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Omnichannel safe customer experience : how should it be measured? Does it affect customer well-being and retailers’ performance?
No full textPublisher Copyright: © 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/Customer safety is a fundamental need, so for customer-centric omnichannel retailers operating in competitive and technologically intensive markets, a critical question arises: do customers’ perceptions of a safe customer experience determine their sense of well-being, as well as the retailers’ performance? To offer insights into these questions, the current research relies on mixed methods across five studies in six phases to develop a multidimensional scale for safe customer experiences (SafeCX). The formative SafeCX scale, which can be adopted as either a full 48-item or a condensed 12-item version, contains 12 critical safety dimensions that constitute essential considerations for managers, as well as key concepts for researchers dedicated to customer safety considerations. Among these dimensions, several directly capture in-store technologies, such as payment systems, surveillance cameras, and technology-mediated order fulfillment processes. Other dimensions reflect online technologies, such as data protection, social media safety, and practices that bridge physical and digital channels, offering a comprehensive perspective on customer safety. Complementing SafeCX, we also develop a two-dimensional, 8-item customer well-being scale: individual well-being, reflecting effects on one’s own mental, emotional, social, and physical life; and community well-being, reflecting effects on family, friends, and the broader community. This scale enables researchers and retail managers to assess how safety perceptions translate into personal and societal value in omnichannel contexts. In turn, this research establishes that customers who indicate positive appraisals on the SafeCX scale also exhibit a higher share of wallet and stronger intentions to influence others, effects that are mediated by their well-being appraisal.Peer reviewe
Eddy current sensor-based direct tool condition monitoring in milling
No full textPublisher Copyright: © 2025 The AuthorsModern automated manufacturing demands new methods for tool condition monitoring. Effective tool condition monitoring is essential in preventing machine downtime especially in automated machining setups. This study presents a novel eddy current sensor-based direct tool condition monitoring method for milling. In this study, a custom eddy current sensor design is used to monitor wear in carbide face milling inserts by measuring changes in the sensor coil impedance. The wear is measured by scanning the inserts with the sensor. The impedance response from a new insert is compared to the response from a worn tool and the tool condition is determined from the response change. Multiple wear tests were performed to investigate the operation of the sensor. The results demonstrate that the sensor is capable of detecting subtle variations in tool wear. This study also investigated the effect of different coil excitation frequencies and the distance between the coil and the tool. The sensor was slightly more sensitive to wear at lower frequencies. Increasing the distance between the sensor and the tool significantly decreased the impedance response magnitude and therefore the sensitivity of the sensor. Severe tool wear could be detected from approximately 1.2 mm distance. These investigations validate the sensor design for tool condition monitoring for milling. However, further studies are required to investigate the use of the sensor for different machining methods such as drilling, turning, and sawing.Peer reviewe
Choosing Portfolios of Reinforcement Actions for Distribution Grids Based on Partial Information
No full textThe cost-efficiency of individual reinforcement actions in mitigating risks of external hazards in distribution grids depends on the entire portfolio of implemented actions. Thus, when seeking to reinforce distribution grids, it is pertinent to assess portfolios of reinforcement actions to account for dependencies between them. Motivated by this recognition, we develop a systemic framework to support Distribution System Operators (DSOs) in allocating scarce resources to portfolios of reinforcement actions that help protect multiple grids against hazards in the light of complementary reliability indices. This decision problem is structured as an influence diagram that contains scenarios representing combinations of realizations for different types of hazards. For cases where scenario probabilities, perceived importance of the grids, and relevance of reliability indices are known, the framework solves a mixed-integer linear programming problem to determine optimal portfolios. If this is not the case, the framework accommodates partial information about these parameters. Building on this partial information, it computes all the non-dominated portfolios by obtaining optimal portfolios for specific parameters and screening the other feasible portfolios. The non-dominated portfolios are analyzed to guide the choice of reinforcement actions at different budget levels. The framework is illustrated with a case study where the DSO seeks to mitigate risks associated with three types of hazards in three distribution grids. The novelty of the proposed optimization-based framework lies in (i) combining Portfolio Decision Analysis (PDA) and reliability models to determine cost-efficient reinforcement portfolios and (ii) accommodating partial information about parameters required by PDA and reliability models.Peer reviewe
Collaborative water resources management: A multilayer social network analysis
No full textOne of the challenges of water governance lies in the management of trade-offs and synergies between policy issues. To address these interdependencies, both formal institutionalized and voluntary collaborative processes have emerged, bringing together a wide range of stakeholders with various interests to deliberate and seek consensus on management decisions. These stakeholders form networks characterized by complex relationships involving both collaboration and conflict, which can influence power dynamics and, consequently, the outcomes and legitimacy of the processes. In this study, we analyse how stakeholder relationships change when the synergies and trade-offs between policy issues are considered, and identify which stakeholders are influential across multiple parallel processes and the interests they represent. The case study focuses on two Finnish river basins facing pressures from human activities such as hydropower development, agriculture, forestry, and mining, and host multiple collaborative processes to manage these challenges. These processes are conceptualized as a multilayer social network. We introduce a novel method to infer positive and negative relationships between participants based on their interest in issues with synergies or trade-offs. The analysis yielded the following key findings: (i) most stakeholder interactions concern issues with synergies; (ii) the networks reveal complex relationships where synergies and trade-offs can significantly affect power dynamics; and (iii) individuals exert varying degrees of influence regarding synergies and trade-offs. The method developed and tested shows promise for analysing past processes and predicting the power relations, and informing more effective collaborative water resources management and governance.Peer reviewe
Enhancing knowledge graph interactions : A comprehensive Text-to-Cypher pipeline with large language models
No full textKnowledge Graphs (KGs) store structured information but typically require specialized query languages, such as Cypher for Neo4j, creating accessibility challenges for users unfamiliar with graph syntax. Large Language Models (LLMs) offer a solution by translating natural language into Cypher queries. However, existing models—including large-scale LLMs (e.g., ChatGPT) and smaller open-source models (e.g., Llama-7B, 8B) often struggle with accurately generating domain-specific queries due to inadequate alignment with KG schemas and limited domain-specific training data. To address these limitations, we propose a training pipeline tailored specifically for domain-aligned Cypher query generation, emphasizing usability for smaller-scale models. Our method integrates template-based synthetic data generation for diverse, high-quality training samples. We combine supervised fine-tuning with preference learning to enhance domain knowledge and Cypher syntax understanding. Additionally, our approach includes a context-aware retrieval mechanism that dynamically incorporates relevant schema elements at inference, improving alignment with domain-specific knowledge. We evaluated our method on the Hetionet biomedical KG using a benchmark dataset of 240 queries across three complexity levels. Our results show that our context-aware prompting achieves a substantial improvement, increasing component matching accuracy by 23.6% for ChatGPT-4o over the vanilla prompt baseline. When applying our full training pipeline to smaller-scale models, CodeLlama-13B* achieves an execution accuracy of 69.2%, nearly matching ChatGPT-4o's 72.1%. Importantly, our approach significantly narrows the performance gap, enabling smaller models to effectively manage complex, domain-specific tasks previously dominated by larger models. These findings demonstrate that our method is scalable, computationally efficient, and robust for practical Cypher query generation applications.Peer reviewe
Enhancing the responsive capacities of Ethiopian public universities : The role of sustainable professional higher education leadership and management training programmes
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A stochastic fuzzy multicriteria methodology for energy planning decision support : Case study of the electrification of the Greek road transport sector
No full textPublisher Copyright: © 2025 The AuthorsProviding robust planning insights requires transitioning from single-criterion, definitive frameworks to approaches that handle trade-offs and communicate result conditionality. This paper introduces an integrated methodology for energy planning, combining life-cycle impact assessment with decision support. Our framework incorporates often-overlooked aspects in energy planning (e.g., resource depletion and biodiversity) and allows us to reflect stakeholder risk profiles, preferences, and uncertainties about future energy and economic states. Scenarios are handled with the fuzzy group utility and maximum regret measures, while strategies are evaluated with the fuzzy VIKOR and TOPSIS methods. The framework advances existing multicriteria approaches by innovatively combining and contextually adapting existing methods, embedding them within a robust modelling framework. It provides comprehensive decision support by identifying optimal strategies across decision-maker profiles and explicitly communicating result contingency and diversity linked to uncertainties and policy preferences. The methodology is demonstrated for Greece's road transport sector, evaluating three fleet electrification strategies for 2040. Results revealed that aggressive electrification may induce significant negative repercussions on resources, human health, and ecosystems. Conversely, moderate electrification emerged as the most effective strategy in 79 % of cases across risk profiles and policy objective portfolios, suggesting the combination of technological shifts with resource-neutral lifestyle changes for road transport decarbonization.Peer reviewe
Aetheras: Characterizing exoplanetary atmospheric escape with NIR and UV spectroscopy
No full textTo date, many exoplanets have been discovered which exhibit distinct characteristics not observed within our own Solar System, raising numerous unresolved questions regarding their compositions, atmospheres, formation processes, and evolutionary pathways. Several missions have been dedicated to enhance the understanding of the exoplanets like James Webb and Hubble Space Telescopes. However, they have a limited spectral range and resolution to allow for a complete characterisation of atmospheric dynamics. The Aetheras mission proposal was developed at the Summer School Alpbach 2023 and presents a satellite mission to overcome these limitations to better understand the formation, evolution and characteristics of exoplanets. This mission aims to unravel key enigmas in contemporary exoplanetary research by investigating atmospheric escape mechanisms and measuring proxies of magnetic fields’ influence on atmospheric loss. Focusing on objects in the Radius Valley and the Hot Neptune desert, the mission seeks to discover their origins. By defining mission needs and designing a potential instrument based on derived requirements, a space mission architecture is envisioned to fulfil the proposed mission objectives. A spacecraft design has been made with top down systems engineering approach. Employing transit spectroscopy in the near-infrared range (1070 nm to 1090 nm) and ultraviolet range (115 nm to 285 nm) outside the geocoronal influence, the mission gains valuable insights to planetary formation and evolution. The mission architecture comprises a 1302 kg spacecraft equipped with a 1.5 m main mirror to observe the sky over a mission lifetime of three years.Peer reviewe
Robust optimization for integrated production and energy scheduling in low-carbon factories with captive power plants under decision-dependent uncertainty
No full textPublisher Copyright: © 2024 Elsevier LtdLow-carbon factories with captive power plants represent a new industrial microgrid paradigm of energy conservation and emission reduction in many countries. However, one of the most common challenges of low-carbon management is the joint regulation of factory production and power plant operations under uncertainty. To meet this challenge, a robust optimization-based integrated production and energy (IPE) scheduling approach is proposed in this paper. Firstly, a two-stage adaptive robust optimization model is established to cover all possible realizations of decision-independent uncertainties (e.g. market demands and output power of renewable sources) and decision-dependent uncertainties (e.g. carbon emission densities depending on the choice of production lines). Secondly, a novel parametric column-and-constraint generation algorithm is utilized to derive robust scheduling schemes. The non-trivial scenarios of decision-dependent uncertainties identified in the subproblem are parametrically characterized based on Karush–Kuhn–Tucker conditions, which can be included in the master problem. Finally, simulations on different cases are conducted to test the rationality and validity of the proposed approach. Compared with the separate scheduling of production and energy, IPE scheduling may increase production and energy costs to ensure the robustness of the resulting schemes. Moreover, the proposed approach can mitigate the impacts of uncertainties on IPE scheduling without significantly increasing the computational complexity.Peer reviewe
Self-assembled La0.7Sr0.3Fe0.9Ni0.1O3-δ-Ce0.8Sm0.2O2-δ composite cathode with a three-dimensional ordered macroporous structure for protonic ceramic fuel cells
No full textPublisher Copyright: © 2024 Elsevier B.V.Self-assembled La0.7Sr0.3Fe0.9Ni0.1O3-δ-Ce0.8Sm0.2O1.9-δ (LSFN-SDC) composite cathode with a three-dimensionally ordered macroporous (3DOM) structure is synthesized using poly(methyl methacrylate) as the template for protonic ceramic fuel cells. The LSFN and SDC phases both distribute uniformly in the cathode. The SDC phase reduces the thickness of the walls of the 3DOM structure and thus hinders the bulk conduction of electrons. SDC also decreases the specific surface area and the surface oxygen reactivity of the cathode, leading to the suppression of the adsorption and dissociation of O2. However, the SDC phase provides the conduction pathway for oxygen ions and enlarges three phase boundary consequently, which facilitates the charge transfer steps. The thickness of the walls and the specific surface area of the composite cathode are both increased with the rise of the concentration of the nitrate precursor solution, resulting in the acceleration of the cathode process. Nevertheless, an excessive precursor concentration leads to the destroy of the 3DOM structure. The 3DOM composite cathode exhibits the lowest Rp of 0.039 Ω cm2, and a single cell with that cathode shows maximum power density of 1484 mW cm−2 at 700 °C. The cell exhibits a short-term stability of 120 h at 700 °C without noticeable degradation.Peer reviewe