jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Azren Aida Asmawi | Fatmawati Adam | Nurul Aini Mohd Azman | Mohd Basyaruddin Abdul Rahman
Source: Heliyon (review, 2024-08-28)
Field: Materials Science — Biomaterials

Advancements in the nanodelivery of azole-based fungicides to control oil palm pathogenic fungi The cultivation of oil palms is of great importance in the global agricultural industry due to its role as a primary source of vegetable oil with a wide range of applications. However, the sustainability of this industry is threatened by the presence of pathogenic fungi, particularly <i>Ganoderma</i> spp., which cause detrimental oil palm disease known as basal stem rot (BSR). This unfavorable condition eventually leads to significant productivity losses in the harvest, with reported yield reductions of 50-80 % in severely affected plantations. Azole-based fungicides offer potential solutions to control BSR, but their efficacy is hampered by limited solubility, penetration, distribution, and bioavailability. Recent advances in nanotechnology have paved the way for the development of nanosized delivery systems. These systems enable effective fungicide delivery to target pathogens and enhance the bioavailability of azole fungicides while minimising environmental and human health risks. In field trials, the application of azole-based nanofungicides resulted in up to 75 % reduction in disease incidence compared to conventional fungicide treatments. These innovations offer opportunities for the development of sustainable agricultural practices. This review highlights the importance of oil palm cultivation concerning the ongoing challenges posed by pathogenic fungi and examines the potential of azole-based fungicides for disease control. It also reviews recent advances in nanotechnology for fungicide delivery, explores the mechanisms behind these nanodelivery systems, and emphasises the opportunities and challenges associated with azole-based nanofungicides. Hence, this review provides valuable insights for future nanofungicide development in effective oil palm disease control. Fungicide ; Palm oil ; Azole ; Palm ; Chemical control ; Biology ; Antifungal ; Biotechnology ; Toxicology ; Agronomy ; Agroforestry ; Microbiology Nanocomposite Films for Food Packaging ; Oil Palm Production and Sustainability ; Studies on Chitinases and Chitosanases
Authors: Nawel Meftah | Badreddine Ratni | Mohammed Nabil El Korso | Shah Nawaz Burokur
Source: Advanced Electronic Materials (article, 2024-09-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhanced‐Resolution Learning‐Based Direction of Arrival Estimation by Programmable Metasurface Abstract Due to its growing importance and wide range of applications, direction‐of‐arrival (DOA) estimation has become a major research topic, particularly in the field of communication systems. While traditional DOA estimation methods rely on antenna arrays and complex algorithms, recent progress achieved in the design and implementation of metasurfaces has proved their effectiveness as promising alternatives. This study presents a distinct approach for DOA estimation that combines the use of a programmable metasurface with deep learning. The programmable metasurface together with a radio‐frequency power detector placed at the focal point, acts as a parabolic reflector antenna with an adjustable pointing direction, which scans the azimuth plane in 5° increments to receive the power level of incoming signals. The collected data is then fed into a pre‐trained multilayer neural network to enable DOA estimation with a resolution of lower than 1° without requiring fine‐tuning of the scanning procedure. This approach ensures accurate and fast estimations, paving the way for advanced solutions in detection and localization for various applications. Materials science ; Direction of arrival ; Resolution (logic) ; Superresolution ; Optics ; Optoelectronics ; Computer science ; Artificial intelligence ; Telecommunications ; Physics Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Miriam Medina‐García | Esteban A. Roca-Nasser | Miguel Ángel Martínez‐Domingo | Eva M. Valero | Alejandra Arroyo-Cerezo | Luis Cuadros-Rodrı́guez | Ana M. Jiménez–Carvelo
Source: Food Control (article, 2024-07-09)
Field: Chemistry — Analytical Chemistry

Towards the establishment of a green and sustainable analytical methodology for hyperspectral imaging-based authentication of wholemeal bread The increased interest in whole-grain products, along non unified European regulations on the composition of wholemeal bread could lead to its misleading labelling. Therefore, to ensure the safety of consumers, both health-wise and from possible fraud, a novel hyperspectral imaging-based authentication method is being proposed, given that no previous study has combined imaging techniques with authentication of wholemeal flour content. Quantification based on pixel counting by classification (QPC) utilizes multivariate analysis methods such as PLS-DA and SVM to classify pixels within a bread sample (as Wholemeal and White flour), based on their visible-near-infrared spectra, to later estimate its proportion of wholemeal flour. This is in accordance with the heterogeneous nature of bread samples, in which individual pixels belonging to both wholemeal and white flour can be accounted for, which was proved by implementing unsupervised training techniques such as hierarchical cluster analysis (HCA). Results show that the quantification model was able to successfully predict wholemeal flour content with a maximum deviation of 8 g wholemeal flour/100 g flour from the estimated value. Hyperspectral imaging ; Mathematics ; Pixel ; Food science ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Chemistry Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Advanced Chemical Sensor Technologies
Authors: Oluwadunni F. Elebiju | Gbolahan O. Oduselu | Temitope A. Ogunnupebi | Olayinka O. Ajani | Ezekiel Adebiyi
Source: Pharmaceuticals (article, 2024-04-23)
Field: Chemistry — Organic Chemistry

In Silico Design of Potential Small-Molecule Antibiotic Adjuvants against Salmonella typhimurium Ortho Acetyl Sulphydrylase Synthase to Address Antimicrobial Resistance The inhibition of <i>O</i>-acetyl sulphydrylase synthase isoforms has been reported to represent a promising approach for the development of antibiotic adjuvants. This occurs via the organism developing an unpaired oxidative stress response, causing a reduction in antibiotic resistance in vegetative and swarm cell populations. This consequently increases the effectiveness of conventional antibiotics at lower doses. This study aimed to predict potential inhibitors of <i>Salmonella typhimurium</i> ortho acetyl sulphydrylase synthase (<i>St</i>OASS), which has lower binding energy than the cocrystalized ligand pyridoxal 5 phosphate (PLP), using a computer-aided drug design approach including pharmacophore modeling, virtual screening, and in silico ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) evaluation. The screening and molecular docking of 4254 compounds obtained from the PubChem database were carried out using AutoDock vina, while a post-screening analysis was carried out using Discovery Studio. The best three hits were compounds with the PubChem IDs 118614633, 135715279, and 155773276, possessing binding affinities of -9.1, -8.9, and -8.8 kcal/mol, respectively. The in silico ADMET prediction showed that the pharmacokinetic properties of the best hits were relatively good. The optimization of the best three hits via scaffold hopping gave rise to 187 compounds, and they were docked against <i>St</i>OASS; this revealed that lead compound <b>1</b> had the lowest binding energy (-9.3 kcal/mol) and performed better than its parent compound 155773276. Lead compound <b>1</b>, with the best binding affinity, has a hydroxyl group in its structure and a change in the core heterocycle of its parent compound to benzimidazole, and pyrimidine introduces a synergistic effect and consequently increases the binding energy. The stability of the best hit and optimized compound at the <i>St</i>OASS active site was determined using RMSD, RMSF, radius of gyration, and SASA plots generated from a molecular dynamics simulation. The MD simulation results were also used to monitor how the introduction of new functional groups of optimized compounds contributes to the stability of ligands at the target active site. The improved binding affinity of these compounds compared to PLP and their toxicity profile, which is predicted to be mild, highlights them as good inhibitors of <i>St</i>OASS, and hence, possible antimicrobial adjuvants. AutoDock ; PubChem ; In silico ; Pharmacophore ; Chemistry ; Virtual screening ; Docking (animal) ; ADME ; Lipinski's rule of five ; Biochemistry ; Stereochemistry ; Combinatorial chemistry ; Medicine Synthesis and biological activity ; Pharmaceutical and Antibiotic Environmental Impacts ; Antibiotic Resistance in Bacteria
Authors: Tommy Liu | Zhi Yang Tho | Amanda S. Barnard
Source: Digital Discovery (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Understanding the importance of individual samples and their effects on materials data using explainable artificial intelligence Explaining the influence of data instances (materials) to predictions such as structure/property relationships in materials informatics can complement structural feature importance profiling, and guide data generation, cleaning, and verification. Psychology ; Artificial intelligence ; Computer science ; Data science Machine Learning in Materials Science ; Mineral Processing and Grinding ; Industrial Vision Systems and Defect Detection
Authors: Seok Won Jeong | Jae Il Lyu | HwangWeon Jeong | Jeongho Baek | Jung‐Kyung Moon | Chaewon Lee | Myoung-Goo Choi | Kyoung-hwan Kim | Youn‐Il Park
Source: Plant Cell Reports (article, 2024-06-09)
Field: Chemistry — Analytical Chemistry

SUnSeT: spectral unmixing of hyperspectral images for phenotyping soybean seed traits Hyperspectral features enable accurate classification of soybean seeds using linear discriminant analysis and GWAS for novel seed trait genes. Evaluating crop seed traits such as size, shape, and color is crucial for assessing seed quality and improving agricultural productivity. The introduction of the SUnSet toolbox, which employs hyperspectral sensor-derived image analysis, addresses this necessity. In a validation test involving 420 seed accessions from the Korean Soybean Core Collections, the pixel purity index algorithm identified seed- specific hyperspectral endmembers to facilitate segmentation. Various metrics extracted from ventral and lateral side images facilitated the categorization of seeds into three size groups and four shape groups. Additionally, quantitative RGB triplets representing seven seed coat colors, averaged reflectance spectra, and pigment indices were acquired. Machine learning models, trained on a dataset comprising 420 accession seeds and 199 predictors encompassing seed size, shape, and reflectance spectra, achieved accuracy rates of 95.8% for linear discriminant analysis model. Furthermore, a genome-wide association study utilizing hyperspectral features uncovered associations between seed traits and genes governing seed pigmentation and shapes. This comprehensive approach underscores the effectiveness of SUnSet in advancing precision agriculture through meticulous seed trait analysis. Biology ; Hyperspectral imaging ; Sunset ; Spectral signature ; Botany ; Remote sensing Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Dayang Liu | Huiting Zhang | Feng Lv | Yanrong Tao | Liangkuan Zhu
Source: Journal of Food Science (article, 2024-04-01)
Field: Chemistry — Analytical Chemistry

Combining transfer learning and hyperspectral imaging to identify bruises of pears across different bruise types Mechanical bruise is one of the most crucial factors affecting the quality of pears, which has a huge influence on postharvest transportation, storage, and sale of pears. To rapidly detect early bruises of pears across different bruise types, hyperspectral imaging technology coupled with transfer learning methods was performed in this study. Two transfer learning methods, that is, transfer component analysis (TCA) and manifold embedded distribution alignment (MEDA), were applied for two tasks (impact bruise → crush bruise, crush bruise → impact bruise). Supporting vector machine (SVM) was set as a baseline to conduct analysis and comparison of the transferability of the models. The result showed that, for task 1 (impact bruise → crush bruise), MEDA and TCA-SVM model achieved a classification accuracy of 93.33% and 91.11% in target domain, individually. For task 2 (crush bruise →impact bruise), MEDA and TCA-SVM model achieved an accuracy of 88.89% and 85.19% in target domain, respectively. Both the two models improved the accuracy compared with SVM models (84.44% for task 1; 77.04% for task 2). Overall, the results indicated that transfer learning approaches could perform pear bruise detection across different bruise types. Hyperspectral imaging in combination with transfer learning methods is a promising possibility for the efficient and cost-saving field detection of fruit bruises among different bruise types. PRACTICAL APPLICATION: The production and export of pears are faced with problems of mechanical damage due to vibration, collision, impact, and other factors, which cause chemical changes in color, odor, and taste. Sometimes the bruise was too slight to be ignored which would infect with other fruits in the future. In this study, we used hyperspectral imaging combined with transfer learning method could detect these slight bruises caused by different factors. Distinguishing different types of damage can provide a reference for quick judgment of the process causing damage and take prompt measures to reduce economic losses. Bruise ; Artificial intelligence ; Computer science ; Transfer of learning ; Pattern recognition (psychology) ; Support vector machine ; Hyperspectral imaging ; Computer vision ; Machine learning ; Medicine ; Surgery Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Bee Products Chemical Analysis
Authors: Cheng‐You Chen | Kung-Wen Du | Yi-Cheng Chung | Chun-I Wu
Source: Energies (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Advancements in Thermoelectric Generator Design: Exploring Heat Exchanger Efficiency and Material Properties This paper presents a comprehensive study on the application and optimization of automotive thermoelectric generators (ATEGs), focusing on the crucial role of heat exchangers in enhancing energy conversion efficiency. Recognizing the substantial waste of thermal energy in internal combustion engines, our research delves into the potential of TEGs to convert engine waste heat into electrical energy, thereby improving fuel efficiency and reducing environmental impact. We meticulously analyze various heat exchanger designs, assessing their influence on the TEG’s output power under different exhaust gas flow rates and temperatures. Furthermore, we explore the impact of TEG material properties on the overall energy conversion effectiveness. Our findings reveal that specific heat exchanger designs significantly enhance the efficiency of waste gas heat exchange, leading to an improved performance of the TEG system. We also highlight the importance of thermal insulation in maximizing TEG output. This study not only contributes to the ongoing efforts to develop more sustainable and efficient vehicles but also provides valuable insights into the practical application of thermoelectric technology in automotive engineering. Through this research, we aim to pave the way for more environmentally friendly transportation solutions, aligning with global efforts to reduce fossil fuel dependence and mitigate environmental pollution. Thermoelectric generator ; Waste heat ; Heat exchanger ; Waste heat recovery unit ; Thermal efficiency ; Efficient energy use ; Automotive industry ; Process engineering ; Combustion ; Automotive engineering ; Environmental science ; Fossil fuel ; Internal combustion engine ; Automotive engine ; Energy recovery ; Environmental pollution ; Thermal energy ; Thermoelectric effect ; Mechanical engineering ; Waste management ; Engineering ; Energy (signal processing) ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Xia Wu | Xiaoyu Xie | Alessandro Troisi
Source: Journal of Materials Chemistry C (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Calibration of several first excited state properties for organic molecules through systematic comparison of TDDFT with experimental spectra Time-dependent density functional theory (TDDFT) is a powerful computational tool for investigating excitation properties in organic electronics, and it holds significant potential for high-throughput virtual screening (HTVS) in this field. While most benchmarks focus on excitation energies, less attention has been paid to evaluating the accuracy of computed oscillator strengths and exciton reorganization energies against experimental data. In this work, we provide a systematic approach to evaluate in parallel the accuracy of these three quantities on the basis of a suitable fitting of the experimental absorption spectra of 71 molecules in solution. After considering 18 computational methodologies, the results from the M06-2X/def2-TZVP/PCM method demonstrate the strongest correlation with experimental data across the desired properties. For HTVS, the M06-2X/6-31G(d)/PCM method appears to be a particularly convenient choice among all methodologies due to its balance of computational efficiency and accuracy. Our results provide an additional benchmark needed before employing TDDFT methods for the discovery and design of organic electronic molecules. Time-dependent density functional theory ; Materials science ; Excited state ; Calibration ; Spectral line ; Molecule ; Organic molecules ; Molecular physics ; Atomic physics ; Physics ; Quantum mechanics Photochemistry and Electron Transfer Studies ; Organic Light-Emitting Diodes Research ; Luminescence and Fluorescent Materials
Authors: Yuhao Zhong | Jun Sun | Kunshan Yao | Jiehong Cheng | Xiao‐Jiao Du
Source: Journal of Food Safety (article, 2024-03-15)
Field: Chemistry — Analytical Chemistry

Detection of rice (with husk) moisture content based on hyperspectral imaging technology combined with <scp>MSLPP–ESMA–SVR</scp> model Abstract Moisture content detection has guiding significance for the storage and quality detection of rice. To detect moisture content rapidly and non‐destructively, hyperspectral imaging technology (400‐1000 nm) was employed to analyze rice with different moisture content, and Savitzky–Golay mixed standard normalized variable algorithm (SG‐SNV) was used for spectral data pretreatment. Furthermore, a modified supervised locality preserving projections (MSLPP) method was proposed to extract spectral features. The modeling results showed that MSLPP had better spectral feature extraction performance. Finally, to improve prediction accuracy, the equilibrium slime mold algorithm (ESMA) was introduced to obtain the optimal parameters (c, g) of the support vector regression (SVR) model. And MSLPP–ESMA–SVR model had higher prediction accuracy and stronger robustness, with R 2 p reaching 0.9755 and root mean square error of prediction reaching 0.8597%. Therefore, hyperspectral imaging technology combined with MSLPP–ESMA–SVR model is feasible to detect rice moisture content. Husk ; Hyperspectral imaging ; Water content ; Food science ; Chemistry ; Environmental science ; Computer science ; Artificial intelligence ; Biology ; Engineering ; Botany ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Thanh Mai Nguyen Tran | Xuan-Phương Dang | M. N. Prabhakar | Jung‐il Song
Source: Polymer Composites (article, 2024-07-05)
Field: Materials Science — Polymers and Plastics

Multi‐objective optimization of hybrid polypropylene composites for enhanced mechanical, thermal, and flame‐retardant properties Abstract Optimizing composite materials is crucial for engineering advancements because it allows for the creation of materials tailored to specific functional requirements, thereby enhancing efficiency, safety, and sustainability. This study examines both the combined and individual effects of long flax fiber (LFF) bundles, short basalt fibers (BF), and rice husk powder (RHP) on the properties of polypropylene (PP) hybrid composites. LFF primarily provides mechanical strength, BF enhances mechanical, thermal, and flame‐retardant properties by filling gaps, and RHP reinforces and improves the overall composite. Contrary to previous studies that relied on random combinations, this research employs a systematic Box–Behnken design (BBD) for three variables: LFF plies, BF, and RHP by weight percentage. This approach facilitated the development of a new second‐order regression equation via response surface methodology (RSM), clarifying the contribution of each component, with R 2 values exceeding 0.85, indicating high predictability. Optimization, conducted using a non‐dominated sorting genetic algorithm (NSGA‐II), demonstrated that the optimal composites significantly outperformed the non‐optimized ones in mechanical strength, thermal stability, and flame retardancy. Compared to the average values before optimization, improvements post‐optimization included increases of 49.86% in tensile strength (TS), 38.82% in TS modulus, 73.93% in char yield (YC), and 29.81% in peak heat release rate (pHRR). Specifically, the fire performance index improved by 237.5% and the fire growth index by 101.33%, compared to pure PP, highlighting significant advancements in fire safety. This study shows that mathematical equations can predict composite properties, helping to select balanced compositions without sacrificing flammability. Highlights Multi‐filler approach technique used for manufacturing of PP composites. LFF, BF &amp; RHP optimize mechanics &amp; flammability. Box–Behnken design predicts optimal composition for superior properties. NSGA‐II optimization improves tensile, thermal &amp; flame retardant properties. Fire retardant ; Materials science ; Composite material ; Polypropylene ; Thermal Flame retardant materials and properties ; Natural Fiber Reinforced Composites ; Polymer crystallization and properties