Improved cash flow forecasts lead to significant cost savings.
problem The impact of forecast accuracy on cash management costs.
method Analysis of two real data sets from the textile industry.
result Predictive accuracy is highly correlated with cost savings.
Paper encodes textile structures and classifies them up to complexity five.
problem Topological classification of textile structures.
method Extends Gauss code to textile structures, introduces a linear time algorithm.
result First classification of all oriented textile structures up to complexity five.
Study finds WACC negatively impacts firm profitability in Bangladesh's food industry.
problem Determining the impact of Weighted Average Cost of Capital (WACC) on firm profitability.
method Fixed Effects Panel Regression Model using 12 food and allied industry companies from 2005-2019.
result WACC negatively correlates with firm profitability (ROA), significant relationship.
Study links in knitted textiles using knot theory.
problem Identify and classify links in knitted textiles.
method Correspondence between links in thickened torus and 2-periodic weft-knitted textiles, using link invariants and ribbon knots.
result New stitch patterns and links in knitted textiles identified.
Study examines cash conversion cycle in manufacturing firms, finding negative relationships with profitability and size.
problem Understanding cash conversion cycle in manufacturing firms and its impact on profitability and size.
method Empirical study of 30 manufacturing firms in Dhaka Stock Exchanges, categorizing them into six industries, analyzing industry averages and relationships with size and profitability.
result Negative relationship between cash conversion cycle and profitability, especially ROE; negative relationship with firm size in terms of net sales.
We study a stochastic equation modeling the lay-down of fibers in the production process of nonwovens. The equation can be formulated as some manifold-valued Stratonovich stochastic differential equation. Especially, we study the long time behaviour of the stochastic process. Demanding mathematical difficulties arising…
Knitted and woven textile structures are examples of doubly periodic structures in a thickened plane made out of intertwining strands of yarn. Factoring out the group of translation symmetries of such a structure gives rise to a link diagram in a thickened torus. Such a diagram on a standard torus is converted into a c…
New methods assess topological entanglement in periodic systems.
problem Assessing topological entanglement in systems with periodic boundary conditions.
method Introducing Periodic Jones polynomial and Cell Jones polynomial.
result Periodic Jones polynomial is a recurring factor of Jones polynomial of finite cutoffs.
A new model for simulating cloth manipulation in robots, accurate to within 1cm.
problem Accurately simulating cloth manipulation in robots, especially in moderate stress environments.
method A continuous, isometric strain model for textiles, treating them as inextensible surfaces with only isometric motions. Aerodynamic effects are incorporated through virtual uncoupling of mass.
result Simulations are accurate to within 1cm compared to real-world manipulation, even with coarse meshes.
Paper characterizes isotopies and hyperbolicity of weaves using geodesics.
problem Characterizing isotopies and hyperbolicity of weaves.
method Using diagrams of closed geodesics and normal positions of essential surfaces.
result Weaves are hyperbolic and cannot have essential Conway spheres.
Method captures fabric mechanics from depth images without expensive setups.
problem Estimating mechanical parameters of fabrics accurately and efficiently.
method Sim-to-real strategy using learning-based framework trained on synthetic data.
result Metric correlates with human judgments of fabric drape similarity.
Paper generates cartoon giraffes from few original drawings.
problem Creating consistent cartoon sketches with limited data.
method Expressive augmentations and GANs trained on complexity layers.
result Generated sketches are consistent with designer's style.
Paper proposes conditional multidimensional scaling for better data reduction.
problem Mapping high-dimensional data to low-dimensional space with known features.
method Developed a broad class of methods called conditional multidimensional scaling (MDS) with an optimization algorithm.
result Conditional MDS improves estimation quality and simplifies visualization and knowledge discovery.
We study the structure of inter-industry relationships using networks of money flows between industries in 20 national economies. We find these networks vary around a typical structure characterized by a Weibull link weight distribution, exponential industry size distribution, and a common community structure. The comm…
Industry evolution caused by various reasons, among which technology progress driving industry development has been approved, but with the new trend of industry convergence, inter-industry convergence also plays an increasing important role. This paper plans to probe the industry synergetic evolution mechanism based on…
Improves industry classification for diversified companies.
problem Traditional industry classification struggles with multi-sector conglomerates.
method Bayesian Non-Parametrics, Markov Updating, and hierarchical modeling.
result MIS-2 provides a measurable improvement over GICS in predicting future correlations.
Develops MIS, a probabilistic model for multi-industry classification.
problem GICS's limitation of assigning each firm to exactly one industry, especially for diversified firms.
method Topic modeling to probabilistically assign firms to multiple industries based on business descriptions.
result Demonstrates MIS's ability to flexibly assign firms to multiple industries with relevance probabilities.
Study finds environmental liability insurance reduces industrial carbon emissions.
problem Reduction of industrial carbon emissions.
method Two-way fixed effect model using provincial (city) level panel data from 2010 to 2020.
result Environmental liability insurance reduces industrial carbon emissions at both direct and indirect levels, with varying effects.
We offer open-source code for a fundamental industry classification.
problem Creating accurate industry classifications from public data.
method Developed open-source code with modules for data handling.
result Improved trading signals through various industry classifications.
Develops algorithms for constructing statistical industry classifications.
problem Creating accurate industry classifications for quantitative trading.
method Combines clustering algorithms with correlation analysis to improve fundamental industry classifications.
result Shows that clustering methods improve off-the-shelf industry classifications.
Study reveals similarities in knowledge flows between pharmaceutical and AI industries.
problem Understanding the dynamics of drug pipelines in global pharmaceutical industry.
method Multilayer network analysis of drug pipeline, global supply chain, and ownership data.
result Proven similarities in knowledge flows between pharmaceutical and AI industries.
Analyzes Indian chemical industry post-Covid.
problem Global uncertainty impacts chemical industry performance.
method Fundamental analysis of key players and trends.
result Various geopolitical and macroeconomic trends shape industry performance.
This work analyzes industrial IoT data for security using machine learning.
problem Security vulnerabilities in industrial IoT networks.
method Transformed industrial network data into time series and analyzed with three algorithms.
result Matrix Profiles outperform other methods with minimal parameterization.
Extends tangle theory to include undetermined crossings in periodic structures.
problem Classical tangle theory's limitations in handling undetermined crossings.
method Introduces pseudo DP tangles, defined as liftings of pseudo motifs in the thickened torus, and analyzes them through diagrammatic methods.
result Defines equivalence for pseudo DP tangles and proves an analogue of Reidemeister theorem.
New online learning algorithms improve cyberattack detection in industrial control systems.
problem Detecting cyberattacks in industrial control systems with limited resources.
method Online learning algorithms to process continuous data streams and address class imbalance.
result Improved detection rate of cyberattacks in industrial control systems.
Industry-specific knowledge boosts pioneer firms' survival and growth.
problem How do regions diversify their economies?
method Measuring industry, occupation, and location-specific knowledge from work histories, using Bartik instruments.
result Industry-specific knowledge is crucial for pioneer firms' survival and growth.
Quantum computing offers financial industry new optimization and risk management tools.
problem Traditional computing limits financial industry's problem-solving capabilities.
method Structured review of quantum computing platforms, algorithms, and use cases.
result Quantum computing can enhance financial industry applications like optimization and risk management.
Study reveals hierarchical structure of global geo-industrial clusters using labor flow network.
problem Identifying and analyzing the hierarchical structure of geo-industrial clusters globally.
method Constructed a labor flow network from LinkedIn data and applied recursive network community detection.
result Geo-industrial clusters defined by labor flow show stronger association with financial performance.
Study uses ML and statistical models to analyze climate impacts of industrial growth.
problem Understanding and predicting environmental impacts of industrial activities.
method Comparative analysis of ML and statistical models on time series data.
result ML models outperform statistical models in predicting environmental impacts.
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
problem Impact of oil price volatility on Tehran stock and industry indices.
method Feed-forward neural networks analysis of two periods: sanctions and post-sanctions.
result Neural networks predict stock and industry indices well, showing significant oil price volatility impact.
GTApprox improves industrial design with new surrogate modeling tools.
problem Efficient surrogate modeling for industrial design problems.
method Novel approximation algorithms, automated model selection methods, and customizable hints.
result Demonstrated efficiency on a large set of test problems and real engineering applications.
Prototype technique for anomaly detection in industrial big data.
problem Real-time condition monitoring and failure analysis of industrial systems.
method Independent ordinations of repeated bootstrapped partitions and inspection of ordinal distances.
result Prototype technique operates at industrial scale for anomaly detection.
New dataset for industrial machine sounds to aid maintenance.
problem Lack of public datasets for industrial machine sounds.
method Recorded normal and anomalous sounds of industrial machines.
result Assists in automated facility maintenance development.
Tool converts industrial systems to RL environments for optimization.
problem Lack of RL methods for optimizing industrial systems.
method Develops a tool to convert FSM models to OpenAI Gym wrappers.
result Traditional RL methods can be applied to industrial systems.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
problem The amplification of demand variability (Bullwhip Effect) in supply chains during the pandemic.
method Extensive industry-level data analysis using traditional and advanced empirical techniques.
result COVID-19 significantly amplified the Bullwhip Effect across different U.S. industries.
Paper proposes machine learning for industrial plant fault prediction.
problem Fault detection in industrial plants using sensor data.
method Classification methods like logistic regression, random forest, and gradient boosting.
result The algorithms predicted fault start and end times effectively.
Deep learning predicts employment changes and industry health.
problem Forecasting short-term employment changes and assessing long-term industry health.
method LSTNet, a multi-scale deep learning model, processes multivariate time series data.
result LSTNet outperforms baseline models in most sectors, especially stable ones.
Industry lacks tools to secure ML systems, study finds.
problem Insufficient security tools for ML systems in industry.
method Interviews with 28 organizations to identify gaps.
result Need revised Security Development Lifecycle for ML.
Study shows insurance industry in North Macedonia declined 10% due to COVID-19.
problem Impact of COVID-19 on insurance industry activity.
method Seasonal autoregressive models and data analysis for 11 insurance classes.
result Insurance activity in North Macedonia decreased by more than 10% during the pandemic.
Company2Vec creates embeddings from company websites for fine-grained business analytics.
problem Lack of fine-grained company labels for analytics.
method Word2Vec and dimensionality reduction on company website data.
result Semantic company embeddings for various applications.
Novel financial time-series data representation improves industry sector classification.
problem Classifying industries using historical stock returns time-series data.
method Proposed a novel representation based on stock returns embeddings for time-series data, overcoming representational challenges of conventional approaches.
result Substantial performance improvements over baselines using conventional representations.
Bayesian methods improve industrial modeling under uncertainty.
problem Limited data, uncertainty in physics models, and high costs in experiments.
method Bayesian Hybrid Modeling (GEBHM) framework.
result Improved decision-making under uncertainty in industrial contexts.
Neural model learns company embeddings from data and news.
problem Subjective industry classification schemes in finance.
method Multimodal neural model training company embeddings.
result Objective company representations capture nuanced relationships.
BayPrAnoMeta tackles few-shot industrial image anomaly detection with Bayesian methods.
problem Challenges in industrial image anomaly detection, especially class imbalance and scarcity of labeled samples.
method Bayesian Proto-MAML approach with probabilistic normality models and Bayesian posterior predictive likelihood.
result Consistent and significant AUROC improvements over existing methods in few-shot anomaly detection.
This research develops a dynamic risk management system for industrial companies.
problem Risk assessment and management in industrial enterprises.
method Qualitative and quantitative analysis, systematic risk classification, dynamic system development.
result Effective risk management strategies formed through dynamic risk management system and risk assessment methods.
Study uses Bayesian regression to analyze consumer behavior changes in restaurants post-COVID-19.
problem Impact of COVID-19 on consumer behavior in the restaurant industry.
method Bayesian regression with Hamiltonian Monte Carlo.
result Estimates change in consumer behavior before and after the pandemic.
We consider the problem of pricing derivatives written on some industrial loss index via utility indifference pricing. The industrial loss index is modelled by a compound Poisson process and the insurer can adjust her portfolio by choosing the risk loading, which in turn determines the demand. We compute the price of a…
Nestedness in global trade network revealed using multi-layer approach.
problem Understanding nested structures in international trade networks.
method Constructed a multi-layer network with countries, industries, and transactions; computed nestedness based on buyers' and sellers' involvement.
result Identified variations of nestedness over time and contributing countries and industries.