jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Roman Karpenko | Yevhenii Arkadiiovych Bondarenko
Source: Technology audit and production reserves (article, 2024-12-31)
Field: Chemistry — Analytical Chemistry

Control of boiler equipments during starting and stopping periods and methods of optimizing these processes through the use of a decision support system with a machine vision subsystem The object of research is the automation of starting and stopping steam power boilers. The problem of automating the starting and stopping of steam power boilers is important for thermal power plants (TPP) and industrial enterprises. These processes require significant efforts from service personnel due to their complexity, partial automation and the need to take into account the human factor. It is emphasized that full automation of starting and stopping steam boilers is economically impractical, since most of the time the boilers operate in continuous automatic operations and only a short time is allocated for periodic procedures, which are mostly performed manually. However, the significant impact of the human factor at critical stages of boiler operation requires the introduction of new technologies that can increase the efficiency and safety of such operations. The study outlines the main challenges associated with steam boiler control and proposes new approaches to solving these problems. It is noted that operators often perform actions during boiler starting and stopping based on instructions or their own experience. This knowledge can be formalized and integrated into the database of an expert decision support system (DSS), which automates some of the manual actions and helps operators avoid errors. For this purpose, it is proposed to use machine vision subsystems that can validate the operator's actions, analyze the interaction of personnel with the equipment and signal about possible incorrect actions. This approach can not only reduce the risk of errors due to fatigue or stress of personnel, but also make the starting and stopping processes safer and more efficient. It is proposed to integrate machine vision subsystems to obtain information that is difficult to measure by traditional means, in particular, regarding the operator's interaction with manual mechanisms or its presence at the workplace. The structure of the proposed DSS also takes into account the possibility of transferring knowledge bases between different objects, which ensures the scalability and adaptability of the system. The implementation of such a system is based on modern international automation standards, in particular ISA-88, ISA-106 and VDI/VDE/VDMA 2632. Boiler (water heating) ; Engineering ; Environmental science ; Reliability engineering ; Automotive engineering ; Computer science ; Waste management Spectroscopy and Chemometric Analyses ; Industrial Technology and Control Systems ; Advanced Algorithms and Applications
Authors: Saule Sadykova | Zh.S. Turlan | Zh.K. Sarkulova
Source: Қорқыт Ата атындағы Қызылорда университетінің Хабаршысы Педагогика және психология сериясы (article, 2024-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

STICKY LEARNING STRATEGIES AS A PEDAGOGICAL APPROACH TO ENHANCING RESEARCH SKILLS IN ACADEMIC WRITING: AN INTEGRATED ANALYTICAL STUDY In today's academic environment, the development of research skills, particularly in academic writing, is essential for student success. Research extends beyond information gathering, encompassing critical thinking and synthesis to generate new insights. Sticky learning strategies, which concentrate on making knowledge durable for long-term retention, present innovative methods for improving students' research skills. These strategies promote deeper engagement with the research process by emphasizing active learning, spaced repetition, interleaved practice, and metacognition. The intervention described herein evaluates the influence of sticky learning strategies on augmenting research skills in academic writing. It integrates various methods into the curriculum to assess their effectiveness in fostering retention, critical thinking, and knowledge application. This contribution offers practical value by adding to existing research on effective teaching strategies and carries the potential to refine instructional practices in higher education. A mixed-methods approach was employed, collecting both quantitative and qualitative data. Pre- and post-tests measured changes in students' research skills, while reflections, interviews, and surveys provided qualitative insights. Descriptive statistics and thematic analysis were used to evaluate student outcomes. Results indicated that sticky learning strategies substantially enhanced research skills, as observed in improved test scores, writing quality, and student engagement. Students reported that active learning and spaced repetition improved their ability to retain and apply knowledge. The findings suggest that incorporating these strategies into academic curricula can lead to sustained improvements in research skills and academic writing. This work provides empirical support for sticky learning strategies and offers practical recommendations for educators seeking to improve student outcomes in research and writing. Active learning (machine learning) ; Curriculum ; Thematic analysis ; Process (computing) ; Critical thinking ; Higher-order thinking ; Qualitative research ; Empirical research ; Student engagement ; Transferable skills analysis Various Chemistry Research Topics ; Academic Writing and Publishing ; Problem and Project Based Learning
Authors: Badiea Soliman | Mokhtar M. Mabrouk | Sherin F. Hammad | Amira H. Kamal
Source: Journal of Advanced Medical and Pharmaceutical Research (article, 2024-02-08)
Field: Chemistry — Analytical Chemistry

Development and Validation of RP-HPLC Method for Analysis of Counterfeit Clopidogrel Isocratic RP-HPLC method was developed and validated for the analysis of clopidogrel bisulfate counterfeited with acetyl salicylic acid and applied for the determination of clopidogrel bisulfate in pharmaceutical dosage forms. Clopidogrel bisulfate is commonly counterfeited with acetyl salicylic acid and it is not labeled to contain acetyl salicylic acid due to their similar pharmacological effect in inhibiting platelet aggregation. The mobile phase was composed of potassium dihydrogen phosphate buffer 0.02M (pH 2.8) and methanol 30:70 (v/v). The flow rate was 1.5mL/min. Diode array detector was set at 220 nm. The linearity ranges were 10-70 µg/mL and 6-50 µg/mL for clopidogrel bisulfate and acetyl salicylic acid, respectively. The retention time was 2.5 min and 10.23 min for clopidogrel bisulfate and acetyl salicylic acid, respectively. The method was validated according to ICH guidelines. The developed method was successfully applied for the determination of clopidogrel bisulfate counterfeited with acetyl salicylic acid as well as estimation of clopidogrel bisulfate in its different dosage forms. Salicylic acid ; Chemistry ; Chromatography ; Methanol ; Clopidogrel ; High-performance liquid chromatography ; Aspirin ; Organic chemistry ; Biochemistry Analytical Methods in Pharmaceuticals ; Analytical Chemistry and Chromatography ; Analytical chemistry methods development
Authors: Roman Kvit | Петро Пукач | Tetyana Salo | Myroslava Vovk
Source: Modelling—International Open Access Journal of Modelling in Engineering Science (article, 2024-10-19)
Field: Materials Science — Materials Chemistry

Statistical Modeling and Probable Calculation of the Strength of Materials with Random Distribution of Surface Defects Based on the solutions of deterministic fracture mechanics and the methods of probability theory, the algorithm for calculating the probabilistic strength characteristics of plate elements of structures with an arbitrary stochastic distribution of surface defects is outlined. On the plate surface, there are uniformly distributed cracks that do not interact with each other, the plane of which is normal to the surface, and the depth is much less than its length on the surface. The cracks’ depth and angle of orientation are random values, and their joint distribution density is specified. Plates made of this material are under the influence of biaxial loading. The probability of failure, along with the mean value, the dispersion, and the variation coefficient of the plate’s strength, taking into account the surface defects under different types of stress, were determined. Their dependence on the type of loading, the size of the plate, and the surface structural heterogeneity of the material were studied graphically. Joint consideration of the influence of the interrelated properties of real materials, such as defectiveness and stochasticity, on strength and fracture, opens up new opportunities in creating a theory of strength and fracture of deformable solids. Surface (topology) ; Distribution (mathematics) ; Statistical physics ; Materials science ; Mathematics ; Geometry ; Physics ; Mathematical analysis Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Non-Destructive Testing Techniques
Authors: Collin Malone | Holly Flynn
Source:  (article, 2024-07-15)
Field: Materials Science — Materials Chemistry

Algorithm Review for the Development of a Real-Time Monitoring and Accountancy Open Framework for Fusion Energy Outline • Tritium as a Fuel Source • Motivation for Improved Accountancy Methods • SRNL Process Modeling • Proof-of-Concept Study • 5-year Framework Development • Acquisition and Curation of Data Sets • Best Fit Algorithm and Metrics • Best Fit Algorithm – Bayes Method • Best Fit Algorithm – Dimensionality • Summary Computer science ; Process (computing) ; Curse of dimensionality ; Sensor fusion ; Best practice ; Energy (signal processing) ; Data mining ; Software engineering ; Algorithm ; Machine learning ; Mathematics ; Programming language ; Statistics Fusion materials and technologies ; Nuclear reactor physics and engineering ; Radioactive contamination and transfer
Authors: Àngels Mateu‐Regué | Luca Mariani | Frederik Otzen Bagger | Muthiah Bose | Finn Cilius Nielsen
Source: medRxiv (preprint, 2024-05-23)
Field: Materials Science — Materials Chemistry

Functional Assessment of Protein Variants in Structured Domains by Fluorescence Cross-Correlation Spectroscopy ABSTRACT With the expanding catalogue of novel disease-genes, there is an increasing need to establish the significance of potential disease-causing variants. Based on the idea that pathogenic variants in structured protein domains disturb folding and association with macromolecular assemblies, we employed Fluorescence Correlation and Cross-Correlation Spectroscopy (FCS and FCCS) to assess in vivo protein complex formation. Since the molecular underpinning of BRCA-associated breast and ovarian cancers is well defined and data from a recent genome editing screening allowed us to compare variant binding data with a reliable functional HRD test in addition to ClinVar and AlphaMissense data, we examined the binding of mutated wild-type BRCA1 or isolated RING and BRCT domains to BARD1 and RBBP8, respectively. The results demonstrate that FCCS, whether applied to full-length BRCA1 in live cells and/or to isolated domains in cellular lysates identified pathogenic BRCA1 RING or BRCT domain variants. We moreover demonstrate the feasibility of employing FCCS for analysis of HNPCC-related factor MSH2 and MEN1 factor Menin variants in combination with DNA mismatch repair factor MSH6 and transcription factor JUND, respectively. We propose that FCCS may be an appealing complement to current clinical procedures for classifying variants, for many monogenic diseases given its generic nature and ease of use. Fluorescence correlation spectroscopy ; Fluorescence cross-correlation spectroscopy ; Fluorescence spectroscopy ; Computational biology ; Fluorescence ; Spectroscopy ; Correlation ; Chemistry ; Computer science ; Biology ; Physics ; Mathematics ; Optics Enzyme Structure and Function ; Microbial Metabolic Engineering and Bioproduction ; Protein Structure and Dynamics
Authors: Jacob Earnshaw | Nicholas Syortiuk | Oliver Duncan | Łukasz Kaczmarczyk | Fabrizio Scarpa | Stefan Szyniszewski
Source:  (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Metamaterials genome: progress towards a community toolbox for ai metamaterials discovery Understanding the limits of the design space is a key aspect in optimising complex hierarchical structures and is vital for exploring and designing novel Metamaterials. Simultaneously, abundant data (mostly text, images, and location) aggregated by multinational corporations accelerated the development of machine learning and artificial intelligence technologies. Although increasingly conceptually advanced, the origins of machine learning can be traced back to traditional statistical methods and datacentric analysis. These techniques have been used in fields where establishing relationships and using differential equations or closed-form descriptions have been challenging due to the systems’ complexity. However, well-established and validated physics-based modelling tools offer direct solutions for various physical domains relevant to metamaterials. What is the right place for the emerging machine learning techniques in that context? Metamaterial ; Toolbox ; Computer science ; Context (archaeology) ; Key (lock) ; Data science ; Artificial intelligence ; Physics ; Programming language Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Siddharth Chaini | A. Mahabal | Ajit Kembhavi | Federica Bianco
Source: arXiv (Cornell University) (preprint, 2024-03-18)
Field: Chemistry — Analytical Chemistry

Light Curve Classification with DistClassiPy: a new distance-based classifier The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (e.g. Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrophysical objects. We developed DistClassiPy, a new distance metric based classifier. The direct use of distance metrics is unexplored in time-domain astronomy, but distance-based methods can help make classification more interpretable and decrease computational costs. In particular, we applied DistClassiPy to classify light curves of variable stars, comparing the distances between objects of different classes. Using 18 distance metrics on a catalog of 6,000 variable stars across 10 classes, we demonstrate classification and dimensionality reduction. Our classifier meets state-of-the-art performance but has lower computational requirements and improved interpretability. Additionally, DistClassiPy can be tailored to specific objects by identifying the most effective distance metric for that classification. To facilitate broader applications within and beyond astronomy, we have made DistClassiPy open-source and available at https://pypi.org/project/distclassipy/. Artificial intelligence ; Classifier (UML) ; Pattern recognition (psychology) ; Computer science Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification
Authors: Jalal Faraj | Wassim Salameh | Ahmad Al Takash | Georges El Achakr | Hicham El Hajj | Cathy Castelain | Mahmoud Khaled
Source: Journal of Physics Conference Series (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Dual harvesting from exhaust gas of diesel generators using thermoelectric generators and cold water tank Abstract The present work concerns a dual harvesting concept applied to exhaust gas of Diesel generators using thermoelectric generators and cold water tank. To proceed, a simplified thermal modelling is developed and appropriate parametric analysis of power generation with the TEGs and heat recovered is conducted in function of the Diesel generator power and the TEG thermal conductivity and thickness. It was shown that powers up to 534 W can be generated with the TEGs and heat recovery rates up to 4463 W can be obtained for a Diesel generator power of 125 kW. Also, it was shown that the temperature difference across the TEGs and the power generated increase exponentially when the Diesel generator power and ratio of thickness to thermal conductivity of TEGs increase. Dual (grammatical number) ; Exhaust gas ; Thermoelectric generator ; Dual purpose ; Environmental science ; Diesel fuel ; Diesel exhaust ; Exhaust gas recirculation ; Automotive engineering ; Waste management ; Thermoelectric effect ; Engineering ; Mechanical engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Advanced Thermodynamic Systems and Engines ; Innovative Energy Harvesting Technologies
Authors: Luca Capriotti
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-08-12)
Field: Chemistry — Analytical Chemistry

Characterization of Legacy Fuels: Progress to Fuel Cladding Chemical Interaction Characterization of Legacy Fuels: Progress to Fuel Cladding Chemical Interaction Presentation for Metal fuel workshop 14-16 may 2024 Cladding (metalworking) ; Characterization (materials science) ; Materials science ; Environmental science ; Nanotechnology ; Metallurgy Petroleum Processing and Analysis ; Heat transfer and supercritical fluids ; Nuclear Materials and Properties
Authors: Yu-Ao Chen | Chenghong Zhu | Keming He | Yingjian Liu | Xin Wang
Source: arXiv (Cornell University) (preprint, 2024-11-21)
Field: Chemistry — Physical and Theoretical Chemistry

Hypothesis testing of symmetry in quantum dynamics Symmetry plays a crucial role in quantum physics, dictating the behavior and dynamics of physical systems. In this paper, we develop a hypothesis-testing framework for quantum dynamics symmetry using a limited number of queries to the unknown unitary operation and establish the quantum max-relative entropy lower bound for the type-II error. We construct optimal ancilla-free protocols that achieve optimal type-II error probability for testing time-reversal symmetry (T-symmetry) and diagonal symmetry (Z-symmetry) with limited queries. Contrasting with the advantages of indefinite causal order strategies in various quantum information processing tasks, we show that parallel, adaptive, and indefinite causal order strategies have equal power for our tasks. We establish optimal protocols for T-symmetry testing and Z-symmetry testing for 6 and 5 queries, respectively, from which we infer that the type-II error exhibits a decay rate of $\mathcal{O}(m^{-2})$ with respect to the number of queries $m$. This represents a significant improvement over the basic repetition protocols without using global entanglement, where the error decays at a slower rate of $\mathcal{O}(m^{-1})$. Symmetry (geometry) ; Dynamics (music) ; Quantum ; Statistical physics ; Theoretical physics ; Physics ; Quantum dynamics ; Quantum mechanics ; Mathematics ; Geometry History and advancements in chemistry
Authors: Bart Van Puyvelde
Source: Faculty of 1000 Research Ltd (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Referee report. For: Interrogation of an ovine serum peptide spectral library to annotate ambiguous clinicopathological biomarkers using data-independent acquisition [version 1; peer review: 1 approved with reservations] Interrogation ; Peptide ; Antibody ; Peptide mapping ; Biomarker Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; S100 Proteins and Annexins
Authors: Lin Deng | Ziqiang Cai | Yongmin Liu
Source: arXiv (Cornell University) (preprint, 2024-03-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Polarization-Encoded Lenticular Nano-Printing with Single-Layer Metasurfaces Metasurface-based nano-printing has enabled ultrahigh-resolution grayscale or color image display. However, the maximum number of independent nano-printing images allowed by one single-layer metasurface is still limited despite many multiplexing methods that have been proposed to increase the design degree of freedom. In this work, we substantially push the multiplexing limit of nano-printing by transforming images at different observation angles into mapping the corresponding images to different positions in the Fourier space, and simultaneously controlling the complex electric field across multiple polarization channels. Our proposed Polarization-Encoded Lenticular Nano-Printing (Pollen), aided by a modified evolutionary algorithm, allows the display of several images based on the viewing angle, similar to traditional lenticular printing but without requiring a lenticular layer. In addition, it extends the display capability to encompass multiple polarization states. Empowered by the ability to control the complex amplitude of three polarization channels, we numerically and experimentally demonstrate the generation of 13 distinguished gray-scale Chinese ink wash painting images, 49 binary patterns, and three sets of 3D nano-printing images, totaling 25 unique visuals. These results present the largest number of recorded images with ultra-high resolution to date. Our innovative Pollen technique is expected to benefit the development of modern optical applications, including but not limited to optical encryption, optical data storage, lightweight display, and augmented reality and virtual reality. Nano- ; Polarization (electrochemistry) ; Materials science ; Nanotechnology ; Layer (electronics) ; Optics ; Optoelectronics ; Physics ; Chemistry ; Composite material Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Advanced Materials and Mechanics
Authors: Ying Chung
Source: Institutional Repositories DataBase (IRDB) (article, 2024-03-01)
Field: Materials Science — Ceramics and Composites

Effects of solid solution, microstructure and sintering atmosphere on thermal and electrical properties of porous SiC ceramics Microstructure ; Sintering ; Ceramic ; Porosity ; Thermal ; Atmosphere (unit) Advanced ceramic materials synthesis ; Aerogels and thermal insulation ; Silicon Carbide Semiconductor Technologies
Authors: K.S. Kim | Taehoon Kim | Jihyun Cha
Source: Preprints.org (preprint, 2024-03-08)
Field: Chemistry — Analytical Chemistry

Advanced Detection of Cardiac Beat Intervals in Functional Near-Infrared Spectroscopy(fNIRS) through Signal Integration Reconstruction In this study, we propose the Signal Integration Reconstruction Peak Detection (SIRPD) algorithm for reliable Cardiac Beat Interval (CBI) measurement using functional Near-Infrared Spectroscopy (fNIRS) signals. This algorithm enhances reliability by identifying a regular waveform in cerebral blood flow associated with heartbeats. The SIRPD algorithm assesses the Signal Quality Index (SQI) per channel, discards signals from unavailable channels, and interpolates oxygenated and total hemoglobin concentration signals, thereby boosting resolution and integrating the signal into a single channel. The integrated and smoothed signal is transformed to identify the most significant periodic waveform, from which the CBI is extracted. Validated with data from 20 healthy subjects who simultaneously recorded fNIRS and ECG signals, our findings confirm the equivalence of the CBI derived from ECG and calculated through the SIRPD algorithm from fNIRS (R^2 = .98, RMSE = .011, ICC = .991). We also extracted heart rate variability (HRV) features, confirming no significant difference in all features between ECG and fNIRS devices. Uniquely, this study regards the fNIRS heart signal not as an artifact source but as a valuable bio signal. The proposed method, which allows for the detection of cardiac beat intervals from cerebral blood flow oscillations, suggests the potential clinical applicability of fNIRS as an auxiliary indicator for various cardiovascular factors. Beat (acoustics) ; Functional near-infrared spectroscopy ; Heart beat ; SIGNAL (programming language) ; Speech recognition ; Pattern recognition (psychology) ; Computer science ; Artificial intelligence ; Acoustics ; Internal medicine ; Physics ; Psychology ; Neuroscience ; Medicine Spectroscopy and Chemometric Analyses