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Comfort foods consumption and linkages to stress coping strategies have received little attention in the business research on food products and services. This paper aims to explore comfort foods consumption among older Americans and how stress-coping strategies are related to their consumption frequency and variety of comfort foods.

Older Americans aged 50–99 years (N = 1,428) in the Health and Retirement Study were surveyed on their frequency and variety of comfort foods consumption and their consumption coping strategies. Data were analyzed and regression models were estimated.

Demographically, baby boomer, male, and non-Hispanic whites reported higher frequency and variety of comfort foods consumption. Comfort foods consumption in frequency and variety was significantly higher (lower) when “eat more” (“use alcohol”) was the endorsed coping strategy.

Research findings furthered research on the consumption of comfort foods among older American adults and added new insights into their coping behavior, both of which may help businesses be more targeted in serving comfort foods to the mature market and the public sector to tailor their services to older adults.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

This study aims to discover a practical and effective way to apply the quality cost concept in Strategic Cost Management (SCM) framework. The interaction of preventive, appraisal and failure (PAF) activities in a company's internal value chain will be the starting point of SCM implementation.

This study begins by establishing value chain and quality costs as the scope of conceptual analysis. Discussions on the interrelationships between activities, quality and costs were gathered to clarify conceptual and practical gaps in the scope of the study. The PAF quality cost model is applied to find viable, practical solutions. The costs of activities will serve as performance indicators.

The PAF quality cost model depicts opportunities to lower costs and increase profit in a business simultaneously; current poor quality costs are the benchmark. Identifying PAF activities and costs in the business value chain and linking it with others is crucial in evaluating SCM applications. These linkages will generate a Quality Cost Chain (QCC). The leading indicator of improvement is a higher ratio between new possible failure costs (FC) and the combination of prevention and appraisal costs (PAC) than the current value, followed by a lower total quality cost (TQC). The subsequent attention is a lower ratio between the appraisal cost (AC) and prevention cost (PC). Mathematically, for assessing the operability of new quality-related activities, ΔPAC_new < ΔFC_new, TQC_new < TQC_current, (FC/PC)_new>(FC/PC)_current and (AC/PC)_new<(AC/PC)_current are proposed as feasible conditional-quantitative improvement criteria.

This study only discusses the relationship between quality costs and activities related to quality management in the PAF quality cost model, not cost behavior. This limitation opens up opportunities for future research that intends to link QCC with cost behavior in the context of managerial accounting and Strategic Cost Management. The use of QCC in certain industrial areas is the next research opportunity. The variety of PAF activities this study addresses originates from a wide range of industrial sectors; QCC research by sector may produce unique industrial quality cost phenomena.

QCC will make it easier for managers to evaluate how strategically their departments or activities contribute to quality costs at the departmental or organizational level, as well as to effectively and efficiently improve quality cost performance.

The quality-related activity and quality cost issues are still rarely treated as subjects of research studies in the field of Strategic Cost Management. Even so, the discussion tends to be very broad, complex and difficult to apply. This study combines a simple diagrammatic and mathematical approach to simplify the discussion and, at the same time, manage the value of strategic quality management.

This paper aims to explore the process of implementing solar photovoltaic (PV) systems in construction to contribute to the understanding of systemic innovation in construction.

The exploratory research presented is based on qualitative data collected in workshops and interviews with 76 construction- and solar-industry actors experienced in solar PV projects. Actor-specific barriers were identified and analysed using an abductive approach.

In light of established definitions of systemic innovation, the process of implementing solar PV systems in construction involves challenges regarding technical and material issues, competencies, and informal and formal institutions. The specificities of this case highlight the necessity of paying attention to details in the process and to develop knowledge of systemic innovation in construction since the industry’s involvement in addressing societal challenges related to the energy transition will require implementing such innovations much more in the future.

New knowledge of solar PV systems as an innovation in professional construction is collected, enabling the adaptation of management strategies for its implementation. This knowledge can also be applied generally to other challenges encountered in highly systemic innovation implementation. Solar industry actors can gain an understanding of solar-specific challenges for the construction industry, challenges for which they must adapt their activities.

The exploration of actor-specific experiences of solar PV projects has resulted in a novel understanding of this specific innovation and its implementation. The findings illustrate a case of a high level of systemic innovation and the need to use a finer-grained scale for classification when studying innovation in construction.

The purpose of this study was to explore how transport companies in Uganda execute strategies in a turbulent business environment.

The study adopted an exploratory qualitative methodology using the data collected through an open-ended instrument. Utilizing the qualitative data analysis software QSR NVivo9, the data were analyzed following the Gioia's methodology. Verbatim texts were used to explain the emergent themes.

The study's findings show that to successfully execute strategies, companies in Uganda communicate, coordinate and put control systems in their operations. The activities undertaken include customer care, timely settlement of complaints, comfortable seats, playing local music, partnerships with reliable fuel stations, setting up strategic offices, cost management, use of experienced drivers, sub-renting vehicles and inspections.

The study produces a pioneering result of how transport companies execute strategies in a turbulent business environment, an aspect that has not been adequately highlighted in previous studies.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

Limited geometric accuracy is one of the major challenges that hinder the wider application of additive manufacturing (AM). This paper aims to predict in-plane shape deviation for online inspection and compensation to prevent error accumulation and improve shape fidelity in AM.

A sequence-to-sequence model with an attention mechanism (Seq2Seq+Attention) is proposed and implemented to predict subsequent layers or the occluded toolpath deviations after the multiresolution alignment. A shape compensation plan can be performed for the large deviation predicted.

The proposed Seq2Seq+Attention model is able to provide consistent prediction accuracy. The compensation plan proposed based on the predicted deviation can significantly improve the printing fidelity for those layers detected with large deviations.

Based on the experiments conducted on the knee joint samples, the proposed method outperforms the other three machine learning methods for both subsequent layer and occluded toolpath deviation prediction.

This work fills a research gap for predicting in-plane deviation not only for subsequent layers but also for occluded paths due to the missing scanning measurements. It is also combined with the multiresolution alignment and change point detection to determine the necessity of a compensation plan with updated G-code.

Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.

A quantitative theoretical model is proposed to measure how productivity performance can be affected by strategic decisions related to specific competitive priorities.

This study proposes the Primary Transformation Model (PTM) and an equation to measure cause-and-effect relationships between productivity and competitive priorities.

The interdependence between productivity and competitive priorities was studied using the PTM and the proposed model indicates that strategies that improve external performance also impact internal productivity. It was also observed that the compatibility between competitive priorities depends on the initial manufacturing conditions and the implementation method adopted.

The proposed model is theoretical and, as such, is an abstraction of reality and does not consider all possible aspects. It consists of a novel approach that still requires further empirical testing. The PTM provides insights about the trade-offs between productivity and strategic objectives, as well, contributes to the ongoing research on manufacturing strategy and can be further developed in future studies.

The main practical implication is to allow companies to relate their strategic decisions to their productivity performance.

This research also contributes to societal issues by enabling firms to better align strategic objectives and operations, which ultimately allows offering products more suited to the needs of customers, thus making better use of the required resources and favoring economic growth.

The model proposed allows objective assessment of actions aiming at operational efficiency and effectiveness, in addition to providing insights into cause-and-effect relationships between productivity and competitive priorities. The model can also be used in empirical investigations on manufacturing strategy.

In this paper, the authors take the central environmental protection inspection (CEPI) as an exogenous shock to study the reaction of the stock market in China. Using the event study method, the authors check how the first round of the first batch of CEPI supervision affects the cumulative abnormal return (CAR) of the listed firms on the Shenzhen or Shanghai stock exchange. This paper aims to discuss the aforementioned objective.

In this paper, the authors take the first round of the first batch of CEPI supervision as a clean exogenous shock to study its effects on the capital market. The authors collect daily trading data from the China stock market and accounting research (CSMAR) database, with the sample containing 1,950 Chinese firms listed on either the Shenzhen or Shanghai stock exchanges. And detailed information on CEPI supervision is obtained from the official website of the Ministry of Ecology and Environment of the People's Republic of China. The event study method is adopted to analyze the reaction of the stock market under CEPI supervision. Specifically, the authors constructed the cumulative abnormal return of each firm around the event day of CEPI. To capture the deterrent effects of CEPI supervision, the authors examine the situation of polluting and non-polluting firms in the supervised provinces, adjacent provinces and provinces that are not supervised or close to the supervised provinces, respectively.

This paper throws light on the following: (1) the polluting firms in the supervised provinces were negatively impacted by CEPI within 20 trading days of the event day, and its effects spread to the polluting firms in the neighboring provinces; (2) CEPI had a favorable impact on the non-polluting businesses in the provinces that are neither supervised nor close to the supervised provinces. The authors contend that it is because the investment is being forced out of the polluting sector and into the non-polluting sector, which is more pronounced in the provinces not directly or indirectly targeted by CEPI; (3) by comparison, the “looking back monitoring of the first round” has had no discernible detrimental impact on the firms' CAR, indicating an important role of psychology anticipation of investors in the stock market performance; (4) although not physically located in the supervised provinces, the downstream enterprises of the polluting firms suffer significantly from CEPI shock; (5) the effectiveness of CEPI supervision in the supervised provinces depends on the level of local environmental regulation and the ownership structure of the company. Private firms in the provinces with stronger environmental regulations suffer more from the CEPI shock; (6) the multivariate analysis shows that while enterprises with high ROE and financial leverage may be at risk of CAR loss, older, larger firms are less likely to experience CEPI shock; (7) the study of persistent effect reveals that the strike of CEPI supervision can last for at least 10 months after the event day and deterrent effect can be spread within the whole polluting industry.

In this paper, the authors only concentrate on the market reaction within 20 trading days after the event day. An analysis of long-term effects should be valuable to get a deeper knowledge of the capital market reaction to the CEPI policy. In addition, the paper only focuses on the first round of the first batch of CEPI. Since CEPI has been built as a constant regulation of local environmental performance, further study may need to track both the reaction of listed firms and investment behavior in the capital market.

Policy implications of the paper are as follows: First, for the policymakers, it is important to construct a constant environmental regulation system instead of a campaign movement. Second, for investors, as environmental issues are receiving increasing attention from both the government and the public, investment decisions should take into account firms' environmental performance, which can help reduce the risk from environmental regulations. Third, the firms in the polluting industry should take more action to reduce pollutant releases and adopt green technology, which is essential for sustainable development under environmental protection.

This paper contributes to the existing literature in the following aspects. First, the authors provide new evidence on the effects of environmental regulations as a shock to the stock market, which has been wildly concentrated in the literature about environmental policies evaluation and capital market reaction. Second, the authors supplement the literature on green finance and sustainability transformation, which has got increasing attention in recent years. Theoretically, by guiding investment and affecting the stock market performance, environmental regulations are considered to be an efficient way to stimulate polluting firms to transform into green development. The results of the paper support this intuition by showing that the CAR of the non-polluting firms in non-supervised provinces in fact benefit from the CEPI supervision.