jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hany A. Dahish
Source: International Journal of Geomate (article, 2023-03-06)
Field: Materials Science — Materials Chemistry

PREDICTING THE COMPRESSIVE STRENGTH OF CONCRETE CONTAINING CRUMB RUBBER AND RECYCLED AGGREGATE USING RESPONSE SURFACE METHODOLOGY The reuse of construction demolition waste and rubber waste has been a key challenge in recent decades due to the decrease in natural material resources and the huge amounts of abundantly available waste.When recycled aggregates (RCA) and crumb rubber (RBR) are used together as partial substitutions for coarse and fine aggregates, respectively, in concrete, it leads to a decrease in the compressive strength when compared to natural aggregate concrete, so it is needed to construct models that can predict the strength before using them.In this paper, response surface methodology was utilized to develop predictive models for the influence of the combined incorporation of RCA and RBR in concrete on the compressive strength.The predictions were based on the results of 100 various concrete mixes obtained from previous works and from the literature.The variables included w/c ratio (0.39 and 0.5), RBR replacement (0% to 40%), levels of RCA replacement (0% to 100%), and durations of 7 and 28 days.The compressive strength was taken as a response.All models and their terms were validated using various statistical tests.The results showed that linear and quadratic models and their terms were significant, and can be utilized for the prediction of the compressive strength with less error. Crumb rubber ; Compressive strength ; Aggregate (composite) ; Materials science ; Composite material ; Natural rubber ; Geotechnical engineering ; Geology Structural mechanics and materials ; Innovations in Concrete and Construction Materials ; BIM and Construction Integration
Authors: Yunting Yang | Jiachen Jiang | Jiafu Zeng | Zhangxiong Chen | Xiao-Song Zhu | Yi-Wei Shi
Source: Sensors (article, 2023-01-27)
Field: Chemistry — Spectroscopy

CH4, C2H6, and CO2 Multi-Gas Sensing Based on Portable Mid-Infrared Spectroscopy and PCA-BP Algorithm A multi-gas sensing system was developed based on the detection principle of the non-dispersive infrared (NDIR) method, which used a broad-spectra light source, a tunable Fabry-Pérot (FP) filter detector, and a flexible low-loss infrared waveguide as an absorption cell. CH<sub>4</sub>, C<sub>2</sub>H<sub>6</sub>, and CO<sub>2</sub> gases were detected by the system. The concentration of CO<sub>2</sub> could be detected directly, and the concentrations of CH<sub>4</sub> and C<sub>2</sub>H<sub>6</sub> were detected using a PCA-BP neural network algorithm because of the interference of CH<sub>4</sub> and C<sub>2</sub>H<sub>6</sub>. The detection limits were achieved to be 2.59 ppm, 926 ppb, and 114 ppb for CH<sub>4</sub>, C<sub>2</sub>H<sub>6</sub>, and CO<sub>2</sub> with an averaging time of 429 s, 462 s, and 297 s, respectively. The root mean square error of prediction (RMSEP) of CH<sub>4</sub> and C<sub>2</sub>H<sub>6</sub> were 10.97 ppm and 2.00 ppm, respectively. The proposed system and method take full advantage of the multi-component gas measurement capability of the mid-infrared broadband source and achieve a compromise between performance and system cost. Algorithm ; Spectroscopy ; Infrared ; Infrared spectroscopy ; Computer science ; Chemistry ; Physics ; Optics ; Organic chemistry Spectroscopy and Laser Applications ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Olfa Boussadia | Hatem Zgallai | Nada Mzid | Rihem Zaabar | Mohamed Braham | Georgios Doupis | Georgios Koubouris
Source: Plants (article, 2023-05-09)
Field: Chemistry — Organic Chemistry

Physiological Responses of Two Olive Cultivars to Salt Stress The olive tree (<i>Olea europaea</i> L.) is the main fruit tree in most of the arid and semi-arid regions of Tunisia, which is where the problem of salinity is more pronounced. Salinity is one of the main factors that affects the productivity of olive trees, so the objective of this experiment was to study the effects of salinity on the photosynthesis, water relations, mineral status, and enzymatic activity of two cultivars of <i>Olea europaea</i> L., 'Chemlali' and 'Koroneiki'. The trial was conducted under controlled conditions in a greenhouse for a period of 49 days and included two treatments: T0 control and T100 (irrigation with 100 mM of NaCl solution). Under salinity stress, the photosynthesis, stomatal conductance, and leaves of both cultivars were negatively affected. 'Chemlali' showed greater tolerance to NaCl salinity, based on a progressive decrease in osmotic potential (Ψπ) followed by a progressive and synchronous decrease in gs, without a comparable decrease in photosynthesis. The water use efficiency (WUE) improved as a result. In addition, the K<sup>+</sup>/Na<sup>+</sup> ratio in 'Chemlali' rose. This appears to be crucial for managing stress. Conversely, enzymatic activity showed an accumulation of glutathione peroxidase (GPX) in stressed plants. The catalase (CAT) and ascorbate peroxidase (APX) content decreased in both stressed varieties. It can be concluded that the cultivar 'Koroneiki' is more susceptible to salt stress than the cultivar 'Chemlali', because the accumulation of GPX and the decreases in CAT and APX were more pronounced in this cultivar. Olea ; APX ; Cultivar ; Salinity ; Stomatal conductance ; Horticulture ; Photosynthesis ; Transpiration ; Olive trees ; Chemistry ; Saline water ; Botany ; Catalase ; Agronomy ; Biology ; Antioxidant ; Ecology Edible Oils Quality and Analysis ; Plant Stress Responses and Tolerance ; Plant responses to elevated CO2
Authors: Xiaohong Wu | Yiheng Fang | Bin Wu | Man Liu
Source: Foods (article, 2023-10-26)
Field: Chemistry — Analytical Chemistry

Application of Near-Infrared Spectroscopy and Fuzzy Improved Null Linear Discriminant Analysis for Rapid Discrimination of Milk Brands The quality of milk is tightly linked to its brand. A famous brand of milk always has good quality. Therefore, this study seeks to design a new fuzzy feature extraction method, called fuzzy improved null linear discriminant analysis (FiNLDA), to cluster the spectra of collected milk for identifying milk brands. To elevate the classification accuracy, FiNLDA was applied to process the near-infrared (NIR) spectra of milk acquired by the portable near-infrared spectrometer. The principal component analysis and Savitzky-Golay (SG) filtering algorithm were employed to lower dimensionality and eliminate noise in this system, respectively. Thereafter, improved null linear discriminant analysis (iNLDA) and FiNLDA were applied to attain the discriminant information of the NIR spectra. At last, the K-nearest neighbor classifier was utilized for assessing the performance of the identification system. The results indicated that the maximum classification accuracies of LDA, iNLDA and FiNLDA were 74.7%, 88% and 94.67%, respectively. Accordingly, the portable NIR spectrometer in combination with FiNLDA can classify milk brands correctly and effectively. Linear discriminant analysis ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Fuzzy logic ; Mathematics ; Near-infrared spectroscopy ; Spectrometer ; Dimensionality reduction ; Discriminant ; Classifier (UML) ; Null (SQL) ; Curse of dimensionality ; Computer science ; Data mining ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Licia Elvira Prestagiacomo | Giuseppe Tradigo | Federica Aracri | Caterina Gabriele | Maria Antonietta Rota | Stefano Alba | Giovanni Cuda | Rocco Damiano | Pierangelo Veltri | Marco Gaspari
Source: ACS Omega (article, 2023-02-07)
Field: Chemistry — Spectroscopy

Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer Prostate cancer (PCa) is annually the most frequently diagnosed cancer in the male population. To date, the diagnostic path for PCa detection includes the dosage of serum prostate-specific antigen (PSA) and the digital rectal exam (DRE). However, PSA-based screening has insufficient specificity and sensitivity; besides, it cannot discriminate between the aggressive and indolent types of PCa. For this reason, the improvement of new clinical approaches and the discovery of new biomarkers are necessary. In this work, expressed prostatic secretion (EPS)-urine samples from PCa patients and benign prostatic hyperplasia (BPH) patients were analyzed with the aim of detecting differentially expressed proteins between the two analyzed groups. To map the urinary proteome, EPS-urine samples were analyzed by data-independent acquisition (DIA), a high-sensitivity method particularly suitable for detecting proteins at low abundance. Overall, in our analysis, 2615 proteins were identified in 133 EPS-urine specimens obtaining the highest proteomic coverage for this type of sample; of these 2615 proteins, 1670 were consistently identified across the entire data set. The matrix containing the quantified proteins in each patient was integrated with clinical parameters such as the PSA level and gland size, and the complete matrix was analyzed by machine learning algorithms (by exploiting 90% of samples for training/testing using a 10-fold cross-validation approach, and 10% of samples for validation). The best predictive model was based on the following components: semaphorin-7A (sema7A), secreted protein acidic and rich in cysteine (SPARC), FT ratio, and prostate gland size. The classifier could predict disease conditions (BPH, PCa) correctly in 83% of samples in the validation set. Data are available via ProteomeXchange with the identifier PXD035942. Prostate cancer ; Urine ; Prostate-specific antigen ; Prostate ; PCA3 ; Population ; Medicine ; Internal medicine ; Cancer ; Urology ; Oncology Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Hormonal and reproductive studies
Authors: Bin Wang | Hua Yang | Shujuan Zhang | Lili Li
Source: Applied Sciences (article, 2023-03-03)
Field: Chemistry — Analytical Chemistry

Detection of Defective Features in Cerasus Humilis Fruit Based on Hyperspectral Imaging Technology Detection of skin defects in Cerasus humilis fruit is a critical process to guarantee its quality and price. This study presents a valid method for the detection of defective features in Cerasus humilis fruits based on hyperspectral imaging. A total of 420 sample images were acquired that included three types of natural defects and undamaged samples. After acquiring hyperspectral images of Cerasus humilis fruits, the spectral data were extracted from the region of interest (ROI). Five spectral preprocessing methods were used to preprocess the original spectral data, including Savitsky–Golay (S-G), standard normal variate (SNV), multiplicative scatter correction (MSC), baseline correction (BC), and de-trending (De-T). Regression coefficient (RC), successive projections algorithm (SPA), and competitive adaptive reweighed sampling (CARS) were conducted to select optimal sensitive wavelengths (SWs); as a result, 11 SWs, 17 SWs, and 13 SWs were selected, respectively. Then, the least squares-support vector machine (LS-SVM) discrimination model was established using the selected SWs. The results showed that the discriminate accuracy of the CARS-LS-SVM method was 91.43%. Based on the characteristics of image information, images corresponding to eight sensitive wavebands (950, 994, 1071, 1263, 1336, 1457, 1542, and 1628 nm) selected by CARS were subjected to principal component analysis (PCA). Then, an effective approach for detecting the defective features was exploited based on the imfill function, canny operator, region growing algorithm, bwareaopen function, and the images of PCA. The location and area of defect feature of 105 Cerasus humilis fruits could be recognized; the detect precision was 88.57%. This investigation demonstrated that hyperspectral imaging combined with an image processing technique could achieve the rapid identification of undamaged samples and natural defects in Cerasus humilis fruit. This provides a theoretical basis for the development of Cerasus humilis fruit grading and sorting equipment. Hyperspectral imaging ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Preprocessor ; Support vector machine ; Principal component analysis ; Computer science Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Essential Oils and Antimicrobial Activity
Authors: Simon Wredh | Yunzheng Wang | Joel K. W. Yang | Robert E. Simpson
Source: Advanced Optical Materials (article, 2023-11-13)
Field: Materials Science — Materials Chemistry

Multi‐Level Optical Switching by Amorphization in Single‐ and Multi‐Phase Change Material Structures Abstract The optical properties of phase‐change materials (PCMs) can be tuned to multiple levels by controlling the transition between their amorphous and crystalline phases. In multi‐material PCM structures, the number of discrete reflectance levels can be increased according to the number of PCM layers. However, the effect of increasing the number of layers on quenching and reversibility has not been thoroughly studied. In this work, the phase‐change physics and thermal conditions required for reversible switching of single and multi‐material PCM switches are discussed based on thermo‐optical phase‐change models and laser switching experiments. By using nanosecond laser pulses, 16 different reflectance levels in Ge 2 Sb 2 Te 5 are demonstrated via amorphization. Furthermore, a multi‐material switch based on Ge 2 Sb 2 Te 5 and GeTe with four discrete reflectance levels is experimentally proven with a reversible multi‐level response. The results and design principles presented herein will impact active photonics applications that rely on dynamic multi‐level operation, such as optical computing, beam steering, and next‐generation display technologies. Materials science ; Nanosecond ; Photonics ; Amorphous solid ; Phase-change material ; Optical switch ; Laser ; Optoelectronics ; Phase (matter) ; Phase change ; Thermal ; Phase transition ; Quenching (fluorescence) ; Optics ; Engineering physics ; Thermodynamics ; Fluorescence Phase-change materials and chalcogenides ; Nonlinear Optical Materials Studies ; Chalcogenide Semiconductor Thin Films
Authors: Shaoli Liu | Ting Lei | Guipu Li | Shuming Liu | Chu Xiao-jun | Donghai Hao | Gongnian Xiao | Ayaz Ali Khan | Taqweem Ul Haq | Manal Y. Sameeh | Tariq Aziz | Manal Tashkandi | Guanghua He
Source: Frontiers in Nutrition (article, 2023-09-22)
Field: Chemistry — Analytical Chemistry

Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy In order to achieve rapid detection of galactooligosaccharides (GOS), fructooligosaccharides (FOS), calcium (Ca), and vitamin C (Vc), four micronutrient components in infant formula milk powder, this study employed four methods, namely Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Normalization (Nor), and Savitzky-Golay Smoothing (SG), to preprocess the acquired original spectra of the milk powder. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm and Random Frog (RF) algorithm were used to extract representative characteristic wavelengths. Furthermore, Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models were established to predict the contents of GOS, FOS, Ca, and Vc in infant formula milk powder. The results indicated that after SNV preprocessing, the original spectra of GOS and FOS could effectively extract feature wavelengths using the CARS algorithm, leading to favorable predictive results through the CARS-SVR model. Similarly, after MSC preprocessing, the original spectra of Ca and Vc could efficiently extract feature wavelengths using the CARS algorithm, resulting in optimal predictive outcomes via the CARS-SVR model. This study provides insights for the realization of online nutritional component detection and optimization control in the production process of infant formula. Micronutrient ; Infant formula ; Near-infrared spectroscopy ; Spectroscopy ; Materials science ; Infrared spectroscopy ; Food science ; Chemistry ; Analytical Chemistry (journal) ; Environmental chemistry ; Optics ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Ming Zhang | Najiao Zhang | Peng Dong | Lin Yang | Baozhu Wang | Ruihong Wu | Weimin Hou
Source: Photonics (article, 2023-08-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

All-Metal Coding Metasurfaces for Broadband Terahertz RCS Reduction and Infrared Invisibility With the rapid advancement of modern technology and radar detection systems, electromagnetic (EM) stealth technology has become increasingly significant, particularly in aircraft stealth and military radar applications. In this work, an all-metal metasurface is designed for broadband terahertz radar cross-section (RCS) reduction and infrared invisibility. The all-metal metasurface possesses extremely low infrared emissivity and high polarization conversion in the terahertz band. Through the joint simulation of MATLAB and CST, a genetic algorithm is used to optimize the random phase distribution of 2, 3, and 4-bit metasurfaces, so that the reflected wave is scattered to achieve broadband terahertz RCS reduction. Simulation results show that the metasurface can simultaneously achieve broadband terahertz RCS reduction in 3–5 THz and infrared invisibility in 24–38 THz (8–12.5 μm). The RCS reduction of the coding metasurface is greater than 10 dB compared to the metal plate, and the maximum RCS reduction of the 4-bit metasurface can reach 21.1 dB. Compared to the traditional design method, the proposed method can reduce time consumption and find the optimal result to achieve high performance. We believe the proposed method can provide significant guidance for surface coating in camouflage applications and opens up new possibilities for improving the information capacity of coding metasurfaces. Terahertz radiation ; Broadband ; Optics ; Materials science ; Metamaterial ; Radar cross-section ; Infrared ; Camouflage ; Optoelectronics ; Radar ; Reduction (mathematics) ; Cloaking ; Computer science ; Physics ; Telecommunications Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Ammara Muazzam | Matt Spick | Olivier Cexus | Bethany Geary | Fowz Azhar | Hardev Pandha | Agnieszka Michael | Rachel Reed | Sarah Lennon | Lee A. Gethings | Robert S. Plumb | Anthony D. Whetton | Nophar Geifman | Paul A. Townsend
Source: Cancers (article, 2023-02-07)
Field: Chemistry — Spectroscopy

A Novel Blood Proteomic Signature for Prostate Cancer Prostate cancer is the most common malignant tumour in men. Improved testing for diagnosis, risk prediction, and response to treatment would improve care. Here, we identified a proteomic signature of prostate cancer in peripheral blood using data-independent acquisition mass spectrometry combined with machine learning. A highly predictive signature was derived, which was associated with relevant pathways, including the coagulation, complement, and clotting cascades, as well as plasma lipoprotein particle remodeling. We further validated the identified biomarkers against a second cohort, identifying a panel of five key markers (GP5, SERPINA5, ECM1, IGHG1, and THBS1) which retained most of the diagnostic power of the overall dataset, achieving an AUC of 0.91. Taken together, this study provides a proteomic signature complementary to PSA for the diagnosis of patients with localised prostate cancer, with the further potential for assessing risk of future development of prostate cancer. Data are available via ProteomeXchange with identifier PXD025484. Prostate cancer ; Medicine ; Prostate ; Oncology ; Cancer ; Internal medicine ; Proteomics ; Bioinformatics ; Computational biology ; Biology Advanced Proteomics Techniques and Applications ; Prostate Cancer Treatment and Research ; Prostate Cancer Diagnosis and Treatment
Authors: Qingge Ji | Chaofeng Li | Xian‐Shu Fu | Jinyan Liao | Xuezhen Hong | Xiaoping Yu | Zihong Ye | Mingzhou Zhang | Yulou Qiu
Source: Molecules (article, 2023-03-20)
Field: Chemistry — Analytical Chemistry

Protected Geographical Indication Discrimination of Zhejiang and Non-Zhejiang Ophiopogonis japonicus by Near-Infrared (NIR) Spectroscopy Combined with Chemometrics: The Influence of Different Stoichiometric and Spectrogram Pretreatment Methods This paper presents a method for the protected geographical indication discrimination of Ophiopogon japonicus from Zhejiang and elsewhere using near-infrared (NIR) spectroscopy combined with chemometrics. A total of 3657 <i>Ophiopogon japonicus</i> samples from five major production areas in China were analyzed by NIR spectroscopy, and divided into 2127 from Zhejiang and 1530 from other areas ('non-Zhejiang'). Principal component analysis (PCA) was selected to screen outliers and eliminate them. Monte Carlo cross validation (MCCV) was introduced to divide the training set and test set according to a ratio of 3:7. The raw spectra were preprocessed by nine single and partial combination methods such as the standard normal variable (SNV) and derivative, and then modeled by partial least squares regression (PLSR), a support vector machine (SVM), and soft independent modeling of class analogies (SIMCA). The effects of different pretreatment and chemometrics methods on the model are discussed. The results showed that the three pattern recognition methods were effective in geographical origin tracing, and selecting the appropriate preprocessing method could improve the traceability accuracy. The accuracy of PLSR after the standard normal variable was better, with R<sup>2</sup> reaching 0.9979, while that of the second derivative was the lowest with an R<sup>2</sup> of 0.9656. After the SNV pretreatment, the accuracy of the training set and test set of SVM reached the highest values, which were 99.73% and 98.40%, respectively. The accuracy of SIMCA pretreated with SNV and MSC was the highest for the origin traceability of <i>Ophiopogon japonicus</i>, which could reach 100%. The distance between the two classification models of SIMCA-SNV and SIMCA-MSC is greater than 3, indicating that the SIMCA model has good performance. Chemometrics ; Partial least squares regression ; Principal component analysis ; Pattern recognition (psychology) ; Near-infrared spectroscopy ; Mathematics ; Artificial intelligence ; Test set ; Outlier ; Training set ; Coefficient of determination ; Statistics ; Chemistry ; Computer science ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies