Search Results (528)

Achieving the construction sector’s dual carbon objectives necessitates scaling green energy adoption in new residential buildings. The current literature critically overlooks four unresolved problems: oversimplified penalty mechanisms, ignoring escalating regulatory costs; static subsidies misaligned with market maturity evolution; systematic exclusion of innovation feedback from energy suppliers; and underexplored behavioral evolution of building owners. This study establishes a government–suppliers–owners evolutionary game framework with dynamically calibrated policies, simulated using MATLAB multi-scenario analysis. Novel findings demonstrate: (1) A dual-threshold penalty effect where excessive fines diminish policy returns due to regulatory costs, requiring dynamic calibration distinct from fixed-penalty approaches; (2) Market-maturity-phased subsidies increasing owner adoption probability by 30% through staged progression; (3) Energy suppliers’ cost-reducing innovations as pivotal feedback drivers resolving coordination failures, overlooked in prior tripartite models; (4) Owners’ adoption motivation shifts from short-term economic incentives to environmentally driven decisions under policy guidance. The framework resolves these gaps through integrated dynamic mechanisms, providing policymakers with evidence-based regulatory thresholds, energy suppliers with cost-reduction targets, and academia with replicable modeling tools. Full article

Background/Objectives: In many parts of the world, pharmacists hold the primary responsibility for providing safe and effective pharmacotherapy. A key aspect is the availability of appropriate medicines for each individual patient. When industrially manufactured medicines are unsuitable or unavailable, pharmacists can prepare tailor-made medicines. While this principle applies globally, practices vary between countries. In the Netherlands, the preparation of medicines in pharmacies is well-established and integrated into routine healthcare. This narrative review explores the role and significance of extemporaneous compounding, pharmacy preparations and related product care in the Netherlands. Methods: Pharmacists involved in pharmacy preparations across various professional sectors, including community and hospital pharmacies, central compounding facilities, academia, and the professional pharmacists’ organisation, provided detailed and expert insights based on the literature and policy documents while also sharing their critical perspectives. Results: We present arguments supporting the need for pharmacy preparations and examine their position and role in community and hospital pharmacies in the Netherlands. Additional topics are discussed, including the regulatory and legal framework, outsourcing, quality assurance, standardisation, education, and international context. Specific pharmacy preparation topics, often with a research component and a strong focus on product care, are highlighted, including paediatric dosage forms, swallowing difficulties and feeding tubes, hospital-at-home care, reconstitution of oncolytic drugs and biologicals, total parenteral nutrition (TPN), advanced therapy medicinal products (ATMPs), radiopharmaceuticals and optical tracers, clinical trial medication, robotisation in reconstitution, and patient-centric solid oral dosage forms. Conclusions: The widespread acceptance of pharmacy preparations in the Netherlands is the result of a unique combination of strict adherence to tailored regulations that ensure quality and safety, and patient-oriented flexibility in design, formulation, and production. This approach is further reinforced by the standardisation of a broad range of formulations and procedures across primary, secondary and tertiary care, as well as by continuous research-driven innovation to develop new medicines, formulations, and production methods. Full article

Ongoing interest in the intersection of spirituality and health has prompted a need for integrated research. This report proposes a distinct approach in a model that allows for successful and harmonious cross-fertilization within these latter two areas of interest. Our work is especially pertinent to inquiries around the role of spirituality in mental health, with special attention to chronic pain conditions. The latter have become an open channel for novel avenues to explore the field of spirituality-based interventions within the arena of psychological inquiry. To address this, the authors developed and implemented the Church and Academia Model, a prototype for an innovative collaborative research project, with the aim of exploring the role of devotional practices, and their potential to be used as therapeutic co-adjuvants or tools to enhance the coping skills of patients with chronic pain. Keeping in mind that the church presents a rich landscape for clinical inquiry with broad relevance for clinicians and society at large, we created a unique hybrid research model. This is a new paradigm that focuses on distinct and well-defined studies where the funding, protocol writing, study design, and implementation are shared by experts from both the pastoral and clinical spaces. A team of theologians, researchers, and healthcare providers, including clinical pain psychologists, built a coalition leveraging their respective skill sets. Each expert is housed in their own environs, creating a functional network that has proven academically productive and pastorally effective. Key outputs include the creation and validation of a new psychometric measure, the Pain-related PRAYER Scale (PPRAYERS), an associated bedside prayer tool and a full-scale dissemination strategy through journal publications and specialty society conferences. This collaborative prototype is also an ideal fit for integrated knowledge translation platforms, and it is a promising paradigm for future collaborative projects focused on spirituality and mental health. Full article

The age of entrepreneurship calls for the evolving of learning and development (L&D) models to meet the dynamic demands of innovation, sustainability, and technology innovation. This study examines the trends and issues of L&D models for entrepreneurs, more so focusing on how these models influence business success in a rapidly changing global landscape. The research employs bibliometric analysis, VOSviewer cluster analysis, and co-citation analysis to explore the literature from 1994 to 2024. Data collected from the Web of Science Core Collection database reflect significant trends in entrepreneurial L&D, with particular emphasis on the use of digital tools, sustainability processes, and governance systems. Findings emphasize the imperative role of L&D in fostering entrepreneurship, more so in areas such as digital transformation and the adoption of new technologies. The study also identifies central regions propelling this field, such as UK and USA. Future studies will be centered on the role of digital technologies, innovation, and green business models within entrepreneurial L&D frameworks. This study provides useful insight into the future of L&D within the entrepreneurial domain, guiding academia and companies alike in the planning of effective learning strategies to foster innovation and sustainable business growth. Full article

Counties are important vehicles for implementing the new-type urbanization strategy in China. However, some counties are experiencing population shrinkage and ageing, and the decline in the working-age population will constrain the process of new-type urbanization, but this has not received sufficient attention from academia and government policymakers. This paper takes Jilin Province, China, which is experiencing significant population shrinkage and ageing, as a case study to examine the spatial–temporal characteristics of population shrinkage and ageing at the county level and their relationship with new-type urbanization. This study found the following: (1) From 2014 to 2020, counties in Jilin Province experienced significant population shrinkage and ageing. (2) There is a certain correlation between population shrinkage and ageing. Ageing is the most obvious in counties with moderate shrinkage, followed by counties with mild shrinkage. (3) Although a new-type urbanization strategy has been implemented, the level of new-type urbanization in counties with population shrinkage and ageing is generally low and shows a trend of rising first and then falling. (4) Population shrinkage and ageing are positively correlated with new-type urbanization. The more severe the population shrinkage or ageing, the lower the level of new-type urbanization. It is necessary to take measures to alleviate the new-type urbanization dilemma in areas with population shrinkage and ageing, in order to promote sustainable regional development. Full article

This paper presents the application of four machine learning algorithms to segment suppliers in a real case. The algorithms used were K-Means, Hierarchical K-Means, Agglomerative Nesting (AGNES), and Fuzzy Clustering. The analyzed company has suppliers that have been clustered using responses such as the number of non-conformities, location, and quantity supplied, among others. The CRISP-DM methodology was used for the work development. The proposed methodology is important for both industry and academia, as it helps managers make decisions about the quality of their suppliers and compares the use of four different algorithms for this purpose, which is an important insight for new studies. The K-Means algorithm obtained the best performance both for the metrics obtained and the simplicity of use. It is important to highlight that no studies to date have been conducted using the four algorithms proposed here applied in an industrial case, and this work shows this application. The use of artificial intelligence in industry is essential in this Industry 4.0 era for companies to make decisions, i.e., to have ways to make better decisions using data-driven concepts. Full article

Native forest timber supplies are declining, and industry needs to do more with less to meet growing demand for wood products. An Australian-based, vertically integrated timber manufacturing business is commissioning a spindleless lathe to produce engineered wood products from small logs. The literature on innovation in timber manufacturing was found to generally focus on technical innovation, with relatively little use of market-oriented concepts and theory. This was particularly true in the Australian context. Using a market-oriented case study approach, this research assessed innovation in the business. It aimed to inform industry-wide innovation approaches to meet market demand in the face of timber supply challenges. Interviews were conducted with key personnel at the firm. Data and outputs were produced to facilitate comparison to existing research and conceptual frameworks. The business was found to empower key staff and willingly access knowledge, information and data from outside its corporate domain. It was also found to prioritise corporate goals outside of traditional goals of profit and competitive advantage. This was shown to increase willingness to try new things at the mill and increase the chances that new approaches would succeed. Thinking outside of the corporate domain was shown to allow access to resources that the firm could not otherwise count on. It is recommended that wood processing businesses seek to emulate this element of the case study, and that academia and the broader sector examine further the potential benefits of using enterprise and market-oriented lenses to better utilise available resources and maintain progress towards corporate goals. Full article

Cambuci is a native fruit from Brazil, and during the processing of this fruit, the peel is typically discarded due to limited knowledge of its physicochemical characteristics, which restricts its potential applications across various industries. Given the lack of detailed physicochemical characterization of this by-product in the literature, this study aimed to analyze key parameters to expand on our understanding of this raw material and stimulate interest from both academia and industry. The cambuci peel was found to have a moisture content of 9.41 ± 1.69% dw (dry weight), total solids of 90.59 ± 1.69% dw, and volatile solids of 87.41 ± 1.69%. Its ash content was 3.18 ± 0.41%, while the chemical oxygen demand (COD) reached 420.54 ± 9.88 mg L⁻¹. The total protein content was 4.93 ± 0.04 g/100 g dw, with reducing sugars at 108.22 ± 3.71 mg g⁻¹ and non-reducing sugars at 30.58 ± 3.16 mg g⁻¹. Neutral detergent fiber (NDF) and acid detergent fiber (ADF) were determined as 36.65 ± 0.19% dw and 18.91 ± 0.05% dw, respectively, with hemicellulose content of 17.74 ± 0.20% dw. Chromatographic analysis identified key bioactive compounds, including ellagic and gallic acid, which hold significant potential for pharmaceutical and food industry applications. Thermogravimetric analysis revealed three distinct decomposition zones, corresponding to physisorbed water, hemicellulose decomposition, and cellulose degradation, respectively. The results demonstrate the valuable physicochemical and biochemical properties of cambuci peel, supporting its potential for the development of new bioproducts aligned with circular economy principles. This study lays the foundation for further research into this underutilized by-product and its application in diverse industrial sectors. Full article

In today’s business environment, emails are essential across all sectors, including finance and academia. There are two main types of emails: ham (legitimate) and spam (unsolicited). Spam wastes consumers’ time and resources and poses risks to sensitive data, with volumes doubling daily. Current spam identification methods, such as Blocklist approaches and content-based techniques, have limitations, highlighting the need for more effective solutions. These constraints call for detailed and more accurate approaches, such as machine learning (ML) and deep learning (DL), for realistic detection of new scams. Emphasis has since been placed on the possibility that ML and DL technologies are present in detecting email spam. In this work, we have succeeded in developing a hybrid deep learning model, where Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) are applied distinctly to identify spam email. Despite the fact that the other models have been applied independently (CNNs, LSTM, GRU, or ensemble machine learning classifier) in previous studies, the given research has provided a contribution to the existing body of literature since it has managed to combine the advantage of LSTM in capturing the long-term dependency and the effectiveness of GRU in terms of computational efficiency. In this hybridization, we have addressed key issues such as the vanishing gradient problem and outrageous resource consumption that are usually encountered in applying standalone deep learning. Moreover, our proposed model is superior regarding the detection accuracy (90%) and AUC (98.99%). Though Transformer-based models are significantly lighter and can be used in real-time applications, they require extensive computation resources. The proposed work presents a substantive and scalable foundation to spam detection that is technically and practically dissimilar to the familiar approaches due to the powerful preprocessing steps, including particular stop-word removal, TF-IDF vectorization, and model testing on large, real-world size dataset (Enron-Spam). Additionally, delays in the feature comparison technique within the model minimize false positives and false negatives. Full article

The Dynamic Multi-objective Optimization Problem (DMOP) is one of the common problem types in academia and industry. The Dynamic Multi-Objective Evolutionary Algorithm (DMOEA) is an effective way for solving DMOPs. Despite the existence of many research works proposing a variety of DMOEAs, the demand for efficient solutions to DMOPs in drastically changing scenarios is still not well met. To this end, this paper is oriented towards DMOEA and innovatively proposes to explore the correlation between different points of the optimal frontier (PF) to improve the accuracy of predicting new PFs for new environments, which is the first attempt, to our best knowledge. Specifically, when the DMOP environment changes, this paper first constructs a spatio-temporal correlation model between various key points of the PF based on the linear regression algorithm; then, based on the constructed model, predicts a new location for each key point in the new environment; subsequently, constructs a sub-population by introducing the Gaussian noise into the predicted location to improve the generalization ability; and then, utilizes the idea of NSGA-II-B to construct another sub-population to further improve the population diversity; finally, combining the previous two sub-populations, re-initializing a new population to adapt to the new environment through a random replacement strategy. The proposed method was evaluated by experiments on the CEC 2018 test suite, and the experimental results show that the proposed method can obtain the optimal MIGD value on six DMOPs and the optimal MHVD value on five DMOPs, compared with six recent research results. Full article

In recent decades, access to powerful computational resources has brought about a major transformation in science, with supercomputers drawing significant attention from academia, industry, and governments. Among these resources, high-performance computing (HPC) has emerged as one of the most critical processing infrastructures, providing a suitable platform for evaluating and implementing novel technologies. In this context, the development of emerging computing technologies has opened up new horizons in information processing and the delivery of computing services. In this regard, this paper systematically reviews and classifies emerging HPC-related computing technologies, including quantum computing, nanocomputing, in-memory architectures, neuromorphic systems, serverless paradigms, adiabatic technology, and biological solutions. Within the scope of this research, 142 studies which were mostly published between 2018 and 2025 are analyzed, and relevant hardware solutions, domain-specific programming languages, frameworks, development tools, and simulation platforms are examined. The primary objective of this study is to identify the software and hardware dimensions of these technologies and analyze their roles in improving the performance, scalability, and efficiency of HPC systems. To this end, in addition to a literature review, statistical analysis methods are employed to assess the practical applicability and impact of these technologies across various domains, including scientific simulation, artificial intelligence, big data analytics, and cloud computing. The findings of this study indicate that emerging HPC-related computing technologies can serve as complements or alternatives to classical computing architectures, driving substantial transformations in the design, implementation, and operation of high-performance computing infrastructures. This article concludes by identifying existing challenges and future research directions in this rapidly evolving field. Full article

Background: The digital transformation in dentistry is increasingly reshaping treatment procedures, offering new opportunities and advancements. While digitalization promises enhanced efficiency and quality of care through the standardization, acceleration, and simplification of workflows, it also introduces challenges related to mental health. Studies investigating digitization-associated stressors and resources, as well as health- and work-related outcomes, in the dental sector are still rare. In the context of ongoing digitalization, further studies are needed to examine the need for and the current status of the implementation of measures preventing techno-stress and stress-related outcomes. This study explores the use of digital tools in dental practices and their relationship to the techno-stress among German dentists. It identifies key stressors and resources associated with digital technologies, aiming to inform preventive measures, as well as training and support strategies to mitigate digital stress. Methods: A qualitative study was employed, involving ten problem-centered, guideline-based expert interviews with German dentists. The interviews were analyzed using MAXQDA software, following the focused interview analysis framework by Kuckartz and Rädiker. Coding and thematic analysis adhered to the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist and qualitative research quality criteria by Mayring. Results: This study identified a dual impact of digitalization in dentistry. On the one hand, digital tools significantly enhance workflow efficiency, diagnostic accuracy, and patient outcomes. On the other hand, they pose challenges like technostress, high financial costs, and the need for continuous learning. Findings reveal that the perceived usefulness of digital technologies is closely linked to the level of techno-stress experienced, while the amount, intuitiveness, and ease of use significantly influence stress levels. Conclusions: Digital transformation offers substantial benefits for dental practices but requires a balanced approach to implementation. Participants highlighted the need for proactive measures, such as targeted training, technical support, and stress-reducing interventions to reduce techno-stress levels. The digital transformation must be supported by coordinated efforts across academia, industry, and policy to strengthen digital competencies—creating a healthier, more resilient digital work environment. Future research should focus on the causal relationship between techno-stress and adverse long-term consequences, such as burnout or mental disorders, among dentists. Full article

Recently, the electrification and automation of heavy-duty trucks has gained significant attention from both industry and academia, driven by new legislation introduced by the European Union. During a typical drive cycle, the mass of an urban service truck can vary substantially as waste is collected, yet most existing studies rely on a single controller with fixed gains. This limits the ability to adapt to mass changes and results in suboptimal energy usage. Within the framework of the EU-funded OBELICS and ESCALATE projects, this study proposes a novel control strategy for a semi-autonomous refuse truck. The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. The main objectives of the proposed method are to (i) optimize controller parameters, (ii) reduce overall energy consumption, and (iii) minimize speed tracking error. A cost function addressing these objectives is formulated for both autonomous and manual driving modes. The strategy is evaluated using a real-world drive cycle from Eskişehir City, Turkiye. Simulation results show that the proposed MMC-based method improves vehicle performance by $5.19\%$ in autonomous mode and $0.534\%$ in manual mode compared to traditional fixed-gain approaches. Full article

Digital transformation of industry has gained emphasis in recent years in academia and industry. Organizations need to be more competitive and efficient and improve their processes and performance to cope with changes in environmental legislation, efficient management of resources and energy, and the trend toward zero waste. These factors have led to the emergence of a new concept. This paper studies data-driven fuzzy-based models for process monitoring focused on Wastewater Treatment Plants (WWTPs). This work aims to study interpretable industrial process monitoring models, which must be easily interpretable by expert process operators. For this purpose, different fuzzy-based models were studied. Exhaustive validations are performed. The studied models employ 16 key variables at 14 different points throughout the waterline of a treatment plant. The learning and testing of each model for every key variable at each involved point use distinct sets of input variables and varied learning model parameters. The impact of the selected input variables and the learning parameters on the model accuracy, and the accuracy versus interpretability tradeoff are analyzed. The best model for each key variable is developed based on the accuracy versus interpretability tradeoff. Full article

In this essay, we propose that the prevalence of cheating by accounting students and serial cheating by accounting practitioners at Big-4 accounting firms are related. Our model of this problem suggests that students who cheat in school become practitioners who cheat in practice, and practitioners, in turn, model dishonest behavior for students. We propose that this vicious cycle of dishonesty poses a threat to the public’s trust in the accounting profession, and this crisis calls for drastic measures, both in academia and in practice, akin to measures like the Sarbanes–Oxley Act of 2002. As an honorable profession, dishonesty cannot be tolerated. Brief overviews of the prevalence of cheating, both by students and by Big-4 accounting practitioners are presented. Suggestions are included for a three-prong approach by accounting stakeholders to reduce this egregious ethical problem—a problem that, we suggest, is causing a new crisis in confidence for the accounting profession. Full article