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Background: Diverse marketing channels have been developed with the advent of digitalization. In the fresh food and grocery retail sector, consumers may have a large choice of channels to shop from. In this case, an analysis of their behavior is crucial for retailers. Therefore, the present paper provides a state-of-the-art review of existing papers dealing with consumers' channel choice when buying groceries. Methods: A systematic literature review (SLR) is performed following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) procedure, identifying 36 research papers published in the last decade. Results: The results present the principal methodologies adopted by the analyzed research papers to address this topic, along with the various channels available to consumers to date. Other important outcomes of this review include the main variables that can affect consumer choice when deciding between the available channels and the limitations of the analyzed papers, along with suggestions for future research directions to address these limitations. Conclusions: This paper discusses the essential effects of consumers' channel choice on the logistics and operation services of grocery retailers. It also provides an integrative framework linking the influencing factors of consumer choice with outcomes directly impacting grocery retailers' logistics services. [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of the preferences of different customer generations in Slovakia in the field of marketing communication and its influence on consumer behaviour. Currently, marketing communication is an integral part of company strategies and has a key influence on how customers choose products or services. With the increasing importance of the generational segmentation of customers, understanding their individual preferences and tendencies in communication becomes an essential prerequisite for success. In this paper, we explore how preferences differ between Baby Boomers, Generation X, Generation Y, and Generation Z, and how these preferences influence their consumer behaviour. Based on the analysis of primary and secondary data, the main trends and preferences of generations in the field of marketing communication were identified. This paper provides useful information that can be used by companies in the creation and implementation of marketing strategies. At the same time, the paper enables a better understanding of the dynamics of consumer behaviour in the context of generational preferences and will help identify opportunities for improving marketing activities and more effective communication with target groups. [ABSTRACT FROM AUTHOR]

This paper proposes a profit maximization model for a data consumer when it buys personal data from data providers (by obtaining consent) through data brokers and provides their new services to data providers (i.e., service consumers). To observe the behavioral models of data providers, the data consumer, and service consumers, this paper proposes the willingness-to-sell model of personal data of data providers (which is affected by data providers' behavior related to explicit consent), the service quality model obtained by the collected personal data from the data consumer's perspective, and the willingness-to-pay model of service consumers regarding provided new services from the data consumer. Particularly, this paper jointly considers the behavior of data providers and service users under a limited budget. With parameters inspired by real-world surveys on data providers, this paper shows various numerical results to check the feasibility of the proposed models. [ABSTRACT FROM AUTHOR]

Over the last two decades, there has been growing interest from all stakeholders (government, manufacturers, and consumers) to make packaging more sustainable. Paper is considered one of the most environmentally friendly materials available. A qualitative study investigating consumers' expectations and opinions of sustainable paper-based packaging materials was conducted where 60 participants took part in focus group sessions organized in two stages. In the first stage, participants expressed their opinions about currently available packages in the market and their expectations about a sustainable packaging material. In the second stage of the study, they evaluated five paper-based prototype packages for two product categories (biscuits and meat). Too much plastic and over-packaging were the key issues raised for current packages. Price and quality were the main driving forces for consumers' purchase intent. While participants were impressed by the sustainable nature of the prototypes, the design did not necessarily meet their expectations, and they were not willing to pay more for a sustainable package. The key message that emerged from the discussions was the "3Rs"—Reduce, Reuse, and Recycle"—which should be the main points to consider when designing a sustainable packaging. [ABSTRACT FROM AUTHOR]

With the advent of big data, the swift advancement of diverse algorithmic technologies has enhanced the transaction efficiency of the e-commerce business. Nevertheless, it is crucial to acknowledge that e-commerce platforms might employ algorithmic technology to enforce differential pricing for various consumers with the aim of maximizing profits, thus infringing upon the lawful rights and interests of consumers. This paper focuses on the algorithmic price discrimination commonly observed on e-commerce platforms. To effectively regulate this behavior, the paper utilizes evolutionary game theory (EGT) to analyze the strategies employed by e-commerce platforms, consumers, and market regulators to achieve stability. This research employs a real-life situation and utilizes parametric simulation to visualize and analyze the process and outcomes of the three-party evolutionary game. The results demonstrate the credibility and feasibility of the article's findings. Based on our research, we have reached the following findings: During the process of evolution, the strategic decisions made by the game participants from the three parties will mutually impact each other, and various elements exert varying degrees of influence on the strategic choices made by the game participants from each party. Collaborative governance can enable consumers and market regulators to regulate the discriminatory pricing behavior of e-commerce platforms effectively. This article offers valuable insights into the governance of violations in the e-commerce sector based on robust data and model research. [ABSTRACT FROM AUTHOR]

Retrial queues are used extensively to model many practical problems in service systems, call centers, data centers, and computer network systems. The average waiting time is the main observable characteristic of the retrial queues. Long queues may cause negative impacts such as waste of manpower and unnecessary crowding leading to suffocation, and can even cause trouble for customers and institutions. Applying control chart technology can help managers analyze customers' waiting times to improve the effective performance of service and attention. This paper pioneers the developing and detailed study of a waiting time control chart for a retrial queue with general service times. Two waiting time control charts, the Shewhart control chart, and a control chart using the weighted variance method are constructed in this paper. We present three cases for the Shewhart control chart in which the service time obeys special distributions, such as exponential, Erlang, and hyper-exponential distributions. The case of an exponentially distributed service time is also presented for the control chart using the weighted variance method. Based on the numerical simulations conducted herein, managers can better monitor and analyze the customers' waiting times for their service systems and take preventive measures. [ABSTRACT FROM AUTHOR]

This paper introduces a groundbreaking approach to demand response management, aiming to empower consumers through innovative strategies. The key contribution is the concept of "acquiring flexibility rights", wherein consumers engage with power aggregators to curtail energy usage during peak-load periods, receiving incentives in return. A flexibility right coefficient is introduced, allowing consumers to tailor their participation in demand response programs, ensuring their well-being. Additionally, a lighting intensity control system is developed to enhance residential lighting network efficiency. The study demonstrates that high-energy consumers, adopting a satisfaction factor of 10, can achieve over 61% in electricity cost savings by combining the lighting control system and active participation in demand response programs. This not only reduces expenses but also generates income through the sale of flexibility rights. Conversely, low-energy consumers can fully offset their expenses and accumulate over USD 33 in earnings through the installation of solar panels. This paper formulates an optimization problem considering flexibility rights, lighting control, and time-of-use tariff rates. An algorithm is proposed for a distributed solution, and a sensitivity analysis is conducted for evaluation. The proposed method showcases significant benefits, including cost savings and income generation for consumers, while contributing to grid stability and reduced blackout occurrences. Real data from a residential district in Tehran validates the method's effectiveness. This study concludes that this approach holds promise for demand response management in smart grids, emphasizing the importance of consumer empowerment and sustainable energy practices. [ABSTRACT FROM AUTHOR]

Priced oblivious transfer (POT) is a cryptographic protocol designed for privacy-preserving e-commerce of digital content. It involves two parties: the merchant, who provides a set of priced items as input, and a customer, who acquires one of them. After the protocol has run, the customer obtains the item they chose, while the merchant cannot determine which one. Moreover, the protocol guarantees that the customer gets the content only if they have paid the price established by the merchant. In a recent paper, the authors proposed a POT system where the payments employed e-coin transactions. The strong point of the proposal was the absence of zero-knowledge proofs required in preceding systems to guarantee the correctness of payments. In this paper, we propose a novel e-coin-based POT system with a fast item retrieval procedure whose running time does not depend on the number of items for sale. This is an improvement over the aforementioned existing proposal whose execution time becomes prohibitively long when the catalog is extensive. The use of zero-knowledge proofs is neither required. [ABSTRACT FROM AUTHOR]

To ensure the precise dimensioning and effective testing of drivetrain components, it is crucial to have a thorough understanding of customer requirements, with a particular emphasis on customer stress on these components. An accurate interpretation of customer data is essential for determining representative customer requirements, such as load collectives. The automobile industry has faced challenges in analyzing large amounts of customer driving data to obtain representative load collectives as target values in durability design. However, due to technical limitations and cost constraints, collecting data from a large sample size is not feasible. The ongoing digitalization of the automotive industry, driven by an increasing number of connected vehicles, enhances data-based and customer-oriented development. This paper investigates representative customer load collectives using cloud data from over 40,000 customer vehicles to lay the groundwork for realizing robust requirement engineering. A systematic method for analyzing big data on the cloud was introduced. The derived component-specific damage distribution from these collectives adopts a unique approach, utilizing the 1% vehicle term instead of the common 1% customer term to represent typical customer stress. This study shows that the driven mileage and the number of vehicles are crucial factors in 1% vehicle analysis. An analysis of the characteristics of the 1% vehicle is conducted, followed by an exploration to determine the required vehicle quantity for obtaining stable results. The shape parameter of the damage distribution determines the necessary number of vehicles for a reliable conclusion. Additionally, a comparative analysis of market-specific customer requirements between the US and Europe is presented, and real usage differences in customer operations are explained using an operating point frequency heatmap. The information presented in this paper provides valuable input for optimizing durability design and conducting efficient, customer-oriented tests, resulting in significant reductions in development time and costs. [ABSTRACT FROM AUTHOR]

Background: Possible solutions to overcome the many challenges of home delivery are collection and delivery points (CDPs). In addition to commercial facilities, the role of CDPs can also be played by users' households, providing a crowd storage service. Key decisions regarding CDPs relate to their location, as well as the allocation of users to selected locations, so that the distance of users from CDPs is minimal. Methods: In this paper, the described problem is defined as a p-median problem and solved for the area of the city of Belgrade, using the heuristic "greedy" and the simulated annealing algorithm. Results: Fifty locations of CDPs were selected and the users allocated to them were distributed in over 950 zones. The individual distances between users and the nearest CDPs and the sum of these distances, multiplied by the number of requests, were obtained. An example of modification of the number of CDPs is presented as a way of obtaining solutions that correspond to different preferences of operators and/or users in terms of their distances from the CDPs. Conclusions: User households can be used as CDPs to achieve various benefits. Locating CDPs, i.e., selecting households, can be solved as a p-median problem, using a combination of heuristic and metaheuristic algorithms. In addition, by modifying the number of medians, the total and average distances between users and CDPs can be better managed. The main contributions of the paper are the establishment of users' households as potential locations of CDPs, the establishment of a framework for analysis of impact of the number of CDPs on the sum and average distances from the customers, as well as the creation of a basis for upgrading and modifying the model for implementation in the business practice. [ABSTRACT FROM AUTHOR]

Existing studies have examined unhealthy food packaging information, mainly focusing on aspects such as the content, color, and text, whilst paying less attention to the boundaries of information. This paper investigates unhealthy foods through three experiments, revealing that the presence (vs. absence) of packaging information boundaries on unhealthy foods has a negative impact on consumers' purchasing intentions (p = 0.040) (Experiment 1). The feeling of constraint mediates this effect (β = −0.078, CI: [−0.1911, −0.0111]) (Experiment 2). Additionally, consumers with an independent self-construal exhibit reduced purchasing intentions when unhealthy food packaging information boundaries are present (vs. absent) (p < 0.001), whereas those with an interdependent self-construal show increased purchasing intentions under the same conditions (p = 0.024) (Experiment 3). This paper reveals the psychological mechanism and boundary conditions of unhealthy food packaging information boundaries affecting consumers' purchasing intention and provides practical inspiration for government policy-making related to unhealthy food packaging. [ABSTRACT FROM AUTHOR]

This paper analyses Keynes's 1930 prediction that technical advances would cut people's working week to 15 h by 2030 and investigates why actual working hours are significantly higher in the United States. Elaborating on Keynes's forecast to provide a general productivity formula while keeping its simplicity, we ran tests on macro-data from 1929 to 2019 and on estimates for 2030, demonstrating that productivity is surprisingly still insufficient to allow for a reduction in working hours across the US economy. This finding represents a substantial contribution to the literature, which has mostly explained long working hours by means of new consumer needs. Even by using microdata, we show that consumption does not explain the stickiness of working hours to the bottom. Hence, this paper combines a macroeconomic, logical-analytical approach based on historical time series with rigorously constructed time series at the microeconomic level. Finally, we also provide policies to narrow the productivity differential to Keynes's prediction for 2030 while fostering work-life balance and sustainable growth. To understand long working hours in the US despite technical advances—this being one of our main findings—productivity remains crucial. [ABSTRACT FROM AUTHOR]

This paper introduces an evaluated initial model for how product lifecycles can be extended considering new and emerging existential design aspects concerning both general as well as digital/connected products. The initial model, which is cyclic, includes reverse logistics of components and raw materials, as well as information on how to manage data at the end of lifecycles. The aim is to improve long-term sustainability with a high degree of circularity while also achieving increased profitability and competitiveness. Further, we highlighted that product providers must start to evaluate and prepare for how to improve product durability, manage long and extendable lifespans, and achieve circularity with reverse logistics to close the loops. Additionally, updatability and upgradability are also required to stay current with time and create value while being cybersecure. Otherwise, customers' expectations, various legal and regulatory aspects, as well as other existential design aspects can halt or even terminate a product's lifecycle. [ABSTRACT FROM AUTHOR]

With the continuous development of electric vehicle (EV) technology, there is an increasing need to analyze the factors influencing customers' purchase intentions. According to the data of customers' vehicle experience evaluation and personal information, this paper develops the analysis models of influencing factors using the analysis of variance algorithm (ANOVA) and Kruskal–Wallis algorithm. Then, the purchase intention model for EVs is proposed using the random forest method. Finally, the optimization model for the EV sales plan was built. The results show that the main factors influencing customers' purchases are different for different vehicle brands. However, the customer's evaluation of the vehicle experience has a greater influence on the customer's purchase. Compared to other prediction models, the random forest model has the highest accuracy. For 3 EV brands, the prediction accuracies are 97.8%, 98.9%, and 97.6%. In addition, this paper predicts the purchase intentions of 15 customers. By optimizing the sales plans for 3 EV brands, the predicted purchase rate of 15 customers increased from 40% to 53%. The research work contributes to the sales of electric vehicles, the accurate positioning of customers, and the identification of more potential customers. [ABSTRACT FROM AUTHOR]

The abstraction of the network node functions using virtualization methods introduced an innovative architecture called Network Function Virtualization (NFV). In NFV, every virtualization software hosts a network service recognized as a Virtual Network Function (VNF). In general, the network provider creates a Service Function Chain (SFC) for every sequence of multiple requested VNFs by the customers. Although NFV allows for a more flexible and economical approach, it is more prone to error and failure. Therefore, providing reliable provisioning for VNF chaining is one of the key issues in NFV. In this paper, we present a systematic literature review to study the pioneer research efforts that provide reliable provisioning for VNF chaining by guaranteeing the availability of the service and resource optimization. Our review is the result of the analysis of 21 screened papers. This paper presents the result of our analysis, including different aspects of a reliable provisioning algorithm, various adopted techniques for reliable provisioning, and the superiority and drawbacks of each algorithm based on the proposed criteria for the evaluation of the provisioning algorithms. [ABSTRACT FROM AUTHOR]

Consumer neuroscience is a new paradigm for studying consumer behavior, focusing on neuroscientific tools to explore the underlying neural processes and behavioral implications of consumption. Based on the bibliometric analysis tools, this paper provides a review of progress in research on consumer neuroscience during 2000–2021. In this paper, we identify research hotspots and frontiers in the field through a statistical analysis of bibliometric indicators, including the number of publications, countries, institutions, and keywords. Aiming at facilitating carbon neutrality via sustainable consumption, this paper discusses the prospects of applying neuroscience to sustainable consumption. The results show 364 publications in the field during 2000–2021, showing a rapid upward trend, indicating that consumer neuroscience research is gaining ground. The majority of these consumer neuroscience studies chose to use electroencephalogram tools, accounting for 63.8% of the total publications; the cutting-edge research mainly involved event-related potential (ERP) studies of various marketing stimuli interventions, functional magnetic resonance imaging (fMRI)-based studies of consumer decision-making and emotion-specific brain regions, and machine-learning-based studies of consumer decision-making optimization models. [ABSTRACT FROM AUTHOR]

The adoption of recommender systems in business-to-business (B2B) can make the management of companies more efficient. Although the importance of recommendation is increasing with the expansion of B2B e-commerce, not enough studies on B2B recommendations have been conducted. Due to several differences between B2B and business-to-consumer (B2C), the B2B recommender system should be defined differently. This paper presents a new perspective on the explainable B2B recommender system using the knowledge graph attention network for recommendation (KGAT). Unlike traditional recommendation systems that suggest products to consumers, this study focuses on recommending potential buyers to sellers. Additionally, the utilization of the KGAT attention mechanisms enables the provision of explanations for each company's recommendations. The Korea Electronic Taxation System Association provides the Market Transaction Dataset in South Korea, and this research shows how the dataset is utilized in the knowledge graph (KG). The main tasks can be summarized in three points: (i) suggesting the application of an explainable recommender system in B2B for recommending potential customers, (ii) extracting the performance-enhancing features of a knowledge graph, and (iii) enhancing keyword extraction for trading items to improve recommendation performance. We can anticipate providing good insight into the development of the industry via the utilization of the B2B recommendation of potential customer prediction. [ABSTRACT FROM AUTHOR]

With the rapid advancement of battery technology and the demand for environmental sustainability, new energy vehicles (NEVs) are becoming more and more popular. This research paper delves into the impact of marketing strategies employed by new energy vehicle companies on consumers' purchase intentions. This paper begins by highlighting the unique benefits of NEVs, such as energy efficiency, environmental friendliness, and improved driving experience. Then, this research identifies gaps in existing research, particularly the lack of micro-market demand data and systematic empirical analyses of consumer purchase intentions for NEVs. The paper employs a quantitative analysis approach grounded in marketing theory to address these gaps and examine the relationship between NEV companies' marketing strategies and consumers' purchase intentions. The research design involves a questionnaire survey based on the 4C marketing theory, focusing on consumer demand, cost, communication, and convenience. The survey targets potential NEV buyers and car owners, and 247 valid responses were analyzed. The results reveal that various factors, including the price and environmental performance of NEVs, non-fiscal policies, vehicle performance, and consumer attributes such as education level and environmental awareness, influence consumers' willingness to purchase NEVs. This study also employs structural equation modeling to explore the correlations between different issues and identifies three main factors: basic car needs, new energy needs, and consumer subjective perception. Lastly, the study concludes that while NEV companies have made significant strides in marketing strategies, there is still room for improvement. We suggest that companies should offer competitive pricing, enhance vehicle performance, and address consumer concerns to boost purchase intentions. [ABSTRACT FROM AUTHOR]

With the development of global clean energy and the implementation of carbon emission reduction policies, the direct purchase of electricity by large consumers has been increasingly promoted as a special form of electricity trading. Therefore, on the basis of the completion of low-carbon emission reduction targets in each country, how to rationalize the electricity purchase by large consumers in the electricity market so as to reduce their electricity purchase costs has become the main target of attention in each country. Currently, there are fewer studies in existing research on the direct electricity purchase strategy of large consumers under the consideration of the weight of consumption responsibility and risk. Based on this, this paper constructs a dual-game model for direct electricity purchase by large consumers based on the Stackelberg game and non-cooperative game theory. The concept of value at risk is further introduced, and the optimal strategy of direct electricity purchase by large consumers is proposed. The results of this study show that when market players make decisions on the purchase and sale of electricity, power suppliers will increase their biddings to obtain the highest returns, and large consumers can reduce the transaction costs by combining the medium- and long-term market with the spot market to purchase electricity. In the choice of electricity purchase market, with the increasing risk factor, large consumers shift from the risky spot market to the less risky medium- and long-term market and option market. This paper provides a reference for the issues of power suppliers' contract bidding and large consumers' electricity purchase strategy in the medium- and long-term contract transactions. [ABSTRACT FROM AUTHOR]

The agribusiness sector is constantly seeking solutions to enhance food security, sustainability, and resilience. Recent estimates indicate that one-third of the total food production remains unused due to waste or limited shelf life, resulting in negative environmental and ethical consequences. Consequently, exploring technological solutions to extend the shelf life of food products could be a crucial option to address this issue. However, the success of these technological solutions is closely linked to the perception of the end-consumers, particularly in the short term. Based on these considerations, this paper presents a systematic literature review of the main technological innovations in the fresh meat industry and of consumers' perceptions of such innovations. Regarding innovative technologies, this review focused on active and smart packaging. Amidst various technological innovations, including the utilization of fundamental matrices and natural additives, a noticeable gap exists in consumer perception studies. This study represents the first comprehensive compilation of research on consumers' perceptions and acceptance of innovations designed to extend the shelf life of fresh meat. Moreover, it sheds light on the existing barriers that hinder the complete embrace of these innovations. [ABSTRACT FROM AUTHOR]

This paper presents a study of fork–join systems. The fork–join system breaks down each customer into numerous tasks and processes them on separate servers. Once all tasks are finished, the customer is considered completed. This design enables the efficient handling of customers. The customers enter the system in a MAP flow. This helps create a more realistic and flexible representation of how customers arrive. It is important for modeling various real-life scenarios. Customers are divided into K ≥ 2 tasks and assigned to different subsystems. The number of tasks matches the number of subsystems. Each subsystem has a server that processes tasks, and a buffer that temporarily stores tasks waiting to be processed. The service time of a task by the k-th server follows a PH (phase-type) distribution with an irreducible representation ( β k , S k ), 1 ≤ k ≤ K . An analytical solution was derived for the case of K = 2 when the input MAP flow and service time follow a PH distribution. We have efficient algorithms to calculate the stationary distribution and performance characteristics of the fork–join system for this case. In general cases, this paper suggests using a combination of Monte Carlo and machine learning methods to study the performance of fork–join systems. In this paper, we present the results of our numerical experiments. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic approach to assessing the hosting capacity for solar PV. The method is part of the optimal techniques for the integration of renewables. There are two types of uncertainties, namely aleatory and epistemic uncertainties. The epistemic and aleatory uncertainties influence distribution networks' hosting capacity differently. The combination of the two uncertainties influences the planning of distribution networks. The study introduces and considers the epistemic probability distribution function (PDF). DSO does take levels of risk for a parameter violation when planning. Epistemic PDF is a range of values of the planning risk margin for quantifying the hosting capacity. The planning risk acknowledges that overvoltages may occur at weaker conceivable locations in a distribution network. In the paper, it has been shown that the number of customers who will be able to connect solar PV in future is influenced by the DSO's planning risk margin. The DSO can be stricter or less strict in planning risk margin. It has been concluded that fewer customers can connect solar PV to a distribution network when a DSO takes a stricter planning risk. Alternatively, more customers can connect solar PV units for a less strict planning risk. How stricter or less strict the DSO is with the planning risk margin determines the investment needed for mitigation measures. The mitigation measures in the future will lead to not exceeding the overvoltage limit when solar PV is connected to the weaker conceivable points of the distribution network. [ABSTRACT FROM AUTHOR]

Traceability is a cornerstone of sustainable honey production and consumption. Honey fraud and a lack of traceability have been recently highlighted by the European Commission. Innovative systems aimed at guaranteeing food safety 'from farm to fork' and improved controls are highly recommended. Within the framework of the BPRACTICES project, part of the European Union's Horizon 2020 research and innovation program, and the ERA-Net SusAn initiative—focused on Sustainable Animal Production Systems—an advanced traceability system has been developed. This system utilizes QR code and radio-frequency identification (RFID) technology, along with a user-friendly web application, to facilitate direct interactions between producers and consumers. Despite existing research, studies on the information needs of Italian consumers regarding honey and its traceability remain limited. Understanding these needs is vital for creating effective communication strategies that enhance consumer satisfaction and trust. This study aims to identify the needs of Italian consumers' honey during the purchasing and consumption decisions. To explore consumer perceptions, behaviors, expectations, and needs regarding honey, we employed diverse social research methodologies, including a quantitative online survey, paper-and-pencil interviews, and focus groups. The results of this study indicate a robust demand for more information on honey's origin, production processes, and beekeeping practices, aligning with the recent EU Directive 2024/1438, which mandates clear labeling. Italian consumers would be willing to pay a premium for honey that offers detailed information about production practices and transparency. The positive reception of QR code technology by consumers suggests a growing openness to digital tools that enhance transparency and access to information. Ultimately, this research emphasizes the need for the beekeeping sector to adopt sustainable practices, improve traceability systems, and actively engage with consumers to foster trust and ensure long-term viability in the honey market. By addressing these information needs, the sector can align itself with increasing consumer demand for quality, sustainability, and transparency. [ABSTRACT FROM AUTHOR]

In the realm of human–robot interaction, the integration of visual and verbal cues has become increasingly significant. This paper focuses on the challenges and advancements in referring image segmentation (RIS), a task that involves segmenting images based on textual descriptions. Traditional approaches to RIS have primarily focused on pixel-level classification. These methods, although effective, often overlook the interconnectedness of pixels, which can be crucial for interpreting complex visual scenes. Furthermore, while the PolyFormer model has shown impressive performance in RIS, its large number of parameters and high training data requirements pose significant challenges. These factors restrict its adaptability and optimization on standard consumer hardware, hindering further enhancements in subsequent research. Addressing these issues, our study introduces a novel two-branch decoder framework with SAM (segment anything model) for RIS. This framework incorporates an MLP decoder and a KAN decoder with a multi-scale feature fusion module, enhancing the model's capacity to discern fine details within images. The framework's robustness is further bolstered by an ensemble learning strategy that consolidates the insights from both the MLP and KAN decoder branches. More importantly, we collect the segmentation target edge coordinates and bounding box coordinates as input cues for the SAM model. This strategy leverages SAM's zero-sample learning capabilities to refine and optimize the segmentation outcomes. Our experimental findings, based on the widely recognized RefCOCO, RefCOCO+, and RefCOCOg datasets, confirm the effectiveness of this method. The results not only achieve state-of-the-art (SOTA) performance in segmentation but are also supported by ablation studies that highlight the contributions of each component to the overall improvement in performance. [ABSTRACT FROM AUTHOR]

In many industries, companies receive customer orders that include multiple products. To simplify the use of optimization models for planning purposes, these orders are broken down, and the quantities of each product are grouped with the same products from other orders to be completed in the same period. Consequently, traditional production planning models enforce minimum demand constraints by product and period rather than by individual orders. An important drawback of this aggregation procedure is that it requires a fixed order fulfillment period, potentially missing opportunities for more efficient resource use through early completion. This paper introduces a novel mathematical formulation that preserves the integrity of customer orders, allowing for early fulfillment when possible. We compare a traditional linear programming model with a new mixed-integer programming approach using a sawmill case study. Although more complex than the traditional model, the proposed formulation reduces costs by approximately 6% by enabling early order completion and offers greater flexibility and control over the production process. This approach leads to better resource utilization and more precise order management, presenting a valuable alternative to conventional production planning models. [ABSTRACT FROM AUTHOR]

With the rapid development of distribution networks and increasing demand for electricity, the pressure of power supply for medium- and low-voltage distribution networks (M&LVDNs) is increasingly significant, especially considering the large scale of customers at the low-voltage (LV) level. In this paper, an outage sequence optimization method for low-voltage distribution networks (LVDNs) that considers the importance of users is proposed. The method aims to develop an optimal outage sequence strategy for LV customers in case of medium-voltage (MV) failure events. First, a multi-dimensional importance indicator system for LV users is constructed, and the customers are ranked using a modified Analytic Hierarchy Process–Entropy Weight (AHP-EW) method to determine their priorities during outages. Then, an elastic net regression-based method is used to identify the topology of the LV network. Finally, an outage sequence optimization model based on the user importance is proposed to reduce the load-shedding level. Extensive case studies are conducted in the modified LV distribution network. The results show that the proposed method results in fewer outage losses throughout the restoration periods than traditional methods and effectively improves the reliability of the power supply to LV users. [ABSTRACT FROM AUTHOR]

With advanced digital technologies as the key support, many scholars and researchers have proposed various random warranty models by integrating mission cycles into the warranty stage. However, these existing warranty models are designed only from the manufacturer's subjective perspective, ignoring certain consumer requirements. For instance, they overlook a wide range of warranty coverage, the pursuit of reliability improvement rather than mere minimal repair, and the need to limit the delay in repair. To address these consumer requirements, this paper proposes a novel random collaborative preventive maintenance warranty with repair-time threshold (RCPMW-RTT). This model incorporates terms that are jointly designed by manufacturers and consumers to meet specific consumer needs, thereby overcoming the limitations of existing warranty models. The introduction of a repair-time threshold aims to limit the time delay in repairing failures and to compensate for any losses incurred by consumers. Using probability theory, the RCPMW-RTT is evaluated in terms of cost and time, and relevant variants are derived by analyzing key parameters. As an exemplary representation of the RCPMW-RTT, two random replacement policies named the discrete random renewable back replacement (DRRBR) and the discrete random renewable front replacement (DRRFR) are proposed and modelled to ensure reliability after the expiration of the RCPMW-RTT. In both policies, product replacement is triggered either by the occurrence of the first extreme mission cycle or by reaching the limit on the number of non-extreme mission cycles, whichever comes first. Probability theory is used to present cost rates for both policies in order to determine optimal values for decision variables. Finally, numerical analysis is performed on the RCPMW-RTT to reveal hidden variation tendencies and mechanisms; numerical analysis is also performed on the DRRBR and the DRRFR. The numerical results show that the proposed random replacement policies are feasible and unique; the replacement time within the post-warranty coverage increases as the maintenance quality improves and the cost rate can be reduced by setting a smaller repair-time threshold. [ABSTRACT FROM AUTHOR]

The split delivery vehicle routing problem (SDVRP) is a relaxed variant of the capacitated vehicle routing problem (CVRP) where the restriction that each customer is visited precisely once is removed. Compared with CVRP, the SDVRP allows a reduction in the total cost of the routes traveled by vehicles. The exact methods to solve the SDVRP are computationally expensive. Moreover, the complexity and difficult implementation of the state-of-the-art heuristic approaches hinder their application in real-life scenarios of the SDVRP. In this paper, we propose an easily understandable and effective approach to solve the SDVPR based on an a priori adaptive splitting algorithm (AASA) that improves the existing state of the art on a priori split strategy in terms of both solution accuracy and time complexity. In this approach, the demand of the customers is split into smaller demand values using a splitting rule in advance. Consequently, the original SDVRP instance is converted to a CVRP instance which is solved using an existing CVRP solver. While the proposed a priori splitting rule in the literature is fixed for all customers regardless of their demand and location, we suggest an adaptive splitting rule that takes into account the distance of the customers to the depot and their demand values. Our experiments show that AASA can generate solutions comparable to the state of the art, but much faster. [ABSTRACT FROM AUTHOR]

In this paper, the impact of initial clusters on the stability of customer segmentation methods based on K-means is investigated. We propose a novel customer segmentation framework, Gms-Afkmc2, based on the Gaussian mixture model and ASSUMPTION-FREE K-MC2, a better cluster-based K-means method, to obtain greater customer segmentation by generating better initial clusters. Firstly, a dataset sampling method based on the Gaussian mixture model is designed to generate a sample dataset of custom size. Secondly, a data clustering approach based on ASSUMPTION-FREE K-MC2 is presented to produce initialized clusters with the proposed dataset. Thirdly, the enhanced ASSUMPTION-FREE K-MC2 is utilized to obtain the final customer segmentation on the original dataset with the initialized clusters from the previous stage. In addition, we conduct a series of experiments, and the result shows the effectiveness of Gms-Afkmc2. [ABSTRACT FROM AUTHOR]

An electrical measurement network designed for analyzing power quality within microgrids is presented in this paper. It is very portable and easy to install across various types of microgrids. Data collected by the system meet the standards for measuring electrical parameters, calculating waveforms spectra and comparing results from different microgrid nodes. The measurements provided by the network are useful for both utility and consumer sides. The system's effectiveness is verified through two experimental setups, specifically built ad hoc: one for testing the accuracy of the measurements obtained and the other for assessing the suitability of these measurements for power quality analysis. [ABSTRACT FROM AUTHOR]

In the realm of supply chain logistics, the Multi-Depot Multi-Supplier Vehicle Routing Problem (MDMSVRP) poses a significant challenge in optimizing the transportation process to minimize costs and enhance operational efficiency. This problem involves determining the most cost-effective routes for a fleet of vehicles to deliver goods from multiple suppliers to multiple depots, considering various constraints and non-linear relationships. The routing problem (RP) is a critical element of many logistics systems that involve the routing and scheduling of vehicles from a depot to a set of customer nodes. One of the most studied versions of the RP is the Vehicle Routing Problem with Time Windows (VRPTW), in which each customer must be visited at certain time intervals, called time windows. In this paper, it is considered that there are multiple depots (supply centers) and multiple suppliers, along with a fleet of vehicles. The goal is to efficiently plan routes for these vehicles to deliver goods from the suppliers to various customers while considering relaxed time windows. This research is intended to establish a new relaxation scheme that relaxes the time window constraints in order to lead to feasible and good solutions. In addition, this study develops a discrete optimization model as an alternative model for the time-dependent VRPTW involving multi-suppliers. This research also develops a metaheuristic algorithm with an initial solution that is determined through time window relaxation. [ABSTRACT FROM AUTHOR]

Background: This paper examines how logistics mangers increase performance by incorporating VAS in their distribution centres in the context of different natures of customer demand. The study is underpinned by the principles of lean and agile strategies as two key concepts that can be applied to respond to different customer expectations. Methods: Based on the phenomenological interpretivist paradigm, an empirical multiple-case study was conducted in German distribution centres operated by six companies. The empirical data were collected through semi-structured interviews, built on the triangulation of sources. Open, axial, and selective coding were employed to analyse data collected through eighteen in-depth interviews with managers from the distribution centres. Results: The findings indicated that the construct of customer demand forms different benefits that the logistics service providers can achieve through VAS. Simultaneously, various customer demands on VAS requires the distribution centres to focus on developing different operational capabilities to gain superior performance. Conclusions: Based on the research findings, a conceptual model was created. This model can support logistics service providers in improving company performance through effectively managing VAS in their distribution centres. The high dynamic VAS customer demand can bring more financial and non-financial benefits but needs higher flexibility in the warehouse operation system. Stable and predictable VAS, in turn, require a higher degree of standardisation. [ABSTRACT FROM AUTHOR]

The concepts of sustainable consumption and production increased in popularity after the World Summit on Sustainable Development in Johannesburg in 2002. Moreover, the focus of attention was turned to the consumer and their key importance in achieving the goals of more sustainable practices. Hence, the main aim of this article is to present the influence of factors related to pro-ecological consumer behaviors on sustainable consumption including energy-use reduction. Epsilon regression was implemented as the research method. This statistical tool enabled the authors to carry out research on such complex phenomena and make the analysis immune to the high correlation among explanatory variables. Moreover, it allowed the authors to consider interactions in the set of diagnostic variables. The research presented in this paper is based on data drawn from the Barometer of Consumer Social Responsibility in Poland for 2022. As the final result of this research, a ranking of pro-ecological consumer behavior factors was constructed. On the one hand, not buying products using excessive packaging can be considered the most important factor of Polish pro-ecological consumer behaviors. On the other hand, trying to choose more ecological products with comparable prices can be regarded as the factor which has the smallest impact. Furthermore, the presented method also allowed for determining the contribution of individual factors to the pro-ecological behavior of consumers. [ABSTRACT FROM AUTHOR]

This paper investigates voltage regulation in low voltage (LV) networks under different loading conditions of a supply network, with increased levels of distributed generation, and in particular with a diverse range of locational solar photovoltaic (PV) penetration. This topic has been researched extensively, with beneficial impacts expected up to a certain point when reverse power flows begin to negatively impact customers connected to the distribution system. In this paper, a voltage-based approach that utilizes novel voltage-based reliability indices is proposed to analyse the risk and reliability of the LV supply feeder, as well as its PV hosting capacity. The proposed indices are directly comparable to results from a probabilistic reliability assessment. The operation of the network is simulated for different PV scenarios to investigate the impacts of increased PV penetration, the location of PV on the feeder, and loading conditions of the MV supply network on the reliability results. It can be seen that all reliability indices improve with increased PV penetration levels when the supply network is heavily loaded and conversely deteriorate when the supply network is lightly loaded. Moreover, bus voltages improve when an on-load tap changer is fitted at the secondary trans-former which leads to better reliability performance as the occurrence and duration of low voltage violations are reduced in all PV scenarios. The approach in this paper is opposed to the conventional reliability assessment, which considers sustained interruptions to customers caused by failure of network components, and thus contributes to a comprehensive analysis of quality of service by considering transient events (i.e., voltage related) in the LV distribution network. [ABSTRACT FROM AUTHOR]

New-energy vehicles (NEV), particularly electric vehicles, are globally popular with political and financial support from governments, which aim at reducing energy consumption and environmental pollution in the transportation sector. This paper designs a matrix model which identifies the factors influencing the decision-making process on purchasing NEVs, and applies the model on a Chinese case to investigate the impact of influencing factors on Chinese consumers' purchase behavior to NEVs. The influencing factors are divided into five groups: economic, political, social, technological and environmental. Through a detailed questionnaire survey of 526 consumers in China, this paper conducts a correlation and difference analysis between these consumers' status and the influencing factors on the purchase decision-making of NEVs through SPSS software. The results indicate that economic, technological and political factors have a strong influence on purchase behavior, while social and environmental factors have a weaker influence. Additional principal component analysis on influencing factors' preference reveals that consumers' preferences have two tendencies: practicability or appearance, with the former is attracting far more attention. Rather than practicability or appearance, consumers care more about economic and political factors. According to these results, this paper suggests several policy implications on enhancing the consumers' willingness to purchase NEVs. [ABSTRACT FROM AUTHOR]

This paper describes the results from a study designed to illustrate the use of machine learning analytical techniques from a household consumer perspective. The outcome of interest in this study is a household's degree of financial preparedness as indicated by the presence of an emergency fund. In this study, six machine learning algorithms were evaluated and then compared to predictions made using a conventional regression technique. The selected ML algorithms showed better prediction performance. Among the six ML algorithms, Gradient Boosting, kNN, and SVM were found to provide the most robust degree of prediction and classification. This paper contributes to the methodological literature in consumer studies as it relates to household financial behavior by showing that when prediction is the main purpose of a study, machine learning techniques provide detailed yet nuanced insights into behavior beyond traditional analytic methods. [ABSTRACT FROM AUTHOR]

Dynamic visual vending machines are rapidly growing in popularity, offering convenience and speed to customers. However, there is a prevalent issue with consumers damaging goods and then returning them to the machine, severely affecting business interests. This paper addresses the issue from the standpoint of defect detection. Although existing industrial defect detection algorithms, such as PatchCore, perform well, they face challenges, including handling goods in various orientations, detection speeds that do not meet real-time monitoring requirements, and complex backgrounds that hinder detection accuracy. These challenges hinder their application in dynamic vending environments. It is crucial to note that efficient visual features play a vital role in memory banks, yet current memory repositories for industrial inspection algorithms do not adequately address the problem of location-specific feature redundancy. To tackle these issues, this paper introduces a novel defect detection algorithm for goods using adaptive subsampling and partitioned memory banks. Firstly, Grad-CAM is utilized to extract deep features, which, in combination with shallow features, mitigate the impact of complex backgrounds on detection accuracy. Next, graph convolutional networks extract rotationally invariant features. The adaptive subsampling partitioned memory bank is then employed to store features of non-defective goods, which reduces memory consumption and enhances training speed. Experimental results on the MVTec AD dataset demonstrate that the proposed algorithm achieves a marked improvement in detection speed while maintaining accuracy that is comparable to state-of-the-art models. [ABSTRACT FROM AUTHOR]

Taste sensitivity can have a significant impact on consumers' food choices. Consumers' taster status and emotions can be guided by sensory information of sugared products. This paper aimed to develop emotional lexicons for sugar-free chocolates based on consumers' taster status applying the check-all-that-apply (CATA) methodology. South African respondents' (n = 153) bitter perception was evaluated with n-propylthiouracil (PROP) paper strips. Respondents received one sugar-free dark chocolate and one sugar-free milk chocolate and completed an electronic questionnaire. Respondents mainly purchased chocolate for its flavour, and enjoyed the taste of the sugar-free dark chocolate more than sugar-free milk chocolate. The non-tasters (>50%) chose positive emotions for sugar-free milk chocolate, while the medium tasters, selected more positive emotions for dark chocolate. The supertasters selected the most negative emotions for the sugar-free dark chocolate. Practical significant associations were found between the non-tasters and the emotion guilty, as well as between the supertasters and the emotions, discontented and disgust. Each taster status requires the development of a distinctive lexicon to be emotionally satisfied by sugar-free products. [ABSTRACT FROM AUTHOR]

In South Africa, there has been a rapid adoption of solar power, particularly inverter-based solar sources, in low-voltage (LV) networks due to factors such as load shedding, rising electricity costs and greenhouse gas emissions reduction. In residential LV networks, the alignment between solar supply and energy demand is less precise, necessitating larger battery storage systems to effectively utilize solar energy. Residential areas experience peak energy demand in the morning and evening when solar irradiance is limited. As a result, substantial energy storage is important to fully utilize the potential of solar energy. However, increasing inverter-based, customer-generated power creates an imbalance in the utility supply. This is because utility LV supply transformers have three phases, while individual customers have single-phase connections and no load balancing control mechanism. This supply imbalance adversely affects the overall power quality, causing energy losses, damage to devices and other issues. To address these problems, the paper proposes a smart control approach to minimize power imbalances within utility LV supply transformers. The controller uses customer battery storage in residential areas to balance the utility transformer phases. A laboratory model was built to simulate a three-phase low-voltage network with single-phase customers, both with and without a smart controller. The results show that closely monitoring and controlling individual inverters through a central controller can significantly improve the balance of the supply network. [ABSTRACT FROM AUTHOR]

Mobile phone consumers have been motivated by the rapid growth of technology and encouraged to update their devices regularly to keep up with new innovations, architectures, and capabilities. Consequently, mobile/cell phone waste has risen significantly in the last decade. Due to their small size, it is convenient for users to keep outdated or unused mobile phones at home or the office, rather than recycling them appropriately. A reverse supply chain (RSC) is one possible method of mitigating the questionable e-waste activity present in the ecosystem. RSC has been significant for the mobile phone industry, user states, analysts, and scholars. This paper reviews the available literature in the RSC management (RSCM) domain, along with its processes and strategies. The paper reviews 223 published scholarly papers in the domain of mobile phone waste recycling and investigates 22 papers related to factors influencing consumers’ intention with regard to electronic recycling, focusing on mobile devices, which contribute significantly to electronic waste management practices. [ABSTRACT FROM AUTHOR]

At the beginning of the COVID-19 pandemic, the entire world was waiting for a medical solution (for example, vaccines) in order to return to normality. Sanitary restrictions changed our consumption behaviors and feelings. Therefore, this paper analyzes the stochastic properties of consumer sentiment during the COVID-19 episode and the appearance of vaccines against the virus in December 2020 in the United States of America. This study adds a new dimension to the literature because it is the first research paper that uses advanced methodologies based on fractional integration and fractional cointegration analysis to understand the statistical properties of these time series and their behavior in the long term. The results using fractional integration methodologies exhibit a high degree of persistence, finding behavior of mean reversion during the pandemic episode. Therefore, the shock duration in consumer sentiment will be transitory, recovering to its previous trend in the short run. Focusing on the cointegrating part, we arrive at two main conclusions. First, an increase in total vaccination produces a positive reaction or impact on the behavior of consumers. On the other hand, an increase in new COVID-19 cases negatively affects the behavior of the consumer. [ABSTRACT FROM AUTHOR]

This paper studies the single-machine scheduling problem with truncated learning effect, time-dependent processing time, and past-sequence-dependent delivery time. The delivery time is the time that the job is delivered to the customer after processing is complete. The goal is to determine an optimal job schedule to minimize the total weighted completion time and maximum tardiness. In order to solve the general situation of the problem, we propose a branch-and-bound algorithm and other heuristic algorithms. Computational experiments also prove the effectiveness of the given algorithms. [ABSTRACT FROM AUTHOR]

The aspiration of this paper is to examine the impact of corporate social responsibility (CSR) and service quality on customer loyalty and their relationship in the Indian telecommunication industry. A model was proposed and a total of 377 responses were collected using a structured questionnaire. Data were assessed and analyzed using PLS SEM. Multi-group analysis (MGA) was carried out to comprehend the moderating effect of gender, age, education, and income within the model. The results suggest that CSR does not have a direct impact on customer loyalty (CL), but there is an indirect effect when it is mediated through customer satisfaction (CS) and trust (Tr). Service quality (SQ) was found to have a direct impact on CL and while it is also mediated through CS. The results of the MGA revealed that customer satisfaction increases commitment towards customer loyalty and trust among male users. This study highlights that the modern customers are knowledgeable, more aware, and value companies which are focused on CSR activities. [ABSTRACT FROM AUTHOR]

Product quality is a key factor affecting consumers' willingness to buy, providing greater advantages to an enterprise than product price. This paper investigates the impact of two factors, price and quality, on the operational decisions of hybrid competing supply chains. Supply chain I, which consists of a manufacturer and a retailer, is a decentralized structure. Supply chain II, where the manufacturer and retailer are integrated, is a centralized structure. Quality investment and vertical shareholding are introduced into the decentralized supply chain. Models are constructed for three different scenarios, examining whether the manufacturer makes a quality investment and whether the retailer holds shares in the quality investment. By comparing the equilibrium results, solved by the Stackelberg game method, the following conclusions are drawn: (1) Quality investment and shareholding can enhance product quality and price. (2) The retail price in a centralized supply chain is consistently lower than that in a decentralized one, leading to generally higher total profits for centralized supply chain. (3) The total profit of the decentralized supply chain only exceeds that of the centralized ones when the degree of substitution between products is lower than 0.6285 and the quality effort cost factor is within a specific range. While centralized supply chain is generally more advantageous, decentralized supply chain can outperform him under specific conditions. [ABSTRACT FROM AUTHOR]

We consider a system of two coupled parallel queues with infinite waiting rooms. The time setting is discrete. In either queue, the service of a customer requires exactly one discrete time slot. Arrivals of new customers occur independently from slot to slot, but the numbers of arrivals into both queues within a slot may be mutually dependent. Their joint probability generating function (pgf) is indicated as A (z 1 , z 2) and characterizes the whole model. In general, determining the steady-state joint probability mass function (pmf) u (m , n) , m , n ≥ 0 or the corresponding joint pgf U (z 1 , z 2) of the numbers of customers present in both queues is a formidable task. Only for very specific choices of the arrival pgf A (z 1 , z 2) are explicit results known. In this paper, we identify a multi-parameter, generic class of arrival pgfs A (z 1 , z 2) , for which we can explicitly determine the system-content pgf U (z 1 , z 2) . We find that, for arrival pgfs of this class, U (z 1 , z 2) has a denominator that is a product, say r 1 (z 1) r 2 (z 2) , of two univariate functions. This property allows a straightforward inversion of U (z 1 , z 2) , resulting in a pmf u (m , n) which can be expressed as a finite linear combination of bivariate geometric terms. We observe that our generic model encompasses most of the previously known results as special cases. [ABSTRACT FROM AUTHOR]

This paper focuses on the design and implementation of a versatile delivery robot which integrates a secure locking system, GPS navigation, customer authentication, and a camera system for real-time monitoring. The advanced locking system ensures the protection of delivered goods during transit, addressing security concerns. GPS navigation optimizes routes for efficient last-mile deliveries, reducing overall delivery times. Customer authentication adds an extra layer of security, allowing only the designated customer, equipped with a unique authentication method, to unlock and retrieve their parcel. The integrated camera system provides continuous monitoring throughout the delivery process. The combination of these features showcases technological innovation and also addresses critical aspects of security and customer trust in autonomous deliveries. The successful integration of these features positions the delivery robot as a reliable and customer-friendly solution for the evolving landscape of last-mile logistics. [ABSTRACT FROM AUTHOR]

The path planning problem is an important issue in maritime search and rescue. This paper models the path planning problem as a dynamic vehicle routing problem. It first designs a dynamic generator that transforms the existing benchmark sets for the static vehicle routing problem into dynamic scenarios. Subsequently, it proposes an effective Dynamic Ant Colony Optimization (DACO) algorithm, whose novelty lies in that it dynamically adjusts the pheromone matrix to efficiently handle customers' changes. Moreover, DACO incorporates simulated annealing to increase population diversity and employs a local search operator that is dedicated to route modification for continuous performance maximization of the route. The experimental results demonstrated that the proposed DACO outperformed existing approaches in generating better routes across various benchmark sets. Specifically, DACO achieved significant improvements in the route cost, serviced customer quantity, and adherence to time window requirements. These results highlight the superiority of DACO in the dynamic vehicle routing problem, providing an effective solution for similar problems. [ABSTRACT FROM AUTHOR]

Supply chains comprise various interconnected components like suppliers, manufacturers, distributors, retailers, and customers, each with unique variables and interactions. Managing dynamic supply chains is highly challenging, particularly when considering various sources of risk factors. This paper extensively explores dynamical analysis and multistability analysis to understand nonlinear behaviors and pinpoint potential risks within supply chains. Different phase portraits are used to demonstrate the impact of various factors such as transportation risk, quality risk, distortion, contingency reserves, and safety stock on both customers and retailers. We introduced a sliding mode control method that computes the sliding surface and its derivative by considering the error and its derivative. The equivalent control law based on the sliding surface and its derivative is derived and validated for control purposes. Our results show that the controller SMC can significantly enhance supply chain stability and efficiency. This research provides a robust framework for understanding complex supply chain dynamics and offers practical solutions to enhance supply chain resilience and flexibility. [ABSTRACT FROM AUTHOR]

The toxicity of food additives is widely studied and concerns many consumers worldwide. Synthetic food colors are often considered an unnecessary risk to consumer health. Since the European Food Safety Authority's (EFSA) re-evaluation between 2009 and 2014, the body of scientific literature on food colors has grown, and new evaluations are being published by the Joint FAO/WHO Expert Committee on Food Additives (JECFA). Therefore, this narrative review aims to review the toxicological data that have become available since 2014. The reviewed colors are Quinoline Yellow, Sunset Yellow, Azorubine, Amaranth, Ponceau 4R, Erythrosine, Allura Red, Patent Blue, Indigo Carmine, Brilliant Blue FCF, Green S, Brilliant Black, Brown HT, and Lithol Rubine BK. Tartrazine was not included in this paper; the overwhelming amount of recent data on Tartrazine toxicity requires more space than this review can provide. The issues regarding the toxicity of synthetic food colors and real population exposures are being regularly examined and reviewed by relevant authorities, such as the EFSA and JECFA. The current ADI limits set by the authorities are mostly in agreement, and they seem safe. However, the EFSA and JECFA assessments of some of the colors are more than a decade old, and new evidence will soon be required. [ABSTRACT FROM AUTHOR]

The explicit demand response (DR) is a key program for reinforcing the participation of end customers and making the most out of the potential of the smart grid. The DR is a key topic in the field of buildings to make use of the flexibility that they can offer. However, in order to guarantee the correct functionality of a DR system, it is fundamental to perform interoperability tests among the various components/actors. In this paper, we take into consideration the technological solutions suggested in the framework of the DRIMPAC project to enable the DR in buildings. We consider all actors/devices involved in order to reach the objective of executing a flexibility order by an asset. Following a structured interoperability testing methodology created by the Joint Research Centre, we perform interoperability tests regarding all critical links of the full chain of interacting actors to obtain the DR in buildings. The results show that the system functions properly and the benefits from the DR can be exploited. On the other hand, we provide a concrete example of how to apply the interoperability methodology in the field of testing the DR in buildings. [ABSTRACT FROM AUTHOR]