Miracle-dz/UniFAR-FTUs huggingface dataset:
Terrain factors in Capitan aquifer recharge, northeastern Guadalupe escarpment, New Mexico

The Guadalupe escarpment southwest of Carlsbad, New Mexico, is under accelerating oil and gas development and also comprises the most direct recharge area for the Capitan aquifer. Key infiltration areas on the escarpment landscape must be identified for special ground water quality protection measures during petroleum drilling and production. The landscape has 120–180 m of local relief, with 22% of surface area in uplands, 61% in slopes, and 17% in canyon floors. Infiltration on uplands and slopes must generally pass through soil-filled fracture openings and move downdip on open bedding planes. Slope areas include many headwater stream channels in V-shaped valleys, with abundant signs of ephemeral ground water seepage. On the floor of Dark Canyon, the most important drainageway traversing the area, bedrock pavement sections have some potential for storm-flow infiltration via abrasion-formed basins along fracture lines, and by eastwarddipping strata with unusual levels of karst conduit development. Depth of boulder fill along some reaches of the canyon exceeds 10 m, providing a large-volume medium for stream-flow transfer to the subsurface during and after floods. It is likely that water moving slowly downdip through backreef units will only feed the reef aquifer if it does not again contact the surface. Two simplified end-member models can be postulated for escarpment area recharge to the Capitan aquifer. In the first model, infiltrating water descends vertically through carbonate units and then runs downdip on the major sandstone beds. The second model treats the entire mass of the backreef as a material with strongly anisotropic permeability, much enhanced along the dip direction, with infiltrating water proceeding in a downdip direction immediately on reaching bedrock. The likely real condition resides between these end members, but the two models agree in identifying the following portions of the escarpment landscape as recharge areas that should be given special attention in terms of water quality protection: (1) the floors and downdip side slopes of Dark Canyon and other major canyons, (2) upland surface and slope areas that are updip of major fracture lines, and (3) the outer escarpment, extending 2 km west from the escarpment front. In the latter area, zones of rectangular, fracture-associated drainage warrant special attention as potential quick recharge sites.
Design and fabrication of infrared holographic optical elements with both beam splitting and convergence functions

Holographic optical element (HOE) are widely used due to their lightweight, miniaturization, and multifunctional characteristics. However, making HOE that can be applied in the infrared band is a challenging task, especially when HOE has multiple function. This paper proposes a design and fabrication method for a near-infrared multifunctional HOE that can be recorded using visible light. The three recording beams of this multifunctional HOE include a plane beam and two spherical beams. Therefore, on the basis of achieving beam redirection, it can also achieve beam splitting and the convergence of each split beam. To solve the problem of wavelength mismatch while achieving multiple functions of HOE. The decrease in diffraction efficiency caused by wavelength shift is compensated by the angle shift of recording light. The wavefront aberration caused by wavelength mismatch is compensated by introducing a cylindrical lens in the recording optical path. The multifunctional HOE has the potential to be used in infrared optical systems to achieve system miniaturization, and this paper provides an application example for laser Doppler velocimeter (LDV). The designed HOE successfully achieved two sets of stripes with opposite slopes in the measurement volume of LDV at different working wavelengths, proving the feasibility of the design and fabrication methods. Furthermore, the method proposed in this paper can provide reference for the design and fabrication of other HOEs working in the near-infrared band.
Machine Learning Detection of Cloud Services Abuse as C&C Infrastructure

The proliferation of cloud and public legitimate services (CLS) on a global scale has resulted in increasingly sophisticated malware attacks that abuse these services as command-and-control (C&C) communication channels. Conventional security solutions are inadequate for detecting malicious C&C traffic because it blends with legitimate traffic. This motivates the development of advanced detection techniques. We make the following contributions: First, we introduce a novel labeled dataset. This dataset serves as a valuable resource for training and evaluating detection techniques aimed at identifying malicious bots that abuse CLS as C&C channels. Second, we tailor our feature engineering to behaviors indicative of CLS abuse, such as connections to known CLS domains and potential C&C API calls. Third, to identify the most relevant features, we introduced a custom feature elimination (CFE) method designed to determine the exact number of features needed for filter selection approaches. Fourth, our approach focuses on both static and derivative features of Portable Executable (PE) files. After evaluating various machine learning (ML) classifiers, the random forest emerges as the most effective classifier, achieving a 98.26% detection rate. Fifth, we introduce the “Replace Misclassified Parameter (RMCP)” adversarial attack. This white-box strategy is designed to evaluate our system’s detection robustness. The RMCP attack modifies feature values in malicious samples to make them appear as benign samples, thereby bypassing the ML model’s classification while maintaining the malware’s malicious capabilities. The results of the robustness evaluation demonstrate that our proposed method successfully maintains a high accuracy level of 84%. In sum, our comprehensive approach offers a robust solution to the growing threat of malware abusing CLS as C&C infrastructure.
Application of Distinct Element Method into Simulation of the Red River Fault Displacement

The numerical methods have been applied in decades in the field of crustal engineering to simulate the processes of material alteration as well as the stress field distribution during movement. One of such numerical methods is the Distinct Element Method by Cundall and Strack (1979). The Red River fault in the North of Vietnam is the extended part of the Ailao Shan Red River shear zone from the Tibet to the offshore area in the Gulf of Tonkin Vietnam under the influence of India-Eurasian collision. Development of the fault mainly controlled the formation of the Song Hong basin in the north of Vietnam. Though many researches have been carried out along the Red River fault in order understand either mechanism and structural styles of the Song Hong basin or dynamic mechanism of the Red River fault displacement those problems are still in controversy with many different hypotheses. In this paper, Distinct Element Method (DEM) has been used to simulate the development of the Red River fault system for revealing the stress field distribution along the Red River fault and its propagation toward offshore area. The results are prospective to be applied into interpretation of structural configurations of the Song Hong basin and the displacements of the Red River fault.
Sensor self-diagnosis method based on a graph neural network

Many types of sensors are used in industrial processes, and their reliability is high. However, the traditional method of regularly detecting and evaluating their health status is time-consuming and laborious, and is not suitable for the development of intelligent sensors. In this work, the relative entropy method is first used to quantitatively evaluate the redundancy relationship between sensors, and a sensor graph network is established based on this relationship. Secondly, an unsupervised multi-sensor self-diagnosis model, called attention-based pruning graph convolutional network, is proposed. In order to capture the strong redundancy among sensors by the attention mechanism, multi-sensor timing prediction is realised using a graph convolutional neural network, and the health status of each sensor can be independently judged by the changes in redundancy among the sensors. Finally, a temperature measurement system in a nickel flash furnace is considered as a case study to verify the feasibility and effectiveness of the proposed method.
Ultraviolet A and Blue Light Transiently Regulate Total Phenolic and Anthocyanin Concentrations in Indoor-grown Red-leaf Lettuce

In controlled environments, supplementing a light spectrum with ultraviolet A (UVA; 315–399 nm) or blue (B; 400–499 nm) light increases the concentrations of phenolic compounds that can increase quality attributes, such as leaf pigmentation and nutritional quality of lettuce (Lactuca sativa). However, B light and sometimes UVA light can inhibit leaf expansion and biomass accumulation when continuously applied, whereas applying it only at the end of the production cycle can increase lettuce quality with little to no effect on crop yield. Our objective was to quantify the persistency of periodic supplemental UVA or B light and compare end-of-production with continuously applied supplemental light during indoor lettuce production on quality attributes and biomass accumulation. We hypothesized that supplemental UVA or B light would be more effective later, rather than earlier, during production with increasing lettuce quality attributes. We grew ‘Rouxai’ red-leaf lettuce hydroponically at an air temperature of 23 °C under 75 μmol⋅m−2⋅s−1 of red (peak = 664 nm) plus 75 μmol⋅m−2⋅s−1 of warm-white light provided by light-emitting diodes. The supplemental lighting treatments consisted of adding 30 μmol⋅m−2⋅s−1 of UVA (peak= 386 nm) or B (peak = 449 nm) light during the seedling phase (P1; days 4–12), growth phase (P2; days 12–20), finishing phase (P3; days 20–28), or the entire time (ET; days 4–28). Supplemental UVA or B light applied at any individual phase did not inhibit biomass accumulation, whereas enriched B light during the entire production period inhibited fresh mass compared with no supplemental light. Additionally, supplemental UVA or B light during P3 or ET similarly increased total phenolic and anthocyanin concentrations. Finally, applying UVA or B light during P1 or P2 had no residual effect on mature plant growth or quality at harvest. We concluded that the end of the production cycle is the optimal time to apply supplemental UVA or B light to improve lettuce coloration and phenolic content, that earlier application elicits transient responses, and that continuous application improves lettuce quality but inhibits biomass accumulation. Finally, there are potential energy savings by using end-of-production supplemental light compared with continuous application of the same spectrum.
MC-Net: multi-scale contextual information aggregation network for image captioning on remote sensing images

ABSTRACT Remote Sensing Image Captioning (RSIC) plays a crucial role in advancing semantic understanding and has increasingly become a focal point of research. Nevertheless, existing RSIC methods grapple with challenges due to the intricate multi-scale nature and multifaceted backgrounds inherent in Remote Sensing Images (RSIs). Compounding these challenges are the perceptible information disparities across diverse modalities. In response to these challenges, we propose a novel multi-scale contextual information aggregation image captioning network (MC-Net). This network incorporates an image encoder enhanced with a multi-scale feature extraction module, a feature fusion module, and a finely tuned adaptive decoder equipped with a visual-text alignment module. Notably, MC-Net possesses the capability to extract informative multiscale features, facilitated by the multilayer perceptron and transformer. We also introduce an adaptive gating mechanism during the decoding phase to ensure precise alignment between visual regions and their corresponding text descriptions. Empirical studies conducted on four publicly recognized cross-modal datasets unequivocally demonstrate the superior robustness and efficacy of MC-Net in comparison to contemporaneous RSIC methods.
Environmental and behavioural determinants of geographic variation in coronary heart disease in England : an ecological study

4 Acknowledgements. 5 The role of the author. 7 Chapter 1: Introduction . 8 Chapter 2: Literature review . 19 Chapter 3: Study design . 50 Chapter 4: Statistical techniques used. 72 Chapter 5: The association between environmental variables and coronary heart disease mortality and hospitalisation rates in England . 88 Chapter 6: Validation of synthetic estimates of the prevalence of behavioural risk factors for coronary heart disease at the ward-level in England . 102 Chapter 7: The association between behavioural risk factor profiles of populations and coronary heart disease mortality and hospitalisation rates in England. 140 Chapter 8: The impact of confounding on the relationship between environmental variables, behavioural risk factor profiles of populations and coronary heart disease mortality and hospitalisation rates in England . 161 Chapter 9: Conclusions . 186
Working outside the comfort of competence in a corrections centre: toward collective competence

Contrary to popular myth, modern prison environments are often workplaces that embrace change and reform. When an innovative model for offender rehabilitation at a corrections centre requires staff to work differently as an inter-professional care team, new ways of learning to work in the overlaps emerge. Using a qualitative case study approach to investigate daily work and crucial incidents that act as change triggers, we discuss how this group experience has challenged individual and group work roles and changed organizational work practices. Our findings suggest a useful nexus of theories of workplace learning with theories of organizational change and development. The emergent quality of human interactions supports the concept of collective competence (Boreham 2004, 2007) rather than the comfort of individual competence. Enhanced understandings of collective learning as relationally-constructed and actively improvised with others argue for a new vocabulary of competence in which more interdisciplinary research can add further insights.
Subthreshold Bias-Induced Threshold Voltage Shift of the Ovonic Threshold Switch

In this study, we report on the subthreshold bias-induced threshold voltage shift (SITS) phenomenon of an ovonic threshold switch (OTS) that can occur during array inhibit operation in a cross-point memory. We found that the threshold voltage (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {th}}{)}$ </tex-math></inline-formula> of the OTS increases after applying a bias to the subthreshold region of the OTS. In particular, the closer the subthreshold bias is to the <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {th}}$ </tex-math></inline-formula> of the OTS, and the longer the width of the bias pulse, the greater the SITS effect. As this SITS phenomenon can occur at half-biased cells during the inhibit operations of read and write, it should be considered as a factor affecting <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\text {th}}$ </tex-math></inline-formula> instability. Based on the changes in drift characteristics after subthreshold biasing and the analysis of off-leakage fitting parameters, it can be concluded that the SITS phenomenon is likely to be associated with the acceleration of the defect annihilation in the OTS film.
Effects of a Bottom Boundary Layer Parameterization in a Coarse-Resolution Model of the North Atlantic Ocean

Abstract The bottom boundary layer model approach of Beckmann and Doscher has been adopted for application in a coarse-resolution model of the North Atlantic Ocean. Both components of the approach (advective and conditional diffusive) are found to affect the deep water stratification and circulation. A significant deepening of the downward spreading North Atlantic Deep Water (NADW) is the major effect. This is associated with an enhanced spatial coverage of the NADW cell in the meridional circulation.
Generate Adversarial Attack on Graph Neural Network using K-Means Clustering and Class Activation Mapping

—Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing complex structured data, including social networks, biological networks, and recommendation systems. However, their susceptibility to adversarial attacks poses a significant challenge, especially in critical tasks such as node classification and link prediction. Adversarial attacks on GNNs can introduce harmful input graphs, leading to biased model predictions and compromising the integrity of the network. We propose a novel adversarial attack method that leverages the combination of K-Means clustering and Class Activation Mapping (CAM) to conduct subtle yet effective attacks against GNNs. The clustering algorithm identifies critical nodes within the graph, whose perturbations are likely to have a substantial impact on model performance. Additionally, CAM highlights regions of the graph that significantly influence GNN predictions, enabling more targeted and efficient attacks. We assess the efficacy of state-of-the-art GNN defenses against our proposed attack, underscoring the pressing need for robust defense mechanisms. Our study focuses on countering attacks on GNN networks by utilizing K-Means clustering and CAM to enhance the effectiveness and efficiency of the adversarial strategy. Through our observations, we emphasize the necessity for stronger security measures to safeguard GNN-based applications, particularly in sensitive environments. Furthermore, our research highlights the importance of developing robust GNNs that can withstand adversarial attacks, ensuring the reliability and trustworthiness of these models in critical applications. Strengthening the robustness of GNNs against adversarial manipulation is crucial for maintaining the security and integrity of systems that heavily rely on these advanced analytical tools. Our findings underscore the ongoing efforts required to fortify GNN-based applications, urging the research community and practitioners to collaborate in developing and implementing more robust security measures for these powerful neural network models.
Self-regulatory employability attributes and competency: the strengthening role of grit

Introduction This study examines grit as psychological mindsets that explain the link between self-regulatory employability attributes and perceived employability competency expectations in a sample of South African adults (N = 308). Methods A quantitative, cross-sectional research design approach was used to collect primary data. Results Results of a mediation analysis through structural equation modelling revealed grit as an important mechanism to strengthen the association between employability attributes (career agility, cultural ingenuity, proactive career resilience) and employability competency expectations (autonomy/leadership skills and personal employability qualities). Discussion This study makes an important contribution to the role of learning and training through understanding the role of grit in enhancing prospects of employability. This study further adds to the grit literature, highlighting the role that grit plays in the contemporary employment context. Practical implications include supportive practices that strengthen individual workers’ grit when confronted with the turbulent changes of today’s work world.
The Red Sanders and its Future

Pterocarpus santalinus , commonly known as red sanders, produces a highly ornamental wood which is much in demand in foreign countries, particularly Japan. The figured wood of best quality fetches an average price of Rs. 3,000/- per ton, but the nonfigured wood obtains only about Rs. 300/- per ton. Red Sanders has a localised distribution in dry and hilly areas. Fires and thefts are common. Red sanders forests are worked under simple coppice system. To preserve the locality factors and obtain the marketable timber, these forests should be worked under shelterwood system.
Generation of Multiple Plasmonic Fano Resonances and Detection of Distinct Analytes

A technique of simultaneous detection of multiple analytes through Fano resonance based plasmonic waveguide is implemented. A rectangular cavity coupled through a narrow channel to the metal-insulator-metal waveguide and supported with two vertical stubs induce six Fano resonances. The rectangular cavity and vertical stubs induce the Fano resonance at distinct resonant wavelength which helps in sensing the multiple analyte samples simultaneously. The sensing analysis is done through the refractive index variation in the range of 1.00 – 1.05. The proposed device operates over the wavelength range of $ {\lambda =450-1150 \text {nm}}$ , providing the sensitivity of 1057.142 nm/RIU, full-width at half minima as 3.16 nm, figure of merit of 334.53 RIU−1, and quality factor of 337.16 with the two number of samples under test.
HOFA-Net: A High-Order Feature Association Network for Dense Object Detection in Remote Sensing

In the remote sensing field, deep learning-based methods have become mainstream for remote sensing image object detection in recent years. However, traditional methods, such as convolutional neural networks (CNNs), mainly ignore the dependencies between features, failing to capture the spatial relationships and relative positions of objects, which affects the detection performance of dense objects, especially small-size objects. To this end, a high-order feature association network (HOFA-Net) for dense object detection in remote sensing has been proposed to better capture the interdependencies between features of channel and spatial dimensions, yielding more distinguishable features. First, we employ CNNs to learn high-level but low-resolution features of objects. To capture feature interdependencies while retaining crucial information, we design a feature association module based on size adaptation nonlocal. This module partitions the low-resolution and high-level features into local regions and utilizes nonlocal residual connections to capture the local contextual information of objects. In addition, we introduce a high-order feature association (HFA) module designed to learn nonlinear feature correlations and interdependencies within the features. In addition, a covariance normalization acceleration strategy is introduced to accelerate computation. Experimental results on two public remote sensing datasets, including the DOTA dataset and the Tiny Person dataset, demonstrate the superiority and effectiveness of the proposed method through comparative experiments.