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Digital transformation (DT) has emerged as a key strategic lever for enhancing firm resilience and competitiveness, yet its influence on non-productive investment behaviors, such as corporate financial investment, remains underexplored. Existing studies have largely focused on DT’s role in innovation and operational efficiency, leaving a significant gap in understanding how DT reshapes firms’ financial asset allocation. Drawing on a unique panel dataset of A-share main board-listed firms in China from 2011 to 2023, this study provides novel empirical evidence that DT significantly restrains financial investment, with pronounced heterogeneity across ownership types. More importantly, this paper uncovers a multi-layered mechanism: DT enhances the corporate information environment, which subsequently reduces financial investment. In addition, the analysis reveals a moderated mediation mechanism wherein economic uncertainty dampens the information-enhancing effect of DT. Unlike previous research that treats corporate risk-taking as a parallel mediator, this study identifies a sequential mediation pathway, where improved information environments suppress financial investment indirectly by influencing firms’ risk-taking behavior. These findings offer new theoretical insights into the financial implications of DT and contribute to the broader understanding of enterprise behavior in the context of digitalization and economic volatility. Full article

With the explosive growth of Internet of Things (IoT) devices, massive amounts of heterogeneous data are continuously generated. However, IoT data transactions and sharing face multiple challenges such as limited device resources, untrustworthy network environment, highly sensitive user privacy, and serious data silos. How to achieve fine-grained access control and privacy protection for massive devices while ensuring secure and reliable data circulation has become a key issue that needs to be urgently addressed in the current IoT field. To address the above challenges, this paper proposes a blockchain-based data transaction and privacy protection framework. First, the framework builds a multi-layer security architecture that integrates blockchain and IPFS and adapts to the “end–edge–cloud” collaborative characteristics of IoT. Secondly, a data sharing mechanism that takes into account both access control and interest balance is designed. On the one hand, the mechanism uses attribute-based encryption (ABE) technology to achieve dynamic and fine-grained access control for massive heterogeneous IoT devices; on the other hand, it introduces a game theory-driven dynamic pricing model to effectively balance the interests of both data supply and demand. Finally, in response to the needs of confidential analysis of IoT data, a secure computing scheme based on CKKS fully homomorphic encryption is proposed, which supports efficient statistical analysis of encrypted sensor data without leaking privacy. Security analysis and experimental results show that this scheme is secure under standard cryptographic assumptions and can effectively resist common attacks in the IoT environment. Prototype system testing verifies the functional completeness and performance feasibility of the scheme, providing a complete and effective technical solution to address the challenges of data integrity, verifiable transactions, and fine-grained access control, while mitigating the reliance on a trusted central authority in IoT data sharing. Full article

Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted local features, resulting in the loss of critical directional information and an increase in computational complexity which affect the detector’s performance. To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. Specifically, we first refine scale modeling through the Multi-kernel Context Interaction (MCI) module and Hierarchical Expansion Attention (HEA) mechanism, achieving sufficient extraction of local features and global information for targets of different scales. Additionally, the Midpoint Offset Loss Function is employed to mitigate the impact of gradual scale changes on target direction perception, enabling precise regression for targets across various scales. We conducted comparative experiments on three commonly used remote sensing target datasets (DOTA, HRSC2016, and UCAS-AOD), with mean average precision (mAP) as the core evaluation metric. The mAP values of the method in this paper on the three datasets reached 81.72%, 92.43%, and 91.86% respectively, which were 0.65%, 1.93%, and 1.87% higher than those of the optimal method, significantly outperforming existing one-stage and two-stage detectors. Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection. Full article

Salient object detection is a challenging task in the field of computer vision. The graph-based model has attracted lots of research attention and achieved remarkable progress in this task, which constructs graphs to formulate the intrinsic structure of any image. Nevertheless, the existing graph-based salient object detection methods still have certain limitations and face two major challenges: (1) Previous graphs are constructed by the Gaussian kernel, but they are often corrupted by original noise. (2) They fail to capture common representations and complementary diversity of multi-view features. Both of these degrade saliency performance. In this paper, we propose a novel method, called multi-scale pure graphs with multi-view subspace clustering for salient object detection. Its main contribution is a new, two-stage graph, constructed and constrained by multi-view subspace clustering with sparsity and low rank. One of the advantages is that the multi-scale pure graphs upgrade the saliency performance from the propagation of noise in the graph matrix. Another advantage is that the multi-scale pure graphs exploit consistency and complementary information among multi-view features, which can effectively boost the capability of the graphs. In addition, to verify the impact of the symmetry of the multi-scale pure graphs on the salient object detection performance, we compared the proposed two-stage graphs, which included cases considering the multi-scale pure graphs and those not considering the multi-scale pure graphs. The experimental results were derived using several RGB benchmark datasets and several state-of-the-art algorithms for comparison. The results demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of multiple standard evaluation metrics. This paper reveals that multi-view subspace clustering is beneficial in promoting graph-based saliency detection tasks. Full article

Background/Objectives: Ultrasound imaging is widely employed to assess kidney health and diagnose renal diseases. Accurate segmentation of renal structures in ultrasound images plays a critical role in the diagnosis and treatment of related kidney diseases. However, challenges such as speckle noise and low contrast still hinder precise segmentation. Methods: In this work, we propose an encoder–decoder architecture, named MAT-UNet, which incorporates two distinct attention mechanisms to enhance segmentation accuracy. Specifically, the multi-convolution pixel-wise attention module utilizes the pixel-wise attention to enable the network to focus more effectively on important features at each stage. Furthermore, the triple-branch multi-head self-attention mechanism leverages the different convolution layers to obtain diverse receptive fields, capture global contextual information, compensate for the local receptive field limitations of convolution operations, and boost the segmentation performance. We evaluate the segmentation performance of the proposed MAT-UNet using the Open Kidney US Data Set (OKUD). Results: For renal capsule segmentation, MAT-UNet achieves a Dice Similarity Coefficient (DSC) of 93.83%, a 95% Hausdorff Distance (HD95) of 32.02 mm, an Average Surface Distance (ASD) of 9.80 mm, and an Intersection over Union (IOU) of 88.74%. Additionally, MAT-UNet achieves a DSC of 84.34%, HD95 of 35.79 mm, ASD of 11.17 mm, and IOU of 74.26% for central echo complex segmentation; a DSC of 66.34%, HD95 of 82.54 mm, ASD of 19.52 mm, and IOU of 51.78% for renal medulla segmentation; and a DSC of 58.93%, HD95 of 107.02 mm, ASD of 21.69 mm, and IOU of 43.61% for renal cortex segmentation. Conclusions: The experimental results demonstrate that our proposed MAT-UNet achieves superior performance in multiple renal structure segmentation in ultrasound images. Full article

Circadian rhythms are intrinsic 24 h cycles that regulate metabolic processes across multiple tissues, with skeletal muscle emerging as a central node in this temporal network. Muscle clocks govern gene expression, fuel utilisation, mitochondrial function, and insulin sensitivity, thereby maintaining systemic energy homeostasis. However, circadian misalignment, whether due to behavioural disruption, nutrient excess, or metabolic disease, impairs these rhythms and contributes to insulin resistance, and the development of obesity, and type 2 diabetes mellitus. Notably, the muscle clock remains responsive to non-photic cues, particularly exercise, which can reset and amplify circadian rhythms even in metabolically impaired states. This work synthesises multi-level evidence from rodent models, human trials, and in vitro studies to elucidate the role of skeletal muscle clocks in circadian metabolic health. It explores how exercise entrains the muscle clock via molecular pathways involving AMPK, SIRT1, and PGC-1α, and highlights the time-of-day dependency of these effects. Emerging data demonstrate that optimally timed exercise enhances glucose uptake, mitochondrial biogenesis, and circadian gene expression more effectively than time-agnostic training, especially in individuals with metabolic dysfunction. Finally, findings are integrated from multi-omic approaches that have uncovered dynamic, time-dependent molecular signatures that underpin circadian regulation and its disruption in obesity. These technologies are uncovering biomarkers and signalling nodes that may inform personalised, temporally targeted interventions. By combining mechanistic insights with translational implications, this review positions skeletal muscle clocks as both regulators and therapeutic targets in metabolic disease. It offers a conceptual framework for chrono-exercise strategies and highlights the promise of multi-omics in developing precision chrono-medicine approaches aimed at restoring circadian alignment and improving metabolic health outcomes. Full article

The changing global climate and rapid urbanization make extreme rainstorm events frequent, and the flood disaster caused by rainstorm has become a prominent problem of urban public safety in China, which severely restricts the healthy and sustainable development of social economy. The weight calculation method of traditional risk assessment model is single and ignores the difference of multi-dimensional information space involved in risk analysis. This study constructs a flood risk assessment model by incorporating natural, social, and economic factors into an indicator system structured around four dimensions: hazard, exposure, vulnerability, and disaster prevention and mitigation capacity. A combination of the Analytic Hierarchy Process (AHP) and the entropy weight method is employed to optimize both subjective and objective weights. Taking the central urban area of Wuhan with a high flood risk as an example, based on the risk assessment values, spatial autocorrelation analysis, cluster analysis, outlier analysis, and hotspot analysis are applied to explore the spatial clustering characteristics of risks. The results show that the overall assessment level of flood hazard in central urban area of Wuhan is medium, the overall assessment level of exposure and vulnerability is low, and the overall disaster prevention and mitigation capability is medium. The overall flood risk levels in Wuchang and Jianghan are the highest, while some areas in Qingshan and Hanyang have the lowest levels. The spatial characteristics of each dimension evaluation index show obvious autocorrelation and spatial differentiation. These findings aim to provide valuable suggestions and references for reducing urban disaster risks and achieving sustainable urban development. Full article

The growing use of mobile health (mHealth) technologies adds complexity and risk to the healthcare environment. This paper presents a multi-layered cybersecurity assessment of an in-house mHealth platform (PHGL-COVID), comprising a Docker-based server infrastructure, a Samsung Galaxy A55 smartphone, and a Galaxy Watch 7 wearable. The objective was to identify vulnerabilities across the server, mobile, and wearable components by emulating real-world attacks and conducting systematic penetration tests on each layer. Tools and methods specifically tailored to each technology were applied, revealing exploitable configurations, insecure Bluetooth Low Energy (BLE) communications, and exposure of Personal Health Records (PHRs). Key findings included incomplete container isolation, BLE metadata leakage, and persistent abuse of Android privacy permissions. This work delivers both a set of actionable recommendations for developers and system architects to strengthen the security of mHealth platforms, and a reproducible audit methodology that has been validated in a real-world deployment, effectively bridging the gap between theoretical threat models and practical cybersecurity practices in healthcare systems. Full article

Visual–inertial odometry (VIO) is the primary supporting technology for autonomous systems, but it faces three major challenges: initialization sensitivity, dynamic illumination, and multi-sensor fusion. In order to overcome these challenges, this paper proposes stereo direct sparse visual–inertial odometry with efficient second-order minimization. It is entirely implemented using the direct method, which includes a depth initialization module based on visual–inertial alignment, a stereo image tracking module, and a marginalization module. Inertial measurement unit (IMU) data is first aligned with a stereo image to initialize the system effectively. Then, based on the efficient second-order minimization (ESM) algorithm, the photometric error and the inertial error are minimized to jointly optimize camera poses and sparse scene geometry. IMU information is accumulated between several frames using measurement preintegration and is inserted into the optimization as an additional constraint between keyframes. A marginalization module is added to reduce the computation complexity of the optimization and maintain the information about the previous states. The proposed system is evaluated on the KITTI visual odometry benchmark and the EuRoC dataset. The experimental results demonstrate that the proposed system achieves state-of-the-art performance in terms of accuracy and robustness. Full article

The increasing frequency and intensity of wildfires pose serious threats to ecosystems, property, and human safety worldwide. Accurate semantic segmentation of wildfire images is essential for real-time fire monitoring, spread prediction, and disaster response. However, existing deep learning methods heavily rely on large volumes of pixel-level annotated data, which are difficult and costly to obtain in real-world wildfire scenarios due to complex environments and urgent time constraints. To address this challenge, we propose a semi-supervised wildfire image segmentation framework that enhances segmentation performance under limited annotation conditions by integrating multi-scale structural information fusion and pixel-level contrastive consistency learning. Specifically, a Lagrange Interpolation Module (LIM) is designed to construct structured interpolation representations between multi-scale feature maps during the decoding stage, enabling effective fusion of spatial details and semantic information, and improving the model’s ability to capture flame boundaries and complex textures. Meanwhile, a Pixel Contrast Consistency (PCC) mechanism is introduced to establish pixel-level semantic constraints between CutMix and Flip augmented views, guiding the model to learn consistent intra-class and discriminative inter-class feature representations, thereby reducing the reliance on large labeled datasets. Extensive experiments on two public wildfire image datasets, Flame and D-Fire, demonstrate that our method consistently outperforms other approaches under various annotation ratios. For example, with only half of the labeled data, our model achieves 5.0% and 6.4% mIoU improvements on the Flame and D-Fire datasets, respectively, compared to the baseline. This work provides technical support for efficient wildfire perception and response in practical applications. Full article

Periodic visual inspections are currently conducted to maintain the condition of railway bridges. These inspections rely on direct visual assessments by human inspectors, often requiring specialized equipment such as aerial ladders. However, this method is not only time-consuming and costly but also involves significant safety risks. Therefore, there is a growing need for a more efficient and reliable alternative to traditional visual inspections of railway bridges. In this study, we evaluated and compared the performance of damage detection using U-Net-based deep learning models on images captured by unmanned aerial vehicles (UAVs). The target damage types include cracks, concrete spalling and delamination, water leakage, exposed reinforcement, and paint peeling. To enable multi-class segmentation, the U-Net model was trained using three different loss functions: Cross-Entropy Loss, Focal Loss, and Intersection over Union (IoU) Loss. We compared these methods to determine their ability to distinguish actual structural damage from environmental factors and surface contamination, particularly under real-world site conditions. The results showed that the U-Net model trained with IoU Loss outperformed the others in terms of detection accuracy. When applied to field inspection scenarios, this approach demonstrates strong potential for objective and precise damage detection. Furthermore, the use of UAVs in the inspection process is expected to significantly reduce both time and cost in railway infrastructure maintenance. Future research will focus on extending the detection capabilities to additional damage types such as efflorescence and corrosion, aiming to ultimately replace manual visual inspections of railway bridge surfaces with deep-learning-based methods. Full article

As part of contemporary urban culture, murals support place making and city identity. While much attention has been paid to their role in activating public space during daylight hours, their presence after dark remains largely unexamined. This paper analyzes how mural content interacts with night-time illumination. The research draws on case studies, photographs, luminance measurements, and lighting simulations. It evaluates how existing lighting systems support or undermine the legibility and impact of commercial murals in urban environments. It explores whether standardized architectural lighting guidelines suit murals, how color and surface affect visibility, and which practices improve night-time legibility. The study identifies a gap in existing lighting strategies, noting that uneven lighting distorts intent and reduces public engagement. In response, a new design tool—the Floodlighting Content Readability Map—is proposed to support artists and planners in creating night-visible murals. This paper situates mural illumination within broader debates on creative urbanism and argues that lighting is not just infrastructure, but a cultural and aesthetic tool that extends the reach and resonance of public art in the 24 h city. It further emphasizes the need for interdisciplinary collaboration and a multi-contextual perspective—encompassing visual, social, environmental, and regulatory dimensions—when designing murals in cities. Full article

The transportation sector is a major consumer of primary energy and is a significant contributor to greenhouse gas emissions. Sustainable transportation requires identifying and quantifying factors influencing transport-related CO₂ emissions. This research aims to establish an adaptable, precise, and transparent forecasting structure for transport CO₂ emissions of the United States. For this reason, we proposed a multi-stage method that incorporates explainable Machine Learning (ML) and Feature Selection (FS), guaranteeing interpretability in comparison to conventional black-box models. Due to high multicollinearity among 24 initial variables, hierarchical feature clustering and multi-step FS were applied, resulting in five key predictors: Total Primary Energy Imports (TPEI), Total Fossil Fuels Consumed (FFT), Annual Vehicle Miles Traveled (AVMT), Air Passengers-Domestic and International (APDI), and Unemployment Rate (UR). Four ML methods—Support Vector Regression, eXtreme Gradient Boosting, ElasticNet, and Multilayer Perceptron—were employed, with ElasticNet outperforming the others with RMSE = 45.53, MAE = 30.6, and MAPE = 0.016. SHAP analysis revealed AVMT, FFT, and APDI as the top contributors to CO₂ emissions. This framework aids policymakers in making informed decisions and setting precise investments. Full article

Flexible sensors have emerged as essential components in next-generation technologies such as wearable electronics, smart healthcare, soft robotics, and human–machine interfaces, owing to their outstanding mechanical flexibility and multifunctional sensing capabilities. Despite significant advancements, challenges such as the trade-off between sensitivity and detection range, and poor signal stability under cyclic deformation remain unresolved. To overcome the aforementioned limitations, this work introduces a high-performance soft sensor featuring a dual-layered electrode system, comprising silver nanoparticles (AgNPs) and a composite of multi-walled carbon nanotubes (MWCNTs) with carbon black (CB), coupled with a laser-engraved crack-gradient microstructure. This structural strategy facilitates progressive crack formation under applied strain, thereby achieving enhanced sensitivity (1.56 kPa⁻¹), broad operational bandwidth (50–600 Hz), fine frequency resolution (0.5 Hz), and a rapid signal response. The synergistic structure also improves signal repeatability, durability, and noise immunity. The sensor demonstrates strong applicability in health monitoring, motion tracking, and intelligent interfaces, offering a promising pathway for reliable, multifunctional sensing in wearable health monitoring, motion tracking, and soft robotic systems. Full article

Osteosarcoma (OS) is the most common primary bone malignancy in children and adolescents, which is also considered an aggressive disease due to its rapid growth rate, ability to metastasize early, and complex and heterogeneous tumor microenvironment (TME). Although we are developing improved surgical and chemotherapeutic approaches, the presence of metastatic or recurrent disease is still detrimental to the patient’s outcome. Major advances in understanding the molecular mechanisms of OS are needed to substantially improve outcomes for patients being treated for OS. This review integrates new data on the molecular biology, pathophysiology, and immune landscape of OS, as well as introducing salient areas of tumorigenesis underpinning these findings, such as chromothripsis; kataegis; cancer stem cell dynamics; and updated genetic, epigenetic, and glycosylation modifiers. In addition, we review promising biomarkers, diagnostic platforms, and treatments, including immunotherapy, targeted small molecule inhibitors, and nanomedicine. Using genomic techniques, we have defined OS for its significant genomic instability due to TP53 and RB1 mutations, chromosomal rearrangements, and aberrant glycosylation. The TME is also characterized as immunosuppressive and populated by tumor-associated macrophages, myeloid-derived suppressor cells, and regulatory T cells, ultimately inhibiting immune checkpoint inhibitors. Emerging fields such as glycomics and epigenetics, as well as stem cell biology, have defined promising biomarkers and targets. Preclinical studies have identified that glycan-directed CAR therapies could be possible, as well as metabolic inhibitors and 3D tumor models, which presented some preclinical success and could allow for tumoral specificity and enhanced efficacy. OS is a biologically and clinically complex disease; however, advances in exploring the molecular and immunologic landscape of OS present new opportunities in biomarkers and the development of new treatment options with adjunctive care. Successful treatments in the future will require personalized, multi-targeted approaches to account for tumor heterogeneity and immune evasion. This will help us turn the corner in providing improved outcomes for patients with this resilient malignancy. Full article