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The first book to address the underlying premises of systems integration and how to exposit them into a practical and productive manner, this book prepares systems managers and systems engineers to consider their decisions in light of systems integration metrics. The book addresses two questions: Is there a way to express the interplay of human actions and the result of system interactions of a product with its environment, and are there methods that combine to improve the integration of systems? The systems integration theory and integration frameworks proposed in the book tie General Systems Theory with practice.

We develop a comprehensive theory of conformal graph directed Markov systems in the non-Riemannian setting of Carnot groups equipped with a sub-Riemannian metric. In particular, we develop the thermodynamic formalism and show that, under natural hypotheses, the limit set of an Carnot conformal GDMS has Hausdorff dimension given by Bowen’s parameter. We illustrate our results for a variety of examples of both linear and nonlinear iterated function systems and graph directed Markov systems in such sub-Riemannian spaces. These include the Heisenberg continued fractions introduced by Lukyanenko and Vandehey as well as Kleinian and Schottky groups associated to the non-real classical rank one hyperbolic spaces.

In this paper we develop a new approach for studying overlapping iterated function systems. This approach is inspired by a famous result due to Khintchine from Diophantine approximation which shows that for a family of limsup sets, their Lebesgue measure is determined by the convergence or divergence of naturally occurring volume sums. For many parameterised families of overlapping iterated function systems, we prove that a typical member will exhibit similar Khintchine like behaviour. Families of iterated function systems that our results apply to include those arising from Bernoulli convolutions, the For each Last of all, we introduce a property of an iterated function system that we call being consistently separated with respect to a measure. We prove that this property implies that the pushforward of the measure is absolutely continuous. We include several explicit examples of consistently separated iterated function systems.

Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.

ContextNon-functional requirements (NFRs) (also referred to as system qualities) are essential for developing high-quality software. Notwithstanding its importance, NFR testing remains challenging, especially in terms of automation. Compared to manual verification, automated testing shows the potential to improve the efficiency and effectiveness of quality assurance, especially in the context of Continuous Integration (CI). However, studies on how companies manage automated NFR testing through CI are limited.ObjectiveThis study examines how automated NFR testing can be enabled and supported using CI environments in software development companies.MethodWe performed a multi-case study at four companies by conducting 22 semi-structured interviews with industrial practitioners.ResultsMaintainability, reliability, performance, security and scalability, were found to be evaluated with automated tests in CI environments. Testing practices, quality metrics, and challenges for measuring NFRs were reported.ConclusionsThis study presents an empirically derived model that shows how data produced by CI environments can be used for evaluation and monitoring of implemented NFR quality. Additionally, the manuscript presents explicit metrics, CI components, tools, and challenges that shall be considered while performing NFR testing in practice.

The aim of this paper is to provide new characterizations of the curvature dimension condition in the context of metric measure spaces (X,\\mathsf d,\\mathfrak m). On the geometric side, the authors' new approach takes into account suitable weighted action functionals which provide the natural modulus of K-convexity when one investigates the convexity properties of N-dimensional entropies. On the side of diffusion semigroups and evolution variational inequalities, the authors' new approach uses the nonlinear diffusion semigroup induced by the N-dimensional entropy, in place of the heat flow. Under suitable assumptions (most notably the quadraticity of Cheeger's energy relative to the metric measure structure) both approaches are shown to be equivalent to the strong \\mathrm {CD}^{*}(K,N) condition of Bacher-Sturm.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

Background Interaction fingerprints (IFP) have been repeatedly shown to be valuable tools in virtual screening to identify novel hit compounds that can subsequently be optimized to drug candidates. As a complementary method to ligand docking, IFPs can be applied to quantify the similarity of predicted binding poses to a reference binding pose. For this purpose, a large number of similarity metrics can be applied, and various parameters of the IFPs themselves can be customized. In a large-scale comparison, we have assessed the effect of similarity metrics and IFP configurations to a number of virtual screening scenarios with ten different protein targets and thousands of molecules. Particularly, the effect of considering general interaction definitions (such as Any Contact, Backbone Interaction and Sidechain Interaction), the effect of filtering methods and the different groups of similarity metrics were studied. Results The performances were primarily compared based on AUC values, but we have also used the original similarity data for the comparison of similarity metrics with several statistical tests and the novel, robust sum of ranking differences (SRD) algorithm. With SRD, we can evaluate the consistency (or concordance) of the various similarity metrics to an ideal reference metric, which is provided by data fusion from the existing metrics. Different aspects of IFP configurations and similarity metrics were examined based on SRD values with analysis of variance (ANOVA) tests. Conclusion A general approach is provided that can be applied for the reliable interpretation and usage of similarity measures with interaction fingerprints. Metrics that are viable alternatives to the commonly used Tanimoto coefficient were identified based on a comparison with an ideal reference metric (consensus). A careful selection of the applied bits (interaction definitions) and IFP filtering rules can improve the results of virtual screening (in terms of their agreement with the consensus metric). The open-source Python package FPKit was introduced for the similarity calculations and IFP filtering; it is available at: https://github.com/davidbajusz/fpkit .

The cement industry continues to experience substantial production pressure driven by steadily increasing global demand. In this context, the present study investigates the relationship between safety integration and safety performance, with production pressure examined as a mediating variable. Structural Equation Modelling (SEM) was employed to analyze these relationships within the cement industry. Safety Integration included Labor Safety Accountability, Management Safety Accountability, and Contractor Safety Management, while Safety Performance was categorized into incident measurements (SPx), management actions (SPy), and continuous improvement efforts (SPz). Production pressure encompassed the Normalization of Unsafe Practices, Disruptions in Safety Protocols, and Production Pressure Intensity. Data from 238 participants, collected over 3 months were analyzed using SPSS and AMOS (version 23). Labor Safety Accountability consistently influenced safety performance ( p  < 0.001 on SPx, p  < 0.001 on SPy, p  = 0.001 on SPz), while Contractor Safety Management and Management Safety Accountability also showed significant effects ( p  = 0.011 on SPx, p  < 0.001 on SPz). Production Pressure Intensity and Disruptions in Safety Protocols negatively affected safety performance ( p  = 0.014 on SPx, p  = 0.026 on SPy) respectively, while Normalization of Unsafe Practices exhibited a weak influence. The findings confirm that safety integration significantly enhances safety performance, whereas production pressure exerts a substantial negative impact, diminishing overall safety outcomes in the cement industry. Unlike prior studies that largely examine safety performance in isolation, this research uniquely demonstrates how production pressure mediates the link between safety integration and safety outcomes in the cement industry, highlighting the dual challenge of maintaining productivity while safeguarding workers and offering new insights for both scholars and practitioners in high demand industrial sectors.

The increasing integration of variable renewable energy sources (VRESs) into modern power systems presents significant challenges in ensuring operational flexibility, highlighting the need for robust methodologies to evaluate and ensure system reliability. The Insufficient Ramping Resource Expectation (IRRE) has emerged as a critical metric for quantifying the probability of ramping deficiencies in power systems. However, its traditional application, designed primarily for capacity-constrained systems, may not fully capture the operational dynamics of energy-constrained systems, such as those dominated by hydropower generation. This study analyzes the IRRE methodology and proposes enhancements to incorporate additional constraints, including seasonal and monthly hydrological variability and operational reserve requirements, to better reflect the flexibility limitations in energy-constrained systems. A case study of the Brazilian electricity system evaluates these modifications by comparing traditional and enhanced IRRE results across varying scenarios, including higher VRES penetration. Results reveal that, under stricter constraints, IRRE values increased by over 11 times for monthly hydrological limits in the Northeast subsystem, compared to the traditional IRRE. Additionally, combining these constraints with a 5% operational reserve requirement led to ramping deficits in up to 5% of the hours in a year for the same subsystem, highlighting the critical impact of operational constraints. Furthermore, scenarios with 30% and 100% VRES growth resulted in deficits increasing by 56 times and 418 occurrences, respectively, in certain subsystems. These findings demonstrate the enhanced IRRE’s effectiveness in evaluating flexibility challenges and its relevance for supporting planning and operational strategies in systems undergoing rapid renewable energy expansion.