jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jayapal P | P. Saminathan | Kar thika | Nirmala Devi N | Kishore Surendra
Source: International Journal of Electronics and Communication Engineering (article, 2025-01-30)
Field: Chemistry — Analytical Chemistry

Developing a Hybrid Faster Recurrent Convolutional Neural Network with an Improved Weighted Artificial Bee Optimization Method for Grape Disease Detection at an Early Stage In India’s diverse range of crops, fruits play a crucial role in generating significant revenue for farmers. Among these fruits, grapes are extensively grown. However, grape plants are susceptible to various diseases affecting their fruits, stems, and leaves, ultimately impacting their yield. Grape disease detection at an early stage plays a crucial role in ensuring crop health and maximizing yield. To address this issue, early detection and effective treatment of these diseases are essential to ensure food safety. This study focuses on analyzing different methods for diagnosing and classifying diseases that affect grapevines, with particular emphasis on grape leaf diseases. Monitoring the condition of grape leaves provides valuable insights into the overall health of the grape plants. The research aims to provide a comprehensive overview of techniques used for identifying and categorizing these diseases. Automated disease detection algorithms are proposed to enhance diagnosis accuracy and enable timely control actions. Image processing, a widely used method, is endorsed for leaf disease identification and classification in plants. In this study, we propose a novel approach by integrating a hybrid Faster Recurrent Convolutional Neural Network (FRCNN) with an improved Weighted Artificial Bee Optimization (WABO) method for efficient grape disease detection. The FRCNN architecture combines the strengths of recurrent and convolutional neural networks to capture both spatial and temporal information from grape images. Additionally, the WABO method enhances the training process by optimizing the network’s weights to improve detection accuracy. Experiments conducted on a large-scale dataset show that the proposed approach is more effective than current approaches in terms of computational efficiency and detection accuracy when it comes to correctly diagnosing grape diseases early on. The proposed framework has a lot of potential for practical uses in vineyard management and precision agriculture. Convolutional neural network ; Stage (stratigraphy) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Doğan Özcan | Neslihan Özsoy | Yunus Zübeyir Turgut | Murat Özsoy
Source: Materials Testing (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Effects of tool geometry on cutting performance in CW511L brass drilling Abstract CW511L lead-free brass is a newly developed alloy that has found widespread application across various industrial sectors. It is particularly preferred as a raw material for watering and pumping components, which typically require machining before use. This study investigates the influence of cutting tool geometry on drilling performance, focusing on critical parameters such as radial rake angles, axial rake angles, tip radius, and helix angle. An L27 Taguchi experimental design was employed using the following levels: radial rake angle (4°, 8°, 12°), axial rake angle (−2°, 0°, 2°), tip radius (10, 20, 30 µm), and helix angle (0°, 5°, 10°). Drilling experiments were performed in accordance with this design to evaluate cutting forces, chip breakability, burr height, and surface quality. The effects of tool geometry were analyzed using three-dimensional surface plots and a correlation matrix to reveal significant relationships between input variables and drilling responses. Rake angle ; Brass ; Rake ; Taguchi methods ; Drilling ; Helix angle ; Machining ; RADIUS Metallurgy and Material Science ; Advanced Machining and Optimization Techniques ; Advanced machining processes and optimization
Authors: Chinnasami Sivaji
Source: Building Materials and Engineering Structures (article, 2025-09-10)
Field: Materials Science — Polymers and Plastics

Advanced Fire-Resistant Materials for Future Construction Using the Weighted Product Method Advanced fire-resistant materials are at the forefront of mitigating fire hazards in modern buildings. These materials improve the structural stability of buildings during a fire and improve the safety and well-being of occupants. Key innovations include high-performance composites, intumescent coatings and advanced ceramics. Each of these materials offers unique advantages that address different aspects of fire resistance, from delaying the spread of flames to maintaining structural stability. Research significance: The primary goal of developing advanced fire-resistant materials is to improve safety for occupants and protect property from fire damage. By studying and using materials with improved fire-resistance properties, researchers can help reduce the frequency of fire-related injuries and deaths. This is especially important in densely populated urban areas and skyscrapers, where fire safety is a significant issue. Mythology: Alternative: Magnesium oxide board, calcium silicate board, gypsum board with additives, cement-bonded particle board and fiber-reinforced polymer (FRP) composites. Evaluation Preference: Thermal Stability (°C), Mechanical Strength (MPa), Weight (kg/m²), Emissions (kg CO₂/m²). Result: The results indicate that Fibre-Reinforced Polymer (FRP) Composites achieved the highest rank, while Gypsum Board with Additives had the lowest rank being attained. Conclusion: “The value of the dataset for Advanced Fire-Resistant Materials for Future Construction, according to the weighted product method, Fibre-Reinforced Polymer (FRP) Composites achieves the highest ranking.” Gypsum ; Fire safety ; Intumescent ; Product (mathematics) ; Thermal stability ; Fire performance ; Key (lock) Flame retardant materials and properties ; Magnesium Oxide Properties and Applications ; Natural Fiber Reinforced Composites
Authors: Muhammad Umair Kamal | Raja Adil Sarfraz | Rizwan Ashraf | Muhammad Kashif | Muhammad Imran
Source: Analytical Science Advances (article, 2025-10-03)
Field: Chemistry — Analytical Chemistry

ATR–FTIR‐Based Direct and Rapid Quantification of Levofloxacin, Degradation and Adulterations via Multivariate Analysis The rapid and direct drug quantification is indispensable for quality control units in the pharmaceutical sector to assure safe formulation and delivery of the right medicine to patients. Spectral features of Levofloxacin (LFX) were examined to provide a Fourier transform infrared spectroscopy (FTIR)-based rapid and pragmatic analytical method for direct quantification of LFX in solid formulations. A chemometric model was developed for frequency region of 1252.39-1218.84 cm<sup>-1</sup> which is linear over the range of 30%-90% (w/w) with a coefficient of determination (<i>R</i> <sup>2</sup>) 0.995 and meets the requirements of International Council for Harmonization (ICH) and AOAC for the development of the method. The limit of detection (LOD) and limit of quantification (LOQ) were found to 7.616% w/w and 23.079% w/w, respectively. Principal component analysis (PCA) was performed to identify adulteration or degradation of active pharmaceutical ingredients (APIs). It was observed that PC1 and PC2 explained 99.93% and 99.91% of the IR spectral variance respectively. Consequently, this method can be applied for routine analysis of LFX and quality control in pharmaceutical regulatory laboratories. Principal component analysis ; Process analytical technology ; Forced degradation ; Active ingredient ; Control limits ; Pharmaceutical formulation ; Degradation (telecommunications) ; Detection limit ; Spectral analysis Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Essential Oils and Antimicrobial Activity
Authors: Zhehua Jiang | Jianhua Zhang | Mengyuan Zhan | Jianxiao Si | Junchao Huang | Hua Tong | Ning Xu
Source: ArXiv.org (preprint, 2025-04-14)
Field: Materials Science — Materials Chemistry

A mechanical approach to facilitate the formation of dodecagonal quasicrystals and their approximants The conditions for forming quasicrystals and their approximants are stringent, normally requiring multiple length scales to stabilize the quasicrystalline order. Here we report an unexpected finding that the approximants and motifs of dodecagonal quasicrystals can be spontaneously formed in the simplest system of identical hard disks, utilizing the unstable feature of the initial square packing subject to mechanical perturbations. Because there is only one length scale involved, this finding challenges existing theories of quasicrystals and their approximants. By applying the same approach to a system known to form a dodecagonal quasicrystal, we develop decent quasicrystalline order in a purely mechanical manner. With the aid of thermal treatment, we achieve a significantly better quasicrystalline order than that from the direct self-assembly of the liquid state within the same period of time. In sufficiently low temperatures where the self-assembly of a liquid is significantly hindered, our approach still promotes the formation of quasicrystals. Our study thus opens a venue for high-efficiency search and formation of quasicrystals, and may have broader implications for the design and synthesis of quasicrystalline materials. Quasicrystal ; Phason ; Order (exchange) ; Thermal ; Square (algebra) ; Scale (ratio) Quasicrystal Structures and Properties
Authors: Yajie Song | Shangbo Han | Teng Hu | Hongyu Lyu | Nuo Chen | Xiao Zhang | Saisai Lin | Chenghang Zheng | Peng Liu | Xiang Gao
Source: Energies (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Comparable Study on Celadon Production Fueled by Methanol and Liquefied Petroleum Gas at Industry Scale As a major contributor to industrial energy consumption and carbon emissions, the kiln industry faces increasing pressure to adopt cleaner energy sources. This study investigated the combustion characteristics, redox processes in celadon firing, product quality, and pollutant emissions for an industry furnace with methanol and liquefied petroleum gas (LPG) as kiln fuels. Methanol combustion reduced firing time by 17.4% due to the faster temperature rise during oxidation and holding phases and provided a more uniform and stable flame, compared with LPG cases. Significant reductions in emissions were observed when methanol is used as fuel. For example, NO concentration is reduced by 70.89%, 37.43% for SO2, 93.67% for CO, 45.07% for CO2, and 85.89% for CH4. The methanol-fired celadon exhibited better quality in terms of the appearance and threshold stress–strain value. The chemical analysis results show that K/O element ratio increased from 8.439% to 11.706%, Fe/O decreased from 4.793% to 3.735%, Al/O decreased from 33.445% to 31.696%, and Si/O increased from 76.169% to 89.825%. These findings demonstrate the potential of methanol as a sustainable kiln fuel, offering enhanced combustion efficiency, reduced emissions, and improved ceramic quality. Liquefied petroleum gas ; Waste management ; Scale (ratio) ; Production (economics) ; Methanol ; Environmental science ; Engineering ; Chemistry ; Economics ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Hydrocarbon exploration and reservoir analysis ; Thermochemical Biomass Conversion Processes
Authors: Djarot B. Darmadi | Angga Saputra | Slamet Prasetyo Utomo | Marco Talice
Source: Mechanical Engineering for Society and Industry (article, 2025-06-28)
Field: Materials Science — Metals and Alloys

The stress corrosion cracking (SCC) susceptibility of the dissimilar ASTM A36 steels and 316L stainless steels welding in varied temperature of FeCl2 Obtaining perfect dissimilar welding joints, which are exposed to corrosive environments still a problem up to now. The ASTM A36 and 316L stainless steels, dissimilar metals, were joined using Capacitive Discharge Welding (CDW). The welding parameters, such as distance between metals, pressure, applied energy, and surface parameters, were kept constant. The FeCl2 corrosive solution concentration was also kept constant at 0.5M. The temperature of the solution was controlled at varied temperatures, those are: 30 °C, 40 °C, and 50 °C, respectively. The resistance to the Stress Corrosion Cracking (SCC) load was evaluated by time to fracture for certain dead tensile loads and corrosive media. The SEM EDS data were retrieved to have a deep insight into the SCC mechanism. The results show that, with a 10 °C increase in temperature, the SCC Threshold is decreased by 40% which is supported by the data of time to failure for certain loads and also the SEM EDS. Welding ; Stress corrosion cracking ; Cracking ; Ultimate tensile strength ; Corrosion ; Heat-affected zone ; Stress (linguistics) Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Material Properties and Failure Mechanisms
Authors: Richard M. Beteck | Lesetja J. Legoabe | Phelelisiwe S. Dube | Audrey Jordaan | Digby F. Warner
Source: Medicinal Chemistry Research (article, 2025-04-04)
Field: Chemistry — Organic Chemistry

6-Phenoxyl-4-aminoquinoline: synthesis and preliminary antitubercular-structure activity relationship analyses Abstract Diphenyl ether and quinoline based compounds have been reported to show antibacterial activity. Against Mycobacterium tuberculosis , drug targets inhibited by diphenyl ether compounds are reportedly different from those perturbed by quinoline based antitubercular hits/drugs. Herein, we conceptualized and synthesized novel molecules incorporating quinoline and diphenyl ether moieties. The antitubercular property of the synthesized compounds were measured in vitro using Tween 80 and Tyloxapol supplemented growth media. Compounds in this study generally showed sub micromolar antitubercular activity in tween 80/albumin supplemented growth medium, and moderate to poor activity in tyloxapol/casitone supplemented growth medium. Compound 4e , havin a trimethylenediamine moiety and low melting point of 68 °C, emerged as the hit compound, possessing MIC 90 value of 0.2 µM. 4e is non-cytotoxic when tested against normal human cell line, exhibiting CC 50 value &gt; 20 µM. Chemistry ; Combinatorial chemistry ; Stereochemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Bioactive Compounds and Antitumor Agents
Authors: Deni Kurnia | Agus Sutanto | Hanif Fakhrurroja
Source: Journal of Scientific & Industrial Research (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

AI and IoT for Yarn Defect Detection in the Textile Industry: A Systematic Literature Review The textile industry continues to face yarn defects, which, in turn, reduce product quality and make production inefficient. Conventional manual inspection methods are unreliable and labour-intensive; therefore, combining AI and IoT enables intelligent quality control. This work conducts a Systematic Literature Review (SLR) on the applications of AI and IoT in yarn defect detection, using the PRISMA methodology to select and synthesise articles. A review of publication records across Scopus, ScienceDirect, IEEE Xplore, and Google Scholar, and thus collected 25 peer-reviewed papers from 2014 to 2024. The research trends were classified by production stage, algorithm type, and global distribution. The results show that AI methods, especially image processing, neural networks, and deep learning (including CNN-based models), play a leading role in yarn defect detection, achieving accuracies exceeding 95%. However, the implementation of IoT for real-time monitoring remains underdeveloped, and few studies have examined in-process defect detection. Post-production inspection receives the vast majority of contributions, while pre-production and on-production stages receive less attention. China leads in the number of published papers, followed by Turkey, Egypt, and India. The main challenges lie in combining AI and IoT with legacy systems, ensuring the reliability of data supply, and handling computational and cost constraints. This review concludes that when AI harmonises with IoT, it drives transformative shifts in predictive monitoring and smart manufacturing of textiles. Further studies are expected to focus on real-time IoT-based monitoring, model optimisation, and low-cost implementation towards fully automated, data-driven yarn defect detection systems. Yarn ; Systematic review ; Textile ; Internet of Things ; Systematic error Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Advanced Sensor and Energy Harvesting Materials
Authors: Manoj Kumar
Source: Journal of Ayurveda and Holistic medicine. (article, 2025-06-19)
Field: Chemistry — Organic Chemistry

Exploring Quinovic Acid as a potential lung cancer therapy: Insights from network pharmacology and molecular docking Background: Lung cancer is the predominant cause of cancer-related death globally, attributed to delayed diagnosis and limited therapeutic effectiveness. Natural chemicals provide potential avenues for the development of innovative cancer therapeutics. Objective: This study seeks to investigate the anticancer efficacy of quinovic acid in lung cancer via a network pharmacology approach combined with molecular docking techniques. Methods: A network pharmacology analysis was conducted to discover the proteins targeted by Quinovic acid. The targets were also analysed for potential overlap with genes linked to lung cancer. A Protein-Protein Interaction (PPI) network was constructed to identify pivotal hub genes. Molecular docking simulations were conducted to evaluate the binding affinity of quinovic acid with the indicated targets. Results: Network pharmacology indicates that quinovic acid interacts with a diverse array of proteins, predominantly phosphatases (33.3%) and phosphodiesterases (26.7%). A substantial overlap of 49 genes was identified between quinovic acid targets and lung cancer-associated genes, suggesting potential therapeutic relevance. PPI analysis identified essential hub genes including TP53, EGFR, KRAS, BRAF, and PIK3CA, which are involved in significant signalling pathways such as PI3K-AKT, MAPK, and apoptosis. Computer-simulated ligand binding analyses demonstrated substantial binding affinities of quinovic acid, particularly with BRAF and PIK3CA (-9.2 kcal/mol). Conclusion: The results indicate that quinovic acid may inhibit cancer proliferation by altering many critical oncogenic pathways, rendering it a promising option for lung cancer treatment. Additional experimental validation is necessary to demonstrate its therapeutic effectiveness. Lung cancer ; Docking (animal) ; KRAS ; Computational biology ; PI3K/AKT/mTOR pathway ; Biology ; Cancer ; Pharmacology ; Cancer research ; Apoptosis ; Medicine ; Biochemistry ; Genetics ; Oncology ; Colorectal cancer Synthesis and biological activity ; Computational Drug Discovery Methods ; Enzyme function and inhibition
Authors: Muhmmad Muzamal | Manzoor Hussain | Aryo De Wibowo
Source:  (article, 2025-09-26)
Field: Chemistry — Analytical Chemistry

Non-Destructive Mango Quality Prediction Using Machine Learning Algorithms The quality of mangoes is a crucial factor in both domestic and commercial markets that directly influences consumer satisfaction and economic value. Traditional methods of checking mango quality often involve destructive techniques, which lead to the loss of the fruit in the testing process. This study presents an advanced approach that could predict the quality of mangoes using advance non-destructive methods leveraging machine learning algorithms to predict quality parameters such as ripeness, sweetness and overall freshness without damaging the fruit. In this research, a dataset consisting of various mango samples was collected, with attributes including color, texture, size, weight and acidity levels. Sensors, such as pH sensors (for acidity) and e-nose sensors (for aroma and sweetness detection), were used to gather data, while a combination of machine learning models such as Decision Tree, K-Nearest Neighbors (KNN), and Automated Machine Learning (AutoMLP), Naive Bayes were applied to predict the mangoes’ quality. The accuracy of each model was measured based on its ability to classify mangoes as fresh, ripe, or rotten. The results determine that the AutoMLP model performs the best out of the traditional models, achieving an accuracy of 98.46%, making it the most suitable model for mango quality prediction. The research explains the significance of feature extraction methods, model optimization, and sensor data pretreatment in reaching a high prediction accuracy. Sweetness ; Quality (philosophy) ; Naive Bayes classifier ; Predictive modelling ; Feature (linguistics) ; Feature extraction Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Smart Agriculture and AI
Authors: Yanchao Wang | Jingyan Chen | Meiling Xu | Jie Yang | Jiaqi Feng | Xiaodong Zhou | Yinwei Li
Source: Research Square (preprint, 2025-07-28)
Field: Materials Science — Materials Chemistry

Phase-Transition-Induced Ferroelectric and Magnetic Switching in Two-Dimensional CuMnP2Se6 under Ultra-Low Electric Fields Ferroelectricity ; Condensed matter physics ; Phase transition ; Materials science ; Electric field ; Transition (genetics) ; Physics ; Optoelectronics ; Chemistry ; Dielectric ; Quantum mechanics 2D Materials and Applications ; Phase-change materials and chalcogenides ; Solid-state spectroscopy and crystallography