Configuration Of Components Research Articles

Digital transformation, innovation capabilities, and servitization as drivers of ESG performance in manufacturing SMEs

This study investigates the relationship between digital transformation and Environmental, Social, and Governance (ESG) performance in the context of SMEs. Drawing upon Resource Orchestration Theory, this research proposes a theoretical model that examines the direct effect of digital transformation on ESG performance and the mediating roles of innovation capabilities and servitization level in this relationship. PLS-SEM and fsQCA were employed to analyze survey data from 215 SME executives. The results reveal that digital transformation positively influences ESG performance. Moreover, innovation capabilities and servitization level partially mediate the relationship between digital transformation and ESG performance. This research contributes to the literature by proposing and validating a comprehensive model that integrates these constructs, offering actionable insights for SME managers and policymakers to enhance ESG outcomes through strategic digital initiatives. Notably, fsQCA results highlight three distinct configurations of digital transformation components leading to high ESG performance, providing nuanced pathways for SMEs to achieve sustainable development.

Optimization on initial configuration of monolithic component for machining deformation control

Optimization on initial configuration of monolithic component for machining deformation control

Understanding Solid Oxide Fuel Cell Hybridization: A Critical Review

A holistic literature review of 399 articles is presented on hybrid solid oxide fuel cell (SOFC) systems and taxonomized through a unique classification methodology that considers primary energy sources, component configurations, system outputs and relevant analysis. In the reviewed research articles, primarily four different types of technologies are used to complement the energy production of the fuel cell stack. As a result, four respective categories were established in terms of the coupling components, inclusive of (1) wind turbines (WTs), (2) solar systems (SS), (3) thermal power plant turbines (TPPTs) and (4) piston engines (PEs). Additional classification was needed to further subdivide the evaluated system configurations, due to the wide spectrum of system outputs encountered in the reviewed literature. That involved (a) electrical power generation (PG), (b) four types of cogeneration (CG), (c) four types of trigeneration (TG) and (d) two types of multigeneration (MG). Depending on the type of assessment in respective research, literature was further categorized as computational, experimental, or both computational and experimental. The most prevalent hybrid system amongst the 102 designs, as adapted from the reviewed literature, is the SOFC/gas turbine (GT) application, with the overwhelming majority producing exclusively electrical power output. The particular system is physically demonstrated in an existing 220-kW experimental facility established by the National Fuel Cell Research Center (NFCRC). The identified systems were predominantly computationally examined and research focused on operating conditions around specific design load ratings. Energy, exergy, economic, and environmental assessments were typically followed by sensitivity studies in the reviewed literature. The primary decision variables in most cases revolved around SOFC operating conditions, such as temperatures over 1000 °C as well as elevated pressures. Those resulted in the deterioration of the electrochemical cell and failed to be addressed by the majority of encountered studies. Improvements in the conversion efficiency of PG applications were enabled by natural gas (NG)-fed pressurized SOFCs, directly coupled to GTs. The most efficient power configuration in terms of thermodynamic performance was achieved by coupling PEs to SOFCs and a metal hydride reactor (MHR) to unlock a maximum net system efficiency of 79.54%. CG systems integrating solar cells (SCs) and photovoltaic panels (PVs) to SOFC subsystems can result in a combined efficiency of approximately 90% and sustain this over a broad energy demand range. TG systems incorporating gasifiers to SOFC/GT subsystems achieved a combined efficiency of 93%, whereas the optimal combined efficiency in MG configurations was identified at 79.5% by coupling SCs to the SOFC/GT/organic Rankine cycle turbine (ORCT) subsystems. In terms of cost-effectiveness, configurations that relied on increasing levels of renewable energy (RE) technologies resulted in high overall levelized cost of electricity (LCOE). This cost was increased further when power storage units were deployed to address wind and solar intermittency challenges. Configurations classified under TPPTs operating on low carbon-content fuels were preferred in terms of providing lower LCOE, while applications operating on gasified biomass and coal blends should deploy additional CO2 capture and storage units to mitigate respective emissions.

Deformation Characteristics and Influence Factors of Shear Force Lateral Stiffness Matching Index for Non-Rigid Plate Bent Frame Structures

The period ratio and the drift ratio are commonly used as plane regularity control indices for multi-story buildings. However, they fail to reasonably reflect the regularity of lateral force-resisting component configuration and deformation characteristics in non-rigid plate bent frame structures. This study focuses on the analysis of non-rigid single-span bent frames, examining the variation patterns of a suitable regularity index for non-rigid plate bent frame structures, referred to as the shear force lateral stiffness matching index, under various parameters. Additionally, it introduces indices to quantify the deformation response of non-rigid plate bent frame structures, providing a detailed analysis of the impact of factors such as eccentricity, torsional stiffness, and roof slab stiffness on the deformation characteristics of non-rigid plate bent frame structures and the shear force lateral stiffness matching index. The results show that the shear force lateral stiffness matching index can reflect the inconsistency in the horizontal displacement response of lateral force-resisting components caused by deformations in the roof slab. The proposed indices for torsional and bending deformations accurately quantify the roof slab’s deformation response, revealing the horizontal deformation characteristics of lateral force-resisting components in non-rigid frames. When eccentricity is present, the stiffness of the roof slab has a non-monotonic effect on the torsional component of the structural seismic response.

Airline business models as complex systems: assessing component interdependencies through interpretive structural modelling

PurposeThis paper explores the intricate interdependencies among core components of airline business models (BMs). In the airline industry, where BMs are complex systems, a successful BM requires an orchestrated configuration of various components. However, there is a paucity of research in BM literature pertaining to the interrelationships among key components of airline BMs.Design/methodology/approachEmploying interpretive structural modelling, we gathered input from experts in Iran to assess the driving power and dependency of elements within airline BMs.FindingsOur findings highlight the significance of operating environment conditions and competitive market dynamics as pivotal external components shaping the foundational structure. Value proposition, customer relationship management, and process monitoring are crucial linkage components that drive power and dependency. Notably, capturing value is positioned with the highest dependency.Practical implicationsWe utilised the ISM technique to visualize interdependencies within airline business models, aiding strategic decision-making. Our findings suggest aligning business and operational strategies with market needs ensures effective value creation and capture, maintaining competitive advantage in the airline industry. In addition, our research reveals critical factors affecting value creation and capture, emphasising monitoring the operating environment and competitive market, and strategically managing value propositions and customer relationship initiatives in the airline industry. We advise adapting business models to external changes for sustained growth and recommend regular monitoring of industry trends and customer expectations.Originality/valueFramed within complexity theory, these insights offer valuable perspectives on identifying and situating critical BM components in the airline industry. The practical implications derived from this study serve as strategic tools for airline managers and potential investors to optimise the design of their airline BMs.

Determining optimal component configurations for flexible biogas plants based on power prices of 2020–2022 and the legislation framework in Germany

The aim of this study is to find optimal component configurations for flexible biogas plants considering electricity prices in Germany, for maximum annuities considering costs and revenues. Cases for solely power-price driven operation and cogeneration-led operation were investigated for electricity prices in 2020, 2021, and 2022 in hourly resolution.Additional revenue from flexible operation increases as price volatility increased from 2020 to 2022. Interestingly, the choice of capacity for the combined heat and power unit (CHPU) is determined by the expected service life of the CHPU. Irrespective of electricity market signals, power quotients (PQ) of at least PQ = 4 avoid reinvestment for the CHPU within 20 years.Another result is that the the optimality region for CHPU capacity flattens for 2022 and shift to capacities above PQ = 4. Thus, high price volatility will allow plant operators to amortise even larger CHPUs, which could stimulate the installation of peak load by flexible biogas plants. Considering the day-ahead market can provide clear recommendations for optimal component configurations.The presented Smart-Force-Optimisation (SFO) combines a prefiltering of schedules matching given constraints and a brute force optimisation to the filtered subset. Thus, SFO has an improved performance comparing to bare brute force approaches.

The Advancement of Artificial Intelligence's Application in Hybrid Solar and Wind Power Plant Optimization: A Study of the Literature

The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, adopting hybrid technologies has engendered a positive trend, marked by broader considerations of configurations and applications within these systems. This study analytically examines the potential of hybrid solar and wind energy harvesting devices. As such, the project aims to extensively evaluate relevant literature and statistical analysis of data extracted from journal papers published between 2004 and 2023. A specific objective is to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures. Moreover, an assessment of the socio-economic, environmental, and ecological impacts of these systems is undertaken to ascertain their salience. Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. Critical lacunae identified during this review pertain to more emphasis on optimization metrics for PV-wind hybrid energy systems, impeding a holistic understanding of their implications on energy, economics, environment, and society. Our findings underscore prevalent methodologies such as computational modelling utilizing software suites like MATLAB/Simulink, HOMER, and others to derive empirical data. Additionally, parametric analyses emerge as the predominant approach, characterized by the application of algorithms such as Particle Swarm Optimization (PSO), Fuzzy Logic Control (FLC), and Genetic Algorithms (GA), among others. PV-wind hybrid energy systems are classified into autonomous and grid-interconnected configurations, with primary components comprising PV-wind generators. The anticipated trajectory suggests a burgeoning development of these hybrid energy harvesting systems, underpinned by their potential as clean, sustainable, and eco-friendly energy sources.

Multi-class segmentation of temporomandibular joint using ensemble deep learning

Temporomandibular joint disorders are prevalent causes of orofacial discomfort. Diagnosis predominantly relies on assessing the configuration and positions of temporomandibular joint components in magnetic resonance images. The complex anatomy of the temporomandibular joint, coupled with the variability in magnetic resonance image quality, often hinders an accurate diagnosis. To surmount this challenge, we developed deep learning models tailored to the automatic segmentation of temporomandibular joint components, including the temporal bone, disc, and condyle. These models underwent rigorous training and validation utilizing a dataset of 3693 magnetic resonance images from 542 patients. Upon evaluation, our ensemble model, which combines five individual models, yielded average Dice similarity coefficients of 0.867, 0.733, 0.904, and 0.952 for the temporal bone, disc, condyle, and background class during internal testing. In the external validation, the average Dice similarity coefficients values for the temporal bone, disc, condyle, and background were 0.720, 0.604, 0.800, and 0.869, respectively. When applied in a clinical setting, these artificial intelligence-augmented tools enhanced the diagnostic accuracy of physicians, especially when discerning between temporomandibular joint anterior disc displacement and osteoarthritis. In essence, automated temporomandibular joint segmentation by our deep learning approach, stands as a promising aid in refining temporomandibular joint disorders diagnosis and treatment strategies.

Metal-Organic Frameworks Derived Carbon-Supported Metal Electrocatalysts for Energy-Related Reduction Reactions.

Electrochemical reduction reactions, as cathodic processes in many energy-related devices, significantly impact the overall efficiency determined mainly by the performance of electrocatalysts. Metal-organic frameworks (MOFs) derived carbon-supported metal materials have become one of star electrocatalysts due to their tunable structure and composition through ligand design and metal screening. However, for different electroreduction reactions, the required active metal species vary in phase component, electronic state, and catalytic center configuration, hence requiring effective customization. From this perspective, this review comprehensively analyzes the structural design principles, metal loading strategies, practical electroreduction performance, and complex catalytic mechanisms, thereby providing insights and guidance for the future rational design of such electroreduction catalysts.

Metal–Organic Frameworks Derived Carbon‐Supported Metal Electrocatalysts for Energy‐Related Reduction Reactions

AbstractElectrochemical reduction reactions, as cathodic processes in many energy‐related devices, significantly impact the overall efficiency determined mainly by the performance of electrocatalysts. Metal–organic frameworks (MOFs) derived carbon‐supported metal materials have become one of star electrocatalysts due to their tunable structure and composition through ligand design and metal screening. However, for different electroreduction reactions, the required active metal species vary in phase component, electronic state, and catalytic center configuration, hence requiring effective customization. From this perspective, this review comprehensively analyzes the structural design principles, metal loading strategies, practical electroreduction performance, and complex catalytic mechanisms, thereby providing insights and guidance for the future rational design of such electroreduction catalysts.

Usefulness of color Doppler and strain elastography adjunctive to B-mode ultrasonography in the diagnosis of non-mass abnormalities of the breast: results of the BC-07 multicenter study of 385 cases.

The concept of non-mass abnormalities of the breast has been employed in Japan for approximately 20years. Although B-mode findings are classified as non-mass abnormalities, the usefulness of adding color Doppler ultrasonography (US) and strain elastography to B-mode US is unclear. Therefore, we conducted a multicenter study (JABTS BC-07) to establish the diagnostic criteria for breast US, including color Doppler and elastography, for non-mass abnormalities of the breast and verify their diagnostic usefulness. We registered US images of non-mass abnormalities of the breast and their clinical and histopathological data from 13 institutions (202 malignant and 183 benign non-mass lesions). Furthermore, we evaluated the centralized image interpretation usefulness of the diagnostic criteria for B-mode and color Doppler US, as well as the sensitivity and specificity when color Doppler US and elastography were added to B-mode US. Echogenic foci in the mammary gland (odds ratio 3.45, 95% confidence interval [CI] 1.92-6.19, p < 0.0001) and the configuration of internal solid components of the ducts (odds ratio 0.056, 95% CI 0.005-0.591, p < 0.0165) significantly differentiated benign and malignant non-mass abnormalities. The sensitivity of B-mode alone (83.7%) was significantly improved by adding color Doppler US (93.1%) (p = 0.0004); however, adding color Doppler US and elastography to B-mode US made no significant difference in either sensitivity or specificity. Although adding color Doppler US and elastography to B-mode US improved sensitivity, the diagnostic significance was limited. Therefore, a comprehensive diagnostic method comprising mammography and magnetic resonance imaging is warranted.

Identification of landscape components for heat mitigation in Bahrain’s arid climate urban waterfront parks

Abstract Heat mitigation became a significant issue in urban spaces because of increased urban temperature due to urban heat phenomena in cities worldwide, particularly in regions with arid climates. This paper discusses the potential of landscape component configurations that mitigate heat in urban parks in arid climate regions. This paper explores the possibility of landscape components in arid climates to mitigate heat in the microthermal areas of urban parks. A case study area and systematic literature review methodology were applied to analyze combining landscape components to generate outdoor heat mitigation in arid climate regions. The study found that combinations of ground asphalt and solid building provide the lowest temperature compared to other configurations in Bahrain’s arid urban waterfront park, which might provide a solid shadow and supported by wind speeds.

Orbits in lattices

Let L be a lattice. We exhibit algorithms for calculating Tits buildings and orbits of vectors in L for certain subgroups of the orthogonal group O(L). We discuss how these algorithms can be applied to determine the configuration of boundary components in the Baily-Borel compactification of orthogonal modular varieties and to improve the performance of computer arithmetic of orthogonal modular forms.

Strategy-based optimization and life cycle environmental impact assessment of a stable photovoltaic power-to-hydrogen system

With the growing global emphasis on reducing greenhouse gas emissions and transitioning to a low-carbon economy, renewable hydrogen is expected to play a vital role in the chemical industry. However, there is a contradiction between the continuous and stable hydrogen demand of chemical process systems and the intermittent and fluctuating nature of renewable energy systems when coupling them together in the industrial chemical production. There are currently few studies addressing year-round stable hydrogen load systems while considering renewable energy intermittency and capacity optimization. Moreover, the comprehensive life cycle environmental impact study of such stable renewable-hydrogen production system is extremely limited. Therefore, this study focuses on a photovoltaic power-to-hydrogen system that can operate stably for 8,760 h per year, specifically targeting its application in the chemical industry. Two operating strategies of the hydrogen system were considered: fully off-grid and ultra-light grid-connected scenarios. The capacity configuration under each scenario was optimized using gird search algorithm, with the objective of minimizing the levelized cost of hydrogen. Based on the optimization results, the energy flow network, cost-effectiveness, and comprehensive life cycle environmental impacts of such system were analyzed. The results show that the ultra-light grid-connected hydrogen production system, due to the support of the power grid, has improved the reliability of the hydrogen production system and significantly reduced the capacity configuration of system components, thereby demonstrating better performance in terms of economics and overall lifecycle environmental impact compared to a completely off-grid hydrogen production system. Its global warming potential (GWP) and levelized cost of hydrogen under electricity sales scenario are 12% and 31% lower, respectively, compared to fully off-grid hydrogen production. Considering China’s future demand for large-scale renewable hydrogen, ultra-light grid-connected hydrogen production may become the primary transitional pathway in the near to medium term.

Optimal sizing of a fixed-tilt ground-mounted grid-connected photovoltaic system with bifacial modules using Harris Hawks Optimization

This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm’s stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system’s Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.

Advancing Evidence-Based Public Health Policy: How Core Component Thinking Can Illuminate the Multilevel Nature of Public Health Policy.

A growing body of literature uses the concept of core components to better understand small-scale programmatic interventions. Instead of interventions being viewed as unitary "black boxes," interventions are viewed as configurations of core components, which are the parts of interventions that carry their causal potential and therefore need to be reproduced with fidelity to produce the intended effect. To date, the concept of core components has not been as widely applied to public health policy interventions as it has to programmatic interventions. The purpose of this topical review is to familiarize public health practitioners and policy makers with the concept of core components as applied to public health policy interventions. Raising the profile of core component thinking can foster mindful adaptation and implementation of public health policy interventions while encouraging further research to enhance the supporting evidence base. We present 3 types of multilevel interactions in which the core components of a public health policy intervention produce effects at the population level by (1) seeking to directly affect individual behavior, (2) facilitating adoption of programmatic interventions by intermediaries, and (3) encouraging intermediaries to take action that can shape changes in upstream drivers of population health. Changing the unit of analysis from whole policies to core components can provide a basis for understanding how policies work and for facilitating novel evidence-generating strategies and rapid evidence reviews that can inform future adaptation efforts.

Detecting Line Segments in Motion-Blurred Images With Events.

Making line segment detectors more reliable under motion blurs is one of the most important challenges for practical applications, such as visual SLAM and 3D line mapping. Existing line segment detection methods face severe performance degradation for accurately detecting and locating line segments when motion blur occurs. While event data shows strong complementary characteristics to images for minimal blur and edge awareness at high-temporal resolution, potentially beneficial for reliable line segment recognition. To robustly detect line segments over motion blurs, we propose to leverage the complementary information of images and events. Specifically, we first design a general frame-event feature fusion network to extract and fuse the detailed image textures and low-latency event edges, which consists of a channel-attention-based shallow fusion module and a self-attention-based dual hourglass module. We then utilize the state-of-the-art wireframe parsing networks to detect line segments on the fused feature map. Moreover, due to the lack of line segment detection datasets with pairwise motion-blurred images and events, we contribute two datasets, i.e., synthetic FE-Wireframe and realistic FE-Blurframe, for network training and evaluation. Extensive analyses on the component configurations demonstrate the design effectiveness of our fusion network. When compared to the state-of-the-arts, the proposed approach achieves the highest detection accuracy while maintaining comparable real-time performance. In addition to being robust to motion blur, our method also exhibits superior performance for line detection under high dynamic range scenes.

Mission Statement Components and Social Enterprise Sustainability: Findings from a Mixed-Method Approach

Mission statement components are crucial for the sustainability of social enterprises. Based on content analysis and fuzzy-set qualitative comparative analysis (fsQCA), we investigate the link between mission statement components and sustainability in a sample of 148 Chinese social enterprises. Our findings indicate that social enterprises’ mission statements are both socially and commercially focused. An individual mission statement component does not constitute a necessary condition for the high sustainability of social enterprises, but products or services and enterprise philosophy play a universal role in this regard. There are four configurations of mission statement components that lead to high social enterprise sustainability. This study helps to reveal the complexity of the impact of mission statement components on social enterprise sustainability.

ILight2: A near-infrared optogenetic tool for gene transcription with low background activation.

Optogenetic tools (OTs) operating in the far-red and near-infrared (NIR) region offer advantages for light-controlling biological processes in deep tissues and spectral multiplexing with fluorescent probes and OTs acting in the visible range. However, many NIR OTs suffer from background activation in darkness. Through shortening linkers, we engineered a novel NIR OT, iLight2, which exhibits a significantly reduced background activity in darkness, thereby increasing the light-to-dark activation contrast. The resultant optimal configuration of iLight2 components suggests a molecular mechanism of iLight2 action. Using a biliverdin reductase knock-out mouse model, we show that iLight2 exhibits advanced performance in mouse primary cells and deep tissues in vivo. Efficient light-controlled cell migration in wound healing cellular model demonstrates the possibility of using iLight2 in therapy and, overall, positions it as a valuable addition to the NIR OT toolkit for gene transcription applications.

Forward-Oriented Programming: A meta-DSL for fast development of component libraries

Libraries that implement Domain-Specific Language (DSL) components keep gaining traction when it comes to developing software for specific application domains. However, creating components that can be organically weaved into use cases is an extremely complex task. In this work, we introduce a meta-DSL to assist library development, called Forward-Oriented Programming (FOP). This combines lazy evaluation and aspect-oriented programming principles to align crosscutting component configurations and alter their execution outcomes depending on usage in subsequent code. Theoretical analysis shows that FOP simplifies component development and makes their combination logic learnable by library users. We realize the paradigm with a Python package, called pyfop, and conduct a case study that compares it with purely functional and object-oriented library implementations. In the study, source code quality metrics demonstrate reduced time and effort to write library components, and increased comprehensibility. Configurations are shared without modifying distant code segments.