Search Results (486)

We present a concise and self-contained extension of the Finite Ring Continuum (FRC) program, showing that symmetry-complete prime shells ${\mathbb{F}}_{\mathsf{p}}$ with $\mathsf{p}=4\mathsf{t}+1$ exhibit a fundamental Euclidean-Lorentzian dichotomy. A genuine Lorentzian quadratic form cannot be realized within a single space-like prime shell ${\mathbb{F}}_{\mathsf{p}}$, since to split time from space one requires a time coefficient $c^2$ in the nonsquare class of ${\mathbb{F}}_{\mathsf{p}}^{\times }$, but then $c\notin {\mathbb{F}}_{\mathsf{p}}$. An explicit finite-field Lorentz transformation is subsequently derived that preserves the Minkowski form and generates a finite orthogonal group $O(Q_{\nu },{\mathbb{F}}_{p^2})$ of split type (Witt index 1). These results demonstrate that the essential algebraic features of special relativity—the invariant interval and Lorentz symmetry—emerge naturally within finite-field arithmetic, thereby establishing an intrinsic relativistic algebra within FRC. Finally, this dichotomy implies the algebraic origin of causality: Euclidean invariants reside within a space-like shell ${\mathbb{F}}_{\mathsf{p}}$, while Lorentzian structure and causal separation arise in its quadratic spacetime extension ${\mathbb{F}}_{{\mathsf{p}}^2}$. Full article

The conformational landscape of 3-indoleacetamide, a key intermediate in plant hormone biosynthesis, has been comprehensively investigated using state-of-the-art laser-ablation chirped-pulse Fourier transform microwave (LA-CP-FTMW) and laser-ablation molecular beam Fourier transform microwave (LA-MB-FTMW) spectroscopy. Remarkably, 3-indoleacetamide exhibits unprecedented conformational rigidity within the tryptophan-derived molecule family, displaying only a single stable conformer characterized by distinctive a-, b-, and c-type rotational transitions. This singular conformational behavior contrasts dramatically with the structural flexibility observed in closely related tryptophan derivatives such as tryptophan, serotonin, tryptamine, and 3-indoleacetic acid. The unique structural constraint imposed by the acetamide functional group provides unprecedented insights into the molecular determinants governing the distinct biological roles of tryptophan-derived compounds. This work establishes a potential correlation between conformational flexibility and biological function, from neurotransmission to plant hormone regulation, offering new perspectives on structure-activity relationships in bioactive natural products. Full article

Background: Knowledge distillation (KD) compresses deep neural networks by transferring knowledge from a high-capacity teacher model to a lightweight student model. However, conventional evaluation metrics such as accuracy, mAP, IoU, or RMSE focus mainly on task performance and overlook how effectively the student internalizes the teacher’s knowledge. Methods: This study introduces the Knowledge Retention Score (KRS), a composite metric that integrates intermediate feature similarity and output agreement into a single interpretable score to quantify knowledge retention. KRS was primarily validated in computer vision (CV) through 36 experiments covering image classification, object detection, and semantic segmentation using diverse datasets and eight representative KD methods. Supplementary experiments were conducted in natural language processing (NLP) using transformer-based models on SST-2, and in time series regression with convolutional teacher–student pairs. Results: Across all domains, KRS correlated strongly with standard performance metrics while revealing internal retention dynamics that conventional evaluations often overlook. By reporting feature similarity and output agreement separately alongside the composite score, KRS provides transparent and interpretable insights into knowledge transfer. Conclusions: KRS offers a stable diagnostic tool and a complementary evaluation metric for KD research. Its generality across domains demonstrates its potential as a standardized framework for assessing knowledge retention beyond task-specific performance measures. Full article

Amid global efforts to reach carbon neutrality, quantifying the cradle-to-gate carbon footprint of panel kitchen cabinets is vital for the transformation of China’s furniture industry to low carbon emissions. This study aims to quantify and compare the cradle-to-gate carbon footprints of three L-shaped panel cabinets made of different materials and to identify the most effective carbon reduction strategies for the Chinese furniture industry. The emission factor method proposed by the Intergovernmental Panel on Climate Change (IPCC) was utilized. The results revealed significant differences in the carbon footprints among the three cabinet products. Specifically, Product A, featuring a DuPont stone countertop from the United States and domestically produced double-sided decorative door panels, exhibited the highest carbon footprint which was 998.5 kgCO₂eq. Product B, with an Italian natural marble countertop and single-sided acrylic door panels, had the lowest carbon footprint which was 610.7 kgCO₂eq. The carbon footprints indicated that key stages such as cabinet bodies, countertops, hardware, and cabinet doors were substantial contributors. Raw material production and processing emerged as the primary sources of carbon emissions, with countertop transportation also contributing significantly. Based on the results, this paper proposed several carbon reduction suggestions. These include optimizing material selection, enhancing energy efficiency in raw material production and processing, optimizing transportation methods, emphasizing the carbon reduction potential of hardware components, and strengthening carbon footprint monitoring and management. Full article

The rise of digital platforms has transformed heritage interpretation from a single official narrative to multi-stakeholder participation. This study investigates how such platforms mediate the formation of a sense of place at the Kulangsu World Heritage Site (WHS). Data were collected from official narrative texts and user-generated content (UGC) on Dianping and Ctrip, and analyzed using high-frequency word statistics and semantic network analysis. The results reveal a clear divergence between official narratives, which emphasize Outstanding Universal Value (OUV), and tourist perceptions, which focus on visual landmarks and “check-in” practices shaped by the “digital gaze.” Moreover, the sense of place is shown to be a dynamic process, co-constructed through pre-visit expectations, on-site experiences, and post-visit reflections. The findings also highlight a transformation in tourists’ roles, shifting from passive cultural consumers to active participants in the co-construction of heritage values, with digital platforms serving as critical mediators. Theoretically, the study advances digital heritage scholarship by clarifying the mechanism of the digital gaze and the dynamic nature of sense of place. Practically, it underscores the importance of integrating official narratives with UGC to strengthen OUV communication, foster broader public engagement, and support the sustainable development of WHSs. Full article

Financial news has a significant impact on investor sentiment and short-term stock price trends. While many studies have applied natural language processing (NLP) techniques to financial forecasting, most have focused on single tasks or English corpora, with limited research in non-English language contexts such as Taiwan. This study develops a joint framework to perform sentiment classification and short-term stock price prediction using Chinese financial news from Taiwan’s top 50 listed companies. Five types of word embeddings—one-hot, TF-IDF, CBOW, skip-gram, and BERT—are systematically compared across 17 traditional, deep, and Transformer models, as well as a large language model (LLaMA3) fully fine-tuned on the Chinese financial texts. To ensure annotation quality, sentiment labels were manually assigned by annotators with finance backgrounds and validated through a double-checking process. Experimental results show that a CNN using skip-gram embeddings achieves the strongest performance among deep learning models, while LLaMA3 yields the highest overall F1-score for sentiment classification. For regression, LSTM consistently provides the most reliable predictive power across different volatility groups, with Bayesian Linear Regression remaining competitive for low-volatility firms. LLaMA3 is the only Transformer-based model to achieve a positive $R^2$ under high-volatility conditions. Furthermore, forecasting accuracy is higher for the five-day horizon than for the fifteen-day horizon, underscoring the increasing difficulty of medium-term forecasting. These findings confirm that financial news provides valuable predictive signals for emerging markets and that short-term sentiment-informed forecasts enhance real-time investment decisions. Full article

The transformation of energy systems towards low emission is one of the key assumptions of the climate and energy policy of the European Union and many countries around the world. These changes include not only the power and transport sectors but also the heating of residential buildings, which consume significant amounts of energy and emit large amounts of greenhouse gases. This article presents a detailed comparative analysis of the costs of heating using an air-to-water heat pump and a condensing gas boiler. The study concerned a retrofitted single-family building from the 1990s, located in southern Poland. The calculations were made taking into account daily meteorological data for two full heating seasons: 2022/2023 and 2023/2024. This approach made it possible to more precisely reproduce real operating conditions. The study was conducted for various configurations of the central heating system: surface and radiator. The following parameters were also taken into account: (1) variable heat pump parameters, such as supply temperature LWT and coefficient of performance COP; (2) current tariffs for electricity and natural gas; and (3) forecasted tariffs for electricity and natural gas in the conditions of market liberalization and phasing out of protective mechanisms. A comparison of the two heating seasons revealed lower costs with a heat pump. In some cases, the cost of heat generated by a gas boiler was over 100% higher than with a heat pump. This applies to both heating seasons. Under the current tariffs, the calculated gas cost for the first season was PLN 6856 (EUR 1605) (1 EUR = 4.27 PLN) compared to heat pump heating costs ranging from PLN 3191 to PLN 4576 (EUR 747 to 1072). For future gas and electricity tariffs, the costs were PLN 8227 (EUR 1926) for gas and PLN 3841 to PLN 5304 (EUR 899 to 1242) for a heat pump. Similarly, for the second heating season, these values were PLN 6055 (EUR 1418) for gas heating and PLN 2741–3917 (EUR 642–917) for a heat pump under the current tariffs, and PLN 7267 (EUR 1702) and PLN 3307–4540 (EUR 774–1064) under future tariffs. This means percentage savings of between approximately 33% and 55%, depending on the heating type and tariff. Therefore, the obtained results indicate the higher profitability of using an air heat pump compared to a gas boiler. This advantage was maintained in all the discussed scenarios, and its scale depended on the type of installation, supply temperature, and the selected electricity tariff. The highest economic profitability was noted for low-temperature systems. These results can provide a basis for making rational investment and design decisions in the context of the energy transformation of single-family housing. Full article

In many advanced materials production processes, the analysis must be non-invasive, rapid, and, if possible, operando. The Raman signal of the various forms of alumina, especially transition alumina, is very weak due to the highly ionic nature of the Al-O bond, which requires long exposure times that are incompatible with monitoring transitions. Here, we explore the use of the fluorescence signal of chromium, a natural impurity in alumina, and the Rayleigh wing to follow the crystallization process up to alpha alumina. To clarify the assignment of the fluorescence components, we compare the transformation of beta alumina single crystals into transition (gamma and theta) alumina and then into alpha alumina with the transformation of optically transparent alumina xerogel and glass, obtained by very slow hydrolysis-polycondensation of aluminum sec-butoxide, into alpha alumina. Vibrational modes are better resolved in thermally treated single crystals than in thermally treated xerogels. Measurements of the Rayleigh wing, the Boson peak, and the fluorescence signal are easier than those of vibrational modes for studying the evolution from amorphous to alpha alumina phases. The fluorescence spectra allow almost instantaneous (<1 s) quantitative control of the phases present. Full article

Jiangsu Province is an important economic province on the eastern coast of China, revealing the spatial–temporal characteristics, dynamic degree, and transition direction of land use/cover change, and its main driving factors are significant for the effective use of land resources and the promotion of regional human–land coordinated development. Based on land use data of Jiangsu Province from 2000 to 2020, this study investigates the spatiotemporal evolution characteristics of land use/cover using the dynamics model and the transfer matrix model, and examines the influence and interaction of the driving factors between human activities and the natural environment based on 10-factor data using Geodetector. The results showed that (1) In the past 20 years, the type of land use/cover in Jiangsu Province primarily comprises cropland, water, and impervious, with the land use/cover change mode mainly consisting of a dramatic change in cropland and impervious and relatively little change in forest, grassland, water, and barren. (2) From the perspective of the dynamic rate of land use/cover change, the single land use dynamic degree showed that impervious is the only land type whose dynamics have positively increased from 2000 to 2010 and 2010 to 2020, with values of 3.67% and 3.03%, respectively. According to the classification of comprehensive motivation, the comprehensive land use motivation in Jiangsu Province in each time period from 2000 to 2010 and 2010 to 2020 is 0.46% and 0.43%, respectively, which belongs to the extremely slow change type. (3) From the perspective of land use/cover transfer, Jiangsu Province is mainly characterized by a large area of cropland transfer (−7954.30 km²) and a large area of impervious transfer (8759.58 km²). The increase in impervious is mainly attributed to the transformation of cropland and water, accounting for 4066.07 km² and 513.73 km² from 2010 to 2020, which indicates that the non-agricultural phenomenon of cropland in Jiangsu Province, i.e., the process of transforming cropland into non-agricultural construction land, is significant. (4) From the perspective of driving factors, population density (q = 0.154) and night light brightness (q = 0.156) have always been important drivers of land use/cover change in Jiangsu Province. The interaction detection indicates that the land use/cover change is driven by both socio-economic factors and natural geographic factors. (5) In response to the dual pressures of climate change and rapid urbanization, coordinating the multiple objectives of socio-economic development, food security, and ecological protection is the fundamental path to achieving sustainable land use in Jiangsu Province and similar developed coastal areas. By revealing the characteristics and driving factors of land use/cover change in Jiangsu Province, this study provides qualitative and quantitative theoretical support for the coordinated decision-making of economic development and land use planning in Jiangsu Province, specifically contributing to sustainable land planning, climate adaptation policy-making, and the enhancement of community well-being through optimized land use. Full article

Hearing-impaired people face challenges in expressing and perceiving emotions, and traditional single-modal emotion recognition methods demonstrate limited effectiveness in complex environments. To enhance recognition performance, this paper proposes a multimodal fusion neural network based on a multimodal multi-head attention fusion neural network (MMHA-FNN). This method utilizes differential entropy (DE) and bilinear interpolation features as inputs, learning the spatial–temporal characteristics of brain regions through an MBConv-based module. By incorporating the Transformer-based multi-head self-attention mechanism, we dynamically model the dependencies between EEG and facial expression features, enabling adaptive weighting and deep interaction of cross-modal characteristics. The experiment conducted a four-classification task on the MED-HI dataset (15 subjects, 300 trials). The taxonomy included happy, sad, fear, and calmness, where ‘calmness’ corresponds to a low-arousal neutral state as defined in the MED-HI protocol. Results indicate that the proposed method achieved an average accuracy of 81.14%, significantly outperforming feature concatenation (71.02%) and decision layer fusion (69.45%). This study demonstrates the complementary nature of EEG and facial expressions in emotion recognition among hearing-impaired individuals and validates the effectiveness of feature layer interaction fusion based on attention mechanisms in enhancing emotion recognition performance. Full article

With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while maintaining occupant comfort. This critical balance between energy efficiency and human comfort has traditionally relied on rule-based and model predictive control strategies. Given the multi-objective nature and complexity of modern HVAC systems, these approaches fall short in satisfying both objectives. Recently, reinforcement learning (RL) has emerged as a method capable of learning optimal control policies directly from system interactions without requiring explicit models. However, standard RL approaches frequently violate comfort constraints during exploration, making them unsuitable for real-world deployment where occupant comfort cannot be compromised. This paper addresses two fundamental challenges in HVAC control: the difficulty of constrained optimization in RL and the challenge of defining appropriate comfort constraints across diverse conditions. We adopt a safe RL with a neural barrier certificate framework that (1) transforms the constrained HVAC problem into an unconstrained optimization and (2) constructs these certificates in a data-driven manner using neural networks, adapting to building-specific comfort patterns without manual threshold setting. This approach enables the agent to almost guarantee solutions that improve energy efficiency and ensure defined comfort limits. We validate our approach through seven experiments spanning residential and commercial buildings, from single-zone heat pump control to five-zone variable air volume (VAV) systems. Our safe RL framework achieves energy reduction compared to baseline operation while maintaining higher comfort compliance than unconstrained RL. The data-driven barrier construction discovers building-specific comfort patterns, enabling context-aware optimization impossible with fixed thresholds. While neural approximation prevents absolute safety guarantees, reducing catastrophic safety failures compared to unconstrained RL while maintaining adaptability positions this approach as a developmental bridge between RL theory and real-world building automation, though the considerable gap in both safety and energy performance relative to rule-based control indicates the method requires substantial improvement for practical deployment. Full article

Thiochromen-4-ones are known to possess useful optical properties and rich bioactivities, including antioxidant, antimicrobial, and anticancer properties. They are known to inhibit tumor cell growth, induce apoptosis, and have antiplatelet aggregation effects. Thiochromen-4-ones are also used as synthons and precursors in organic synthesis for bioactive agents. Although many synthetic approaches to oxygen-containing counterparts, chromones, have been reported, research on the synthesis of thiochromen-4-ones is scarce. The synthesis of thiochromen-4-ones can be challenging due to the inherent nature of sulfur, including its multiple oxidation states and tendency to form diverse bonding patterns. Here, we report the one-pot synthesis of thiochromen-4-ones, where two transformations of the starting material, 3-(arylthio)propanoic acid, are performed within a single reaction vessel, eliminating the need for an intermediate purification step. This one-pot reaction worked well with a variety of substrates with both electron-withdrawing and donating groups on the aromatic ring of 3-(arylthio)propanoic acids to give thiochromen-4-ones with good yields (up to 81%). This approach offers advantages like time and cost savings, increased efficiency, and reduced waste. This synthetic approach will allow access to a broader scope of thiochromen-4-ones due to the readily available thiophenols. Full article

In this work, we combine the Natural Transform and generalized-Laplace Transform into a new transform called, the Natural Generalized-Laplace Transform, (NGLT) and some of its properties are provided. Moreover, the existence condition, convolution theorem, periodic theorem, and non-constant coefficient partial derivatives are proved with some details. The (NGLT) is applied to gain the solutions of linear telegraph and partial integro-differential equations. Also, we obtained the solution of the singular one-dimensional Boussinesq equation by employing the Natural Generalized-Laplace Transform Decomposition Method, (NGLTDM). Full article

This study presents a qualitative analysis of the fractional coupled nonlinear Schrödinger equation (FCNSE) to obtain its complete set of solutions. An appropriate wave transformation is applied to reduce the FCNSE to a fourth-order dynamical system. Due to its non-Hamiltonian nature, this system poses significant analytical challenges. To overcome this complexity, the dynamical behavior is examined within a specific phase–space subspace, where the system simplifies to a two-dimensional, single-degree-of-freedom Hamiltonian system. The qualitative theory of planar dynamical systems is then employed to characterize the corresponding phase portraits. Bifurcation analysis identifies the physical parameter conditions that give rise to super-periodic, periodic, and solitary wave solutions. These solutions are derived analytically and illustrated graphically to highlight the influence of the fractional derivative order on their spatial and temporal evolution. Furthermore, when an external generalized periodic force is introduced, the model exhibits quasi-periodic behavior followed by chaotic dynamics. Both configurations are depicted through 3D and 2D phase portraits in addition to the time-series graphs. The presence of chaos is quantitatively verified by calculating the Lyapunov exponents. Numerical simulations demonstrate that the system’s behavior is highly sensitive to variations in the frequency and amplitude of the external force. Full article

Global efforts to achieve carbon neutrality increasingly rely on institutional green policy that reshape corporate environmental behavior. This study examines whether green policy improves corporate environmental performance (EP). Using panel data of the A-share listed firms from 2010 to 2022, we exploit the rollout of pilot cities as a quasi-natural experiment and apply a difference-in-differences (DID) framework, supplemented by double machine learning (DML) and robustness tests. The results show that the New Energy Demonstration City (NEDC) policy notably increases EP, with stronger effects for state-owned enterprises, large firms, and regulated industries. Mechanism analysis indicates that artificial intelligence innovation capacity and the stringency of regional environmental regulation amplify the policy’s effectiveness, revealing a “innovation–regulation” dual mechanism. By focusing on integrated EP rather than single outcomes, this paper extends the literature on green policy instruments. It demonstrates that structural policies combining fiscal incentives and regulatory constraints can correct market failures and foster long-term green transition. Beyond China, the findings provide insights for other developing economies where market-based instruments alone may be insufficient to trigger low-carbon transformation. Full article