Square-lattice s=1/2 XY model and the Jordan-Wigner fermions: The ground-state and thermodynamic properties

Using the 2D Jordan-Wigner transformation we reformulate the square-lattice s=1/2 XY (XZ) model in terms of noninteracting spinless fermions and examine the ground-state and thermodynamic properties of this spin system. We consider the model with two types of anisotropy: the spatial anisotropy interpolating between 2D and 1D lattices and the anisotropy of the exchange interaction interpolating between isotropic XY and Ising interactions. We compare the obtained (approximate) results with exact ones (1D limit, square-lattice Ising model) and other approximate ones (linear spin-wave theory and exact diagonalization data for finite lattices of up to N=36 sites supplemented by finite-size scaling). We discuss the ground-state and thermodynamic properties in dependence on the spatial and exchange interaction anisotropies. We pay special attention to the quantum phase transition driven by the exchange interaction anisotropy as well as to the appearance/disappearance of the zero-temperature magnetization in the quasi-1D limit.Comment: 28 pages, 7 figures include

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception

Model pre-training is essential in human-centric perception. In this paper, we first introduce masked image modeling (MIM) as a pre-training approach for this task. Upon revisiting the MIM training strategy, we reveal that human structure priors offer significant potential. Motivated by this insight, we further incorporate an intuitive human structure prior - human parts - into pre-training. Specifically, we employ this prior to guide the mask sampling process. Image patches, corresponding to human part regions, have high priority to be masked out. This encourages the model to concentrate more on body structure information during pre-training, yielding substantial benefits across a range of human-centric perception tasks. To further capture human characteristics, we propose a structure-invariant alignment loss that enforces different masked views, guided by the human part prior, to be closely aligned for the same image. We term the entire method as HAP. HAP simply uses a plain ViT as the encoder yet establishes new state-of-the-art performance on 11 human-centric benchmarks, and on-par result on one dataset. For example, HAP achieves 78.1% mAP on MSMT17 for person re-identification, 86.54% mA on PA-100K for pedestrian attribute recognition, 78.2% AP on MS COCO for 2D pose estimation, and 56.0 PA-MPJPE on 3DPW for 3D pose and shape estimation.Comment: Accepted by NeurIPS 202

Markup in Engineering Design: A Discourse

Today’s engineering companies are facing unprecedented competition in a global market place. There is now a knowledge intensive shift towards whole product lifecycle support, and collaborative environments. It has become particularly important to capture information, knowledge and experiences about previous design and following stages during their product lifecycle, so as to retrieve and reuse such information in new and follow-on designs activities. Recently, with the rapid development and adoption of digital technologies, annotation and markup are becoming important tools for information communication, retrieval and management. Such techniques are being increasingly applied to an array of applications and different digital items, such as text documents, 2D images and 3D models. This paper presents a state-of-the-art review of recent research in markup for engineering design, including a number of core markup languages and main markup strategies. Their applications and future utilization in engineering design, including multi-viewpoint of product models, capture of information and rationale across the whole product lifecycle, integration of engineering design processes, and engineering document management, are comprehensively discussed

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Study on Adsorption Performance of Lignite Activated Carbon for Coal Gasification Wastewater

Abstract In order to ensure the excellent adsorption performance, this paper choosed lignite activated carbon to study the adsorption performance of typical pollutants phenol, indole and quinoline in coal gasification wastewater, and analyzed its adsorption performance under single matrix and co-substrate conditions. The experimental results showed that among the three kinds of organic substances, lignite activated coke has the strongest adsorption capacity for phenol, while the adsorption capacity for quinoline was relatively low. Under mixed matrix conditions, activated coke has strong adsorption selectivity for phenol and indole.</jats:p

Mechanism of Influence of Spatial Perception on Residents’ Emotion in Child-Friendly Urban Streets of Fuzhou City

ObjectiveAmid China’s strategic push for child-friendly urbanization and its evolving demographic policies, this research explores how urban street environments affect children’s emotional well-being. Focusing on Fuzhou, a national pilot city for child-friendly initiatives, the research addresses a critical gap in urban planning literature: The lack of empirical evidence linking micro-scale street design to the emotional dynamics of children and their caregivers. Existing research primarily prioritizes physical safety and functional infrastructure, while often neglecting the psychosocial dimensions of urban spaces, such as how sensory stimuli, spatial aesthetics, and perceived safety collectively influence residents’ daily emotional states. By examining interactions between street environment elements and residents’ emotional responses, this research aims to generate actionable insights for creating emotionally supportive urban environments that align with China’s child-friendly urbanization goals.MethodsThe research employs a multi-modal analytical framework integrating geospatial data, machine learning, and participatory scoring. Data sources include 53,771 Baidu Street View images, 1,474 social media texts (from platforms like Weibo and government portals), and human − machine adversarial scores derived from 40 children − caregiver dyads evaluating street safety perceptions. Three machine learning architectures are deployed: CNN-BiLSTM Hybrid Model, FCN-RF Semantic Segmentation, and XGBoost-SHAP Interpretability Framework. For FCN-RF Semantic Segmentation, street view images are processed by fully convolutional networks to quantify 10 spatial metrics, validated against human-scored safety perceptions via random forest-based adversarial training; for XGBoost-SHAP Interpretability Framework, the nonlinear relationships between 12 street environment indicators and emotional indices are modeled through extreme gradient boosting, with SHapley additive explanations (SHAP) decoding feature contributions and interaction effects. This combination of methods enables detailed analysis of how spatial metrics and perceptions shape emotions.ResultsKey findings highlight the nonlinear effects of street environment elements on residents’ emotion. Traffic flow: Moderate traffic flow enhances urban vitality, but excessive traffic flow leads to negative emotion due to noise and safety concerns. SHAP analysis reveals a threshold effect, whereby emotion scores peak and then decline at a given traffic flow: Balanced visual stimuli promote positive emotion, while overly cluttered or monotonous streetscapes reduce emotional satisfaction. Areas such as Academy Road in Gulou District are optimized for visual diversity and exhibit higher emotion scores. Higher safety scores enhance positive emotion, especially in areas with adequate lighting, visible safety facilities, and caregiver-friendly infrastructure. However, poorly maintained security facilities reduce emotional benefits, despite high design scores. For example, in terms of guardrail density, guardrails improve emotion in high-traffic areas, but may create unwelcoming environments that are overly safe in recreational areas, suggesting a dependence on environmental influence. Spatial analysis finds that clusters of low-emotion areas are associated with fragmented pedestrian networks, insufficient green space, and mismatched security measures. Notably, child-friendly renovations in Fuzhou perform poor emotionally due to disjointed maintenance and environmental mismatches, emphasizing the need for adaptive design strategies. In view of this, a three-level optimization path of “traffic control (base layer) — safety creation (middle layer) — spatial quality (enhancement layer)” is proposed.ConclusionThis research advances child-friendly urban planning by street spatial perceptions to residents’ emotional outcomes. Methodologically, the research demonstrates the efficacy of combining machine learning (CNN-BiLSTM, XGBoost) with participatory human − machine scoring. Key practical implications include prioritizing traffic calming measures near schools and residential areas, balancing visual complexity through context-sensitive landscaping to avoid sensory overload or monotony, ensuring that safety infrastructure is supplemented by regular maintenance and caregiver-centered amenities, and employing adaptive fencing strategies that are consistent with spatial functions. Although limited by data granularity and area specificity, this research highlights the importance of embedding sentiment analysis into urban governance. Machine learning and SHAP methodology provide nuanced analysis of how urban environments impact residents’ emotions. These methods not only expand the data base for research on the built environment of child-friendly urban streets, but also validate the feasibility of multi-source fusion of subjective perception data and built environment data in emotion perception measurement, providing an effective methodological reference for the field of spatial research on child-friendly city streets. The present research has made important progress, but there are still limitations in data sources and methods of analyzing residents’ emotions. Future research should expand the diversity of data and refine sentiment recognition models to address cultural and environmental variability. By combining spatial indicators with emotional experiences, this research may contribute to the creation of inclusive, resilient and emotionally supportive child-friendly cities that prioritize safety and well-being

Dynamic Monitoring of Soil Salinization in Oasis Regions Using Spatiotemporal Fusion Algorithms

Accurate dynamic monitoring of soil salinization in arid oasis regions is crucial for sustainable regional development. Remote sensing is widely used for large-scale, long-term monitoring, but its effectiveness is often limited by image quality and spatiotemporal resolution. Spatiotemporal fusion algorithms, due to their low cost and accessibility, are frequently applied to generate missing images. However, the applicability of these fused images for soil salinization inversion, the impact of different fusion strategies on image quality, and the potential for using multiple fused images to improve model accuracy remain unclear. This study evaluates the performance of three typical spatiotemporal fusion algorithms on raw spectral bands and compares two fusion strategies: fusion-then-index (FI) and index-then-fusion (IF), for two vegetation indices (NDVI and EVI) and two salinity indices (SI and SI2) related to soil salinization. Additionally, the inclusion of multiple fused images during the sampling period is examined for its effect on model accuracy. The results show that (1) spatiotemporal fusion images are suitable for soil salinization inversion, with accuracy depending on image quality; (2) for vegetation indices (NDVI and EVI), the IF strategy yields better results, while for salinity indices (SI and SI2), the FI strategy is more effective; and (3) combining multi-year and multiple fused images significantly improves model accuracy, though using fused images as auxiliary datasets or variables does not further enhance accuracy. These findings provide valuable insights for large-scale, long-term monitoring of soil salinization in arid regions

Friction and wear behaviors of polyamide-based composites blended with polyphenylene sulfide

Polymer blending has attracted wide concern for modification of polymer materials especially in the tribological field. In this article, the structure, mechanical and tribological properties were investigated carefully for the polyamide-6-based composites blended with polyphenylene sulfide (PA6-PPS). It was found that the composites were partially miscible, and the modulus of PA6 was improved apparently with the addition of PPS. The average friction coefficient of PA6-PPS composites decreased as the content of PPS increased at the stable stage. The friction coefficients and wear rate of the PA6-PPS composites increased under higher load, while the friction coefficient decreased and the wear rate increased at higher sliding speed. Besides, scanning electron micrograph of worn surface morphology revealed the primary wear mechanism of PA6-PPS composites gradually transformed from the micro-cutting wear to adhesive wear and abrasive wear. </jats:p