Search Results (10)

The prevailing challenges of fading characteristics and identity crises in historical and cultural districts of small and medium-sized cities have been identified. Traditional analytical methods have been found to be deficient in systematically capturing the unique forms and urban memory of these districts. The present study thus adopts the Shangcheng Historical and Cultural District of Huangling County as a case study, proposing a comprehensive analytical framework that integrates urban memory and multi-dimensional methods such as space syntax, grounded-theory-inspired coding, and urban image analysis. The district is subject to a systematic assessment of its spatial form, structural design, and the mechanisms by which urban memory is conveyed. The proposal sets out targeted renewal strategies for four aspects: paths, edges, nodes and landmarks, and districts. The research findings are as follows: (1) Paths with high integration and connection degrees simultaneously serve as both sacrificial axes and carriers of folk narratives. (2) Edges are composed of the city wall ruins, Loess Plateau landform, and street spaces. The fishbone-like street structure leads to significant differences in the connection degrees of main and secondary roads. (3) Nodes such as Guanyv Temple-Confucian Temple, the South Gate, and the North City Wall Ruins Square have high visual control, while the visual integration and visual control of the Qiaoshan Middle School and Gongsun Road historical nodes are relatively low, and their spatial accessibility is insufficient. (4) Based on the “memory–space” coupling relationship, the district is divided into the Academy Life Area, the Historical and Cultural Core Experience Area, and the Comprehensive Service Area, providing an effective path to alleviate the problem of functional homogenization. The present study proffers a novel perspective on the revitalization mechanisms of historical districts in small and medium-sized cities, encompassing both theoretical integration and practical strategy levels. It further contributes methodological inspirations and localized planning experiences for addressing the cultural disconnection and spatial inactivity problems of historical urban areas on a global scale. Full article

Facing the challenges of temperature gradient-induced soil cracking and delamination in earthen ruins like Gaochang Ancient City, this study develops a novel phase change microcapsule (MPCM) grouting material designed for dual functionality: structural reinforcement and thermal regulation. We systematically evaluated its performance in both aspects. Experimental results demonstrated excellent thermal cycling stability. For instance, the maximum mass loss was only 0.65% after 200 cycles, indicating its reliability for long-term service. Evaluation showed that the material effectively provides structural strength compatible with the original soil. With 15% MPCM content, the compressive strength reached 1.39 MPa. Simultaneously, it effectively mitigates temperature fluctuations, significantly regulating temperature gradients. The MPCM-15 sample, for example, reduced the heating rate by 9.7 °C/h and peak temperature by 6.0 °C compared to the control group. Field application further validated its effectiveness in both restoring structural integrity by filling cracks and significantly reducing temperature gradient effects within the site. This dual-function MPCM grouting offers a promising new technical approach for the sustainable preservation of earthen cultural heritage. Full article

Viewing high-redshift sources at near-opposite directions on the sky can ensure, using light-travel-time arguments, acausality between their emitted photons. One utility would be true random-number generation through sensing these via two independent telescopes that each flip a switch based on the latest-arrived colours; for example, to autonomously control a quantum-mechanical (QM) experiment. Although demonstrated with distant quasars, those were not fully acausal pairs, which are restricted when simultaneously viewed from the ground at any single observatory. In optical light, such faint sources also require a large telescope aperture to avoid sampling assumptions when imaged at fast camera framerates: unsensed intrinsic correlations between them or equivalently correlated noise may ruin the expectation of pure randomness. One such case that could spoil a QM test is considered. Based on that, the allowed geometries and instrumental limits are modelled for any two ground-based sites, and their data are simulated. For comparison, an analysis of photometry from the Gemini twin 8 m telescopes is presented using the archival data of well-separated bright stars obtained with the instruments ‘Alopeke (on Gemini North in Hawai’i) and Zorro (on Gemini-South in Chile) simultaneously in two bands (centred at $562\mathrm{nm}$ and $832\mathrm{nm}$) with 17 Hz framerate. No flux correlation is found; these results were used to calibrate an analytic model predicting where a search with a signal-to-noise over 50 at 50 Hz can be made using the same instrumentation. Finally, the software PDQ (Predict Different Quasars) is presented, which searches a large catalogue of known quasars, reporting those with a brightness and visibility suitable to verify acausal, uncorrelated photons at these limits. Full article

We consider a two-dimensional risk model with simultaneous Poisson arrivals of claims. Each claim of the first input process is at least as large as the corresponding claim of the second input process. In addition, the two net cumulative claim processes share a common Brownian motion component. For this model we determine the Gerber–Shiu metrics, covering the probability of ruin of each of the two reserve processes before an exponentially distributed time along with the ruin times and the undershoots and overshoots at ruin. Full article

Edible flowers, including roselle, contain a varied composition of phenolic compounds that may inhibit the oxidative stress mechanism. Roselle-based tisane with appealing sensory properties is commonly consumed worldwide. However, the conventional hot-brew method may ruin the stability of thermolabile phenolic compounds during the tisane preparation. Hence, this study aimed to develop a new alternative brewing method linked with the new cold-brew method, which involves a lower temperature and applying ultrasound to maximize the extraction of phenolic compounds and to avoid degradation during the tisane preparation. The brewing factors, including particle size (10, 20, 30 mesh), temperature (4, 15, 26 °C), time (10, 20, 30 min), and ultrasound amplitude (20, 60, 100% of the maximum amplitude) have been optimized simultaneously using Box–Behnken design in conjunction with response surface methodology. Seven major phenolic compounds were identified by HPLC-DAD and classified into hydroxycinnamic acid derivatives (HCA) and flavonoids. The optimum extraction condition to reach the highest level of the studied phenolic compounds was set to brew roselle with particle size of 30 ± 3.25 mesh at 26 ± 1.32 °C for 18 ± 2.00 min applying 78 ± 5.64% ultrasound amplitude. This method successfully extracted almost all HCA and flavonoid during the first cycle with less than 10% CV and provided higher antioxidant activity in terms of DPPH (IC₅₀ 9.77 ± 0.01 µg mL⁻¹), ABTS (IC₅₀ 8.05 ± 0.02 µg mL⁻¹), and FRAP (IC₅₀ 10.34 ± 0.03 µg mL⁻¹) than the roselle tisane prepared using the conventional method. Additionally, the resulting cold-brew product was stable for up to five days of storage. Full article

Macao is a tourist city. It is home to the Ruins of Saint Paul’s, a unique 100-year-old landmark, which is still standing with manual maintenance, even after three fires and reconstruction events. Therefore, the continuous preservation of its culture, heritage education, and construction are important issues for Macao. With the development of digital technology in recent years, users can quickly search historical sites and save two-dimensional and three-dimensional images and videos through smartphones. These methods also enhance the communication power of culture. Virtual browsing on a smartphone requires computing power and storage space; yet, virtual reality devices are not widely used. Therefore, augmented reality and virtual reality are rarely used simultaneously for three-dimensional interactive guided tours and operation experiences on the same theme. However, by quickly creating virtual reality scenarios and preserving historical sites on mobile devices, 4DAGE’s 4DKanKan technology can provide augmented reality and metaverse virtual reality experiences. 4DKanKan can also integrate mobile guides and navigation software to connect mobile devices and assist in cultural inheritance and conduct sustainable education. This research linked this technology to the web by incorporating augmented reality and virtual reality technology to make designs and discussed the influences among service design, behavioral intentions, and learning effects. We collated and analyzed relevant data and text materials through systematic testing, observation, operation processes, and semi-structured interviews. The PLS multigroup structural model was used to explore and analyze the degree of influence and explanatory power of system quality, information quality, behavioral intention, and learning effects among themselves. The results of this study show that most users accepted the proposed innovative mode of operation and found it to be interesting and fun. Augmented reality is not limited by space or time; however, virtual reality devices taking too long to operate, switching too frequently, and having too many functional interfaces can cause operational problems. This study identified and modified the influencing factors and problems of the proposed system, with the aim of continuing to expand the applications of 4DKanKan to other cultural attractions or museums in the future. In addition, the research results can provide a reference for the sustainable development of related cultural sites. Full article

Visual pollution (VP) is the deterioration or disruption of natural and man-made landscapes that ruins the aesthetic appeal of an area. It also refers to physical elements that limit the movability of people on public roads, such as excavation barriers, potholes, and dilapidated sidewalks. In this paper, an end-to-end visual pollution prediction (VPP) framework based on a deep active learning (DAL) approach is proposed to simultaneously detect and classify visual pollutants from whole public road images. The proposed framework is architected around the following steps: real VP dataset collection, pre-processing, a DAL approach for automatic data annotation, data splitting as well as augmentation, and simultaneous VP detection and classification. This framework is designed to predict VP localization and classify it into three categories: excavation barriers, potholes, and dilapidated sidewalks. A real dataset with 34,460 VP images was collected from various regions across the Kingdom of Saudi Arabia (KSA) via the Ministry of Municipal and Rural Affairs and Housing (MOMRAH), and this was used to develop and fine-tune the proposed artificial intelligence (AI) framework via the use of five AI predictors: MobileNetSSDv2, EfficientDet, Faster RCNN, Detectron2, and YOLO. The proposed VPP-based YOLO framework outperforms competitor AI predictors with superior prediction performance at 89% precision, 88% recall, 89% F1-score, and 93% mAP. The DAL approach plays a crucial role in automatically annotating the VP images and supporting the VPP framework to improve prediction performance by 18% precision, 27% recall, and 25% mAP. The proposed VPP framework is able to simultaneously detect and classify distinct visual pollutants from annotated images via the DAL strategy. This technique is applicable for real-time monitoring applications. Full article

We consider the optimal dividend problem in the so-called degenerate bivariate risk model under the assumption that the surplus of one branch may become negative. More specific, we solve the stochastic control problem of maximizing discounted dividends until simultaneous ruin of both branches of an insurance company by showing that the optimal value function satisfies a certain Hamilton–Jacobi–Bellman (HJB) equation. Further, we prove that the optimal value function is the smallest viscosity solution of said HJB equation, satisfying certain growth conditions. Under some additional assumptions, we show that the optimal strategy lies within a certain subclass of all admissible strategies and reduce the two-dimensional control problem to a one-dimensional one. The results are illustrated by a numerical example and Monte Carlo simulated value functions. Full article

Cultural heritage conservation is a constant process of preserving the valuable historical legacy and transferring it to future generations. The ability to adapt the matter under conservation to changing needs and environmental conditions is an essential element of this process. In this context, climate change and its consequences are a growing challenge, requiring innovative and often simultaneous efforts. This study was conducted in response to the discovery of previously unknown documents on nineteenth-century impoundment structures of the Krakow Fortress’s defensive system. At present, the facilities are almost entirely ruined, yet the need to restore and preserve the memory of their culturally valuable legacy merits investigation. The conditions and requirements of the management of Krakow’s changing hydrological environment became a vital component of this study. The uncovered archival documents were subjected to historical-interpretative analysis. Virtual modeling contributed to identifying the original scope of the dams’ impact. Analysis of the city’s spatial planning documents pointed to their contemporary potential. The entirety of the material collected aided in determining the framework in which protective measures targeting this dying heritage are currently possible. This study features a proposal for a new form of recreating the structures under investigation by assigning them a range of possible simultaneous uses. Thus, the presented research proposal is a form of concern for preserving this historical legacy and an attempt at rising to contemporary challenges posed by an intensively changing environment. Full article

The ruin probability is used to determine the overall operating risk of an insurance company. Modeling risks through the characteristics of the historical data of an insurance business, such as premium income, dividends and reinvestments, can usually produce an integral differential equation that is satisfied by the ruin probability. However, the distribution function of the claim inter-arrival times is more complicated, which makes it difficult to find an analytical solution of the ruin probability. Therefore, based on the principles of artificial intelligence and machine learning, we propose a novel numerical method for solving the ruin probability equation. The initial asset $u$ is used as the input vector and the ruin probability as the only output. A trigonometric exponential function is proposed as the projection mapping in the hidden layer, then a block trigonometric exponential neural network (BTENN) model with a symmetrical structure is established. Trial solution is set to meet the initial value condition, simultaneously, connection weights are optimized by solving a linear system using the extreme learning machine (ELM) algorithm. Three numerical experiments were carried out by Python. The results show that the BTENN model can obtain the approximate solution of the ruin probability under the classical risk model and the Erlang(2) risk model at any time point. Comparing with existing methods such as Legendre neural networks (LNN) and trigonometric neural networks (TNN), the proposed BTENN model has a higher stability and lower deviation, which proves that it is feasible and superior to use a BTENN model to estimate the ruin probability. Full article