Search Results (596)

This paper investigates the fundamental mechanisms of technological change in complex systems by analyzing how the evolution of embedded subsystems dictates the trajectory and sets the tempo of a host technology. Building on the theoretical framework of technological parasitism, the study conceptualizes host systems having a modular architecture—such as smartphones—as evolving through dynamic, coevolutionary interactions with their constituent subsystems. These relations gradually shift from parasitic reliance to mutualistic and ultimately symbiotic interactions. Central to this research is the concept of subsystems as pacemakers. Methodologically, this research employs a longitudinal, mixed-methods approach, combining an 18-year case study of the iPhone (2007–2025) with time-series regression and log–log hedonic pricing models. Key findings are: (a) Temporal precedence: Advances in subsystems (e.g., Bluetooth protocols) consistently precede host releases. The integration lag has contracted from three years to one, signaling an acceleration in symbiotic coupling and highlighting Bluetooth as a systemic pacemaker whose evolutionary tempo anticipates shifts in the wider smartphone architecture. (b) Differential evolutionary pressure in technological host systems: While camera resolution exhibited the highest exponential growth (+16.73%), it remained a secondary driver of systemic evolution. (c) Economic pacemakers: Hedonic analysis identifies battery life as the dominant evolutionary predictor (standardized beta = 0.77). With an elasticity of approximately 0.30, a 1% gain in battery performance correlates to a 0.3% increase in nominal price, whereas display and camera resolution exert significantly less influence on the system’s valuation and trajectory. These findings reveal that subsystems evolve—and exert influence—at different speeds and with different degrees of systemic leverage. Overall, the proposed theory shows that subsystem evolution functions as a leading indicator of forthcoming host–system transitions. By identifying which subsystems act as temporal pacemakers, this research contributes new design rules for forecasting technological generations and optimizing R&D strategies in complex, multi-component innovations. Hence, the study demonstrates that mastering complex innovation requires a granular understanding of the asynchronous rhythms between a host technology and its constitutive parts. Full article

In many organizations, business intelligence systems support analytical reporting and operational decision making. As data volumes grow and analytical tasks become more complex, architectures based on centralized processing pipelines increasingly face limitations related to scalability and timely response. These challenges motivate the development of alternative architectural approaches capable of operating efficiently in data-intensive environments. This study presents a modular multi-agent business intelligence framework that distributes analytical tasks across autonomous agents and applies lightweight reinforcement learning at the decision-making stage. The analytical workflow is decomposed into agents responsible for data collection, preprocessing, analytical modeling, and decision execution. Decision adaptation relies on localized policy updates driven by operational feedback, which avoids complex learning coordination and helps preserve system stability and interpretability. The proposed framework is evaluated using real-world transactional data from an electronic commerce setting. Experimental results show that the approach consistently outperforms centralized analytical pipelines and non-agent machine learning baselines in terms of processing efficiency, classification accuracy, and balanced classification performance. Threshold-independent evaluation further confirms stronger discriminative behavior across varying decision thresholds. In addition, stability analysis across repeated experimental runs indicates reduced performance variance and more predictable system behavior. These findings suggest that the proposed multi-agent business intelligence framework provides a practical and scalable alternative to centralized analytical architectures for data-intensive decision-support environments, while maintaining the robustness and transparency required in enterprise systems. The evaluation is limited to a single dataset and a classification task, and results should be interpreted within this scope. Experiments on the Online Retail dataset (UCI Machine Learning Repository) show an average accuracy of 0.89 ± 0.012 (baseline: 0.74 ± 0.029) and decision latency of 94 ± 9 ms (baseline: 137 ± 16 ms) across 10 independent runs, indicating stable behavior under repeated execution. Full article

Life cycle assessment (LCA) studies show that electricity supply and consumption are often a dominant contributor to environmental impacts, yet these results are highly sensitive to the choice of inventory database and its embedded assumptions. This study examines how database structure and scenario flexibility shape electricity-related impacts by comparing three approaches for the 2022 Northeast Power Coordinating Council (NPCC) region in the USA: the Ecoinvent “market for electricity” dataset, the modular U.S. electricity model from the Sphera database, and a customized NPCC model built from Ecoinvent unit processes. Impacts were assessed with both ReCiPe 2016 and TRACI 2.1. While climate change and fossil resource depletion results were consistent across databases and impact assessment methods, toxicity-related categories diverged substantially, with substantially higher values from Ecoinvent inventories. These high toxicity values were directly linked to assumptions about the use of copper in grid infrastructure (66%), including incineration at its end of life (18%), a disposal technique that is not relevant to the NPCC area. A case study of residential heating electrification further highlighted that while heat pumps with a decarbonized grid consistently reduced climate impacts, conclusions for other categories varied depending on the database used. These findings underscore the importance of transparent electricity models and cross-database sensitivity analysis in prospective LCAs when evaluating the overall environmental and health benefits of a sustainable energy future (UN SDG 7, 13). Without such practices, non-climate results, particularly toxicity outcomes, risk reflecting database assumptions and artifacts rather than real technological and environmental differences. Full article

Background/Objectives: Strain-level detection of bacteria is essential for applications such as diagnostics, food safety, and microbial monitoring. While 16S rRNA gene sequencing provides genus- or species-level resolution, it cannot reliably discriminate closely related strains. Whole-genome sequencing (WGS) offers high-resolution strain differentiation but remains impractical for routine detection due to cost and analytical complexity. This study aims to enable the translation of WGS data into accurate and cost-effective strain-specific PCR assays. Methods: We developed Erimin, a modular, shell-based bioinformatics pipeline for the automated identification of strain-specific genomic regions from short-read WGS data. Erimin systematically analyzes all available reference genomes for a given bacterial species in combination with sequencing data from a target strain. The workflow integrates reference-based read alignment, extraction of unmapped reads, de novo assembly, contig filtering and validation, genome annotation, and in silico PCR primer design and specificity evaluation. Results: Erimin was applied to Lactiplantibacillus pentosus whole-genome sequencing data to identify genomic regions specific to strain L33 through comparative analysis against a comprehensive set of reference genome assemblies representing multiple Lactiplantibacillus species. These regions were used for in silico PCR primer design and computational specificity assessment against non-target bacterial genomes, supporting discrimination of closely related strains. Conclusions: Erimin provides a structured computational approach for identifying strain-specific genomic regions from WGS data and for supporting the in silico design of PCR primers. This framework facilitates strain-level discrimination using targeted molecular assays. Full article

The immersive, multisensory experiences offered by virtual reality have been transformative across multiple disciplines, enhancing practical and theoretical skills while increasing user motivation and learning. On the other hand, multi-agent systems have proven to be effective in facilitating the expansion and modularity of computer systems. This paper presents an application developed in a virtual reality environment based on multi-agent systems for the conceptual design of mechanical assemblies from primitives. As a main novelty, the primitives can be defined by the user of the application from a set of models and images, and an Excel document, without the need for programming knowledge, taking advantage of the possibilities offered by multi-agent systems. In addition, for each primitive, it is possible to define a set of geometric and dimensional modifications, as well as a set of position relations with respect to other primitives to generate mechanical assemblies. Full article

The transition toward carbon-neutral energy systems has revived interest in nuclear technologies, particularly small and micro modular reactors (SMRs and MMRs) as flexible, safe and efficient alternatives to conventional large-scale power plans. In the Czech Republic, Centrum výzkumu Řez (CVŘ) is developing Energy Well (EW), a molten salt-cooled micro modular reactor concept employing FLiBe (Fluoride Lithium Beryllium) as primary and secondary coolant and a supercritical CO₂ (sCO₂) tertiary loop. A dedicated experimental facility was built to reproduce EW operating conditions and provide critical data on thermohydraulic behavior, fuel properties and heat-transfer mechanisms. This paper presents the development and assessment of a TRACE (TRAC/RELAP Advanced Computational Engine) model of the experimental facility, including specific methodologies for the main heater and the heat exchanger. Model accuracy was assessed through comparison with experimental commissioning data. The simulations demonstrated overall model consistency, especially regarding the heat exchanger and the main heater general performances, while some discrepancies were observed inside the main heater graphitic core. Other discrepancies were observed along the loop, mainly resulting from modeling simplifications and lack of information regarding certain experimental loop phenomena. In particular, the pressure calculation showed large inconsistencies mainly connected to the complexity of pressure measurements in molten salt circuits and the lack of specific head loss correlations. This study also helped identify broader issues in both the code (persistent error in generating CO₂ property tables and instabilities resulting from FLiBe interactions with non-condensable gases) and the experimental loop (defect in the heat exchanger filling and uncertainties on sensors location), also contributing to resolving sensor-related inconsistencies in the facility. Results confirm TRACE as a reliable tool for modeling molten salt systems, regarding the temperature distribution and the heat transfer. However, depending on the specific experimental case, this paper introduces specific limitations, such as some inconsistencies in the pressure drops distribution, in order to support the future development of TRACE code. Beyond technical advances, this work provides unique experimental data and fosters international collaboration in advancing SMR and molten salt reactor technologies. Full article

The adoption of blockchain-based smart contracts for the trading of goods and services promises greater transparency, automation, and trustlessness, but also raises challenges related to payment integration and modularity. While business analysts (BAs) can express business logic and control flow using BPMN and decision rules using DMN, payment tasks that involve concrete transfers (on-chain, off-chain, cross-chain, or hybrid) require careful implementation by developers due to platform-specific constraints and semantic richness. To address this separation of concerns, we introduce a methodology within the context of the smart contract-as-a-service (SCaaS) approach that supports (1) identifying and mapping generic payment tasks in BPMN to pre-deployed payment smart contracts, (2) augmenting BPMN models with matching payment fragments from a pattern repository, and (3) automatically transforming the augmented models into smart contracts that invoke the appropriate payment services. Our approach builds on prior work in automated BPMN-to-smart contract transformation using Discrete Event–Hierarchical State Machine (DE-HSM) multi-modal modeling to capture process semantics and nested transactions, while enabling payment service reuse, extensibility, and the separation of concerns. We illustrate this methodology via representative use cases spanning conventional, DeFi, and cross-chain payments, and discuss the implications for modular contract deployment and maintainability. Full article

The decarbonization of shipping and the transformation towards digitally assisted or automated ship operation require new methods to analyze, predict, and optimize energy demand during maneuvering. The SimPleShip-SigMa sub-project of Hochschule Wismar developed and validated a comprehensive simulation-based framework combining real-time capable fast-time simulation of ship motion, detailed thermodynamic engine modeling, and hybrid data exchange via Functional Mock-up Units (FMU/FMI). The approach enables consistent coupling between navigation-related and machinery-related simulations and supports energy-optimized decision-making on the bridge. Operational relevance and validation of use cases were supported through collaboration with Carnival Maritime GmbH, providing practical feedback on large passenger-ship operations. The study presents the architecture of the simulation environment, the implementation of energy- and emission-prediction models, and the result of validation runs and simulator-based trials. The developed method was applied to a virtual cruise-ship scenario representing a confined coastal environment similar to the Geiranger Fjord. The work builds upon earlier research on simulation-augmented maneuvering and extends it toward a modular digital-twin concept linking hydrodynamic and thermodynamic models. The paper concludes with an outlook on applying the system for crew training, on-board support, and gradual automation of sustainable ship operations. Full article

Background/Objectives: Alzheimer’s disease (AD) is characterized by progressive neurodegeneration driven by interconnected mechanisms, including oxidative stress, mitochondrial dysfunction, neuroinflammation, synaptic impairment, and abnormal protein aggregation. MicroRNAs (miRNAs) have emerged as post-transcriptional regulators of these complex pathways; however, efficient delivery remains a major limitation. Small extracellular vesicles (sEVs) have been proposed as biologically compatible carriers for miRNA delivery. Methods: In this study, milk-derived sEVs were isolated, characterized, and loaded with microRNA-137-5p (miR-137-5p). Their effects were evaluated in an amyloid-β (Aβ)-induced in vitro AD model using SH-SY5Y human neuroblastoma cells. Oxidative stress markers, including reactive oxygen species (ROS), malondialdehyde (MDA), superoxide dismutase (SOD), lactate dehydrogenase (LDH), and glutathione peroxidase 1 (GPX1), were assessed. Inflammation- and neuroprotection-related gene expression analyses included intercellular adhesion molecule 1 (ICAM1), tumor necrosis factor alpha (TNF-α), and brain-derived neurotrophic factor (BDNF). Cytoskeletal injury was evaluated using neurofilament light chain (NfL). Mitochondrial stress markers included cytochrome c (Cyt-c), 8-hydroxy-2′-deoxyguanosine (8-OHdG), PTEN-induced kinase 1 (PINK1), dynamin-1-like protein (DNM1L), and mitochondrial transcription factor A (TFAM). Synaptic and extracellular matrix-associated proteins, including complexin-2 (CPLX2), SPARC-related modular calcium-binding protein 1 (SMOC1), and receptor tyrosine kinase-like orphan receptor 1 (ROR1), as well as AD-related biomarkers, including total tau, phosphorylated tau at threonine 181 (pTau-181), phosphorylated tau at threonine 217 (pTau-217), and amyloid-β 1–40 (Aβ1–40), were evaluated using molecular and biochemical approaches. Results: Aβ exposure was associated with increased oxidative stress, inflammatory activation, mitochondrial and cytoskeletal alterations, synaptic-related disturbances, and elevations in tau- and amyloid-associated proteins. Treatment with unloaded sEVs was associated with partial modulation of several parameters, whereas miR-137-5p-loaded sEVs were consistently associated with normalization of multiple pathological markers toward control levels. Conclusions: These findings indicate that miR-137-5p-enriched sEVs may represent a useful experimental platform for multi-target modulation of AD-related cellular alterations. Further mechanistic and in vivo studies are required to clarify translational relevance. Full article

In this paper, we prove that, on the one hand, a duality relationship between different types of modular T-transitive relations and the reciprocal modular generalized metrics exists and, on the other hand, that, based on this duality, a construction of functions that aggregate modular T-transitive relations can be made from functions aggregating generalized metrics. Furthermore, we provide a guide for the families of that type of functions that can be used for the aforementioned purpose. Finally, illustrative examples of how to create such functions via the duality are given. Full article

This study proposes a new formalized approach to the stabilization of linear transformations in artificial neural networks, based on discrete algebraic properties. In contrast to existing stability methods that rely on spectral norms, regularization techniques, or empirical heuristics, this work introduces the concept of algebraic stabilization—stability that arises from the structural properties of the matrices defining linear operators. The central object of investigation is the class of integer-valued matrices for which exponentiation to a form of the type ${\mathrm{W}}^{\mathrm{k}}=\mathrm{I}+\mathsf{\mu }\mathrm{D}$ is possible, where $\mathrm{D}\in {\mathbb{Z}}^{\mathrm{n}\times \mathrm{n}},\mathsf{\mu }\in {\mathbb{Z}}_{>1}$. A well-known problem in group algebra is considered that guarantees the existence of such an exponent under the condition that $\mathsf{\mu }$ is coprime with the determinant of $\mathrm{W}$. Within this framework, modular arithmetic, reduction modulo $\mathsf{\mu }$, and the group structure of ${\mathrm{G}\mathrm{L}}_{\mathrm{n}}\left({\mathbb{Z}}_{\mathsf{\mu }}\right)$ are employed, thereby linking the proposed method to the theory of finite groups and linear automata. The advantages of the approach are discussed, including formal control over the iterative behavior of transformations, compatibility with quantized and finitely representable networks, the possibility of embedding stabilizing conditions directly into the network architecture, and the potential to improve model interpretability and reliability. At the same time, limitations are identified, particularly those related to constructive implementation, the selection of suitable hyperparameters, and generalization to broader classes of transformations. Full article

Clinical diagnostic laboratories continue to face growing pressure from rising test volumes, increasingly complex testing menus, significant workforce shortages, and expectations for faster turnaround times at sustainable cost. Total laboratory automation (TLA) has become a central strategy for improving efficiency in high-volume laboratories, where integrated systems from Abbott, Roche, Siemens Healthineers, and Beckman Coulter have demonstrated substantial reductions in turnaround time, error rates, and labor requirements. Evidence across multiple health systems shows that TLA improves performance and stabilizes laboratory operations even during workload peaks. Despite these gains, large segments of pre-analytical and post-analytical workflows remain manual, especially tasks related to specimen transportation, bench-level manipulation, instrument tending, and troubleshooting. Recent progress in collaborative robotics (cobots), autonomous mobile robots (AMRs), and hospital service robots demonstrates that these technologies can complement TLA by addressing not only the logistical and dexterous tasks that fixed automation lines cannot reach but also enabling robots that can work safely right alongside humans in a shared space. Cobots have shown sub-millimeter precision in colony picking and other fine-motor tasks, though typically at lower throughputs than dedicated track modules, and AMRs have demonstrated reliable transport of pathology carts and medical supplies through large clinical environments. Meanwhile, humanoid-capable mobile manipulators, like Moxi from Diligent Robotics, deployed in hospitals are already completing hundreds of thousands of supply deliveries, indicating real-world significance. Here, we integrate technical, regulatory, operational, and business perspectives on TLA, collaborative robotics, and mobile platforms. We discuss real-world efficiency gains, regulatory expectations under the CLIA and United States FDA, and the emerging case for hybrid automation ecosystems that combine TLA islands, cobotic workcells, AMRs, and AI-enabled orchestration. We argue that the next decade of laboratory automation will move beyond monolithic tracks with robots toward flexible, modular robotic systems designed to operate safely together with humans and to augment the increasingly strained laboratory workforce. This not only allows clinical staff to dedicate more time to patient care but also ensures greater reliability and scalability for essential services throughout demanding hospital environments. Full article

Human pose classification, along with related tasks such as action recognition, is a crucial area in deep learning due to its wide range of applications in assisting human activities. Despite significant progress, it remains a challenging problem because of high inter-class similarity, dataset noise, and the large variability in human poses. In this paper, we propose a lightweight yet highly effective modular attention-based architecture for human pose classification, built upon a Swin Transformer backbone for robust multi-scale feature extraction. The proposed design integrates the Spatial Attention module, the Context-Aware Channel Attention Module, and a novel Dual Weighted Cross Attention module, enabling effective fusion of spatial and channel-wise cues. Additionally, explainable AI techniques are employed to improve the reliability and interpretability of the model. We train and evaluate our approach on two distinct datasets: Yoga-82 (in both main-class and subclass configurations) and Stanford 40 Actions. Experimental results show that our model outperforms state-of-the-art baselines across accuracy, precision, recall, F1-score, and mean average precision, while maintaining an extremely low parameter count of only 0.79 million. Specifically, our method achieves accuracies of 90.40% and 87.44% for the 6-class and 20-class Yoga-82 configurations, respectively, and 94.28% for the Stanford 40 Actions dataset. Full article

Shagamite, KFe₁₁O₁₇ (IMA 2020-091) was discovered in the ferrite zone of gehlenite hornfels from the Hatrurim Complex exposed near Mt. Ye’elim, Hatrurim Basin, Israel. The mineral occurs in outer zones of gehlenite rock blocks that were heterogeneously altered by high-temperature (>1200 °C) ferritization. Ferritization was induced by K-bearing fluids or melts, generated as a by-product of late combustion processes. Shagamite crystallized from a thin melt that formed on the rock surface during cooling to approximately 800–900 °C. It is mainly associated with minerals of the magnetoplumbite group like barioferrite, Sr-analog of barioferrite, and gorerite but also with magnetite, maghemite, harmunite, devilliersite and K(Sr,Ca)Fe₂₃O₃₆ hexaferrite. Shagamite is a modular compound with a β-alumina-type structure (P6₃/mmc, a = 5.9327 (5), c = 23.782 (3) Å, γ = 120°, V = 724.91 (13) ų, Z = 2), and it is isostructural with diaoyudaoite, NaAl₁₁O₁₇, and kahlenbergite, KAl₁₁O₁₇. Its structure is also closely related, though non-isotypic, to those of the magnetoplumbite-group minerals. Shagamite is dark brown with a semi-metallic luster and forms platy crystals flattened on (001). Its mean empirical formula is: (K_1.00Ca_0.15Mn²⁺_0.05Na_0.04Rb_0.01)_Σ1.25(Fe_10.36Mn²⁺_0.15Al_0.14Mg_0.12Zn_0.10Ni_0.07Cu_0.03Cr³⁺_0.02Ti⁴⁺_0.01)_Σ11.00O₁₇. The Vickers microhardness VHN₂₅ = 507 kg/mm² corresponds to a Mohs hardness of ~5. The calculated density, based on the empirical formula and unit-cell parameters, is 4.12 g·cm⁻³. The main bands in the Raman spectrum of shagamite occur at 685 and 715 cm⁻¹ and are assigned to ν₁(FeO₄)⁵⁻ tetrahedral vibrations. Full article

Accurate and timely diagnosis of rice leaf diseases is crucial for smart agriculture leveraging vision sensors. However, existing lightweight convolutional neural networks (CNNs) often struggle in complex field environments, where small lesions, cluttered backgrounds, and varying illumination complicate recognition. This paper presents I-GhostNetV3, an incrementally improved GhostNetV3-based network for RGB rice leaf disease recognition. I-GhostNetV3 introduces two modular enhancements with controlled overhead: (1) Adaptive Parallel Attention (APA), which integrates edge-guided spatial and channel cues and is selectively inserted to enhance lesion-related representations (at the cost of additional computation), and (2) Fusion Coordinate-Channel Attention (FCCA), a near-neutral SE replacement that enables efficient spatial–channel feature fusion to suppress background interference. Experiments on the Rice Leaf Bacterial and Fungal Disease (RLBF) dataset show that I-GhostNetV3 achieves 90.02% Top-1 accuracy with 1.831 million parameters and 248.694 million FLOPs, outperforming MobileNetV2 and EfficientNet-B0 under our experimental setup while remaining compact relative to the original GhostNetV3. In addition, evaluation on PlantVillage-Corn serves as a supplementary transfer sanity check; further validation on independent real-field target domains and on-device profiling will be explored in future work. These results indicate that I-GhostNetV3 is a promising efficient backbone for future edge deployment in precision agriculture. Full article