Miracle-dz/UniFAR-FTUs huggingface dataset:
Role of epigenetic transgenerational inheritance in generational toxicology

Abstract Many environmental toxicants have been shown to be associated with the transgenerational inheritance of increased disease susceptibility. This review describes the generational toxicity of some of these chemicals and their role in the induction of epigenetic transgenerational inheritance of disease. Epigenetic factors include DNA methylation, histone modifications, retention of histones in sperm, changes to chromatin structure, and expression of non-coding RNAs. For toxicant-induced epigenetic transgenerational inheritance to occur, exposure to a toxicant must result in epigenetic changes to germ cells (sperm or eggs) since it is the germ cells that carry molecular information to subsequent generations. In addition, the epigenetic changes induced in transgenerational generation animals must cause alterations in gene expression in these animals’ somatic cells. In some cases of generational toxicology, negligible changes are seen in the directly exposed generations, but increased disease rates are seen in transgenerational descendants. Governmental policies regulating toxicant exposure should take generational effects into account. A new approach that takes into consideration generational toxicity will be needed to protect our future populations.
Modeling fracture propagation in a rock‐water‐air system with the assumed enhanced strain method

This article presents a fully coupled finite element method for the single cohesive fracture propagation in a deformable porous medium (rock) interacting with two immiscible pore fluids, that is, water and air. The cohesive fracture is represented with the assumed enhanced strain method which can be cast into a classical plasticity framework. The governing equations for the solid skeleton deformation, pore water flow, and pore air flow are derived based on the mixture theory. The coupled nonlinear global system is then solved by the standard Galerkin finite element method. The numerical implementation is verified against the numerical and analytical solutions previously reported in the literature. The proposed model is capable of capturing fluid transfer between the fracture and the host rock due to the pressure difference. The numerical examples demonstrate that the behavior of fracture propagation can be significantly influenced by the capillary pressure inside fracture and vice versa. Since the fracture is represented at the level of Gauss points, the proposed numerical model can be easily implemented on any standard finite element code base.
Alteration in amino acid contents in different genotypes of bread wheat at different developmental stages

The aim of this study was to determine changes of amino acid levels in thirteen bread wheat genotypes during growth stages of wheat (tillering, flowering, maturity stages and seeds) in growing periods of 2018-2019. Results suggest that important variations and differences occurred among bread wheat genotypes and their growing stages. The amino acid content was found as higher in tillering and flowering, lower in maturity stage. Once changes in amino acid levels increase/ decrease depending upon biochemical activities and crop growth stages, genotypic capacity and features significantly control of amino acid levels in bread wheat. Amino acid levels are higher until the flowering period than during the maturation period indicates that amino acids are of vital importance in plant development. Especially amino acids responsible for growth and development are very important in bread wheat. In addition, differences between genotypes and plant growth stages indicate that amino acids are highly affected by the genotype x environment interaction.
eSardine: A General Purpose Platform with Autonomous AI and Explainable Outputs

: The combination of high computational power and data awareness triggered an increasing demand for business applications from industrial players. However, harnessing the knowledge from data requires expertise, usually being a time-consuming task. Additionally, the users’ trust in the results obtained is commonly compromised due to the black box behavior of most Machine Learning models. This paper proposes a general-purpose platform, eSardine, that leverages automatic machine learning and explainability to produce fast, reliable, and interpretable results. The eSardine platform integrates forefront tools to enhance, and automate the data science process, with minimal human interaction. For any tabular supervised classiﬁcation and regression problems, predicted outputs are given, as well as an explainability report of each prediction. The inclusion of AutoML tools, i . e . , automatic model tuning and selection, presented a strong baseline whose capabilities are ampliﬁed by built-in, yet customizable, autonomous processing mechanisms. The explainable reports aim to increase users’ conﬁdence in the models’ quality and robustness. Furthermore, in the industrial context, understanding key factors unveiled in these reports is determinant to increase the business model’s proﬁtability. The platform was evaluated in two public datasets, where it outperformed state-of-the-art results.
Institutional Ecology, `Translations' and Boundary Objects: Amateurs and Professionals in Berkeley's Museum of Vertebrate Zoology, 1907-39

Scientific work is heterogeneous, requiring many different actors and viewpoints. It also requires cooperation. The two create tension between divergent viewpoints and the need for generalizable findings. We present a model of how one group of actors managed this tension. It draws on the work of amateurs, professionals, administrators and others connected to the Museum of Vertebrate Zoology at the University of California, Berkeley, during its early years. Extending the Latour-Callon model of interessement, two major activities are central for translating between viewpoints: standardization of methods, and the development of `boundary objects'. Boundary objects are both adaptable to different viewpoints and robust enough to maintain identity across them. We distinguish four types of boundary objects: repositories, ideal types, coincident boundaries and standardized forms.
Machine Learning for DDoS Attack Detection in Industry 4.0 CPPSs

The Fourth Industrial Revolution (Industry 4.0) has transformed factories into smart Cyber-Physical Production Systems (CPPSs), where man, product, and machine are fully interconnected across the whole supply chain. Although this digitalization brings enormous advantages through customized, transparent, and agile manufacturing, it introduces a significant number of new attack vectors—e.g., through vulnerable Internet-of-Things (IoT) nodes—that can be leveraged by attackers to launch sophisticated Distributed Denial-of-Service (DDoS) attacks threatening the availability of the production line, business services, or even the human lives. In this article, we adopt a Machine Learning (ML) approach for network anomaly detection and construct different data-driven models to detect DDoS attacks on Industry 4.0 CPPSs. Existing techniques use data either artificially synthesized or collected from Information Technology (IT) networks or small-scale lab testbeds. To address this limitation, we use network traffic data captured from a real-world semiconductor production factory. We extract 45 bidirectional network flow features and construct several labeled datasets for training and testing ML models. We investigate 11 different supervised, unsupervised, and semi-supervised algorithms and assess their performance through extensive simulations. The results show that, in terms of the detection performance, supervised algorithms outperform both unsupervised and semi-supervised ones. In particular, the Decision Tree model attains an Accuracy of 0.999 while confining the False Positive Rate to 0.001.
Dynamic road pricing incorporating dynamic traffic assignment

Road pricing has been widely advocated as an efficient instrument to alleviate traffic congestion. Current static pricing schemes are designed for long term transportation planning as traffic conditions are assumed not to vary dramatically. When dealing with short time period, especially during the peak period, such assumption cannot be valid due to variable conditions of traffic over the whole network. Thus, pricing should be dynamic to accommodate the dynamic traffic evolution. This research aims to develop a theoretical dynamic road pricing model incorporating dynamic traffic assignment. The framework for the optimal road pricing problem incorporating traffic assignment on a network was a Stackelberg game. Road pricing schemes have to be announced firstly and the travellers’ behaviour were assumed to follow the concept of Wardrop’s user equilibrium. This problem can also be expressed as a Mathematical Problem with Equilibrium Constraints (MPEC). Traffic assignment model under fixed tolling level was expressed as the constraint. The toll level was expected to have an effect on travel time. After reviewing various algorithms for the MPEC problems, three different algorithms were tested for a static road pricing problem: Differential Evolutionary (DE), Pattern Search (PS), and Non-linear Problem with Equilibrium Constraints (NLPEC) solver. For this well-defined problem, NLPEC solver showed to be the most efficient in computation time for a given xvi ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library
Strategic Repositioning of Koforidua Polytechnic

The study seeks to find out what changes are taking place in strategy in Koforidua Polytechnic and why? The methodology used in the study is based on qualitative approaches of documentary analysis. Data is collected through a content analysis of relevant documents. The scope of the study is limited to the researcher's interpretation of the strategic changes identified and articulated in official polytechnic documents. A framework for assessing and modeling changes in strategy provided by Ginsberg (1988) is adapted to suit the higher education context and applied in the analysis. The adapted framework is used as a tool to conceptualize changes in strategy in Koforidua Polytechnic. The findings indicate significant changes in strategy in Koforidua Polytechnic. The leadership of the polytechnic has introduced new strategic directions for the polytechnic, focusing on the provision of infrastructure facilities, improving financial resource mobilization, promoting research, strengthening collaborative partnership at home and abroad, and promoting good governance and planning. External and internal conditions provide pressure for the shift in strategic direction. The change in direction is a necessary measure which ensures the future of the polytechnic. Keywords: Strategy, Strategic change, Strategic planning, Higher education, Koforidua Polytechnic.
Motor Imagery BCI Classification Based on Multivariate Variational Mode Decomposition

In this article, a novel computer-aided diagnosis framework is proposed for the classification of motor imagery (MI) electroencephalogram (EEG) signals. First, a multivariate variational mode decomposition (MVMD) method was employed to obtain joint modes in frequency scale across all channels. Second, several multi-domain features (time domain, frequency domain, nonlinear and geometrical) were extracted from each EEG signal, and to further enhance the classification performance of different MI EEG signals, a variety of wrapper and filter feature selection methods were utilized with different channel combinations. Finally, to avoid a large number of training sessions for a particular device, extensive subject-independent experiments were performed. The MVMD applied to 18-channel EEG from the motor cortex area in combination with the ReliefF feature selection method achieved an average classification accuracy of 99.8% for a subject-dependent while 98.3% for subject-independent experiments. Besides the aforementioned combination provide above 99% accuracy for subjects with sufficient or small training samples for both subject-dependent or independent cases. These promising findings suggest that the proposed framework is flexible to use for subject-dependent or independent BCI systems.
Weight Health Among Hispanic Women in Albuquerque: A Preliminary Study to Evaluate the De las Mías App Prototype

Our 3-year study tested a culturally tailored digital platform to improve weight health among Hispanic women. Phase I evaluated a prototype (n = 54). Phase II tested the De Las Mías app (n = 195). Both included Hispanic women with BMI 25 to 39. In Phase II weight was measured at 0-, 3-, and 6-months. A telephone interview was conducted at 9-months. Primary outcome was >5% weight loss at 6 months. Experimental participants were approximately 6 times more likely to experience >5% weight loss at 6 months. Greater benefit was observed for participants age 35 to 50. Among participants age 35 to 50, 29.4% of the experimental group, compared with 12.1% in the control group, had >5% weight loss at 6 months (p-value = .08); and 46.9% of the experimental group, compared with 27.3% of control group participants (p-value = .10), had a gain in self-efficacy in making changes in physical activity. Nine of 10 participants overall maintained some healthy changes at 9 months.
A circularly polarized square microstrip parasitic antenna with an improved bandwidth

This paper presents a novel design for an optimized circularly polarized microstrip antenna. Circular polarized microstrip antennas have many uses due to its low profile design. However, one of the microstrip antenna's main limitations arises from its narrow frequency bandwidth. Therefore, it is necessary to use special design techniques in order to increase the operating frequency bandwidth while maintaining all of the other respectable optimum radiation characteristics. The proposed design involves using a dual‐feed square patch, which is gap coupled with two parasitic patches in a mirrored L‐shaped configuration. This design resulted in an axial ratio bandwidth of 11%, S11 bandwidth of 14% and a gain of 9 dB at the 2.15 GHz center frequency. The proposed design was built, and the measured results were in agreement with the simulation results. Additionally, this antenna design was used in an array structure in order to increase the gain, while maintaining a good axial ratio. These enhanced radiation results allow the use of the proposed antenna design in many domains such as wireless and military applications without facing the limitations of the narrow frequency bandwidth. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:597–603, 2016
A comparative study of the 5 mm-layer Vickers hardness model with bulk-fill resin-based composites

Aim: The aim of this study was to compare the Vickers hardness numbers (VHNs) of two bulk-fill resin-based composites (BFRBC) and a conventional hybrid resin-based composite (RBC) through the layers of a 5mm thickness model with two different light-curing time intervals. Methodology: In the present study, a sonic-activated and dual-cure BFRBC, and a conventional hybrid RBC were used. Semi-cylindrical specimens 4 mm in radius and 5 mm in height were prepared using a two-piece stainless-steel mold (n=10). The BFRBCs allowed a single 5mm increment to be introduced into the molds, whereas hybrid RBC was incremented (2+2+1 mm). Two different time intervals were applied for the light-curing (irradiance of 1200 mW/cm2) of each material (hybrid-sonic-activated bulk-fill, 20 s and 40 s; dual-cure bulk-fill, 7 s and 15 s). VHN measurements were carried out from top to bottom at every 1 mm of the specimen thickness. Data were analyzed using three-way and two-way ANOVA for the VHN and bottom/top ratios and Bonferroni correction for multiple comparisons (p=0.05). Results: For each layer and time interval groups, there was a significant difference between the materials. The highest VHN was found within hybrid groups, whereas dual-cure bulk-fill groups showed the lowest results. Sonic-activated bulk-fill had the lowest bottom/top ratios, which were significantly different from those of the other materials. There was no significant difference between the different time intervals for bottom/top ratios within each material. Conclusion: Increased irradiation intervals positively affected the VHN of hybrid and dual-cure bulk-fill. BFRBCs showed clinically acceptable bottom/top hardness ratios. How to cite this article: Aytaç Bal F, Ağaccıoğlu M, Demir O. A comparative study of the 5 mm-layer Vickers hardness model with bulk-fill resin-based composites. Int Dent Res 2021;11(2):172-9. https://doi.org/10.5577/intdentres.2021. vol11.no3. 6 Linguistic Revision: The English in this manuscript has been checked by at least two professional editors, both native speakers of English.
Application of classification models on maintenance records through text mining approach in industrial environment

PurposeThe data created from regular maintenance activities of equipment are stored as text in industrial plants. The size of these data is increasing rapidly nowadays. Text mining provides a chance to handle this huge amount of text data and extract meaningful information to improve various processes of an industrial environment. This paper represents the application of classification models on maintenance text records to classify failure for improving maintenance programs in the industry.Design/methodology/approachThis paper is presented as an implementation study, where text mining approaches are used for binary classification of text data. Naive Bayes and Support Vector Machine (SVM), two classification algorithms are applied for training and testing of the models as per the labeled data. The reason behind this is, these algorithms perform better on text data for classifying failure and they are easy to handle. A methodology is proposed for the development of maintenance programs, including classification of potential failure in advance by analyzing the regular maintenance data as well as comparing the performance of both models on the data.FindingsThe accuracy of both models falls within the acceptable limit, and performance evaluation of the models concludes the validation of the results. Other performance measures exhibit excellent values for both of the models.Practical implicationsThe proposed approach provides the maintenance team an opportunity to know about the upcoming breakdown in advance so that necessary measures can be taken to prevent failure in an industrial environment. As predictive maintenance incurs a high expense, it could be a better replacement for small and medium industrial plants.Originality/valueNowadays, maintenance is preventive-based rather than a corrective approach. The proposed technique is facilitating the concept of a proactive approach by minimizing the cost of additional maintenance steps. As predictive maintenance is efficient but incurs high expenses, this proposed method can minimize unnecessary maintenance operations and keep control over the budget. This is a significant way of developing maintenance programs and will make maintenance personnel ready for the machine breakdown.
Cross-Sectional Study of HPV Self-Sampling among Indian Women—A Way Forward

Molecular human papillomavirus (HPV) DNA is a recommended test for any country planning cervical cancer screening as a national policy. The emerging literature proposes HPV self-sampling (HPV-SS) as a feasible implementing strategy in low-income settings. The success of this strategy would depend on developing impactful health education materials, understanding modalities toward generating awareness, and precision in performing the screening test among beneficiaries. The current paper is an interim analysis of ongoing research undertaken to understand the acceptability of HPV-SS among Indian women across different community settings. The study design has two modalities for generating awareness: (1) health education arm wherein the awareness and steps of collecting self-sample are explained by health personnel, and (2) the pamphlet arm wherein pictorial illustrations depicting the steps to conduct HPV-SS are distributed among women. The quality of samples is compared with primary health worker samples (PHW-S). Initial results of this study support the acceptance of HPV-SS (97%) among urban slum settings. An agreement between HPV-SS and PHW-S was demonstrated to be 95.1%. The results of the pamphlet arm were comparable to the health education arm in every aspect. The art-based strategy seems like a promising communication modality for generating awareness toward cervical cancer screening using HPV-SS in low-resource settings.
The Mental Health of Black Youth Affected by Community Violence: Family and School Context as Pathways to Resilience

Black youth who experience community violence occupy multiple environments with varying levels of influence on how they display resiliency to prevent adverse mental health outcomes. Considering the recent rise of mental health concerns (i.e., increase in suicidal outcomes) among Black youth, along with the abundance of research illustrating the detrimental impact of community violence, more research is needed to examine how different environmental factors (e.g., family and school) shape how youth protect their mental health while displaying resiliency navigating community violence. The purpose of this study was to examine how family and school contexts predict Black youths’ ability to display resiliency to navigate community violence and prevent adverse mental health outcomes. This study utilized a path analysis to examine the associations between parent relationships, parent bonding, school climate, resilience to adverse community experiences, community violence, and mental health among 548 Black adolescents in Chicago. Findings highlight that parent relationships, parent bonding, and school climate influence the association between resilience to community violence and mental health outcomes among Black youth. Implications for mental health practice and policy among Black youth are discussed.
A Simple and Effective Convolutional Filter Pruning based on Filter Dissimilarity Analysis

In this paper, a simple and effective filter pruning method is proposed to simplify the deep convolutional neural network (CNN) and accelerate learning. The proposed method selects the important filters and discards the unimportant ones based on filter dissimilarity analysis. The proposed method searches for filters with decent representative ability and less redundancy, discarding the others. The representative ability and redundancy contained in the filter is evaluated by its correlation with currently selected filters and left over unselected filters. Moreover, the proposed method uses an iterative procedure, so that less representative filters can be discarded evenly from the entire model. The experimental analysis confirmed that a simple filter pruning method can reduce floating point operations (FLOPs) of TernausNet by up to 89.65% on an INRIA Aerial Image Labeling dataset with an only marginal drop in the original accuracy. Furthermore, the proposed method shows promising results in comparison with other state-of-the-art methods.