Miracle-dz/UniFAR-FTUs huggingface dataset:
DeviceVeri-A Graphical Programming Based Test Script Generation Framework for Measurement Devices

Manufacturing and Process Industries use a complex and wide range of measurement devices to measure real-time process variables like temperature, pressure, and flow rate. Control systems use the measured process variables to manage various industrial operations like opening/closing a valve, raising alarms, etc. Inaccurate recording of process variables can lead to industrial failures or affect the production quality of manufactured goods. Verification of measurement devices thus becomes imperative to ensure maximum performance of industrial processes. Traditionally, subject matter experts and testers define the pseudo logic of a test case, which software developers further convert to executable test scripts to be run on devices. However, this manual generation of test scripts is cumbersome, time-consuming, lacks standardization, and depends mainly on the software developers’ competency. Automatic generation of test scripts thus becomes necessary for quick, reliable, and efficient testing of measurement devices.This paper presents a device verification framework called DeviceVeri, based on graphical programming to generate executable test scripts for verifying measurement devices. General and device-specific graphical blocks represent specific functionalities of device verification logic. Graphical blocks abstract technical details from users, making the test script generation process simple, less time-intensive, and user-friendly. The framework will be used by process engineers, testers and subject matter experts to generate test scripts and use them to verify measurement devices. In addition, the proposed framework minimizes the dependency on software developers to write test scripts manually, thus enhancing the testing process of measurement devices.
The impact of performance appraisal methods on enterprises in the Internet era

The purpose of this study is to explore the impact of performance appraisal methods on enterprises in the Internet era. Through literature review, empirical data analysis and case studies, we study the emerging performance appraisal methods in the Internet era, as well as their positive and negative impacts on enterprises. The study found that in the Internet era, factors such as data-driven performance reviews, the use of technology tools, and flexible remote work environments have had a profound impact on business performance. However, there are also some challenges and issues that companies need to overcome. This study is helpful for enterprises to better understand and cope with the changes of performance appraisal methods in the Internet era.
Decoding seasonal changes: soil parameters and microbial communities in tropical dry deciduous forests

In dry deciduous tropical forests, both seasons (winter and summer) offer habitats that are essential ecologically. How these seasonal changes affect soil properties and microbial communities is not yet fully understood. This study aimed to investigate the influence of seasonal fluctuations on soil characteristics and microbial populations. The soil moisture content dramatically increases in the summer. However, the soil pH only gradually shifts from acidic to slightly neutral. During the summer, electrical conductivity (EC) values range from 0.62 to 1.03 ds m-1, in contrast to their decline in the winter. The levels of soil macronutrients and micronutrients increase during the summer, as does the quantity of soil organic carbon (SOC). A two-way ANOVA analysis reveals limited impacts of seasonal fluctuations and specific geographic locations on the amounts of accessible nitrogen (N) and phosphorus (P). Moreover, dehydrogenase, nitrate reductase, and urease activities rise in the summer, while chitinase, protease, and acid phosphatase activities are more pronounced in the winter. The soil microbes were identified in both seasons through 16S rRNA and ITS (Internal Transcribed Spacer) gene sequencing. Results revealed Proteobacteria and Ascomycota as predominant bacterial and fungal phyla. However, Bacillus, Pseudomonas, and Burkholderia are dominant bacterial genera, and Aspergillus, Alternaria, and Trichoderma are dominant fungal genera in the forest soil samples. Dominant bacterial and fungal genera may play a role in essential ecosystem services such as soil health management and nutrient cycling. In both seasons, clear relationships exist between soil properties, including pH, moisture, iron (Fe), zinc (Zn), and microbial diversity. Enzymatic activities and microbial shift relate positively with soil parameters. This study highlights robust soil-microbial interactions that persist mainly in the top layers of tropical dry deciduous forests in the summer and winter seasons. It provides insights into the responses of soil-microbial communities to seasonal changes, advancing our understanding of ecosystem dynamics and biodiversity preservation.
Conservation easements in the Adirondack Park of New York state

The use of conservation easements to keep private lands undeveloped and protect open space and large scale landscapes has grown rapidly. The New York State Adirondack Park includes 2.5 million acres (1 million ha) of state owned land and 3 million acres (1.2 million ha) of private lands; over 781,000 acres (316,194 ha) of these private lands were under publicly held conservation easement by 2012. Private landowners include commercial forest and agricultural owners, second homeowners, residential landowners, conservation organizations, educational institutions, and others. The conservation easements convey property rights from the landowner to the state in perpetuity. The specifics of each conservation easement are individual and varied, with the main purposes being to protect natural resource and landscape values such as open space, scenic areas, wetlands, riparian areas, critical ecosystems, water quality, wildlife and fisheries habitats, and other purposes.
Genomic surveillance indicates clonal replacement of hypervirulent Klebsiella pneumoniae ST881 and ST29 lineage strains in vivo

The emergence of hypervirulent Klebsiella pneumoniae (hvKp) poses a significant public health threat, particularly regarding its carriage in the healthy population. However, the genomic epidemiological characteristics and population dynamics of hvKp within a single patient across distinct infection episodes remain largely unknown. This study aimed to investigate the clonal replacement of hvKp K2-ST881 and K54-ST29 lineage strains in a single patient experiencing multiple-site infections during two independent episodes. Two strains, designated EDhvKp-1 and EDhvKp-2, were obtained from blood and cerebrospinal fluid during the first admission, and the strain isolated from blood on the second admission was named EDhvKp-3. Whole-genome sequencing, utilizing both short-read Illumina and long-read Oxford Nanopore platforms, was conducted. In silico multilocus sequence typing (MLST), identification of antimicrobial resistance and virulence genes, and the phylogenetic relationship between our strains and other K. pneumoniae ST881 and ST29 genomes retrieved from the public database were performed. Virulence potentials were assessed through a mouse lethality assay. Our study indicated that the strains were highly susceptible to multiple antimicrobial agents. Plasmid sequence analysis confirmed that both virulence plasmids, pEDhvKp-1 (166,008 bp) and pEDhvKp-3 (210,948 bp), belonged to IncFIB type. Multiple virulence genes, including rmpA, rmpA2, rmpC, rmpD, iroBCDN, iucABCD, and iutA, were identified. EDhvKp-1 and EDhvKp-2 showed the closest relationship to strain 502 (differing by 51 SNPs), while EDhvKp-3 exhibited 69 SNPs differences compared to strain TAKPN-1, which all recovered from Chinese patients in 2020. In the mouse infection experiment, both ST881 EDhvKp-1 and ST29 EDhvKp-3 displayed similar virulence traits, causing 90 and 100% of the mice to die within 72 h after intraperitoneal infection, respectively. Our study expands the spectrum of hvKp lineages and highlights genomic alterations associated with clonal switching between two distinct lineages of hvKP that successively replaced each other in vivo. The development of novel strategies for the surveillance, diagnosis, and treatment of high-risk hvKp is urgently needed.
Benchmarking Artificial Neural Network Architectures for High-Performance Spiking Neural Networks

Organizations managing high-performance computing systems face a multitude of challenges, including overarching concerns such as overall energy consumption, microprocessor clock frequency limitations, and the escalating costs associated with chip production. Evidently, processor speeds have plateaued over the last decade, persisting within the range of 2 GHz to 5 GHz. Scholars assert that brain-inspired computing holds substantial promise for mitigating these challenges. The spiking neural network (SNN) particularly stands out for its commendable power efficiency when juxtaposed with conventional design paradigms. Nevertheless, our scrutiny has brought to light several pivotal challenges impeding the seamless implementation of large-scale neural networks (NNs) on silicon. These challenges encompass the absence of automated tools, the need for multifaceted domain expertise, and the inadequacy of existing algorithms to efficiently partition and place extensive SNN computations onto hardware infrastructure. In this paper, we posit the development of an automated tool flow capable of transmuting any NN into an SNN. This undertaking involves the creation of a novel graph-partitioning algorithm designed to strategically place SNNs on a network-on-chip (NoC), thereby paving the way for future energy-efficient and high-performance computing paradigms. The presented methodology showcases its effectiveness by successfully transforming ANN architectures into SNNs with a marginal average error penalty of merely 2.65%. The proposed graph-partitioning algorithm enables a 14.22% decrease in inter-synaptic communication and an 87.58% reduction in intra-synaptic communication, on average, underscoring the effectiveness of the proposed algorithm in optimizing NN communication pathways. Compared to a baseline graph-partitioning algorithm, the proposed approach exhibits an average decrease of 79.74% in latency and a 14.67% reduction in energy consumption. Using existing NoC tools, the energy-latency product of SNN architectures is, on average, 82.71% lower than that of the baseline architectures.
Analytical Solution of Generalized Bratu-Type Fractional Differential Equations Using the Homotopy Perturbation Transform Method

In this study, we present the generalized form of the higher-order nonlinear fractional Bratu-type equation. In this generalization, we deal with a generalized fractional derivative, which is quite useful from an application point of view. Furthermore, some special cases of the generalized fractional Bratu equation are recognized and examined. To solve these nonlinear differential equations of fractional order, we employ the homotopy perturbation transform method. This work presents a useful computational method for solving these equations and advances our understanding of them. We also plot some numerical outcomes to show the efficiency of the obtained results.
Predicting Individual Tree Mortality of Larix gmelinii var. Principis-rupprechtii in Temperate Forests Using Machine Learning Methods

Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques including random forest, logistic regression, artificial neural network, generalized additive model, support vector machine, gradient boosting machine, k-nearest neighbors, and naive Bayes models were employed, to construct an ensemble learning model based on comprehensive dataset from this specific ecosystem. The random forest model emerged as the most accurate, demonstrating 92.9% accuracy and 92.8% sensitivity, making it the best model among those tested. We identified key variables impacting tree mortality, and the results showed that a basal area larger than the target trees (BAL), a diameter at 130 cm (DBH), a basal area (BA), an elevation, a slope, NH4-N, soil moisture, crown density, and the soil’s available phosphorus are important variables in the Larix Principis-rupprechtii individual mortality model. The variable importance calculation results showed that BAL is the most important variable with an importance value of 1.0 in a random forest individual tree mortality model. By analyzing the complex relationships of individual tree factors, stand factors, environmental, and soil factors, our model aids in decision making for temperate Larix gmelinii var. principis-rupprechtii forest conservation.
Competition or Silence? The Double-Edged Sword Effect of ESM Affordance on Employee Creativity

Enterprise social media (ESM) refers to a platform built on Web 2.0 technology dedicated to online collaboration and information sharing among employees within an organization. With the advent of the information age, ESM as a kind of information tool improves the internal communication and management mode of the enterprise, which is crucial for its sustainable development. The current research on ESM affordance has a single perspective and lacks a double-edged sword perspective. Studying ESM affordance from a double-edged sword perspective enables managers to recognize the complex impact of ESM use. Based on communication visibility theory and regulatory focus theory, we used prestige-striving behavior (PSB) and quiescent silence (QS) as mediating variables. In addition, we used need for uniqueness (NFU) as the moderating variable. The impact of ESM affordance on employee creativity (EC) was explored from a double-edged sword perspective. There were 428 questionnaires collected in China from October to November of 2023. The results indicate that ESM affordance promotes EC, PSB and QS. ESM affordance positively influences employee creativity by promoting PSB. ESM affordance negatively affects employee creativity by promoting QS. The moderating effect of NFU is significant.
Interpreting Map Usage Patterns using Geovisual Analytics and Spatio-Temporal Clustering

Title Interpreting map usage patterns using geovisual analytics and spatio-temporal clustering Authors(s) McArdle, Gavin; Tahir, Ali; Bertolotto, Michela Publication date 2015 Publication information International Journal of Digital Earth, 8 (8): Publisher Taylor and Francis Item record/more information http://hdl.handle. net/10197/5704 Publisher's statement This is an electronic version of an article published in the International Journal of Digital Earth, 2014-03. The International Journal of Digital Earth is available online at: www.tandfonline. com//doi/abs/10.1080/17538947.2014. 898704 Publisher's version (DOI) 10.1080/17538947.2014. 898704
Enhanced model iteration algorithm with graph neural network for diffuse optical tomography

Diffuse optical tomography (DOT) employs near-infrared light to reveal the optical parameters of biological tissues. Due to the strong scattering of photons in tissues and the limited surface measurements, DOT reconstruction is severely ill-posed. The Levenberg-Marquardt (LM) is a popular iteration method for DOT, however, it is computationally expensive and its reconstruction accuracy needs improvement. In this study, we propose a neural model based iteration algorithm which combines the graph neural network with Levenberg-Marquardt (GNNLM), which utilizes a graph data structure to represent the finite element mesh. In order to verify the performance of the graph neural network, two GNN variants, namely graph convolutional neural network (GCN) and graph attention neural network (GAT) were employed in the experiments. The results showed that GCNLM performs best in the simulation experiments within the training data distribution. However, GATLM exhibits superior performance in the simulation experiments outside the training data distribution and real experiments with breast-like phantoms. It demonstrated that the GATLM trained with simulation data can generalize well to situations outside the training data distribution without transfer training. This offers the possibility to provide more accurate absorption coefficient distributions in clinical practice.
ERP as Biomarkers of Early Selective Attentional Bias, Reward Sensitivity, and Vulnerability to Excessive Internet Use

Within a precision medicine perspective, this study investigated electrophysiological biomarkers of early selective attention and reward process, and psychosocial risk factors related to excessive internet use in a non-clinical population. The degree of internet use [evaluated through Internet Addiction Test (IAT)], anxiety and depression levels were assessed in 24 young healthy adults. N1 and P2 Event Related-Potential (ERPs) components were monitored as biomarkers during a Go/NoGo Task performance with different addiction-related background pictures. Results indicated IAT scores were positively correlated with trait anxiety, depression levels, Go N1 amplitude in Pz, and Go P2 amplitude in Cz for videogames stimuli. In contrast, a negative correlation was found between IAT and No-Go P2 amplitude in Cz. Overall, trait anxiety and depression levels are confirmed as risk factors for excessive internet use. Moreover, early selective attention and reward sensitivity seem to be partially biased towards videogames stimuli in individuals with higher scores at IAT. The analysis of specific biomarkers (ERP components), which mark the vulnerability to the development of a clinical disorder, can be useful for developing tailor made neurocognitive preventive interventions.
Moho depth variation in southern California from teleseismic receiver functions

The number of broadband three-component seismic stations in southern California has more than tripled recently. In this study we use the teleseismic receiver function technique to determine the crustal thicknesses and 1/,/1' ratios for these stations and map out the lateral variation of Moho depth under southern California. It is shown that a receiver function can provide a. very good "point" measurement of crustal thickness under a broadband station and is not sensitive to crustal P velocity. However, the crustal thickness estimated only from the delay time of the Moho P-to-S converted phase trades off strongly with the crustal ratio. The ambiguity can be reduced significantly by incorporating the later multiple converted phases, namely, the PpPs and PpSs+PsPs. We propose a stacking algorithm which sums the amplitudes of receiver function a,t the predicted a, rrival times of these phases by different crustal thicknesses H and V,/I/ ratios. This transforms the time domain receiver functions directly into the H-V,/I, domain without need to identify these phases and to pick their arrival times. The best estimations of crustal thickness and F}/Iratio are found when the three phases are stacked coherently. By stacking receiver functions from different dista, nces and directions, effects of lateral structural variation are suppressed, and an average crustal model is obtained. Applying this technique to 84 digital broadband sta,tions in southern California reveals that the Moho depth is 29 km on average and varies from 21 to 37 kin. Deeper Mohos are found under the eastern Transverse Range, the Peninsular Range, and the Sierra Nevada Range. The central Transverse Range, however, does not have a crustal root. Thin crusts exist in the Inner California Borderland (21-22 kin) and the Salton Trough (22 kin). The Moho is relatively fia, t at the average depth in the western and central Mojave Desert and becomes shallower to the east under the Eastern California Shear Zone (ECSZ). Southern California, crust has an average Vp/lra,tio of 1.78, with higher ratios of 1.8 to 1.85 in the mountain ranges with Mesozoic basement and lower ratios in the Mojave Block except for the ECSZ, where the ratio increases.
Three-Dimensional Model for Bioventing: Mathematical Solution, Calibration and Validation

Bioventing is an established technique extensively employed in the remediation of soil contaminated with petroleum hydrocarbons. In this study, the objective was to develop an improved foundational bioventing model that characterizes gas flow in vadose zones where aqueous and non-aqueous phase liquid (NAPL) are present and immobile, accounting for interphase mass transfer and first order biodegradation kinetics. By incorporating a correlation for the biodegradation rate constant, which is a function of soil properties including initial population of petroleum degrader microorganisms in soil, sand content, clay content, water content, and soil organic matter content, this model offers the ability to integrate a specific biodegradation rate constant tailored to the soil properties for each site. The governing equations were solved using the finite volume method in OpenFOAM employing the “porousMultiphaseFoam v2107” (PMF) toolbox. The equation describing gas flow in unsaturated soil was solved using a mixed pressure-saturation method, where calculated values were employed to solve the component transport equations. Calibration was done against a set of experimental data for a meso-scale reactor considering contaminant volatilization rate as the pre-calibration parameter and the mass transfer coefficient between aqueous and NAPL phase as the main calibration parameter. The calibrated model then was validated by simulating a large-scale reactor. The modelling results showed an error of 2.9% for calibrated case and 4.7% error for validation case which present the fitness to the experimental data, proving that the enhanced bioventing model holds the potential to improve predictions of bioventing and facilitate the development of efficient strategies to remediate soil contaminated with petroleum hydrocarbons.
Advancement in SLAM Techniques and Their Diverse Applications

Simultaneous Localization and Mapping (SLAM) is a pivotal technology at the intersection of robotics, computer vision, and autonomous systems. This comprehensive review paper explores the dynamic landscape of SLAM techniques and their multifaceted applications. Beginning with an illuminating background on the fundamental principles of SLAM, we traverse the rich tapestry of advancements in SLAM methodologies. The paper categorizes SLAM techniques into distinct paradigms, including feature-based SLAM, pose-graph SLAM, visual SLAM, and sensor fusion, elucidating their underlying algorithms, strengths, and limitations. From the intricate interplay of sensors and data sources, SLAM has matured into a versatile toolset, facilitating a broad spectrum of applications. Our exploration extends to the diverse domains benefiting from SLAM, including robotics and autonomous navigation, augmented reality, autonomous vehicles, indoor mapping, agriculture, healthcare, and more. Each application domain is examined, emphasizing the specific challenges encountered and the innovative solutions that have arisen.
Revisting high-resolution schemes with van-Albada slope limiter

Slope limiters play an essential role in maintaining the non-oscillatory behavior of high-resolution methods for nonlinear conservation laws. The family of minmod limiters serves as the prototype example. Here, we revisit the question of non-oscillatory behavior of high-resolution central schemes in terms of the slope limiter proposed by van Albada et. al. 1982. The van Albada (vA) limiter is smoother near extrema, and consequently, in many cases, it outperforms the results obtained using the standard minmod limiter. In particular, we prove that the vA limiter ensures 1D TVD stability and demonstrate that it yields noticeable improvement in computation of one- and two-dimensional systems.