About the Journal
Call for Papers
International Scientific Technical and Economic Research
Dear Authors.
We cordially invite you to submit your manuscripts to the journal International Scientific
Technical and Economic Research. The journal is an international academic publication with the international issue number: ISSN 2959-1309. It is dedicated to the publication of high quality research in the fields of science, technology and economics.
We welcome original research papers in all fields, including but not limited to the
following topics:
1. scientific research: research results in the fields of physics, chemistry, biology, earth
sciences, mathematics, etc;
2. technological developments: technological innovations in the fields of engineering,
computer science, information technology, biotechnology, etc;
3. economic research: economic theory and empirical research in the fields of macroeconomics, microeconomics, international economics, finance, etc;
4. interdisciplinary: interdisciplinary research in multiple fields, such as the relationship
between science and technology innovation and economic development, the impact of
technology applications on society and the environment, etc.
Please follow the following requirements for the call for papers:
1. Originality: Your submitted paper must be original, with a repetition rate of less than
20%, and not published or submitted in other journals or conferences.
2. Academic quality: We value academic rigor and quality, so please ensure that your
research methods, data analysis, and paper structure meet academic standards.
3. Article format: Please write and format your paper according to the journal's author
guidelines. We accept submissions in English language only.
4. Submission method: Submit your paper via our email address. email (istaer@126.com).
5. Collaboration and number of authors: We encourage collaborative research, but please
ensure that all authors have substantial contributions and are clearly listed in the paper.
6. Review process: Our review process includes peer review to ensure fairness and
anonymity of the review. Please wait patiently for the review results and make revisions based
on the review comments, all of which will be returned in the email.
7. The journal charges a small page fee according to the quality of the paper; it is
recommended to indicate the fund project to the teacher; if there is a fund project, the page fee will be significantly reduced and other publication and mailing costs will be charged, and the publication cycle will take about 3 months.
We are committed to completing the review process in a short time and providing high
quality publication services. Successfully published papers will be published in full in both the print and online versions of the journal, providing a valuable reference for the global academic community and industry.
If you have any questions or require further information, please feel free to contact our
editorial team. We look forward to receiving your valuable submissions!
Good luck!
Editorial Board of International Scientific Technical and Economic Research
Announcements
An interpretable machine learning framework for automated mitosis detection in gastrointestinal stromal tumors
Digital capability and rural household development resilience: A double machine learning approach
Classifying demonstration format and presenter identity in imitative learning task: EEG-based explainable machine learning
A blockchain solution for decentralized training in machine learning for IoT
Comprehensive Serum Glycopeptide Spectrum Analysis with Machine Learning for Non-Invasive Early Detection of Gastrointestinal Cancers
Integrating machine learning and CFD for enhanced trailing edge serration design on a NACA 0012 wind turbine blade
Predictive modeling and stability analysis of tetradecanoic acid-modified zinc/zinc oxide coatings using machine learning
Determination of (p, n) reaction cross-section for various nuclei at 7.5 MeV by using machine learning models
Prediction of fast-charging capabilities in LiFePO₄/graphite lithium-ion batteries using internal resistance and machine learning
Tunable graphene-based metamaterial thermal absorber design for thermal sensing applications with behaviour prediction using machine learning
Determination of mechanical properties of physical vapor deposition tool coatings using machine learning
Heuristic Custom Similarity Index (HCSI): A novel machine learning approach for link prediction
Sinkhole susceptibility mapping in Greene county, Missouri through machine learning algorithms
Machine learning based prediction of nitrogenous product yield in biomass pyrolysis oil
Machine learning modeling of melt-spinning for yarn property prediction
OpenPyStruct: Open-source toolkit for machine learning-driven structural optimization
Multidimensional strategy for discovering saltiness-enhancing peptides in shrimp heads integrating ultra-high pressure hydrolysis and machine learning
Machine confirming: Validating financial theories with transfer learning
A multilevel machine learning algorithm to predict session-by-session outcome for patients receiving cognitive-behavioural therapy
AI-driven extraction and intelligent retrieval of missionary archives in Malabar: advancing preservation and accessibility with machine learning
Pyrolysis characteristics of microalgae and machine learning modelling for activation energy
Application of machine learning in python for temporal groundwater level prediction
HyperGraph-based Minimax Probability Machines for Semi-Supervised Learning
Exploring the coupling of ecosystem services and human well-being: evidence from Chinese cities through interpretable machine learning
Beyond the last surprise: Reviving PEAD with machine learning and historical earnings
Building energy prediction in a changing climate: an interpretable machine learning approach
2D and 3D QSAR-Based Machine Learning Models for Predicting Pyrazole Corrosion Inhibitors for Mild Steel in HCl
Machine learning-driven insights into the microstructure and properties of high-entropy alloys
Moisture content prediction in durian husk biomass via near infrared spectroscopy coupled with aquaphotomics and explainable machine learning
Foreign aid's double-edged sword effect on carbon emissions: A machine learning approach
A rapid method for determination of plasmid types and transformations based on combining the Fourier-transform infrared spectral data with machine learning
New models for estimating pure shear fracture toughness of confined quasi-brittle PTS specimens: Empirical and machine learning framework
Machine learning-based damage classification and comparative life cycle assessment of Origami Pill Bug for emergency shelters
Roughness-informed machine learning – A call for fractal and fractional calculi
Prospectivity mapping and exploration targeting for sediment-hosted Pb–Zn deposits in NW Guizhou of SW China using an integrated machine learning framework
Determining the geographical origin of Fritillaria by terahertz spectroscopy and machine learning algorithms
Sysmon event logs for machine learning-based malware detection
Advancing ovarian cancer outcomes with CTGAN-enhanced hybrid machine learning approach
Material property prediction of perovskite oxides based on machine learning
A machine learning based calibration method for differential scanning calorimetry
A machine learning assisted approach to classify rose species and varieties with laser induced breakdown spectroscopy
Adversarial learning enhanced multi-agent cooperative reinforcement learning for parallel batch processing machine scheduling in wafer fabrication
Exploring the Nonlinear and Spatial Effects of Urban Activity Heterogeneity on the Nighttime Thermal Environment Using Machine Learning and GWR
Identification of 37 kinds of herbs containing oligosaccharides by combining data fusion and machine learning
A comprehensive prediction framework for offshore downhole collapse pressure based on machine learning and multi-attribute decision analysis: Insights from the East China Sea,
Sampling strategies for machine learning-based linear erosion studies: a review approaching contributing area
Enhanced prediction of ammonia nitrogen levels in reverse osmosis brine from a full-scale water reclamation plant using machine learning
Using machine learning to predict child active transportation prevalence
Application of latent variable models for hidden pattern identification and machine learning prediction improvement in structural engineering
Machine learning from a “Universe” of signals: The role of feature engineering
Machine learning prediction of heat capacity of polymers as a function of temperature
Machine Learning-Based Prediction of Stand Biomass Using Multi-Source Environmental Data in the Hulunbuir Mixed Forests, Inner Mongolia
Quantitative radiomic analysis of computed tomography scans using machine and deep learning techniques accurately predicts histological subtypes of non-small cell lung cancer: A retrospective analysis
Prediction and optimization of transverse thermal conductivity of green fiber composites based on interpretable machine learning
Recent advances in the high entropy materials for advanced energy storage with machine learning
Discovering Biomarkers for Asymptomatic Tuberculosis via Olink Proteomics and Machine Learning
Application of machine learning in analysing flexible plate governed by the Mooney Rivlin model
Reverse design of cylindrical shell metasurface structures based on machine learning methods
Applied machine learning for adiabatic gas–liquid flow pattern prediction in small diameter circular tubes: Effect of dimensionality reduction
Machine learning integrated approach for modeling crop production in Bangladesh
Evaluating generalization of arm movement identification using machine learning: From structured to semi-structured environments
Predicting the surface tension of mixed surfactant systems through theory and machine learning
Machine learning models to predict hot carcass weight of Angus feedlot cattle at induction in commercial conditions
Reconstructed precipitation isotopes in China during the past six decades based on machine learning
Machine learning modelling of sonochemical systems using physically-derived dimensionless groups
Agricultural drought forecasting using remote sensing: A hybrid modeling framework by integrating wavelet transformation and machine learning techniques
Pressure-induced structural changes in liquid SiO2 investigated by molecular dynamics and machine learning approaches
Particle number emissions on mountainous roads: machine learning insights from on-road testing
Precision nitrogen management for maize based on crop modeling, remote sensing and machine learning
Machine Learning surrogate models for Hertzian contact stress prediction in gear design: A comparative study of multiple approaches
Machine learning-optimized porous thermally responsive SS-PCM with switchable transparency for adaptive building envelope coatings
Physics-informed machine learning model for peak stress prediction generated from cylindrical charges in concrete
Bandgap prediction and design of halide double perovskites via ensemble machine learning
Machine learning assisted chitin Extraction: Source and yield prediction from crustacean biomass using deep eutectic solvents
High-Fidelity Machine Learning Framework for Fracture Energy Prediction in Fiber-Reinforced Concrete
Machine learning-optimised carbon nanotube sensors for simultaneous monitoring of multiple environmental contaminants
Enhanced grid integration through machine-learning optimized bidirectional EV chargers
Machine learning prediction model combined with network toxicology analysis identifies potential cardiotoxic components and mechanisms among 741 pesticides
Microparameter calibration method for concrete DEM using metaheuristic-based explainable machine learning and multi-objective optimization
A linear-response and stretchable capacitive electronic skin integrated with machine learning for wireless health monitoring and smart robotic grasping
Empowering econometric methods with machine learning for policy making: A comparative study in maritime transportation
Integrating advanced frequency-domain signal processing with machine learning for accurate leak detection in subsurface CO2 storage
Body movements as biomarkers: Machine Learning-based prediction of HPA axis reactivity to stress
The impact of oxides of cementitious materials on mortar strength: A machine learning perspective
A feature engineering technique for enhancing the generalization of machine learning models in estimating crop evapotranspiration
Machine learning of microstructure–property relationships in materials leveraging microstructure representation from foundational vision transformers
Optimization of genomic breeding value prediction for growth traits in Rongchang pigs through machine learning techniques
Biofuel consumption and emission prediction for harbour craft using Machine learning methods
Modelling over-reading correction factors for ultrasonic flow meters in wet gas measurement using advanced regression and machine learning techniques
Explainable machine learning-based cardiovascular disease prediction in patients with hypertension: Algorithm comparison and SHapley Additive exPlanations (SHAP) analysis
A comparative study of machine learning for particle morphology discrimination using interferometric particle imaging
Unveiling the adsorption behaviour of nitrogen-doped porous carbons (NDPCs) towards carbon dioxide capture using machine learning techniques
Development and Validation of an Interpretable Machine Learning Model for Predicting Hospital Mortality in ICU-Admitted Ovarian Cancer Patients: A Multicenter Study
An improved machine learning strategy for shear analysis in structural concrete based on the Newton’s method and the solvability region of the steel constitutive model
Prediction of fire-induced steel beam deformation using machine learning algorithms
Comparative analysis and evaluation of PEMFC machine learning surrogates by bridging CFD and experimental data
Identification of airfoil stall using airborne acoustic signature under room conditions by machine learning
Source apportionment of on-site paper-based combustion residues through interpretable machine learning and HS-GC-IMS fingerprint analysis in public security
Prediction of luminescence lifetimes of Mn4+/Eu3+ doped phosphors based on interpretable machine learning
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