Search Results (13,854)

Using mobile crowd sourcing/sensing (MCS) noise monitoring can lead to false sound level reporting. The methods used for recruiting mobile phones in an area of interest vary from selecting full populations to randomly selecting a single phone. Other methods apply a clustering algorithm based on spatial or noise parameters to recruit mobile phones to MCS platforms. However, statistical t tests have revealed dissimilarities between these selection methods. In this paper, we assign these dissimilarities to (1) acoustic characteristics and (2) outlier mobile phones affecting the noise level. We propose two clustering phases for noise level monitoring in MCS platforms. The approach starts by applying spatial clustering to form focused clusters and removing spatial outliers. Then, noise level clustering is applied to eliminate noise level outliers. This creates subsets of mobile phones that are used to calculate the noise level. We conducted a real-world experiment with 25 mobile phones and performed a statistical t test evaluation of the selection methodologies. The statistical values indicated dissimilarities. Then, we compared our proposed method with the noise level clustering method in terms of properly detecting and eliminating outliers. Our method offers $4\%$ to $12\%$ higher performance than the noise clustering method. Full article

Lunar-rover path planning is a key topic in lunar exploration research, with safety and computational efficiency critical for achieving long-distance planning. This paper proposes a distributed path-planning method that considers multiple lunar environmental factors, addressing the issues of inadequate safety considerations and low computational efficiency in current research. First, a set of safety evaluation rules is constructed by considering factors such as terrain slope, roughness, illumination, and rock abundance. Second, a distributed path-planning strategy based on a safety-map tile pyramid (DPPS-STP) is proposed, using a weighted A* algorithm with hash table-based open and closed lists (OC-WHT-A*) on a Spark cluster for efficient and safer path planning. Additionally, high-resolution digital orthophoto maps (DOM) are utilized for small crater detection, enabling more refined path planning built upon the overall mission-planning result. The method was validated in four lunar regions with distinct characteristics. The results show that DPPS-STP, which considers multiple environmental factors, effectively reduces the number of hazardous nodes and avoids crater obstacles. For long-distance tasks, it achieves an average speedup of up to 11.5 times compared to the single-machine OC-WHT-A*, significantly improving computational efficiency. Full article

Sensor measurements are often affected by complex ambient noise and complicating signal processing tasks. The singular spectrum decomposition (SSA) algorithm, while widely used, faces challenges such as the difficulty of determining the number of decomposition layers, requiring iterative adjustments that reduce precision and increase processing time. This paper proposes an improved adaptive singular spectrum analysis (ASSA) algorithm that integrates a deep residual network (Res-Net) for automatic recognition. A comprehensive interference signal database was constructed to train the Deep Res-Net, and common interferences were restored through the combination of different signals, enabling greater frequency resolution performance. Meanwhile, a novel correlation detection reconstruction method based on a clustering algorithm for adaptive signal classification was developed to suppress background noise and extract meaningful signals. ASSA addresses the challenge of determining the optimal number of decomposition layers, eliminating the parameter adjusting process and enhancing the measurement efficiency of sensor systems. Through experiments, magnetotelluric (MT) observation data with complex interferences were applied to demonstrate the performance of ASSA, and promising results with an RMSE of 0.2 were obtained. The experiments also showed that the accuracy of ASSA was improved by 14% compared to other signal extraction algorithms, proving that ASSA can achieve excellent results when applied to other data processing fields. Full article

To tackle the challenges arising from missing real-time measurement data and dynamic changes in network topology in optimizing and controlling distribution networks, this study proposes a data-driven collaborative optimization strategy tailored for multi-area clusters. Firstly, the distribution network is clustered based on electrical distance modularity and power balance indicators. Next, a collaborative optimization model for multiple area clusters is constructed with the objectives of minimizing node voltage deviations and active power losses. Then, a locally observable Markov decision model within the clusters is developed to characterize the relationship between the temporal operating states of the distribution network and the decision-making instructions. Using the Actor–Critic framework, the cluster agents are trained while considering the changes in cluster boundaries due to topology variations. A Critic network based on an attention encoder is designed to map the dynamically changing cluster observations to a fixed-dimensional space, enabling agents to learn control strategies under topology changes. Finally, case studies show the effectiveness and superiority of the proposed method. Full article

Individuals with high psychological resilience cope with stress more effectively. It is crucial to select a suitable psychological resilience tool for workers in high-risk industries to identify and help those with lower resilience early on, protecting their health and reducing accidents. The CD-RISC-10 is widely used, and this study assessed its validity and reliability among Chinese construction workers, focusing on workers on elevated platforms. A total of 325 valid CD-RISC-10 scales were collected and analyzed using statistical methods, such as exploratory factor analysis, confirmatory factor analysis, and K-means cluster analysis. The results show that the CD-RISC-10 can effectively measure psychological resilience with a high scale reliability of 0.857, and it had an acceptable model fit (CFI = 0.947) and good item discrimination. About 17.23% of the measured sample of Chinese workers working at height were identified as having resilience impairments, and demographic variables such as age, length of service, educational level, and accident experience had a significant impact on the level of resilience, revealing the heterogeneity of the workers. This study validated the measurement validity of the CD-RISC-10 scale among Chinese high-place workers, and the analysis results were conducive to conducting psychological resilience assessments, improving workers’ occupational health, and promoting the sustainable development of construction enterprises. Full article

With the rapid development of geographic information technology, the expression of topographical spatial semantic relationships has become a research hotspot in the field of intelligent geographic information systems. Geographical spatial semantic relationships refer to the spatial relationships and inherent meanings between geographical entities, including topological relationships, metric relationships, etc. This study proposes a novel method of viewshed analysis, which solves the limitation of treating the viewshed as a unified unit in traditional viewshed analysis by decomposing the viewshed into multiple viewsheds and quantifying their spatial semantic relationships. The method uses a DBSCAN clustering algorithm with terrain adaptability to divide a viewshed into spatially different viewsheds and characterizes these viewsheds through a systematic measurement framework, including azimuth, area, and sparsity. The method was applied to a case study of Purple Mountain in Nanjing. The experiment used 12.5 m accuracy topographic data from Purple Mountain, and two observation points were selected. For the first observation point near the mountain park, during the DBSCAN clustering partition of the viewshed, the number of clusters and the number of noise points were compared with determine the neighborhood radius of 18 m and the minimum sample point number of 4. Five viewsheds were successfully generated, with the largest viewshed having 468 visible points and the smallest only 16, located in different locations from the observer, reflecting the spatial variability of terrain features. All viewsheds are basically distributed to the north of the observer, two of which also share the northeast 87° direction with the observer in a straight line distribution but at different distances. In three-dimensional space, the distance between the two viewsheds is 317.298 m. Azimuth angle verification showed significant aggregation in the northeast direction. The second point is near the ridgeline, where one viewshed accounts for 87.52% of the total viewshed, showing significant visual effects. One viewshed is 3121.113 m away from the observer, with only 113 visible points, and is not located at a low altitude, so it is suitable for a long-distance fixed-point intermittent observation. The experimental results of the two observation points reveal the directional dominance and distance stratification of viewshed spatial relationships. This paper proposes a model to express topographical viewshed spatial relationships. The model analyzes and describes the spatial features of the viewshed through quantitative and qualitative methods. These metric features provide a basis for constructing spatial topological relationships between observation points and viewsheds, helping optimize viewpoint selection and enhance landscape planning. Compared with traditional methods, the proposed method significantly improves the resolution of spatial semantic relationship expression and has practical application value in fields such as archaeology, tourism planning, and urban design. Full article

Background/Objectives: We aimed to explore long-term trajectories of thyroid-stimulating hormone receptor antibody (TRAb) in patients with Graves’ disease (GD) and to identify key factors associated with TRAb normalization. We also investigated whether these trajectories correlate with Graves’ orbitopathy (GO) comorbidity. Methods: We retrospectively reviewed 403 patients with GD who had an initial TRAb level ≥ 1.5 IU/L between 2010 and 2021, monitoring their TRAb levels for at least 3 years. K-means clustering was performed to categorize patients into distinct TRAb change patterns (A, B, C, D). We employed a Cox regression–based time-to-event model, expressing results as “Survival ratio” rather than the conventional Hazard ratio, to reflect the proportion of patients achieving TRAb normalization over time. Key variables included age, sex, initial TRAb, and GO comorbidity. Results: Four unique TRAb patterns emerged, differing primarily in baseline TRAb levels, duration of GD, and treatment approaches. Pattern A demonstrated the highest TRAb normalization rate (96%), whereas Patterns B (80%), C (29%), and D (13%) showed lower probabilities. Regrouping into A vs. BCD further emphasized the distinct normalization profile of Pattern A. A higher “Survival ratio” was observed in female patients and those with baseline TRAb < 6.14 IU/L. In contrast, patients whose TRAb levels were ≥6.14 IU/L frequently exhibited persistently elevated values over a decade. GO comorbidity did not significantly differ among the four patterns. Conclusions: K-means clustering revealed four unique TRAb change patterns in GD, with baseline TRAb (stratified by the median of 6.14 IU/L) and sex emerging as significant predictors of normalization. These findings highlight the importance of early TRAb monitoring and tailored therapeutic strategies, particularly for those with persistently elevated TRAb levels. Full article

The identification of the multifunctional combination of production–living–ecological spaces (PLES) in urban agglomerations, particularly in urban cores and peri–urban areas, is a critical issue in the urbanization process. This study, using the Hohhot–Baotou–Ordos–Yulin (HBOY) urban agglomeration, a key node in China’s “Two Horizontals and Three Verticals” urbanization strategy, proposes a hexagonal grid–based PLES quantification framework using POI data. A three–level POI classification system was developed, with functional element weights determined via the Analytic Hierarchy Process and public perception surveys. The framework quantifies PLES within hexagonal grids and analyzes its patterns and functional coupling mechanisms using spatial overlay, Average Nearest Neighbor Index (ANNI), kernel density analysis, and spatial autocorrelation analysis. The following results were obtained. (1) PLES classification accuracy reached 90.83%, confirming the reliability of the method. (2) The HBOY urban agglomeration exhibits a dominant production space (40.84%), balanced living and ecological spaces (29.37% and 29.36%, respectively), and a severe shortage of mixed spaces (0.43%). (3) Production and living spaces show significant clustering ($ANNI$ ≤ 0.581), mixed spaces follow ($ANNI$ = 0.660), and ecological spaces are relatively evenly distributed ($ANNI$ = 0.870). (4) The spatial distribution patterns show that production and living spaces exhibit “core concentration with peripheral dispersion”, ecological spaces show “block concentration with point–like distribution”, and mixed spaces show “point–like dispersion”. (5) Production and living spaces exhibit strong spatial autocorrelation ($Moran’s I$ > 0.7) and the highest spatial correlation ($Bivariate Moran’s I$ = 0.692), while the spatial correlation with ecological spaces is weakest ($Bivariate Moran’s I$ = 0.150). The proposed PLES identification framework, with its efficiency and dynamic updating potential, provides an innovative approach to urban spatial governance under the global Sustainable Development Goals. The findings offer integrated decision–making support for spatial diagnosis and functional regulation in the ecologically vulnerable areas of northwest China’s new urbanization. Full article

The research of revenue management (RM) practices is widespread in the accommodation sector, but not in the restaurant industry. This study aims to ascertain which RM practices are the most used in the restaurant industry, organizing them by clusters, identifying those that imply profit maximization and describing the challenges of their implementation. Mixed methods were used as the methodology through a systematic literature review, which was submitted to a brief descriptive analysis and content analysis. Data were retrieved from the Scopus database, and, using the PRISMA diagram, 70 papers were collected for comprehensive analysis of their content. The results of the studies identified five main areas of RM and 21 practices, some specific to the restaurant industry, with reservations and meal duration management being the most used practices. Reservations have been implemented in many restaurants but are not a reality for all of them. A well-managed meal duration increases restaurant capacity. Furthermore, customer satisfaction implies the success of all other practices since customers must understand and accept the RM practices for their success. As a theoretical implication, this study contributes to the development of research into the RM practices of restaurants, and as practical implications, restaurant managers should implement the following practices: meal duration management, indicators, and table mix. This study contributes to future research, such as analyzing the relationship between sustainability and RM, applying RM to the beverages department, and including RM in consumer behavior in the context of future crises. Full article

Inland lakes and reservoirs are critical components of global freshwater resources. However, traditional water level monitoring stations are costly to establish and maintain, particularly in remote areas. As an alternative, satellite altimetry has become a key tool for lake water level monitoring. Nevertheless, conventional radar altimetry techniques face accuracy limitations when monitoring small water bodies. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), equipped with a single-photon counting lidar system, offers enhanced precision and a smaller ground footprint, making it more suitable for small-scale water body monitoring. However, the water level data obtained from the ICESat-2 ATL13 inland water surface height product are limited in quantity, while the lake water level accuracy derived from the ATL08 product is relatively low. To overcome these challenges, this study proposes a Spatial Distribution-Based Hierarchical Clustering for Photon-Counting Laser altimeter (SD-HCPLA) for enhanced water level extraction, validated through experiments conducted at the Danjiangkou Reservoir. The proposed method first employs Landsat 8/9 imagery and the Normalized Difference Water Index (NDWI) to generate a water mask, which is then used to filter ATL03 photon data within the water body boundaries. Subsequently, a Minimum Spanning Tree (MST) is constructed by traversing all photon points, where the vertical distance between adjacent photons replaces the traditional Euclidean distance as the edge length, thereby facilitating the clustering and denoising of the point cloud data. The SD-HCPLA algorithm successfully obtained 41 days of valid water level data for the Danjiangkou Reservoir, achieving a correlation coefficient of 0.99 and an average error of 0.14 m. Compared with ATL08 and ATL13, the SD-HCPLA method yields higher data availability and improved accuracy in water level estimation. Furthermore, the proposed algorithm was applied to extract water level data for five lakes and reservoirs in Hubei Province from 2018 to 2023. The temporal variations and inter-correlations of water levels were analyzed, providing valuable insights for regional ecological environment monitoring and water resource management. Full article

Background: Cattle production is a cornerstone of U.S. agriculture but faces increasing pressure to balance profitability with environmental sustainability. Optimizing the ruminal microbiome to enhance feed efficiency could help address both challenges. Members of the Streptococcus bovis/Streptococcus equinus complex (SBSEC) are key contributors to ruminal acidosis and related digestive disorders due to their role in carbohydrate fermentation and lactic acid production. Bacteriophages targeting this bacterial group present a promising approach to mitigate this problem with high precision and without promoting the spread of antibiotic resistance. Methods: A collection of SBSEC-targeting bacteriophages were isolated from cattle rumen fluid and feces and further characterized. Characterization included host-range evaluation, whole genome sequencing, and growth inhibition assessment via optical density measurements. Selected bacteriophages underwent training to enhance infectivity. Results: Eleven lytic and one lysogenic phage were isolated. Several phages demonstrated sustained bacterial growth suppression, showing efficacy against SBSEC bacteria from diverse sources despite narrow host ranges. Co-evolutionary training was done in a subset of phages to improve bacteriolytic activity but had an inconsistent effect on the ability of phages to inhibit the growth of their naïve host. Genomic sequencing and phylogenetic analysis revealed uniqueness and clustering into three distinct groups that matched phenotypic characteristics. Conclusions: This study demonstrates the potential of bacteriophages as precise biological control agents, with successful isolation and enhancement of phages targeting SBSEC bacteria. Eleven lytic genome-sequenced phages show promise for development as cattle feed additives, though further research is needed to optimize their application in agricultural settings. Full article

Educational AI systems often do not employ proper sophistication techniques to enhance learner interactions, organize their contextual knowledge or even deliver personalized feedback. To address this gap, this paper seeks to reform the way ChatGPT supports learners by employing fuzzy memory retention and thematic clustering. To achieve this, three modules have been developed: (a) the Fuzzy Memory Module which models human memory retention using time decay fuzzy weights to assign relevance to user interactions, (b) the Schema Manager which then organizes these prioritized interactions into thematic clusters for structured contextual representation, and (c) the Response Generator which uses the output of the other two modules to provide feedback to ChatGPT by synthesizing personalized responses. The synergy of these three modules is a novel approach to intelligent and AI tutoring that enhances the output of ChatGPT to learners for a more personalized learning experience. The system was evaluated by 120 undergraduate students in the course of Java programming, and the results are very promising, showing memory retrieval accuracy, schema relevance and personalized response quality. The results also show the system outperforms traditional methods in delivering adaptive and contextually enriched educational feedback. Full article

In this paper, we investigate the evolution characteristics of floor failure during pressured mining in extra-thick coal seams. A mechanical expression relating floor failure depth to seam thickness is established based on soil mechanics and mine pressure theory. The findings reveal a linear relationship between seam thickness and floor failure depth; specifically, as the coal seam thickens, the depth of floor failure increases. To simulate the mining process of extra-thick coal seams, FLAC3D numerical simulation software is utilized. We analyze the failure process, failure depth, and the behavior of water barriers at the coal seam floor under the influence of extra-thick coal seam mining from three perspectives: rock displacement evolution in the floor, stress evolution in the floor, and plastic deformation. Based on geological characteristics observed in the Longwanggou mine field, we establish a main control index system for assessing floor water-inrush risk. This system comprises 11 primary control factors: water abundance, permeability, water pressure, complexity of geological structure, structural intersection points, thickness of both actual and equivalent water barriers, thickness ratio of brittle–plastic rocks to coal seams, as well as depths related to both coal seams and instances of floor failure. Furthermore, drawing upon grey system theory and fuzzy mathematics within uncertainty mathematics frameworks leads us to propose an innovative approach—the interval grey optimal clustering model—designed specifically for risk assessment concerning potential floor water inrush during pressured mining operations involving extra-thick coal seams. This method of mine water inrush risk assessment is applicable for popularization and implementation in mines with analogous conditions, and it holds practical significance for the prevention of mine water damage. Full article

In wireless sensor networks, sensors often collect and transmit a large amount of redundant data, which can lead to excessive battery consumption and subsequent performance degradation. To solve this problem, this paper proposes a Zoom-In Zoom-Out (ZIZO) method based on sparse vectors (SV-ZIZO). It operates in two parts: At the sensor level, given the temporal similarity of the data, a new compression method based on the sparse vector representation of segmented regions is proposed. This method can not only effectively ensure the compression ratio but also improve the accuracy of data restoration. At the cluster-head (CH) level, by utilizing the spatial similarity of the data, the fuzzy clustering theory is introduced to put some sensors into hibernation mode, thereby reducing data transmission. Meanwhile, the sampling frequency of the sensors is dynamically adjusted by calculating the redundancy rate of the collected periodic data. The experimental results show that compared with other existing methods, the algorithm proposed in this paper increases the data compression ratio by 21.8% and can reduce energy consumption by up to 95%. Full article

As an efficient and economical method to enhance oil recovery (EOR), it is very important to explore the applicability of chemical flooding under harsh reservoir conditions, such as high temperature and high salinity. We designed microscopic visualization oil displacement experiments to comprehensively evaluate the oil displacement performance of the zwitterionic surfactant betaine (BSB), a temperature- and salinity-resistant hydrophobically modified polymer (BHR), and surfactant–polymer (SP) binary systems. Based on macroscopic properties and microscopic oil displacement effects, we confirmed that the BSB/BHR binary solution has the potential to synergistically improve oil displacement efficiency and quantified the reduction in residual oil and oil displacement efficiency within the swept range. The experimental results show that after water flooding, a large amount of residual oil remains in the porous media in the form of clusters, porous structures, and columnar formations. After water flooding, only slight emulsification occurred after the injection of BSB solution, and the residual oil could not be activated. The injection of polymer after water flooding can expand the swept range to a certain extent. However, the distribution of residual oil in the swept range is similar to that of water flooding, and the oil washing efficiency is low. The SP binary flooding process can expand sweep coverage and effectively decompose large oil clusters simultaneously. This enhances the oil washing efficiency within the swept area and can significantly improve oil recovery. Finally, we obtained the microscopic oil displacement mechanism of BSB/BHR binary system to synergistically increase the swept volume and effectively activate the residual oil after water flooding. It is the result of the combined action of low interfacial tension (IFT) and suitable bulk viscosity. These findings provide critical insights for optimizing chemical flooding strategies in high-temperature and high-salinity reservoirs, significantly advancing EOR applications in harsh environments. Full article