Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating knowledge dissemination and social media use in the farming network to build trust in smart farming technology adoption

Purpose This paper aims to investigate how actors in the farmer’s network influence the adoption of smart farming technology (SFT) and to understand how social media affects this adoption process, in particular focusing on the influence of social media on trust in knowledge dissemination within the network. Design/methodology/approach The methodology used a two-stage process, with semi-structured interviews of farmers, augmented by a netnographic approach appropriate to the social media context. Findings The analysis illustrates the key role of the farmer network in the dissemination of SFT knowledge, bringing insight into an important B2B context. While social media emerges as a valuable way to connect farmers and promote discussion, it remains underused in knowledge dissemination on SFT. Also, farmers exhibit more trust in the content from peers online rather than from SFT vendors. Originality/value Novel insights are gained into the influence of the farming network on the accelerated adoption of SFT, including the potential role of social media in mitigating the homophilous nature of peer-to-peer interactions among farmers through exposure to more diverse actors and information. The use of a social network theory lens has provided new insights into the role of trust in shaping social media influence on the farmer, with variances in farmer trust of information from technology vendors and from peers.
A local regression approach to analyze the orographic effect on the spatial variability of sub-daily rainfall annual maxima

Abstract In this work we investigate the spatial variability of sub-daily rainfall extremes over Italy considering the influence of local orographic effects. We consider the average annual maxima computed from the recently-released Improved Italian – Rainfall Extreme Dataset (I2-RED) in about 3800 time series with at least 10 years of data (1916–2020 period) and we analyze the orographic effects through a local regression approach which gathers stations in a grid cell-centered area of 1 km resolution. Several constraints are considered to tackle problems determined by the low data density of some areas and by the extrapolation at low/high elevations. Different criteria for selecting the local sample are examined. This work confirms with increased detail previous findings, such as a generally positive gradient of the 24 h average annual maxima and the evidence of negative gradients in large mountainous areas for the 1 h maxima. The use of a local regression approach allows to identify the areas showing the reverse orographic effect, providing material for future investigations on the physical explanation of this evidence. Moreover, the reconstructed maps will allow to apply more accurate approaches in works related to the spatial variability of other rainfall statistics, such as the quantiles required for hydrologic design.
Levelling up or widening the gap? An analysis of community renewal fund allocation in English regions using an economic resilience index

ABSTRACT This paper assesses the relationship between economic resilience in English regions and the allocation of the UK government’s Community Renewal Fund (CRF). The CRF is part of the government’s ‘Levelling Up’ agenda to address place-based inequalities. Economic resilience is measured using a composite index comprising productivity, skills, unemployment, population density and household income. Data were obtained for the five indicators at local authority (LA) level in England. Regional resilience scores were produced by calculating the weighted mean score of the constituent LAs. Regional resilience values were used to generate an ‘expected share’ funding allocation, which was compared with the actual allocation using differences and correlation. Regional resilience scores ranged from 28.5 (North East) to 66.6 (London). A total of £125.56 million was allocated in the first round of the CRF. All regions in the North received less than their expected share allocation of the CRF, with the least resilient region in England (North East) receiving £13.4 million less. The South West received £9.9 million more than their expected share. All regions in the North of England received less than their expected share allocation of the CRF. There was no significant correlation between resilience values and CRF allocations (r = −0.16, p = 0.68). The current method for CRF allocation may therefore widen existing inequalities rather than ‘level up’.
Deep learning-based concrete defects classification and detection using semantic segmentation

Visual damage detection of infrastructure using deep learning (DL)-based computational approaches can facilitate a potential solution to reduce subjectivity yet increase the accuracy of the damage diagnoses and accessibility in a structural health monitoring (SHM) system. However, despite remarkable advances with DL-based SHM, the most significant challenges to achieving the real-time implication are the limited available defects image databases and the selection of DL networks depth. To address these challenges, this research has created a diverse dataset with concrete crack (4087) and spalling (1100) images and used it for damage condition assessment by applying convolutional neural network (CNN) algorithms. CNN-classifier models are used to identify different types of defects and semantic segmentation for labeling the defect patterns within an image. Three CNN-based models—Visual Geometry Group (VGG)19, ResNet50, and InceptionV3 are incorporated as CNN-classifiers. For semantic segmentation, two encoder-decoder models, U-Net and pyramid scene parsing network architecture are developed based on four backbone models, including VGG19, ResNet50, InceptionV3, and EfficientNetB3. The CNN-classifier models are analyzed on two optimizers—stochastic gradient descent (SGD), root mean square propagation (RMSprop), and learning rates—0.1, 0.001, and 0.0001. However, the CNN-segmentation models are analyzed for SGD and adaptive moment estimation, trained with three different learning rates—0.1, 0.01, and 0.0001, and evaluated based on accuracy, intersection over union, precision, recall, and F1-score. InceptionV3 achieves the best performance for defects classification with an accuracy of 91.98% using the RMSprop optimizer. For crack segmentation, EfficientNetB3-based U-Net, and for spalling segmentation, IncenptionV3-based U-Net model outperformed all other algorithms, with an F1-score of 95.66 and 89.43%, respectively.
Suporte dos Padrões à Evolução de Processos de Desenvolvimento de Software

Este artigo apresenta uma infra-estrutura que suporta a evolução de processos de desenvolvimento de software. Esta infra-estrutura permite que o conhecimento sobre processos de desenvolvimento de software seja registrado em um ADS, utilizando-se dos padrões de software para representar as descrições de processos, e inclui uma ferramenta para a reutilização deste conhecimento. A infra-estrutura além de permitir a evolução de descrições de processos, também contribui para as funcionalidades do ADS que são importantes para o suporte destes à evolução de processos.
Democratizing photogrammetry: an accuracy perspective

ABSTRACT Photogrammetry is experiencing an era of democratization mostly due to the popularity and availability of many commercial off-the-shelf devices, such as drones and smartphones. They are used as the most convenient and effective tools for high-resolution image acquisition for a wide range of applications in science, engineering, management, and cultural heritage. However, the quality, particularly the geometric accuracy, of the outcomes from such consumer sensors is still unclear. Furthermore, the expected quality under different control schemes has yet to be thoroughly investigated. This paper intends to answer those questions with a comprehensive comparative evaluation. Photogrammetry, in particular, structure from motion, has been used to reconstruct a 3D building model from smartphone and consumer drone images, as well as from professional drone images, all under various ground control schemes. Results from this study show that the positioning accuracy of smartphone images under direct geo-referencing is 165.4 cm, however, this could be improved to 43.3 cm and 14.5 cm when introducing aerial lidar data and total station surveys as ground control, respectively. Similar results are found for consumer drone images as well. For comparison, this study shows the use of the professional drone is able to achieve a positioning accuracy of 3.7 cm. Furthermore, we demonstrate that through the combined use of drone and smartphone images we are able to obtain full coverage of the entire target with a 2.3 cm positioning accuracy. Our study concludes that smartphone images can achieve an accuracy equivalent to consumer drone images and can be used as the primary data source for building facade data collection.
Varied Histories of Outlier Polar Ice Deposits on Mars

Martian ice holds an important key to interpreting Mars' past climate, but much is still unknown regarding the distribution and properties of Mars' ice deposits. Previous surveys identified craters that contain “outlying” deposits of ice separate from, but nearby, the north and south polar layered deposits (NPLD and SPLD). There are many differences between the characteristics of the NPLD and SPLD, which may or may not be shared by these outlying deposits, and may provide clues to the climate conditions under which ice in the polar regions formed. Ground penetrating radar is one of the crucial datasets for understanding Martian ice, as it can probe the subsurface and place constraints on the properties of buried materials. We have analyzed 517 SHARAD radar tracks across 24 ice deposits housed within craters, including quantifying surface reflectivity and identifying the presence of subsurface reflectors. After examining the subsurface radar observations, we determined that the northern outlier deposits share many common characteristics with the NPLD, and thus may have been emplaced concurrently or at least under similar environmental conditions. The southern outlying crater deposits exhibit a variety of subsurface characteristics, and likely represent two or more populations of ice‐rich deposits that may have differing emplacement histories.
A Review of Recurrent Neural Network Based Camera Localization for Indoor Environments

Camera localization involves the estimation of the camera pose of an image from a random scene. We used a single image or sequence of images or videos as the input. The output depends on the representation of the scene and method used. Several computer vision applications, such as robot navigation and safety inspection, can benefit from camera localization. Camera localization is used to determine the position of an object on the camera in an image containing multiple images in a sequence. Structure-based localization techniques have achieved considerable success owing to a combination of image matching and coordinate regression. Absolute and relative pose regression techniques can provide end-to-end learning; however, they exhibit poor accuracies. Despite the rapid growth in computer vision, there has been no thorough review of the categorization, evaluation, and synthesis of structures and regression-based techniques. Input format and loss strategies for recurrent neural networks (RNN) have not been adequately described in the literature. The main topic is indoor camera pose regression, which is a part of the camera localization techniques. First, we discuss certain application areas for camera localization. We then discuss different camera localization techniques, such as feature and structure-based, absolute and relative pose regression techniques, and simultaneous localization and mapping (SLAM). We evaluated the frequently used datasets and qualitatively compared the absolute and relative camera pose estimation approaches. Finally, we discuss potential directions for future research, such as optimizing the computational cost of the features and evaluating the end-to-end characteristics of multiple cameras.
Ralstonia solanacearum type III effector RipAS associates with potato type one protein phosphatase StTOPP6 to promote bacterial wilt

Abstract The bacterial pathogen Ralstonia solanacearum (R. solanacearum) delivered type III secretion effectors to inhibit the immune system and cause bacterial wilt on potato. Protein phosphatases are key regulators in plant immunity, which pathogens can manipulate to alter host processes. Here, we show that a type III effector RipAS can reduce the nucleolar accumulation of a type one protein phosphatase (PP1) StTOPP6 to promote bacterial wilt. StTOPP6 was used as bait in the Yeast two-Hybrid (Y2H) assay and acquired an effector RipAS that interacts with it. RipAS was characterized as a virulence effector to contribute to R. solanacearum infection, and stable expression of RipAS in potato impaired plant resistance against R. solanacearum. Overexpression of StTOPP6 showed enhanced disease symptoms when inoculated with wild strain UW551 but not the ripAS deletion mutant, indicating that the expression of StTOPP6 facilitates the virulence of RipAS. RipAS reduced the nucleolar accumulation of StTOPP6, which occurred during R. solanacearum infection. Moreover, the association also widely existed between other PP1s and RipAS. We argue that RipAS is a virulence effector associated with PP1s to promote bacterial wilt.
An IoT-based Framework for Detecting Heart Conditions using Machine Learning

—A lot of diseases may be preventable if they can be analyzed or predicted from patient historical and family data. Predicting diagnosis depends on the gathered clinical and physiological data of patients. The more collected clinical and medical healthcare data, the more knowledge the medical support system may support. Hence, real monitoring clinical and healthcare data for patients is the trend of this decade based on Internet of Things technologies (IoT). IoT models facilitate human life by easily collecting clinical data remotely for recognizing diseases that are easily treatable if it is diagnosed early. This paper proposes a framework consisting of two models: (i) heart attack detection model (HADM); (ii) Electrocardiosignal ECG heartbeat multiclass-classification model (ECG-HMCM). Gridsearch is used to the hyperparameters optimization for different machine learning (ML) techniques. The used dataset in HADM consists of 1190 patients and 14 features. As the foundation of diagnosing cardiovascular disease is arrhythmia detection hence, we propose an ECG heartbeat multi-class classification model using MIT-BIH Arrhythmia and PTB Diagnostic ECG signals dataset which contains five categories with 109446 samples. K Nearest Neighbor (KNN) technique is applied to build ECG-HMCM in addition to the using of Gridsearch algorithm for hyperparameter optimization aiming to improve the accuracy of classification which achieved 97.5%. The proposed framework aims to facilitate human life by easily collecting clinical data remotely. The outcomes of the experiments show that the suggested framework works well in a practical setting
Can salivary lactate be used as an anaerobic biomarker?

Background Salivary lactate has been suggested as a non-invasive anaerobic biomarker in sports medicine for decades, yet has not been widely applied until now. This study aimed to explore possible issues related to its application and suggest directions for future method improvement. Methods A liquid chromatography–mass spectrometry (LC-MS) method for the determination of salivary lactate was developed, validated and applied on saliva samples collected from a group of professional sprinters (n = 20). The samples were collected via chewing a cotton ball for one minute and centrifuging it afterwards. The evaluation included variation with mouth rinse times, consistency at different sampling times, change after treadmill or cycle ergometer trainings, and association with blood lactate. Sample sizes were calculated prior to the study. One-way analysis of variance (ANOVA), intra-class correlation coefficients (ICC) and relative standard deviation (RSD) were used to evaluate data variances. Pearson correlation was applied to show correlation between salivary and blood lactate. Effect sizes and power were calculated following ANOVA and correlation analyses. Results The RSD of the LC-MS method was 19.70%. Salivary lactate concentration was affected by mouth rinse times before sampling (ANOVA p = 0.025, η2 = 0.40, 1 − β = 0.99, ICC = 0.23, mean RSD of four sampling = 55.30%), and stabilized after mouth rinsing for three times. The concentrations at resting state across three weeks were consistent at group level (ANOVA p = 0.57, η2 = 0.03, 1 − β = 0.20), but varied greatly individually (ICC = 0.22, mean RSD = 56.16%). Salivary lactate level significantly increased after treadmill and cycle ergometer trainings (ANOVA p = 0.0002, η2 = 0.46, 1 − β = 0.9999 and ANOVA p = 0.0019, η2 = 0.40, 1 − β = 0.9993, respectively), and displayed positive correlation with blood lactate concentration (r = 0.61, p = 0.0004, 1 − β = 0.9596). Significant difference between male and female participants was observed in none of the tests conducted in this study. Discussion Salivary lactate was found to be a potential anaerobic biomarker. However, reproducible methods for sample collection and analysis, as well as more knowledge on the secretion mechanism and pattern of salivary lactate are required to make it a practical anaerobic biomarker.
An indentation scheme

1.2 \everypar TEX performs another action when it starts a paragraph: it inserts whatever is currently the contents of the token list\everypar. Usually you don’t notice this, because the token list is empty in plain TEX (the TEX book [3] gives only a simple example, and the exhortation ‘if you let your imagination run you will think of better applications’). LATEX [5], however, makes regular use of \everypar. Some mega-trickery with \everypar can be found in [2].
Substation smoking behavior detection based on improved decoupling head

In the complex environment of the substation, small objects such as long-distance cigarettes, cigarette boxes, and lighters have few imaging pixels and lack texture information, making it difficult for convolutional neural networks to extract small object features. In the case of multiple targets, the missed detection rate and false detection rate of small targets are high, and the fusion of model features is insufficient, making it difficult to accurately identify and detect. Aiming at the above problems, a multi-scale small target detection algorithm is proposed. In the prediction part of the network, a more effective decoupling head is designed. In addition, shallow features are introduced to improve the feature pyramid, extract small target features, increase the correlation between multiple targets, and prevent the loss of small target feature information. At the same time, a multi-layer attention mechanism is embedded in the backbone network to increase the regional features of invisible small targets and reduce the missed detection rate. In the post-processing stage, the Focal Loss loss function is introduced to increase the model's learning of positive sample targets and further reduce the rate of missed detection and false detection. The experimental results show that the method achieves a 𝑚𝐴𝑃@. 5: . 95 of 0.6350 on the homemade smoking dataset, and 𝑚𝐴𝑃@0.5 achieves 0.9569. For the self-made multi-scene and multi-scale smoking data set, this model has advantages in detection accuracy compared with the current excellent target detection models such as YOLOX and YOLOv5. The experimental results show that the model method can realize the identification and detection of small targets such as cigarettes and lighters under multi-scale targets, which has a certain reference value for anti-smoking measures.
Statistical Mechanics of Soft Core Fluid Mixtures

In this thesis we investigate the statistical mechanics of binary mixtures of soft-core fluids. The soft-core pair potentials between the fluid particles are those obtained by considering the effective potential between polymers in solution. The effective pair potential between the centers of mass of polymers is well approximated by a repulsive Gaussian potential. A binary mixture of Gaussian particles can phase separate, and we calculate the phase diagrams for various size ratios of the two species. Using a simple mean field density functional theory (DFT) , which generates the random phase approximation for the bulk pair direct correlation functions, we calculate the surface tension and density profiles for the free interface between the demixed fluid phases. We find that the asymptotic decay of the interfacial profiles into bulk can be oscillatory on both sides of the interface. We also calculate density profiles for the binary Gaussian core model (GCM) adsorbed at a planar wall and find a wetting transition from partial to complete wetting for certain purely repulsive wall potentials. By applying a general DFT approach for calculating the force between two big particles immersed in a solvent of smaller ones we calculate the solvent mediated (SM) potential between two big Gaussian core particles in the phase separating binary mixture of smaller GCM particles. We show that the theory for calculating the SM potential captures effects of thick adsorbed films surrounding the big solute particles and we find extremely attractive, long ranged SM potentials between the big particles whose range is determined by the film thickness. In the region of the solvent critical point we also find extremely attractive SM potentials whose range is now set by the bulk correlation length in the binary solvent. In addition to the GCM, we also consider the effective potential between the central monomers on each polymer chain; this potential features a weak (logarithmic) divergence as the particle separation r → 0. A binary mixture of these particles can also phase separate and displays many features in common with the binary GCM. To my family: Sarah, Mum, Dad and Alice.
Evaluation of the Antioxidant Properties and GC-MSD Analysis of Commercial Essential Oils from Plants of the Lamiaceae Family

Plants of the Lamiaceae family have been used for thousands of years in cooking, as well as phyto- and aromatherapy. Their essential oils are characterized by high antioxidant and other types of biological activities. In our study, the phytochemical profile and quantification of the essential oil components of thyme, marjoram, and sage were analyzed by gas chromatography with mass-spectrometric detection (GC-MSD). The antioxidant properties of the samples were evaluated using total antioxidant parameters (total antioxidant capacity (TAC), ferric reducing power (FRP), antioxidant activity (AOA) towards 2,2-diphenyl-1-picrylhydrazyl (DPPH•), and total phenolics by Folin–Ciocalteu method). The obtained FRP was 46–321-fold lower than TAC, which is consistent with the contents of phenolics identified in the samples. Terpenes, isopropylmethylphenols, and eugenol turned out to be the major components of all essential oils and determined their TAC and AOA. The Folin–Ciocalteu method was applicable to the thyme essential oil only. Its FRP, which is based on the reaction of phenolic antioxidants with electrogenerated ferricyanide ions, agreed well with the total phenolic contents (329 ± 17 and 334 ± 15 mg of carvacrol per mL, respectively). The thyme essential oil had the highest antioxidant parameters, while sage showed the weakest antioxidant properties. Positive correlations (r = 0.8846–0.9964) of the antioxidant parameters were obtained.