Renewal Process Research Articles

Thermoelectric Performance of YSZ Additions to ZnO Ceramics: Processing and Investigation

The increasing global demand for alternative energy sources and the environmental consequences of climate change necessitate research into renewable energy sources and energy conversion processes. Thermoelectric technology, which converts heat directly to electricity, can solve the energy issue and provide greener energy sources. The effects of yttria-stabilized zirconia (YSZ) on thermoelectric properties, such as electrical resistivity and Seebeck coefficients, of YSZ/ZnO composite have been investigated in this research. The study investigates the influence of sintering ZnO ceramics in an argon atmosphere at 1450°C with various quantities of YSZ addition to the microstructure and thermoelectric properties. The solid-state reaction method was used to synthesize all ZnO ceramic samples without YSZ and with YSZ of 0.5, 2 and 3 wt%. The Seebeck coefficient of the sintered samples increased 44-92% with YSZ additions at 800°C. At 800°C, the nominal composition of 0.5 wt% YSZ had power factor 1505 μW.m−1.K−2

Analytical model of a computing system with software rejuvenation

The foundational basis of present technological advancements worldwide largely relies on computing systems. The integration of virtualisation amplifies the capabilities of these computing systems. The incorporation of virtualisation requires a virtual machine monitor (VMM) or hypervisor, host or server, and virtual machines (VMs). Over the time, as the system ages, the hypervisor requires rejuvenation, in order to ensure the system continues operating at its full capacity. Security of virtual system also compromises due to ageing. This article aims to examine the impact of software rejuvenation on the reliability, security and the performance of virtualised computing systems by comparing two probabilistic models. In Model-I, VMM quickly recovers from failure, restoring access to the robust state. Whereas, in Model-II, despite prompt repair, access shifts to the rejuvenation state before returning to the robust state. The models are built on the foundation of the theory of semi-Markov processes, employing the regenerative point approach from renewal theory to deduce results for diverse performance indicators. The findings from this research will contribute for making computing systems more resilient and long-lasting in different fields of applications.

Revising the Motion Control Parameter Optimization Research of a Two-Wheel Differential Car

This paper proposes a solution based on the particle swarm optimization algorithm to address the issue of Proportional Integral Derivative parameter selection in the motion control of a two-wheel differential car. The mathematical motion model is established based on the driving principle of a two-wheel differential car. The transfer function of the DC motor is derived in detail, based on Kirchhoff’s law and the Laplace transform. The pose renewal equation and error renewal equation of the car are based on the mathematical motion model. Finally, a numerical simulation and experimental analysis were conducted using MATLAB R2022a, Simulink 9.1 (part of R2018a), VOFA 1.3.10 software, an STM32 microcontroller, an L298N driver chip, and other hardware components. The results indicate that the particle swarm optimization algorithm enables the rapid acquisition of optimal Proportional Integral Derivative parameters. The optimized parameter of the motor speed convergence time is set to 10 ms, with an overshoot of 1 r/min and an enhanced anti-interference ability. The optimized parameters effectively regulate the car’s motion, ensuring a maximum error control of approximately 0.003 m.

FAKTOR-FAKTOR YANG MENGHAMBAT MASYARAKAT UNTUK BERJALAN KAKI DAERAH PERKOTAAN

Walking is very important in cities as it helps people to travel from one place to another. Urban areas in Bali are experiencing rapid development as tourism normalises, gradually entering a process of urban renewal. Improving the quality of the pedestrian environment is an important issue. The purpose of this research is to study how a person's physical condition, concepts, and activities affect their physical character and interest in walking. A quantitative method was used using randomly distributed open-ended questionnaires. Pedestrian criteria and facilities will be the source of data categorised in inhibiting pedestrians. A person who wants to walk should have good access and well-maintained facilities so that urban areas can be specialised for pedestrian paths.

Distribution theory for dependent renewal–reward processes and their first-passage times using saddlepoint and residue expansions

Distribution theory for dependent renewal–reward processes and their first-passage times using saddlepoint and residue expansions

IBN SINA’S CONTRIBUTIONS TO MODERNITY: INTEGRATING MEDIEVAL PHILOSOPHY, NEUROPHILOSOPHY, AND EDUCATIONAL REFORM IN ISLAMIC THOUGHT AND CIVILISATION

This study explores the enduring legacy of Ibn Sina, a seminal figure in Islamic medieval philosophy, focusing on his theory of the soul and its implications for modern neurophilosophy, educational reform, and the renewal of Islamic thought and civilisation. By bridging the intellectual heritage of Ibn Sina with contemporary discussions in neurophilosophy, this paper highlights his contributions to understanding the nature of consciousness, perception, and the human mind. Furthermore, it examines how Ibn Sina's insights can inform modern educational practices, advocating for an integration of classical wisdom and scientific advancements within the curriculum of Islamic studies. This integration not only pays homage to the rich intellectual tradition of Islamic civilisation but also fosters a dialogue between the medieval and the modern, contributing to the ongoing process of renewal and reform in Islamic thought. By applying Ibn Sina’s philosophical principles to contemporary challenges in education and society, this research underscores the potential for medieval Islamic philosophy to contribute to modernity, offering innovative pathways for navigating the complexities of the 21st century.

Development of Robust Dual-Functional NiX (X: Fe, TiO2) Electrocatalysts via Atmospheric Plasma Spray for Efficient Water Splitting Applications

As traditional energy resources become scarcer, the need for cleaner and more efficient energy carriers becomes increasingly urgent. In response to this challenge, hydrogen energy has gained attention as a practical and clean alternative with its potential for zero emissions and ability to be generated through renewable processes. Water splitting is essential for sustainable energy production as it generates hydrogen, a clean fuel, and oxygen through electrochemical reactions. In this study, we fabricated robust dual-functional nickel-based NiX (X: Fe, TiO2) electrocatalysts, using atmospheric plasma spray (APS) for water splitting. The nickel-based electrocatalysts demonstrated excellent catalytic activity for both the oxygen evolution reaction (OER) and the hydrogen evolution reaction (HER). Specifically, the NiX electrocatalyst exhibited a remarkable OER performance with an overpotential of 209 mV at a current density of 50 mA cm-2 and a Tafel value of 173.54 mV/Dec. For HER, it exhibited the overpotential of 298 mV at 20 mA cm-2 with a Tafel value of 85.86 mV/Dec, making NiFe particularly efficient in achieving high current densities with minimal energy input. NiTiO2 electrocatalyst exhibits stability in acidic conditions, making it suitable for long-term applications. The surface morphology and wettability of the materials were assessed before and after electrochemical reactions. Overall, this study highlights the single-step fabrication process of highly stable and efficient dual-functional electrocatalysts for water splitting.

Humin accelerates interspecies electron transfer between Shewanella oneidensis MR-1 and Methanosarcina barkeri.

Existing researches involving accelerated interspecies electron transfer (IET) by solid redox mediators focus mainly on the conductive nature of these materials. Although non-conductive solid redox mediator-humin has been reported to promote methanogenic performance of anaerobic granular sludge, likely through accelerating IET of microorganisms, this phenomenon has not been validly proven. In this study, a wetland sediment sourced HM (HMWS) was added into a co-culture of a syntrophic bacteria Shewanella oneidensis MR-1 and an archaeal Methanosarcina barkeri with ethanol as sole electron donor to examine whether HMWS can accelerate the IET between these two species. Results showed that the average ethanol consumption rate and methanogenic rate in the HMWS-containing co-culture were 1.6-fold and 2.9-fold higher than that without HMWS, indicating that HMWS enhanced the cooperation of S. oneidensis MR-1 and M. barkeri. Further experiment suggested that except for interspecies acetate transfer, an alternative HMWS-mediated IET pathway occurred between M. barkeri and S. oneidensis MR-1. The estimated total IET rate in the HMWS-containing co-culture was 0.53 ± 0.1 meq/(d-1·g cell-dry weight-1), which was 2.2-fold higher than that without HMWS. CO/CO and NO/NH2 were the functional groups of HMWS contributing to HMWS-mediated IET, and flavin and cytochrome c of microorganisms participated in the reduction and oxidation of HMWS. The findings are of significance for understanding the diverse IET occurred in natural environments and providing a novel strategy for renewable bioenergy processes with high efficiency.

Renewable energy transition in Europe in the context of renewable energy transition processes in the world. A review

Renewable energy transition in Europe in the context of renewable energy transition processes in the world. A review

Wastewater-based effective reproduction number and prediction under the absence of shedding information.

Estimating effective reproduction number (Re) and predicting disease incidences are essential to formulate effective strategies for disease control. Although recent studies developed models for inferring Re from wastewater-based data, they require information on shedding dynamics. Here, we proposed a framework of Re estimation and prediction without shedding information. The framework consists of a space-state model for smoothing wastewater-based data and a renewal equation modified for wastewater-based data. The applicability of the framework was tested with simulated data and real-world data on Influenza A virus (IAV) and SARS-CoV-2 concentration in wastewater in 2022/2023 season in the USA. We confirmed the state-space model effectively fits various simulated epidemic curves and real-world data. In simulations, we found wastewater-based Re (Reww) closely aligns with instantaneous clinical Re when shedding dynamics are rapid. For more prolonged shedding, Reww approximates a smoothed Re over time. We also observed the necessary sampling frequency to trace dynamics of wastewater concentration and Reww accurately in the framework varies depending on the precision of detection methods, the epidemic status, the transmissibility of infectious diseases, and shedding dynamics. By applying our framework to real-world data, we found Reww for SARS-CoV-2 showed similar trend and values to clinically-based Re. Reww for IAV ranged from 0.66 to 1.52 with a clear peak in the winter season, which agrees with previously reported Re. We also succeeded in predicting wastewater concentration in a few weeks from available wastewater-based data. These results indicate that our framework potentially enables near real-time monitoring of approximated Re and prediction of infectious disease dynamics through wastewater surveillance, which limits the delay between infection and reporting. Our framework is useful especially for regions where reliable clinical surveillance is not available and notifiable surveillance is abolished, and can be expanded to multiple infectious diseases that have been detected from wastewater.

Technoeconomic analysis of supercritical water gasification of canola straw for hydrogen production

Production of hydrogen from renewable sources is gaining popularity to reduce our dependency on non-renewable fossil fuels to meet growing hydrogen demand. However, despite the great prospect of production of hydrogen from sustainable sources such as lignocellulosic biomass via supercritical water gasification (SCWG), it has not been commercialized at a large industrial scale. This is due to the lack of detailed economic analysis of SCWG of lignocellulosic biomass, owing to the complexity of the SCWG process and the heterogeneous nature of biomass. Therefore, to address this knowledge gap, in this study, a detailed technoeconomic analysis (TEA) of a conceptual SCWG pilot having the capacity to process 200 tons/day of canola straw for the production of green hydrogen was conducted. Mass and energy balance of conceptual pilot was performed using Aspen Plus ® simulation by utilizing experimental data and hydrogen yield of 41.62 mmol/g was obtained at optimized reaction conditions of 500 °C, 23 MPa, and 10 wt%. Economic analysis based on calculated mass and energy balance was performed using SuperPro software. Cash flow analysis for capital expenses (CAPEX) of 81 Million USD showed a high internal rate of return (IRR) of 38.9% and an undiscounted net present value (NPV) of 548 million USD. A minimum selling price (MSP) of 3.38 USD/kg H2 for produced hydrogen was estimated, which is lower than other renewable hydrogen production processes and comparable to non-renewable hydrogen production technologies. A high positive IRR and NPV, while a lower MSP showed that despite having a low technological readiness level (TRL) of 4, SCWG of lignocellulosic biomass is a technically feasible and economically viable process for the production of hydrogen. Furthermore, sensitivity analysis also revealed that capital expenses (CAPEX) and canola straw price had the highest influence on net present value (NPV) and MSP. However, overall NPV and MSP were highly stable to changes in parameters highlighting the robustness of the economic analysis.

Applicable Descriptors under Weak Metal‐Oxygen d–p Interaction for the Oxygen Evolution Reaction

AbstractThe oxygen evolution reaction (OER) plays a crucial role in water electrolysis and renewable energy conversion processes. Descriptors are utilized to elucidate the structure‐performance relationships of OER catalytic materials, yet each descriptor exhibits specificity to particular systems. Currently, there is a lack of effective descriptors to describe the relationship between electronic structure and OER performance in ionic systems. This study reveals for the first time that widely used OER descriptors, the d‐band center and charge transfer energy, are limited in their effectiveness for oxide systems dominated by ionic bonds, in which ionic interactions significantly enhance or suppress the catalytic activity. Furthermore, composite descriptors tailored for ionic systems are proposed, with findings extended to complex multi‐component and high‐entropy oxides. The results indicate that the metal d‐band unoccupied states parameter and the active states parameter can serve as effective OER descriptors for ionic catalytic materials. This work addresses the gap in OER descriptors for ionic systems, offering a new theoretical foundation and guidance for the development of efficient OER catalytic materials.

Applicable Descriptors under Weak Metal-Oxygen d-p Interaction for the Oxygen Evolution Reaction.

The oxygen evolution reaction (OER) plays a crucial role in water electrolysis and renewable energy conversion processes. Descriptors are utilized to elucidate the structure-performance relationships of OER catalytic materials, yet each descriptor exhibits specificity to particular systems. Currently, there is a lack of effective descriptors to describe the relationship between electronic structure and OER performance in ionic systems. This study reveals for the first time that widely used OER descriptors, the d-band center and charge transfer energy, are limited in their effectiveness for oxide systems dominated by ionic bonds, in which ionic interactions significantly enhance or suppress the catalytic activity. Furthermore, composite descriptors tailored for ionic systems are proposed, with findings extended to complex multi-component and high-entropy oxides. The results indicate that the metal d-band unoccupied states parameter and the active states parameter can serve as effective OER descriptors for ionic catalytic materials. This work addresses the gap in OER descriptors for ionic systems, offering a new theoretical foundation and guidance for the development of efficient OER catalytic materials.

Mathematical Optimization of Wind Turbine Maintenance Using Repair Rate Thresholds

As reliance on wind energy intensifies globally, optimizing the efficiency and reliability of wind turbines is becoming vital. This paper explores sophisticated maintenance strategies, crucial for enhancing the operational sustainability of wind turbines. It introduces an innovative approach to maintenance scheduling that utilizes a mathematical model incorporating an alternating renewal process for accurately determining repair rate thresholds. These thresholds are important for identifying optimal maintenance timings, thereby averting failures and minimizing downtime. Central to this study are the obtained generalized analytical expressions that can be used to predict the total repair time for an observed entity. Four key lemmas are developed to establish formal proofs for the probability density function (PDF) and cumulative distribution function (CDF) of repair rates, both above and below critical repair rate thresholds. The core innovation of this study lies in the methodological application of PDFs and CDFs to set repair time thresholds that refine maintenance schedules. The model’s effectiveness is illustrated using simulated data based on typical wind turbine components such as gearboxes, generators, and converters, validating its potential for improving system availability and operational readiness. By establishing measurable repair rate thresholds, the model effectively prioritizes maintenance tasks, extending the life of crucial turbine components and ensuring consistent energy output. Beyond enhancing theoretical understanding, this research provides practical insights that could inform broader maintenance strategies across various renewable energy systems, marking a significant advancement in the field of maintenance engineering

A method for estimating vehicle delays at parking lot exits in urban networks

Parking lot entrances and exits are places where traffic conflicts are serious and traffic delays often occur. The accurate calculation of vehicle delays is crucial for parking lot entrance and exit management and is beneficial for reducing the interaction between static and dynamic traffic. Field data can be used to determine the vehicle delay, but the process of collecting field data is costly and time-consuming. Therefore, it is necessary to determine the vehicle delay for vehicles at the entrance and exit of the parking lot through mathematical models, as this method is efficient and cost-saving. This paper provides an estimation model based on renewal theory, the Poisson distribution, and the negative exponential distribution to study the delay time of a vehicle preparing to make a right turn at the curb of a parking lot exit (VCPLE). The study focuses on the yielding behaviors of the closest approaching vehicle (CAV) that is travelling in the lane into which the VCPLE is about to merge, and that will be positioned immediately behind the VCPLE once the merge is completed. Furthermore, the headway distance between the VCPLE and the CAV is also considered in addressing the problem. Additionally, a comparative analysis of vehicle delays was conducted via field data alongside predictions from the proposed model and the Cowan M3 model. The results demonstrate the success of the model developed in this study in estimating vehicle delays for the VCPLE. Furthermore, upon comparing the calculation results with those of the M3 model, it becomes evident that the model constructed in this study offers a more precise estimation of vehicle delays at parking lot exits. By calculating the delays experienced by vehicles within the parking lot entrance and exit zones, a comprehensive understanding of the traffic operational status in these areas can be obtained. This, in turn, aids in assessing the efficiency of the transportation system. This analysis enables traffic management authorities to promptly identify and address traffic bottlenecks, ultimately contributing to an improvement in road capacity.

A Robust Spectrum Allocation Framework Towards Inference Management in Multichannel Cognitive Radio Networks

ABSTRACTRecently, there has been research on using cognitive radio to increase the effectiveness of spectrum resources. Secondary users (SUs) sense, identify, and access frequency bands presently unoccupied by primary users (PUs). Indeed, the precise communication methods of PUs remain consistently unknown to SUs. Designing an appropriate system for spectrum monitoring along with access is necessary to address the challenging problem of guaranteeing the quality of service (QoS) for PUs and satisfying the demand of SUs for spectrum resources. When several SUs and different channels are available, spectrum allocation and sharing must be considered. This work proposes a system for allocating spectrum to multiple users and sharing multiple channels for SUs dealing with PUs with uncertain behavior. The study focuses on analyzing the interference caused to PUs by the dynamic access of SUs. The analysis of the interference produced by SUs is conducted using renewal theory. Queuing theory determines the highest average data rate achievable by SUs while considering the limitations imposed by interference to PUs and the need to maintain stability in the secondary network. Comprehensive simulations validate the efficacy of this approach.

Inpp5e Is Critical for Photoreceptor Outer Segment Maintenance.

In humans, inositol polyphosphate-5-phosphatase e (INPP5E) mutations cause retinal degeneration as part of Joubert and MORM syndromes and can also cause non-syndromic blindness. In mice, mutations cause a spectrum of brain, kidney, and other anomalies and prevent the formation of photoreceptor outer segments. To further explore the function of Inpp5e in photoreceptors, we generated conditional and inducible knockouts of mouse Inpp5e where the gene was deleted either during outer segment formation or after outer segments were fully formed. In both cases, the loss of Inpp5e led to severe defects in photoreceptor outer segment morphology and ultimately photoreceptor cell loss. The primary morphological defect consisted of outer segment shortening and reduction in the number of newly forming discs at the outer segment base. This was accompanied by structural abnormalities of the Golgi apparatus, mislocalized rhodopsin, and an accumulation of extracellular vesicles. In addition, knockout cells showed a reduction in the size and prevalence of the actin network at the site of new disc morphogenesis and the occasional formation of membrane whorls instead of discs in a subset of cells. Together, these data demonstrate that Inpp5e plays a critical role in maintaining the outer segment and the normal process of outer segment renewal depends on the activity of this enzyme.

Digital Gig Work Under Unstable Energy Infrastructures: Invisible Workarounds and Alternative Imaginaries

Digital upskilling and remote work are frequently presented as pathways for displaced communities who face political, economic crises and employment barriers. The viability and stability of available energy infrastructures for these communities is critical to the long-term success of these proposed job trajectories. In this paper, we investigate how 17 Syrian refugees living in Lebanon navigate their local unstable energy infrastructures to conduct digital gig work and receive digital training. We found that digital gig work and training are workarounds to the political and social inaccessibility of local labor markets for refugees and that participants rely on a series of material energy strategies to optimize their electricity access. We argue that to support refugees conducting digital gig work and training, we must recognize and account for ecological, social, and technological limitations and frailties in the development of technological solutions and draw our attention to how infrastructures are perpetually undergoing processes of breakdown, repair, and renewal. We argue that this attention to ongoing transformation and renewal creates new opportunities for productive and creative reconfiguration as well as modes through which CSCW may intervene in unstable energy contexts. From this perspective, we emphasize the importance of better resourcing displaced communities with information regarding energy access and supporting them in establishing and strengthening their own visions for alternative energy systems and employment pathways.

Digital twin for decision support system of industrial utility management

Manufacturing and industrial operations rely heavily on energy that is generated mostly from fossil fuels, such as coal, oil, and natural gas, which harm the environment and contribute to climate change. Thus, renewable energy is being integrated into industrial processes to lessen environmental effects and reduce fossil fuel usage. However, the renewable source performance process can be greatly affected by disturbances and constraints, such as ambient air temperature and relative humidity, minimum utility consumption, and the total energy required, making effective control difficult. This study proposes a digital twin built with machine learning regression techniques for load demand forecasting as a decision-support system for industrial utility management. From the results, the ensemble tree (ET) model produced the highest accuracy, based on validation and test dataset root mean squared error values of 23.164 and 27.558, respectively, and R2 values of 0.96 and 0.95, respectively. The digital twin and load demand forecasting approaches effectively created an efficient operating schedule for industrial utility management. The ET model had a total error of 23.86%, substantially lower than the average load demand's total error of 65.29%. Therefore, the ET model with weather conditions in four scenarios could be recommended to optimize energy utilization when creating the operating schedule.

Research on Age-Friendly Renewal Design of Community Parks Based on Elderly Participatory Construction: A Case Study of Xinmenkou Community Park Renewal

Age-friendly design embodies a pivotal urban development endeavor in addressing the aging society during China’s urban renewal era. Among the key components of this renewal process, community parks occupy a prominent position, and their adaptation to cater to the older population stands as a significant area of research and development. Drawing upon an analysis of the characteristics and requirements of the senior population, along with factors influencing their participatory construction of community parks, this study proposes methodologies for elderly-involved construction activities encompassing design, planting, maintenance, and activity participation. Capitalizing on a user-centric approach, the study targets three primary aspects—space, facilities, and activities—to proceed with an age-friendly renovation for the Xinmenkou Community Park in Gulou District, Nanjing, China. By introducing tailored zones such as a self-created space zone, a planting experience zone, a voluntary maintenance zone, and an activity participation zone, the design aims to fulfill the aspirations of older residents for the participatory construction of community parks. This innovative scheme revitalizes aging urban communities and presents a fresh perspective for urban renewal strategies.