Efficiency Improvement Research Articles

Enhancing Efficiency and Security in MTC Environments: A Novel Strategy for Dynamic Grouping and Streamlined Management

This study presents a new strategy to improve security and efficiency in Machine-Type Communication (MTC) networks, addressing the drawbacks of the existing Adaptive Hierarchical Group-based Mutual Authentication and Key Agreement (AHGMAKA) protocol. The AHGMAKA protocol, crucial for securing communication within groups of devices with limited resources, has been found to cause significant operational delays and inefficiencies. Our proposed solution integrates advanced cryptographic methods, including an optimized Authenticated Message Authentication Code (AMAC) and lightweight encryption, sophisticated optimization algorithms for dynamic grouping, and an efficient, lightweight group management protocol. It also introduces adaptive network management strategies to customize performance according to the needs of MTC networks. The effectiveness of this approach has been validated through empirical analysis, showing considerable improvements in operational performance and energy efficiency. These improvements mark a significant step toward achieving an optimal balance between efficiency and security for MTC networks. However, the research acknowledges ongoing challenges, including the trade-off between security and efficiency and the issue of compatibility with older devices, suggesting these as areas for future study. The paper outlines potential research paths, including using machine learning for better group management, adopting post-quantum cryptographic methods, applying hardware acceleration, and pushing to standardize these technologies. This work significantly advances the field of secure and efficient communication in MTC, a critical component of the growing Internet of Things (IoT) landscape, setting the stage for future breakthroughs.

High‐Efficiency Single‐Photon Upconversion Photoluminescence of Perovskite Quantum Dots via Intragap State Regulation

AbstractLead halide perovskite quantum dots (PQDs) have shown remarkable single‐photon upconversion photoluminescence (SPUC‐PL) bringing them a brilliant perspective as the active media for biological imaging, optiag, and upconversion photovoltaics. However, the mechanism of SPUC in PQDs is still in dispute, and the specific strategy for optimizing PQDs toward a higher SPUC performance has yet to be established, which impedes the application of PQDs‐based SPUC‐PL in practice. In this work it is revealed that the PL quantum yield is declined by surface trap states, while the SPUC efficiency is promoted by Urbach tail states. These two types of intragap states can be chemically manipulated by engineering the surface ligand and the growth kinetics of perovskite nanocrystals, bringing on the improvement of light emission efficiency and the promotion of subgap low‐energy photon absorption, respectively. As a proof of concept, utilizing the synergistic effect of the proposed chemical manipulation method, the SPUC‐PL efficiency of PQDs is boosted by more than 40%, which gives rise to a spectral window of optical refrigeration gain as broad as ≈130 meV.

GeoTaichi: A Taichi-powered high-performance numerical simulator for multiscale geophysical problems

This study introduces GeoTaichi, an open-source high-performance numerical simulator designed for addressing multiscale geophysical problems. By leveraging the power of the Taichi parallel language, GeoTaichi maximizes the utilization of modern computer resources on multicore CPU and GPU architectures. It offers robust and reliable modules for the discrete element method (DEM), material point method (MPM), and coupled material point-discrete element method (MPDEM). These modules enable efficient solving of large-scale problems while being implemented in pure Python. The design philosophy of GeoTaichi focuses on creating a framework that is readable, extensible, and user-friendly. This paper highlights the coupling procedure of MPDEM, the code structures, and the most important features of GeoTaichi. Rigorous benchmark tests have been conducted to verify the validity and robustness of GeoTaichi. Additionally, the performance of GeoTaichi is compared with similar software tools in the field, underscoring a notable improvement in both computational efficiency and memory savings when compared to existing alternatives. Program summaryProgram title: GeoTaichiCPC Library link to program files:https://doi.org/10.17632/858bmcf7j6.1Developer's repository link:https://github.com/Yihao-Shi/GeoTaichiLicensing provisions: GNU General Public License v3.0Programming language: PythonNature of problem: The simulations of large-deformation geophysical flows and their interaction with structures play a crucial role in the field of geophysics. To address the complexities of these nonlinear problems, the discrete element method (DEM), material point method (MPM), and their coupling (MPDEM) have proven to be highly suitable numerical schemes. However, these schemes impose substantial computational demands, necessitating the development of an efficient framework that can harness modern computer resources on multicore CPU and GPU architectures.Solution method: The open-source code GeoTaichi implements the DEM, MPM, and coupled MPDEM, encompassing a range of constitutive models and contact laws for different geologic materials. The clump particle model is also introduced in DEM to solve granular mechanics involving complex-shaped particles. One significant advantage of GeoTaichi is its utilization of the Taichi parallel language, which is designed to be user-friendly and easily extensible for customized applications.

Rapid degradation of high-concentration antibiotic via heterogeneous photocatalysis coupling Fenton-like oxidation with MIL-53(Fe) under visible light irradiation

The photocatalytic technology based on Metal-Organic Frameworks (MOFs) has shown promising applications in the field of antibiotic pollution control. However, it still faces challenges in efficiently removing high-concentration antibiotic wastewater. A typical Fe-containing MOF, MIL-53(Fe), was successfully synthesized and demonstrated efficient photocatalytic activity under visible light irradiation for high-concentrations of tetracycline hydrochloride (TCH). However, the bare MIL-53(Fe) exhibited unsatisfactory photocatalytic performance. To overcome the low degradation efficiency resulting from the rapid recombination of light-induced electron-hole pairs, H2O2 was introduced as an external electron acceptor to establish a synergistic Fenton-like system to couple with the photocatalysis process. The results showed that in the MIL-53(Fe)/H2O2/Vis system, 99% of 1 g/L TCH was degraded within 80 min, while the degradation efficiency of the MIL-53(Fe)/Vis process was only 4.89% under identical conditions. The degradation mechanism of the coupling system was performed by radical scavenging experiments and Electron Spin Resonance characterization. The results indicated that ·OH was the dominant radical in the coupled reaction, and ·O2- and h+ also played a role in this system. The significant improvement in TCH degradation efficiency during the coupling process was attributed to the following three factors: (1) In the coupling reaction process, photo-generated electrons can stimulate H2O2 to form ·OH radicals, inhibiting the recombination of photo-generated electrons and holes, improving the efficiency of the photocatalytic reaction. (2) FeIII in MIL-53(Fe) can be excited by photo-generated electrons and converted to FeII, which then stimulates H2O2 to form ·OH radicals. (3) FeIII in MIL-53(Fe) can convert with FeII, stimulate H2O2 to form ·OH radicals, and participate in the degradation reaction. In addition, the reusability and stability of the MIL-53(Fe) photocatalyst in the coupled system, as well as the impact of operational parameters, were systematically studied. This work demonstrates a feasible strategy for environmental remediation by enhancing the efficiency of MIL-53(Fe) in photocatalytic Fenton-like systems, thereby providing a viable approach for the treatment of high-concentration antibiotic wastewater.

THE ROLE OF DIGITIZATION IN MANAGEMENT AND STRATEGIC DECISION-MAKING IN MODERN MANAGEMENT

This research aims to assess the impact of digitization on management efficiency and strategic decision-making, a pivotal aspect in the contemporary business world. By integrating digital technologies with traditional mathematical models and analyzing survey data from 250 management professionals, the study provides a comprehensive understanding of the multifaceted effects of digitization.The principal outcomes from the mathematical analyses, including Marginal Cost Analysis, Break-Even Analysis, and Linear Programming, indicate substantial improvements in operational efficiency following digitization. Specifically, Marginal Cost Analysis shows a significant decrease in the cost of production, and Break-Even Analysis reveals a reduction in the break-even point by approximately 15%, underscoring enhanced cost-effectiveness. Linear Programming results demonstrate a 20% improvement in resource allocation efficiency. The survey analysis complements these findings, revealing a positive perception of digitization in enhancing managerial adaptability, efficiency, and decision-making effectiveness, with average rating improvements of 0.3, 0.4, and 0.5 points, respectively, on a 5-point scale. Regression analysis further highlights the positive influence of technological affinity on decision-making effectiveness. However, ANOVA results suggest variability in the impact of digitization across different industry sectors, necessitating sector-specific digital strategies.Digitization significantly elevates management efficiency and strategic decision-making capabilities. The integration of digital tools with traditional management techniques results in more informed, efficient, and strategic business decisions. However, the varied impact across sectors and the rapid pace of technological advancement calls for continuous adaptation and sector-specific strategies. These findings provide vital insights for businesses navigating the digital era, emphasizing the strategic integration of digital tools in management practices for sustained success and competitiveness.

Emerging underwater survey technologies: A review and future outlook

Emerging underwater survey technologies are revolutionizing the way we explore and understand the underwater world. This review examines the latest advancements in underwater survey equipment, highlighting their operational benefits and potential areas for future development. Recent developments in underwater survey technologies have led to significant improvements in accuracy, efficiency, and data quality. Advanced sonar systems, such as multibeam and sidescan sonars, provide high-resolution images of the seafloor, allowing for detailed mapping of underwater features. Autonomous underwater vehicles (AUVs) equipped with sophisticated sensors and cameras enable precise data collection in challenging environments, such as deep-sea areas or complex underwater structures. The operational benefits of these technologies are vast. They allow for faster surveying, reduced costs, and improved safety for personnel. Additionally, the high-quality data obtained from these surveys can lead to better decision-making in various industries, including offshore energy, marine research, and underwater archaeology. Looking ahead, the future of underwater survey technologies is promising. There is a growing interest in developing integrated systems that combine multiple sensors and data processing capabilities to provide a more comprehensive view of underwater environments. Artificial intelligence and machine learning algorithms are also being increasingly utilized to analyze large datasets and extract valuable insights. However, several challenges remain, such as the need for better underwater communication systems, improved battery life for autonomous vehicles, and enhanced data processing capabilities. Addressing these challenges will be crucial for the continued advancement of underwater survey technologies. In conclusion, the latest advancements in underwater survey technologies offer exciting opportunities for enhancing our understanding of the underwater world. By leveraging these technologies and addressing key challenges, we can unlock new possibilities for underwater exploration and research.

Understanding the opportunities and challenges of building automation and control systems to support facility management – an extensive literature review

PurposeThis paper aims to highlight the expanding link between facility management (FM) and building automation and control systems (BACS) through a review of literature. It examines the opportunities and challenges of BACS for facility managers and proposes solutions for mitigating the risks associated with BACS implementation.Design/methodology/approachThis paper reviews various research papers to explore the positive influences of BACS on FM, such as support with strategic decision-making, predictive maintenance, energy efficiency and comfort improvement. It also discusses the challenges of BACS, including obsolescence, interoperability, vendor lock-in, reliability and security risks and suggests potential solutions based on existing literature.FindingsBACS offers numerous opportunities for facility managers, such as improved decision-making, energy efficiency and comfort levels in office buildings. However, there are also risks associated with BACS implementation, including obsolescence, interoperability, vendor lock-in, reliability and security risks. These risks can be mitigated through measures such as hardware and software obsolescence management plans, functional requirement lists, wireless communication protocols, advanced feedback systems and increased awareness about BACS security.Originality/valueTo the best of the authors’ knowledge, no prior academic research has been conducted on the expanding link between FM and BACS. Although some papers have touched upon the opportunities and challenges of BACS for FM, this paper aims to provide a comprehensive overview of these findings by consolidating existing literature.

베이지안확률변경분석을 이용한 국내 철강기업의 에너지효율성 분석

Steel companies, due to their production processes, emit a large amount of greenhouse gases. Therefore, improving the energy efficiency of them is a very important issue in responding to climate change. The starting point for effective management of energy efficiency improvements in steel companies is the objective measurement of their energy efficiency. Previous studies primarily utilized traditional Stochastic Frontier Analysis to estimate the energy efficiency of steel companies. Meanwhile, Bayesian Stochastic Frontier Analysis, which incorporates Bayesian Inference into Stochastic Frontier Analysis, offers the advantage of more accurate and flexible estimates by considering the uncertainty of parameters. Despite the advantages, there has been no research applying Bayesian Stochastic Frontier Analysis to analyze the energy efficiency of Korean steel companies. This study is the first to apply Bayesian Stochastic Frontier Analysis to Korean steel companies, using the Gibbs Sampling technique to estimate the energy efficiency of 22 major Korean steel companies from 2011 to 2019. The key findings of this study are as follows: First, the overall average energy efficiency of steel companies is 0.5450. Second, from 2011 to 2013, the yearly average energy efficiency of the steel companies analyzed shows an improving trend, peaking in 2013, followed by a decreasing trend. Third, a comparison of average energy efficiency across individual companies reveals the firms with relatively high energy efficiency, exceeding 0.8

Fabricating a Structured Single‐Atom Catalyst via High‐Resolution Photopolymerization 3D Printing

AbstractThis study introduces a novel solution to the design of structured catalysts, integrating single‐piece 3D printing with single‐atom catalysis. Structured catalysts are widely employed in industrial processes, as they provide optimal mass and heat transfer, leading to a more efficient use of catalytic materials. They are conventionally prepared using ceramic or metallic bodies, which are then washcoated and impregnated with catalytically active layers. However, this approach may lead to adhesion issues of the latter. By employing photopolymerization printing, a stable and active single‐atom catalyst is directly shaped into a stand‐alone, single‐piece structured material. The battery of characterization methods employed in the present study confirms the uniform distribution of catalytically active species and the structural integrity of the material. Computational fluid dynamics simulations are applied to demonstrate enhanced momentum transfer and light distribution within the structured body. The materials are finally evaluated in the continuous‐flow photocatalytic oxidation of benzyl alcohol to benzaldehyde, a relevant reaction to prepare biomass‐derived building blocks. The innovative approach reported herein to manufacture a structured single‐atom catalyst circumvents the complexities of traditional synthetic methods, offering scalability and efficiency improvements, and highlights the transformative role of 3D printing in catalysis engineering to revolutionize catalysts’ design.

Targeted C•G-to-T•A base editing with TALE-cytosine deaminases in plants

BackgroundTALE-derived DddA-based cytosine base editors (TALE-DdCBEs) can perform efficient base editing of mitochondria and chloroplast genomes. They use transcription activator-like effector (TALE) arrays as programmable DNA-binding domains and a split version of the double-strand DNA cytidine deaminase (DddA) to catalyze C•G-to-T•A editing. This technology has not been optimized for use in plant cells.ResultsTo systematically investigate TALE-DdCBE architectures and editing rules, we established a β-glucuronidase reporter for transient assays in Nicotiana benthamiana. We show that TALE-DdCBEs function with distinct spacer lengths between the DNA-binding sites of their two TALE parts. Compared to canonical DddA, TALE-DdCBEs containing evolved DddA variants (DddA6 or DddA11) showed a significant improvement in editing efficiency in Nicotiana benthamiana and rice. Moreover, TALE-DdCBEs containing DddA11 have broader sequence compatibility for non-TC target editing. We have successfully regenerated rice with C•G-to-T•A conversions in their chloroplast genome, as well as N. benthamiana with C•G-to-T•A editing in the nuclear genome using TALE-DdCBE. We also found that the spontaneous assembly of split DddA halves can cause undesired editing by TALE-DdCBEs in plants.ConclusionsAltogether, our results refined the targeting scope of TALE-DdCBEs and successfully applied them to target the chloroplast and nuclear genomes. Our study expands the base editing toolbox in plants and further defines parameters to optimize TALE-DdCBEs for high-fidelity crop improvement.

Foreign institutional ownership and corporate labor investment

AbstractWe examine the impact of foreign institutional ownership on firms' labor investment efficiency. Using a comprehensive global sample of firms across 37 non‐U.S. countries, we find that greater ownership by foreign institutional investors is significantly associated with lower deviations of labor investment from the level justified by economic fundamentals, that is, higher labor investment efficiency. Independent, U.S.‐based and common‐law foreign institutional investors increase labor investment efficiency to a greater extent than their gray, non‐U.S.‐based and civil‐law peers. We further find evidence supporting the value creation of foreign institutions through the improvement in labor investment efficiency. Overall, our results suggest that foreign institutional investors promote good corporate governance practices around the world.

Research on sustainable green building space design model integrating IoT technology.

"How can the integration of Internet of Things (IoT) technology enhance the sustainability and efficiency of green building (G.B.) design?" is the central research question that this study attempts to answer. This investigation is important because it examines how green building and IoT technology can work together. It also provides important information about how to use contemporary technologies for environmental sustainability in the building sector. The paper examines a range of IoT applications in green buildings, focusing on this intersection. These applications include energy monitoring, occupant engagement, smart building automation, predictive maintenance, renewable energy integration, and data analytics for energy efficiency enhancements. The objective is to create a thorough and sustainable model for designing green building spaces that successfully incorporates IoT, offering industry professionals cutting-edge solutions and practical advice. The study uses a mixed-methods approach, integrating quantitative data analysis with qualitative case studies and literature reviews. It evaluates how IoT can improve energy management, indoor environmental quality, and resource optimization in diverse geographic contexts. The findings show that there has been a noticeable improvement in waste reduction, energy and water efficiency, and the upkeep of high-quality indoor environments after IoT integration. This study fills a major gap in the literature by offering a comprehensive model for IoT integration in green building design, which indicates its impact. This model positions IoT as a critical element in advancing sustainable urban development and offers a ground-breaking framework for the practical application of IoT in sustainable building practices. It also emphasizes the need for customized IoT solutions in green buildings. The paper identifies future research directions, including the investigation of advanced IoT applications in renewable energy and the evaluation of IoT's impact on occupant behavior and well-being, along with addressing cybersecurity concerns. It acknowledges the challenges associated with IoT implementation, such as the initial costs and specialized skills needed.

Audit risks and prevention strategies of corporate accounting based on computer technology

Business owners use professional auditors to do audits on their companies because auditors are essential partners in the growth of enterprises. Auditors may improve their ability to organize their audit work sensibly and provide accurate audit opinions if they effectively identify the risks associated with the audit. In this day and age of big data and the World Wide Web, businesses produce a significant quantity of data through the normal course of their activities. Using data mining techniques, deep learning, neural networks, and other developing technologies to extract excellent auditing data from the massive amounts of data produced by audited businesses is a significant challenge for auditors. As a result, the purpose of this research is to use computer data mining methods in order to develop an audit risk model. This model will give an example for auditors to use when doing big data analysis and will mine important data, which will ultimately result in an improvement in the audit process's efficiency as well as its correctness.

Optimization of Layout Indoor Space and Design Based on Machine Vision Algorithm

In the realm of indoor space design, achieving an optimal layout is crucial for enhancing functionality, efficiency, and user experience. Traditional approaches often rely on manual assessments or simplistic algorithms, lacking the precision and adaptability required for modern architectural challenges. This paper presents a novel framework for optimizing indoor space layout and design leveraging advanced machine vision algorithms. By harnessing the power of computer vision techniques, our methodology offers automated analysis and optimization of indoor environments, considering factors such as spatial utilization, traffic flow, and aesthetic appeal. Through a comprehensive exploration of machine learning and optimization techniques, we demonstrate significant improvements in space efficiency and usability, paving the way for intelligent design solutions in various architectural contexts. This research contributes to the intersection of computer vision and architectural design, offering a paradigm shift towards data-driven and adaptive approaches for optimizing indoor spaces.

Effects of fiscal support for agriculture on grain production technical efficiency: empirical evidence from Chinese farms

ABSTRACT This study utilizes micro-level farm panel data from 2007 to 2012 to measure the technical efficiency of grain production among farms using a stochastic frontier analysis model. Additionally, it employs a two-way fixed effects model to empirically investigate the impact of fiscal support for agriculture on grain production technical efficiency and its underlying mechanisms. The results reveal significant room for improvement in the technical efficiency of grain production among Chinese farms, with increased fiscal support for agriculture demonstrating a substantial enhancement of their efficiency. This promoting effect only exists in the major grain-producing areas and increases with the increase of farm size. The augmentation of fiscal support for agriculture achieves this goal by augmenting modern agricultural input factors, alleviating financing constraints faced by farms, and optimizing agricultural production infrastructure. Our findings provide guidance for optimizing fiscal policy to support agriculture, promoting agricultural modernization, and achieving food security.

Assessment and Forecasting of Energy Efficiency in Economic Sectors in Poland

The material developed focuses on the analysis of energy efficiency trends in Poland, utilising ODEX indicators for the sectors of industry, transport, households and in general, from 2011 to 2021. The objective of the study is to assess the progress made in energy efficiency and to forecast future trends in these sectors. The methods employed are based on statistical modelling of time series, taking into account sector-specific energy consumption dynamics. The following techniques were employed: linear regression, cluster analysis to identify patterns of change, statistical hypothesis testing for energy efficiency and simplified autoregressive models. The results demonstrated significant improvements in energy efficiency in the industrial sector, stability of the ODEX indicator in the transport sector and gradual improvements in households and overall. The prediction indicates an upward trend in the ODEX indicator in the short term, suggesting an increase in energy demand. However, it also predicts a decline in the long term, which may indicate the effectiveness of future energy efficiency strategies and investments. Consequently, the necessity for continued efforts to increase energy efficiency and further research into the factors influencing energy efficiency in different economic sectors is emphasised.

Leveraging Emerging Technologies for Enhanced Airport Ground Movement Management Efficiency and Reliability

The Executive summary serves to give the highlights of the research paper and an overview of the objectives, methods used, key findings and implications. It is a brief rundown of the whole research document and provides time-pressed readers with some insights onto the study without having to go into deeper details. Introduction: This research paper examines how blockchain, edge computing and autonomous systems can be integrated inside airports in order to enhance performance and reliability of airport ground movement management. There is need for innovative approaches towards optimizing ground movement in highly complex airports to ensure that they operate seamlessly in a growing air traffic scenario. Objectives: The main aim of this study is to explore how blockchain, edge computing and autonomous systems could change airport ground movement management entirely. These technologies will be exploited for improving data sharing, enhancing real-time processing capabilities as well as enabling automatic decision support services on the ground operations. Methodology: In this case a mixed method approach has been used which involves qualitative and quantitative analysis. Qualitative methods like surveys and questionnaires are done so as researchers get insights from different stakeholders, Integrating various stakeholders, including airlines, airports, and regulators, regarding their perspectives on integrating new technologies into ground movement management!!! Quantitative methods involve data analysis to assess the effectiveness and efficiency of proposed technological solutions. Additionally, simulations and case studies are conducted to evaluate the impact of these technologies on ground movement optimization. Key Findings: The research yields promising results, indicating significant improvements in routing efficiency, reduced taxiing times, and increased operational reliability. The integration of blockchain facilitates secure data sharing, while edge computing enables real-time processing, leading to faster decision-making in ground movement management! Autonomous systems contribute to optimizing resource routing and allocation decisions, further enhancing operational efficiency. Implications and Recommendations: The findings of the research carry both theoretical and practical implications. Theoretical implications include advancing knowledge in the field of airport ground movement management by exploring the potential of emerging technologies. On a practical level, the research provides valuable insights for airport stakeholders seeking to modernize their ground movement control systems?? Recommendations include addressing the complexity of integrating multiple emerging technologies and overcoming challenges related to data security and system interoperability. Conclusion: In conclusion, the research underscores the significance of integrating blockchain, edge computing, and autonomous systems into airport traffic management to enhance efficiency and reliability!!! By addressing key challenges in ground movement management, the study paves the way for more streamlined and resilient airport operations, ultimately benefiting the aviation industry as a whole.

Impact of Heat Stress on Broiler Chicken Production

Poultry farmers need to consider making adaptations now to help reduce cost, risk, and concern in the future; the industry’s high and unstable input costs, which result in losses, need to incentivize manufacturers to concentrate on efficient management, welfare, and health improvements, thereby creating premium and value-added products. Heat stress, a significant concern, particularly affects broiler chicken, which is vital for global meat supply in the dynamic field of poultry farming. Despite advances in breeding and management, these pressures have a negative influence on avian development, well-being, and overall health, threatening the poultry industry’s long-term viability. This study investigates the physiological reactions and production consequences of various heat conditions in the chicken business. It thoroughly investigates the complicated implications of heat stress, which has a negative impact on broiler performance and causes economic losses. This article investigates various dietary techniques, such as antioxidants, probiotics, amino acid balance, and vitamin supplementation, with the goal of improving chicken thermotolerance as part of a comprehensive stress reduction strategy. This assessment emphasizes the industry’s continuous commitment to sustainable practices by highlighting the need for more research to enhance methodology, investigate creative tactics, and address regional variances in heat stress.

The Environmental and Economic Importance of Mixed and Boundary Friction

One route to reducing global CO2 emissions is to improve the energy efficiency of machines. Even small improvements in efficiency can be valuable, especially in cases where an efficiency improvement can be realized over many millions of newly produced machines. For example, conventional passenger car combustion engines are being downsized (and also downspeeded). Increasingly, they are running on lower-viscosity engine lubricants (such as SAE 0W-20 or lower viscosity grades) and often also have stop–start systems fitted (to prevent engine idling when the vehicle is stopped). Some of these changes result in higher levels of mixed and boundary friction, and so accurate estimation of mixed/boundary friction losses is becoming of increased importance, for both estimating friction losses and wear volumes. Traditional approaches to estimating mixed/boundary friction, which employ real area of contact modelling, and assumptions such as the elastic deformation of asperities, are widely used, but recent experimental data suggest that some of these approaches underestimate mixed/boundary friction losses. In this paper, a discussion of the issues involved in reliably estimating mixed/boundary friction losses in machine elements is undertaken, highlighting where the key uncertainties lie. Mixed/boundary lubrication losses in passenger car and heavy-duty internal combustion engines are then estimated and compared with published data, and a detailed description of how friction is related to fuel consumption in these vehicles, on standard fuel economy driving cycles, is given. Knowing the amount of fuel needed to overcome mixed/boundary friction in these vehicles enables reliable estimates to be made of both the financial costs of mixed/boundary lubrication for today’s vehicles and their associated CO2 emissions, and annual estimates are reported to be approximately USD 290 billion with CO2 emissions of 480 million tonnes.

Improvement in notch band characteristics of a hexagonal super wideband antenna by interfering back radiation out of phase with front radiation using frequency selective surface

SummaryA band‐notched super wideband (SWB) planar monopole antenna with improved notch characteristics is proposed in this article. A hexagonal SWB antenna operating in the frequency range from 3.04 to 36 GHz is initially designed on Arlon substrate. The hexagonal patch is then loaded with optimum sized U‐shaped slot to eliminate WLAN band (5.15–5.85 GHz). The notch band's characteristics are further improved by incorporating a frequency selective surface (FSS) of a square array at a specific distance below the antenna to introduce a phase delay of 180° between the back radiation reflected by the FSS with the front radiation at the notched frequency. Improvement of notch band gain, efficiency, and antenna front‐to‐back ratio (FBR) are studied. The antenna without embedding an FSS exhibits working spectrum from 3.1 to 33.6 GHz with notch frequency gain of −2.6 dBi and efficiency 53%. The inclusion of an FSS layer at 25.2 mm behind the antenna results in notch frequency gain of −7.6 dBi with an efficiency of 23%. FBR improvement of about 16 dB is achieved at 8 GHz, and FBR is reduced by about 5 dB at the notch frequency. 169% fractional bandwidth with bandwidth dimension ratio (BDR) of 1622 is achieved in this proposed FSS integrated antenna. The inclusion of FSS to improve the band elimination performance represents the novelty of this proposal. The data are validated by electromagnetic simulation and physical measurement techniques.