Integration Costs Research Articles

Microscale chemiresistive hydrogen sensors: Current status and recent developments

Hydrogen is known for its efficient combustion, abundant natural availability, and environmentally friendly characteristics. It is recognized as a promising energy source for the future and is already utilized in various industries, including petrochemicals, electronics, food processing, aerospace, and new energy vehicles. However, challenges arise in the storage and use of hydrogen owing to its tendency to leak, its potential for explosion within a specific concentration range of 4%–75%, and itslow ignition energy requirements. Consequently, there is a demand for hydrogen sensors capable of quickly and accurately detecting low levels of hydrogen leaks. Microelectromechanical systems-based chemiresistive hydrogen sensors offer advantages such as low cost, compact size, low energy consumption, and superior sensing performance, making them a major focus of recent research. This article provides a comprehensive overview and comparison of the sensing principles of various hydrogen sensors, including chemiresistive sensors, electrochemical sensors, thermocatalytic sensors, acoustic sensors, and mechanical sensors. Micro-chemiresistive hydrogen sensors exhibit high sensitivity, low cost, and ease of integration, making them highly promising for practical applications. In response to the challenges encountered in practical applications of chemiresistive hydrogen sensors, such as high operating temperatures and high power consumption, this review explores emerging trends in chemiresistive hydrogen sensor technology from the perspectives of novel materials and activation methods. Finally, it discusses the applications and potential further developments of chemiresistive hydrogen sensors.

Streamlining budgeting and forecasting across multi-cloud environments with dynamic financial models

This review paper explores the complexities of financial management in multi-cloud environments, emphasizing the need for streamlined budgeting and forecasting processes. It begins by addressing the challenges inherent in managing finances across multiple cloud platforms, such as integration difficulties, data silos, and cost management issues. The paper then provides an overview of dynamic financial models, detailing their components and benefits in overcoming these challenges. It highlights the role of automation, real-time data utilization, and predictive analytics in enhancing financial accuracy and efficiency. Furthermore, the paper discusses the importance of collaborative platforms in fostering stakeholder communication and coordination. Finally, strategic planning, best practices for multi-cloud financial management, and emerging trends and prospects are outlined. The review underscores the significance of aligning financial models with organizational goals and leveraging advanced technologies to achieve optimal financial outcomes in multi-cloud settings. Keywords: Multi-Cloud Financial Management, Budgeting and Forecasting, Dynamic Financial Models, Automation in Finance, Predictive Analytics.

Block Medcare: Advancing Healthcare Through Blockchain Integration

In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings underscore the transformative potential of blockchain technology in healthcare, pointing towards a new era of patient-centric and secure healthcare services. By addressing current limitations and exploring future enhancements, such as integration with IoT devices and AI-driven analytics, this research contributes to the ongoing evolution of secure, efficient, and interoperable healthcare systems.

Using break-even analysis to explore the cost and carbon reduction benefits of solar and wind energy integration in microgrids for convenience stores

In response to the increasing demands for net-zero carbon emissions in Taiwan and globally, this study explores the feasibility of implementing microgrid technologies in convenience stores to reduce carbon emissions. The study utilizes the commercial simulation tool HomerPro to evaluate the performance of integrating solar and wind power into microgrid systems, aligning with Taiwan's current priorities in renewable energy development. The analysis incorporates carbon pricing scenarios, grid reliability considerations, and financial variables such as discount and inflation rates. We simulated 256 system configurations to identify the most economically viable solutions under various conditions. The results indicate that integrating solar and wind energy into microgrid systems can significantly lower energy costs and carbon emissions, especially in high carbon-price scenarios. These benefits provide strong incentives for businesses to adopt carbon-neutral strategies. These findings provide valuable insights for policymakers and businesses, highlighting the critical role of reducing green power purchase prices in improving economic feasibility. Future research explores the impacts of integrating other renewable energy sources, offering broader technical support for achieving carbon neutrality.

Floating body motion coupling study and economic evaluation of different floating photovoltaic arrangement schemes under hybrid solar-wind power generation

Abstract Reducing the number of anchor points and moorings for floating platforms is one of the ways to reduce the integration and maintenance costs of floating offshore wind and solar farms. This paper focuses on the technical feasibility and economic viability of reducing the number of anchor points and moorings for floating offshore PV platforms by using a monopile of a stationary wind turbine as a mooring anchor point and introducing a shared line system. In this paper, three different floating solar layouts and their corresponding mooring systems are numerically evaluated and optimized under different environmental loading conditions using the commercial software OrcaFlex. The results of the study show that the implementation of a shared mooring solution maximizes the cost-effectiveness economically while technically ensuring stable operation.

Optimasi Ukuran Lot Persediaan dalam Model Integrasi Single Vendor dan Single Buyer dengan Mempertimbangkan Kekurangan Buyer

<p><strong>The supply chain is the main factor influencing the production process in achieving goals. This research develops a model to determine the optimal production lot size in the supply chain system between vendors and buyers by considering the inventory shortage at the buyer. Minimizing the combined inventory cost is the goal of optimizing the production lot size by developing an integration model to determine the optimal lot size. The proposed model uses a periodic delivery method from the vendor to the buyer and allows for inventory shortages at the buyer. An iterative algorithm is applied to find the optimal lot size and minimum integrated inventory cost. The results showed that the initial model with n=1 resulted in a total cost of 2252 and an optimal production size (Q*) of 457. The proposed model reduces the total cost to 1605 with an optimal production size (Q*) of 737. Sensitivity analysis showed that an increase in the frequency of deliveries to vendors and vendor set-up costs contributed to a rise in total cost, while an increase in vendor storage costs led to a decrease in production lot size. This research can improve the efficiency of production planning and inventory management in the supply chain.</strong></p><p><strong><em>Keywords</em></strong> - <em>Inventory, Shortages, Single Buyer</em><em>, Single Vendor.</em></p>

The high price of equity in pulse oximetry: A cost evaluation and need for interim solutions.

Disparities in pulse oximetry accuracy, disproportionately affecting patients of color, have been associated with serious clinical outcomes. Although many have called for pulse oximetry hardware replacement, the cost associated with this replacement is not known. The objective of this study was to estimate the cost of replacing all current pulse oximetry hardware throughout a hospital system via a single-center survey in 2023 at an academic medical center (Duke University) with three hospitals. The main outcome was the cost of total hardware replacement as identified by current day prices for hardware. New and used prices for 3,542/4,136 (85.6%) across three hospitals for pulse oximetry devices were found. The average cost to replace current pulse oximetry hardware is $6,834.61 per bed. Replacement and integration costs are estimated at $14.2-17.4 million for the entire medical system. Extrapolating these costs to 5,564 hospitals in the United States results in an estimated cost of $8.72 billion. "Simply replacing" current pulse oximetry hardware to address disparities may not be simple, cheap, or timely. Solutions for addressing pulse oximetry accuracy disparities leveraging current technology may be necessary, and might also be better. Trial Registration: Pro00113724, exempt.

A dynamic pricing strategy and charging coordination of PEV in a renewable-grid integrated charging station

The increasing penetration of Plug-in Electric Vehicles (PEV) in the transportation system has increased the burden on the power system. This has made peak load demand management a challenging task for the power grid. To address this issue, a novel dynamic demand response pricing strategy in a grid-renewable generation integrated charging station environment is proposed in this paper. Renewable energy sources reduce the cost of generation and grid integration makes the system reliable. The proposed strategy models a Stackelberg game to provide dynamic prices for charging, discharging and grid power supplied for charging stations. Uncertainty and economics of renewable generation are considered for effective analysis and evaluation of the feasibility of the proposed strategy. The study considers the bidirectional flow of power and the battery degradation cost. Charging coordination is performed to optimize the cost of charging and discharging and support the grid in peak load demand management. Random charging behaviour and other parameters of PEVs are simulated using a random distribution function to resemble the real-time environment. A numerical case study validates that the proposed strategy has optimized the cost of charging and discharging and the serving capabilities of the charging station are enhanced with existing infrastructure.

CIPerf: a benchmark for continuous integration services performance and cost analysis

Continuous Integration is a crucial practice in modern software development, enabling teams to automate the process of building, testing, and merging code increments to ensure continuous delivery of high-quality software. Despite its growing adoption, the cost and performance of Continuous Integration services often go unexamined in sufficient detail. This paper presents CIPerf, a comprehensive benchmark designed to analyze both the performance and cost of cloud-based and self-hosted Continuous Integration services. The study centers on a comparison between two specific services: Bitbucket Pipelines, a cloud-based offering by Atlassian, and Hetzner, a self-hosted solution. By focusing on these platforms, the research aims to provide practical insights into the real-world costs and execution performance of Continuous Integration services. To achieve this, CIPerf conducted automated tests on an hourly basis over a two-month period, measuring critical timeframes such as resource provisioning, environment setup, and the actual test execution times. The results showed significant differences in both the cost efficiency and the consistency of performance between the two services. For instance, Bitbucket Pipelines, while convenient in its cloud-based offering, demonstrated higher variability in provisioning times compared to the stable, predictable performance of Hetzner’s self-hosted environment. The analysis also explored how these performance metrics influence key software development metrics, including deployment frequency and developer productivity. CIPerf provides a clear methodology for developers and organizations to objectively assess their Continuous Integration service options, ultimately helping them optimize their workflows. Moreover, this benchmark can serve as an ongoing tool to monitor service performance over time, identifying potential degradations or improvements in service quality, thus offering longterm value for teams that rely on Continuous Integration for their development processes.

A Multi-Sized Unit Cell Method for the Design of LPBF Lattice Support Structures Concerning Complex Geometries

Abstract Composed of individual unit cells strategically arranged to achieve a desired function, lattices are a promising solution for laser powder bed fusion (LPBF) support structure design in additive manufacturing. Despite their many advantages (e.g., multifunctionality and reduced material cost), prior work in lattice support structure design primarily focuses on horizontal support domains that are not translatable to support domains for complex geometries, thereby limiting their application. This work introduces a multi-sized unit cell design optimization (MSO) method to create lattice support structures (LSS) for parts with complex geometries. The proposed method utilizes voxelization to generate LSS using box-like unit cells of different sizes. It also allows for efficient, high-dimensional design optimization for the types and locations of user-specified unit cells through a modified simulated annealing-based optimization algorithm. The effectiveness and efficiency of the MSO method are demonstrated through the case study of an adapter pipe for a high-temperature heat exchanger. For this demonstration, LSS using multi-sized unit cells are designed to increase heat transfer rate while satisfying structural integrity and material cost constraints. The case study results indicate that the design of the LSS derived from the MSO method fulfills all constraints, including the design constraint of 50% material cost reduction, compared to the solid support structure. In contrast, the lattice support structure designs derived from equal-sized unit cell methods either cannot satisfy all design constraints or have a lower heat transfer rate than the design of the MSO method.

PSV-10 Vaccination of beef cattle to reduce enteric methane emissions

Abstract Livestock methane (CH4) emissions total over 3 billion tonnes per year of carbon dioxide equivalents (CO2e) and are responsible for approximately 6% of total annual greenhouse gas emissions. The contribution of livestock CH4 emissions are only to be exacerbated as the global demand for meat and dairy products increases. Greater than 75% of livestock CH4 emissions are generated in dispersed production environments (e.g., cow-calf and stocker segments), which are not addressable by methane mitigation strategies requiring constant inputs. Thus, strategies that fit into standard agronomic practices, in particular dispersed production environments, are urgently needed to address the increasing carbon footprint associated with livestock production. Methane-reducing vaccines are a promising solution for addressing this need, due to their longevity of action, low cost, and ease of integration into standard agronomic practices, which could lower the barrier for adoption. Thus, this study aimed to assess the effects of a prototype vaccine on total and antigen-specific immune response, CH4 yield, and average daily gain (ADG) in cattle. Angus crossbred steers [n = 20; initial body weight (BW) = 537 ± 15 kg) fed on a high-forage diet were randomly assigned to either placebo- or vaccine-treated groups blocked by BW, breed, and feed intake. Steers were subcutaneously inoculated (2 mL dose) in the anterior region of the neck. Blood and saliva samples were collected, and total sera and salivary immunoglobulin (Ig) G and IgA were quantified using ELISA (Bethyl laboratories, Montgomery, TX). Antigen-specific sera IgG was measured using ELISA. Daily CH4 emissions and dry matter intake (DMI) were measured using GreenFeed systems (C-Lock Inc. Rapid City, SD) and GrowSafe feed bunks (Vytelle, Lenexa, KS), respectively. Body weights were recorded bi-weekly. Total sera IgA and IgG did not differ between the placebo-treated and vaccinated steers (P = 0.584 and P = 0.425, respectively), nor did total salivary IgA and IgG (P = 0.577 and P = 0.548, respectively). However, antigen-specific sera IgG did significantly increase following booster vaccination in the vaccinated steers, as compared with placebo-treated steers (d 0 vs. d 28; P < 0.001). CH4 yield was significantly reduced in vaccinated steers following booster vaccination despite significantly increasing in placebo-treated steers over the same period (P = 0.002). This reduction in CH4 yield was not associated with a reduction in DMI nor ADG (P = 0.181 and P = 0.314, respectively), suggesting that vaccination did not negatively affect treated animals. These findings underscore the potential of vaccine-based solutions to mitigate the environmental impact of livestock CH4 emissions, especially from dispersed production environments where current options for mitigation strategies are limited.

CLOUD SECURITY POSTURE MANAGEMENT AUTOMATING RISK IDENTIFICATION AND RESPONSE IN CLOUD INFRASTRUCTURES

Cloud Security Posture Management (CSPM) tools have become essential in addressing the growing security challenges faced by organizations as they migrate to cloud environments. This study explores the effectiveness of CSPM tools in automating the identification and response to security risks within cloud infrastructures, highlighting their role in reducing misconfigurations, improving compliance, and enhancing overall security posture. Through a mixed-method approach, combining a comprehensive literature review, a survey of IT security professionals, and detailed case study analyses, this research provides a robust evaluation of CSPM tools' capabilities and the challenges associated with their implementation. The findings reveal that organizations utilizing CSPM tools experience significant reductions in security incidents and operational inefficiencies, with automation playing a crucial role in enabling real-time threat detection and response. However, the study also identifies critical barriers to CSPM adoption, including integration complexities, cost concerns, and organizational resistance to automated security solutions. These challenges suggest that while CSPM tools offer substantial benefits, their successful deployment requires careful planning, adequate resource allocation, and strategic change management to address both technical and human factors. This study contributes to the existing literature by providing detailed insights into the practical applications and limitations of CSPM tools, offering valuable guidance for organizations seeking to enhance their cloud security strategies through automation.

How Can Genetically Informative Research Contribute to Life Course Research?

Genetically informative studies have established a new research field that crosscuts disciplinary boundaries within the social sciences, as well as between social science and biology, with proprietary aims and research questions. This happens, however, at the cost of appropriate integration into the current theoretical and conceptual streams in the social sciences, e.g., sociology. That such a fruitful integration is possible is demonstrated for the case of life course research. The focus in dominantly, though not exclusively, on sociological concepts of the life course. This article first introduces central concepts of genetically informative research and life course research and then discusses possible ways to integrate genetic information into the life course research agenda, giving a brief overview of the main methodological tools available.

A Robust Parallel Predictive Torque Control Based on a Proportional Integral Cost Function for PMSM

A Robust Parallel Predictive Torque Control Based on a Proportional Integral Cost Function for PMSM

Audio Signal-Stimulated Multilayered HfOx/TiOy Spiking Neuron Network for Neuromorphic Computing.

As the key hardware of a brain-like chip based on a spiking neuron network (SNN), memristor has attracted more attention due to its similarity with biological neurons and synapses to deal with the audio signal. However, designing stable artificial neurons and synapse devices with a controllable switching pathway to form a hardware network is a challenge. For the first time, we report that artificial neurons and synapses based on multilayered HfOx/TiOy memristor crossbar arrays can be used for the SNN training of audio signals, which display the tunable threshold switching and memory switching characteristics. It is found that tunable volatile and nonvolatile switching from the multilayered HfOx/TiOy memristor is induced by the size-controlled atomic oxygen vacancy pathway, which depends on the atomic sublayer in the multilayered structure. The successful emulation of the biological neuron's integrate-and-fire function can be achieved through the utilization of the tunable threshold switching characteristic. Based on the stable performance of the multilayered HfOx/TiOy neuron and synapse, we constructed a hardware SNN architecture for processing audio signals, which provides a base for the recognition of audio signals through the function of integration and firing. Our design of an atomic conductive pathway by using a multilayered TiOy/HfOx memristor supplies a new method for the construction of an artificial neuron and synapse in the same matrix, which can reduce the cost of integration in an AI chip. The implementation of synaptic functionalities by the hardware of SNNs paves the way for novel neuromorphic computing paradigms in the AI era.

High-speed, low-voltage, low-bit-energy silicon photonic crystal slow-light modulator with impedance-engineered distributed electrodes

Silicon modulators in optical transceivers feature high-density integration and low manufacturing cost, but they also need to deliver high speed and low power consumption to meet the demands of future data centers and high-performance computing. This paper demonstrates a significantly improved 64 Gbps silicon Mach–Zehnder modulator incorporating photonic crystal slow-light phase shifters. By employing distributed electrodes and engineering their impedance, electro-optic phase matching and electrical impedance matching were obtained simultaneously, and the driving voltage was reduced to 0.87 V, which is compatible with fin-type field effect transistors and eliminates the need for additional electrical amplifiers. The bit energy of as low as 59 fJ/bit is comparable to that of microring modulators, while this modulator does not require temperature control like that used for microring modulators, due to its wide working spectrum of 6 nm. These results indicate the potential for addressing power issues in next-generation data infrastructures.

The impact of family urban integration on migrant worker mental health in China.

As China has undergone the processes of urbanization and economic development, a large migrant population has emerged, creating new family migration trends. Family migration brings about changes in urban integration costs and benefits, affecting health investment. The primary objective of this research is to investigate the influence of urban integration of migrant workers' families on their mental wellbeing, with the aim of offering policy recommendations conducive to the realization of a comprehensive public health strategy in China. This paper uses multi-dimensional indexes to measure family urban integration, covering economic, social and psychological dimensions, which may consider the complexity of integration. Utilizing a machine learning clustering algorithm, the research endeavors to assess the level of urban integration experienced by migrant workers and their respective families. The analysis discerns three distinct clusters denoting varying degrees of urban integration within these familial units, namely high-level, medium-level, and low-level urban integration. We applied binary logit regression models to analyze the influence of family urban integration on the mental health among migrant workers. Then we conducted a series of robustness tests. The results show that family urban integration decreases the probability of depressive symptoms by 14.6 percentage points. Further mechanism tests show that family economic integration enhances the psychological wellbeing of migrant workers by elevating their income status. Family social integration decreases depressive symptoms by increasing social status. Family psychological integration increases the psychological health of migrant workers by making them more satisfied with their lives. The heterogeneity test shows that family urban integration and its different dimensions have a strong impact on the depressive symptoms of women, first-generation, and less-educated groups. This study finds that family urban integration and its economic, social, and psychological dimensions significantly reduced the depressive symptoms of migrant workers. The results of this study lead the authors to recommend formulating a family-centered policy for migrant workers to reside in urban areas, optimizing the allocation of medical resources and public services, and improving family urban integration among migrant workers in order to avoid mental health problems in the process of urban integration.

Computer-Aided Design and Additive Manufacturing for Automotive Prototypes: A Review

This study investigated the integration of computer-aided design (CAD) and additive manufacturing (AM) in prototype production, particularly in the automotive industry. It explores how these technologies redefine prototyping practices, with a focus on design flexibility, material efficiency, and production speed. Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines, this study encompasses a systematic review of 28 scholarly articles. It undertakes a comprehensive analysis to identify key themes, trends, and gaps in the existing research on CAD and AM integration in automotive prototyping. This study revealed the significant advantages of CAD and AM in prototype manufacturing, including improved design capabilities, efficient material usage, and the creation of complex geometries. It also addresses ongoing challenges, such as technology integration costs, scalability, and sustainability. Furthermore, this study foresees future developments by focusing on enhancing CAD and AM technologies to meet evolving market demands and optimize performance. This study makes a unique contribution to the literature by providing a detailed overview of the integration of CAD and AM in the context of automotive prototyping. This study incorporates valuable insights into the current practices and challenges and future prospects, potentially leading to more advanced, sustainable, and customer-oriented prototyping methods in the automotive sector.

Processing causal structure sentences in Mandarin Chinese: an eye movements study.

It remains uncertain whether causal structure prediction can improve comprehension in Chinese sentences and whether the position of the headword mediates the prediction effect. We conducted an experiment to explore the effect of causal prediction and headword position in Chinese sentence reading. Participants were asked to read sentences containing causal connectives with their eye movements recorded. In the experiment, we manipulated the causal structure of the sentence and the position of the headword. We found a promoting effect of causal structure on first-pass reading time and a hindering impact on total reading time. However, the effect was not mediated by the headword position. The results show that causal syntactic prediction facilitated early-stage processing and increased the integration cost in the late stage of Chinese sentence processing. These findings also support the constraint-based approach, which suggests an isolation between semantic and syntactic processing.

Efficacy of Motor Imagery in the Rehabilitation of Stroke Patients: A Scope Review

This review examines the efficacy of motor imagery (MI) as a supplementary rehabilitation technique for stroke patients. Nine randomized controlled trials (RCTs) were analyzed, highlighting MI's potential to enhance motor recovery, mobility, balance, and psychological well-being. Significant improvements in upper-limb function were observed with combined mental and physical practice, evidenced by notable gains in Fugl-Meyer Assessment (FMA) and Action Research Arm Test (ARAT) scores. MI-based exercise programs improved mobility and balance in elderly patients, reducing fall risk as measured by the Timed Up and Go (TUG) test and Berg Balance Scale (BBS). MI was also found to enhance self-efficacy and functional performance, with significant increases in Functional Independence Measure (FIM) and General Self-Efficacy Scale (GSES) scores. Neuroimaging studies revealed that MI activates cortical areas associated with motor control, supporting its role in promoting neural plasticity. Despite these promising results, the heterogeneity in participant characteristics, stroke severity, and MI protocols across studies poses challenges to standardization. Additionally, small sample sizes and reliance on self-report measures limit the generalizability of findings. Nevertheless, MI's low cost, minimal risk, and ease of integration into existing rehabilitation protocols make it a valuable adjunct to physical therapy. Standardized guidelines and personalized MI exercises tailored to individual needs are essential for maximizing benefits. Integrating MI into clinical practice can significantly enhance both physical and psychological recovery outcomes for stroke patients, offering a comprehensive approach to rehabilitation.