Skill UI

This guide helps researchers determine the primary focus of their work—whether it lies in Information Processing (algorithms, estimation) or Sensor Platforms, Tools, and Design Methods (hardware, deployment). It provides a structured methodology for selecting the most appropriate academic venue (e.g., SenSys, MobiSys, INFOCOM) based on the project's technical contribution and maturity.

This comprehensive guide details the rigorous methodological standards required for validating new statistical estimators, tests, or algorithms. It moves beyond simple causal inference, emphasizing foundational proofs of identification, regularity conditions, asymptotic consistency, and finite-sample robustness. It serves as a checklist for assuring the credibility and reliability of methods in academic statistical journals.

This guide assists authors in preparing research materials for submission to the Journal of Financial Intermediation (JFI), adhering to Elsevier Option C. It provides detailed instructions on managing, depositing, citing, and linking research data, code, and algorithms—whether the data is public, proprietary, or restricted (e.g., central bank registers). The goal is to ensure methodological rigor and reproducibility through the Data Statement.

A comprehensive framework for analyzing and presenting algorithmic contributions in academic papers. It guides users beyond mere computational speed, requiring rigorous proof of complexity (Big O), convergence (rate and conditions), or approximation bounds (rho). Essential for optimizing and validating complex computational models in scientific fields.

This guide helps researchers determine if their manuscript is suitable for highly quantitative, theory-driven journals. It emphasizes evaluating the core deliverable—whether it consists of a formal model, provable properties, and an algorithm, rather than relying solely on empirical data or policy recommendations. Essential for optimizing journal submission strategy.

A guide for designing rigorous numerical experiments and simulations for academic manuscripts in management science. This methodology focuses on validating proven theoretical properties, assessing algorithm performance, exploring parameter sensitivity, and extracting actionable managerial insights. It emphasizes that simulations must complement, but never replace, formal mathematical proofs.

This comprehensive guide teaches how to structure and write professional rebuttal letters (R&R) for top-tier academic journals, particularly in management science. It focuses on methodically addressing reviewer concerns regarding assumptions, proof gaps, algorithmic complexity, and empirical evidence, requiring precise, point-by-point references to the revised manuscript to ensure academic rigor.

This structured workflow guide is designed for researchers submitting to top-tier management science journals. It acts as a comprehensive router, guiding the manuscript through the rigorous academic process: from problem definition and model construction, through theoretical proofs, algorithm analysis, and numerical validation, ensuring methodological depth over mere empirical findings.

A specialized tool designed to assess the suitability and refine the structure of a manuscript targeting the Journal of Industrial Engineering and Engineering Management (IJEM). It helps authors understand the journal's specific scope, preferred methodologies (modeling, algorithms, simulation), structural requirements, and common rejection pitfalls, optimizing the paper before submission.

This guide details the rigorous requirements for publishing in top-tier quantitative management journals. It specifically targets research outputs that utilize mathematical models, theoretical proofs, and computational algorithms (e.g., operations research, decision theory). It strongly advises against purely empirical surveys or case studies, emphasizing methodological contribution over policy implication.

A comprehensive guide for authors writing mathematical finance manuscripts. It specifies the rigorous standards for presenting figures, numerical tables, algorithms, and dependency maps, ensuring every visual exhibit supports a formal theorem or proposition. It advises on reporting numerical settings, avoiding empirical claims, and structuring evidence to aid theoretical comprehension rather than serving as standalone proof.

A comprehensive guide for rigorously positioning an Operations Research (OR) manuscript against prior art. It teaches authors to move beyond vague 'gap' statements by constructing a precise, technical delta against 3-5 closest papers. Focus areas include weakening assumptions, improving bounds, enhancing algorithmic complexity, and defining new operational regimes, often presented in a structured comparison table to satisfy technical reviewers.