Quantitative Performance Analysis Research Articles

The impacts of contact explosions on ultra-high performance reinforced concrete slabs: experimental study and dimensional analysis

Ultra-high performance concrete (UHPC) is becoming a prevailing construction material in protective engineering. However, an insufficient research basis causes difficulty in the anti-explosion structural design of UHPC. To investigate the blast resistance of the UHPC slab (UHPCS), contact explosion tests were carried out. UHPCS exhibited superior blast resistance and a lower threshold range of failure modes than the normal reinforced concrete slab (NRCS). Compared with the NRCS, the UHPCS reached a lower damage level under the same scaled distance and had smaller craters and spalls, fewer cracks, and an absence of cross-shaped cracks. For NRCS, failure modes of medium spall, severe spall, and perforation were reached under charges of 1.0, 1.6, and 2.2 kg. In contrast, UHPCS reached medium spall, severe spall, and perforation under charges of 1.6, 3.3, and 5.0 kg. The scaled slab thicknesses (T/W1/3) of the medium spall, severe spall, and perforation of the NRCSs were 1.17, 1.27, and 1.32 times larger than those of the UHPCSs, respectively. The formulae for predicting the spall and perforation thresholds of the UHPCSs were proposed. The reduction factors were used to evaluate the mitigation effect of the blast damage on the UHPC compared to that on the NRC. The reduction factors of the crater diameter (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\gamma _\\Phi }_{C}$$\\end{document}) and the spall diameter (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\gamma _{\\Phi s}}$$\\end{document}) were determined to be 0.507 and 0.721, respectively. For quantitative analysis of the anti-explosion performance of UHPC, a simple estimation method for predicting the dimensions of the crater and spall of the UHPCS was proposed and verified based on the dimensional analysis method.

Investing in Netflix: Accounting for Content Assets*

ABSTRACTThis case describes an equity investment scenario in the context of the COVID‐19 pandemic. A fictional decision‐maker, Amandeep Singh, is considering whether or not to invest in Netflix, Inc. (Netflix). Netflix has benefited from changing habits during the pandemic, but Singh questions whether those benefits will persist post‐pandemic. To make his decision, Singh needs to perform a qualitative analysis of the video streaming industry and Netflix itself before moving to a quantitative analysis of Netflix's performance. To properly assess performance, Singh needs to fully understand Netflix's accounting methods and estimates for content assets—especially original content. The case is ideally suited to an MBA or Executive MBA audience. Students can easily engage with the case since they will be familiar with the company and industry. This familiarity allows for a general discussion about the industry's future and key success factors. The case provides an opportunity to cover core financial statement analysis concepts and drives home the importance of understanding accounting judgments when assessing firm performance.

Quantitative analysis of motor performance in patients with Mowat Wilson Syndrome using inertial sensors

Quantitative analysis of motor performance in patients with Mowat Wilson Syndrome using inertial sensors

Lexical retrieval difficulties in post-COVID-19 syndrome: Insights from verbal fluency and naming tasks.

Although considerable research has been conducted on post-COVID-19 syndrome (PCS), cognitive symptoms, particularly those related to language, are still not well understood. To provide a detailed quantitative and qualitative analysis of language performance in PCS patients using a comprehensive set of semantic and verbal production tasks. The study involved 195 PCS patients aged 26-64 years and 50 healthy controls aged 25-61 years. Participants were assessed using two semantic tasks, three naming tasks and four types of verbal fluency tasks, designed to evaluate different aspects of language processing. PCS patients demonstrated significantly poorer performance compared with controls across all verbal fluency tasks. This was evident in both the total number of words generated and their types, with patients tending to choose more easily accessible words. In naming tasks, the pattern of errors was similar in both groups, although patients showed a higher number of non-responses and made more errors, reflecting difficulties in word retrieval. The analysis highlighted the impact of factors such as stimulus availability, educational level and cognitive reserve on performance. Notably, younger patients performed worse than older, a paradoxical trend also observed in previous research. These findings reveal significant word retrieval difficulties in PCS patients, suggesting that cognitive impairment related to language may be more pronounced than previously understood. The results underscore the need for a thorough evaluation of language functions in PCS patients and the development of more targeted and individualized language rehabilitation strategies to address these specific challenges. What is already known on the subject Studies on the cognitive characteristics of CPS have focused mainly on broad-spectrum neuropsychological assessments covering all cognitive functions. However, there are very few studies analysing oral production with specific lexical and semantic system tasks. Furthermore, no work has specifically included tasks assessing semantic processing or conducted qualitative analyses of the psycholinguistic variables affecting performance. Such analyses could undoubtedly help clarify the nature of the language impairments in patients with PCS. What this paper adds to the existing knowledge This study explores in depth the evaluation and analysis of the oral production of patients with PCS using several lexical and semantic tasks. In addition, psycholinguistic variables are analysed that could undoubtedly help clarify the nature of the language impairments in patients with PCS. What are the potential or actual clinical implications of this work? The study allows the identification of specific lexical-semantic deficits in the spoken language in patients with this PCS. A more detailed assessment of the oral language of these patients, keeping in mind the psycholinguistic variables that may affect the performance, will facilitate the design of more efficient and individualized rehabilitation programmes.

IDENTITIES. PEDAGOGICAL METHODS FOR TEACHING SHORT MULTIPLICATION FORMULAS

The effective teaching of short multiplication formulas, which play a crucial role in algebra, requires innovative pedagogical methods. This paper explores how different teaching strategies impact students' understanding and retention of these algebraic identities. The study employs a mixed-methods approach, combining quantitative analysis of student performance with qualitative feedback from both students and teachers. Results suggest that interactive methods, such as problem-based learning and visual aids, significantly enhance comprehension. These findings have important implications for improving the quality of algebra education in secondary and tertiary settings.

Leveraging AI and ML for Next-Generation Cloud Security: Innovations in Risk-Based Access Management

In the increasing reliance on the cloud computing era, securing digital assets against sophisticated cyber threats has become a critical concern for organizations globally. Traditional security mechanisms, which often rely on static and pre-defined access control policies, must be revised to address these threats' dynamic and evolving nature. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud security through the development of advanced Risk-Based Access Management (RBAM) systems. The primary objective is to evaluate how AI and ML can improve dynamic access control, threat prediction, and mitigation strategies within cloud environments. The research adopts a mixed-methods approach, combining quantitative analysis of RBAM system performance with qualitative insights from cybersecurity experts. AI/ML models were developed using extensive historical access log datasets and integrated into a cloud-based RBAM prototype. The system's performance was assessed based on its accuracy in threat detection, reduction in false positives, and effectiveness in dynamically adjusting access controls. Results indicate that the AI-enhanced RBAM system significantly outperforms traditional methods, achieving a 30% reduction in false positives and a 25% decrease in unauthorized access incidents. Additionally, AI-driven threat prediction models demonstrated high accuracy, enabling preemptive actions to mitigate potential security breaches. These findings highlight the transformative potential of AI and ML in cloud security, providing a more adaptive and proactive defense against emerging threats. The study concludes with recommendations for refining AI/ML models and exploring their application in other areas of cloud security, emphasizing the need for continued innovation to safeguard the increasingly complex digital landscapes that organizations operate within today.

Quantitative analysis of impression-taking performance - a pilot study to visualize invisible technical steps in dental procedures

Quantitative analysis of impression-taking performance - a pilot study to visualize invisible technical steps in dental procedures

Quantitative performance analysis of anion-exchange membrane fuel cells with in-plane and through-plane water transport

Effective water management is crucial in anion-exchange membrane fuel cells (AEMFCs) due to water's dual role as a reactant during alkaline oxygen reduction and a product of alkaline hydroxide oxidation. Meanwhile, both hydroxide conduction and water transport in polymer electrolytes are highly dependent on the hydration level. Therefore, a three-dimensional multi-physics model is developed to investigate the critical role of ionic and water transport parameters of the electrolyte on AEMFC performance. Additionally, detailed water distribution analysis is used to reveal its fundamental mechanism. The results reveal that extremely inhomogeneous water distribution and cathode water starvation are the main causes of the poor cell performance. Moreover, increasing membrane water diffusivity, water conversion, and decreasing the electro-osmotic drag coefficient all contribute to better cell performance through improved water balance and cathode water supply. Among these, cell performance is most sensitive to membrane water diffusivity, with a 40 % increase in peak power density when it is increased from 10−10 m2 s−1 to 10−9 m2 s−1. Although high ionic conductivity improves cell performance by directly reducing ohmic impedance, it also results in a more inhomogeneous distribution of water in the whole cell, so that a four-fold increase in ionic conductivity only yields a 62 % increase in peak power density.

CyberCortex.AI: An AI‐based operating system for autonomous robotics and complex automation

AbstractThe underlying framework for controlling autonomous robots and complex automation applications is Operating Systems (OS) capable of scheduling perception‐and‐control tasks, as well as providing real‐time data communication to other robotic peers and remote cloud computers. In this paper, we introduce CyberCortex.AI, a robotics OS designed to enable heterogeneous AI‐based robotics and complex automation applications. CyberCortex.AI is a decentralized distributed OS which enables robots to talk to each other, as well as to High Performance Computers (HPC) in the cloud. Sensory and control data from the robots is streamed toward HPC systems with the purpose of training AI algorithms, which are afterwards deployed on the robots. Each functionality of a robot (e.g., sensory data acquisition, path planning, motion control, etc.) is executed within a so‐called DataBlock of Filters shared through the internet, where each filter is computed either locally on the robot itself or remotely on a different robotic system. The data is stored and accessed via a so‐called Temporal Addressable Memory (TAM), which acts as a gateway between each filter's input and output. CyberCortex.AI has two main components: (i) the CyberCortex.AI.inference system, which is a real‐time implementation of the DataBlock running on the robots' embedded hardware, and (ii) the CyberCortex.AI.dojo, which runs on an HPC computer in the cloud, and it is used to design, train and deploy AI algorithms. We present a quantitative and qualitative performance analysis of the proposed approach using two collaborative robotics applications: (i) a forest fires prevention system based on an Unitree A1 legged robot and an Anafi Parrot 4K drone, as well as (ii) an autonomous driving system which uses CyberCortex.AI for collaborative perception and motion control.

A Comparative Analysis of Virtual Education Technology, E-Learning Systems Research Advances, and Digital Divide in the Global South

This pioneering study evaluates the digital divide and advances in virtual education (VE) and e-learning research in the Global South Countries (GSCs). Using metadata from bibliographic and World Bank data on research and development (R&D), we conduct quantitative bibliometric performance analyses and evaluate the connection between R&D expenditures on VE/e-learning research advances in GSCs. The results show that ‘East Asia and the Pacific’ (EAP) spent significantly more on (R&D) and achieved the highest scientific literature publication (SLP), with significant impacts. Other GSCs’ R&D expenditure was flat until 2020 (during COVID-19), when R&D funding increased, achieving a corresponding 42% rise in SLPs. About 67% of ‘Arab States’ (AS) SLPs and 60% of citation impact came from SLPs produced from global north and other GSCs regions, indicating high dependence. Also, 51% of high-impact SLPs were ‘Multiple Country Publications’, mainly from non-GSC institutions, indicating high collaboration impact. The EAP, AS, and ‘South Asia’ (SA) regions experienced lower disparity. In contrast, the less developed countries (LDCs), including ‘Sub-Sahara Africa’, ‘Latin America and the Caribbean’, and ‘Europe (Eastern) and Central Asia’, showed few dominant countries with high SLPs and higher digital divides. We advocate for increased educational research funding to enhance innovative R&D in GSCs, especially in LDCs.

Liquid-Phase Exfoliation of Arsenic Trisulfide (As2S3) Nanosheets and Their Use as Anodes in Potassium-Ion Batteries.

Here, we demonstrate the production of 2D nanosheets of arsenic disulfide (As2S3) via liquid-phase exfoliation of the naturally occurring mineral, orpiment. The resultant nanosheets had mean lateral dimensions and thicknesses of 400 and 10 nm, and had structures indistinguishable from the bulk. The nanosheets were solution mixed with carbon nanotubes and cast into nanocomposite films for use as anodes in potassium-ion batteries. These anodes exhibited outstanding electrochemical performance, demonstrating an impressive discharge capacity of 619 mAh/g at a current density of 50 mA/g. Even after 1000 cycles at 500 mA/g, the anodes retained an impressive 94% of their capacity. Quantitative analysis of the rate performance yielded a capacity at a very low rate of 838 mAh/g, about two-thirds of the theoretical capacity of As2S3 (1305 mAh/g). However, this analysis also implied As2S3 to have a very small solid-state diffusion coefficient (∼10-17 m2/s), somewhat limiting its potential for high-rate applications.

Quantitative analysis of the polishing performance of Wurtzite-SiC surface texture on surface quality and material removal rate

Molecular dynamics simulation is used to study Wurtzite-SiC's structural changes after polishing process. For realistic planarization conditions, various surface-textured structures are also used. Polishing speed greatly impacts dislocation distribution. Increasing velocity to 2 Å/ps reduces dislocation length from 800 to 500 Å quickly. Increasing polishing depth from 15 to 30 Å improves surface roughness by 25 %. Besides, textures such as RPrt-s (Rectangular prism textured) and TPrt-s (Triangular prism textured) show surface improvement but still result in high values greater than 42 Å. The amorphous transition rate (ATR) and material removal rate (MRR) closely correlate and display similar variations. The material's triangular prism textured surface enables for minimal SDL (subsurface damage layer) thickness, dislocation density, and atom removal while preserving low groove surface roughness.

Exploring the synergy of problem‐based learning and computational fluid dynamics in university fluid mechanics instruction

AbstractRecently, the growing demand for computational fluid dynamics (CFD) skills in industry has highlighted the importance of their incorporation into university academic programs at both the undergraduate and graduate levels. However, many academic programs treat CFD tools as a “black box” in which users simply enter data without fully understanding the inner workings of the software or its application in real‐world situations. Therefore, in the context of a civil engineering program in Chile, a novel approach combining problem‐based learning (PBL) with CFD was introduced into the curriculum of a fluid mechanics course to foster crucial competencies. This comprehensive methodology allows students to acquire fundamental theoretical knowledge that is directly related to specific problems in the classroom. Subsequently, students measure relevant variables in the laboratory, ultimately using these data to build computational models for comparing and contrasting reality with simulations. To gauge the effectiveness and impact of this PBL strategy, both quantitative analysis of student performance and qualitative analysis through surveys were conducted. The results reveal a significant improvement in student performance with the implementation of the PBL methodology, alongside a positive perception among students regarding its implementation. This underscores its benefits for learning, motivation, and academic performance. Additionally, the implementation of PBL was found to enhance both theoretical and practical understanding of concepts related to fluid dynamics and CFD simulation.

Advancements in object detection: From machine learning to deep learning paradigms

The evolution of object detection from traditional machine learning approaches to advanced deep learning techniques marks a significant milestone in the field of computer vision. Initially, object detection relied on algorithms such as Support Vector Machines (SVMs) and decision trees, leveraging handcrafted features like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) for classification and recognition tasks. However, these methods exhibited limitations in scalability and adaptability to complex environments. The breakthrough came with the adoption of Convolutional Neural Networks (CNNs), which transformed the landscape by automating feature extraction, thereby enhancing detection accuracy and efficiency. Subsequent innovations in network architectures, such as R-CNN, YOLO, and SSD, have continually refined object detection capabilities, optimizing both speed and precision. This paper examines the progression of object detection technologies, focusing on the impact of deep learning models and the optimization of network structures. It also delves into the quantitative analysis of model performance, highlighting the role of data augmentation and advanced training techniques in overcoming real-world detection challenges. Through this exploration, the paper aims to provide comprehensive insights into the current state and future directions of object detection techniques.

Evaluating the Performance of ETSI-ITS Multi-Stack Protocols for V2V Communication in VANETs: A Simulation Study

The research evaluates various multi-stack protocols for Vehicular Ad-hoc Networks (VANETs), focusing on Vehicle-to-Vehicle (V2V) communication scenarios with Emergency Vehicle (EV) simulations. The study uses the ns-3 network and SUMO (Simulation of Urban MObility) traffic simulators to test these protocols in diverse scenarios, including fluctuating data rates and dense network conditions. By implementing the IEEE 802.11p protocol alongside vehicular message dissemination stacks compliant with ETSI (European Telecommunications Standards Institute) ITS (Intelligent Transport Systems) standards, the study performs simulation experiments with varying vehicle counts, ranging from 20 to 35. It employs two distinct data rate configurations while maintaining a constant transmission power of 23 dBm. The results indicate a decline in the average Packet Reception Ratio (PRR) as vehicle density increases, indicating heightened contention and interference. At the same time, there is an observed increase in average latency, contributing to increased message transmission and reception delays. The quantitative analysis demonstrates an inverse relationship between the average PRR and the total vehicle count when the SEND_CAM message is enabled. On the other hand, disabling SEND_CAM maintains a relatively consistent average PRR across scenarios. Additionally, a positive correlation between vehicle count and average latency underlines the impact of network congestion and interference on communication efficacy within VANETs. Despite suboptimal PRR values falling between 41% and 47%, latency performance remains satisfactory, with average latency durations ranging from 0.154 s to 0.187 s. Notably, the SEND_CAM parameter status shows negligible impact on protocol performance, suggesting that network density plays a more pivotal role. Finally, this study offers valuable insights into the trade-offs and challenges of multi-stack protocols in V2V communication within VANETs. Further optimization efforts are recommended to improve packet reception ratios, especially in high-vehicle-density environments, while maintaining acceptable latency levels. These findings contribute to the ongoing efforts to enhance the reliability and efficiency of communication protocols in VANETs, thus advancing the development of intelligent transportation systems. The study's quantitative protocol performance analysis under varying network conditions provides valuable guidance for optimizing V2V communication deployments in VANETs.

Thermal runaway and gas production characteristics of semi-solid electrolyte and liquid electrolyte lithium-Ion batteries: A comparative study

Solid-state electrolytes have attracted considerable attention as an alternative to liquid electrolytes for lithium-ion batteries. This study compares the thermal runaway and gas production of two commercially available lithium-ion batteries (i.e., the liquid electrolyte lithium iron phosphate battery (LFP-L) and the semi-solid electrolyte lithium iron phosphate battery (LFP-SS)) under different environments and states of charge (SOCs). The main findings include the following aspects: Regarding thermal runaway characteristics and gas production kinetics, LFP-SS exhibits a slower temperature rise rate but a higher per Ah pressure increase. The total gas production volume of LFP-SS is lower, but the gas production volume per Ah is approximately 1.5 times that of LFP-L batteries. The LFP-L produces gas rate per Ah more rapidly than the LFP-SS. As for the produced gas components, LFP-L has higher concentrations of H2 and hydrocarbons than LFP-SS. Concerning explosion limits, both the upper and lower explosion limits of LFP-SS are higher than those of LFP-L at 100 % SOC. However, for 110 % SOC, the upper explosion limit of LFP-SS is lower than that of LFP-L. Quantitative analysis of the safety performance of these batteries under different conditions shows that LFP-SS exhibits the best safety performance at 100 % SOC in a nitrogen environment. These results are hoped to guide battery selection and safety protection design for lithium-ion battery packs.

Quantitative analysis of temporal stability and instrument performance during field experiments of an airborne QCLAS via Allan–Werle-plots

Allan–Werle-plots are an established tool in infrared absorption spectroscopy to quantify temporal stability, maximum integration time and best achievable precision of a measurement instrument. In field measurements aboard a moving platform, however, long integration times reduce time resolution and smooth atmospheric variability. A high accuracy and time resolution are necessary as well as an appropriate estimate of the measurement uncertainty. In this study, Allan-Werle-plots of calibration gas measurements are studied to analyze the temporal characteristics of a Quantum Cascade Laser Absorption Spectrometer (QCLAS) instrument for airborne operation. Via least-squares fitting the individual noise contributions can be quantified and different dominant regimes can be identified. Through simulation of data according to the characteristics from the Allan-Werle-plot, the effects of selected intervals between in-flight calibrations can be analyzed. An interval of 30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$30\\,$$\\end{document}min is found sufficient for successful drift correction during ground operation. The linear interpolation of the sensitivity increases the accuracy and lowers the measurement uncertainty from 1.1%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.1\\,\\%$$\\end{document} to 0.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.2\\,\\%$$\\end{document}. Airborne operation yields similar results during segments of stable flight but suffers from additional flicker and sinusoidal contributions. Simulations verify an appropriate interval of 30\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$30\\,$$\\end{document}min in airborne operation. The expected airborne measurement uncertainty is 2.45 ppbv.

Assessment of visuospatial functions in post-Covid 19 patients: Beyond the traditional paradigm

Several studies indicate that some cognitive changes occur after COVID-19. Visuospatial alterations have been reported in 24–40 %. These alterations may be useful as early biomarkers of neurodegenerative disease. Thus, we can emphasize the importance of visuospatial processes in cognition through quantitative and qualitative analysis of performance on the Clock Test (CDT) and the Rey-Osterrieth Complex Figure (FCRO). Our objective was to describe the performance of post COVID 19 patients in visuospatial tests, with different degrees of respiratory impairment and to perform a qualitative analysis of the performance to check its relationship with alterations in attention and executive functions. This will allow highlighting the executive component of the performance of the CDT and ROCF and differentiate patients with possible cognitive impairment. 77 patients with SARS-CoV-2 infection were evaluated (3 months post-infection) with a complete neuropsychological battery and MRI. Overall, there is a significant difference between FCRO and CDT, with FCRO having only 9 % change and CDT having 51.9 % change. Regarding the correlations observed between groups (VM Inv, VM non I and non hospitalized) the highest correlations were observed between Boston with FCRO copy (r=0.497; p=0.001) and with FCRO memory (r=0.429; p=0.001). Comparing the performance between groups by severity, significant differences were observed only in the TMT A (13.706 p=0.001) and B (9.583 p=0.008) tests and in the phonological fluency letter A (13.445 p=0.001), we observed that the group of non-hospitalized patients had a better performance. Neuropsychological deficits often have a direct impact on daily life by affecting the ability to learn and adapt. Thus, a useful strategy for the neuropsychological characterization of post-COVID-19 patients is the qualitative analysis of visuospatial abilities in conjunction with executive functions that cannot be analyzed in isolation.

Study on the Long-Term Salt Release Characteristics of Self-Melting Ice Asphalt Mixtures and Their Impact on Pavement Performance.

Self-melting ice asphalt pavement materials inhibit pavement freezing and improve driving safety. This paper aims to study the long-term salt release characteristics of self-melting ice asphalt mixtures and the impact on pavement after complete salt release. Firstly, a method to accelerate the rapid release of salt based on the Los Angeles abrasion tester. Then, long-term salt release patterns were elucidated under the influence of deicing agent dosage, type of asphalt, and type of gradation. Finally, a quantitative analysis of the pavement performance after complete salt release is conducted. The results indicate that the release efficiency of the Los Angeles abrasion tester method has increased by 91 times compared to the magnetic stirrer immersion flushing method and by 114 times compared to the natural soaking method. The SBS-modified self-melting ice asphalt mixture possesses a longer duration of salt release, but the uniformity of salt release is inferior. Salt release duration is directly proportional to the dosage of deicing agents. SMA-13 self-melting ice asphalt mixture exhibits poorer uniformity in salt release. After complete salt release, high-temperature stability of self-melting ice asphalt mixtures decreased by 31.6%, low-temperature performance decreased by 15.4%, water stability decreased by 26.7%, and fatigue life decreased by 35.9%.

Marketing innovation for sustainability: Review, trends, and way forward

AbstractSustainable development goals are aligning marketing innovations to meet sustainability interventions. Recently, marketing has evolved to incorporate sustainability in outreach objectives. Heightened literature on the interplay between sustainability, innovations, and marketing demands a holistic understanding to guide future research direction. The current review bridges the research gap using quantitative performance analysis and qualitative intellectual structure analysis. The thematic and content analysis points towards permeating sustainability focus across the business verticals and value chain for differentiated brand positioning and sustainability‐based competitive advantage. The emergent conceptual framework underpins the moderated‐mediation role of sustainability and economic enablers with marketing initiatives to promote business innovations beneficial to all stakeholders. The sustainability‐aligned innovations in products and processes reflect in gaining cost advantage, revenue generation, access to newer markets, and differentiation. It can be inferred from the study that sustainability and conducive marketing strategies can co‐create value across the value chain, providing tangible and non‐tangible corporate payoffs. This cross‐section in the research domain calls for environmental and business focus at the micro and macro levels, supported by pro‐business strategies, system efficiencies, productivity, and technological changes in synergy with the societal landscape. The findings discern strategic directions for practitioners to chart out an organizational portfolio centered around marketing innovations to create business value for the shareholders and sustainability value for the stakeholders, ecosystem, and society. The conceptual framework can help academicians comprehend the business model around sustainability, innovations, and marketing. The evolutionary mapping of the research domain can be used to discern corporate or macro‐level policies and transcend reforms aligned to the dynamic market/stakeholder expectations.