jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wentian Jin | Liang Chen | Subed Lamichhane | Mohammadamir Kavousi | Sheldon X.-D. Tan
Source: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design (article, 2022-10-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

HierPINN-EM Electromigration (EM) becomes a major concern for VLSI circuits as the technology advances in the nanometer regime. The crux of problem is to solve the partial differential Korhonen equations, which remains challenging due to the increasing integrated density. Recently, scientific machine learning has been explored to solve partial differential equations (PDE) due to breakthrough success in deep neural networks and existing approach such as physics-informed neural networks (PINN) shows promising results for some small PDE problems. However, for large engineering problems like EM analysis for large interconnect trees, it was shown that the plain PINN does not work well due the to large number of variables. In this work, we propose a novel hierarchical PINN approach, HierPINN-EM for fast EM induced stress analysis for multi-segment interconnects. Instead of solving the interconnect tree as a whole, we first solve EM problem for one wire segment under different boundary and geometrical parameters using supervised learning. Then we apply unsupervised PINN concept to solve the whole interconnects by enforcing the physics laws in the boundaries for all wire segments. In this way, HierPINN-EM can significantly reduce the number of variables at plain PINN solver. Numerical results on a number of synthetic interconnect trees show that HierPINN-EM can lead to orders of magnitude speedup in training and more than 79× better accuracy over the plain PINN method. Furthermore, HierPINN-EM yields 19% better accuracy with 99% reduction in training cost over recently proposed Graph Neural Network-based EM solver, EMGraph. Solver ; Computer science ; Speedup ; Artificial neural network ; Interconnection ; Artificial intelligence ; Algorithm ; Parallel computing Copper Interconnects and Reliability ; Semiconductor materials and devices ; Power Transformer Diagnostics and Insulation
Authors: Wanru Zhao | Xinchi Qiu | Javier Fernández-Marqués | Pedro Porto Buarque de Gusmão | Nicholas D. Lane
Source:  (preprint, 2022-10-17)
Field: Chemistry — Spectroscopy

Protea Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users. While successful, FL research is currently limited by the difficulties of establishing a realistic large-scale FL system at the early stages of experimentation. Simulation can help accelerate this process. To facilitate efficient scalable FL simulation of heterogeneous clients, we design and implement Protea, a flexible and lightweight client profiling component within federated systems using the FL framework Flower. It allows automatically collecting system-level statistics and estimating the resources needed for each client, thus running the simulation in a resource-aware fashion. The results show that our design successfully increases parallelism for 1.66 X faster wall-clock time and 2.6X better GPU utilisation, which enables large-scale experiments on heterogeneous clients. Computer science Advanced Proteomics Techniques and Applications ; Metabolism and Genetic Disorders
Authors: Ricardo Cortez | Gabriel Cubas | Marco Antonio Sandoval-Chileño | Luis Ángel Castañeda Briones | Norma Lozada‐Castillo | Alberto Luviano‐Juárez
Source: Sensors (article, 2022-10-01)
Field: Materials Science — Materials Chemistry

Position Measurements Using Magnetic Sensors for a Shape Memory Alloy Linear Actuator This article presents the design and implementation of a linear actuator based on NiTi Shape Memory Alloys with temperature and position measurements based on a magnetic sensor array and a set of thermistors. The position instrumentation is contact free to avoid friction perturbations; the position signal conditioning is carried out through the calculation of the response of each magnetic sensor, selecting the closest sensor to ensure accurate results on the full range of movement. Experimental results validate the accuracy of the position sensing with a competitive behaviour. Actuator ; Thermistor ; Position (finance) ; Position sensor ; Signal conditioning ; Instrumentation (computer programming) ; Acoustics ; Shape-memory alloy ; Nickel titanium ; Control theory (sociology) ; SIGNAL (programming language) ; Computer science ; Materials science ; Engineering ; Electrical engineering ; Physics ; Artificial intelligence ; Control (management) ; Angular displacement Shape Memory Alloy Transformations ; Transition Metal Oxide Nanomaterials ; Soft Robotics and Applications
Authors: Omar Hussein Dib | Ali Assaf | Alexia Pean | Marie‐José Durand | Sulivan Jouanneau | Ramakrishnan Ramanathan | Gérald Thouand
Source: Sustainability (article, 2022-12-22)
Field: Chemistry — Analytical Chemistry

Raman Spectroscopy Application in Food Waste Analysis: A Step towards a Portable Food Quality-Warning System Food waste is one of the main problems contributing to climate change, as its piling up in landfills produces the greenhouse gas methane. Food waste occurs at every stage of food production; however, a major source of food waste occurs at businesses that supply food to consumers. Industry 4.0 technologies have shown promise in helping to reduce food waste in food supply chains. However, more innovative technologies, such as Raman spectroscopy, hold great promise in helping to reduce food waste, although this has largely been ignored in the literature. In this context, we propose a portable Raman platform to monitor food quality during transportation. The developed system was tested in conditions mimicking those present in a refrigerated truck by analyzing chicken samples stored at temperatures of 4 °C. Raman spectra were acquired for non-packaged and packaged samples over the duration of 30 days resulting in 6000 spectra. The analysis of Raman spectra revealed that the system was able to detect noticeable changes in chicken quality starting on day six. The main Raman bands contributing to this change are amide I and tyrosine. The proposed system will offer the potential to reduce food losses during transportation by consistently checking the food quality over time. Food waste ; Context (archaeology) ; Raman spectroscopy ; Food quality ; Food systems ; Environmental science ; Food industry ; Greenhouse gas ; Food supply ; Food processing ; Quality (philosophy) ; Waste management ; Business ; Food science ; Food security ; Agricultural science ; Chemistry ; Engineering ; Agriculture ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: John R. Helliwell
Source: Acta Crystallographica Section D Structural Biology (article, 2022-05-18)
Field: Materials Science — Materials Chemistry

Raw diffraction data are our ground truth from which all subsequent workflows develop Defining best practice in science is challenging. International consensus is facilitated by the International Science Council via its members such as the International Union of Crystallography (IUCr). The crystallographic community has many decades of tradition linking articles with the underpinning data, and is admired across all sciences accordingly. Crystallography has always been at the forefront of harnessing new technology in the service of consensus. Technology has provided new vast data-archiving opportunities, allowing the preservation of raw diffraction data, along with article and database depositions of a model's coordinates and associated structure factors. The raw diffraction data, which can now be preserved, are the ground truth from which all subsequent workflows develop. Journal editorial boards provide a practical forum for setting the criteria to decide if a study's files are truly the version of record. Within that, reality involves a variance of reasonable workflows. But what is a reasonable variance? Workflows must be detailed carefully by authors in explaining what they have done. There is a great, and increasing, diversity of macromolecular crystallography analyses, and yet an increased constraint on how much can be written in an article about the workflow used. Raw data provide the ultimate reproducibility evidence. A part of reproducibility and replicability is using an agreed vocabulary; the meaning of words such as precision and accuracy and, more recently, the confidence of a protein structure prediction should feature in approaching `truth'. Workflow ; Raw data ; Computer science ; Variance (accounting) ; Data science ; Ground truth ; Database ; Artificial intelligence ; Business Enzyme Structure and Function ; Protein Structure and Dynamics ; Metabolomics and Mass Spectrometry Studies
Authors: Stav Biton Hayun | Rajendra P. Shukla | Hadar Ben‐Yoav
Source: Polymers (article, 2022-02-13)
Field: Chemistry — Electrochemistry

Diffusion- and Chemometric-Based Separation of Complex Electrochemical Signals That Originated from Multiple Redox-Active Molecules In situ analysis of multiple biomarkers in the body provides better diagnosis and enables personalized health management. Since many of these biomarkers are redox-active, electrochemical sensors have shown promising analytical capabilities to measure multiple redox-active molecules. However, the analytical performance of electrochemical sensors rapidly decreases in the presence of multicomponent biofluids due to their limited ability to separate overlapping electrochemical signals generated by multiple molecules. Here we report a novel approach to use charged chitosan-modified electrodes to alter the diffusion of ascorbic acid, clozapine, L-homocysteine, and uric acid-test molecules with various molecular charges and molecular weights. Moreover, we present a complementary approach to use chemometrics to decipher the complex set of overlapping signals generated from a mixture of differentially charged redox molecules. The partial least square regression model predicted three out of four redox-active molecules with root mean square error, Pearson correlation coefficient, and R-squared values of 125 µM, 0.947, and 0.894; 51.8 µM, 0.877, and 0.753; 55.7 µM, 0.903, and 0.809, respectively. By further enhancing our understanding of the diffusion of redox-active molecules in chitosan, the in-situ separation of multiple molecules can be enabled, which will be used to establish guidelines for the effective separation of biomarkers. Redox ; Molecule ; Chemometrics ; Electrochemistry ; Chemistry ; Diffusion ; Ascorbic acid ; Analytical Chemistry (journal) ; Electrode ; Physical chemistry ; Chromatography ; Inorganic chemistry ; Organic chemistry ; Thermodynamics Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Advanced Chemical Sensor Technologies
Authors: Tanzeela Anis | Syed Mujtaba ul Hassan | Ahmat Khurshid | M. Fakhar-e-Alam | Faisal Shahzad | Amjad Ali | Jamil Ahmad | Nazia Hossain
Source: Journal of Cluster Science (review, 2022-05-18)
Field: Materials Science — Biomaterials

Hollow Multicomponent Capsules for Biomedical Applications: A Comprehensive Review Abstract Hollow capsules with multi-shelled or multicomponent structures are essential materials for various applications. Biomedical applications like disease diagnosis, therapy, and monitoring have special significance as they aim to improve health conditions. This review demonstrated a comprehensive overview of hollow, multifunctional structures incorporating meaningful use of nanotechnology and its’ unique prospects in medicine such as patient-specific treatment, multimodal imaging, multimodal therapy, simultaneous delivery of drugs and imaging probes, and actively targeted delivery. The internal hollow cavity provides safe and controlled drug release while also enabling transport of functional moieties to target sites. This review explored the performance of different organic, inorganic, and metallic multicomponent capsules that have been reported for biomedical applications, mainly diagnostic imaging and drug delivery. Material compositions, morphologies, and synthesis strategies involved in fabricating such multifunctional systems have been discussed in detail. It is expected that with time, more sophisticated and precise systems will come to light as the outcome of ongoing concentrated research efforts. Nanotechnology ; Drug delivery ; Materials science ; Nanochemistry ; Computer science ; Biochemical engineering ; Systems engineering ; Engineering Electrospun Nanofibers in Biomedical Applications ; Dendrimers and Hyperbranched Polymers ; Nanomaterials for catalytic reactions
Authors: Sen Yang | Houqing ZHANG | Wenmin FAN
Source: Food Science and Technology (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Characteristic wavelengths selection of rice spectrum based on adaptive sliding window permutation entropy Due to the redundancy of rice spectral wavelengths and the strong correlation between adjacent wavelengths, the modeling classification accuracy based on traditional characteristic wavelengths selection methods is insufficient. Thus, a rice spectral characteristic wavelengths selection method based on adaptive sliding window permutation entropy (ASW-PE) was proposed in this paper. Firstly, the ASW-PE algorithm is constructed by combining the adaptive sliding window (ASW) method and permutation entropy (PE) method. Then, for the spectral data of rice varieties WC, XS, YS and YG, based on ASW-PE, sliding window permutation entropy (SW-PE), analysis of variance (ANOVA), competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) to carry out the characteristic wavelengths selection experiment, and evaluated the computational efficiency of the five algorithms from the perspective of time complexity. Finally, a partial least squares (PLS) rice varieties classification model was established based on the spectral characteristic wavelengths selected by the above algorithms, and the characteristic selection performance of the five algorithms was evaluated with the classification accuracy. Experimental results show that the ASW-PE algorithm has a speed advantage in selecting characteristic wavelengths for large sample spectral data. Compared with SW-PE, ANOVA, CARS and SPA algorithms, the accuracy of modeling classification based on ASW-PE method is improved by 5.6%, 22.6%, 8.6% and 15.2%, respectively. Sliding window protocol ; Algorithm ; Entropy (arrow of time) ; Wavelength ; Permutation (music) ; Mathematics ; Redundancy (engineering) ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Window (computing) ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Remote Sensing and Land Use ; Advanced Chemical Sensor Technologies
Authors: Huanle Zhang | Nicharee Wisuthiphaet | Hemiao Cui | Nitin Nitin | Xin Liu | Qing Zhao
Source: Frontiers in Artificial Intelligence (article, 2022-06-22)
Field: Chemistry — Analytical Chemistry

Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-supervised Learning The past decade witnessed rapid development in the measurement and monitoring technologies for food science. Among these technologies, spectroscopy has been widely used for the analysis of food quality, safety, and nutritional properties. Due to the complexity of food systems and the lack of comprehensive predictive models, rapid and simple measurements to predict complex properties in food systems are largely missing. Machine Learning (ML) has shown great potential to improve the classification and prediction of these properties. However, the barriers to collecting large datasets for ML applications still persists. In this paper, we explore different approaches of data annotation and model training to improve data efficiency for ML applications. Specifically, we leverage Active Learning (AL) and Semi-Supervised Learning (SSL) and investigate four approaches: baseline passive learning, AL, SSL, and a hybrid of AL and SSL. To evaluate these approaches, we collect two spectroscopy datasets: predicting plasma dosage and detecting foodborne pathogen. Our experimental results show that, compared to the <i>de facto</i> passive learning approach, advanced approaches (AL, SSL, and the hybrid) can greatly reduce the number of labeled samples, with some cases decreasing the number of labeled samples by more than half. Leverage (statistics) ; Computer science ; Machine learning ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: In Ho Cho | Sinchul Yeom | Tanmoy Sarkar | Tae-Sik Oh
Source: Scientific Reports (article, 2022-02-24)
Field: Materials Science — Materials Chemistry

Unraveling hidden rules behind the wet-to-dry transition of bubble array by glass-box physics rule learner A liquid-gas foam, here called bubble array, is a ubiquitous phenomenon widely observed in daily lives, food, pharmaceutical and cosmetic products, and even bio- and nano-technologies. This intriguing phenomenon has been often studied in a well-controlled environment in laboratories, computations, or analytical models. Still, real-world bubble undergoes complex nonlinear transitions from wet to dry conditions, which are hard to describe by unified rules as a whole. Here, we show that a few early-phase snapshots of bubble array can be learned by a glass-box physics rule learner (GPRL) leading to prediction rules of future bubble array. Unlike the black-box machine learning approach, the glass-box approach seeks to unravel expressive rules of the phenomenon that can evolve. Without known principles, GPRL identifies plausible rules of bubble prediction with an elongated bubble array data that transitions from wet to dry states. Then, the best-so-far GPRL-identified rule is applied to an independent circular bubble array, demonstrating the potential generality of the rule. We explain how GPRL uses the spatio-temporal convolved information of early bubbles to mimic the scientist's perception of bubble sides, shapes, and inter-bubble influences. This research will help combine foam physics and machine learning to better understand and control bubbles. Bubble ; Black box ; Computer science ; Phenomenon ; Bubble point ; Phase transition ; Liquid bubble ; Generality ; Physics ; Theoretical physics ; Mechanics ; Artificial intelligence ; Thermodynamics Pickering emulsions and particle stabilization ; Enhanced Oil Recovery Techniques ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Chonge Wang | B. Dramé | Lucien Niare | Yuegang Fu
Source: International Journal of Optics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Optimization of the Shell Thickness of the ZnO/CdS Core‐Shell Nanowire Arrays in a CZTS Absorber Copper‐zinc‐tin‐sulfide (CZTS) solar cells have now become a topic of interest in the solar power generation industry. These are used as an absorber in the zinc oxide (ZnO)/cadmium sulfide (CdS) core‐shell nanowire arrays, in order to improve the performance of solar cells. The relationship between the average increase in absorption rates and CdS shell thickness (compared to the thin film) reveals that the optimum thickness with the maximum average absorption rate (39.95%) compared to thin film is 30 nm. The cells’ electrical and optical performance was significantly improved with the introduction of graphene between the ZnO and CdS layers. The shell thicknesses for a better performance of these nanowire solar cells were 30 and 40 nm, with almost the same open‐circuit voltage, the similar short‐circuit current density, and efficiency, which were 630 mV, 6.39 mA/cm 2 , and 16.8%, respectively. Furthermore, a minimum reflection of 40% was obtained with these same shell thicknesses. Materials science ; CZTS ; Nanowire ; Shell (structure) ; Cadmium sulfide ; Open-circuit voltage ; Optoelectronics ; Graphene ; Absorption (acoustics) ; Zinc ; Solar cell ; Short circuit ; Tin ; Voltage ; Nanotechnology ; Composite material ; Metallurgy ; Electrical engineering Quantum Dots Synthesis And Properties ; Chalcogenide Semiconductor Thin Films ; ZnO doping and properties
Authors: Chao Zhang | Yue Shi | Zhonghui Wei | Ruiqi Wang | Ting Li | Yubin Wang | Xiaoyan Zhao | Xiaohe Gu
Source: Frontiers in Physics (article, 2022-10-14)
Field: Chemistry — Analytical Chemistry

Hyperspectral estimation of the soluble solid content of intact netted melons decomposed by continuous wavelet transform Netted melons are welcomed for their soft and sweet pulp and strong aroma during the best-tasting period. The best-tasting period was highly correlated with its soluble solid content (SSC). However, the SSC of the intact melon was difficult to determine due to the low relationship between the hardness, color, or appearance of fruit peel and its SSC. Consequently, a rapid, accurate, and non-destructive method to determine the SSC of netted melons was the key to determining the best-tasting period. A hyperspectral model was constructed to estimate the SSC of intact netted melons. The combination of continuous wavelet transform and partial least squares or random forest algorithm was employed to improve the estimation accuracy of the hyperspectral model. Specifically, the hyperspectra of the diffuse reflection and SSC of 261 fruit samples were collected. The sensitivity band was screened based on the correlation analysis and continuous wavelet transform decomposition. The correlation coefficient and RMSE of the random forest regression model decomposed by the continuous wavelet transform were 0.72 and 0.98%, respectively. The decomposition of the continuous wavelet transform improved the correlation coefficient by 5 and 1.178 times at 754 and 880 nm, respectively. The random forest regression model enhanced the determination coefficient by at least 56.5% than the partial least squares regression model, and the continuous wavelet transform decomposition further enhanced the determination coefficient of the random forest regression model by 4.34%. Meanwhile, the RMSE of the random forest regression model was reduced. Therefore, the decomposition of the continuous wavelet transform improved the stability and prediction ability of the random forest regression model. Partial least squares regression ; Hyperspectral imaging ; Mathematics ; Wavelet ; Continuous wavelet transform ; Mean squared error ; Correlation coefficient ; Wavelet transform ; Random forest ; Coefficient of determination ; Regression ; Regression analysis ; Statistics ; Pattern recognition (psychology) ; Artificial intelligence ; Discrete wavelet transform ; Computer science Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Phytochemicals and Antioxidant Activities