Search Results (1,342)

This article presents a novel mathematical formalism for advanced manifold–metric pairs, enhancing the frameworks of geometry and topology. We construct various D-dimensional manifolds and their associated metric spaces using functional methods, with a focus on integrating concepts from mathematical physics, field theory, topology, algebra, probability, and statistics. Our methodology employs rigorous mathematical construction proofs and logical foundations to develop generalized manifold–metric pairs, including homogeneous and isotropic expanding manifolds, as well as probabilistic and entropic variants. Key results include the establishment of metrizability for topological manifolds via the Urysohn Metrization Theorem, the formulation of higher-rank tensor metrics, and the exploration of complex and quaternionic codomains with applications to cosmological models like the expanding spacetime. By combining spacetime generalized sets with information-theoretic and probabilistic approaches, we achieve a unified framework that advances the understanding of manifold–metric interactions and their physical implications. Full article

This study investigates the critical capabilities and integration mechanisms that enable organizations to achieve substantive sustainability transformations. Using a mixed-methods approach combining survey data (n = 234), in-depth interviews (n = 42), and comparative case studies (n = 6), the research identifies how organizations effectively navigate sustainability paradoxes while developing integration practices that embed sustainability throughout organizational systems. Our research is primarily grounded in paradox theory, complemented by insights from organizational learning theory, institutional logics, and power dynamics perspectives to develop a comprehensive theoretical framework. Statistical analysis reveals strong relationships between paradox navigation capabilities and transformation outcomes (β = 0.31, p < 0.01), with integration practices emerging as the strongest predictor of sustainability success (β = 0.42, p < 0.01). Qualitative findings illuminate four essential integration mechanisms—governance integration, strategic integration, operational integration, and performance integration—and their temporal development. The significant interaction between power mobilization and integration practices (β = 0.19, p < 0.01) demonstrates that structural interventions are insufficient without attention to power relationships. The research contributes to sustainability science by advancing theory on paradoxical tensions in transformation processes, demonstrating how organizations can transcend the gap between sustainability rhetoric and substantive action through both structural integration and power-conscious approaches. By identifying contextual contingencies across sectors and organizational types, the study challenges universal prescriptions for sustainability transformation, offering instead a nuanced framework for creating organizational conditions conducive to context-specific transformation toward more sustainable futures. Our findings offer practical guidance for organizations navigating the complex landscape of sustainability transformation and contribute to the implementation of UN Sustainable Development Goals, particularly SDG 12 (Responsible Consumption and Production) and SDG 17 (Partnerships for the Goals). Full article

This study presents a cross-cultural comparison of two fortified residences—Palazzo Farnese in Italy and Dong’s Fortified Compound in China—through a triadic analytical framework encompassing architectural form, symbolic ornamentation, and public perception. By combining field observation, iconographic interpretation, and digital ethnography, the research investigates how heritage meaning is constructed, encoded, and reinterpreted across distinct sociocultural contexts. Empirical materials include architectural documentation, decorative analysis, and a curated dataset of 4947 user-generated images and 1467 textual comments collected from Chinese and international platforms between 2020 and 2024. Methods such as CLIP-based visual clustering and BERTopic-enabled sentiment modelling were applied to extract patterns of perception and symbolic emphasis. The findings reveal contrasting representational logics: Palazzo Farnese encodes dynastic authority and Renaissance cosmology through geometric order and immersive frescoes, while Dong’s Compound conveys Confucian ethics and frontier identity via nested courtyards and traditional ornamentation. Digital responses diverge accordingly: international users highlight formal aesthetics and photogenic elements; Chinese users engage with symbolic motifs, family memory, and ritual significance. This study illustrates how historically fortified residences are reinterpreted through culturally specific digital practices, offering an interdisciplinary approach that bridges architectural history, symbolic analysis, and digital heritage studies. Full article

The aim was to explore using machine learning (including cluster mapping and k-means methods) to classify types of mead based on sensory analysis and aromatic compounds. Machine learning is a modern tool that helps with detailed analysis, especially because verifying aromatic compounds is challenging. In the first stage, a cluster map analysis was conducted, allowing for the exploratory identification of the most characteristic features of mead. Based on this, k-means clustering was performed to evaluate how well the identified sensory features align with logically consistent groups of observations. In the next stage, experiments were carried out to classify the type of mead using algorithms such as Random Forest (RF), adaptive boosting (AdaBoost), Bootstrap aggregation (Bagging), K-Nearest Neighbors (KNN), and Decision Tree (DT). The analysis revealed that the RF and KNN algorithms were the most effective in classifying mead based on sensory characteristics, achieving the highest accuracy. In contrast, the AdaBoost algorithm consistently produced the lowest accuracy results. However, the Decision Tree algorithm achieved the highest accuracy value (0.909), demonstrating its potential for precise classification based on aroma characteristics. The error matrix analysis also indicated that acacia mead was easier for the algorithms to identify than tilia or buckwheat mead. The results show the potential of combining an exploratory approach (cluster map with the k-means method) with machine learning. It is also important to focus on selecting and optimizing classification models used in practice because, as the results so far indicate, choosing the right algorithm greatly affects the success of mead identification. Full article

This article presents a comprehensive review of methods used for forecasting electricity consumption. The studies analyzed by the authors encompass both classical statistical models and modern approaches based on artificial intelligence, including machine-learning and deep-learning techniques. Electricity load forecasting is categorized into four time horizons: very short term, short term, medium term, and long term. The authors conducted a comparative analysis of various models, such as autoregressive models, neural networks, fuzzy logic systems, hybrid models, and evolutionary algorithms. Particular attention was paid to the effectiveness of these methods in the context of variable input data, such as weather conditions, seasonal fluctuations, and changes in energy consumption patterns. The article emphasizes the growing importance of accurate forecasts in the context of the energy transition, integration of renewable energy sources, and the management of the evolving electricity system, shaped by decentralization, renewable integration, and data-intensive forecasting demands. In conclusion, the authors highlight the lack of a universal forecasting approach and the need for further research on hybrid models that combine interpretability with high predictive accuracy. This review can serve as a valuable resource for decision-makers, grid operators, and researchers involved in energy system planning. Full article

Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data ($R^2$ = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article

Driverless electric formula racing cars are affected by nonlinear vehicle characteristics, perturbations, and parameter uncertainties during races, which can cause problems such as low accuracy and instability in trajectory tracking. Aiming to address such problems, this paper proposes a control method combining a prescribed performance control with adaptive backstepping fuzzy control (PPC-ABFC) to solve the aforementioned issues and improve the trajectory tracking accuracy and stability of racing cars. This control method is achieved by constructing a combined error model and confining the error within a prescribed performance function. The nonlinear terms, disturbances, and unknown parameters of the model are approximated by a fuzzy logic system (FLS). An adaptive parameter update law is designed to update the learning parameters in real time. The virtual control law and the real control law were designed by using the backstepping method. The stability of the PPC-ABFC closed-loop system was rigorously proved by applying the Lyapunov stability theory. Finally, simulations were conducted to compare the proposed PPC-ABFC method with other algorithms at different speeds. The results demonstrated that the PPC-ABFC method effectively enhances the trajectory tracking performance of driverless electric formula racing cars. Full article

Background/Objective: Dysbiosis is implicated in the pathogenesis of ulcerative colitis. Hydrogen has been reported to promote intestinal microbiota diversity and suppress ulcerative colitis progression in mice models. In this study, we investigated changes in the intestinal microbiota, therapeutic effects, and safety of hydrogen inhalation in patients with ulcerative colitis. Methods: In this randomised, double-blind, placebo-controlled trial, 10 active patients with ulcerative colitis (aged ≥20 years; Lichtiger’s clinical activity index, 3–10; and Mayo endoscopic subscores ≥1) participated, and they were assigned to either a hydrogen or air inhalation group (hydrogen and placebo groups, respectively). All patients inhaled gas for 4 h every day for 8 weeks. Subsequently, we performed clinical indices and microbiota analyses using the metagenomic sequencing of stool samples before and after inhalation. Results: There was significant difference in the sum of the Mayo endoscopic subscores before and after inhalation in the clinical assessment indices. The hydrogen group showed higher α-diversity (p = 0.19), and the variation in β-diversity was markedly different, compared to the placebo group, in intestinal microbiota analysis (p = 0.02). Functional gene analysis revealed 115 significant genetic changes in the hydrogen group following treatment. No inhalation-related adverse events were observed. Conclusions: Hydrogen inhalation appeared to improve intestinal microbiota diversity; however, no clear therapeutic effect on ulcerative colitis was observed. Further studies are needed, and hydrogen inhalation may possibly lead to a logical solution combined with microbiome therapy, such as faecal microbiota transplantation, with fewer adverse events. Full article

This work presents the development of NOVA, an educational virtual assistant designed for the Parallel Computing course, built using a Retrieval-Augmented Generation (RAG) architecture combined with Large Language Models (LLMs). The assistant operates entirely in Spanish, supporting native-language learning and increasing accessibility for students in Latin American academic settings. It integrates vector and relational databases to provide an interactive, personalized learning experience that supports the understanding of complex technical concepts. Its core functionalities include the automatic generation of questions and answers, quizzes, and practical guides, all tailored to promote autonomous learning. NOVA was deployed in an academic setting at Universidad Politécnica Salesiana. Its modular architecture includes five components: a relational database for logging, a vector database for semantic retrieval, a FastAPI backend for managing logic, a Next.js frontend for user interaction, and an integration server for workflow automation. The system uses the GPT-4o mini model to generate context-aware, pedagogically aligned responses. To evaluate its effectiveness, a test suite of 100 academic tasks was executed—55 question-and-answer prompts, 25 practical guides, and 20 quizzes. NOVA achieved a 92% excellence rating, a 21-second average response time, and 72% retrieval coverage, confirming its potential as a reliable AI-driven tool for enhancing technical education. Full article

This article offers a critical reading of the fishing neighbourhood of Paramos, located on the northern coast of Portugal, through a methodological approach that combines documentary photography and cognitive cartography. The study investigates the relationships between identity, landscape, and power within a territory marked by spatial fragmentation, symbolic exclusion, and functional indeterminacy. By means of a structured visual essay supported by field observation and interpretive maps, Paramos is examined as a liminal urban enclave whose ambiguities reveal tensions between memory, informal appropriation, and control devices. Drawing on authors such as Lefebvre, Augé, Hayden, Domingues, Foucault, and Latour, the article argues that the photographic image, used as a critical tool, can unveil hidden territorial logics and contribute to a more inclusive and situated spatial discourse. Full article

Encryption and decryption are essential components in signal processing and optical communication systems, providing data confidentiality, integrity, and secure high-speed transmission. We present a novel design and simulation of an all-optical encryption and decryption system operating at 120 Gb/s using carrier reservoir semiconductor optical amplifiers (CR-SOAs) embedded in Mach–Zehnder interferometers (MZIs). The architecture relies on two consecutive exclusive-OR (XOR) logic gates, implemented through phase-sensitive interference in the CR-SOA-MZI structure. The first XOR gate performs encryption by combining the input data signal with a secure optical key, while the second gate decrypts the encoded signal using the same key. The fast gain recovery and efficient carrier dynamics of CR-SOAs enable a high-speed, low-latency operation suitable for modern photonic networks. The system is modeled and simulated using Mathematica Wolfram, and the output quality factors of the encrypted and decrypted signals are found to be 28.57 and 14.48, respectively, confirming excellent signal integrity and logic performance. The influence of key operating parameters, including the impact of amplified spontaneous emission noise, on system behavior is also examined. This work highlights the potential of CR-SOA-MZI-based designs for scalable, ultrafast, and energy-efficient all-optical security applications. Full article

What motivates industrial companies to decarbonise? While climate policy has intensified, the specific factors driving corporate decisions remain underexplored. This article addresses that gap through a mixed-methods study combining qualitative insights from a leading automotive supplier with quantitative data from over 800 manufacturing companies in Germany. The study distinguishes between internal motivators—such as risk reduction, future-proofing, and competitive positioning—and external drivers like regulation, supply chain pressure, and investor expectations. Results show that internal economic logic is the strongest trigger: companies act more ambitiously when decarbonisation aligns with their strategic interests. Positive motivators outperform external drivers in both influence and impact on ambition levels. For instance, long-term cost risks were rated more relevant than reputational gains or regulatory compliance. The analysis also reveals how company size, energy intensity, and supply chain position shape motivation patterns. The findings suggest a new framing for climate policy: rather than relying solely on mandates, policies should strengthen intrinsic motivators. Aligning business interests with societal goals is not only possible—it is a pathway to more ambitious, resilient, and timely decarbonisation. By turning external pressure into internal logic, companies can move from compliance to leadership in the climate transition. Full article

Although fuzzy logic is regarded as an old modelling technique, its application in recent studies cannot be overemphasised. Therefore, the study aims to provide recent developments and ideas based on the scholarly contribution from the literature on how uncertainty can be reduced and to enhance decision-making through fuzzy logic in relation to bioenergy technologies. This is necessary to address the potential of uncertainty, inherently subjective information, and handling imprecise data, as well as identifying sustainable determinants in bioenergy technologies. Fuzzy logic application is an essential modelling technique in this regard. In this paper, a review focusing on the comprehensive and detailed applications of fuzzy logic models in bioenergy technologies is presented. From the review, it is found that the integration and combination of a fuzzy logic model plus other modelling techniques provides a better performance and is known to be effective and efficient. The review demonstrates how fuzzy logic can help to manage complicated variables, thereby ultimately promoting more effective and sustainable bioenergy solutions. Hence, for maximum attention on the review, it is suitable for stakeholders, planners, and decision makers in bioenergy research and industry. Full article

The bis-thiourea chiral solvating agent (CSA) BTDA enables the NMR-based determination of absolute configuration in N-3,5-dinitrobenzoyl (DNB) amino acid derivatives without requiring covalent derivatization. A reliable trend of the sense of nonequivalence and absolute configuration is found in both ¹H and ¹³C NMR spectra. A dual-enantiomer approach, using (R,R)- and (S,S)-BTDA, generates diastereomeric complexes with the enantiopure substrate, and distinct spatial arrangements are reflected in consistent and interpretable Δδ values. The observed chemical shift differences correlate reliably with the stereochemistry of the chiral center and are further supported by ROESY (Rotating-frame Overhauser Enhancement SpectroscopY) experiments and binding constants’ measurements, confirming the formation of stereoselective non-covalent complexes. This methodology extends the logic of Mosher’s analysis to solvating agents and remains effective even in samples containing single pure enantiomers of the amino acid derivative. The BTDA-based dual-CSA system thus represents a robust, non-derivatizing strategy for stereochemical assignment by NMR, combining operational simplicity with broad applicability to DNB derivatives of amino acids with free carboxyl function. Full article

Rear-end collisions represent a major concern in automotive safety, particularly due to the risk of whiplash injuries among vehicle occupants. The accurate simulation of occupant kinematics during such impacts is critical for the development of advanced safety systems. This paper presents an enhanced multibody simulation model specifically designed for rear-end crash scenarios, incorporating integrated active headrest mechanisms and sensor-based activation logic. The model combines detailed representations of vehicle structures, suspension systems, restraint systems, and occupant biomechanics, allowing for the precise prediction of crash dynamics and occupant responses. The system was developed using Simscape Multibody, with CAD-derived components interconnected through physical joints and validated using controlled experimental crash tests. Special attention was given to modelling contact forces, suspension behaviour, and actuator response times for the active headrest system. The model achieved a root mean square error (RMSE) of 4.19 m/s² and a mean absolute percentage error (MAPE) of 0.71% when comparing head acceleration in frontal collision tests, confirming its high accuracy. Validation results demonstrate that the model accurately reproduces occupant kinematics and head acceleration profiles, confirming its reliability and effectiveness as a predictive tool. This research highlights the critical role of integrated sensor-actuator systems in improving occupant safety and provides a flexible platform for future studies on intelligent vehicle safety technologies. Full article