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The purpose of this paper is to measure price risk transfer from futures prices to spot prices in the markets of energy commodities.

To this end, it estimates CoVaR functions for five futures-spot prices pairs of energy commodities. To account for the effects of the Covid-19 pandemic as well as for the effects of the Russo−Ukrainian conflict, the total sample has been split into three sub-samples. The first one contains observations from 01/01/2010–3/11/2020, which marks the official declaration of the coronavirus as a global pandemic. The second sub-sample uses observations from 3/12/2020–2/24/2022, which marks the beginning of the Russo−Ukrainian conflict, and the third one includes observations from 2/25/2022 up to 8/31/2023.

Results indicate that the effect of the coronavirus pandemic was to increase the risk of price transfer from futures markets to spot markets, in all of the energy commodities examined. On the other hand, the effect of the escalation of the Russo−Ukrainian conflict was to significantly reduce the price risk transfer from the futures markets to the spot markets, in all five energy commodities.

To the best of the authors’ knowledge, this is the first study to use CoVaR functions to estimate risk transfer among the energy commodities. In addition, it separates and estimates the effects of the Covid-19 pandemic as well as the effects of the Russo−Ukrainian conflict.

Many researchers and analysts are interested in evaluating the performance of a system with a network structure as a decision-making unit. In this regard, fuzzy network data envelopment analysis (FNDEA) is a noticeable and worthy method for evaluating the efficiency of a system with fuzzy data. Based on the structure of a fuzzy network system, which consists of at least two serial stages, an intermediate factor has an output nature for the first stage and an input nature for the second stage. Hence, it is inappropriate to allocate the same weight for each stage using this factor. Unfortunately, contrary to real-world conditions, all previous conventional FNDEA studies have considered the same role for intermediate factors to linearize or simplify models. For the first time, this study attempts to determine the upper and lower bounds of the overall efficiencies of a fuzzy two-stage series system and its subprocesses with unequal intermediate product weights.

The proposed model remains in its original nature as a complex combinatorial problem in the nonlinear programming category of NP-hard problems. A genetic algorithm (GA) is utilized as a metaheuristic algorithm, and a novel hybrid GA-FNDEA algorithm is presented to solve the problem.

The findings of the study outlined several theoretical contributions and practical implications, including as compensatory property of DEA, determining upper and lower bounds, improving efficiency in nonlinear systems, reducing computational burden, enhancing evolutionary algorithms and retaining real-world conditions.

Contrary to real-world conditions, all previous conventional FNDEA studies have considered the same role for intermediate factors to linearize or simplify models. For the first time, this study attempts to determine the upper and lower bounds of the overall efficiencies of a fuzzy two-stage series system and its subprocesses with unequal intermediate product weights.

On account of the flexibility, large working space and system openness, manipulators are often adopted in automatic grinding and polishing operations. In the flexible roboticized polishing process for complex surfaces with narrow spatial structures, such as aero-engine blades, the contact mode between the tool and the workpiece changes with the transformation of the manipulator’s end posture and the alternation of the workpiece curvature, which often leads to processing contact faults. These faults result in the obsolescence of expensive aerospace components and reduced efficiency. The purpose of this study is to collect vibration signals during the machining process and extract fault characteristic parameters for monitoring and diagnosis for diagnosing faults in automated flexible polishing to protect the workpiece.

This paper proposes a whale optimization algorithm (WAO)-support vector machine model based on the support vector machine and WAO. From the original grinding and polishing vibration signal, 11 time-domain features that can reflect the fluctuation of the vibration signal are extracted as detection features.

Experimental results indicate that this method effectively reflects the relationship between contact faults and diagnostic results, demonstrating good real-time performance and diagnostic capability.

This method provides a crucial theoretical basis for real-time fault diagnosis and monitoring in automatic flexible machining, ensuring reliable automatic flexible polishing processes.

This study examines the role of logotypes in advertising effectiveness on s-commerce platforms by analyzing the visual attention paid by the consumer to fashion branding – wordmarks or combination marks – and their subsequent recall.

The study examines the main areas of visual representation of the brand (VRB) on the Instagram network and the user’s corresponding areas of interest on a mobile-device screen. Attention and recall of the VRB are assessed in light of different classification variables (users’ gender, age and level of experience in s-commerce tools) to better understand how VRB may be leveraged by fashion retailers to encourage purchasing behavior. To achieve this objective, a mixed experiment design based on the eye-tracking methodology and a self-administered questionnaire is carried out.

The results indicate that visual attention, gender, age and s-commerce experience all contribute to determining users’ recall of the brand logo to which they are exposed on-screen. By considering the different s-commerce user profiles that exhibit different visualization behaviors, fashion retailers will be better placed to improve their online advertising campaigns and, ultimately, increase brand sales. The findings also point to promising future research directions on the effectiveness of branding strategies.

This highly innovative study provides in-depth insights into advertising effectiveness in terms of attention and recall, according to the main types of VRB for two specific s-commerce tools used by a high-street fashion brand, namely, its profile on Instagram Shop and its profile on Instagram Stories.

This study bridges the gap in quality control strategies for high-volume production by balancing the cost and effectiveness of inspection strategies. Using the cost of quality (CoQ) to manage cost and external failures (EF) to gauge effectiveness, this research introduces an innovative inspection strategy chart that serves as a decision-making tool for optimizing inspection processes.

This paper presents a scenario-based framework designed to support strategic decision-making in inspection processes by integrating empirical data analysis with inspection strategy charts. This approach allows for a dynamic assessment and visualization of the relationship between CoQ and EF, facilitating more informed decision-making in quality management. Notably, it contrasts the traditional models with a novel approach that more accurately captures the uncertainty and correlation among key quality indicators, showcasing its potential for more refined decision-making in quality management.

Application of the framework illustrates its effectiveness in offering a nuanced understanding of the cost implications and effectiveness of various quality control strategies. This facilitates enhanced strategic decision-making, optimizing inspection processes and reducing external failures in high-volume production settings.

The study focuses on a single industry case study, limiting the generalizability of findings across different high-volume production contexts. Future research could explore the framework’s applicability in other sectors and refine the model based on additional empirical data.

The research introduces a versatile framework that navigates the unique challenges of high-volume manufacturing environments. Diverging from models optimized for low-volume settings, this approach provides a valuable tool for adapting inspection strategies to complex production demands, marking a significant contribution to quality management and control literature.

This paper aims to propose the sensory stimulation spillover effect phenomenon, defined as the process by which sensory stimulation in one area generates positive impressions and favorably impacts opinions in other areas. Specifically, this paper demonstrates that the spillover effect of sensory priming via an advertised brand impacts the viewer’s self-brand connections (the mental representation of a brand connected to an individual’s self-concept), brand attitude and brand purchase intention.

Across six experiments, 883 participants considered advertised brands from diverse product categories (food snacks, electronics and detergent). The multisensory prime in Studies 1–3 uses positively valenced sensory imagery and text, whereas the multisensory prime in Studies 4–6 is a sensory imaging task. Studies 1–4 examine the spillover effect of the multisensory prime on consumers’ self-brand connections, as well as downstream brand-related variables. Studies 5 and 6, respectively, examined the moderating roles of advertising appeal, regulatory focus (promotion vs prevention) and cognitive versus affective tone.

Results provide robust evidence of the proposed sensory stimulation spillover effect. Sensory priming strengthens self-brand connections and positively impacts brand attitude and purchase intention; self-brand connections mediate the relationship between a multisensory prime and brand attitude and purchase intention. The sensory stimulation spillover effect is stronger when advertisements have a promotion (vs prevention) focus and particularly for participants with a stronger intrinsic promotion (vs prevention) orientation, as well as for advertisements with an affective (vs a cognitive) tone.

The authors manipulated sensory stimulation using visual images and text as well as using a multisensory-imaging task. Future work can explore the use of actual sensory stimulation, and retail spaces or public venues may provide opportunities for field experiments to study sensory stimulation in situ.

The research focuses on spillover effects in an advertising context with broader implications for consumers’ in-store shopping experiences based on multisensory store architecture and atmospherics, as well as online shopping that is impacted by multisensory information.

This paper introduces the phenomenon of sensory stimulation spillover effect, the process by which sensory stimulation in one area generates positive impressions and favorably impacts opinions in other areas and demonstrates that multisensory priming strengthens self-brand connections and downstream brand-related variables, with self-brand connections as the mediator. The results are robust across multiple product categories and are contingent upon the type of advertising appeal. The research focuses on spillover effects in an advertising context with broader implications for consumers’ in-store shopping experiences based on multisensory store architecture and atmospherics, as well as online shopping which is impacted by multisensory information.

This study evaluates the impact of cutting fluids on energy consumption and tool life in machining, focusing on sustainable practices to reduce environmental impact and improve efficiency. By comparing vegetable-based soyabean oil with mineral-based blasocut oil, the study assesses their effects on power usage and tool life.

This study introduces a novel approach by applying both response surface methodology (RSM) and artificial neural network (ANN) models to validate the performance of vegetable-based cutting fluids, specifically soyabean oil, in machining operations.

Results indicate that soyabean oil reduces energy use by 9% and extends tool life by 29% compared to blasocut oil, with strong alignment between model predictions and actual results.

The findings, though specific to certain fluids and conditions, suggest that soyabean oil offers a viable eco-friendly alternative for machining processes.

Adoption of such fluids could lower greenhouse gas emissions, reduce dependency on mineral oils and benefit farmers by creating additional demand for vegetable oils.

This dual-model validation of cutting fluid performance marks an innovative contribution to sustainable machining, supporting the adoption of greener, resource-efficient manufacturing practices. This study underscores the potential of vegetable-based cutting fluids to enhance sustainability in manufacturing.

One of the central tenets of heritage protection is preserving heritage significance. This can be traced to the “conserve as found” philosophy espoused by Ruskin and Morris in the 1840s. A consensus of values framing what is conserved and the processes facilitating conservation has evolved. This paper examines those processes in land-use planning regulation.

After examining several cases selected for their high profile, revisions are suggested. A methodology is developed to promote a new way of applying the legal tests of preservation.

These include the development of a new methodology to assess cumulative heritage impacts, the removal of any artificial divide between tests of heritage harm and greater harmony between heritage policy and heritage legislative duties when confronting the delivery of preservation and enhancement within conservation practice.

Beyond the calls for reform from such bodies as Historic England and Cadw, no real examination has been undertaken of the relationship between legal strictures and policy detail when assessing the rigours of heritage significance.

Stock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.

Ensemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.

We find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.

The paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.

The proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.

Development of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

The paper aims to elucidate effective strategies for promoting the adoption of green technology innovation within the private sector, thereby enhancing the value of public–private partnership (PPP) projects during the operational phase.

Utilizing prospect theory, the paper considers the government and the public as external driving forces. It establishes a tripartite evolutionary game model composed of government regulators, the private sector and the public. The paper uses numerical simulations to explore the evolutionary stable equilibrium strategies and the determinants influencing each stakeholder.

The paper demonstrates that government intervention and public participation substantially promote green technology innovation within the private sector. Major influencing factors encompass the intensity of pollution taxation, governmental information disclosure and public attention. However, an optimal threshold exists for environmental publicity and innovation subsidies, as excessive levels might inhibit technological innovation. Furthermore, within government intervention strategies, compensating the public for their participation costs is essential to circumvent the public's “free-rider” tendencies and encourage active public collaboration in PPP project innovation.

By constructing a tripartite evolutionary game model, the paper comprehensively examines the roles of government intervention and public participation in promoting green technology innovation within the private sector, offering fresh perspectives and strategies for the operational phase of PPP projects.