Search Results (75)

As a key enabling technology for energy transition, the smart grid is propelling the global power system to evolve toward greater efficiency, reliability, and sustainability. Based on the three-dimensional analysis framework of “technology–policy–application”, this study systematically sorts out the technical architecture, regional development mode, and typical application scenarios of the smart grid, revealing the multi-dimensional challenges that it faces. By using the methods of literature review, cross-national case comparison, and technology–policy collaborative analysis, the differentiated paths of China, the United States, and Europe in the development of smart grids are compared, aiming to promote the integration and development of smart grid technologies. From a technical perspective, this paper proposes a collaborative framework comprising the perception layer, network layer, and decision-making layer. Additionally, it analyzes the integration pathways of critical technologies, including sensors, communication protocols, and artificial intelligence. At the policy level, by comparing the differentiated characteristics in policy orientation and market mechanisms among China, the United States, and Europe, the complementarity between government-led and market-driven approaches is pointed out. At the application level, this study validates the practical value of smart grids in optimizing energy management, enhancing power supply reliability, and promoting renewable energy consumption through case analyses in urban smart energy systems, rural electrification, and industrial sectors. Further research indicates that insufficient technical standardization, data security risks, and the lack of policy coordination are the core bottlenecks restricting the large-scale development of smart grids. This paper proposes that a new type of intelligent and resilient power system needs to be constructed through technological innovation, policy coordination, and international cooperation, providing theoretical references and practical paths for energy transition. Full article

The transition from a linear to a more circular economy has pressured companies from different sectors to implement circular business strategies and redesign their existing business models or even create new ones. The aim of this investigation is to identify the different circular business strategies adopted by Portuguese companies in the textile and clothing industry and evaluate their impact on the sustainability of the business. This article presents a framework of strategies to guide managers in addressing the challenges of moving from fast to more sustainable fashion. This exploratory research is based on a qualitative methodology, relying on semi-structured interviews with the managers of six companies in the textile and clothing sector in Portugal that have implemented circular practices. The primary data collection took place between 20 July and 30 September 2022. The results show that companies have supported their circular economy practices mainly through product life extension strategies (mostly based on durable product design) and resource use reduction strategies, with resource recovery being the most common. The use of personalized product design and clothing repair strategies is still largely unexplored by companies. The findings also suggest that companies have to adapt their way of production and market relationships with consumers in order to accommodate the practices of a circular economy in their businesses. In the future, a quantitative approach could also provide new insights, as well as longitudinal and cross-country comparison studies. Full article

Background: Ultra-processed foods (UPFs) represent a substantial part of modern diets, with a growing prevalence in food environments worldwide. Their unfavourable nutritional composition and adverse health effects present growing public health concerns. Methods: This study examines the prevalence of UPFs in the Slovenian food supply, their nutritional quality and the use of different food symbols and labelling schemes on food packaging. A cross-sectional analysis was conducted using the representative Slovenian branded foods database. A total of 23,173 prepacked foods and beverages were categorised into levels of processing according to the NOVA classification system. The nutritional composition of UPFs was compared to less processed products within 16 narrow subcategories. Additionally, the prevalence in the use of front-of-package nutrition labelling (FOPNL) and subjectively nutrition-related elements (SNREs) (such as EU Organic, Vegan labels etc.) were assessed across different food categories and processing levels. Results: Results show that UPFs represent 54.5% of the available products in the Slovenian food supply, with the highest prevalence in Confectionery (93%), Bread and bakery products (83%), Meat, meat products and alternatives (77%) and Convenience foods (74%). Comparison of nutritional composition indicated that UPFs had significantly poorer nutritional composition compared to less processed counterparts, including higher levels of sugar, salt and saturated fats, and a lower protein content. Breakfast cereals, Snack foods, Meat alternatives and Pre-prepared salads and sandwiches showed the most significant differences between UPFs and less processed counterparts. Analysis of the prevalence of symbols and labelling schemes revealed that 33.8% of products carried at least one FOPNL (15.0%) or SNRE (19.1%), with SNREs being more prevalent on less processed products and FOPNL predominantly used on UPFs (p < 0.05). The most prevalent SNRE was the EU Organic logo (12.7%), followed by the Vegan (4.7%) and Non-GMO (3.1%) logos, whereas the most frequent FOPNL was Reference Intakes (RI), presenting only energy value RI-Energy (12.5%), followed by nutrient-specific RI (1.6%), while other FOPNL were scarce and limited to certain categories. An additional comparison of visual presentation highlighted the potentially selective use of voluntary FOPNL to improve product framing. This raises concerns about their role in guiding consumer choices versus serving as marketing tools, especially when it comes to UPFs. Conclusions: Our findings highlight the need for monitoring UPFs in the food supply together with harmonised, mandatory labelling regulations to ensure transparency and empower consumers to make healthier choices. Full article

Due to increased competition in the marketplace, companies in all industries are facing the problem of customer attrition. In order to expand their market share and increase profits, companies have shifted from the concept of ‘acquiring new customers’ to ‘retaining old customers’. In this study, we design a deep learning model based on multi-network feature extraction and an attention mechanism, convolutional neural network–bidirectional long and short-term memory network–fully connected layer–coordinate attention (CNN-BiLSTM-FC-CoAttention), and apply it to customer churn risk assessment. In the data preprocessing stage, the imbalanced dataset was processed using the SMOTE-ENN hybrid sampling method. In the feature extraction stage, a sequence-based CNN and time-based BiLSTM are combined to extract the local and time series features of the customer data. In the feature transformation stage, high-level features are extracted using a fully connected layer of 64 Relu neurons and the sequence features are reshaped into matrix features. In the attention enhancement stage, the extracted feature information is refined using a coordinate attention learning module to fully learn the channel and spatial location information of the feature map. To evaluate the performance of the proposed model, we include public datasets from telecom, bank and insurance industries for ten-fold cross-validation experiments, and the results show that the CNN-BiLSTM-FC-CoAttention model outperforms the comparison models in all metrics. Our proposed model improves the accuracy and generalisation of the model prediction by combining multiple algorithms, enabling it to be widely used in multiple industries. As a result, the model gives enterprises a better and more general decision-making reference for the timely identification of potential churn customers. Full article

Background/Purpose: In an increasingly competitive educational landscape, the school principal’s role has expanded beyond instructional leadership to include strategic marketing responsibilities. Yet the role of marketing in school leadership remains underexplored. Our objective was to investigate how Greek secondary school principals perceive marketing’s key dimensions (Customer Orientation, Competitor Orientation, and Inter-Functional Coordination) within schools, in comparison to other school staff members. Design/Methodology: A cross-sectional survey design was employed, collecting data using a structured questionnaire adapted from Oplatka and Hemsley-Brown. The sample comprised 350 respondents, including school principals and teaching staff. One-way ANOVA, supplemented by Games–Howell and Kruscall–Wallis tests, was employed to examine group differences across the three dimensions of market orientation. Results: Our findings indicate that principals demonstrate a higher level of Customer Orientation than other staff. However, no significant differences were found in Competitor Orientation or Inter-Functional Coordination across roles. Conclusions/Novelty: The findings suggest that while principals play a key role in aligning school functions and engaging with internal stakeholders, their sensitivity to external competition remains limited. The study highlights the evolving nature of school leadership while showing that marketing strategies in Greek schools are insufficiently implemented, calling for research into how leadership training can enhance market responsiveness. Full article

This paper investigates wine tourists’ preferences for the attributes of the wine tourism experience (WTEXP) in Italy and Turkey, exploring cross-cultural differences and similarities in two countries with diverse wine tourism development. Data were collected through an online survey of 253 wine consumers, and the Best–Worst Scaling method was employed in both countries to assess the perceived importance of selected WTEXP attributes that influence tourists’ choices. The samples were then segmented using cluster analysis based on key attitudinal scales (e.g., wine involvement), with BWS applied to each segment to further examine visitor preferences. The results show that both Italian and Turkish wine tourists prioritized expert-led tours but differed in other preferences. Italian tourists valued the winery’s aesthetic appeal, while Turkish tourists favored pre-visit informative sessions. Additionally, Italians placed less importance on accompanying events, while Turks considered the reputation of the wine, winery, or wine region the least significant factor. These preferences also varied within the clusters identified in each sample. This research represents the first comparison of consumer preferences in wine tourism between Turkey, a developing market, and Italy, a traditional Old World wine producer, while considering the diversity within each group. The findings provide key insights for wine tourism stakeholders, such as wineries and tourism managers, offering actionable recommendations to tailor their offerings for specific tourist segments to attract a larger number of wine tourists, enhance their experience, and foster sustainable growth of wine tourism. Full article

Almost a century ago, the first industrial controllers were introduced to the market, labeled as automatic reset and later generalized to hyper-reset or pre-act. Recently, it has been shown that such control solutions can be characterized as model-based solutions with a simplified disturbance observer developed for an integrating model. The aforementioned controllers, albeit under the name of proportional–integral–derivative (PID) controllers, are still the most commonly used control solutions in practice. With the help of a new interpretation, however, it can be shown that PID controllers are also very well suited for controlling processes with complex non-linear dynamics. This paper investigates the design and feasibility of a family of gain-scheduling controllers for saturated non-linear systems described by a first-order differential equation. It is shown that the process can be linearized either by using locally applicable linear models or by using more narrowly applicable ultralocal models. By combining both approaches, an innovative linearization method around the steady states of the process input and output is proposed. This novel approach emphasizes that the entire process input signal has to be constructed by adding the control increment calculated by the linearization to the value of the considered operating point. Thus, it avoids the uncertainties of those methods, which are based on achieving the actual controller output by integrating the calculated differential values. Another advantage of model-based design is that the saturation of the control signal is included in the design from the outset. Therefore, the undesired integration (windup), which is typical for controllers with explicit integral action, is prevented. The proposed design is illustrated using the control of a liquid tank with variable cross-section as a function of the liquid level. The model-based approach is also used in the evaluation of the transients, where homogeneous responses were obtained over the whole range of process output values. Responses were more homogeneous when simple ultralocal models were used, regardless of controller saturation constraints. Finally, all important innovative aspects of the design are highlighted by a comparison with gain-scheduled PI controller design based on velocity implementation. Full article

This study investigates the multifractal properties of daily returns of the Standard and Poor’s 500 Index (SPX), the Dow Jones Industrial Average (DJI), and the Nasdaq Composite Index (IXIC), the three main indices representing the U.S. stock market, from 1 January 2005 to 1 November 2024. The multifractal detrended fluctuation analysis (MF-DFA) method is applied in this study. The origins of the multifractal properties of these returns are both long-range correlation and fat-tail distribution properties. Our findings show that the SPX exhibits the highest multifractal degree, and the DJI exhibits the lowest for the whole sample. This study also examines the multifractal behaviors of cross-correlations among the three major indices through the multifractal detrended cross-correlation analysis (MF-DCCA) method. It is concluded that the indices are cross-correlated and the cross-correlations also exhibit multifractal properties. Meanwhile, these returns exhibit different multifractal properties in different stages of the market, which shows some asymmetrical dynamics of the multifractal properties. These empirical results may have some important managerial and academic implications for investors, policy makers, and other market participants. Full article

This work develops a comprehensive review of the main conventional and emerging desalination processes. It presents the state of knowledge of the most known and investigated techniques, highlights their advantages and drawbacks, and draws appropriate conclusions on their respective performances from various angles including their energy consumption and efficiency, environmental impacts, reliability, and flexibility in operations. This review reveals the recent large dominance and deployment of the reverse osmosis technology in the Gulf countries, mainly in Saudi Arabia; the importance of hybridization; and the slow penetration of promising processes including membrane distillation and forward osmosis into the industrial desalination market. In addition, this work aims to develop some comparison exercises between these processes using specific criteria. A cross approach allowing an easier comparison between various desalination processes could help identify the advantages and drawbacks of each technology and select the appropriate process. Therefore, various criteria allowing a clear picture to be drawn of the performance and capabilities of the main conventional and emerging desalination processes have been proposed in the frame of sustainable development. As an illustration of this general approach from sustainability prospects and considering specific weights for each proposed criterion for the case of Saudi Arabia, a comparison exercise reveals that the superiority of reverse osmosis (RO) is confirmed. Multiple effect distillation (MED) and membrane distillation (MD) processes are potentially competitive to RO while multi-stage flash (MSF) comes last due to several drawbacks. Full article

Accurate forecasts of stock indexes can not only provide reference information for investors to formulate relevant strategies but also provide effective channels for the government to regulate the market. However, due to its volatility and complexity, predicting the stock price index has always been a challenging task. This paper proposes a hybrid forecasting system based on comprehensive feature selection and intelligent optimization for stock price index forecasting. First, a recursive feature elimination with a cross-validation (RFECV) algorithm is designed to filter variables that have a significant impact on the target data from multiple datasets. Then, the stack autoencoder (SAE) algorithm is constructed to compress the feature variables. At last, an enhanced least squares support vector machine (LSSVM) algorithm is established to obtain high-precision point prediction results, and the Gaussian process regression (GPR) algorithm is used to obtain reasonable interval prediction results. Taking the Shanghai Stock Exchange (SSE) as an example, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the model were 6.989 and 0.158%, respectively. In addition, the prediction interval coverage probability (PICP) is 99.792%. Through experimental comparison, the model shows high prediction accuracy and generalization ability. Full article

A wireless sensor network (WSN) consists of sensors with wireless transceivers that link autonomously over many hops. It offers various advantages, including less traffic, more stability, extended wireless communication distances, and broader coverage regions at lower cost. Combining emerging Limited Bandwidth Channel Internet of Things (LBC-IoT) technologies with wireless sensor networks offers interesting applications in the defense, medical, smart conveyance, and marketable sectors. This study initially analyzes WSN and LBC-IoT technologies independently before combining them to look into the networking framework of LBC-IoT and WSN, as well as the associated technologies resulting from the fusion. This article describes the typical network node redeployment strategy for wireless sensors, which can lead to poor node connection and inadequate coverage due to a lack of confined subgroup node exploration. The suggested method for localizing WSN nodes, based on the Hop Count Path Vector (HOP-PV) algorithm, enhances the process of calculating the average hop distance and the number of node hops, resulting in the PVHOP-LBCIOT mechanism. Simulation results indicate that the improved PVHOP-LBCIOT algorithm’s three deployment methods (square, central uniform, and cross) outperform the two approaches of HOP-PV (random deployment) and PVHOP-LBCIOT (border uniform deployment) for an equal number of unknown moving anchor positions (11), a disparate number of unspecified nodes (30-13), and a fixed communication radius (6), with a reduced average error rate of 32.79%, from 38%, and improved accuracy for obtaining unknown node locations. The suggested method for localizing WSN nodes using a single node acting as a mobile anchor point, known as the PVHOP-LBCIOT mechanism, enhances and optimizes the process of computing the average hop distance and the number of node hops. A comparison experiment demonstrates that this hopping algorithm has much greater coverage, node power, linkage, and resilience compared to existing methods. Full article

Soybeans, a vital source of protein for animal feed and an essential industrial raw material, are the most traded agricultural commodity worldwide. Accurate price forecasting is crucial for maintaining a resilient global food supply chain and has significant implications for agricultural economics and policymaking. This review examines over 100 soybean price forecast models published in the last decade, evaluating them based on the specific markets they target—futures or spot—while highlighting how differences between these markets influence critical model design decisions. The models are also classified into AI-powered and traditional categories, with an initial aim to conduct a statistical analysis comparing the performance of these two groups. This process unveiled a fundamental gap in best practices, particularly regarding the use of common benchmarks and standardised performance metrics, which limits the ability to make meaningful cross-study comparisons. Finally, this study underscores another important research gap: the lack of models forecasting soybean futures prices in Brazil, the world’s largest producer and exporter. These insights provide valuable guidance for researchers, market participants, and policymakers in agricultural economics. Full article

Background/Objectives: In this article, we present a literature review of methods used to measure the shaping ability of endodontic rotary files, including the selection of endodontic sample type (extracted teeth versus simulated blocks) and an imaging evaluation method. This review was conducted as background research to identify concerns that arise when designing research studies in this domain and propose how the field can plan more systematic studies going forward. Methods: A literature search was conducted using PubMed, MEDLINE, Embase, ScienceDirect, Scopus, and e B-on databases, including studies published in English from January 2010 to June 2024. Only studies that specified in vitro or ex vivo methods for evaluating the endodontic performance of NiTi rotary files on canal transportation and centering ability were considered. Results: A total of 86 studies met the inclusion criteria from an initial pool of 651. Of these, 67 studies used extracted teeth, while 20 utilized simulated root canals in resin blocks. For evaluation methods, 55 studies employed Micro-Computed Tomography and Cone-Beam Computed Tomography (MCT + CBCT), 30 used Double Digital Images/Radiographs/Photographs (DDIR + DDIP) with software analysis, 1 used both DDIR and MCT, 1 used high-precision nano-CT, and 1 used a digital single-lens reflex (DSLR) camera. Conclusions: The findings indicate that the MCT method and its advanced variations appear superior in many cases for evaluating the quality of root canal instrumentation due to their ability to provide detailed three-dimensional images. We also discuss the pros and cons of other evaluation methods, including CBCT and DDIR. Finally, we identify important factors to consider for optimizing future cross-study comparisons. This work highlights the importance of being familiar with shaping ability assessment methods as new instruments are introduced to the market. Full article

In the knowledge era, intellectual capital has been considered a key factor in creating value within organisations. This study examines the relationships and interactions between the components of intellectual capital and the profitability of Panamanian banking and financial institutions listed on the Latin American Stock Exchange (LATINEX) from 2014 to 2020. A theoretical framework based on agency theories, signalling theory, and stakeholder theory was employed to support the results. The Valued-Added Intellectual Coefficient (VAIC)™ model, which evaluates the intellectual capital of organisations based on information from financial statements, was also utilised. In this study, stepwise regression was applied to select the optimal number of predictors to be included in each multiple regression model to examine the relationship between the return on equity (ROE) and the components of the VAIC™ in addition to control variables such as size and indebtedness. The findings confirm this study’s hypothesis, demonstrating that the structural capital efficiency (SCE) and company size (SIZE) variables explain 57% of the variance in the ROE for the analysed institutions. The results suggest that the intellectual capital (IC) of financial sector institutions listed on LATINEX is significantly influenced by the SCE coefficient, which shows a negative relationship, suggesting that investment in structural capital does not enhance profitability. On the other hand, larger institutions exhibited higher profitability during the study period. This study was limited to the analysis of two sectors: banking and finance in companies listed on LATINEX. However, its rigorous theoretical and empirical foundation opens the way for future research in which other sectors can be considered, and cross-country comparisons can be made, strengthening the research in this field for Latin America. At the same time, this study offers market regulators a scientific methodology to oversee the activities of issuing companies. Full article

The aim of this paper is to evaluate changes in Italy’s competitiveness in the global wood products market, with a particular focus on wooden furniture and wood panels, both final and intermediate products of the crucial wooden furniture supply chain. The analysis is conducted through a cross-country comparison using trade flow matrices and various descriptive indices: Market Share, Trade Competitiveness Index, Balassa’s Revealed Comparative Advantage Index, and the Symmetric Balassa Index. Furthermore, this study also examines intra-industry trade using the Grubel–Lloyd Index. While each index has its limitations when used individually, their combined analysis can provide a more comprehensive view. The study covers the period from 1996 to 2019, using data from FAO and COMTRADE sources. The results show that Italy maintains a significant position in the international furniture market, although this position has deteriorated over time. Conversely, Italy remains a net importer of wood panels. Trade flows have become more concentrated, with Canada and Germany still holding importance in the international market. However, Asian countries have now become the core of the commercial network. China has emerged as the leading exporting country in all product categories considered, with Vietnam and Malaysia also increasing in importance. Noteworthy progress has also been recorded by Russia and Poland in Europe. Additionally, the study discusses the implications of these findings for rural development, particularly in regions dependent on the wood-product sectors. Full article