Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic characterisation and dissection of the onset of resistance to acetyl CoA carboxylase-inhibiting herbicides in a large collection of Digitaria insularis from Brazil

An in-depth genotypic characterisation of a diverse collection of Digitaria insularis was undertaken to explore the neutral genetic variation across the natural expansion range of this weed species in Brazil. With the exception of Minas Gerais, populations from all other states showed high estimates of expected heterozygosity (HE > 0.60) and genetic diversity. There was a lack of population structure based on geographic origin and a low population differentiation between populations across the landscape as evidenced by average Fst value of 0.02. On combining haloxyfop [acetyl CoA carboxylase (ACCase)-inhibiting herbicide] efficacy data with neutral genetic variation, we found evidence of presence of two scenarios of resistance evolution in this weed species. Whilst populations originating from north-eastern region demonstrated an active role of gene flow, populations from the mid-western region displayed multiple, independent resistance evolution as the major evolutionary mechanism. A target-site mutation (Trp2027Cys) in the ACCase gene, observed in less than 1% of resistant populations, could not explain the reduced sensitivity of 15% of the populations to haloxyfop. The genetic architecture of resistance to ACCase-inhibiting herbicides was dissected using a genome wide association study (GWAS) approach. GWAS revealed association of three SNPs with reduced sensitivity to haloxyfop and clethodim. In silico analysis of these SNPs revealed important non-target site genes belonging to families involved in herbicide detoxification, including UDPGT91C1 and GT2, and genes involved in vacuolar sequestration-based degradation pathway. Exploration of five genomic prediction models revealed that the highest prediction power (≥0.80) was achieved with the models Bayes A and RKHS, incorporating SNPs with additive effects and epistatic interactions, respectively.
OPTIMISING EMPLOYEE PERFORMANCE THROUGH QUALITY IMPROVEMENT WORK LIFE AND EMPLOYEE ENGAGEMENT; A CASE STUDY ON THE REGIONAL SECRETARIAT OF PEKALONGAN CITY

This study aims to optimise employee performance at Pekalongan City Regional Secretariat through improving quality of work life and employee engagement, using organisational citizenship behaviour (OCB) as a mediator. Quality of work life and employee engagement are identified as key factors that have the potential to improve employee performance. By utilising mathematical equations, this study illustrates the relationship between these variables. The study population consisted of civil servants in the Regional Secretariat of Pekalongan City. Analysis methods include descriptive analysis, multiple regression, coefficient of determination test, F test (Anova), and mediation test (Sobel test). KEYWORDS: Quality of work life, employee engagement, employee performance, organisational citizenship behaviour
Mathematics for 2D face recognition from real time image data set using deep learning techniques

The recognition of human faces poses a complex challenge within the domains of computer vision and artificial intelligence. Emotions play a pivotal role in human interaction, serving as a primary means of communication. This manuscript aims to develop a robust recommendation system capable of identifying individual faces from rasterized images, encompassing features such as eyes, nose, cheeks, lips, forehead, and chin. Human faces exhibit a wide array of emotions, with some emotions, including anger, sadness, happiness, surprise, fear, disgust, and neutrality, being universally recognizable. To achieve this objective, deep learning techniques are leveraged to detect objects containing human faces. Every human face exhibits common characteristics known as Haar features, which are employed to extract feature values from images containing multiple elements. The process is executed through three distinct stages, starting with the initial image and involving calculations. Real-time images from popular social media platforms like Facebook are employed as the dataset for this endeavor. The utilization of deep learning techniques offers superior results, owing to their computational demands and intricate design when compared to classical computer vision methods using OpenCV. The implementation of deep learning is carried out using PyTorch, further enhancing the precision and efficiency of face recognition.
Oxidative Imbalance in Pediatric Anxiety Disorders: A Preliminary Comparative Study

Background It is important to determine the possible related factors of anxiety disorder, one of the common psychiatric disorders of childhood. Our aims in this study were to compare oxidative stress markers between anxiety disorders in pediatric patients and healthy controls and to examine the relationship between anxiety symptom severity and oxidative stress indicators. Methods The study included 25 patients and 25 healthy controls. We measured the total oxidant capacity (TOS) and total antioxidant capacity (TAS) from the collected serum samples and calculated the oxidative stress index (OSI). We evaluated the clinical severity of the anxiety symptoms by the Revised Child Anxiety and Depression Scale-Child Version (RCADS-CV). Results The groups did not exhibit a noteworthy distinction in terms of TOS (p=0.128) and TAS (p=0.329). However, OSI was markedly elevated in the group with anxiety disorder (p=0.044). In the correlation analysis between anxiety symptom severity and oxidative stress indicators in the group with anxiety disorder, we found a positive correlation between TOS and RCADS total anxiety score (p=0.08). Conclusion These results may point to an oxidative dysfunction in anxiety disorders and the potential role of oxidative stress in their aetiology. Prospective, large-scale, randomized studies are needed to investigate if oxidative stress indicators can be used in the diagnosis of anxiety disorders and as new treatment targets.
On Learning Generalized Wireless MAC Communication Protocols via a Feasible Multi-Agent Reinforcement Learning Framework

Automatically learning medium access control (MAC) communication protocols via multi-agent reinforcement learning (MARL) has received huge attention to cater to the extremely diverse real-world scenarios expected in 6G wireless networks. Several state-of-the-art solutions adopt the centralized training with decentralized execution (CTDE) learning method, where agents learn optimal MAC protocols by exploiting the information exchanged with a central unit. Despite the promising results achieved in these works, two notable challenges are neglected. First, these works were designed to be trained in computer simulations assuming an omniscient environment and neglecting communication overhead issues, thus making the implementation impractical in real-world scenarios. Second, the learned protocols fail to generalize outside of the scenario they were trained on. In this paper, we propose a new feasible learning framework that enables practical implementations of training procedures, thus allowing learned MAC protocols to be tailor-made for the scenario where they will be executed. Moreover, to address the second challenge, we leverage the concept of state abstraction and imbue it into the MARL framework for better generalization. As a result, the policies are learned in an abstracted observation space that contains only useful information extracted from the original high-dimensional and redundant observation space. Simulation results show that our feasible learning framework exhibits performance comparable to that of the infeasible solutions. In addition, the learning frameworks adopting observation abstraction offer better generalization capabilities, in terms of the number of UEs, number of data packets to transmit, and channel conditions.
Pan-Sharpening With Wavelet-Enhanced High-Frequency Information

Pan-sharpening is essentially a panchromatic (PAN)-guided super-resolution process, primarily focused on enhancing multispectral image quality. This methodology intricately incorporates the high-frequency derived from texture-rich PAN images into the low-resolution multispectral (LRMS) counterparts. However, current spatial-domain techniques frequently face challenges in accurately restoring texture details, while frequency-domain methods lack efficient interaction with spatial domains, thus restricting the overall model performance. In response to these challenges, we introduce a novel high-frequency wavelet network that capitalizes on the spatial–frequency interaction and frequency division capabilities inherent in wavelet transform. In particular, our approach consists of two fundamental modules: the wavelet-inspired fusion block (WFB) and the high-frequency enhancement block (HEB). The former is inspired by wavelet lifting schemes, enabling the fusion of frequencies and facilitating information exchange across various subbands. The latter harnesses wavelet’s frequency division attributes to enhance high-frequency information learning. Comprehensive experiments over multiple satellite datasets demonstrate that our approach outperforms state-of-the-art (SOTA) techniques in both quantitative and qualitative assessments. Moreover, our model showcases exceptional generalization capabilities in real-world scenarios. Code is available at https://github.com/alexhe101/WINet.
Evaluation of Jacking Forces in Weathered Phyllite Based on In Situ Pressuremeter Testing and Deep Learning

Pipe jacking is a trenchless technology used to install buried pipelines, such as sewer lines in wastewater management systems. Existing mechanistic approaches based on geomaterial strength parameters (i.e., friction angle and apparent cohesion) can provide an estimation of the potential jacking forces during construction. However, extracting intact rock cores for strength characterisation is challenging when dealing with highly weathered ‘soft rocks’ which exhibit RQD values of zero. Such was the case for a pipe jacking drive traversing the highly weathered lithology underlying Kuching City, Malaysia. Furthermore, mechanistic approaches face limitations during construction when jacking forces are dependent on operation parameters, such as jacking speed and lubrication. To address these knowledge gaps, the primary objectives of this study are the development of rock strength parameters based on in situ pressuremeter testing for the purpose of estimating jacking forces. Furthermore, this study investigates the influence of various pipe jacking operation parameters, with a particular focus on their impact on jacking forces in weathered ‘soft rocks’. To achieve this, a novel deep learning model with an attention mechanism is introduced. The proposed methods of rock strength parameters derived from pressuremeter testing and the utilisation of deep learning will help to provide insights into the key factors affecting the development of jacking forces. This paper successfully shows the use of in situ pressuremeter testing in developing Mohr–Coulomb (MC) parameters directly from the site. In addition, the developed deep learning model with an attention mechanism successfully highlights the significance of pipe jacking operation parameters with an accuracy of 88% in predicting the jacking forces.
Image Quality Evaluation

Image quality evaluation plays an important role in the design of many Hewlett Packard products, including imaging peripherals such as digital cameras, scanners, printers and displays. This chapter describes some of the engineering tools my colleagues and I are developing at Hewlett Packard Laboratories to help us evaluate how our customers perceive the image quality of our products. The engineering tools we use to evaluate image quality can be grouped into three categories: device simulation, subjective evaluation and distortion metrics. Simulation tools enable us to test our understanding of devices, predict their output, and optimize their design. We use software simulators for image capture devices, such as scanners and digital cameras, and rendering devices, such as displays and printers, to determine how adjustments in device parameters affect subjective impressions of image quality. Simulators also enable us to investigate regions of design space that are difficult to build and prototype. In this chapter I briefly review the assumptions underlying our device simulations and give an example of how device simulation enables us to optimize the image quality of H P inkjet and electrophotographic printers. Since our customers are the final arbiter of image quality, we consider their subjective image quality judgements to be key to the success of our imaging product. We
Diosgenin alleviates D-galactose-induced oxidative stress in rats’ brain and liver targeting aging and apoptotic marker genes

The theory of aging is primarily concerned with oxidative stress caused by an imbalance in reactive oxygen species generation and cellular antioxidants. To alleviate the oxidative stress, we investigated the protective effect of diosgenin (DSG) for D-galactose (D-gal) using 20 and 40 mg of DSG/kg/day/orally for 42 days. The findings showed that D-gal caused brain and liver oxidative injuries by upregulating aging and oxidative markers. To counteract the oxidative stress caused by D-gal, DSG upregulated glutathione peroxidase-1, superoxide dismutase-1, and glutathione S-transferase-α. DSG also diminished the expression of p53, p21, Bcl-2-associated X protein, caspase-3, and mammalian target of rapamycin in brain and liver, as well as the build-up of β-galactosidase. DSG, in a dose-dependent manner, decreased the oxidative aging effects of D-gal in brain and liver tissues through targeting of aging and apoptotic marker genes. Finally, it should be noted that consuming DSG supplements is a suggesting natural preventative agent that may counteract aging and preserve health through improvement of body antioxidant status and control aging associated inflammation and cellular apoptosis.
Novel strains of Actinobacteria associated with neotropical social wasps (Vespidae; Polistinae, Epiponini) with antimicrobial potential for natural product discovery

Abstract Antimicrobial resistance has been considered a public health threat. The World Health Organization has warned about the urgency of detecting new antibiotics from novel sources. Social insects could be crucial in the search for new antibiotic metabolites, as some of them survive in places that favor parasite development. Recent studies have shown the potential of social insects to produce antimicrobial metabolites (e.g. ants, bees, and termites). However, most groups of social wasps remain unstudied. Here, we explored whether Actinobacteria are associated with workers in the Neotropical Social Wasps (Epiponini) of Costa Rica and evaluated their putative inhibitory activity against other bacteria. Most isolated strains (67%) have antagonistic effects, mainly against Bacillus thuringensis and Escherichia coli ATCC 25992. Based on genome analysis, some inhibitory Actinobacteria showed biosynthetic gene clusters (BGCs) related to the production of antimicrobial molecules such as Selvamycin, Piericidin A1, and Nystatin. The Actinobacteria could be associated with social wasps to produce antimicrobial compounds. For these reasons, we speculate that Actinobacteria associated with social wasps could be a novel source of antimicrobial compounds, mainly against Gram-negative bacteria.
Geographic origin differentiation of Philippine civet coffee using an electronic nose

1Industrial Technology Development Institute, DOST, Bicutan, Taguig, 1631 Metro Manila, Philippines; 2Graduate School, University of Santo Tomas, 1015 Manila, Philippines; 3CNR-IDASC SENSOR & Brescia University, Via Valotti 9-, 25133 Brescia, Italy; 4Centro di Ricerca Interdipartimentale per il Miglioramento e la Valorizzazione delle Risorse Biologiche Agro-Alimentari BIOGEST-SITEIA, Università degli Studi di Modena e Reggio Emilia, Piazzale Europa 1 (Tecnopolo di Reggio Emilia), 42124 Reggio Emilia, Italy; 5Dipartimento di Scienze Agrarie e degli Alimenti, Universitá di Modena e Reggio Emilia, Via G. Amendola 2, 42122 Reggio Emilia, Italy
An updated method for estimating landslide‐event magnitude

Summary statistics derived from the frequency–area distribution (FAD) of inventories of triggered landslides allows for direct comparison of landslides triggered by one event (e.g. earthquake, rainstorm) with another. Such comparisons are vital to understand links between the landslide‐event and the environmental characteristics of the area affected. This could lead to methods for rapid estimation of landslide‐event magnitude, which in turn could lead to estimates of the total triggered landslide area. Previous studies proposed that the FAD of landslides follows an inverse power‐law, which provides the basis to model the size distribution of landslides and to estimate landslide‐event magnitude (mLS), which quantifies the severity of the event. In this study, we use a much larger collection of earthquake‐induced landslide (EQIL) inventories (n=45) than previous studies to show that size distributions are much more variable than previously assumed. We present an updated model and propose a method for estimating mLS and its uncertainty that better fits the observations and is more reproducible, robust, and consistent than existing methods. We validate our model by computing mLS for all of the inventories in our dataset and comparing that with the total landslide areas of the inventories. We show that our method is able to estimate the total landslide area of the events in this larger inventory dataset more successfully than the existing methods. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.
Cross-Sectional but Not Prospective Association of Accelerometry-Derived Physical Activity With Quality of Life in Children and Adolescents

Objectives: This study aims to quantify the cross-sectional and prospective associations between quality of life (QoL) and moderate-to-vigorous physical activity (MVPA). Methods: This study was based on the Swiss children’s Objectively measured PHYsical Activity cohort. The primary endpoint is the overall QoL score and its six dimensions. The main predictor is the average time spent in MVPA per day. Linear mixed effects and linear regression models respectively were used to investigate the cross-sectional and prospective associations between MVPA and QoL. Results: There were 352 participants in the study with complete data from baseline (2013–2015) and follow-up (2019). MVPA was positively associated with overall QoL and physical wellbeing (p = 0.023 and 0.002 respectively). The between-subject MVPA was positively associated with the overall QoL, physical wellbeing, and social wellbeing (p = 0.030, 0.017, and 0.028 respectively). Within-subject MVPA was positively associated with physical wellbeing and functioning at school (p = 0.039 and 0.013 respectively). Baseline MVPA was not associated with QoL 5 years later. Conclusion: Future longitudinal studies should employ shorter follow-up times and repeat measurements to assess the PA and QoL association.
Stability Control of Narmab Dam and Sensitivity Analysis of Reliability Coefficients

Static and quasi-static stability analysis of embankment dams is of vital importance in different stages of dam’s design, construction and operation. The stability can be studied using different techniques which are generally analyzed through Limit Equilibrium Method. Base on this main method, the critical slip surface is selected and the shear strength required to counter the slip at the selected surface is obtained and compared with shear strength of the soil at that surface in order to obtain confidence coefficient. In the present research, the Geo-studio Slope/w software that is a geotechnical software based on finite element method and is widely used in geotechnical field, is employed in order to analyze the stability of the body and foundation of Narmab dam in Golestan province. Narmab dam is a homogeneous embankment dam with a height of 60 m, crest length of 807 m and reservoir volume of 115 million cubic meters. The confidence coefficients provided by the software are compared to the permissible confidence coefficients. Moreover, the sensitivity of the confidence coefficients values to the changes in the effective factors, adhesion and internal friction coefficient, is analyzed. The analyses were performed on 8 values (±5, ±10, ±15, ±20) of c and φ and the obtained values of confidence coefficients were compared. In addition, a comparison was made between different methods of stability analysis. According to the static and quasi-static conditions, Narmab dam is stable in all loading stages (End of Construction, First Impounding and Steady State Seepage and In general, only for the static conditions of the end of construction stage, the sensitivity of adhesion is greater than the angle of internal friction, but, in other conditions and stages, the sensitivity of friction angle has more effects.
Short-Term Stock Correlation Forecasting Based on CNN-BiLSTM Enhanced by Attention Mechanism

This study utilizes a new approach for short-term stock correlation forecasting using a combination of convolutional neural networks (CNN), bi-directional long and short-term memory (BiLSTM), and attention mechanisms to address the issue of information loss due to excessively long input time series data. The objective is to improve the accuracy of short-term equity correlation forecasts, which is essential for the efficient management of financial asset portfolios. The existing time series models usually only take into account the effect of stock historical data characteristics on stock correlation but ignore the fact that stock correlation is affected by multiple factors and undergoes an unknown unstable trend. More efficient methods or algorithms need to be devised to deal with financial data. This paper explores stock metrics and introduces a multi-factor model to quantify the factors affecting stock returns in order to calculate the correlation between stocks. Meanwhile, to cope with the problem of incomplete input data, this paper simultaneously incorporates stock return data that is directly related to the correlation data. In the CNN-BiLSTM-Attention (CLATT) model, the role of the CNN module is to obtain high-dimensional features from the input time series data, and the role of the BiLSTM module is to process the time series information to capture the time-series features in the stock return data and correlation data in the sample. In order to acquire the inherent correlation between the input stock return data and correlation data and to enhance the prediction accuracy of the model, the attention mechanism is finally implemented and used to optimize the weights of the BiLSTM output. In this paper, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) metrics, mean absolute percentage of error (MAPE), and the coefficient of determination $R^{2}$ , as well as four benchmark models, are used to evaluate the performance of the proposed model. The result shows that the CLATT model has 57.32% and 33% improvement in the correlation prediction accuracy of different stock portfolios compared to the single LSTM model, and the model prediction accuracy has been improved. Compared with the benchmark CNN-BiLSTM, it also has 56.06% and 28.87% improvement.