staining for TOX & CD3. Results: There was statistically significant increase of TOX & CD3 expression in MF than BCID & ability of TOX to detect all true positive cases (100.0%) compared to (76.7%) for CD3, (P < 0.001). Conclusion: TOX had the highest sensitivity (100.0%) & accuracy (88.3%). TOX is useful marker in the diagnosis of early MF & differentiating it from BCID.
A Case of Identity: HOX Genes in Normal and Cancer Stem Cells

Stem cells are undifferentiated cells that have the unique ability to self-renew and differentiate into many different cell types. Their function is controlled by core gene networks whose misregulation can result in aberrant stem cell function and defects of regeneration or neoplasia. HOX genes are master regulators of cell identity and cell fate during embryonic development. They play a crucial role in embryonic stem cell differentiation into specific lineages and their expression is maintained in adult stem cells along differentiation hierarchies. Aberrant HOX gene expression is found in several cancers where they can function as either oncogenes by sustaining cell proliferation or tumor-suppressor genes by controlling cell differentiation. Emerging evidence shows that abnormal expression of HOX genes is involved in the transformation of adult stem cells into cancer stem cells. Cancer stem cells have been identified in most malignancies and proved to be responsible for cancer initiation, recurrence, and metastasis. In this review, we consider the role of HOX genes in normal and cancer stem cells and discuss how the modulation of HOX gene function could lead to the development of novel therapeutic strategies that target cancer stem cells to halt tumor initiation, progression, and resistance to treatment.