Miracle-dz/UniFAR-FTUs huggingface dataset:
A sigmoid‐optimized encoder–decoder network for crack segmentation with copy‐edit‐paste transfer learning

The automatic recognition of cracks is an essential requirement for the cost‐efficient maintenance of concrete structures, such as bridges, buildings, and roads. It should allow the localization and the determination of the crack type and the evaluation of the crack severity by providing information on the shape, orientation, and crack area and width. The first step in this direction is the automatized segmentation of cracks. This paper provides a concrete crack data set (370 images) and proposes two solutions that achieve the best results on two different crack data sets. Our first solution concerns the segmentation architecture. We provide an encoder–decoder‐based network with a particular interconnection of layers between the encoder and decoder parts that outperforms several other methods. In addition, this network is enhanced by squeeze‐and‐excitation blocks equipped with a modified sigmoid activation function. We introduce a stretch coefficient into the sigmoid function and declare it a trainable parameter, allowing more differentiated calibration of the feature map during network training. Our second solution concerns kernel initialization by transfer learning (TL). We propose the Copy‐Edit‐Paste Transfer Learning (CEP TL). By copying, geometric editing, and pasting crack masks onto new concrete background images, we generate thousands of semisynthetic images used to pretrain the network. This CEP TL method increases model performance with significant differences. For data set A (ours), we achieve F1‐scores 76.06 ± 0.06% without CEP TL and 92.32 ± 0.82% with CEP TL. For data set B (DeepCrack data set), we achieve F1‐scores 88.56 ± 0.01% without CEP TL and 90.59 ± 0.80% with CEP TL.
End-to-End Lip-Reading Open Cloud-Based Speech Architecture

Deep learning technology has encouraged research on noise-robust automatic speech recognition (ASR). The combination of cloud computing technologies and artificial intelligence has significantly improved the performance of open cloud-based speech recognition application programming interfaces (OCSR APIs). Noise-robust ASRs for application in different environments are being developed. This study proposes noise-robust OCSR APIs based on an end-to-end lip-reading architecture for practical applications in various environments. Several OCSR APIs, including Google, Microsoft, Amazon, and Naver, were evaluated using the Google Voice Command Dataset v2 to obtain the optimum performance. Based on performance, the Microsoft API was integrated with Google’s trained word2vec model to enhance the keywords with more complete semantic information. The extracted word vector was integrated with the proposed lip-reading architecture for audio-visual speech recognition. Three forms of convolutional neural networks (3D CNN, 3D dense connection CNN, and multilayer 3D CNN) were used in the proposed lip-reading architecture. Vectors extracted from API and vision were classified after concatenation. The proposed architecture enhanced the OCSR API average accuracy rate by 14.42% using standard ASR evaluation measures along with the signal-to-noise ratio. The proposed model exhibits improved performance in various noise settings, increasing the dependability of OCSR APIs for practical applications.
ROLE OF PAST BEHAVIOUR IN APPLICATION OF TPB IN PREDICTING ENERGY SAVING BEHAVIOUR: A REVIEW

The energy sector is the largest source of emissions and accounting for nearly 68% of the world's greenhouse gas emissions. Where 36% of all global final energy use is generated from buildings. Currently, the topic of energy-saving behaviour is attracting the attention of researchers, how to encourage users towards energy-saving behaviour (ESB). Theory of Planned Behaviour (TPB) is empirically recognized as a model widely used in predicting social behaviour. However, the TPB has received much debate and criticism about the narrow sufficiency of the three original determinants: Attitudes, Subjective Norms and Perceived Behaviour Control. This article discusses empirical support by several authors who acknowledged other related factor that need to be considered in developing a theoretical framework by adding Past Behaviour in predicting the energy saving behaviour of users to improve the ESB. An extensive literature search was conducted on 100 published literature in year 2011-2021, to explore the significance of past behaviour as an additional psychological factor of ESB in a building context. Content analysis conducted based on previous research published in leading journals (e.g. Scopus and Web of Science). About 30% of previous studies have found Past Behaviour as a significant contributing predictor of ESB beside the TPB original determinants. This study will help to provide a better understanding about social psychological determinants of energy saving behaviour hence serve as a guide to the building operator and relevant agencies in shaping strategy to promote energy saving behaviour among the occupants.
Highly pathogenic avian influenza virus incursions of subtype H5N8, H5N5, H5N1, H5N4, and H5N3 in Germany during 2020-21

Abstract From October 2020 to July 2021, five different subtypes (H5N8, H5N5, H5N1, H5N4, and H5N3) and seven genotypes of highly pathogenic avian influenza viruses (HPAIV) belonging to clade 2.3. 4.4b were detected in a broad array of avian hosts in Germany. Initial incursion by wild birds with an unprecedented involvement of charadriiforme species at the Wadden Sea coast only carrying subtype H5N3, lateral spread between poultry with detection of novel reassortants and mixed infections in poultry holdings, suspected spillback of HPAIV from poultry to wild birds, and detection of HPAIV-infected wild birds during the following summer in 2021 were hallmarks of this epizootic. Local reassortment events with low pathogenic AIV strains were detected by phylogenetic analyses, with a dominating HP H5N8 and later HP H5N1 strain responsible for most cases. In addition, the first-ever described HPAIV strain of subtype H5N4 could be genetically characterized.
INFLUENCE OF SCHOOL LOCATION ON TEACHERS’ ACCEPTANCE OF WHATSAPP/TELEGRAM FOR ONLINE INSTRUCTION

This study determined the teachers’ level of acceptance of WhatsApp/Telegram for online instructions in secondary schools in Ondo East and Ondo West Local Government Areas of Ondo State. It determined the influence of school location on the teachers’ acceptance of WhatsApp/Telegram for online instructions in the secondary schools. It also investigated the influence of teachers’ characteristics (sex, qualification and years of experience) on the teachers’ level of acceptance of WhatsApp/Telegram for online instructions across the secondary school locations and determined the relationship between the teachers’ attitude towards and acceptance of WhatsApp/Telegram for online instructions in the secondary schools. The study adopted the descriptive survey research design. The population of the study consisted all the secondary school teachers in the local government areas. 299 teachers were selected across the schools using multistage sampling procedure. Teachers’ Acceptance of Telegram/WhatsApp Based Online Classes was used for data gathering. Analysis of data showed that the level of acceptance of WhatsApp/Telegram for online instruction by the teachers is high (82.5%). Also that school location has no significant influence on the teachers’ acceptance of WhatsApp/Telegram for online instructions. Results also revealed that sex has no significant influence on the teachers’ acceptance of the tool (t(194) = 0.67; p > 0.05). Academic qualifications also did not have significant influence on the teachers’ acceptance of WhatsApp/Telegram (F(6,203) = 1.26; p > 0.05). Years of experience as well had no significant influence on the teachers’ acceptance of WhatsApp/Telegram (F(8,209) = 1.52; p > 0.05).
Physiological Effects and Human Health Benefits of Hibiscus sabdariffa: A Review of Clinical Trials

Hibiscus sabdariffa Linn. Malvaceae (HS) is characterized by its edible calyxes. The HS calyxes are widely used for cosmetic, food, and medicinal applications. According to ethnobotanical evidence, decoction, infusion, or maceration extracts from HS calyxes have been used in folk medicine to treat many ailments. Moreover, several in vitro and in vivo studies have demonstrated the pharmacological properties and potential human health benefits of HS consumption. On the other hand, the evaluation of the physiological effects and health benefits of HS in clinical studies is most challenging. Therefore, this narrative review summarizes and discusses the physiological effects and health benefits of HS calyxes reported in clinical trials. Preparations obtained from HS calyxes (extracts, infusions, decoction, teas, beverages, capsules, and pills) are used as non-pharmacological therapies to prevent/control diverse chronic non-communicable diseases. The most-reported HS health benefits are its antihypertensive, antidyslipidemic, hypoglycemic, body fat mass reduction, nephroprotective, antianemic, antioxidant, anti-inflammatory, and anti-xerostomic activities; these effects are associated with the phytochemicals found in HS. Moreover, no adverse effects were reported during the clinical trials. However, clinical studies exhibited some limitations; thus, further studies are required to validate the clinical efficacy of HS in large-scale studies with higher doses and a good experimental design
Coupled Projection Transfer Metric Learning for Cross-Session Emotion Recognition from EEG

Distribution discrepancies between different sessions greatly degenerate the performance of video-evoked electroencephalogram (EEG) emotion recognition. There are discrepancies since the EEG signal is weak and non-stationary and these discrepancies are manifested in different trails in each session and even in some trails which belong to the same emotion. To this end, we propose a Coupled Projection Transfer Metric Learning (CPTML) model to jointly complete domain alignment and graph-based metric learning, which is a unified framework to simultaneously minimize cross-session and cross-trial divergences. By experimenting on the SEED_IV emotional dataset, we show that (1) CPTML exhibits a significantly better performance than several other approaches; (2) the cross-session distribution discrepancies are minimized and emotion metric graph across different trials are optimized in the CPTML-induced subspace, indicating the effectiveness of data alignment and metric exploration; and (3) critical EEG frequency bands and channels for emotion recognition are automatically identified from the learned projection matrices, providing more insights into the occurrence of the effect.
Machine Learning Prediction Models to Evaluate the Strength of Recycled Aggregate Concrete

Compressive and flexural strength are the crucial properties of a material. The strength of recycled aggregate concrete (RAC) is comparatively lower than that of natural aggregate concrete. Several factors, including the recycled aggregate replacement ratio, parent concrete strength, water–cement ratio, water absorption, density of the recycled aggregate, etc., affect the RAC’s strength. Several studies have been performed to study the impact of these factors individually. However, it is challenging to examine their combined impact on the strength of RAC through experimental investigations. Experimental studies involve casting, curing, and testing samples, for which substantial effort, price, and time are needed. For rapid and cost-effective research, it is critical to apply new methods to the stated purpose. In this research, the compressive and flexural strengths of RAC were predicted using ensemble machine learning methods, including gradient boosting and random forest. Twelve input factors were used in the dataset, and their influence on the strength of RAC was analyzed. The models were validated and compared using correlation coefficients (R2), variance between predicted and experimental results, statistical tests, and k-fold analysis. The random forest approach outperformed gradient boosting in anticipating the strength of RAC, with an R2 of 0.91 and 0.86 for compressive and flexural strength, respectively. The models’ decreased error values, such as mean absolute error (MAE) and root-mean-square error (RMSE), confirmed the higher precision of the random forest models. The MAE values for the random forest models were 4.19 MPa and 0.56 MPa, whereas the MAE values for the gradient boosting models were 4.78 MPa and 0.64 MPa, for compressive and flexural strengths, respectively. Machine learning technologies will benefit the construction sector by facilitating the evaluation of material properties in a quick and cost-effective manner.
Application of Low-Frequency Processing Method Based on VMD Algorithm in Blasting Signal Processing

In the field of blasting vibration signal analysis, accurate removal of low-frequency noise in the signal has important practical significance. In view of the shortcomings of the existing methods, such as cumbersome calculation and limited scope of application, a method based on variational mode decomposition (VMD) to remove low-frequency noise was proposed. In this paper, the parameter selection of VMD algorithm to identify low-frequency noise of blasting signal is optimized by simulation experiment. The results show that frequency has little influence on the decomposition effect. When the frequency of low-frequency noise is between 0 and 5 Hz, the influence of frequency on the decomposition effect remains basically unchanged. The amplitude has a significant effect on the decomposition effect. The smaller the amplitude, the worse the decomposition effect of the VMD method. Taking the excavation of the Badaling Great Wall Station as the background, the LSM method, EMD method, and VMD method are used to remove the low-frequency noise of the randomly selected test signals in order. Compared with the LSM method and the EMD method, the signal processed by the VMD method does not have modal mixing and does not have much low-frequency noise residue. It has more extensive applicability in removing low-frequency noise of blasting signal.
Forecasting commodity returns by exploiting climate model forecasts of the El Niño Southern Oscillation

Abstract The physical and socioeconomic environments in which we live are intrinsically linked over a wide range of time and space scales. On monthly intervals, the price of many commodities produced predominantly in tropical regions covary with the dominant mode of climate variability in this region, namely the El Niño Southern Oscillation (ENSO). Here, for the spot prices returns of vegetable oils produced in Asia, we develop autoregressive (AR) models with exogenous ENSO indices, where for the first time these indices are generated by a purpose-built state-of-the-art general circulation model (GCM) climate forecasting system. The GCM is a numerical simulation which couples together the atmosphere, ocean, and sea ice, with the initial conditions tailored to maximize the climate forecast skill at multiyear timescales in the tropics. To serve as additional benchmarks, we also test commodity forecasts using: (a) no ENSO information as a lower bound; (b) perfect future ENSO knowledge as a reference upper bound; and (c) an econometric AR model of ENSO. All models adopting ENSO factors outperform those that do not, indicating the value here of incorporating climate knowledge into investment decision-making. Commodity forecasts adopting perfect ENSO factors have statistically significant skill out to 2 years. When adopting the GCM-ENSO factors, there is predictive power of the commodity beyond 1 year in the best case, which consistently outperforms commodity forecasts adopting an AR econometric model of ENSO.
Calculation of copper losses in case of Litz and twisted wires. 2D Modeling and application for switched reluctance machine

The strong correlation between the level of eddy current losses and the winding geometry shows the necessity to pay attention to the manner of disposition of coils in machine slots. The conductor type, whether it is solid or stranded, also has a crucial influence. Since the transposition of winding strands reduces the degree of skin and proximity effects, it is a recommended solution to reduce eddy current losses. This article suggests an electromagnetic analysis of complex stranded conductors such as Litz and twisted wires. Each conductor is decomposed into individual strands that are twisted or woven in the slot throughout the length of the machine. Combined with several electric circuit relationships that interpret the transposition of the strands, a 2D finite element model is developed in this article so that the copper losses in each strand can be calculated individually.
Language profile, metacognitive reading strategies, and reading comprehension performance among college students

Abstract This study dealt with the students’ language profile, metacognitive reading strategies, and reading comprehension performance adopting the descriptive-correlational method of the four hundred forty-six randomly selected students enrolled in the colleges of Forestry, Education, Engineering, Computer Science, and Information Technology in a Philippine State University. Instruments used to gather data were based on an expert validated 20-point comprehension test taken from a short story selection and an adopted 30-point Metacognitive Awareness Reading Inventory (MARI). Findings revealed that Tagalog was the most commonly spoken by the respondents, followed by Iloco and English with moderately high reading comprehension performance and average level in terms of familiarity and attitude towards the Filipino language. The Problem-Solving Strategies (PROB) was the most frequently used, followed by the Global Reading Strategies (GLOB) and the Support Reading Strategies (SUPP). The familiarity with the Filipino language and the use of Problem-solving Metacognitive Reading Strategies were found to have a significant relationship to students’ reading comprehension performance.
TBP, PPIA, YWHAZ and EF1A1 Are the Most Stably Expressed Genes during Osteogenic Differentiation

RT-qPCR is the gold standard and the most commonly used method for measuring gene expression. Selection of appropriate reference gene(s) for normalization is a crucial part of RT-qPCR experimental design, which allows accurate quantification and reliability of the results. Because there is no universal reference gene and even commonly used housekeeping genes’ expression can vary under certain conditions, careful selection of an appropriate internal control must be performed for each cell type or tissue and experimental design. The aim of this study was to identify the most stable reference genes during osteogenic differentiation of the human osteosarcoma cell lines MG-63, HOS, and SaOS-2 using the geNorm, NormFinder, and BestKeeper statistical algorithms. Our results show that TBP, PPIA, YWHAZ, and EF1A1 are the most stably expressed genes, while ACTB, and 18S rRNA expressions are most variable. These data provide a basis for future RT-qPCR normalizations when studying gene expression during osteogenic differentiation, for example, in studies of osteoporosis and other bone diseases.
Self Supervised Adversarial Domain Adaptation for Cross-Corpus and Cross-Language Speech Emotion Recognition

Despite the recent advancement in speech emotion recognition (SER) within a single corpus setting, the performance of these SER systems degrades significantly for cross-corpus and cross-language scenarios. The key reason is the lack of generalisation in SER systems towards unseen conditions, which causes them to perform poorly in cross-corpus and cross-language settings. Recent studies focus on utilising adversarial methods to learn domain generalised representation for improving cross-corpus and cross-language SER to address this issue. However, many of these methods only focus on cross-corpus SER without addressing the cross-language SER performance degradation due to a larger domain gap between source and target language data. This contribution proposes an adversarial dual discriminator (ADDi) network that uses the three-players adversarial game to learn generalised representations without requiring any target data labels. We also introduce a self-supervised ADDi (sADDi) network that utilises self-supervised pre-training with unlabelled data. We propose synthetic data generation as a pretext task in sADDi, enabling the network to produce emotionally discriminative and domain invariant representations and providing complementary synthetic data to augment the system. The proposed model is rigorously evaluated using five publicly available datasets in three languages and compared with multiple studies on cross-corpus and cross-language SER. Experimental results demonstrate that the proposed model achieves improved performance compared to the state-of-the-art methods.
Receding-Horizon Reinforcement Learning Approach for Kinodynamic Motion Planning of Autonomous Vehicles

Kinodynamic motion planning is critical for autonomous vehicles with high maneuverability in dynamic environments. However, obtaining near-optimal motion planning solutions with low computational costs and inaccurate prior model information is challenging. To address this issue, this paper proposes a receding-horizon reinforcement learning approach for kinodynamic motion planning (RHRL-KDP) of autonomous vehicles in the presence of inaccurate dynamics information and moving obstacles. Specifically, a receding-horizon actor-critic reinforcement learning algorithm is presented, resulting in a neural network-based planning strategy that can be learned both offline and online. A neural network-based model is built and learned online to approximate the modeling uncertainty of the prior nominal model in order to improve planning performance. Furthermore, active collision avoidance in dynamic environments is realized by constructing safety-related terms in actor and critic networks using potential fields. In theory, the uniformly ultimate boundedness property of the modeling uncertainty’s approximation error is proven, and the convergence of the proposed RHRL-KDP is analyzed. Simulation tests show that our approach outperforms the previously developed motion planners based on model predictive control (MPC), safe RL, and RRT${^\star }$ in terms of planning performance. Furthermore, in both online and offline learning scenarios, RHRL-KDP outperforms MPC and RRT$^{\star }$ in terms of computational efficiency.
Load transfer mechanism of concrete screws

Even though the market and development for concrete screws has been increasingly rising in recent years, the load transfer mechanism of concrete screws has not yet been fully investigated. Therefore, different tests of concrete screws made of galvanized and stainless steel were performed in concrete C20/25 and C50/60. The main aim is to measure the strain along the embedment depth. This will be achieved by using strain gauges that get placed in a centrically drilled borehole through the concrete screw. To get a comparison to the mechanism of the screws the same process will be executed in threaded rods used as a part of bonded anchors. Due to the fact that the threaded cuts of concrete screws have geometrical similarities to bonded anchors, it was examined if the load transfer of both fasteners is related and may be compared. The results of the testing have shown that the load transfer mechanism of both fastener types is similar in low-strength concrete showing a concrete cone failure. In high strength concrete due to the mainly occurring steel failure the maximum measured strains at the maximum load step are not comparable. However, at lower load steps where the steel does not exceed the yield strength the results show a similar load transfer mechanism, too.