Fast Tool Research Articles

Machine learning prediction of methane, nitrogen, and natural gas mixture viscosities under normal and harsh conditions

The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and − 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.

Liquid crystal immunosensors for the selective detection of Escherichia coli with a fast analysis tool

The consumption of contaminated food may cause serious illnesses, and traditional methods to detect Escherichia coli are still associated with long waiting times and high costs given the necessity to transport samples to specialized laboratories. There is a need to develop new technologies that allow cheap, fast, and direct monitoring at the site of interest. Thus, in this work, we developed optical immunosensors for the selective detection of E. coli, based on liquid crystal technology, whose molecules can align in different manners depending on the boundary conditions (such as substrates) as well as the environment that they experience. Each glass substrate was functionalized with anti-E. coli antibody using cysteamine as an intermediate, and a vertical alignment was imposed on the liquid crystal molecules by using DMOAP during functionalization. The presence of bacteria disrupts the alignment of the liquid crystal molecules, changing the intensity of light emerging between cross polarizers, measured using a polarized optical microscope and a monochromator. It was possible to detect E. coli in suspensions in the concentration range from 2.8 cells/mL to 2.8×109 cells/mL. Selectivity was also evaluated, and the sensors were used to analyze contaminated water samples. A prototype was developed to allow faster, in-situ, and easier analysis avoiding bulky instruments.

Radial velocities: Direct application of Pierre Connes’ shift-finding algorithm to cross-correlation functions

Context. Pipelines of state-of-the-art spectrographs dedicated to planet detection provide, for each exposure, series of cross-correlation functions (CCFs) built with a binary mask (BM), as well as the absolute radial velocity (RV) derived from the Gaussian fit of a weighted average CCFtot of the CCFs. Aims. Our aim was to test the benefits of the application of the shift-finding algorithm developed by Pierre Connes directly to the total CCFtot, and to compare the resulting RV shifts (DRVs) with the results of the Gaussian fits. In a second step, we investigated how the individual DRV profiles along the velocity grid derived from the shift-finding algorithm can be used as an easy tool for detection of stellar line shape variations. Methods. We developed the corresponding algorithm and tested it on 1151 archived spectra of the K2.5 V star HD 40307 obtained with ESO/ESPRESSO during a one-week campaign in 2018. Tests were performed based on the comparison of DRVs with RVs from Gaussian fits. DRV profiles along the velocity grid (DRV(i)) were scrutinized and compared with direct CCFtot ratios. Results. The dispersion of residuals from a linear fit to RVs from 406 spectra recorded within a single night, a measure of mean error, was found to be σ = 1.03 and 0.83 m s−1 for the Gaussian fit and the new algorithm, respectively, which is a significant 20% improvement in accuracy. The two full one-week series obtained during the campaign were also fitted with a three-planet system Keplerian model. The residual divergence between data and best-fit model is significantly smaller for the new algorithm than for the Gaussian fit. Such a difference was found to be associated in a large part with an increase of ≃1.3 m s−1 in the difference between the two types of RV values between the third and fourth nights. Interestingly, the DRV(i) profiles reveal at the same time a significant variation of line shape. Conclusions. The shift-finding algorithm is a fast and easy tool that provides additional diagnostics on the RV measurements in series of exposures. For observations made in the same instrumental configuration, and if line shapes are not varying significantly, it increases the accuracy of velocity variation determinations. On the other hand, departures from constancy of the DRV(i) profiles, as well as varying differences between RVs from this new method and RVs from a Gaussian fit can detect and report in a simple way line shape variations due to stellar activity.

Spectral characteristics of dissolved organic matter in Plateau Lakes: Identifying eutrophication indicators in Southwest China

Dissolved organic matter (DOM) acts as a chemical intermediary between terrestrial and lacustrine ecosystems and significantly affects the structure and function of lakes. The trophic state of lakes, driven by terrestrial input and phytoplankton biomass, alters the optical properties of DOM. From November 2018 to July 2019, we collected 119 water samples from the Erhai watershed and analyzed them using UV–Vis and EEM-PARAFAC to study the optical properties of DOM in relation to the trophic conditions. Our result indicated that the tyrosine-like protein (C1), tryptophan-like protein (C2), and humic-like compounds (C3) were among the mostly autochthonous components of the DOM. The percentage of the C3 was higher in eutrophic lakes than in mesotrophic and light-eutrophic lakes. The ultraviolet absorption coefficients at 254 nm (aCDOM(254)) and fluorescence intensity at 355 nm (Fn(355)) increased significantly (p < 0.01) with an increased trophic state. Our findings indicate that the influence of nutrients and environmental factors (such as pH and water temperature) on DOM varies with the trophic state. The development of novel predictive models for trophic state assessment was largely based on the significant correlations between TSI and aCDOM(254) (R2 = 0.762, p < 0.01) and Fn(355) (R2 = 0.705, p < 0.01). This neural network model facilitates the creation of a novel fast assessment tool by highlighting the connection between DOM features and the trophic state index. By enabling swift experimental measurements, this model offers a high-resolution monitoring solution for tracking the eutrophication of plateau lakes and rivers.

Contrast-Enhanced Ultrasound in the Diagnosis of Solid Renal Lesions.

The availability of imaging methods has enabled increased detection of kidney lesions, which are a common clinical problem. It is estimated that more than half of patients over the age of 50 have at least one undetermined mass in the kidney. The appropriate characterization and diagnosis of lesions imaged in the kidney allows for proper therapeutic management. Previously, contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) have been used in their extended diagnosis. However, the limitations of these techniques, such as radiation exposure, renal toxicity, and allergies to contrast agents, must be considered. Contrast-enhanced ultrasound (CEUS) is increasingly being used as an examination to resolve interpretive doubts that arise with other diagnostic methods. Indeed, it can be considered both as a problem-solving technique for diagnosing and distinguishing lesions and as a technique used for observation in preservative treatment. Evaluation of the enhancement curve over time on CEUS examination can help to differentiate malignant renal cell carcinoma (RCC) subtypes that should be resected from benign lesions, such as oncocytoma or angiomyolipoma (AML), in which surgery can be avoided. It allows for distinguishing between benign and malignant tumors, renal and pseudotumors, and solid and cystic tumors. Therefore, with recent advances in ultrasound technology, CEUS has emerged as a fast, reliable, and cost-effective imaging tool in the preoperative evaluation and diagnosis of solid renal masses.

Short functional geriatric evaluation: Confirmatory factor analysis to assess the multidimensionality of frailty in community-dwelling older adults

This paper explores the ageing population in Italy, where older adults account for more than 14 million individuals (in January 2023) and constitute 24.1 % of the total population. Frailty, a condition encompassing biological, psychological, social, and economic challenges, is recognised as a significant public health issue. The study introduces the Short Functional Geriatric Evaluation (SFGE) as a large-scale screening tool for frailty in community-dwelling older individuals. A Confirmatory Factor Analysis (CFA) was conducted on the SFGE. The CFA scrutinises the construct validity of SFGE using a sample population from the "Long Live the Elderly!" program in Italy. Initial results indicate an acceptable fit, prompting the incorporation of Modification Indices to enhance model performance. The refined CFA demonstrates that the SFGE model effectively captures the multidimensional nature of frailty. The text underscores the timeliness of identifying frailty, emphasising the need for simple, fast, and predictive tools to screen large populations efficiently.

Line-field Confocal Optical Coherence Tomography (LC-OCT) for real-time differential diagnosis of toxic epidermal necrolysis versus staphylococcal scalded skin syndrome leading to an optimal initial management

We present here the cases of a 31-year-old male and a 68-year-old female, both presented with extensive erythema, a positive Nikolsky’s sign and major pain. Line-field Confocal Optical Coherence Tomography (LC-OCT) LC-OCT is a fast imaging tool able to visualize the cleavage level and epidermal necrosis to distinguish staphylococcal scalded skin syndrome from toxic epidermal necrolysis.

Diagnostic Accuracy of the Recognizing Acute Delirium as Part of Your Routine (RADAR) Scale for Delirium Assessment in Hospitalized Older Adults: A Cross-Sectional Study.

Delirium is highly prevalent among hospitalized older adults and is associated with unfavorable outcomes. However, delirium often remains undiagnosed in the hospital context. Having a valid, simple, and fast screening tool could help in limiting the additional workload for healthcare professionals, without leaving delirium undetected. The aim of this study was to estimate the sensitivity and specificity of the Recognizing Acute Delirium As part of your Routine (RADAR) scale in an Italian hospital. An observational cross-sectional study was conducted. A total of 150 patients aged ≥70 years were enrolled. Receiver operating characteristic (ROC) curves using the Confusion Assessment Method (CAM) criterion-defined delirium as the gold standard were plotted to evaluate the performance of the RADAR scale. The cut-off suggested by previous research was used to estimate the sensitivity, specificity, and positive and negative predictive values of the RADAR scale. The involved patients were mostly females (60%; n = 90), with a median age of 84 years (I-III quartiles: 80-88). According to the CAM and the RADAR scale, 37 (25%) and 58 (39%) patients were classified as experiencing delirium, respectively. The area under the ROC curve of the RADAR scale was 0.916. Furthermore, the RADAR scale showed robust sensitivity (95%), specificity (80%), and positive (60%) and negative predictive values (98%). The RADAR scale is thus suggested to be a valid tool for screening assessment of delirium in hospitalized older adults.

Spectroscopic and Interferometric Sum-Frequency Imaging of Strongly Coupled Phonon Polaritons in SiC Metasurfaces.

Phonon polaritons enable waveguiding and localization of infrared light with extreme confinement and low losses. The spatial propagation and spectral resonances of such polaritons are usually probed with complementary techniques such as near-field optical microscopy and far-field reflection spectroscopy. Here, infrared-visible sum-frequency spectro-microscopy is introduced as a tool for spectroscopic imaging of phonon polaritons. The technique simultaneously provides sub-wavelength spatial resolution and highly-resolved spectral resonance information. This is implemented by resonantly exciting polaritons using a tunable infrared laser and wide-field microscopic detection of the upconverted light. The technique is employed to image hybridization and strong coupling of localized and propagating surface phonon polaritons in a metasurface of SiC micropillars. Spectro-microscopy allows to measure the polariton dispersion simultaneously in momentum space by angle-dependent resonance imaging, and in real space by polariton interferometry. Notably, it is possible to directly image how strong coupling affects the spatial localization of polaritons, inaccessible with conventional spectroscopic techniques. The formation of edge states is observed at excitation frequencies where strong coupling prevents polariton propagation into the metasurface. The technique is applicable to the wide range of polaritonic materials with broken inversion symmetry and can be used as a fast and non-perturbative tool to image polariton hybridization andpropagation.

The performance of A rpob gene mutation linked to A resistant to rifampicin mycobacterium tuberculosis isolate from an Indonesian referral hospital

IntroductionIndonesia has the world's second-highest tuberculosis and is one of the 30 countries with an increased MDR/RR TB burden. Even though the number of people with tuberculosis is rising every year, several cases of latent or active tuberculosis remain unreported. Microscopy has its limitations, and factors such as quality sample, quality smear, bacterial load, and experience of reading AFB influence the sensitivity of the test. Utilizing a quick, point-of-care testing system based on molecular techniques can increase the sensitivity of tuberculosis early diagnosis. Cepheid Xpert ®MTB/RIF is Indonesia's most widely utilized molecular technology. It is a nested real-time PCR diagnostic device that is cartridge-based and semiquantitative. In the TB complex of Mycobacterium tuberculosis, it identifies alterations in the RNA polymerase (rpoB) gene beta-subunit that result in rifampicin resistance (RR). By utilizing the Xpert ® MTB/RIF assay at Soetomo General Hospital, one of Indonesia's primary tertiary referral hospitals, the current study seeks to improve our knowledge of the prevalence of rpoB gene mutations in individuals with tuberculosis. MethodsBetween January 2021 and December 2021, Soetomo General Hospital used the Xpert®MTB/RIF system to screen sputum and extrapulmonary specimens from 3584 for tuberculosis. ResultsOf the 665 samples, 18,5% tested positive for M. tuberculosis, with 7.6% (51 out of 665) showing resistance to Rifampicin Rifampicin compounds at codons 529–533 (probe E) of the rpoB gene were detected in 56.8% (29/51) of rifampicin-resistant tuberculosis (RR-TB) samples. And then Probe D (19.6%). 7.8% for probes A and B, respectively (5.8%). Probe C did not exhibit any mutations. On the other hand, two samples revealed two mutations in probes D and E, while among 13.7% (7/51) of RR TB samples, no mutations in the probe type were observed. ConclusionThe Xpert®MTB/RIF system is a compassionate, fast, accurate, and efficient diagnostic tool for RR-TB, tuberculosis, and the molecular epidemiology of mutations associated with rifampin resistance.

Simulation of nanopore sequencing signal data with tunable parameters.

In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, a transcriptome, or read sequences, and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation, and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols or noise-free "ideal" data, or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.

Modeling 1-Cyano-4-Dimethylaminopyridine Tetrafluoroborate (CDAP) Chemistry to Design Glycoconjugate Vaccines with Desired Structural and Immunological Characteristics.

Glycoconjugation is a well-established technology for vaccine development: linkage of the polysaccharide (PS) antigen to an appropriate carrier protein overcomes the limitations of PS T-independent antigens, making them effective in infants and providing immunological memory. Glycoconjugate vaccines have been successful in reducing the burden of different diseases globally. However, many pathogens still require a vaccine, and many of them display a variety of glycans on their surface that have been proposed as key antigens for the development of high-valency glycoconjugate vaccines. CDAP chemistry represents a generic conjugation strategy that is easily applied to PS with different structures. This chemistry utilizes common groups to a large range of PS and proteins, e.g., hydroxyl groups on the PS and amino groups on the protein. Here, new fast analytical tools to study CDAP reaction have been developed, and reaction conditions for PS activation and conjugation have been extensively investigated. Mathematical models have been built to identify reaction conditions to generate conjugates with wanted characteristics and successfully applied to a large number of bacterial PSs from different pathogens, e.g., Klebsiella pneumoniae, Salmonella Paratyphi A, Salmonella Enteritidis, Salmonella Typhimurium, Shighella sonnei and Shigella flexneri. Furthermore, using Salmonella Paratyphi A O-antigen and CRM197 as models, a design of experiment approach has been used to study the impact of conjugation conditions and conjugate features on immunogenicity in rabbits. The approach used can be rapidly extended to other PSs and accelerate the development of high-valency glycoconjugate vaccines.

PM-profiler: a high-resolution and fast tool for taxonomy annotation of amplicon-based microbiome.

Amplicon sequencing stands as a cornerstone in microbiome profiling, yet concerns persist regarding its resolution and accuracy. The enhancement of reference databases and annotations marks a new era for 16S rRNA-based profiling. Capitalizing on this potential, we introduce PM-profiler, a novel tool for profiling amplicon short reads. PM-profiler is implemented by C++-based advanced algorithms, such as pre-allocated hash for reference construction, hybrid and dynamic short-read matching, big-data-guided dual-mode hierarchical taxonomy annotation strategy, and full-procedure parallel computing. This tool delivers species-level resolution and ultrafast speed for large-scale microbiomes, surpassing alignment-based approaches and the Naïve-Bayesian model. Furthermore, recognizing the global uneven distribution of microbes, we delineate optimal annotation strategies for each sampling habitat based on microbial patterns over 270,000 microbiomes. Integrated with the established workflow of Parallel-Meta Suite and the latest curated reference databases, this endeavor offers a swift and dependable solution for high-precision microbiome surveys.IMPORTANCEOur study introduces PM-profiler, a new tool that deciphers the complexity of microbial communities. With advanced algorithms, flexible annotation strategies, and well-organized big-data, PM-profiler provides a faster and more accurate way to study on microbiomes, paving the way for discoveries that could improve our understanding of microbiomes and their impact on the world.

Numerical modeling of systems based on SMA wires and pulleys: A finite element formulation within the Arbitrary Lagrangian–Eulerian framework

Thanks to their properties of superelasticity and shape memory effects, SMA wires are often considered for the design of damping devices or actuators coupled to pulleys. In order to dimension such devices for commercialization, it is necessary to develop reliable, robust and fast numerical tools. To this end, simplifying the management of the wire/pulley contact using an Arbitrary Lagrangian–Eulerian (ALE) formulation is an original and effective solution. In this work, the wires are modeled by three-dimensional truss thermo-mechanical finite elements associated with a superelastic law. For each node, the curvilinear abscissa is used as an additional degree of freedom in order to deal with material flow from one element to another. After presenting the ALE formalism and the necessary precautions (advection of variables), as well as the material behavior and thermo-mechanical contact laws at the pulleys, elementary validation tests are studied. Finally, two devices taken from the literature are modeled using the ALE formalism. The results obtained demonstrate the relevance and effectiveness of the approach adopted, as well as the importance of not neglecting friction at the pulleys and thermal effects on the material mechanical response.

Short-Term Forecasting of Photovoltaic Power Using Multilayer Perceptron Neural Network, Convolutional Neural Network, and k-Nearest Neighbors’ Algorithms

Governments and energy providers all over the world are moving towards the use of renewable energy sources. Solar photovoltaic (PV) energy is one of the providers’ favourite options because it is comparatively cheaper, clean, available, abundant, and comparatively maintenance-free. Although the PV energy source has many benefits, its output power is dependent on continuously changing weather and environmental factors, so there is a need to forecast the PV output power. Many techniques have been employed to predict the PV output power. This work focuses on the short-term forecast horizon of PV output power. Multilayer perception (MLP), convolutional neural networks (CNN), and k-nearest neighbour (kNN) neural networks have been used singly or in a hybrid (with other algorithms) to forecast solar PV power or global solar irradiance with success. The performances of these three algorithms have been compared with other algorithms singly or in a hybrid (with other methods) but not with themselves. This study aims to compare the predictive performance of a number of neural network algorithms in solar PV energy yield forecasting under different weather conditions and showcase their robustness in making predictions in this regard. The performance of MLPNN, CNN, and kNN are compared using solar PV (hourly) data for Grahamstown, Eastern Cape, South Africa. The choice of location is part of the study parameters to provide insight into renewable energy power integration in specific areas in South Africa that may be prone to extreme weather conditions. Our data does not have lots of missing data and many data spikes. The kNN algorithm was found to have an RMSE value of 4.95%, an MAE value of 2.74% at its worst performance, an RMSE value of 1.49%, and an MAE value of 0.85% at its best performance. It outperformed the others by a good margin, and kNN could serve as a fast, easy, and accurate tool for forecasting solar PV output power. Considering the performance of the kNN algorithm across the different seasons, this study shows that kNN is a reliable and robust algorithm for forecasting solar PV output power.

Fast and space-efficient taxonomic classification of long reads with hierarchical interleaved XOR filters.

Metagenomic long-read sequencing is gaining popularity for various applications, including pathogen detection and microbiome studies. To analyze the large data created in those studies, software tools need to taxonomically classify the sequenced molecules and estimate the relative abundances of organisms in the sequenced sample. Because of the exponential growth of reference genome databases, the current taxonomic classification methods have large computational requirements. This issue motivated us to develop a new data structure for fast and memory-efficient querying of long reads. Here, we present Taxor as a new tool for long-read metagenomic classification using a hierarchical interleaved XOR filter data structure for indexing and querying large reference genome sets. Taxor implements several k-mer-based approaches, such as syncmers, for pseudoalignment to classify reads and an expectation-maximization algorithm for metagenomic profiling. Our results show that Taxor outperforms state-of-the-art tools regarding precision while having a similar recall for long-read taxonomic classification. Most notably, Taxor reduces the memory requirements and index size by >50% and is among the fastest tools regarding query times. This enables real-time metagenomics analysis with large reference databases on a small laptop in the field.

Autumn cold acclimation and freezing tolerance of three oak species in semi-Mediterranean Zagros forests

Aim of study: To identify and compare the early frost resistance mechanisms in three oak species (Quercus brantii, Quercus libani and Quercus infectoria). Area of study: Zagros forests of Iran. Material and methods: The physiological and biochemical variables such as chlorophyll fluorescence, relative water content (RWC), electrolyte leakage, and osmotic metabolite content, such as proline, glucose, and potassium of three oak species seedlings with varying altitudinal and latitudinal ranges were measured under various treatments, including cold treatments (4°C: control, -20°C (1 hour) and -20°C (2 hours)) and four levels of hardening steps with decreasing temperature and photoperiod. Main results: Results showed that decreasing photoperiod and temperature during cold hardening was associated with decreasing Fv/Fm, ΦPSII and electron transport rate, increasing (NPQ) measured from chlorophyll fluorescence, as well as increasing osmotic metabolite content and decline of RWC, except the glucose content decreased in Q. brantii from lower altitudes and south aspect. On the other hand, Q. libanii, which originates from higher altitudes and north aspect showed the strongest cold-resistance and faster developing cold-acclimation capacity using earlier accumulation of osmotic metabolites, diminishing RWC and subsequently lowest EL compared to the other oak species. Research highlights: The physiological and biochemical responses of oak species differed based on origin and there was a positive relation between osmotic metabolite content, NPQ, altitude, and cold stress resistance. These physiological responses, especially NPQ (as a fast and non-invasive tool) provide a quantitative assessment of the risks associated with autumn freezing in different oak species and ecotypes relevant to conservation and reforestation projects of the Zagros forests under changing climatic conditions.

An atomic force microscope-like dual-stage force controlled fast tool servo for in-process inspection of micro-structured surfaces

This study presents the development of a dual-stage force sensor integrated fast tool servo (FS-FTS) for in-process inspection of micro-structures. With the cutting tool serving as a probe, the measurement principle of the FS-FTS is similar to that of an atomic force microscope (AFM). The force modulation method, inspired by the tapping mode AFM, is employed to detect the weak contact between the cutting tool and the workpiece. The primary stage of the dual-stage FTS is responsible for tracking the surface profile of the micro-structures while the secondary stage offers the high-frequency oscillation to modulate the force signal. An experimental method is proposed to determine the optimal oscillation frequency. The transfer function of the FS-FTS, from the input voltage of the primary stage to the output force of the force detection module, is identified, and a notch filter integrated feedback controller is designed for the force control. With the designed dual-stage FS-FTS and force controller, the tip of the cutting tool could follow the unknown workpiece contour with a specified constant contact force, and the surface profile is obtained through the measured displacement of the primary stage. The feasibility and superiority of the developed system are validated through the in-process inspection of micro-lens with a depth of 19μm. The results demonstrate that the developed FS-FTS realizes the sub-micron profile measurement with a scanning speed of 150μm/min, and the depth of the resulting surface scratches is less than 20 nm.

Comparison of Simulation Methods for Glare Risk Assessment with Roller Shades

Daylight discomfort glare evaluation is important when selecting shading properties. New standards recommend allowable glare frequency limits but do not specify the modeling accuracy required for annual glare risk assessment. Fast simulation tools allow users to perform hourly glare evaluations within minutes. However, reliable evaluation of glare through roller shades requires accurate modeling of their specular and diffuse transmission characteristics, affected by color, materials, and weaving technology. This study presents a systematic comparison between commonly used glare simulation methods against the “ground truth” Radiance ray-tracing tool rpict in terms of hourly daylight glare probability (DGP), hourly vertical illuminance (Ev), and annual visual discomfort frequency. The results are presented for two shade fabrics using light transmission models with and without a peak extraction algorithm (Radiance–aBSDF and Radiance–BSDF) for the specular component. The impact of sky/sun discretization on glare prediction is also discussed. The results show that the Radiance 5–Phase Method (5PM) is superior when modeling direct sunlight and DGP through shades, while other investigated methods (3–Phase Method, imageless DGP, ClimateStudio Annual Glare) are not as robust for that purpose. Users are encouraged to understand the underlying assumptions in the imageless methods to avoid errors when simulating glare, especially due to the contrast effects.

Study on automatic lithology identification based on convolutional neural network and deep transfer learning

Automatic and fast rock classification identification is an important part of geotechnical intelligent survey system. Image based supervised deep learning analysis, especially for convolutional neural networks (CNN), has potential in optimizing lithologic classification and interpretation using borehole core images. However, the accuracy and efficiency of lithology identification models are low at present. In this work, a systematic and enormous rock data framework based on the geological rock classification system is firstly established to provide rock learning datasets. The dataset is composed of approximately 150,000 images of rock samples, which covers igneous rocks, sedimentary rocks, and metamorphic rocks. Secondly, based on CNN-deep transfer learning algorithm, an end-to-end, image-to-label rock lithology identification is established. Finally, the generalization of the proposed model and the field drilling core verification test show that the constructed intelligent rock recognition model has an ability to identify rocks quickly and accurately, and the recognition accuracy of 12 kinds of common engineering rocks is more than 95%. The proposed rock intelligent classification model provides a convenient and fast tool for field geologists and scientific researchers.