jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siva Peddareddigari | S. Vijayan | Annamalai Manickavasagan
Source: Applied Sciences (article, 2024-12-26)
Field: Chemistry — Analytical Chemistry

IoT, Blockchain, Big Data and Artificial Intelligence (IBBA) Framework—For Real-Time Food Safety Monitoring Technological advancements in mechanized food production have expanded markets beyond geographical boundaries. At the same time, the risk of contamination has increased severalfold, often resulting in significant damage in terms of food wastage, economic loss to the producers, danger to public health, or all of these. In general, governments across the world have recognized the importance of having food safety processes in place to impose food recalls as required. However, the primary challenges to the existing practices are delays in identifying unsafe food, siloed data handling, delayed decision making, and tracing the source of contamination. Leveraging the Internet of Things (IoT), 5G, blockchains, cloud computing, and big data, a novel framework has been proposed to address the current challenges. The framework enables real-time data gathering and in situ application of machine learning-powered algorithms to predict contamination and facilitate instant decision making. Since the data are processed in real time, the proposed approach enables contamination to be identified early and informed decisions to be made confidently, thereby helping to reduce damage significantly. The proposed approach also throws up new challenges in terms of the implementation of changes to data collection across all phases of food production, onboarding various stockholders, and adaptation to a new process. Blockchain ; Big data ; Computer science ; Internet of Things ; Computer security ; Data science ; Data mining Spectroscopy and Chemometric Analyses
Authors: Krzysztof Banaś | Agnieszka Banaś | Giorgia Pastorin | Nguyễn Thị Hồng | Shikhar Gupta | Katarzyna Dziedzic-Kocurek | Mark B. H. Breese
Source: Sensors (article, 2024-10-31)
Field: Chemistry — Analytical Chemistry

Sensing the Changes in Stratum Corneum Using Fourier Transform Infrared Microspectroscopy and Hyperspectral Data Processing The stratum corneum (SC) forms the outermost layer of the skin, playing a critical role in preventing water loss and protecting against external biological and chemical threats. Approximately 90% of the SC consists of large, flat corneocytes, yet its barrier function primarily relies on the intercellular lipid matrix that surrounds these cells. Traditional methods for characterizing these lipids, such as Fourier transform infrared spectroscopy (FTIR), typically involve macroscopic analysis using attenuated total reflection (ATR) techniques. In this study, we introduce a novel approach for investigating SC samples at a microscopic level to gain detailed chemical insights and assess sample heterogeneity. Special emphasis is placed on advanced hyperspectral data pre-processing to ensure the accuracy and reliability of the results. We also evaluate methods for filtering out spectral data that significantly deviate from the mean and analyze the extracted mean spectra, the intensities of specific infrared peaks, and their ratios. The novelty of this work lies in its microscopic approach to analyzing the SC lipid matrix, diverging from the traditional macroscopic FTIR-ATR methods. By focusing on hyperspectral imaging and developing robust pre-processing techniques, this study provides more localized, high-resolution chemical insights. This microscopic perspective opens up the possibility of detecting subtle heterogeneities within the skin's lipid matrix, offering deeper, previously unattainable understanding of the SC's barrier function. Additionally, the exploration of spectral filtering methods enhances the precision of the analysis, paving the way for more refined and reliable investigations of skin structure and behavior in future research. Hyperspectral imaging ; Stratum corneum ; Infrared ; Fourier transform infrared spectroscopy ; Fourier transform ; Remote sensing ; Fourier transform spectroscopy ; Chemical imaging ; Materials science ; Optics ; Computer science ; Environmental science ; Geology ; Physics ; Medicine ; Pathology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Bee Products Chemical Analysis
Authors: Yasi Dani | Maria Artanta Ginting
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2024-01-26)
Field: Chemistry — Analytical Chemistry

Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms In this study, we apply two classification algorithm methods, namely the Gaussian naïve Bayes (GNB) and the decision tree (DT) classifiers. The Gaussian naïve Bayes classifier is a probability-based classification model that predicts future probabilities based on past experiences. Whereas the decision tree classifier is based on a decision tree, a series of tests that are performed adaptively where the previous test affects the next test. Both of these methods are simulated on the Iris dataset where the dataset consists of three types of Iris: setosa, virginica, and versicolor. The data is divided into two parts, namely training and testing data, in which there are several features as information on flower characteristics. Furthermore, to evaluate the performance of the algorithms on both methods and determine the best algorithm for the dataset, we evaluate it using several metrics on the training and testing data for each method. Some of these metrics are recall, precision, F1-score, and accuracy where the higher the value, the better the algorithm's performance. The results show that the performance of the decision tree classifier algorithm is the most outperformed on the Iris dataset. Decision tree ; Naive Bayes classifier ; Computer science ; Artificial intelligence ; Decision tree learning ; Pattern recognition (psychology) ; Classifier (UML) ; Bayes classifier ; Machine learning ; Bayes' theorem ; Algorithm ; Gaussian ; Test data ; Data mining ; Bayesian probability ; Support vector machine Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Smart Agriculture and AI
Authors: Pattamapan Lomarat | Chutima Phechkrajang | Pawida Sunghad | Natthinee Anantachoke
Source: Food Chemistry X (article, 2024-10-24)
Field: Chemistry — Organic Chemistry

Raman spectroscopy coupled with the PLSR model: A rapid method for analyzing gamma-oryzanol content in rice bran oil Rice bran oil (RBO) is widely used in food, nutraceutical, and cosmetic industries, due to its γ-oryzanol content, a key quality indicator. This study developed a rapid, non-destructive method for quantifying γ-oryzanol in RBO using Raman spectroscopy combined with partial least squares regression (PLSR). The optimal PLSR model, based on orthogonal signal correction (OSC)-pretreated data of Raman spectra from 800 to 1800 cm<sup>-1</sup>, demonstrated high accuracy with a strong R<sup>2</sup>-Pearson correlation coefficient of 0.9827 and low root mean square error of prediction (RMSEP) of 0.5314. Principal component analysis (PCA) of OSC-pretreated data showed improved sample grouping by concentration of γ-oryzanol compared to untreated data. Additionally, Bland-Altman plots comparing results from Raman and HPLC methods showed random scatter within ±2 SD of the mean difference, confirming the method's reliability. This study indicates that Raman spectroscopy can serve as a reliable method for determining γ-oryzanol content in RBO products within the related industries. Bran ; Raman spectroscopy ; Content (measure theory) ; Chemistry ; Food science ; Mathematics ; Physics ; Optics ; Organic chemistry ; Raw material Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Fatty Acid Research and Health
Authors: P. Sruthi | Vanmathi A. Mugasundari | Shubham Nimbkar | J.A. Moses | V. R. Sinija
Source: Food Innovation and Advances (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Microfluidization of tender coconut water and its impact on spoilage enzymes and physicochemical properties In this study, microfluidization was explored to inactivate autolytic spoilage enzymes (polyphenol oxidase, PPO, and peroxidase, POD) that significantly impact the nutritional and sensory qualities of tender coconut water (TCW). TCW was treated at three different pressure levels (70, 140, and 210 MPa) and five different number of passes/cycles (3, 5, 7, 9, and 11). The highest percentage reduction was obtained in the case of PPO (~61% in the 11<sup>th</sup> pass, at 210 MPa), while for POD, ~45% reduction was achieved in the 9<sup>th</sup> pass, at 70 MPa. The impact of different treatment conditions on the physicochemical properties of TCW, such as color, turbidity, total soluble solids (TSS), pH, titratable acidity, total phenolic content (TPC), and protein content was assessed. The pH and TSS remained unaffected; whereas, turbidity showed an increase with treatment intensity from 2.59% ± 0.14% (untreated) to 8.62% ± 0.39% (30,000 psi, 11 passes), and the highest color difference was observed for this sample (ΔE = 4.61 ± 0.018). Furthermore, TPC and antioxidant activity showed minimal changes upon treatment. Overall, the findings of this research provide new insights into the application of microfluidization for the processing of thermally sensitive products such as TCW, extending their shelf life without any additives and providing a clean label solution. Food spoilage ; Food science ; Chemistry ; Pulp and paper industry ; Biotechnology ; Biology ; Engineering Coconut Research and Applications
Authors: Shuai Du | Xian Cheng | Guowei Ge | Tianyun Li | Longyu Guo | Sai Liu
Source: Scientific Reports (article, 2024-08-22)
Field: Materials Science — Materials Chemistry

AC breakdown and decomposition products detection characteristics of eco-friendly insulating medium in gas insulated switchgear Gas Insulated Switchgear (GIS) was a crucial electrical equipment that provides the safety and security of power systems, and finding the eco-friendly alternatives of sulphur hexafluoride (SF6) as insulating medium in GIS has been an urgent demand in past decades. However, few studies reported the specific dielectric strength under various electrodes and partial discharge (PD) decomposition products detection of applying the eco-friendly gas in GIS. In this paper, the insulation properties among C<sub>4</sub>F<sub>7</sub>N/CO<sub>2</sub> mixtures, dry air and CO<sub>2</sub> were explored under the reduced-scale gas-insulated experimental device. The relationship between AC breakdown voltage and gas pressure was gained under the different electrodes and gap distances, and 0.6 MPa 7% C<sub>4</sub>F<sub>7</sub>N/93% CO<sub>2</sub>, 0.9 MPa dry air, 0.9 MPa CO<sub>2</sub> all possess the capacity to be the insulating medium in eco-friendly gas insulated switchgear. Furthermore, the partial discharge experimental was also carried out to realize the decomposition products detection of dry air, which designs three common defect types including metal protrusion defects, air gap defects, and metal contamination defects. The decomposition products detection result shows that the contains of CO<sub>2</sub>, CO, and NO<sub>2</sub> linearly increase with the increasing applied voltages and times, and the partial discharge defects are distinguished according to the ratios of c(CO<sub>2</sub> + CO)/c(NO<sub>2</sub>) and c(CO<sub>2</sub>)/c(CO). The results can provide the basis for the further development of eco-friendly gas insulated switchgear. Switchgear ; Partial discharge ; Sulfur hexafluoride ; Materials science ; Decomposition ; Dielectric gas ; Electrode ; Environmentally friendly ; Voltage ; High voltage ; Environmental science ; Breakdown voltage ; Partial pressure ; Composite material ; Nuclear engineering ; Electrical engineering ; Chemistry ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation
Authors: Biwang Lu | Jing He
Source: Energies (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

Equivalent Thermal Conductivity of Topology-Optimized Composite Structure for Three Typical Conductive Heat Transfer Models Composite materials and structural optimization are important research topics in heat transfer enhancement. The current evaluation parameter for the conductive heat transfer capability of composites is effective thermal conductivity (ETC); however, this parameter has not been studied or analyzed for its applicability to different heat transfer models and composite structures. In addition, the optimized composite structures of a specific object will vary when different optimization methods and criteria are employed. Therefore, it is necessary to investigate a suitable method and parameter for evaluating the heat transfer capability of optimized composites under different heat transfer models. Therefore, this study analyzes and summarizes three typical conductive heat transfer models: surface-to-surface (S-to-S), volume-to-surface (V-to-S), and volume-to-volume (V-to-V) models. The equivalent thermal conductivity (keq) is proposed to evaluate the conductive heat transfer capability of topology-optimized composite structures under the three models. A validated simulation method is used to obtain the key parameters for calculating keq. The influences of the interfacial thermal resistance and size effect on keq are considered. The results show that the composite structure optimized for the V-to-S and V-to-V models has a keq value of only 79.4 W m−1 K−1 under the S-to-S model. However, the keq values are 233.4 W m−1 K−1 and 240.3 W m−1 K−1 under the V-to-S and V-to-V models, respectively, which are approximately 41% greater than those of the in-parallel structure. It can be demonstrated that keq is more suitable than the ETC for evaluating the V-to-S and V-to-V heat transfer capabilities of composite structures. The proposed keq can serve as a characteristic parameter that is beneficial for heat transfer analysis and composite structural optimization. Thermal conductivity ; Composite number ; Heat transfer ; Electrical conductor ; Materials science ; Topology (electrical circuits) ; Thermal conduction ; Thermal ; Composite material ; Mechanics ; Thermodynamics ; Physics ; Engineering ; Electrical engineering Thermal properties of materials ; Composite Material Mechanics ; Heat Transfer and Optimization
Authors: Paul B. Calio | Matthew R. Hermes | Jie J. Bao | Ignacio Fdez. Galván | Roland Lindh | Donald G. Truhlar | Laura Gagliardi
Source: The Journal of Physical Chemistry A (article, 2024-02-26)
Field: Materials Science — Materials Chemistry

Minimum-Energy Conical Intersections by Compressed Multistate Pair-Density Functional Theory Compressed multistate pair-density functional theory (CMS-PDFT) is a multistate version of multiconfiguration pair-density functional theory that can capture the correct topology of coupled potential energy surfaces (PESs) around conical intersections. In this work, we develop interstate coupling vectors (ISCs) for CMS-PDFT in the <i>OpenMolcas</i> and <i>PySCF/mrh</i> electronic structure packages. Yet, the main focus of this work is using ISCs to calculate minimum-energy conical intersections (MECIs) by CMS-PDFT. This is performed using the projected constrained optimization method in <i>OpenMolcas</i>, which uses ISCs to restrain the iterations to the conical intersection seam. We optimize the <i>S</i><sub>1</sub>/<i>S</i><sub>0</sub> MECIs for ethylene, butadiene, and benzene and show that CMS-PDFT gives smooth PESs in the vicinities of the MECIs. Furthermore, the CMS-PDFT MECIs are in good agreement with the MECI calculated by the more expensive XMS-CASPT2 method. Conical intersection ; Conical surface ; Density functional theory ; Intersection (aeronautics) ; Potential energy ; Work (physics) ; Coupling (piping) ; Topology (electrical circuits) ; Statistical physics ; Physics ; Computer science ; Algorithm ; Mathematics ; Geometry ; Engineering ; Classical mechanics ; Quantum mechanics ; Combinatorics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Zeolite Catalysis and Synthesis
Authors: Farag M. Mosallam | Rana Elshimy
Source: AMB Express (article, 2024-05-29)
Field: Materials Science — Materials Chemistry

Eradication of Klebsiella pneumoniae pulmonary infection by silver oxytetracycline nano-structure Targeted bactericidal nanosystems hold significant promise to improve the efficacy of existing antimicrobials for treatment of severe bacterial infections by minimizing the side effects and lowering the risk of antibiotic resistance development. In this work, Silver Oxytetracycline Nano-structure (Ag-OTC-Ns) was developed for selective and effective eradication of Klebsiella pneumoniae pulmonary infection. Ag-OTC-Ns were prepared by simple homogenization-ultrasonication method and were characterized by DLS, Zeta potential, TEM and FT-IR. The antimicrobial activity of Ag-OTC-Ns was evaluated in vitro using broth micro-dilution technique and time-kill methods. Our study showed that MICs of AgNO<sub>3</sub>, OTC, AgNPs and Ag-OTC-Ns were 100, 100, 50 and 6.25 µg/ml, respectively. Ag-OTC-Ns demonstrated higher bactericidal efficacy against the targeted Klebsiella pneumoniae at 12.5 µg/ml compared to the free Oxytetracycline, AgNO<sub>3</sub> and AgNPs. In vivo results confirmed that, Ag-OTC-Ns could significantly eradicate K. pneumoniae from mice lung in compare with free Oxytetracycline, AgNO<sub>3</sub> and AgNPs. In addition, Ag-OTC-Ns could effectually diminish the inflammatory biomarkers levels of Interferon Gamma and IL-12, and as a result it could effectively lower lung damage in K. pneumoniae infected mice. Ag-OTC-Ns has no significant toxicity on tested mice along the experimental period, there was no sign of behavioral abnormality in the surviving mice indicating that the Ag-OTC-Ns is safe at the used concentration. Furthermore, capability of 5 kGy Gamma ray to sterilize Ag-OTC-Ns solution without affecting it stability was proven. Oxytetracycline ; Klebsiella pneumoniae ; Microbiology ; Biology ; Antibiotics ; Escherichia coli Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications
Authors: Wiktor Nisterenko | Damian Kułaga | Mateusz Woziński | Yash Raj Singh | Beata Judzińska | Karolina Jagiełło | Katarzyna Ewa Greber | Wiesław Sawicki | Krzesimir Ciura
Source: Molecules (article, 2024-04-19)
Field: Chemistry — Spectroscopy

Evaluation of Physicochemical Properties of Ipsapirone Derivatives Based on Chromatographic and Chemometric Approaches Drug discovery is a challenging process, with many compounds failing to progress due to unmet pharmacokinetic criteria. Lipophilicity is an important physicochemical parameter that affects various pharmacokinetic processes, including absorption, metabolism, and excretion. This study evaluated the lipophilic properties of a library of ipsapirone derivatives that were previously synthesized to affect dopamine and serotonin receptors. Lipophilicity indices were determined using computational and chromatographic approaches. In addition, the affinity to human serum albumin (HSA) and phospholipids was assessed using biomimetic chromatography protocols. Quantitative Structure-Retention Relationship (QSRR) methodologies were used to determine the impact of theoretical descriptors on experimentally determined properties. A multiple linear regression (MLR) model was calculated to identify the most important features, and genetic algorithms (GAs) were used to assist in the selection of features. The resultant models showed commendable predictive accuracy, minimal error, and good concordance correlation coefficient values of 0.876, 0.149, and 0.930 for the validation group, respectively. Ipsapirone ; Chromatography ; Chemistry ; Chemometrics ; Biochemistry ; Agonist Analytical Chemistry and Chromatography ; Analytical Methods in Pharmaceuticals ; Pharmacogenetics and Drug Metabolism
Authors: Emilio J. Lozano | G. Blázquez | M. Calero | M.A. Martín‐Lara | Salvador Peréz‐Huertas | Antonio Pérez
Source: Processes (article, 2024-02-02)
Field: Chemistry — Analytical Chemistry

Optimizing the Extraction Process of Value-Added Products from Olive Cake Using Neuro-Fuzzy Models The use of olive cake, an abundant residue in the olive oil industry, has been studied by developing a biorefinery scheme. The aim was to develop a novel, efficient, and environmentally friendly strategy for the valorization of olive cake, contributing to sustainable agriculture. A special extraction procedure based on a combination of hydrothermal treatments with liquid/liquid extractions was designed to produce value-added products, along with solids that can be used for energy or adsorbent production. The optimal extraction conditions were determined by exploring the influence of the operating variables (temperature, extraction time, solvent type, solvent/extract ratio, extraction stages, and pH) on the extraction yield. The decision about the optimal conditions was made by adjusting the experimental results to a neuro-fuzzy model. Glucose and inositol showed similar response surfaces, allowing simultaneous concentration in a single process. Under optimal extraction conditions, the concentration of inositol increased by up to 70%, while glucose and fructose increased by 70 and 30 times, respectively, compared to the initial feed. The proposed methodology successfully extracted significant amounts of bioactive polyols (mainly inositol) (1126 mg/L), saccharides (15,960 mg/L glucose, 385 mg/L xylose, 5550 mg/L fructose, 165 mg/L lactose, and 248 mg/L sucrose), and polyphenols (4792 mg/L) under mild conditions, i.e., 30 °C and 30 min. Thus, olive cake extracts have a great unexploited potential for application in several industrial sectors, including, but not limited to, food and pharmaceuticals. Process engineering ; Process (computing) ; Extraction (chemistry) ; Fuzzy logic ; Computer science ; Mathematics ; Pulp and paper industry ; Biological system ; Biochemical engineering ; Artificial intelligence ; Engineering ; Chromatography ; Chemistry ; Biology Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis