Search Results (7,706)

In the existing literature, targeting rules are typically determined separately for monetary and fiscal policy. This article proposes a framework for determining targeting rules that account for the policy mix of both monetary and fiscal policy. The aim of this study is to compare selected optimization methods used to derive targeting rules as solutions to a constrained minimization problem. The constraints are defined by a model that incorporates a monetary and fiscal policy mix. The optimization methods applied include the linear–quadratic regulator, Bellman dynamic programming, and Euler’s calculus of variations. The resulting targeting rules are solutions to a discrete-time optimization problem with a finite horizon and without discounting. In this article, we define targeting rules that take into account the monetary and fiscal policy mix. The derived rules allow for the calculation of optimal values for the interest rate and the balance-to-GDP ratio, which ensure price stability, a stable debt-to-GDP ratio, and the desired GDP growth dynamics. It can be noted that all the optimization methods used yield the same optimal vector of decision variables, and the specific method applied does not affect the form of the targeting rules. Full article

Against the backdrop of global climate change and resource-environmental constraints, land ecological security is paramount to regional sustainable development. This study innovatively integrates the DPSIRM system framework with a CNN-LSTM hybrid neural network model to establish a land ecological security early warning system for China’s top 100 counties, enabling scientific diagnosis and dynamic early warning of security incidents. Findings indicate: (1) From 2010 to 2023, land ecological security conditions across counties showed continuous improvement, with the proportion of counties classified as ‘relatively safe’ or higher rising from 2% in 2010 to 68% in 2023. (2) The comprehensive early warning index exhibited a ‘stepwise leap’ trend, progressing through four stages from ‘relatively unsafe’ to ‘relatively safe’. (3) The six subsystems exhibited markedly divergent evolutionary trajectories, characterised by dual-core leadership from ‘driving-management’, fluctuating improvements in ‘pressure-impact’, and low-amplitude oscillations in ‘state-response’. (4) Over the next five years, the comprehensive early warning index will exhibit a ‘gradual stabilisation and upward trend’, yet subsystems will display a polarised pattern of ‘three rising, two stagnant, and one declining’. The early warning system developed in this study provides local decision-makers with critical leading indicators, supporting differentiated management and source-level interventions. These findings hold significant implications for refining county-level ecological governance and optimising territorial spatial patterns. Full article

Accurate prediction of the International Roughness Index (IRI) is critical for road safety and maintenance decisions. In this study, we propose a novel Physics-Aware Informer (PA-Informer) model that integrates the efficiency of the Informer structure with physics constraints derived from partial differential equations (PDEs). The model addresses two key challenges: (1) performance degradation in short-sequence scenarios, and (2) the lack of physics constraints in conventional data-driven models. By embedding residual PDEs to link IRI with influencing factors such as temperature, precipitation, and joint displacement, and introducing a dynamic weighting strategy for balancing data-driven and physics-informed losses, the PA-Informer achieves robust and accurate predictions. Experimental results, based on data from four climatic regions in China, demonstrate its superior performance. The model achieves a Mean Squared Error (MSE) of 0.0165 and R² of 0.962 with an input window length of 30 weeks, and an MSE of 0.0152 and R² with an input window length of 120 weeks. Its accuracy is superior to that of other models, and the stability of the model when the input window length changes is far better than that of other models. Sensitivity analysis highlights joint displacement and internal stress as the most influential features, with stable sensitivity coefficients (S_p ≈ 0.89 and S_p ≈ 0.81). These findings validate the PA-Informer as a reliable and scalable tool for predicting pavement performance under diverse conditions, offering significant improvements over other IRI prediction models. Full article

Staining-based assays are widely used for cell analysis but are invasive, alter physiology, and prevent longitudinal monitoring. Label-free, morphology-based approaches could enable real-time, non-invasive drug testing, yet detection of subtle and dynamic changes has remained difficult. We developed a deep learning framework for stain-free monitoring of leukemia cell cultures using automated bright-field microscopy in a semi-automated culture system (AICE3, LABMaiTE, Augsburg, Germany). YOLOv8 models were trained on images from K562, HL-60, and Kasumi-1 cells, using an NVIDIA DGX A100 GPU for training and tested on GPU and CPU environments for real-time performance. Comparative benchmarking with RT-DETR and interpretability analyses using Eigen-CAM and radiomics (RedTell) was performed. YOLOv8 achieved high accuracy (mAP@0.5 > 98%, precision/sensitivity > 97%), with reproducibility confirmed on an independent dataset from a second laboratory and an AICE3 setup. The model distinguished between morphologically similar leukemia lines and reliably classified untreated versus differentiated K562 cells (hemin-induced erythroid and PMA-induced megakaryocytic; >95% accuracy). Incorporation of decitabine-treated cells demonstrated applicability to drug testing, revealing treatment-specific and intermediate phenotypes. Longitudinal monitoring captured culture- and time-dependent drift, enabling separation of temporal from drug-induced changes. Radiomics highlighted interpretable features such as size, elongation, and texture, but with lower accuracy than the deep learning approach. To our knowledge, this is the first demonstration that deep learning resolves subtle, drug-induced, and time-dependent morphological changes in unstained leukemia cells in real time. This approach provides a robust, accessible framework for label-free longitudinal drug testing and establishes a foundation for future autonomous, feedback-driven platforms in precision oncology. Ultimately, this approach may also contribute to more precise and adaptive clinical decision-making, advancing the field of personalized medicine. Full article

Inter-saccadic times or eye fixation durations (EFDs) are relatively stable at around 250 ms, equivalent to four saccades per second. However, the mean and standard deviation are not sufficient to describe the frequency histogram distribution of EFD. The exGaussian has been proposed for fitting the EFD histograms. The present report tries to adjust a competition model (C model) between the saccadic and the fixation network to the EFD histograms. This model is at a rather conceptual level (computational level in Marr’s classification). Both models were adjusted to EFD from an open database with data of 179,473 eye fixations. The C model showed to be able, along with exGaussian model, to be compatible with explaining the EFD distributions. The two parameters of the C model can be ascribed to (i) a refractory period for new saccades modeled by a sigmoid equation (A parameter), while (ii) the ps parameter would be related to the continuous competition between the saccadic network related to the saliency map and the eye fixation network, and would be modeled through a geometric probability density function. The model suggests that competition between neural networks would be an organizational property of brain neural networks to facilitate the decision process for action and perception. In the visual scene scanning, the C model dynamic justifies the early post-saccadic stability of the foveated image, and the subsequent exploration of a broad space in the observed image. The code to extract the data and to run the model is added in the ^{Supplementary Materials}. Additionally, entropy of EFD is reported. Full article

The built environment influences urban quality of life, particularly through material properties and design decisions that affect thermal comfort, energy consumption, and environmental performance. Among the physical parameters shaping urban microclimates, surface reflectivity—albedo plays a central role in regulating both surface and ambient temperatures. While high-albedo materials are widely recognized for mitigating the urban heat island (UHI) effect and lowering energy demand, limited attention has been given to how material aging alters albedo and, by extension, thermal performance over time. This study investigates that relationship through field measurements conducted at 18 outdoor locations in Xanthi, Greece, across four dates with varying environmental conditions. Variables such as material color, age, and temperature were analyzed through statistical methods and linear regression. Results confirmed a strong correlation between color and albedo and identified a statistically significant relationship between aging and albedo. Additionally, the expected inverse correlation between albedo and surface temperature was reaffirmed. These findings underscore the dynamic nature of material performance and highlight the need for incorporating aging behavior into sustainable urban design. The study contributes data to the field and supports the development of long-term strategies in urban planning and maintenance aimed at preserving the reflective efficiency of surface materials. Full article

Antimicrobial resistance (AMR) is widely recognized as a major global public health threat, yet its origins and implications extend beyond the simple misuse or overuse of antibiotics. This study explores AMR as a complex, multifactorial phenomenon shaped by biological, clinical, dental, environmental, and social dynamics, with particular attention to the emerging role of dentistry. A narrative literature review was performed, drawing from textbooks, peer-reviewed articles, and official World Health Organization (WHO) reports, with emphasis on recent findings on periodontal biofilms as reservoirs of resistance genes. The analysis shows that AMR develops through bacterial mutations, horizontal gene transfer, environmental contamination, healthcare-associated practices, and patient behaviors, all of which interact to sustain its spread. Within dentistry, subgingival microresistances are gaining relevance, complicating treatment strategies and underscoring the need for more conscious clinical decision-making. The findings suggest that reducing antibiotic prescriptions or developing new drugs alone will not suffice; instead, a systemic, interdisciplinary approach is required, integrating microbiology, clinical practice, public health, and institutional responsibility. Such awareness is essential to confront the significant clinical, economic, and social implications of AMR and to foster strategies capable of addressing its complex and evolving nature. Full article

The energy sector, as a major source of carbon emissions, has a significant impact on the operation of the carbon market and the management of carbon emissions. With the introduction of the “dual carbon” goals, the Chinese government has actively implemented measures to reduce carbon emissions, making the carbon market an important tool for emission reduction. Therefore, characterizing the inter-market relationships helps enhance decision-making for market participants and promotes sustainable economic development. This study selects the price of the Chinese carbon emission trading market, which began trading on 16 July 2021, as a representative of the carbon market price. In terms of energy market selection, the prices of electricity, new energy, and coal are chosen as representatives of the energy market. From the perspective of the nonlinear dependency structure between market prices, a “carbon ↔ electricity ↔ new energy ↔ coal market” multi-to-multi interaction model is constructed, and the MSVAR model is employed to study the nonlinear dependency characteristics between market prices under interactive influences. The results show that there is a significant nonlinear dependency structure between the four market prices, especially between the carbon market and the new energy market. These market prices exhibit different behavioral characteristics under different states, with non-stationary states being the most common. There is a strong positive correlation between the electricity market and new energy market prices, while the relationship between the carbon market and other market prices is relatively weaker. The relevant conclusions provide valuable insights for policymakers and investors, helping them better understand and predict future market dynamics. Full article

Water, energy, and food (WEF) constitute the core strategic resources essential for regional sustainable development, and the governance of the WEF system holds critical significance for the Central Area of the Yangtze River Delta (caYRD)—one of China’s most economically dynamic regions. In this area, however, the potential risks associated with insufficient WEF synergy capacity have become increasingly prominent amid continuous population growth and rapid urbanization. Against this backdrop, this study aimed to evaluate the WEF synergy capacity of 27 prefecture-level cities (PLCs) in the caYRD over the period 2005–2023 using the Projection Pursuit Model (PPM), based on an evaluation framework encompassing 12 indicators. Our results revealed that (1) the WEF system exhibits significant spatiotemporal heterogeneity, which is evident not only in the water resource, energy resource, and food resource subsystems but also in the overall WEF synergy capacity. In the water subsystem, Wenzhou and Ma’anshan achieved the highest and lowest PPM evaluation scores, respectively; in the energy subsystem, Zhoushan and Shanghai recorded the highest and lowest scores, respectively; and in the food subsystem, Yancheng and Zhoushan ranked first and last in terms of PPM scores, respectively. (2) For the integrated WEF synergy capacity evaluation, Yancheng obtained the highest score, whereas Shanghai ranked the lowest; additionally, Chuzhou exhibited the largest fluctuation range in scores, while Taizhou (Jiangsu) exhibited the smallest fluctuation range. (3) Subsequently, based on the PPM evaluation values of WEF synergy capacity, the 27 PLCs were clustered into three groups: the High WEF synergy capacity value cluster, which includes Yancheng and Chuzhou; the Low WEF synergy capacity value cluster, which consists of Shanghai and Suzhou; and the Mid-level WEF synergy capacity value cluster, which comprises the remaining 22 PLCs and is further subdivided into three sub-clusters. The cluster results of WEF synergy capacity imply that special attention to the consumption control of WEF resources is required for different PLCs. The variations in WEF synergy capacity and its spatial distribution patterns provide critical insights for formulating region-specific strategies to optimize the WEF system, which is of great significance for supporting sustainable development decision-making in the caYRD. Full article

Artificial intelligence (AI) is increasingly embedded within organizational infrastructures, yet the foundational role of data in shaping AI outcomes remains underexplored. This study positions data at the center of complexity, uncertainty, and strategic decision-making in AI development, aligning with the emerging paradigm of data-centric AI (DCAI). Based on in-depth interviews with 74 senior AI and data professionals, the research examines how experts conceptualize and operationalize data throughout the AI lifecycle. A thematic analysis reveals five interconnected domains reflecting sociotechnical and organizational challenges—such as data quality, governance, contextualization, and alignment with business objectives. The study proposes a conceptual model depicting data as a dynamic infrastructure underpinning all AI phases, from collection to deployment and monitoring. Findings indicate that data-related issues, more than model sophistication, are the primary bottlenecks undermining system reliability, fairness, and accountability. Practically, this research advocates for increased investment in the development of intelligent systems designed to ensure high-quality data management. Theoretically, it reframes data as a site of labor and negotiation, challenging dominant model-centric narratives. By integrating empirical insights with normative concerns, this study contributes to the design of more trustworthy and ethically grounded AI systems within the DCAI framework. Full article

In the current geopolitical context, sustainable energy development has become one of the pillars of global economic growth. This issue is well recognized in the European Union, which has undertaken a number of measures to achieve sustainable development goals. For these measures to be effective, it is essential to conduct a reliable, multi-variant diagnosis of the state of energy development in the EU-27 countries. This paper addresses this highly topical and important issue. It presents a new proprietary method—the Entropy–Evolutionary Evaluation of Sustainability (E³)—based on a multidimensional approach to researching and evaluating the state of sustainable energy development in the EU-27 countries between 2014 and 2023. Through the integration of 19 indicators representing the adopted dimensions of the study (energy, economic, environmental, and social), the method enabled both a static assessment and a dynamic analysis of energy transition processes across space and time. To determine the weights of the indicators for each dimension of sustainable energy development, the CRITIC, Entropy, and equal weight methods, along with the Laplace criterion, were applied. The Analytic Hierarchy Process method was used to establish the weights of the dimensions themselves. An important component of the approach was the inclusion of scenario studies, which made it possible to assess sustainable energy development under five variants: baseline, level, equilibrium, transformational, and neutral. These scenarios were based on different weight values assigned to three factors: the level of energy development (L), its stability (S), and the trajectory of change ($\tilde{T}$). The results, expressed in the form of a total index value and dimensional indices, reveal significant diversity among the EU-27 countries in terms of sustainable energy development. Sweden, Finland, Denmark, Latvia, and Austria achieved the best results, while Cyprus, Malta, Ireland, and Luxembourg—countries heavily dependent on energy imports, with limited diversification of their energy mix and high energy costs—performed the worst. The developed method and the results obtained should serve as a valuable source of knowledge to support decision-making and the formulation of strategies concerning the pace and direction of actions related to the energy transition. Full article

Against the backdrop of intensifying global technological competition and the deepening of the national innovation-driven strategy, private technology enterprises, as the core entities of technological innovation, have their sustainable innovation dynamics profoundly influenced by the strategic interactions among multiple parties such as the government, enterprises, and users. Based on evolutionary game theory, this paper constructs a tripartite evolutionary game model involving the government, private technology enterprises, and market users in the Chinese context. Through theoretical deduction and multi-scenario numerical simulation using Matlab, it systematically analyzes the logic of strategic choices and the laws of dynamic equilibrium of the three parties in the process of sustainable innovation. The research shows that the strategic evolution of multiple entities presents multiple equilibrium states. There exist critical thresholds for the intensity of policy support, the concentration of market competition, and users’ willingness to choose innovative products; beyond these thresholds, the marginal impact on sustainable innovation dynamics increases significantly. Further research finds that the government and enterprises need to compensate for the profit gap between users’ choice of innovative products and traditional products through a subsidy mechanism to form a positive cycle of “active innovation–market recognition–profit improvement”. This study enriches the theoretical system of multi-entity innovation dynamics by incorporating user behavior and provides a decision-making reference for optimizing innovation governance and fostering the development of sustainable innovation dynamics in private enterprises in China and other similar economies. Full article

Cerastoderma edule, the European edible cockle, is a key species in the coastal ecosystems of Portugal, particularly in Ria de Aveiro, a biodiversity hotspot and a critical area for cockle harvesting. This study aimed to assess the population dynamics of C. edule in Ria de Aveiro, focusing on spatial and seasonal patterns in density, growth, cohort composition, and recruitment areas, to provide baseline data for sustainable management. Our results revealed marked spatial and seasonal variability in cockle density, ranging from complete absence at some upstream sites to peaks of over 5900 ind. m⁻², with recruitment concentrated in summer and early autumn. Environmental gradients, particularly decreasing salinity inland, seasonal temperature shifts, and current velocity, strongly shaped the distribution of recruits and adults, while cohort lifespan and growth performance varied with sediment conditions and lagoon position. Concerningly, the maximum mean shell length observed is close to the legal minimum catch size, raising questions about population sustainability under current harvesting pressures. This interplay of environmental drivers and harvesting pressures poses risks to population viability. Effective management strategies, including adjusted catch sizes, seasonal harvesting bans, and habitat conservation, are essential to ensure the sustainable exploitation of cockles in Ria de Aveiro. Enhanced research and monitoring efforts are recommended to support informed management decisions and protect this valuable resource. Full article

Predicting the quality of freshwater, both surface and groundwater, is essential for the sustainable management of water resources. This study collected 1822 articles from the Scopus database (2000–2024) and filtered them using Topic Modeling to create the study corpus. The B-SLR analysis identified exponential growth in scientific publications since 2020, indicating that this field has reached a stage of maturity. The results showed that the predominant techniques for predicting water quality, both for surface and groundwater, fall into three main categories: (i) ensemble models, with Bagging and Boosting representing 43.07% and 25.91%, respectively, particularly random forest (RF), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGB), along with their optimized variants; (ii) deep neural networks such as long short-term memory (LSTM) and convolutional neural network (CNN), which excel at modeling complex temporal dynamics; and (iii) traditional algorithms like artificial neural network (ANN), support vector machines (SVMs), and decision tree (DT), which remain widely used. Current trends point towards the use of hybrid and explainable architectures, with increased application of interpretability techniques. Emerging approaches such as Generative Adversarial Network (GAN) and Group Method of Data Handling (GMDH) for data-scarce contexts, Transfer Learning for knowledge reuse, and Transformer architectures that outperform LSTM in time series prediction tasks were also identified. Furthermore, the most studied water bodies (e.g., rivers, aquifers) and the most commonly used water quality indicators (e.g., WQI, EWQI, dissolved oxygen, nitrates) were identified. The B-SLR and Topic Modeling methodology provided a more robust, reproducible, and comprehensive overview of AI/ML/DL models for freshwater quality prediction, facilitating the identification of thematic patterns and research opportunities. Full article

To address the issue that prototypical networks in existing few-shot text classification methods suffer from performance limitations due to prototype shift and metric constraints, this paper proposes a meta-learning-based few-shot text classification method: Threshold Dynamic Multi-Source Decisive Prototypical Network (TDMP-Net) to solve these problems. This method designs two core components: the threshold dynamic data augmentation module and the multi-source information Decider. Specifically, the threshold dynamic data augmentation module achieves the optimization of the prototype estimation process by leveraging the multi-source information of query set samples, which thereby alleviates the prototype shift problem; meanwhile, the multi-source information Decider performs classification by relying on the multi-source information of the query set, thus alleviating the metric constraint problem. The effectiveness of the proposed method is verified on four benchmark datasets: under the five-way one-shot and five-way five-shot settings, TDMP-Net achieves average accuracies of 78.3% and 86.5%, respectively, which are an average improvement of 3.3 percentage points compared with current state-of-the-art methods. Experimental results show that this TDMP-Net can effectively alleviate the prototype shift problem and metric constraint problems, and has stronger generalization ability. Full article