Search Results (147)

The European Common Agricultural Policy (CAP) commits, in its Treaty foundation, to a fair standard of living for the agricultural community and, in its post-2014 architecture, to enhanced territorial cohesion. Yet repeated reform cycles have left the regional concentration of payments in many Member States visibly untouched. This paper asks why. We document the persistence of the territorial concentration of CAP transfers across the 13 Greek NUTS-2 regions over the 2010–2025 period (€47.65 bn cumulative), identify the CAP design mechanisms that mechanically reproduce it, and quantify how much of the observed aggregate stationarity is the artefact of compositional shifts versus genuinely offsetting forces. Using the universe of payment disbursements aggregated to 13 NUTS-2 regions and 51 NUTS-3 prefectures, we (i) test for σ- and β-convergence and Lorenz dominance, (ii) decompose Theil-T between and within regions and across Pillar I/Pillar II, and (iii) run four counterfactual simulations: Pillar II share held at its 2010 level, Article: 17-style capping at a 12–15% NUTS-2 ceiling, an Article: 29-style lower-tail floor, and a concentration-elasticity perturbation of the top region. The territorial distribution of support proves strikingly stable: standard inequality measures stay within a narrow band for sixteen consecutive years, and the ranking of regions barely changes, so formal convergence tests detect no narrowing over time. Three messages follow. First, this persistence is not accidental but built into the architecture of the CAP—through historical-reference entitlement values, the per-hectare logic of the Basic Payment Scheme, the geographic concentration of coupled support in cotton and livestock, and the cadastral fragmentation of the island prefectures. Second, the apparent stability conceals two large and opposing forces: the post-2014 expansion of Pillar II has reduced regional disparities, while a widening of the Pillar I distribution has increased them by almost the same amount, so aggregate stationarity reflects policy effort cancelling out, not the absence of it. Third, the instruments already in the CAP toolbox have real redistributive power: capping the largest region’s envelope and redistributing the surplus to lagging regions, or introducing a lower-tail floor, would roughly halve measured inequality. Therefore, the spatial concentration of CAP transfers in Greece is a designed equilibrium rather than an unsolved residual, and reducing it requires instruments that act asymmetrically on the top of the distribution. Full article

Manufacturing systems are undergoing a fundamental transition as efficiency-driven optimisation paradigms prove increasingly inadequate for meeting net-zero, resource-efficiency, and resilience objectives. Digital twins have emerged as a central enabler of this transition, offering continuously coupled physical–digital representations capable of real-time monitoring, prediction, and control. Recent advances in artificial intelligence have accelerated this evolution, transforming digital twins from static simulation artefacts into adaptive, learning-enabled systems embedded within cyber–physical manufacturing environments. However, this shift has also exposed critical challenges related to trust, interpretability, scalability, and sustainability alignment. This review provides a critical synthesis of AI-enabled digital twin research with a specific focus on manufacturing and additive manufacturing systems. It examines the progression from physics-based and data-driven twins toward hybrid AI–physics architectures that balance predictive performance with physical consistency and explainability. Beyond technical performance, the review reframes digital twins as decision-making infrastructures whose value depends on how effectively they integrate energy consumption, material efficiency, carbon intensity, and lifecycle impacts into optimisation and control logic. Particular attention is given to real-time optimisation, predictive maintenance, and intelligent asset management, highlighting persistent gaps in uncertainty propagation, cross-scale coordination, and sustainability-aware governance. The review further identifies structural barriers to large-scale industrial adoption, including data interoperability fragmentation, platform lock-in, organisational resistance, and regulatory ambiguity surrounding AI-driven decisions. Synthesising insights across domains, it argues that many current digital twin implementations remain technically sophisticated yet strategically conservative, reinforcing throughput-centred objectives rather than enabling systemic decarbonisation and circularity. The paper concludes by outlining future research directions and policy-relevant opportunities, emphasising the need for digital twins that reason across timescales, objectives, and lifecycle boundaries. By aligning manufacturing intelligence with measurable sustainability outcomes, AI-enabled digital twins can move from incremental efficiency gains toward transformative impact in net-zero and circular manufacturing systems. Full article

Environmental Impact Assessment (EIA) is intended to function as a predictive, spatially grounded decision-support mechanism. Yet in many developing contexts, its operationalization remains fragmented, descriptive, and weakly standardized. Thus, this study addresses limitations in conventional EIA systems related to transparency, reproducibility, and uncertainty integration by proposing a spatially explicit, digital rule-based decision-support framework that operationalizes hierarchical receptor-based structuring, lifecycle-sensitive modelling, risk classification, and uncertainty propagation within an integrated Geographic Information Systems (GISs) architecture. The academic objective is to advance computational environmental assessment methodologies by formalizing EIA logic into a structured computational workflow that translates spatial interactions (including land use, population density, ecological sensitivity, hydrological zones) and project attributes (including project type, activities and operational conditions) into quantified risk profiles and mitigation mappings. This necessitates combining receptor proximity, overlap intensity, contextual sensitivity, operational conditions, and receptor vulnerability. The framework was applied to three airport case studies in Egypt—representing urban, peri-urban/desert expansion, and coastal–ecological environmental contexts—using standardized spatial preprocessing and normalized analytical scales. Validation was conducted using Monte Carlo uncertainty simulation, sensitivity analysis, Spearman rank correlation, and Cohen’s Kappa agreement analysis. The results demonstrated stable comparative risk classification across receptor categories, lifecycle phases, and impact mechanisms under moderate parameter perturbation (±15%). Cohen’s Kappa agreement values ranging from 0.71 to 0.79 indicated substantial consistency between model-generated exceedance zones and regulatory environmental classifications. In sum, the results demonstrate that receptor proximity, operational intensity, and lifecycle stage function as primary determinants of differentiated environmental risk configurations, and that the proposed framework can support transparent, reproducible, and spatially explicit environmental assessment. Full article

Resource-constrained Multi-Access Edge Computing (MEC) nodes cannot fully replace cloud infrastructure, yet existing service placement models treat edge hosting as an all-or-nothing decision. This paper proposes a queueing-theoretic framework for split-user offloading in hybrid MEC–cloud environments. The system is modeled as a Continuous-Time Markov Chain (CTMC) over a load-vector state space that admits a product-form stationary distribution. A delay-aware greedy orchestration policy determines, at every arrival and departure event, which service occupies the MEC node and how many of its users are offloaded from the cloud. Closed-form expressions are derived for average end-to-end (E2E) delay, MEC occupancy and saturation probabilities, per-service hosting probabilities, and delay-saving indicators. Numerical analysis of a five-service industrial scenario shows that the proposed split-user mechanism keeps the MEC node occupied for most of the observation time (around 97% at the baseline load), naturally prioritizes services with the largest aggregate latency benefit, and substantially reduces the average delay compared with a cloud-only configuration. The analytical results are validated by discrete-event simulation, which matches the CTMC values with relative discrepancy below 1% under the Poisson/exponential assumptions; additional simulations quantify the sensitivity to alternative arrival and service-time distributions. The framework provides analytically tractable, interpretable decision logic with negligible runtime overhead, making it a suitable analytical foundation for cloud service orchestration platforms that must meet strict QoS targets in next-generation edge networks. Full article

A graph-theoretical approach to the analysis of motion and rest in many-body systems is developed. Point bodies are represented as vertices of a complete bi-colored graph, termed the motion–rest graph (MRG). Two vertices are connected by a rust-colored edge when the corresponding bodies are at rest relative to each other; that is, when their mutual distance remains constant in time, bodies moving relative to each other are connected by a cyan edge. It is shown that the logical structure of the relation “to be at rest relative to each other” determines the combinatorial structure of the graph. For one-dimensional motion in classical mechanics and special relativity, this relation is reflexive, symmetric, and transitive, and therefore defines an equivalence relation. As a result, rust edges form disjoint complete cliques corresponding to rest-clusters, and the MRG becomes a semi-transitive complete bi-colored graph that is completely determined by the partition of the bodies into equivalence classes. It is proven that any such graph on five vertices necessarily contains a monochromatic triangle. For two- and three-dimensional motion, the transitivity of relative rest generally fails because constant mutual distance does not imply an equality of velocities in the presence of rotational degrees of freedom. In this case, the MRG is non-transitive, and the Ramsey threshold becomes the classical value R(3, 3) = 6. The approach is extended to mixed sets containing moving bodies and reference points, including the center of mass of the system. Generalizations to general relativity and quantum mechanics are also discussed. In general relativity, transitivity of relative rest is generically lost because global rigid congruences do not generally exist. In quantum mechanics, exact transitivity survives only at the level of idealized delocalized eigenstates, whereas for physically realizable localized states, the notion of mutual rest becomes only approximate. The results demonstrate that the interplay between kinematics, logical properties of relational motion, and Ramsey-type combinatorial constraints gives rise to unavoidable ordered substructures in many-body systems. Full article

Upper tract urothelial carcinoma (UTUC) is a relatively rare malignancy, far less frequent than its counterpart in the bladder, but with a more aggressive course, worse prognosis and unique diagnostic challenges. Despite the histological and molecular similarities between upper and lower tract urothelial tumours, UTUC has many key distinct traits, both clinical and genomic, and must be viewed as a separate entity from bladder urothelial carcinoma (BUC). Ureteroscopy with biopsy is the only means to obtain tissue for histo-logical confirmation of diagnosis and more accurate tumour grading, but is not always feasible or preferable because it carries the risk of potentially severe complications. Aside from the widely available but poorly sensitive urine cytology, a large variety of urine-based diagnostics are increasingly investigated as non-invasive alternatives to ureteroscopy. Fluorescence in situ hybridization (FISH) is the most widely used molecular assay for the diagnosis and monitoring of UTUC, but has failed, as of yet, to display a comparable diagnostic accuracy to the existing gold standards of computed tomography urography (CTU) and ureteroscopy. We herein aimed to comprehensively review all published data on the performance of FISH for the detection of UTUC, in comparison to urine cytology and other assays, while further commenting on the existing challenges and future perspectives in the field of urine-based diagnostics. Across all studies (n = 29) which were included in this review, the sensitivity and specificity of FISH ranged from 36.8% to 100.0% (mean: 75.5%; median: 78.9%) and 34.4% to 100.0% (mean: 84.9%; median: 89.9%), respectively, in the overall patient population, while in the low- versus high-grade subgroups, the sensitivity of FISH ranged from 30.0% to 90.0% (mean: 55.6%; median: 60%) versus 50.0% to 100.0% (mean: 77.9%; median: 78.8%). Furthermore, FISH showed superi-or sensitivity and similar or lower specificity in comparison to cytology, in the over-whelming majority of studies, while Xpert^®BC Detection showed the highest sensitivity values among all evaluated assays, reaching 100% even in the low-grade subgroup, albeit at the cost of a significantly reduced specificity. Despite the adequate overall sensitivity and specificity of FISH, its suboptimal performance in the detection of low-grade UTUC seems to preclude its use as a stand-alone screening test. Full article

An f-HINe diagram represents real-world clinical histories, primarily of chronic patients with multiple pathologies, who therefore interact with multiple specialists. Therefore, considering the different specialties and the fact that a health problem in a clinical history may refer to multiple medical specialties, an f-HINe diagram presents different specialty swimlanes. Furthermore, the health problem can be organized according to different perspectives, creating logical-conceptual spaces or levels of analysis. The presence of swimlanes and levels allows for the generation of different views from a clinical case, extracted and anonymized from an electronic medical record (reference case). Another way to generate a view can be based on focusing attention on the clinical case over a period of time. The goal of this paper is not only to present the various ways of extracting a view from a clinical case but also to identify an indicator (the educational flexibility of a clinical history) for determining the number of views that can be extracted from a reference case. Indeed, the definition of flexibility has many similarities with the view-based search, as the view provides the guide for calculating this indicator. It is also rooted in the psychological and educational construct of flexibility. The value of flexibility depends on the type of view considered and how the specialty swimlanes, levels, analysis levels, and time intervals of analysis are combined. Since not all views are medically interesting, the indicator’s usefulness is to show all potentially extractable views and allow the clinician to choose the most useful and meaningful ones for their teaching and education objectives. Full article

Founded in 1703, St. Petersburg was the capital of the Russian Empire. Its historic center and associated monuments are inscribed as a UNESCO World Heritage Site. Its components are classified as cultural rather than natural or mixed. We hypothesized that a part of them has an additional ecotourism value. We carried out field observations along with a review of the literature. Our results confirmed the hypothesis: many of these sites retain important elements of biodiversity that can be used for environmental education. Large congregations of birds can be observed in close proximity to Heritage monuments. Wintering bats occupy the interiors of historic fortifications, and in summer, concentrations of feeding bats can be found nearby. Seal haul-out sites have been documented on small islands near the city. The ecotourism and nature-conservation value of these Heritage landscapes is usually linked to the original logic of their selection. The best locations were chosen for palace construction—dry, scenic areas with fertile soils suitable for park creation. Proximity to bodies of water was equally important, both for aesthetic reasons and for sanitation. These same qualities also make such areas highly favorable for biodiversity. Even after centuries of development, many natural features have persisted. Full article

A state of terminal stagnation is often reached by software projects despite the presence of advanced tools, and these occurrences are defined within this study as software engineering deadpoints, where the cost of system recovery is frequently found to be higher than the actual value of the software. While many factors are seen to lead toward project failure, it is suggested by the evidence that technical debts are the main cause of such failures. A significant number (23.5%) of these fatal issues is created during the early architectural phases of development, and it is noted that these problems often remain hidden until they become unrecoverable. The data collected during this research show that projects facing technical obstacles (Recovery Score: 4.24) are much harder to save than those suffering with process obstacles (Recovery Score: 5.38). It was also observed that a steady reluctance to refactor old logic and an excessive number of code revisions are seen as the most reliable signs that a project is approaching a point of no return. Because these warning signs are often overlooked by management, the eventual failure of the system is often viewed as an unexpected event rather than a predictable outcome of poor early choices. By defining these terminal states, this work provides those in leadership roles with a method to differentiate between minor delays and total failure, thereby assisting teams in avoiding the heavy economic losses associated with unproductive development paths. Full article

Random number generation is essential in many application domains, including high-energy physics simulations. Implementing Monte Carlo methods that generate samples following a desired probability distribution is particularly challenging on hardware platforms such as FPGAs. Direct implementations of analytical distribution functions are often resource-intensive, making them impractical for real-time systems. An efficient alternative is the use of the inverse cumulative distribution function (CDF), which can be implemented using look-up tables (LUTs). In this approach, a uniformly distributed random number—generated by Linear Feedback Shift Registers (LFSRs)—is used as an address to access LUTs containing discretized x-axis values of the CDF, thereby yielding the target random variable. However, this method presents limited accuracy in low-probability regions of the distribution. To address this issue, this paper proposes a segmented CDF implementation based on multiple LUTs, improving resolution in poorly sampled regions. A cascade of decision logic selects the appropriate memory output, increasing resolution only where necessary while optimizing memory usage. The proposed method was validated through Monte Carlo simulations in particle physics applications, achieving close agreement with theoretical distributions while requiring limited FPGA resources and no DSP blocks. Full article

In recent years, many countries have increased their investment in the field of humanoid robots, promoting significant technological development. This study aims to enable humanoid robots to better adapt to various complex environments, enhancing the robustness of their motion systems and the generalization ability of their motion strategies. Using reinforcement learning algorithms, training on varied terrain is a critical factor for developing adaptable humanoid robots. This paper takes the humanoid robot G1 as the research platform. First, it completes the training, transfer verification, and real-machine deployment of a flat-ground walking model. Then, using fuzzy logic control and a phased training strategy, walking models for ascending/descending stairs and traversing slopes are trained. By systematically varying the stair height and slope gradient, the convergence of the reward function and the task completion success rate are analyzed. Furthermore, the dynamic stability of the robot on complex terrains is validated through qualitative kinematic analysis. The research concludes that as the single-step height and slope gradient increase, the reward value initially rises with more iterations but converges more slowly and at a lower final value. Statistical analysis shows that the success rates of phased training for stair and slope terrains are higher than 86% and 92%, respectively. Full article

Advancing the Sustainable Development Goals (SDGs)—framed around social, environmental, and governance dimensions—offers societies across the world the possibility of achieving long-term prosperity and ensuring that future generations enjoy a high quality of life. Governments pursue the 17 SDGs in accordance with their own socioeconomic and cultural contexts, institutional capacities, and available resources. Because countries differ substantially in structure and capability, their progress toward these goals varies, making the systematic measurement and analysis of SDG performance essential for appropriate timing and efficient resource allocation. This study proposes a hybrid assessment system to evaluate the sustainable development performance of upper-middle-income developing countries under both fuzzy and non-fuzzy environments. This integrated evaluation system consists of four main stages. In the first stage, evaluation criteria and alternative countries are specified, relevant data are obtained, and an initial decision matrix is developed. In the second stage, an efficiency analysis is conducted to identify countries that are efficient and those that are not. In the third stage, evaluation criteria are weighted using AHP and Fuzzy AHP methods. In the final stage, the TOPSIS and Fuzzy TOPSIS methods are used to rank efficient countries depending on sustainable development performance criteria. As a result, six countries were identified as inefficient countries based on sustainable development: China, Kazakhstan, Mongolia, Paraguay, Namibia and Turkmenistan. The AHP and Fuzzy AHP methods produced similar criterion weight values compared to each other. The criteria were prioritized from most important to least one as follows: Life expectancy, expected years of schooling, mean years of schooling, gross national income per capita, CO₂ emissions per capita, and material footprint per capita. While some countries achieved similar rankings using the TOPSIS and Fuzzy TOPSIS methods, most countries achieved different rankings because of the multidimensional nature of sustainable development. When the rankings obtained from the fuzzy and non-fuzzy approaches were compared, a noticeable level of overlap was observed, with a Spearman’s rank correlation coefficient of 68.73%. However, the fuzzy TOPSIS method is considered more reliable for assessing sustainable development performance due to its ability to handle data uncertainty, imprecision, and the multidimensional nature of SDG indicators. The results of this study demonstrate that analyses related to sustainable development, which may not contain precise and clear values and have a complex structure encompassing many areas such as social, environmental, and governance, should preferably be conducted within a fuzzy logic framework to ensure more robust and credible evaluations. Full article

This qualitative study investigates how academic faculty in a UK university conceptualise and implement alternative assessment practices aimed at fostering critical 21st-century skills—such as problem-solving, collaboration, and creativity—in an increasingly diverse higher education context. Drawing on in-depth interviews with six academic faculty members, the study explores the extent to which inclusive and alternative assessment practices are embedded in teaching and examines the institutional and cultural barriers that shape these practices. Thematic analysis reveals that while staff broadly value critical skills, there is considerable variation in how these skills are understood and operationalised in assessment. Many staff face structural constraints, including rigid assessment policies and market-driven accountability frameworks, that limit their ability to innovate. Furthermore, the findings highlight a disjunction between staff awareness of inclusive pedagogies and their capacity to enact them systematically in assessment design. The study contributes to the literature by foregrounding the complex interplay between institutional logics, assessment practices, and inclusive pedagogical aims. It argues that advancing genuinely inclusive and skills-oriented assessment requires systemic change at both institutional and policy levels. Full article

The recently proposed concept of “precursory fingerprint” is a logical consequence of the commonsense statement that seismic structures are unique and that their expected preshock behaviors, including precursory phenomena, are also unique. Our new prediction-related research strategy is conceptually based on the principles of (1) the uniqueness of seismogenic structures, (2) interconnected and interacting geospheres, and (3) non-equivalence of Earth’s surface spots in terms of precursory signal receptivity. The precursory fingerprint of a given seismic structure is a unique assemblage of precursory signals of various natures (seismic, physical, chemical, and biological), detectable in principle by using a system of proper monitoring equipment that consists of a matrix of n sensors placed on the ground at “sensitive” spots identified beforehand and on orbiting satellites. In principle, it is composed of a combination of signals that are emitted by the “responsive sensors”, in addition to the “non-responsive sensors”, coming from the sensor matrix, monitoring as many virtual precursory processes as possible by continuously measuring their relevant parameters. Each measured parameter has a pre-established (by experts) threshold value and an uncertainty interval, discriminating between background and anomalous values that are visualized similarly to traffic light signals (green, yellow, and red). The precursory fingerprint can thus be viewed as a particular configuration of “precursory signals” consisting of anomalous parameter values that are unique and characteristic to the targeted seismogenic structure. Presumably, it is a complex entity that consists of pattern, space, and time components. The “pattern component” is a particular arrangement of the responsive sensors on the master board of the monitoring system yielding anomalous parameter value signals, that can be re-arranged, after a series of experiments, in a spontaneously understandable new pattern. The “space component” is a map position configuration of the signal-detecting sensors, whereas the “time component” is a characteristic time sequence of the anomalous signals including the order, occurrence time before the event, transition time between yellow and red signals, etc. Artificial intelligence using pattern-recognition algorithms can be used to follow, evaluate, and validate the precursory signal assemblage and, finally, to judge, together with an expert board of human operators, its “precursory fingerprint” relevance. Signal interpretation limitations and uncertainties related to dependencies on sensor sensibility, focal depth, and magnitude can be established by completing all three phases (i.e., experimental, validation, and implementation) of the precursory fingerprint-based earthquake prediction research strategy. Full article

Missing data imputation is a critical preprocessing task that directly impacts the quality and reliability of data-driven analyses, yet many existing methods treat numerical and categorical data separately and lack the integration of advanced techniques. We suggest a novel imputation technique to overcome these restrictions that synergistically combines regression imputation using HistGradientBoostingRegressor and fuzzy rule-based systems and is enhanced by a tailored clustering process. This integrated approach effectively handles mixed data types and complex data structures using regression models to predict missing numerical values, fuzzy logic to incorporate expert knowledge and interpretability, and clustering to capture latent data patterns. Categorical variables are managed by mode imputation and label encoding. We evaluated the method on twelve tabular datasets with artificially introduced missingness, employing a comprehensive set of metrics focused on originally missing entries. The results demonstrate that our iterative imputer performs competitively with other established imputation techniques, achieving better and comparable error rates and accuracy. By combining statistical learning with fuzzy and clustering frameworks, the method achieves 15% lower Root Mean Square Error (RMSE), 10% lower Mean Absolute Error (MAE), and 80% higher precision in UCI datasets, thus offering a promising advance in data preprocessing in practical applications. Full article