jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: B Rituraj | K K Hemanga | B Rabijita | D Pallabi | B Utpal | D Nivedita | D K Jayanta | B Debajit | D Chittarajan
Source: Plant Science Today (article, 2026-03-24)
Field: Chemistry — Organic Chemistry

Application of artificial neural networks and random forest algorithms for forecasting marketing security among agarwood growers in Assam Benefits earned from a single agar (Aquilaria malaccensis) plant make it unique and popular among growers in Assam. The present study was conducted in 2024 to assess the marketing security of agar growers using different classifiers viz., artificial neural network (ANN) and random forest. A total of 420 agar growers were selected following the snowball sampling technique. The findings indicated that 45.95 % of the agar growers had a medium level of marketing security. Results of multiple regression analysis showed that 91.55 % variation in the marketing security of agar growers was explained by seven independent variables, namely age, family occupation, annual income, experience in agar cultivation, indebtedness, economic motivation and access to extension services. As per stepwise regression analysis, eight variables viz., age, family occupation, annual income, experience in agar cultivation, indebtedness, economic motivation, access to extension services and risk orientation had a high influence on marketing security, with an R2 value of 96.36 %. The multilayer perceptron neural network (MPN) model indicated that five independent variables viz., age, annual income, economic motivation, access to extension services and risk orientation, generated the greatest number of activated neurons influencing the marketing security of agar growers through the hidden layer H (1:1) and H (2:3). The normalized graph depicted that annual income had the maximum influence on the marketing security of agar growers. The random forest classifier established that age was the most influential variable. Random forest ; Artificial neural network ; Snowball sampling ; Variables ; Regression analysis ; Multilayer perceptron ; Stepwise regression Wood and Agarwood Research ; Forest Ecology and Conservation ; Medicinal Plants and Neuroprotection
Authors: Kazuhisa Nakasho
Source: ArXiv.org (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

IntSeqBERT: Learning Arithmetic Structure in OEIS via Modulo-Spectrum Embeddings Integer sequences in the OEIS span values from single-digit constants to astronomical factorials and exponentials, making prediction challenging for standard tokenised models that cannot handle out-of-vocabulary values or exploit periodic arithmetic structure. We present IntSeqBERT, a dual-stream Transformer encoder for masked integer-sequence modelling on OEIS. Each sequence element is encoded along two complementary axes: a continuous log-scale magnitude embedding and sin/cos modulo embeddings for 100 residues (moduli $2$--$101$), fused via FiLM. Three prediction heads (magnitude regression, sign classification, and modulo prediction for 100 moduli) are trained jointly on 274,705 OEIS sequences. At the Large scale (91.5M parameters), IntSeqBERT achieves 95.85% magnitude accuracy and 50.38% Mean Modulo Accuracy (MMA) on the test set, outperforming a standard tokenised Transformer baseline by $+8.9$ pt and $+4.5$ pt, respectively. An ablation removing the modulo stream confirms it accounts for $+15.2$ pt of the MMA gain and contributes an additional $+6.2$ pt to magnitude accuracy. A probabilistic Chinese Remainder Theorem (CRT)-based Solver converts the model's predictions into concrete integers, yielding a 7.4-fold improvement in next-term prediction over the tokenised-Transformer baseline (Top-1: 19.09% vs. 2.59%). Modulo spectrum analysis reveals a strong negative correlation between Normalised Information Gain (NIG) and Euler's totient ratio $φ(m)/m$ ($r = -0.851$, $p < 10^{-28}$), providing empirical evidence that composite moduli capture OEIS arithmetic structure more efficiently via CRT aggregation. Modulo ; Circulant matrix ; Chinese remainder theorem ; Moduli ; Primitive root modulo n ; Probabilistic logic ; Modular arithmetic ; Modulo operation Machine Learning in Materials Science ; Time Series Analysis and Forecasting ; Machine Learning in Healthcare
Authors: Xinhu Xu | Wanglong Shi | Liang Zhang
Source: Applied Sciences (article, 2026-03-16)
Field: Chemistry — Spectroscopy

Performance Enhancement Study of WMS-TDLAS System for Online Measurement of High-Concentration CO2 in Flue Gas Accurate and stable measurement of carbon dioxide (CO2) concentrations in industrial flue gases is critical for emissions monitoring and carbon management. The present study developed a wavelength-modulated tunable diode laser absorption spectroscopy (WMS-TDLAS) system for measuring high-concentration carbon dioxide (CO2) in flue gases, covering a range of 3–20% (by volume). To mitigate optical intensity fluctuations caused by particle scattering and detector gain drift in harsh flue gas environments, a normalized second harmonic (2f/1f) detection scheme based on a single-harmonic peak was employed. A digital phase-locked amplification algorithm replaces the conventional hardware lock-in amplifier, enabling simultaneous demodulation of multiple harmonic components and enhancing system integration. A comparison of the digital locking method with a commercial lock-in amplifier reveals that the former demonstrates comparable or superior stability, with relative standard deviations of 0.04% for the 2f signal and 0.02% for the first-harmonic signal. In order to address the sensitivity degradation of WMS-TDLAS at elevated CO2 concentrations, a pressure control strategy was introduced. Maintaining the measurement cell pressure at 70 ± 0.005 kPa resulted in a 2.74-fold enhancement in system sensitivity at 13.01% CO2 and a more than one-order-of-magnitude increase at 20.01% CO2 compared to operation at atmospheric pressure. Concentration measurement error under reduced pressure also decreased from 1.101% to 0.075%. The system exhibited 0.6% repeatability in high-concentration CO2 measurements, signifying its aptitude for industrial flue gas monitoring applications. Flue gas ; Carbon dioxide ; Amplifier ; Sensitivity (control systems) ; Lock-in amplifier ; System of measurement ; Carbon dioxide sensor ; Detector ; SIGNAL (programming language) ; Accuracy and precision Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Water Quality Monitoring and Analysis
Authors: Mahbub Arfah | Husni Husin | Nurdin Ali | Akhyar Akhyar
Source: International Journal of Transport Development and Integration (article, 2026-01-20)
Field: Chemistry — Physical and Theoretical Chemistry

Simpson’s Paradox in the Engine Room: Unraveling Waste Generation on Training Ships Accurate shipboard waste prediction is essential for MARPOL compliance, yet maritime research has predominantly relied on fleet-wide aggregated models that may obscure vessel-specific patterns.The occurrence of statistical paradoxes in hierarchical maritime data has not been systematically examined.This study provides the first systematic documentation of Simpson's Paradox in maritime operational environmental data, using shipboard waste generation as a case study.By analyzing engine running hours and waste generation from six Indonesian training ships, we demonstrate the risks of data aggregation in maritime predictive analytics.We compared fleetwide Generalized Linear Models with individual vessel regression approaches using 66 observations over 11 days.Simpson's Paradox emerged in Auxiliary Engine data: strong individual-level correlations (r = 0.993) were masked by weak fleet-wide correlation (r = 0.416), demonstrating how aggregation can fundamentally misrepresent underlying relationships.Individual ship models achieved substantially higher predictive performance (97.38% and 98.60%) than fleet-wide models (89.5% and 17.3%), with cross-validation (CV) confirming robustness.The findings reveal that fleet-wide aggregation can produce misleading predictions with significant operational consequences for waste storage planning and regulatory compliance.This study establishes the necessity of vessel-specific modeling in maritime environmental management and provides methodological guidance for analyzing hierarchical operational data. Training (meteorology) ; Work (physics) ; Product (mathematics) ; Key (lock) History and advancements in chemistry ; Chemical Safety and Risk Management ; Material Selection and Properties
Authors: Fakulti Teknologi dan Kejuruteraan Mekanikal, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia | Nazhatul Syahirah Aziz | Nadlene Razali | Bushra Rashid | Anita Akmar Kamarolzaman | Hasanudin Hamdan
Source: Jurnal Kejuruteraan (article, 2026-01-30)
Field: Materials Science — Polymers and Plastics

The Effect of Partial Replacement of Fiber Glass in Epoxy Composite by Using Dates Palm Fiber for Mechanical Properties In the field of hybrid composites, the combination of date palm fiber(natural fiber) with fiberglass (synthetic fiber) provides an alternative approach that emphasises on the advantages of both materials to create a composite with unique properties and applications. In this experimental study, hybrid composites were fabricated using different mixing ratio of the epoxy, fiberglass and date palm fiber. Every sample have different mixing ratio of glass fiber and date palm fiber but same epoxy with weight fraction 70% of matrix ratio while 30% balanced consist of glass fiber and dates palm fiber. The study evaluates the impact of this substitution on the overall performance of the hybrid epoxy composites, including aspects like tensile strength, flexural, impact test and morphological structure. For the tensile strength, sample A1(26% Glass Fiber, 4% Dates Palm Fiber) obtained the highest value 87.7MPa. Sample A4(30% Glass Fiber) is the highest value of flexural strength with 218.0MPa while Sample A2 with 101.4KJ/m2 for highest value of the impact test. Then, different structures such as fiber breakage, elamination and debonding are also observed by using Scaning Electron Microscope (SEM). However, the results offer valuable guidance for optimizing the mechanical performance of these composites in various applications It delves into the otential advantages and limitations of using date palm fiber as an alternative to conventional fiber glass in composite materials, aiming to assess the feasibility and efficiency of this substitution in various applications. Glass fiber ; Epoxy ; Ultimate tensile strength ; Flexural strength ; Palm ; Fiber ; Composite number ; Mixing (physics) Natural Fiber Reinforced Composites ; Scientific and Engineering Research Topics ; Mechanical Engineering and Vibrations Research
Authors: Osman Goni Ridwan | Gilles Frapper | Hongfei Xue | Qiang Zhu
Source: ArXiv.org (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Crystal Generation using the Fully Differentiable Pipeline and Latent Space Optimization We present a materials generation framework that couples a symmetry-conditioned variational autoencoder (CVAE) with a differentiable SO(3) power spectrum objective to steer candidates toward a specified local environment under the crystallographic constraints. In particular, we implement a fully differentiable pipeline to enable batch-wise optimization on both direct and latent crystallographic representations. Using the GPU acceleration, this implementation achieves about fivefold speed compared to our previous CPU workflow, while yielding comparable outcomes. In addition, we introduce the optimization strategy that alternatively performs optimization on the direct and latent crystal representations. This dual-level relaxation approach can effectively escape local minima defined by different objective gradients, thus increasing the success rate of generating complex structures satisfying the target local environments. This framework can be extended to systems consisting of multi-components and multi-environments, providing a scalable route to generate material structures with the target local environment. Maxima and minima ; Differentiable function ; Pipeline (software) ; Scalability ; Autoencoder ; Relaxation (psychology) ; Optimization problem Machine Learning in Materials Science ; Calcium Carbonate Crystallization and Inhibition ; Topology Optimization in Engineering
Authors: Seshavatharam U V S | Lakshminarayana S | Gunavardhana Naudu T
Source: Springer Link (Chiba Institute of Technology) (article, 2026-01-07)
Field: Chemistry — Organic Chemistry

Empirical formula for specific heat of solids based on atomic constants and a universal subzero limiting temperature We present a novel empirical approach to understand the specific heat capacities of solids by introducing a characteristic subzero reference temperature derived from fundamental atomic constants. Using the total energy of the electron associated with the Bohr radius and the proton- electron mass ratio, we define a universal lower temperature limit as 3.87862×10⁻¹⁴ K. Based on this temperature and a room temperature of 300 K, we propose a simple empirical relation for specific heat capacity expressed in terms of atomic mass number A: C = 25416.852/A J/kg.K. The formula accurately predicts experimental specific heat capacities across various elemental solids, providing a fundamental link between atomic-scale constants and macroscopic thermodynamic behaviour. Considering ‘grams to kilograms’ and ‘moles to kilomoles’ and with reference to Dulong-Petit law, it can be expressed as, C = [3 x 1000 x R]/A. It needs further study. Specific heat ; Bohr radius ; Limiting ; Heat capacity ; Limit (mathematics) ; Atomic mass ; RADIUS ; Constant (computer programming) ; Work (physics) Chemical Thermodynamics and Molecular Structure ; High-pressure geophysics and materials ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Seshavatharam U V S | Lakshminarayana S | Gunavardhana Naudu T
Source: EPJ Web of Conferences (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Empirical formula for specific heat of solids based on atomic constants and a universal subzero limiting temperature We present a novel empirical approach to understand the specific heat capacities of solids by introducing a characteristic subzero reference temperature derived from fundamental atomic constants. Using the total energy of the electron associated with the Bohr radius and the proton- electron mass ratio, we define a universal lower temperature limit as 3.87862×10⁻¹⁴ K. Based on this temperature and a room temperature of 300 K, we propose a simple empirical relation for specific heat capacity expressed in terms of atomic mass number A: C = 25416.852/A J/kg.K. The formula accurately predicts experimental specific heat capacities across various elemental solids, providing a fundamental link between atomic-scale constants and macroscopic thermodynamic behaviour. Considering ‘grams to kilograms’ and ‘moles to kilomoles’ and with reference to Dulong-Petit law, it can be expressed as, C = [3 x 1000 x R]/A . It needs further study. Bohr radius ; Specific heat ; Limiting ; Heat capacity ; Atomic mass ; Atomic number ; Limit (mathematics) ; Bohr model ; RADIUS Chemical Thermodynamics and Molecular Structure ; High-pressure geophysics and materials ; Thermodynamic and Structural Properties of Metals and Alloys
Authors: Miguel Gurumendi-Noriega | Mariela González-Narváez | John Ramos-Veliz | Andrea Rosado-Moncayo | Boris Apolo-Masache | Luis Domínguez-Granda | Julio Bonilla | Christine Van der heyden
Source: Water (article, 2026-03-27)
Field: Materials Science — Biomaterials

Environmental Drivers of Algal Blooms in a Tropical Coastal Riverine System: A Multivariate Statistical Approach Nutrient inputs from human activities, such as agriculture and sewage discharge, influence algal blooms in water bodies. In Ecuador, the Daule River receives wastewater discharges. In addition, poor agricultural practices, including the unsuitable use of fertilisers in combination with soil erosion and surface runoff processes, increase the nutrient load to the river. Considering this, the objective of this study was to evaluate environmental and biological variables using statistical analysis to identify the parameters that influence algal blooms in the main stem of the Daule River. The methodology consisted of two phases: (i) data collection, including water sampling and laboratory work for the analysis of nutrients and phytoplankton, and (ii) statistical analysis, which includes univariate, bivariate, inferential and multivariate analysis (STATICO technique). The results showed that pH and dissolved oxygen were the main drivers of diatoms (Polymyxus coronalis and Aulacoseira granulate) and the charophyte Mougeotia sp. Similarly, ammonium-N was the main driver of the diatom Ulnaria ulna and the cyanobacteria Planktothrix cf. agardhii. The outcomes of this study identified the main environmental variables driving blooms of the five most abundant species, providing a basis for the development of ecological models in the context of land use and climate change. Nutrient ; Context (archaeology) ; Algal bloom ; Surface runoff ; Diatom ; Water quality ; Hydrology (agriculture) ; Water pollution ; Eutrophication Diatoms and Algae Research ; Aquatic Ecosystems and Phytoplankton Dynamics ; Freshwater macroinvertebrate diversity and ecology
Authors: Anil Sharma | Dr. Alok Choudhary
Source: INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND APPLIED SCIENCES (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Microstructure Refinement and Mechanical Isotropy Enhancement in WAAM Ti–6Al–4V via Interpass Rolling inancial technology, or FinTech, has emerged as a transformative force in the Indian banking sector, reshaping how financial services are offered, accessed, and managed. With India aiming to become a developed nation by 2047, the integration of FinTech solutions into banking operations has become a strategic necessity. This paper examines the impact of key technological innovations, including digital payment systems, artificial intelligence-based analytics, and blockchain-enabled platforms, on the modernization of Indian banks. Employing a mixed-method approach based on secondary data from regulatory reports, industry analyses, and financial publications up to 2025, the study evaluates both the advantages and challenges of adopting FinTech. Findings reveal that these innovations improve operational efficiency, promote financial inclusion, and enhance customer engagement, while also introducing regulatory, cybersecurity, and competitive challenges. The study concludes by suggesting strategies for banks and policymakers to ensure FinTech adoption leads to a secure, inclusive, and resilient banking system. Modernization theory ; Payment ; Transformative learning ; Financial services ; Banking industry ; Key (lock) Titanium Alloys Microstructure and Properties ; Magnesium Alloys: Properties and Applications ; Orthopaedic implants and arthroplasty
Authors: Karolina Głogowska | Janusz Sikora
Source: Materials (article, 2026-01-08)
Field: Materials Science — Polymers and Plastics

Predicting the Effect of Mould Material and Design on the Efficiency of the LLDPE Rotational Moulding Process: Thermal and Time Analyses This study evaluates the effect of mould material and mould wall thickness on the thermal behaviour and cycle time of the LLDPE rotational moulding process by using RotoSim-based numerical simulation. This study was performed using three different metallic materials (low-carbon steel, brass, and aluminium), mould wall thicknesses of 3, 5, and 8 mm, and oven temperatures of 230, 250, and 270 °C. The simulations demonstrate that both mould material and wall thickness significantly influence the temperature evolution in the mould cavity and the overall cycle duration. Aluminium moulds provided the mould-cavity temperature closest to the oven conditions, the longest time with the polymer in the plastic/molten state, and the shortest total cycle time compared with steel and brass moulds. Increasing the mould wall thickness prolonged the cycle time for all materials, with the extension occurring primarily during the cooling stage. For a 3 mm wall thickness at 230 °C, the shortest cycle time was 2900 s (Al/3/230) and the longest was 3300 s (S/3/230). For an 8 mm wall thickness at 270 °C, the shortest cycle time was 4400 s (Al/8/270) and the longest was 4900 s (S/8/270). These results indicate that selecting an appropriate mould material and wall thickness can be an effective approach to shortening the cycle time and improving the efficiency of LLDPE rotational moulding. Brass ; Aluminium ; Thermal ; Injection moulding ; Material properties ; Dwell time ; Mold Polymer Foaming and Composites ; Injection Molding Process and Properties ; Epoxy Resin Curing Processes
Authors: Xin Fan | Yang Zhang | Ming Li | Zhilin Tuo | Yibei Wu | Xu Su | Haiyang Wang | Desheng Zhou
Source: Processes (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Study on Corrosion Mechanisms and Inhibitor Dosing Scheme for Tight Sandstone Gas Wells in the X Block of the Ordos Basin With the exploitation of tight sandstone gas, the corrosion problem of wellbores in the X block of the Ordos Basin has become increasingly severe, necessitating the implementation of effective measures to mitigate tubing corrosion and enhance corrosion inhibition efficiency. This study conducted field corrosion monitoring in conjunction with laboratory experiments, employing weight loss method, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD) to comprehensively characterize the corrosion of gas wells from both macro and micro perspectives. The results show that the gas wells in the X block of the Ordos Basin are exposed to a complex corrosion environment, where the electrochemical corrosion risk in the aqueous phase and the acidic gas corrosion risk in the gas phase coexist, posing a potential threat to wellbore integrity. Corrosion in X-1 and X-2 wells is mainly attributed to CO2, while corrosion in X-3 well is primarily caused by sulfides. The field application of corrosion inhibitor M exhibited significant corrosion inhibition effects on steels, with the best performance at a dosage of 2000 mg/L. Based on experimental data, a corrosion inhibitor dosage prediction model for the X block gas wells was constructed. By increasing the dosing frequency and reducing the dosing concentration, the optimized dosing scheme can annually save approximately 566.4 L of corrosion inhibitor per well, providing a scientific basis for extending the service life of the gas well tubing. Given the prevalence of CO2- and H2S-induced corrosion in many global reservoirs, these findings provide valuable insights for corrosion management in similar international oil and gas fields, enhancing the broader applicability of the study. Corrosion ; Dosing ; Corrosion inhibitor ; Pitting corrosion ; Block (permutation group theory) ; Fossil fuel ; Structural basin Corrosion Behavior and Inhibition ; Erosion and Abrasive Machining ; Structural Integrity and Reliability Analysis