jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Baris Ördek | Zaida Ortega | Jake Kelly-Walley | Mark McCourt | Christian Spreafico
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2026-02-28)
Field: Materials Science — Polymers and Plastics

Parametric LCA for optimizing rotational moulding of composite products to support eco-design Abstract Rotational moulding (RM) is a versatile technique for producing plastic products, but its environmental sustainability needs deeper analysis. Existing life cycle assessment (LCA) studies are often limited, focusing on single materials and ignoring variations in process parameters. This study introduces a parametric LCA model designed to minimize the environmental impact of RM composite products by optimizing material composition, specifically blends of polyethylene and natural fibre (e.g., abaca). The model allows users to set control parameters, including product dimensions, mould material and thickness, energy consumption, from gas furnace and motor, number of moulds, geographical location, and the impact category to assess. Applied to case studies with abaca fibre composites, the model demonstrated that optimizing the fibre-to-polymer ratio can significantly reduce environmental impacts. These reductions vary depending on the input parameters, confirming that a one-size-fits-all approach is inadequate. The model’s ability to process multiple heterogeneous factors makes it a robust tool for eco-assessment. It supports systematic sensitivity analysis and offers practical, flexible guidance for eco-design in RM, particularly for multi-material products. Its strengths, flexibility, low cost, and ease of integration make it especially valuable for small and medium-sized enterprises with diverse product lines, enabling more sustainable decision-making without requiring extensive resources. Graphical abstract Life-cycle assessment ; Parametric statistics ; Process (computing) ; Sustainability ; Product (mathematics) ; Composite number ; Sensitivity (control systems) ; Product design ; Material efficiency ; Automotive industry Polymer Foaming and Composites ; Rheology and Fluid Dynamics Studies ; Injection Molding Process and Properties
Authors: P. Kiełczyński | Piotr Szymański | Krzysztof Wieja | A. Balcerzak | S. Ptasznik
Source: Applied Sciences (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Investigation and Discrimination of Hand-Deboned and Mechanically Separated Pork Meat Using Ultrasonic Velocity Measurements In this paper, we present an original ultrasonic technique to investigate and discriminate between different kinds of pork meat, i.e., hand-deboned (HD) meat and mechanically separated (MSM) meat. To this end, we measured the speed c of high-frequency f = 5 MHz ultrasonic waves propagating in the examined pork meat samples. The measured speed of longitudinal ultrasonic waves ranged from 1578 to 1610 m/s. Significant statistical differences were found between the speed of ultrasonic waves in the HD pork loin and pork neck, and those in various types of MSM. The newly proposed measurement (analytical) method, based on the determination of ultrasonic velocity, can be effectively applied to distinguish between manually separated (HD) and mechanically separated (MSM) pork meat. Importantly, the obtained experimental results were supported by a statistical analysis, showing highly significant correlations between the speed of ultrasonic waves and the content of water, protein, fat and sodium in the measured pork meat samples. Ultrasonic sensor ; Loin ; Statistical analysis ; Ultrasound ; Ultrasonic testing Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Food Supply Chain Traceability
Authors: Prabesh Joshi
Source: ChemRxiv (article, 2026-03-30)
Field: Chemistry — Analytical Chemistry

GA-Optimized Bin Difference Partial Least Squares Regression for Spectroscopic Calibration Near-infrared (NIR) spectroscopy is a cornerstone of non-destructive analysis, yet its complexity necessitates sophisticated multivariate modeling.While partial least squares regression (PLSR) is the standard approach, its efficacy depends heavily on independent, sequential steps of preprocessing and variable selection.A significant research gap exists in current workflows: most selection methods focus on absolute intensities at specific wavelengths, failing to exploit the relational information and contrast between spectral regions which are often more resistant to baseline shifts.Furthermore, treating preprocessing and variable selection as isolated tasks ignores their functional interdependencies.This study introduces Genetic Algorithm-Optimized Bin Difference PLSR (GA-OBD PLSR), a framework that unifies feature construction and variable selection.By representing spectra as the difference between integrated intensities of two optimized spectral bins, the method captures relational absorbance patterns directly in the native spectral domain.A genetic algorithm jointly optimizes bin locations, widths, and feature sparsity within a nested crossvalidation framework to ensure unbiased performance estimates.Evaluated across diverse pharmaceutical and agricultural datasets, GA-OBD PLSR consistently reduced model dimensionality by 60 to 80 percent while maintaining or enhancing predictive accuracy.The most substantial gains occurred on raw spectra, where the bin-difference representation functioned as an inherent, targeted noise-reduction step.Analysis of selected features confirmed that the algorithm identifies chemically meaningful regions, while DModX diagnostics showed superior orthogonality to non-predictive variation.Beyond improved generalization, the reduction in predictor variables yielded a 7.36x speedup in prediction time, with the reduced model.These results demonstrate that GA-OBD PLSR provides a robust, interpretable, and computationally tractable alternative to full-spectrum modeling. Bin ; Calibration ; Partial least squares regression ; Least-squares function approximation ; Regression ; Linear regression Spectroscopy and Chemometric Analyses ; Fault Detection and Control Systems ; Advanced Statistical Methods and Models
Authors: Dedy Harianto | Bagus Pradita | Hamdan S. Bintang | Hendri Pujianto
Source: Tekstil ve Mühendis (article, 2026-03-31)
Field: Materials Science — Polymers and Plastics

OPTIMIZING YARN QUALITY THROUGH CLEANING INTERVENTIONS: EFFECTS OF WIRE CYLINDER AND STATIONARY FLAT MAINTENANCE ON NEPS COUNT AND UNEVENNESS PERCENTAGE (U%) IN CARDED SLIVERS Maintaining yarn quality is crucial in textile manufacturing, where neps count and sliver unevenness percentage (U%) significantly impact production consistency and fabric performance. This study evaluates the effect of cleaning wire cylinders and stationary flats on neps count and U% in carded slivers, providing a cost-effective alternative to advanced predictive maintenance techniques. Using a randomized experimental design, measurements were conducted on two production lines (B2 and B3) before and after cleaning interventions. Neps count and U% were assessed using the Advanced Fiber Information System (AFIS) and Uster Tester, respectively. Statistical analysis, including t-tests and effect size calculations, revealed significant reductions in neps count (B2: 76.08 to 39.50; B3: 88.00 to 40.23) and U% (B2: 7.19% to 4.34%; B3: 8.27% to 4.25%), meeting industry benchmarks. The findings demonstrate that routine cleaning enhances fiber alignment, reduces defects, and improves production consistency without the high costs of predictive maintenance systems. This study highlights the importance of structured maintenance practices and provides a foundation for optimizing cost-effective and sustainable textile manufacturing processes. Yarn ; Overall equipment effectiveness ; Textile ; Consistency (knowledge bases) ; Quality (philosophy) ; Fiber ; Production (economics) Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Research in Cotton Cultivation
Authors: Xiuzhi Luo | Shihui Tan | Xin Shi | Jianfei Xing | Xiuying Tang
Source: Journal of Food Process Engineering (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Prediction of the Key Rheological Characteristics of Wheat Dough Based on the Spatial Deformation Difference Before and After the Bursting of the Dough ABSTRACT Dough toughness and extensibility are key rheological properties directly linked to the quality of dough‐based products. Thus, this research employed a custom‐built device to inflate dough sheets into bubbles, with a 3D imaging system recording real‐time point cloud data of these formations. The investigation specifically targeted point cloud imagery capturing both the frame displaying maximum spatial deformation height in dough bubbles and its subsequent frame. Four novel computational methods were developed to quantify four distinct deformation characteristics from the selected two‐frame bubble point cloud images. These parameters were subsequently fed into three machine learning architectures for autonomous prediction of dough toughness and extensibility. Experimental findings revealed optimal detection accuracy when utilizing deformation feature n's immediate recovery index ( E n ) with the ELM algorithm, attaining R p coefficients of 0.882 and0.940 for extensibility and toughness assessments, respectively. Rheology ; Toughness ; Extensibility ; Deformation (meteorology) ; Bubble ; Point (geometry) Spectroscopy and Chemometric Analyses ; Food composition and properties ; Industrial Vision Systems and Defect Detection
Authors: Víctor Sánchez | Sawyer Remillard | Bachir A. Abeid | Lehu Bu | Spencer Breyngelson | Jin Yang | Jon Estrada | Mauro Rodriguez
Source: Soft Matter (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Hierarchical Bayesian constitutive model selection for high-strain-rate soft material characterization The high-fidelity characterization of soft, tissue-like materials under ultra-high-strain-rate conditions is critical in engineering and medicine. Still, it remains challenging due to limited optical access, sensitivity to initial conditions, and... Characterization (materials science) ; Selection (genetic algorithm) ; Sensitivity (control systems) ; Bayesian probability ; Model selection ; Soft sensor ; Bayesian inference High-Velocity Impact and Material Behavior ; Elasticity and Material Modeling ; Cellular and Composite Structures
Authors: Amor Judith A. CABANESAS
Source: Engineering and Technology Journal (article, 2026-02-28)
Field: Materials Science — Polymers and Plastics

Mechanical Performance of Concrete Reinforced with Hybrid Natural Plant Fibers The increasing demand for sustainable construction materials has encouraged the exploration of natural fibers as alternative reinforcement in concrete. This study investigates the mechanical performance of concrete reinforced with hybrid natural fibers derived from Sansevieria trifasciata (Snake Plant) and Ficus elastica (Rubber Tree). Concrete specimens were prepared with fiber contents of 0%, 0.25%, 0.375%, and 0.5% by volume and cured for 14 days under controlled conditions. The compressive and flexural strengths were determined using standard testing procedures, and the results were analysed using descriptive statistics and One-Way Analysis of Variance (ANOVA). Results showed that the addition of hybrid natural fibers significantly affected the compressive strength of concrete (F = 23.56, p = 0.00025), with strength decreasing as fiber content increased. The control specimen (0% fiber) exhibited the highest compressive strength (18.41 MPa), while the 0.5% fiber content yielded the lowest mean compressive strength (7.39 MPa) and the highest variability (CV ≈ 38.97%). In contrast, flexural strength showed no statistically significant difference among the groups (F = 0.44, p = 0.73), indicating that fiber incorporation did not substantially enhance bending performance at 14 days of curing. Overall, the findings suggest that while hybrid Snake Plant and Rubber Tree fibers may contribute to crack-bridging potential, their inclusion at the tested proportions does not improve early-age compressive strength and may reduce mechanical consistency at higher fiber contents. Further studies are recommended to optimize fiber treatment, proportioning, and curing duration to improve performance outcomes. Compressive strength ; Flexural strength ; Fiber ; Curing (chemistry) ; Natural rubber ; Natural fiber Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Microbial Applications in Construction Materials
Authors: O.V. Bisikalo | V.G. Storchak | Yu.S. Zditovetskyi | G.V. Goryachev
Source: Optoelectronic Information-Power Technologies (article, 2026-01-12)
Field: Chemistry — Analytical Chemistry

Method for searching and analyzing e-additives and other components in food products of the population The research is dedicated to the development of a formal method and a corresponding intelligent IT-system that allows consumers to automatically determine the content of food additives (E-additives) and provide an assessment of potential health risks based on EFSA and WHO data by photographing a product label. To implement the proposed approach, a combination of Natural Language Processing (NLP) methods for label text analysis, Computer Vision (CV) for ingredient recognition, and Machine Learning (ML) for classifying their hazard based on EFSA and WHO data was used. The experimental results showed that the system achieved an accuracy of 94% in recognizing E-additives on the test dataset (10,000 images). It was found that 23% of the analyzed products contain additives with potential allergenicity (for example, E320, E621). Furthermore, highly processed products contain a relatively larger number of additives, which is fully consistent with the results of previous studies in the field of food toxicology. The proposed method and the technological means for its implementation are promising for mass monitoring of food quality and consumer informing. Food products ; Product (mathematics) ; Ingredient ; Population ; Product testing ; Quality (philosophy) ; Hazard ; Food safety Dye analysis and toxicity ; Identification and Quantification in Food ; Food Industry and Aquatic Biology
Authors: Nurchim - | Muljono | Edi Noersasongko | Ahmad Zainul Fanani | Deshinta Arrova Dewi
Source: International Journal of Advanced Computer Science and Applications (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Few-Shot Semantic Meta-Learning Framework with CRF for Skill Entity Recognition in Open Innovation Ecosystems The accelerating pace of digital transformation is reshaping labour-market dynamics, driving the emergence of new competencies, and intensifying the need for scalable skill-intelligence systems within open innovation ecosystems. Yet, research on Indonesian Named Entity Recognition (NER) remains limited for skill-extraction tasks, especially in low-resource contexts where annotated data are scarce and novel skill expressions evolve rapidly. To address this gap, this study contributes to applied Natural Language Processing (NLP) by introducing the Few-Shot Semantic Meta-Learning framework with CRF (FSM-CRF) for Indonesian skill entity recognition, which integrates semantic span representations, episodic meta-learning, and BIO-constrained CRF decoding to enhance prototype stability and entity-boundary precision for complex, multi-token skill expressions. Using the NERSkill.id dataset, the proposed model is evaluated under a 3-way, 10-shot episodic setting and achieves a micro-F1 of 73.84%, outperforming traditional supervised approaches (IndoBERT fine-tuning, BiLSTM-CRF) and existing few-shot baselines. Ablation experiments further demonstrate that semantic span modelling and structured CRF inference play pivotal roles in improving robustness, while meta-learning strengthens adaptability across diverse and evolving skill categories. From an open innovation perspective, this framework offers a data-efficient solution for dynamic competency mapping, reducing dependence on costly annotation pipelines and enabling continuous updates to workforce skill taxonomies. Overall, the findings highlight semantic meta-learning as a promising foundation for next-generation skill-intelligence infrastructures that support AI-enabled innovation management, strategic workforce planning, and evidence-informed policy design. Scalability ; Annotation ; Inference ; Pace ; Adaptability ; Consistency (knowledge bases) ; Indonesian ; Knowledge base ; Semantic memory Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Topic Modeling
Authors: Velmurugan Ganesan | Jasgurpreet Singh Chohan | Gowthami Duttaluru | Vasa Anil Kumar | Singarapu Lokesh Sai | Bommepalli Harsha Vardhan Reddy
Source: Springer Link (Chiba Institute of Technology) (article, 2026-03-03)
Field: Materials Science — Polymers and Plastics

Statistical Modelling and Mechanical Behaviour of Biochar-Reinforced Hybrid Composites: A Response Surface Approach The present research investigates the mechanical properties (tensile, flexural & impact) of kenaf-flax-based hybrid composites with the incorporation of various weight percentages of coconut shell biochar. The hybrid composites were fabricated through compression molding with the following parameters: varying flax to kenaf ratios (0-100%), biochar content (2-6 wt.%), and fiber loading (20-40 wt.%) according to a Response Surface Methodology (RSM) and Box-Behnken Design (BBD). The ANOVA analysis of the composites confirmed that the quadratic models were statistically significant for high coefficients of determination, i.e., R²>98. Among the three variables, the flax-to-kenaf weight ratio and fiber loading were found to be the maximum leading variables, while the biochar also provides the best results up to 4 wt.%. The RSM method demonstrates the maximum mechanical properties (tensile 110 MPa, flexural 150 MPa, and impact 13 kJ/m²) were achieved at 60% of flax, 40% of kenaf, 35 wt.% of fiber loading, and 4 wt.% of biochar. The fracture analysis of the hybrid composites was evaluated through scanning electron microscopy (SEM). The obtained results of hybrid composites reveal that the inclusion of biochar content enhanced the performance of natural fiber composites, providing sustainable alternatives for structural applications. Kenaf ; Response surface methodology ; Flexural strength ; Compression molding ; Fiber ; Composite number ; Molding (decorative) ; Scanning electron microscope Natural Fiber Reinforced Composites ; Hygrothermal properties of building materials ; Bamboo properties and applications
Authors: Velmurugan Ganesan | Jasgurpreet Singh Chohan | Gowthami Duttaluru | Vasa Anil Kumar | Singarapu Lokesh Sai | Bommepalli Harsha Vardhan Reddy
Source: EPJ Web of Conferences (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Statistical Modelling and Mechanical Behaviour of Biochar-Reinforced Hybrid Composites: A Response Surface Approach The present research investigates the mechanical properties (tensile, flexural &amp; impact) of kenaf-flax-based hybrid composites with the incorporation of various weight percentages of coconut shell biochar. The hybrid composites were fabricated through compression molding with the following parameters: varying flax to kenaf ratios (0-100%), biochar content (2-6 wt.%), and fiber loading (20-40 wt.%) according to a Response Surface Methodology (RSM) and Box-Behnken Design (BBD). The ANOVA analysis of the composites confirmed that the quadratic models were statistically significant for high coefficients of determination, i.e., R²&gt;98. Among the three variables, the flax-to-kenaf weight ratio and fiber loading were found to be the maximum leading variables, while the biochar also provides the best results up to 4 wt.%. The RSM method demonstrates the maximum mechanical properties (tensile 110 MPa, flexural 150 MPa, and impact 13 kJ/m²) were achieved at 60% of flax, 40% of kenaf, 35 wt.% of fiber loading, and 4 wt.% of biochar. The fracture analysis of the hybrid composites was evaluated through scanning electron microscopy (SEM). The obtained results of hybrid composites reveal that the inclusion of biochar content enhanced the performance of natural fiber composites, providing sustainable alternatives for structural applications. Kenaf ; Response surface methodology ; Flexural strength ; Compression molding ; Fiber ; Composite number ; Molding (decorative) ; Biochar ; Scanning electron microscope Natural Fiber Reinforced Composites ; Bamboo properties and applications ; Hygrothermal properties of building materials
Authors: Deep Pankajbhai Mehta
Source: ArXiv.org (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Placement Semantics for Distributed Deep Learning: A Systematic Framework for Analyzing Parallelism Strategies Training large language models requires distributing computation across many accelerators, yet practitioners select parallelism strategies (data, tensor, pipeline, ZeRO) through trial and error because no unified systematic framework predicts their behavior. We introduce placement semantics: each strategy is specified by how it places four training states (parameters, optimizer, gradients, activations) across devices using five modes (replicated, sharded, sharded-with-gather, materialized, offloaded). From placement alone, without implementation details, we derive memory consumption and communication volume. Our predictions match published results exactly: ZeRO-3 uses 8x less memory than data parallelism at 1.5x communication cost, as reported in the original paper. We prove two conditions (gradient integrity, state consistency) are necessary and sufficient for distributed training to match single-device results, and provide composition rules for combining strategies safely. The framework unifies ZeRO Stages 1-3, Fully Sharded Data Parallel (FSDP), tensor parallelism, and pipeline parallelism as instances with different placement choices. Parallelism (grammar) ; Semantics (computer science) ; Pipeline (software) ; Data parallelism ; Computation ; Scheme (mathematics) ; State (computer science) ; Principle of compositionality Machine Learning in Materials Science ; Advanced Neural Network Applications ; Topic Modeling