Search Results (969)

Human-induced pluripotent stem cells (hiPSCs) have an innate ability to differentiate into the three germ layers: the ectoderm, endoderm, and mesoderm. By using targeted differentiation methods and carefully controlling growth factors, morphogens, and signaling modulators, hiPSCs can be guided to develop into specific lineage cell types. For clinical applications of hiPSCs and their derivatives, it is crucial to use xenogen-free, chemically defined culture media, reagents, recombinant growth factors, morphogens, and extracellular matrix (ECM) scaffolds. One major obstacle is the widespread use of Matrigel as an hiPSC culture matrix. Matrigel, derived from Engelbreth–Holm–Swarm (EHS) mouse sarcoma, is an extract of basement membrane material with a complex, poorly defined, and variable composition. It also exhibits batch-to-batch variability in mechanical and biochemical properties and is difficult to modify, which limits its rational use in the production of therapeutic cells and organoids. Synthetic ECM matrices and scaffolds offer a promising alternative because they can have a fully defined composition, highly tunable physical properties, surface modifications, and functionalization with recombinant signaling peptides and growth factors. This provides a suitable microenvironment for hiPSC culture and the directed differentiation towards lineage-specific cells and organoid development, and can be used in clinical-grade tissue transplantation and regenerative medicine. Full article

Change detection in remote sensing imagery is crucial for monitoring temporal variations in surface characteristics; nevertheless, it presents significant challenges owing to indistinct boundaries, limited semantic differentiation, and inadequate incorporation of multi-scale contextual information. To solve these problems, we propose EIMDGNet (Edge-Induced and Multi-Dimensional Grouped Difference Network), a novel architecture that enhances boundary representation and cross-scale feature interaction for accurate and robust change detection. EIMDGNet adopts a dual-branch ResNet18 backbone to extract multi-scale features from bi-temporal images, capturing both fine spatial detail and high-level semantic context. To improve boundary awareness and reduce pseudo-change interference, we introduce the Edge-Induced Differential Multi-Dimensional Group Enhancement Module (EID-MDGEM). This module enriches fine-grained spatial features through grouped pooling across spatial and channel dimensions, enabling precise localization of change contours. Within EID-MDGEM, the Edge Feature Enhancement Module (EFEM) integrates a parameter-free attention mechanism to generate edge-saliency maps, highlighting true change regions while suppressing background noise and irrelevant variations. To further enhance semantic consistency across feature scales, we design the Multi-Scale Hierarchical Progressive Fusion Module (MSHPM). This component employs a bottom-up progressive strategy to hierarchically integrate low-level spatial details with high-level semantic abstractions, thus increasing the continuity and completeness of detected change regions. By tightly coupling edge-aware enhancement with multi-scale hierarchical fusion, EIMDGNet effectively addresses major obstacles in change detection, including boundary ambiguity, inconsistent scale information, and feature misalignment. We evaluated EIMDGNet on five remote sensing change detection datasets: LEVIR-CD, DSIFN-CD, S2Looking, CLCD-CD and GVLM-CD. Our method consistently outperformed state-of-the-art approaches, achieving 91.49% F1 and 82.93% IoU on LEVIR-CD, 77.32% F1 and 69.39% IoU on DSIFN-CD, the highest 49.19% IoU and 99.20% OA on S2Looking, 81.65% F1 and 72.91% IoU on CLCD-CD, and 85.49% F1 and 76.08% IoU on GVLM-CD. These results demonstrate the superior accuracy and robustness of EIMDGNet across diverse change detection scenarios. Full article

Francis turbines are renowned for their high efficiency and adaptability across a wide range of head and discharge conditions. However, internal mechanical friction losses (IMFLs), resulting from rotational frictional resistance between the rotating runner and the surrounding fluid, remain a significant obstacle to further performance optimisation. This study introduced a CFD-derived integral friction torque framework, validated through theoretical analysis, that enables the spatially resolved quantification of IMFLs in Francis turbine runners. Building on this framework, a comprehensive computational approach was established to quantify IMFLs in a Francis turbine runner using a CFD-derived integral torque method combined with a theoretical verification model. Three runner configurations were analysed: the original runner model (ORM), a modified runner (RM1) with selective exit height reduction, and a modified runner (RM2) with uniform exit height reduction. Transient simulations were conducted at the best efficiency point (BEP) using the shear stress transport (SST) k–ω turbulence model and a sliding mesh approach. The numerical results were verified using the theoretical model and systematically evaluated to assess IMFL mechanisms and runner performance. The findings demonstrate that variations in runner geometry significantly influence internal frictional resistance and turbine efficiency. Compared with ORM, both RM1 and RM2 reduced the rotational friction torque, with RM2 exhibiting the greatest improvement: a 2.83% reduction in total friction resistance torque, a 14.74% reduction in total power losses, and a 1% absolute increase in efficiency. These improvements are primarily attributed to reduced wall shear stress and a more uniform pressure distribution across the runner surface. Full article

Accurate quantification of surface runoff is required for the appropriate design of storage structures, irrigation patterns, waterways, erosion control structures, water harvesting projects, and groundwater development schemes. However, the paucity of runoff data in Iraq and Iran is a serious obstacle. The soil conservation service–curve number (SCS–CN) method is applied in conjunction with remote sensing (RS) and geographic information system (GIS) to predict the surface runoff in the Shatt Al-Arab Region. In the present study, the Shatt Al-Arab Region is defined as the drainage areas and lateral sub-basins that contribute direct surface runoff to the main channel between Qurna city and the Arabian Gulf. Rainfall, land use/land cover (LULC), hydrologic soil group (HSG), and slope maps are developed in a GIS platform and processed to produce surface runoff for 35 years (1979–2013). The surface runoff ranges between 163 mm (2008) and 300 mm (1982) with an average of 233 mm yr⁻¹. The average annual surface runoff in the study area is 33.657 km³. A scatter plot constructed to visualize the connection between annual rainfall and annual runoff reveals a significant positive relation (coefficient of determination (r²) = 0.67, probability value (p) < 0.05). The runoff potential is low in the southern parts of the study area and gradually rises towards the northern parts. Cross-validation of the modeled annual runoff with the annual runoff data shows reasonably close matches (r² = 0.73, p < 0.001) demonstrating the procedure’s suitability. Full article

With the increasing demand for perception in complex road environments in intelligent driving, rapid and accurate identification of paved-road obstacles has become a critical prerequisite for driving safety and comfort. Various types of road obstacles can significantly affect vehicle stability and ride quality. To address this challenge, a lightweight and efficient vision-based obstacle detection framework, termed GroupScale-YOLO, is proposed, in which detection accuracy and computational efficiency are jointly enhanced through the collaborative design of multiple novel modules. First, a dedicated dataset targeting common paved-road obstacles is constructed, and six data augmentation strategies are employed to mitigate the adverse effects of road surface undulations and illumination variations on visual perception. Second, to overcome the limitations of YOLOv11n in paved-road obstacle detection tasks, targeted optimizations are introduced to the backbone network, convolutional blocks, and detection head. Experimental results indicate that GroupScale-YOLO achieves a 29.95% reduction in model parameters while simultaneously increasing mAP@0.5 by 0.6% on the self-built dataset, demonstrating its suitability for deployment in resource-constrained scenarios. Furthermore, real-vehicle road tests confirm that the proposed method maintains stable and accurate obstacle detection performance under practical driving conditions, offering a reliable solution for intelligent vehicle environmental perception. Full article

Industrial robots are a widely used technology in various industries, capable of performing multiple tasks, particularly machining operations. However, compared to CNC machines, industrial robots have certain drawbacks, such as lower rigidity, precision, and repeatability. These limitations are significant obstacles to fully integrating industrial robots into manufacturing processes. The deficiencies in robots directly impact the quality of machined workpieces, leading to reduced surface quality and geometric accuracy. Researchers have been exploring ways to improve production accuracy and quality using robotic arms for decades. Despite technological advancements, the full implementation of robotic arms in machining processes remains in its early stages. This article focuses on robotic milling and the effect of technological parameters on the final quality of the machined material, aiming to determine their importance for designing the main experimental plan. In our experiment, we used an ABB IRB 1100 robotic arm with a payload of 4 kg and a maximum reach of 0.58 m. The milling process was programmed in RobotStudio, which also supports milling complex shapes. Our statistical analysis showed that feed rate had the greatest impact on quality, while depth of field had the least influence. This information will guide further research in developing a mathematical model for robotic milling. Full article

Exploring the lunar complex and extreme terrain presents formidable challenges for conventional lunar rovers. To address these limitations, this study proposes a novel hexapod jumping hybrid robot that incorporates a “figure-of-eight” (butterfly-shaped) six-branched wheel-legged mechanism and a jumping system that stores elastic energy via deformation of its elastic body. Inspired by the multimodal locomotion of grasshoppers, the robot dynamically switches between two operational modes: high-efficiency wheeled locomotion on relatively flat surfaces and agile jumping to traverse steep slopes and surmount large obstacles. A bio-inspired gait, inspired by the crawling patterns of a hexapod insect, is implemented using a Central Pattern Generator (CPG)-based controller to produce coordinated, rhythmic limb movements. Dynamic simulations of the jumping mechanism were conducted to optimize the critical parameters of the elastic structure and its associated control strategy. Experiments on a physical prototype were conducted to validate the robot’s wheeled mobility and jumping performance. The results demonstrate that the robot exhibits excellent adaptability to rugged terrains and obstacle-dense environments. The integration of multimodal locomotion and adaptive gait control significantly enhances the robot’s operational robustness and survivability in the harsh lunar environment, opening new possibilities for future lunar exploration missions. Full article

Background/Objectives: The precise elimination of Schistosoma japonicum eggs within host tissues poses a significant therapeutic obstacle due to the ineffectiveness of existing drugs in penetrating the eggs’ protective shields. This investigation sought to create a surface-modified magnetic nanoparticle (MNP) framework to surmount this hurdle and realize targeted theranostics for combating schistosomiasis. Methods: Fe₃O₄ MNPs, MNP-NH₂, and MNP-COOH were synthesized and characterized before systematically studying their interactions with parasites. The intrinsic autofluorescence of eggs and adult worms served as an optical background for the investigation. In vitro co-incubation assays, confocal microscopy, and Prussian blue staining were utilized to quantify both adsorption and internalization. The in vivo efficacy was assessed in a Schistosoma japonicum murine model following tail vein injection. Results: A pronounced surface chemistry-dependent interaction was noted. Fe₃O₄ MNP and MNP-NH₂ displayed remarkable adsorption and effective internalization into eggs in vitro, while MNP-COOH exhibited limited uptake. This varying effectiveness was similarly observed in vivo, with Fe₃O₄ MNP and MNP-NH₂ predominantly gathering in hepatic granulomas and effectively infiltrating deposited eggs. Within adult worms, Fe₃O₄ MNP and MNP-COOH exhibited distribution on the tegument and within adult worms. Conclusions: We developed a functional MNP platform in which surface charge governs parasiticidal targeting. Among the candidates investigated, MNP-NH₂ proved to be the most efficient for egg-targeted theranostics. This study introduces an innovative nanotechnology-based approach for accurate diagnosis and treatment of schistosomiasis by specifically tackling the challenge of impermeable eggs. Full article

This paper presents a safe guidance strategy for affine formations based on the Interference Vector Method (IVM) to address dynamic formation guidance and collision avoidance for Autonomous Surface Vessels (ASVs) in multi-obstacle environments. An affine formation control framework is first adopted to enable dynamic formation transformations for the Autonomous Surface Vessel (ASV) fleet. Building on this, an IVM-based obstacle avoidance method is developed, enabling the formation to evade both static and dynamic obstacles in real time. Furthermore, a course guidance law based on the Vector Field Method (VFM) and a speed magnitude guidance law based on Control Barrier Functions (CBFs) are proposed to simultaneously achieve formation guidance and prevent inter-vessel collisions. The proposed safe guidance strategy is rigorously validated through theoretical proofs and comprehensive numerical simulations. The simulation results further confirm the robustness of the obstacle avoidance algorithm under ideal perception conditions, as well as the practical applicability of the overall strategy in complex, obstacle-rich environments. Full article

To address the common shortcomings of traditional artificial potential field methods in complex water environments, this paper proposes an improved artificial potential field obstacle avoidance method based on a scoring weighting mechanism. It also designs a real-time obstacle avoidance system for unmanned surface vehicles (USVs) primarily utilizing millimeter-wave radar as the sensing modality. This method utilizes obstacle information from millimeter-wave radar, introducing a scoring mechanism that comprehensively considers distance, azimuth, and motion state to dynamically adjust repulsive weighting within the artificial potential field. This enables adaptive obstacle avoidance decision-making in complex multi-obstacle scenarios. Compared to traditional artificial potential field methods, the proposed approach effectively mitigates local minima and unreachable target issues while enhancing obstacle avoidance path stability and safety without compromising real-time performance. Simulation analysis and real-vessel experiments validate the method’s strong feasibility and engineering applicability in complex environments. Full article

Breast cancer remains the most common and leading cause of cancer deaths among women worldwide. The efficacy of conventional therapies is often hampered by off-target effects and multidrug resistance. Phototherapy, encompassing photodynamic therapy (PDT) and photothermal therapy (PTT), has gained significant attention due to its non-invasiveness, high spatiotemporal selectivity, and minimal side effects. However, its application is hindered by several obstacles, including the tumor hypoxic microenvironment, insufficient light penetration depth, and acquired heat resistance. Metal–organic frameworks (MOFs) have adjustable structures, enormous specific surfaces, and facile functionalization, providing an ideal platform to overcome these limitations. This review summarizes the latest research progress in the application of MOFs for precision phototherapy in breast cancer treatment. It emphasizes their role as a direct photosensitizer (PS), photothermal agent (PTA), or multifunctional nanocarrier for PDT, PTT, and synergistic phototherapy (including PDT/PTT, chemo/phototherapy, and immunotherapy/phototherapy). The design strategy and therapeutic effect of MOFs for phototherapy of breast cancer are critically discussed. In addition, the current bottlenecks and future perspectives are outlined to facilitate the clinical translation of MOF-based breast cancer treatment platforms. Full article

Background: The precise segmentation and classification of dermoscopic images remain prominent obstacles in automated skin cancer evaluation due, in part, to variability in lesions, low-contrast borders, and additional artifacts in the background. There have been recent developments in foundation models, with a particular emphasis on the Segment Anything Model (SAM)—these models exhibit strong generalization potential but require domain-specific adaptation to function effectively in medical imaging. The advent of new architectures, particularly Vision Transformers (ViTs), expands the means of implementing robust lesion identification; however, their strengths are limited without spatial priors. Methods: The proposed study lays out an integrated foundation-model-based framework that utilizes SAM-Adapter-fine-tuning for lesion segmentation and a ViT-based classifier that incorporates lesion-specific cropping derived from segmentation and cross-attention fusion. The SAM encoder is kept frozen while lightweight adapters are fine-tuned only, to introduce skin surface-specific capacity. Segmentation priors are incorporated during the classification stage through fusion with patch-embeddings from the images, creating lesion-centric reasoning. The entire pipeline is trained using a joint multi-task approach using data from the ISIC 2018, HAM10000, and PH2 datasets. Results: From extensive experimentation, the proposed method outperforms the state-of-the-art segmentation and classification across the dataset. On the ISIC 2018 dataset, it achieves a Dice score of 94.27% for segmentation and an accuracy of 95.88% for classification performance. On PH2, a Dice score of 95.62% is achieved, and for HAM10000, an accuracy of 96.37% is achieved. Several ablation analyses confirm that both the SAM-Adapters and lesion-specific cropping and cross-attention fusion contribute substantially to performance. Paired t-tests are used to confirm statistical significance for all the previously stated measures where improvements over strong baselines indicate a $p<0.01$ for most comparisons and with large effect sizes. Conclusions: The results indicate that the combination of prior segmentation from foundation models, plus transformer-based classification, consistently and reliably improves the quality of lesion boundaries and diagnosis accuracy. Thus, the proposed SAM-ViT framework demonstrates a robust, generalizable, and lesion-centric automated dermoscopic analysis, and represents a promising initial step towards clinically deployable skin cancer decision-support system. Next steps will include model compression, improved pseudo-mask refinement and evaluation on real-world multi-center clinical cohorts. Full article

Traditional prebiotic chemistry experiments often isolated single reactions under clean, controlled conditions, yet early Earth was chemically diverse and physically dynamic. Such primordial complexity likely imposed obstacles, including side reactions, low yields, and unstable intermediates, but it also generated opportunities, including redundant routes, parallel pathways, and environmental filters that could bias mixtures toward subsets of persistent and chemically productive compounds. This review examines how heterogeneous prebiotic settings could generate RNA precursors, including nucleobases, ribose, and phosphate-containing species, through multiple concurrent pathways. Although side reactions can sequester carbon in inert tars and reduce yields of specific targets, networked chemistry can also enhance robustness when different routes converge on shared intermediates, or when apparent byproducts reenter productive cycles. Environmental factors such as ultraviolet irradiation, mineral surfaces, wet-dry cycling, and thermal gradients can act as constraints that enrich certain products by differential stability, reactivity, and compartmentalization. In this context, the RNA world hypothesis remains compelling, as RNA can store heritable sequence information and catalyze reactions through sequence dependent folding, thereby linking heredity and chemistry within a single polymer. At the same time, the emergence of functional sequence information and of control architectures that couple sequence to reproducible function remains a central open problem, and it sets clear limits on what chemistry alone can explain. Rather than dismissing messy mixtures as irrelevant noise, it is more accurate to treat them as the native context in which concentration mechanisms, environmental cycling, and selective persistence could enable the accumulation and survival of RNA related molecules. Full article

The blood–brain barrier (BBB) is a major obstacle to the development of brain-targeted drug delivery systems, restricting greater than 98% of small molecules (<500 Da) and virtually all large-molecule drugs from entering the brain tissues from the bloodstream, resulting in suboptimal drug doses and therapeutic failure in the treatment of Alzheimer’s disease (AD). However, the advent of nanotechnology has provided significant solutions to the BBB challenges, enabling particle size reduction, enhanced drug solubility, reduced premature drug degradation, extended and sustained drug release, enhanced drug transport across the BBB, increased drug target specificity and enhanced therapeutic efficacy. In corollary, a library of brain-targeted surface-functionalized nanotherapeutics has been widely reported in the current literature. These promising in vitro, in vivo and pre-clinical results from the existing literature provide quantitative evidence for the relative clinical utility of each of the techniques, indicating remarkable capacity for brain-targeted carrier systems; many of them are still being tested in human clinical trials. However, despite the recorded research successes in drug transport across the BBB, there are currently no clinically proven medications that can slow or reverse the progression of AD because most of the novel therapeutics have not been successful during the clinical trials. Therefore, the main option for the treatment of AD is symptomatic treatment using cholinesterase inhibitors and N-methyl-D-aspartate (NMDA) receptor antagonists. Although these therapies help to alleviate symptoms of AD and improve patients’ quality of life, they neither slow the progression of disease nor cure it. Thus, an effective disease-modifying therapy for the treatment of AD is an unmet clinical need. It is apparent that a deeper understanding of the structural complexity and controlling dynamic functions of the BBB in tandem with a comprehensive elucidation of AD pathogenesis are crucial to the development of novel nanocarriers for the effective treatment of AD. Therefore, this narrative review describes the contextual analysis of several promising strategies that enhance brain-targeted drug delivery across the BBB in AD treatment and recent research efforts on two major AD biomarkers that have revolutionized AD diagnosis, amyloid-beta plaques and phosphorylated tau protein tangle, as potential targets in AD drug development. This has led to the Food and Drug Administration (FDA)’s approval of two intravenous (IV) anti-amyloid monoclonal antibodies, Lecanemab (Leqembi^®) and Donanemab (Kisunla^®), which were developed based on the Aβ cascade hypothesis for the treatment of early AD. This review also discusses the recent shift in the Aβ cascade hypothesis to Aβ oligomer (conformer), a soluble intermediate of Aβ, which is the most toxic mediator of AD and could be the most potent drug target in the future for a more accurate and effective drug development model for the treatment of AD. Furthermore, various promising nanoparticle-based drug carriers (therapeutic nanoparticles) that were developed from intensive research are discussed, including their clinical utility, challenges and prospects in the treatment of AD. Overall, it suffices to state that the advent of nanotechnology provided several innovative techniques for overcoming the BBB and improving drug delivery to the brain; however, their long-term biosafety is a relevant concern. Full article

In this study, a three-dimensional (3D) model is developed based on an actual small forestry crawler tractor, to analyze its overturning and rollover behaviors, and a corresponding simulation model is constructed. The accuracy of the 3D model is validated by comparing its dimensions and center of gravity with those of the physical tractor, and the fidelity of the simulation model is verified using static sidelong falling angle, minimum turning radius, and driving tests. The developed simulation framework was employed to investigate the dynamic behavior of the small forestry crawler tractor, focusing on roll and pitch angular velocities across different obstacle heights, slope angles, and driving speeds. Backward rollover was not observed within the tractor’s realistic operating speed range, indicating that backward rollover is not the dominant risk mode. In contrast, lateral overturning occurs under all driving scenarios, and increases in driving speed and obstacle height lead to higher roll angular velocities, increasing the risk of lateral overturning. Across all conditions, the likelihood of lateral overturning surges when the roll angular velocity enters the 80–100°/s range, with obstacle height exerting the greatest influence. In conclusion, the small forestry crawler tractor is more prone to lateral overturning than backward rollover when driving on inclined surfaces. A distinct threshold roll angular velocity is identified as the onset point of lateral overturning, which will vary according to the tractor’s specifications. This study is a quantitative study of a small forestry crawler tractor and does not correlate with a full-scale tractor. While angular velocity values vary during lateral overturning and backward rollover, this study was conducted to identify trends under various driving conditions. Further work is required to apply the proposed analysis methodology to full-scale agricultural and forestry machinery and validate it with real-world operational data. Full article