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
Development of a litho-structural map for the Upper Mereb area, Eritrea, using multi-source remote sensing data and machine learning models
Quantitative relationships between structure characteristics and mass transfer for chromatographic resin by combining digital material techniques and machine learning algorithms
Three-Dimensional Radiomics and Machine Learning for Predicting Postoperative Outcomes in Laminoplasty for Cervical Spondylotic Myelopathy: A Clinical-Radiomics Model
Data-driven optimization of polycyclic aromatic hydrocarbons removal by organic composites from aquatic environments: Integrating machine learning with theoretical calculations
Identification of Novel Biomarkers for Hypertension and Ventricular Remodeling Based on Transcriptomics and Machine Learning
Impact of drought on de facto reuse and water quality in Lake Mead: Insights from hydrodynamic modeling versus machine learning
Dynamic Prediction of Postprandial Glycemic Response and Personalized Dietary Interventions Based on Machine Learning
A fast seismic assessment technique for reinforced concrete buildings: Machine learning-based Hassan Index
Demand-driven predictive tailoring of anisotropic yield surfaces in origami metamaterials via machine learning
Predicting ammonia solubility in ionic liquids using machine learning models based on critical properties
Enhancing big data analysis in IoT applications and optimizing the performance of machine learning models using hybrid dimensionality optimization approach
Machine learning methods in microscopic pedestrian and evacuation dynamics simulation: a comparative study
Machine learning-driven phase prediction and corrosion behavior of (CoCrNi)(100-x-y) AlxTiy high-entropy alloys in Ringer's solution
What influence digital inclusive finance from policy tones? Based on machine learning
Acid leaching of copper from copper slag based on machine learning analysis
Prediction of mechanical properties of 16Mn large tube plates based on machine learning
Hybrid machine learning and soft computing methods for predicting and optimizing natural convection in a C-shaped cavity with double diffusion effects
Application of explainable machine learning to characterizing apatite fertility in porphyry-skarn deposits
Predicting radiation-acute esophagitis via machine learning algorithms
Perspectives on machine learning: Predicting the combined effects and strategies of water management and biochar treatment on soil Cd activity and Cd accumulation in rice
Progress in CP violating top-Higgs coupling at the LHC with Machine Learning
Decoding weight-gain patterns in tungsten-containing refractory high-entropy alloys under high-temperature oxidation through machine learning
Using machine learning with supplemented NC code to predict machining energy
Developing safety performance functions incorporating pavement roughness using Poisson regression and Machine learning models on Jordan’s Desert Highway
Development and evaluation of neighborhood social risk indices for surgery using outcome-specific machine-learning models
Predicting filter cake thickness in drilling fluids using machine learning techniques
Machine learning-optimized jet-enhanced immersion liquid cooling for high-power data centers
Machine learning for adsorption-related parameters prediction of electronic specialty gases: DFT-based dataset construction and balanced data augmentation
Integrating machine learning with electromechanical impedance for non-destructive detection of bolt looseness in steel structures
Machine Learning in Indoor Localization Prediction Using Received Signal Strength Indicator and Wi-Fi Network
Exploring maximum likelihood and Bayesian approaches for two-dimensional image restoration: A machine learning perspective
Machine learning-based plasma-derived extracellular vesicle signatures for digestive system cancers prediction
MegaEye: Applying multiple machine learning approaches to identify oral compounds with ocular bioactivity
Increasing the prediction efficacy of the thermodynamic properties of R515B refrigerant with machine learning algorithms using SMOGN data augmentation method
Evaluating solar drying effects and machine learning models for nutritional quality of Jerusalem artichoke
A machine learning-based generative design approach for rapid topology optimization of microchannel heat sinks
Intelligent assessment of habitat quality based on multiple machine learning fusion methods
Development of a machine learning-based risk prediction model for perioperative neurocognitive disorders
Classification of toxic element accumulation in rice grains using optimized machine learning models: A comparative study
Democratizing interactivity: An overview of interfaces for multimedia machine learning
Real-time classification of Serengeti wildebeest behaviour with edge machine learning and a long-range IoT network
CVaR-based risk parity model with machine learning
Chirality recognition of amino acid by combining machine learning method and sliding window technique
ANN vs. traditional machine learning models: A comparative study on open switch fault diagnosis in VSIs for solar pumping systems
Evaluating CEO hubris effects on sustainable performance in the IC design industry: An integrated dynamic network DEA framework with machine learning
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
Current Issue