The segregation of dopants at grain boundaries (GB) affects the bulk mechanical and electrical properties, as well as the microstructure evolution in polycrystalline materials. The structure and atomic arrangements at GBs play a crucial role in dictating the segregation behaviors. However, the effect of different GB arrangements within a single GB on the segregation behavior is not well understood yet. For this reason, a near-Σ3 twist boundary was fabricated to introduce GB-site anisotropy using magnesium aluminate spinel single crystals doped with yttrium (Y). The small deviation angle from the exact Σ3 configuration introduced a network of secondary GB dislocations identified as Shockley partials (1/6<112>). Detailed atomic-resolution imaging on conventional cross-sectional projection, as well as a pseudo-plan-view projection, was carried out to directly observe the Y segregation. Three distinct Y segregation behaviors were identified within a single GB: ordered (between dislocation network), disorder (near dislocations), and negligible Y segregation (remaining regions between the dislocation network). Atomic-resolution imaging and atomic configuration models of the GB revealed that the distinct segregation behaviors were correlated to changes in the local atomic configurations, especially the arrangement of O atoms. The ordered Y segregation was spatially correlated to coherent and symmetric segments of the boundary with larger excess free volumes. The regions with no Y segregation presented an O atom arrangement similar to that in the bulk lattice, where the limited free volume hindered the Y segregation. These results provided experimental evidence of how different atomic arrangements led to distinct segregation behaviors within a single GB.

Abstract Defined Solid Angle (DSA) alpha spectrometry is a primary method for absolute activity determination of Radon-222, in which radon is cryogenically depositing onto a polished cold disk under vacuum and counted in a fixed source–detector geometry. In this approach, the detection efficiency is determined exclusively by the normalized solid angle, expressed as the Geometry Factor $$G$$ G . The accuracy of activity determination therefore depends directly on the precise evaluation of $$G$$ G and its associated uncertainty. This work presents a comprehensive numerical investigation of the sensitivity and uncertainty of $$G$$ G with respect to the governing geometrical parameters: source–diaphragm distance $$z$$ z , diaphragm radius $$a$$ a , source radius $$b$$ b , and eccentricity $$e$$ e . Calculations were performed using both the Knoll (Radiation detection and measurement, 4th ed. Wiley, New York, S120, 2010) and Curtis (Nucleus 13:38, 1955. https://doi.org/10.1016/S0168-9002(96)80029-5 ) analytical extensions, enabling systematic parameter variation and direct model comparison. The results show excellent agreement between the two models for concentric configurations ( $$e$$ e = 0 mm). The Geometry Factor is found to be most sensitive to variations in $$z$$ z and $$a$$ a ; however, uncertainty propagation analysis demonstrates that the dominant contribution to the combined uncertainty arises from $$z$$ z , accounting for approximately 88% – 97% of the total variance under typical National Metrology Institute (NMI) conditions. In contrast, the influence of $$b$$ b is significantly smaller, and eccentricities up to 1 mm produce only minor variations in $$G$$ G . These findings quantitatively identify the source–diaphragm distance as the critical parameter limiting uncertainty reduction in DSA-based primary standardization of Radon-222. The study provides a clear metrological framework for optimizing geometry control and supports the harmonization of high-accuracy activity measurements across NMIs, enabling more stable and reliable transfer standards for secondary laboratories and end-users.

The paper presents borophene nanoribbons (BNRs) as a strong candidate for next‐generation on‐chip interconnects, addressing the scaling, surface roughness, and thermal limitations of copper and the performance constraints of graphene at sub‐nanometer technology nodes. Quantum equivalent‐circuit models and an analytical mean free path formulation demonstrate that BNRs exhibit higher normalized conductance and lower effective resistivity, particularly with Fermi energy tuning in the range of 0–0.4 eV. Extending this analysis to multilayer BNRs, temperature‐aware analytical models for armchair and zigzag configurations reveal low resistance, favorable inductance, and capacitance over a wide temperature range (250–500 K) at the 7 nm node. Benchmarking against graphene and conventional copper interconnects shows that MLBNRs deliver comparable or superior electrical and thermal performance, establishing borophene as a highly promising material for future nanoscale interconnect applications.

Abstract The rate of penetration (ROP) is a key metric in drilling performance, directly impacting well construction time and cost. In complex and deviated wellbore trajectories, ROP is often reduced due to lateral forces, torque, drag, and vibration acting on the Bottom Hole Assembly (BHA), leading to dysfunctions like stick-slip and buckling. These mechanical inefficiencies not only affect drilling speed but also compromise the structural integrity of the BHA, potentially increasing non-productive time (NPT). Traditional BHA designs often rely on static analysis and rule of thumb sizing, which may not be adequate in dynamic, high-curvature environments. Therefore, integrating simulation-based analysis becomes essential for anticipating real time mechanical responses. This study presents an iterative strategy BHA design configurations for the 12-1/4” section of the R-3 well using API RP 7G principles integrated with WELLPLAN ™ simulations. The results show a 55.2% reduction in lateral forces and improved ROP in high-curvature zones, demonstrating the effectiveness of dynamic modelling in enhancing drilling performance in challenging directional wells. The findings emphasize the value of a performance-based approach to BHA design, incorporating trajectory-specific load behaviour and stiffness matching to mitigate downhole risks and improve operational efficiency.

Benchmarking machine learning models against full-scale acoustic absorption measurements in gas turbine liners

The rapid adoption of cloud computing has fundamentally reshaped the design, delivery, and governance of Information Technology Service Management (ITSM). Traditional on-premises service management architectures are increasingly inadequate for supporting elastic infrastructure, distributed service ownership, and continuous delivery models. This presents a set of conceptual models for cloud-based IT Service Management architecture and orchestration using the ServiceNow platform as an enterprise workflow backbone. The proposed models integrate principles from cloud-native design, service-oriented architecture, and enterprise governance to enable scalable, resilient, and highly automated ITSM capabilities across complex organizational environments. This conceptualizes ServiceNow not merely as an ITSM tool, but as a unifying digital workflow layer that orchestrates processes, data, and integrations across hybrid and multi-cloud ecosystems. Core architectural constructs are defined, including service abstraction layers, configuration and service mapping models, event-driven orchestration patterns, and API-centric integration frameworks. These constructs support seamless coordination between cloud infrastructure providers, application platforms, and enterprise operations functions such as incident, problem, change, and request management. Emphasis is placed on the role of centralized data models, real-time telemetry ingestion, and policy-driven automation in enabling proactive service assurance and operational agility. Additionally, this examines governance and control mechanisms embedded within the architectural models, including role-based access, compliance automation, and performance visibility across distributed services. By aligning ServiceNow’s platform capabilities with cloud operating models, the framework enables consistent service experiences while accommodating rapid scaling, frequent change, and decentralized delivery teams. The conceptual models are intended to guide enterprise architects, ITSM leaders, and platform owners in designing robust cloud-based service management ecosystems that balance standardization with flexibility. Overall, this work contributes a structured architectural perspective on how ServiceNow can be leveraged to orchestrate cloud-based IT services at scale, supporting improved reliability, transparency, and business alignment in modern digital enterprises. Keywords: Cloud-based ITSM, ServiceNow, IT Service Architecture, Workflow Orchestration, Cloud Governance, Enterprise Service Management, Digital Operations.

Purpose Accurate interpretation of frequency response analysis (FRA) data is crucial for power transformer windings. To address this challenge, this study suggests a new auto-synthesis method to derivate a high-frequency Equivalent Ladder Network Circuit Model (ELNCM) aimed at investigating and interpreting the transformer winding (TRW) characteristics. Design/methodology/approach The precise ELNCM is attained using the transfer function (TF) extracted from the measured FRA data. From where, five winding parameters are recited, mainly, the total ground capacitance Cgeff, the equivalent inductance Leq and capacitance Ceq, the voltage distribution factor α and series capacitance Cs. After that, the precise self and mutual inductances (Ls,Mij) of the proposed ELNCM identification are carried out using artificial intelligence (AI). As the self and mutual inductances cannot be measured directly, this paper presents an AI approach, namely, Logistic Chaotic Archimedes Optimization Algorithm (LCAOA) for improved precision. Findings The physical parameters (Leq,Ceq,Cgeff,α, Cs ) are directly extracted from the FRA data, which is very useful in practical studies. Through the obtained results, actual identification is ensured by faithfully seeing the FRA curves created from TF-AI-ELNCM. Originality/value Going further, unlike previous studies, the proposed method eliminates the need for geometrical data and not necessitate any specific arrangement on the transformer winding. The methodology presented offers substantial practical benefits for transformer winding diagnosis, it eliminates the need for lookup tables or specific configuration models.

Chromatin conformation is thought to be critical for enhancer function, but its dynamic, nanoscale organization is difficult to measure directly. Here we introduce PLOTTED (Probabilistic Localization of Oligopaint Tagged Target Element Distances), an integrated imaging and computational framework that infers chromatin architecture from targeted high-resolution imaging of cis-regulatory modules (CRMs). PLOTTED generates spatial distance distributions between DNA loci, enabling quantitative modeling of chromatin configurations across developmental time, spatial axes, and genotypes. Applying PLOTTED to the brinker locus in Drosophila embryos, we measured distances among three CRMs and used chromatin geometry as a proxy for regulatory activity. In wild type, CRM configurations shift dynamically at nuclear cycle 13, whereas these changes are delayed in mutants and vary along the dorsal-ventral and anterior-posterior axes. Importantly, these conformational changes correlate with altered gene expression. Together, our findings position PLOTTED as a probabilistic, single-locus framework for interpreting chromatin architecture in development and disease.

Performance improvement of a hybrid heat pump system with PVT using DNN and optimization algorithms

A High-Fidelity and High-Efficiency Simulator for 6G-Integrated Space–Ground Networks

Post-earthquake recovery of electric power networks (EPNs) is critical to community resilience. Traditional recovery processes often rely on prolonged and imprecise manual inspections for damage diagnosis, leading to suboptimal repair prioritization and extended service disruptions. Seismic Structural Health Monitoring (SSHM) offers the potential to expedite post-earthquake recovery by enabling more accurate and timely damage assessment. However, the deployment of SSHM comes with a cost and the quantifiable benefit of SSHM in terms of system-level resilience remains underexplored. This study develops an integrated probabilistic simulation framework to quantify the system-level value of SSHM in enhancing EPN resilience. The framework incorporates damage simulations based on EPN configuration, seismic hazard, fragility function, and damage-functionality mapping models, along with recovery simulations considering repair scheduling, resource constraints, transfer and repair durations. System functionality is evaluated via graph-based island detection and optimal power flow analysis under electrical constraints. Resilience is quantified using the Lack of Resilience (LoR) metric derived from the time-evolution functionality restoration curve. The effect of SSHM is incorporated by altering the quality of damage information used to create repair schedules. Specifically, different monitoring scenarios (e.g., no-SSHM baseline, partial SSHM, and full SSHM with various assessing accuracy levels) are modelled using observation matrices that simulate misclassification of component damage states. The results demonstrate that improved damage awareness enabled by SSHM significantly accelerates recovery and reduces LoR by up to 21%. This study provides a quantitative foundation for evaluating the system-level resilience benefits of SSHM and guiding evidence-based sensor investment decisions for critical infrastructures.

The aim of this work is to develop and verify a rationalized approach to the parameters for finalizing industrial coal reserves at coal mining enterprises. The research was conducted in several stages. The first stage involved determining the optimal configuration of productive flows depending on the production situation. The second stage, based on the application of the Analytic Hierarchy Process (AHP method), involved defining the optimal strategy for finalizing industrial reserves.The rationalization of coal mining enterprise parameters consists in substantiating the configuration of productive flows and applying the AHP method to select a strategy for finalizing industrial reserves. The use of the Cobb-Douglas function allows for modeling the technical and economic indicators of a mine's operations under various initial mining and geological conditions and coal quality parameters.Based on the modeling of productive flow configurations, rational volumes of coal extraction were determined under different production scenarios. It was established that coal ash content is of primary importance when choosing a strategy for finalizing industrial reserves. If the coal's ash content is less than 20%, the most effective scenario is to maximize coal extraction. For ash content indicators of 20–50%, the most effective scenario is the processing and utilization of coal mining waste and the maintenance of coal extraction. The utilization of methane and the treatment of wastewater at enterprises that are finalizing industrial reserves are not justified.The application of the AHP method confirmed the hypothesis that the optimal strategy for finalizing industrial coal reserves at coal mining enterprises is to maximize the volume of waste rock processing. The scenario that involves extracting rare-earth materials from the rock proved to be the least optimal. The obtained results made it possible to formulate recommendations regarding the directions for supporting the viability of low-power coal mining enterprises at the stage of finalizing industrial reserves, as well as to determine the most optimal strategy for mineral extraction.

The purpose of this research is to optimize shaping filter parameters in order to improve the accuracy of frequency offset estimation in satellite communication channels, taking into account high Doppler shift values. The study presents comprehensive modeling of various system configurations using Orthogonal Frequency Division Multiplexing, Orthogonal Frequency Division Multiple Access, Localized Frequency Division Multiple Access, and Interleaved Frequency Division Multiple Access technologies with different types of shaping filters, including traditional Raised Cosine and Root Raised Cosine filters, as well as the authors’ developed Band-Type Raised Cosine filter and its optimized version. The offered method, which combines flexible guard interval and optimized shaping filtration, significantly improves the accuracy of frequency offset estimation and ensures reliable data transmission in Low Earth Orbit satellite networks for fifth and sixth generation communications.

This paper addresses the problem of optimizing transaction performance in relational databases used in warehouse management systems. In response to the growing demands for scalability and efficient data processing in high-load environments, the research aims to evaluate the impact of structural configurations—particularly indexing and partitioning—on the execution speed of key business operations. PostgreSQL was selected as the experimental platform due to its advanced optimization capabilities and wide industrial adoption. The study employs a relational data model comprising eight interrelated tables representing a typical warehouse system, including products, categories, suppliers, stock balances, and disposal records. Three architectural configurations of the main operational table (warehouse stock) were developed, including default indexing and combinations of partitioning and composite indexing on warehouse and product identifiers. Each configuration models a distinct approach to database optimization under intensive transactional load. To assess performance objectively, the pgbench benchmarking tool was used with multithreaded workloads and fixed-duration tests. Four core operations—stock availability check, product transfer between warehouses, goods intake, and inventory disposal—were implemented as SQL procedures simulating real-world scenarios. Each test scenario yielded performance metrics in terms of transactions per second, enabling comparative evaluation of all configurations. The proposed methodology allows for a systematic assessment of how architectural decisions influence relational database performance in transaction-heavy systems. The experiment provides a foundation for further research involving larger datasets, alternative partitioning schemes, or comparative evaluation with other DBMS platforms and hardware setups. The work holds practical significance for professionals involved in database architecture, performance optimization, and the development of scalable warehouse management solutions.

ABSTRACT In this paper, we analyze the process that led Afro-Brazilian quilombola communities within a conservation area to incorporate agroforestry systems as a bridge between external and internal demands about agri-food systems and environmental governance. The adoption of agroforestry systems, introduced from the outside as a potential replacement for quilombola swidden practices, was initially resisted by quilombola farmers. We examined three key dimensions underlying agricultural adoption: preexisting conditions, adjusted transfer of technology, and assessment of opportunity costs. Within these dimensions, we examine factors contributing to agroforestry adoption against a background of land tenure insecurity caused by the overlap between a protected area and the quilombolas’ territories. An important factor explaining agroforestry adoption is the high level of flexibility and agency afforded to farmers who were able to adapt and change agroforestry configuration models to fit their social-ecological context and individual preferences.

Electromagnetic radiation of the terahertz range (THz) is used in various fields of science and technology: medical diagnostics, security and control, sensor and communication technologies, scientific research, etc. Computer simulation in the development and research of THz receivers significantly reduces costs and time-to-market by replacing expensive experiments. Advanced software suites, like HFSS, facilitate the accurate modeling of intricate three-dimensional configurations and the analysis of component electrodynamic performance. This study leveraged HFSS to simulate the performance of a THz receiver across the 0.4–1.4 THz band. The accuracy of the simulations relies on the fidelity of the model, encompassing geometric details, material properties, and chosen simulation parameters. This paper proposes a three-dimensional model of an original selective compact receiver of terahertz electromagnetic radiation (with a conversion efficiency of~97 %) for two resonance frequencies, consisting of a sensor based on two open apodized periodic microcavity structures with a fill factor changing according to a linear law, a matching element in the form of an asymmetric irregular triangular strip line, and a detecting diode. The design offers key advantages including high conversion efficiency, suitability for matrix architectures, high selectivity to registered radiation, and the potential for simple readout methods, making it an attractive approach for THz sensing, an important tool across various scientific and practical applications.

ABSTRACT Markerless (ML) motion capture has emerged as a viable option to marker-based (MB) motion capture in estimating movement biomechanics, but limited data exists on the accuracy of ML systems during high-speed throwing. This study evaluated the accuracy and reliability of an in-stadium (Hawk-Eye) and a portable (Theia3D) ML motion-capture system in quantifying baseball pitching kinematics and kinetics relative to an MB reference. Eighteen collegiate pitchers were simultaneously recorded using all three systems. Mean per-joint position error (MPJPE), statistical parametric mapping (SPM), root mean square error (RMSE), Bland-Altman analysis, and concordance correlation coefficients (CCC) were used to assess agreement. Both ML systems demonstrated measurable discrepancies across variables, with MPJPE values of 56.6 ± 9.4 mm (Hawk-Eye) and 52.0 ± 12.3 mm (Theia3D). Stride length exhibited the strongest agreement with MB in both systems (CCC > 0.85), whereas shoulder rotational variables showed greater variability. Error magnitudes in joint positions and kinematic waveforms were comparable to those reported for other ML systems during dynamic movements. These results highlight the influence of system configuration, camera deployment, and pose-estimation models on biomechanical accuracy. Overall, both configurations showed potential for estimating pitching biomechanics, underscoring the trade-offs between criterion and ecological validity in markerless motion capture.

Models of porous electrode systems and configurations have been adapted from manufacturing techniques developed in powder science and technology. As a result, modeling methodologies of porous electrode systems have taken the route of treating this as a network of particles. For example, in electrochemical impedance spectroscopy (EIS), transmission line (TL) models have been used to model a single particle in this network. Generally, in TL models, a circuit analogy for one particle and its contact with other particles is developed as a unit circuit, from which a complete circuit analogy is constructed to model the entire porous electrode based on the connectivity, contact points between particles, apparent thickness, etc., in this porous electrode network. Despite this foundation from the powder science and technology field, key interfacial and electrochemical processes are not fully captured. For example, in plating and stripping systems, particles in a porous electrode can undergo size changes that change the overall particle size distribution of the electrode material and the void volume available for mass transport in the electrolyte. Consequently, the overall resistivity of the porous electrode increases due to particles losing contact with one another and a decrease in the available pathways for current to enter and exit through the current collector. Parasitic reactions at the surface of these particles may lead to passivation, which otherwise ideally induces electroplating upon reversing the polarity across the cell. Some particles may never be re-incorporated into a connected network of particles, effectively rendering those particles electrochemically inactive. Therefore, the complex heterogeneous reaction kinetics, or interfacial processes, that occur on the surface of each electrode particle is a network that governs the behavior of the porous electrode system. Thus, a different modeling paradigm is needed to reconcile the description of these two relevant networks at two scales: (1) the porous electrode network and (2) the heterogeneous reaction kinetics for each electrode particle in the porous electrode network.This paradigm is developed by considering one particle in a porous electrode system and adapting two different modeling approaches into a single framework using zinc as an example: (1) phase-field (PF) modeling and (2) reacting engineering. PF modeling has successfully been applied to moving boundary problems driven by both thermal and electrochemical driving forces, such as the growth of dendritic morphologies in problems regarding supercooling and electrodeposition or plating. However, its diffuse interface description does not accommodate a description of the complex chemical kinetics at the electrode interface, such as those used in reaction engineering, which is not without its shortcomings. In reaction engineering, a surface site balance is used to model complex heterogeneous reaction kinetics, but this description faces an obstacle when applied to moving boundaries. Surface site balances are closed when the total number of surface sites is finite, which is essential to closing this balance for the analysis of heterogeneous reaction networks. This constraint is predicated on the Langmuir description of reaction kinetics, which assumes that the surface area of the interface is constant and, by extension, the total number of surface sites. Therefore, a modeling paradigm that combines key features of both modeling approaches is put forth to understand better porous electrode systems, such as the zinc anode.This work finds (1) key limitations in traditional porous electrode theory and modeling approaches and (2) introduces a framework that merges concepts from interfacial science, reaction engineering, and transport phenomena. The analytical tools used to study mass transfer and reaction kinetics are applied from this modeling framework. For example, the rate of passivation and electrodeposition as a function of surface curvature, temperature, etc., are compared at the surface of the anode. Meanwhile, dendrite formation in electrodeposition, attributed to be a mass transfer limited process, is examined in the same modeling framework. Insight is developed into two networks of interest: (1) the connectivity of particles in the porous electrode and (2) the set of complex coupled reaction kinetics (electrochemical and otherwise). Thus, the reconciliation of these two modeling methodologies provides deeper insight into porous electrode systems, which is demonstrated using zinc and can be extended to other metal-electrode materials in general.

This study optimizes vehicle suspension dynamics by introducing a controllable degree of nonlinearity, characterized by a parameter ε, into the spring element of Inerter-Spring-Damper (ISD) systems. Quarter-vehicle models for parallel and series ISD configurations are established, and a multi-objective genetic algorithm optimizes the parameters under random road excitation to minimize body acceleration (BA), suspension working space (SWS), and dynamic tire load (DTL). Results demonstrate that optimizing ε brings advantages: compared to a conventional passive suspension, the optimized parallel ISD suspension reduces BA, SWS, and DTL by 7.98%, 8.57%, and 1.69%, respectively, with the BA reduction notably improving from 5.94% (achieved by the linear ISD with ε = 0) to 7.98%. Similarly, the optimized series ISD achieves reductions of 2.53%, 7.62%, and 6.42% in BA, SWS, and DTL, showing a more balanced enhancement over its linear counterpart. The analysis reveals how ε distinctly influences the performance trade-offs, validating that strategically tuning the spring nonlinearity degree, in synergy with the inerter and damper, provides an effective method for superior suspension performance customization.

The increasing integration of renewable energy systems into the power grid necessitates enhanced demand-side flexibility in buildings. Data-driven model predictive control (DMPC) has emerged as a promising approach for such energy management task. To advance research in this field, this paper introduces a comprehensive simulation environment designed to facilitate the development and evaluation of DMPC solutions for buildings. This development framework:• encompasses a customizable zone model for multiple room configurations,• is designed with a particular emphasis on thermally activated building structures (TABS) and solar shading control,• integrates DMPC algorithms, with adaptable state space model structures, or using reinforcement learning algorithms, supporting various optimization architectures (centralized, decentralized…).To ensure real-world applicability, the simulation environment has been validated using over one year of data from a living-lab office building. Validation results of the thermal zone models demonstrate good accuracy, with mean absolute errors below 0.5 °C across all zones and simulation time steps (1 and 15 min). The simulation environment exhibits robust performance in simulating complex building systems, effectively capturing the dynamics of thermally activated components and solar shading mechanisms. This versatile framework enables researchers and practitioners to address the challenges posed by increasing building complexity and growing need for decentralized control approaches.