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With the growing application of soft robot technology in complex, dynamic environments, the limitations of traditional rigid robots have become increasingly prominent, urgently demanding novel soft actuation technologies. Dielectric elastomer actuators (DEAs) have gradually emerged as a research focus in soft robotics due to their high energy density, rapid response, low noise, and excellent compliance. This paper systematically reviews the research progress of DEA-based soft robots over the past decade. Using classification and comparative analysis, DEAs are categorized into four basic types according to their initial shape—planar, saddle-shaped, cylindrical, and conical—with detailed elaboration on their working principles, structural features, and typical applications. Furthermore, from two major application scenarios (underwater and terrestrial), this paper analyzes the adaptability of various DEAs in robot design and corresponding optimization strategies and summarizes their performance and research challenges in bionic propulsion, multi-modal motion, and environmental adaptability. Finally, it provides the prospective future research directions of DEAs in material development, structural design, intelligent control, and system integration, providing theoretical support and technical references for their wide application in fields such as medical treatment, detection, and human–robot interaction. Full article

Background: In healthcare institutions, radiologists play an essential role in patients’ care, enabling them to begin treatment and start their recoveries. However, data on the characteristics and distribution of the radiology workforce in Saudi Arabia are limited. Therefore, this study aimed to conduct a comprehensive analysis of the radiology workforce in SA based on national data and identify key distributional and specialty trends relevant to workforce planning and radiology service delivery. Methods: The following data were obtained from the Saudi Commission for Health Specialties (SCFHS) Registry: total number of registered radiologists, age, subspecialty, professional classification, place of qualification, and geographical location. Descriptive statistics were used for data analysis. Additionally, the findings were compared with those of published international benchmarks. Results: There were 5150 radiologists registered with SCFHS in SA, which corresponded to 147 radiologists per 1,000,000 inhabitants. The mean age was 40.8 years (standard deviation [SD] 9.8), with 60% of them being aged 30–44 years. Most of the radiologists specialised in general diagnostic radiology (83.7%), with few of them specialising in interventional radiology (1.8%), paediatric radiology (1.1%), and breast imaging (0.9%). The workforce mainly comprised consultants (35.0%), followed by registrars (29.7%) and senior registrars (22.7%). Two-thirds (65.0%) of the radiologists had obtained their qualifications abroad. More than half of the radiologists resided in three provinces: Riyadh (29%), Mecca (23%), and the Eastern Region (15%), while several provinces had fewer than 2% of the available workforce. Conclusions: The radiology workforce in SA is relatively young and has a higher density than the average in the European Union. Further, most of the radiologists are professionally classified as consultants or registrars. However, there is a clear imbalance in their geographic distribution, which is consistent with the population sizes of the respective cities. Targeted training expansion and reduced reliance on foreign-trained professionals are warranted to meet future service demands in line with the Vision 2030 objectives. Full article

Background/Objectives: Central bone mineral density (cBMD) is widely utilized for assessing bone quality, but its reliability as a predictor of knee bone strength for cementless total knee arthroplasty (TKA) remains unclear. This study aimed to determine whether cBMD reliably estimates bone strength suitable for cementless fixation. Methods: 188 patients scheduled for TKA underwent preoperative cBMD assessment of the lumbar spine and femoral neck. During surgery, femoral bone specimens were collected for indentation tests. We compared distal femoral bone strength among osteoporosis classification groups (normal, osteopenia, osteoporosis) and examined the distribution of cementless suitable versus cemented mandatory cases with chi-square tests. ROC analysis evaluated cBMD’s diagnostic performance in predicting cementless TKA suitability, with AUC, sensitivity, and specificity calculated for both measurement sites. Results: No significant differences in distal femoral bone strength existed between osteopenia and osteoporosis groups (p = 0.845 for lumbar spine, p = 0.857 for femoral neck). Among patients with normal cBMD, 35.4% (lumbar spine) and 30.7% (femoral neck) were unsuitable for cementless TKA, whereas 30.8% and 45.0% of osteoporotic patients, respectively, had adequate bone strength for cementless fixation. The AUC values for predicting cementless suitability were 0.656 (lumbar spine) and 0.669 (femoral neck), with sensitivity and specificity below 0.75 for both measurements. Conclusions: Central BMD does not reliably represent distal femoral bone strength and demonstrates inadequate predictive capability for identifying appropriate candidates for cementless TKA in this predominantly Asian female cohort. Future multi-center, multi-ethnic studies are needed to enhance generalizability. Full article

Urban green spaces (UGSs) are critical to ecological sustainability and human well-being, but equitable access remains a key challenge, particularly in high-density cities. While existing studies have predominantly focused on parks, the role of non-park green spaces (NPGSs) has received limited attention. This study examines the spatial equity of NPGSs—an overlooked but essential component of urban green infrastructure in Inner London—using a typological classification informed by previous research, along with multi-threshold accessibility assessment and spatial justice evaluation. We apply GIS-based buffer analysis, decomposed Gini coefficients, and Moran’s I clustering to quantify distributional disparities. The main findings are as follows: (1) five NPGS types are defined and mapped in Inner London: Natural and Protected, Community and Household, Purpose-Specific, Linear, and Underutilized; (2) significant accessibility inequities exist among NPGS types, with Community and Household demonstrating high equity (Gini coefficient < 0.25), while Underutilized exhibit severe deprivation (Gini coefficient > 0.74); (3) spatial clustering analysis reveals a core–periphery differentiation, characterized by persistent low–low clusters in central boroughs and emerging high–high hot spots in southeastern/northwestern boroughs. This study underscores the critical role of NPGS in complementing park-based greening strategies and provides a transferable framework to assess green equity, thereby contributing to the achievement of the United Nations Sustainable Development Goals (SDGs). Full article

Inter-saccadic times or eye fixation durations (EFDs) are relatively stable at around 250 ms, equivalent to four saccades per second. However, the mean and standard deviation are not sufficient to describe the frequency histogram distribution of EFD. The exGaussian has been proposed for fitting the EFD histograms. The present report tries to adjust a competition model (C model) between the saccadic and the fixation network to the EFD histograms. This model is at a rather conceptual level (computational level in Marr’s classification). Both models were adjusted to EFD from an open database with data of 179,473 eye fixations. The C model showed to be able, along with exGaussian model, to be compatible with explaining the EFD distributions. The two parameters of the C model can be ascribed to (i) a refractory period for new saccades modeled by a sigmoid equation (A parameter), while (ii) the ps parameter would be related to the continuous competition between the saccadic network related to the saliency map and the eye fixation network, and would be modeled through a geometric probability density function. The model suggests that competition between neural networks would be an organizational property of brain neural networks to facilitate the decision process for action and perception. In the visual scene scanning, the C model dynamic justifies the early post-saccadic stability of the foveated image, and the subsequent exploration of a broad space in the observed image. The code to extract the data and to run the model is added in the ^{Supplementary Materials}. Additionally, entropy of EFD is reported. Full article

Post-traumatic lymphedema (PTL) is a chronic and often under-recognized sequela of soft tissue trauma, leading to persistent swelling, functional impairment, and increased risk of infection. While lymphedema is traditionally associated with oncologic interventions, growing evidence highlights the significant burden of PTL in trauma patients. This review provides a comprehensive analysis of the current understanding of PTL, including epidemiology, risk factors, pathophysiology, diagnostic modalities, and treatment strategies. PTL often occurs after high-impact musculoskeletal injuries (such as open fractures with significant soft tissue loss) or burns (especially if deep or circumferential). This risk is increased if injury occurs at critical areas of increased lymphatic density (such as anteromedial leg, medial knee, medial thigh, medial elbow, or medial arm). Advances in imaging techniques, including indocyanine green lymphography and magnetic resonance lymphangiography, have improved early detection and classification of PTL. Management approaches range from conservative therapies, such as complete decongestive therapy (CDT), to surgical interventions, including lymphaticovenous anastomosis (LVA), vascularized lymph node transfer (VLNT), and vascularized lymph vessel transfer (VLVT)/lymph-interpositional-flap transfer (LIFT). We report on our experience with two patients. At our center, we diagnose and stage PTL with ICG lymphography and trial CDT for 6 months. If there is no significant improvement, we recommend LVA. If there is insufficient improvement after 12 months, we recommend LIFT/repeat LVA/VLNT. We also treat open fractures with significant soft tissue defects with LIFT, as prophylaxis against PTL. PTL remains an underdiagnosed condition, necessitating increased awareness and intervention to prevent long-term disability. Full article

As a critical ecological barrier and key dryland agricultural zone in China, the Loess Plateau is faced with acute tensions between food security risks arising from cropland abandonment (CA) and the imperatives of ecological conservation. Yet, existing research has failed to adequately capture the long-term, high-spatiotemporal-resolution dynamics of abandonment in this region or to quantitatively couple its driving mechanisms with implications for food security. To address these gaps, this study establishes a high-precision identification system for CA tailored to the Plateau’s complex topographic conditions, distinguishing among interannual abandonment, multiyear abandonment, conversion to forest/grassland, and reclamation. Leveraging long-term data from 2000 to 2023 and integrating the Mann–Kendall test with the random forest algorithm, we examine the spatiotemporal trajectories, driving forces, and food security consequences of CA. Guided by a “type differentiation–grade classification–temporal tracking” framework, the analysis reveals a marked transition in dominant drivers from “socioeconomic factors” to “topographic–climatic factors.” It further identifies an “increasing loss–slowing growth” effect of abandonment on grain production, alongside a “pressure alleviation” trend in per capita carrying capacity. The results showed that: (1) Between 2000 and 2023, the area of CA on the Loess Plateau expanded from 2.72 million ha to 6.96 million ha, with high-grade abandonment (≥8 years) accounting for 58.9% of the total and being spatially concentrated in the hilly–gully regions of northern Shaanxi and eastern Gansu; (2) The Grain for Green Project (GFGP) peaked at approximately 340,000 hectares in 2018, followed by a slight decline, but has generally remained at around 300,000 hectares since then; (3) The reclamation rate of CA remained between 5% and 12% during 2003–2015, with minimal overall fluctuations, but after 2016, it gradually increased and peaked at 23.4% in 2022; (4) In terms of driving forces, population density (14.99%) was the primary determinant in 2005, whereas by 2020, slope (15.43%) and mean annual precipitation (15.63%) emerged as core factors; and (5) Grain yield losses attributable to abandonment increased from less than 100 t to nearly 450 t, though the growth rate slowed after 2016, accompanied by gradual alleviation of pressure on per capita carrying capacity. Overall, the study offers robust empirical evidence to inform cropland protection, food security strategies, and sustainable agricultural development policies on the Loess Plateau. Full article

Extracting key features for phenotype classification from high-dimensional and complex mass spectrometry (MS) data presents a significant challenge. Conventional data representation methods, such as traditional peak lists or grid-based imaging strategies, are often hampered by information loss and compromised signal integrity, thereby limiting the performance of downstream deep learning models. To address this issue, we propose a novel data representation framework named MSIMG. Inspired by object detection in computer vision, MSIMG introduces a data-driven, “density-peak-centric” patch selection strategy. This strategy employs density map estimation and non-maximum suppression algorithms to locate the centers of signal-dense regions, which serve as anchors for dynamic, content-aware patch extraction. This process transforms raw mass spectrometry data into a multi-channel image representation with higher information fidelity. Extensive experiments conducted on two public clinical mass spectrometry datasets demonstrate that MSIMG significantly outperforms both the traditional peak list method and the grid-based MetImage approach. This study confirms that the MSIMG framework, through its content-aware patch selection, provides a more information-dense and discriminative data representation paradigm for deep learning models. Our findings highlight the decisive impact of data representation on model performance and successfully demonstrate the immense potential of applying computer vision strategies to analytical chemistry data, paving the way for the development of more robust and precise clinical diagnostic models. Full article

Hollow heart, and other crop defects, can be devastating to farmers. Hollow heart is not a disease but a physiological disorder affected by temperature, soil moisture, plant density, and other factors. These defects can cause substantial annual losses for farmers. Currently, potatoes are shipped and inspected from producers to shipping points and markets. At these facilities, samples are inspected for defects. Detection of hollow heart consists of halving potatoes and visually inspecting for defects. The defect size is compared to USDA hollow heart classification charts for acceptance or rejection. An automatic, non-destructive system to identify hollow heart has the potential to improve quality. Two methods have been developed to collect data for such a system: acoustic signal capture and visual/vibration signal capture. Data is collected and stored for one potato at a time. The procedure includes the collection of weight, proportional size, and volume, as well as the generation of an acoustic sound signal through a drop test and a motion signal captured through a vision system. To simulate hollow heart, potatoes are cored and retested by producing a new set of data. Each potato is manually cut and inspected for true hollow heart. The generated data includes over 1000 samples, each comprising proportional volume, weight, proportional size, motion, and acoustic data. Such a dataset does not exist in the current literature and can serve for the development of machine learning algorithms to detect hollow heart nondestructively. In this paper, the data is also analyzed in terms of its statistical properties, as applied for possible feature engineering in machine learning. Full article

To achieve professional roe deer population management and to mitigate wildlife-related agricultural damage, a wildlife population estimation trial was conducted in Hungary using an ultralight aircraft with dual sensors (thermal and DSLR camera) to assess the method’s applicability, using the roe deer as a model species. The test took place in early spring, at an altitude of 400 m above ground level and a flight speed of 150 km/h. The survey targeted a total count of a 1040 hectare area using adjacent 200 m-wide strips. This strip-based design also allowed for a methodological comparison between total count and strip sample count approaches. Object-based image classification was applied, and species-level validation was performed. During the survey, a total of 213 roe deer were localised. The average group size was 9.17 ± 1.7 ($\overline{\mathrm{x}}$ ± SE), with two prominent outliers (28 and 34 individuals). Compared to the density value of 0.205 individuals/ha established through the full-area census, the simulated estimations (50% and 25%) showed considerable under- and overestimation, primarily due to the aggregative behaviour of roe deer. Based on the test, aerial population estimation using dual-sensor technology proved to be effective in agricultural habitats; however, the accuracy of the results is strongly influenced by the sampling design applied. Full article

In the lower and middle sections of the Yangtze River Basin Region (YRBR) in China, challenges posed by climate change and delayed harvesting of preceding crops have hindered the timely sowing of wheat, leading to an increasing prevalence of late-sown wheat fields. This trend has emerged as a significant impediment to achieving high and stable production of wheat in this area. During the growing seasons of 2022–2023 and 2023–2024, an unmanned aerial vehicle (UAV)-based multispectral camera was used to monitor different wheat materials at various growth stages under normal sowing treatment (M1) and late sowing with increased plant density (M2). By assessing yield loss, the wheat tolerance to late sowing was quantified and categorized. The correlation between the differential vegetation indices (D-VIs) and late sowing resistance was examined. The findings revealed that the J2-Logistic model demonstrated optimal classification performance. The precision values of stable type, intermediate type, and sensitive type were 0.92, 0.61, and 1.00, respectively. The recall values were 0.61, 0.92, and 1.00. The mean average precision (mAP) of the model was 0.92. This study proposes a high-throughput and low-cost evaluation method for wheat tolerance to late sowing, which can provide a rapid predictive tool for screening suitable varieties for late sowing and facilitating late-sown wheat breeding. 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

Polymer-based aerogels have recently received considerable research attention as a favorable option for oil–water separation due to their enhanced porous 3D structure with great specific surface area, low density and outstanding sorption behavior. Additionally, polymer-containing aerogels exhibit more favorable characteristic properties, such as being lipophilic–hydrophobic (superhydrophobic–superoleophilic), hydrophilic–lipophobic (superhydrophilic–underwater oleophobic), or other specific wetness forms, including anisotropic and dual-wettability. In this review, cellulose and cellulose-based materials used as an aerogel for oil–water separation are comprehensively reviewed. This review highlights the significance of cellulose and cellulose-based combinations through structure–property interactions, surface modifications (using different hydrophilic and hydrophobic agents), and aerogel formation, focusing on the light density and high surface area of aerogels for effective oil–water separation. This article provides an in-depth review of four primary classifications of cellulose-based aerogels, namely, cellulose aerogels (regenerated cellulose and bacterial cellulose), cellulose with biopolymer-based aerogels (chitosan, lignin, and alginate), cellulose with synthetic polymer aerogels (polyvinyl alcohol, polyetherimide, polydopamine and others), and cellulose with organic/inorganic (such as SiO₂, MTMS, and tannic acid) material-based aerogels. Furthermore, the aspects of performance, scalability, and durability have been explained, alongside potential prospect directions for the advancement of cellulose aerogels aimed at their widespread application. This review article stands apart from previously published review works and represents the comprehensive review on cellulose-based aerogels for oil–water separation, featuring wide-ranging classifications. Full article

Surface drifter trajectory prediction is essential for applications in environmental management, maritime safety, and climate studies. This survey paper reviews research from the past two decades, and systematically classifies the evolution of methodologies into six successive generations, including numerical models, data assimilation, statistical and probabilistic approaches, machine learning, deep learning, and hybrid or AI-based data assimilation (1st–5.5th Generation). To our knowledge, this is the first systematic generational classification of trajectory prediction methods. Each generation revealed distinct strengths and limitations. Numerical models ensured physical consistency but suffered from accumulated forecast errors in observation-sparse regions. Data assimilation improved short-term accuracy as observing networks expanded, while machine learning and deep learning enhanced short-range forecasts but faced challenges such as error accumulation and insufficient physical constraints in longer horizons. More recently, hybrid frameworks and AI-based data assimilation have emerged, combining physical models with deep learning and traditional statistical techniques, thereby opening new possibilities for accuracy improvements. By comparing methodologies across generations, this survey provides a roadmap that helps researchers and practitioners select appropriate approaches depending on observation density, forecast lead time, and application objectives. Finally, this paper highlights that future systems should shift focus from deterministic tracks toward credible uncertainty estimates, region-aware designs, and physically consistent prediction frameworks. Full article

Spatial density, as a key indicator of the quality of the urban residential environment, comprises both physical and perceived dimensions. Physical density refers to objective spatial characteristics (e.g., building density and population density), whereas perceived density denotes residents’ perceptual evaluations (e.g., perceived crowding, visual openness, and overall environmental quality). Clarifying the relationship between physical and perceived density is therefore critical for advancing livability-oriented urban planning and design. This study examines the relationship through an empirical analysis of 50 representative high-density communities in Guangzhou. Using morphological classification, descriptive statistics, and multiple linear regression, the analysis compares objective density indicators with residents’ perceptual evaluations and identifies key environmental factors that shape perceived density. Findings indicate that physical and perceived density are not fully aligned: compact but coherent spatial forms can enhance residents’ perceptual evaluations, whereas overcrowded and deteriorating environments intensify negative perceptions. The identified community typologies—for example, urban villages, traditional walk-up estates, and modern high-rise complexes—exhibit distinct perceptual patterns and influencing factors. These results highlight the need for density regulation to move beyond conventional physical indicators and to incorporate perceptual dimensions into planning frameworks. Overall, the study provides theoretical insights and practical guidance for tailored strategies in the renewal and management of high-density communities. Full article