Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.27560

Restraint-Induced Loss in Software-Intensive Enterprises: Large A Verified Technical Economic Impact Model of Operational Downtime and Delayed Commercialization, with Tail-Risk Security Sizing and Three Enterprise Case Studies

Mahdi Eslamimehr
View original source ↗Read source document ↗Export metadata ↓

Abstract

When an enterprise must stop using a software component (for example, because of an interim court restraint in a technology patent dispute), the economic loss passes through a chain of technical mechanisms. Withdrawing the component exposes dependent capabilities. Fallback paths degrade throughput, correctness or feature coverage. Incident rates and recovery backlogs rise. Customers respond. Remediation programmes compete against the restraint clock, and planned launches are pushed back. This paper develops and verifies a \emph{technical--economic impact model} (TEIM) that quantifies this chain end to end as a probability distribution of present-value loss rather than as a point estimate. The technical layer combines (i)~a lattice-valued, fixed-point semantics of service dependencies with degraded fallbacks, (ii)~continuous-time Markov reliability models evaluated by uniformization and specified in continuous stochastic logic, and (iii)~a fluid queueing model of post-outage backlog. These feed (iv)~convex acute-outage loss functions with regulatory and contractual thresholds, (v)~compound-Poisson excess-incident processes, (vi)~survival-weighted functional, attrition and funding channels that provably avoid double counting, (vii)~a real-options valuation of forcibly delayed launches with an explicit closed form, and (viii)~a COCOMO~II--driven stochastic activity network for design-around and substitution strategies. We prove seven theorems and eight propositions.

Explore related subjects

cs.CE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mahdi Eslamimehr. 2026-10-20. Restraint-Induced Loss in Software-Intensive Enterprises: Large A Verified Technical Economic Impact Model of Operational Downtime and Delayed Commercialization, with Tail-Risk Security Sizing and Three Enterprise Case Studies. https://arxiv.org/abs/2610.27560

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Interpretable Meta-Learning for Multi-Objective Chemical Search

Navigating the vast space of synthetically accessible molecules demands surrogate models that are interpretable and capable of handling multiple competing objectives at the same time. Deep learning approaches struggle to satisfy them under the computational constraints of quantum-level chemistry. Here, we introduce a modular pipeline that combines interpretable linear meta-learning models and adaptive-confidence uncertainty quantification into an Efficient Global Optimization (EGO) framework for multi-objective molecular discovery. For the first time, linear meta-learning is deployed in a multi-objective chemical search setting: by training across chemical objectives and cheap auxiliary properties, the meta-learned surrogates acquire transferable chemical knowledge that adapts rapidly to new objectives from limited data. Evaluated empirically on a real large scale search for spin-crossover metal-organic complexes, the baseline performs 78% worse in Pareto sense than the meta-learning alternative. We also address the calibration challenges inherent to active search. Since optimal candidates typically lie precisely in the distributional tails, standard uncertainty estimates fail. We introduce an adaptive confidence-tuning algorithm that dynamically recalibrates the exploration-exploitation trade-off as the molecular search evolves. Empirically, dynamic confidence tuning further dominates over 50% of the statically calibrated front.

cs.CE↗
arXiv2026

Observability-Aligned Diffusion and Null-Space Completion for EEG Source Localization

Electrophysiological source imaging is severely underdetermined because the lead-field matrix has limited rank relative to the source dimension and strongly correlated columns. Scalp EEG measurements constrain only the observable row-space component, leaving a high-dimensional null space unobservable. We propose a two-stage framework coupling posterior diffusion refinement with deterministic null-space completion. A minimum-norm-initialized, singular-coordinate Posterior-DDIM sampler combines a spatiotemporal diffusion prior with direction-dependent measurement reliability to refine the observable component. A frozen ridge predictor and nonlinear temporal residual network then estimate the null component under explicit projection constraints, preserving the refined row estimate and its predicted EEG exactly. All learned components are trained exclusively on simulated cortical activity and its row-null decomposition, without paired EEG-source training data. Across simulations with varying noise levels, the method achieved higher frame-level area under the receiver operating characteristic curve and average precision, together with lower localization error, than conventional and learning-based baselines. Exploratory evaluations on evoked EEG and simultaneous scalp EEG-stereo-EEG recordings showed condition- and event-dependent concordance with cross-modal and intracranial references.

cs.CE↗
arXiv2026

EBCC v0.2: Error bounded compression for weather and climate applications

As the resolution of weather and climate simulations increases, the amount of data produced is growing rapidly from hundreds of terabytes to tens of petabytes. These data volumes limit broader adoption, and their continued growth will soon exhaust available storage capacity. To address this problem, we present EBCC v0.2 (Error Bounded Climate-data Compressor). EBCC follows a two-layer compression approach: a base layer using JPEG2000 to capture the bulk of the data at high compression ratios, and a residual layer storing sparse wavelet coefficients to eliminate long-tail extreme errors efficiently. It supports three-dimensional chunks through a multicomponent transform (MCT) in the base layer. To achieve error bounded compression, we introduce error-bound-aware rate allocation in the JPEG2000 base layer. It selects coding passes to approximately satisfy the error target while optimizing the trade-off between compression ratio and mean squared error. We evaluate EBCC alongside established compression methods on benchmarks relevant to weather and climate science, including error statistics, sequential and parallel throughput tests, case studies of primitive and derived variables near a tropical cyclone, an analysis of global energy-budget closure, and a Lagrangian air-parcel trajectory simulation. EBCC either outperforms or closely follows the strongest existing baseline across these benchmarks at relative error targets ranging from 0.01% to 10%, achieving compression ratios from 15x to more than 300x. In the energy-budget-closure and Lagrangian-trajectory benchmarks, EBCC achieves more than 100x compression while keeping compression-induced errors below an assimilation-uncertainty reference derived from the ERA5 uncertainty ensemble. These results demonstrate that EBCC can produce heavily compressed weather and climate datasets suitable for downstream applications.

cs.CE↗