Search Results (4,740)

Power-line presence recognition technology for unmanned aerial vehicles (UAVs) is one of the key research directions in the field of UAV remote sensing. With the rapid development of UAV technology, the application of UAVs in various fields has become increasingly widespread. However, when flying in urban and rural areas, UAVs often face the danger of obstacles such as power lines, posing challenges to flight safety and stability. To address this issue, this study proposes a novel method for presence recognition in UAVs for power lines using an improved GhostNet v2 knowledge distillation dual-attention mechanism convolutional neural network. The construction of a real-time UAV power-line presence recognition system involves three aspects: dataset acquisition, a novel network model, and real-time presence recognition. First, by cleaning, enhancing, and segmenting the power-line data collected by UAVs, a UAV power-line presence recognition image dataset is obtained. Second, through comparative experiments with multi-attention modules, the dual-attention mechanism is selected to construct the CNN, and the UAV real-time power-line presence recognition training is conducted using the SGD optimiser and Hard-Swish activation function. Finally, knowledge distillation is employed to transfer the knowledge from the dual-attention mechanism-based CNN to the nonlinear function and Mamba-enhanced GhostNet v2 network, thereby reducing the model’s parameter count and achieving real-time recognition performance suitable for mobile device deployment. Experiments demonstrate that the UAV-based real-time power-line presence recognition method proposed in this paper achieves real-time recognition accuracy rates of over 91.4% across all regions, providing a technical foundation for advancing the development and progress of UAV-based real-time power-line presence recognition. Full article

Background: Second salvage autologous stem cell transplantation (SAT) is a therapeutic option for patients with multiple myeloma (MM) who relapse after a first autologous stem cell transplantation (ASCT) in the era of novel agents. However, the clinical context in which SAT provides benefit relative to contemporary salvage regimens remains unclear. Methods: We retrospectively analyzed 51 patients who underwent SAT after novel agent-based induction and first ASCT, and salvage re-induction, and compared outcomes with 113 patients treated with salvage carfilzomib–lenalidomide–dexamethasone (KRd) without SAT. Results: Median interval from first ASCT to relapse was 27 months. In the SAT cohort, median progression-free survival (PFS) and overall survival (OS) from initiation of salvage therapy were 30 and 99 months, respectively. A time to relapse ≥18 months after first ASCT and receipt of SAT as second-line of therapy were associated with significantly longer PFS and OS. In multivariate analysis, administration of SAT at later lines was independently associated with inferior outcomes, while a time to relapse ≥18 months after first ASCT was associated with significantly longer OS. Compared with the KRd-only cohort, SAT was associated with longer OS, whereas PFS was numerically longer without statistical significance. Among patients who had received both a proteasome inhibitor and an immunomodulatory drug as salvage induction, SAT was associated with longer PFS and OS. Conclusions: SAT may provide clinical benefit in selected patients with MM, particularly those with a durable response to first ASCT and those undergoing SAT at an earlier line of relapse in the novel agent era. Full article

An n-color composition is a colored composition in which a part of size m may come in m colors. This paper gives a new set of n-color-type compositions that admits exhaustive conjugation of its members. Previous attempts at conjugation of n-color compositions have yielded partial results at best. Instead of importing the coloring scheme previously used for partitions, we apply colors directly to the parts of compositions while treating any maximal string of ones as a single part under color assignment. This leads to the definition of n-color compositions of the second kind. As with ordinary compositions, a conjugate may be found using equivalent techniques: symbolic algebra, zig-zag graphs, and line graphs. We conclude with a derivation of the relevant enumeration formulas. Full article

Maintenance of building fire protection facilities is crucial for preventing fires and safeguarding lives and property; the standardization and timeliness of these activities directly determine operational reliability. However, as fire-safety requirements escalate, manually drafting maintenance work orders remains inefficient and prone to omissions. Furthermore, regulatory documents in this domain are inherently complex, and annotated resources are scarce, hampering the digitalization of fire-safety management. To address these challenges, this paper presents an LLM-based method for automatically generating maintenance work orders for building fire protection facilities. The proposed approach integrates a domain-specific knowledge base and incorporates the FS-RAG (Fire Services–Retrieval-Augmented Generation) framework to enhance both the accuracy and practical usability of generated work orders. First, we construct a lightweight domain knowledge base, FSKB (Fire Services Knowledge Base), derived from extensive maintenance regulations, capturing key elements such as equipment types, components, maintenance actions, and frequencies. Second, we design an FS-RAG framework that leverages retrieval-augmented generation to extract critical information from regulations and fuse it with the knowledge base, ensuring high accuracy and operational feasibility. Multi-round evaluations across stages B0–B4 validate the effectiveness of our method. Results indicate significant improvements over traditional approaches: the line-level compliance rate reaches 97.3% (an increase of 5.7% over B1 and 30.4% over B0), and the F1 score achieves 90.42% (an increase of 12.62% over B1 and 29.87% over B0). Full article

Background: Cutaneous squamous cell carcinoma (cSCC) represents the second most common form of non-melanoma skin malignancy, and, when not amenable to curative surgery or radiotherapy, it is a life-threatening disease. The anti-PD-1 monoclonal antibody cemiplimab has transformed the outcome of advanced or metastatic cSCC, with response rates approaching 50% and sustained benefit beyond three years in clinical trials. Cemiplimab is now the first-line standard of care treatment for advanced disease. Methods: This retrospective observational study included consecutive adult patients with locally advanced (lac) or metastatic (m) cSCC who received cemiplimab (350 mg every three weeks) at the Center for Immuno-Oncology, University Hospital of Siena, Italy, either through an Expanded Access Program or routine clinical practice. Clinical outcome and treatment related adverse events (TRAEs) are reported. Results: Between December 2019 and December 2023, 27 patients (24 male; median age 82 years [range 41–90]) diagnosed with lacSCC (n = 20 [74.0%]) or mcSCC (n = 7 [25.9%]) were treated with cemiplimab as first line therapy and were followed until June 2024. Head and neck were the primary tumor location for 88.8% of patients, followed by trunk (7.4%) and lower extremities (3.7%). All patients had comorbidities, including six patients (22.2%) with hematologic malignancies. With a median follow-up of 31 months (data cut-off June 2024), the ORR was 66.6% (complete response 22.2%) and the disease control rate (DCR) 77.7%. Median progression-free survival (mPFS) and overall survival (mOS) were not reached, while 2-year PFS and OS rates were 65.2% and 71%, respectively. Treatment was well-tolerated, with three (11.1%) patients experiencing grade ≥3 TRAEs, and three patients discontinuing treatment due to TRAEs. Conclusions: Our real-world experience confirms the high rate of durable objective responses, good tolerability and long treatment duration of cemiplimab in elderly and frail cSCC patients as well. Full article

Background/Objectives: The treatment of dual disorders (DDs) must be comprehensive and multidisciplinary. Evidence supports the effectiveness of psychotherapy in treating DDs. The second part of this consensus synthesizes the available evidence on psychological treatment for specific DDs. Methods: Two consensus methods were sequentially implemented: the nominal group technique and the Delphi method. Results: This consensus review encompassed a compilation of recommendations for the psychological treatment of neurodevelopmental, anxiety, post-traumatic stress, somatic symptom, eating, and personality disorders. Finally, recommendations for the future research agenda on the psychological treatment of DD were included. Conclusions: (1) Psychological treatment, particularly integrated treatment, is effective. (2) In the case of dual autism, interventions for substance use disorders should be adapted to this population’s characteristics. (3) More research is needed on dual social anxiety, panic, generalized anxiety, somatic symptom, and eating disorders, for which Cognitive Behavioral Therapy (CBT) is the most commonly used treatment. (4) For dual attention deficit hyperactivity disorder, multicomponent treatment is recommended (psychoeducation, CBT, and peer or family support). (5) For dual anxiety disorders, CBT is the first-line treatment. (6) For dual post-traumatic stress disorder, CBT (cognitive processing therapy and prolonged exposure therapy), acceptance and commitment therapy, stress inoculation training, and Eye Movement Desensitization and Reprocessing (EMDR) are effective. (7) For dual personality disorders, evidence is scarce. (8) For borderline personality disorder, dialectical behavior therapy, dynamic deconstructive psychotherapy, and dual-focus schema therapy show promise. (9) For antisocial personality disorder, CBT, contingency management, and counseling on impulsive lifestyles may be useful. (10) Much more evidence is needed from studies that overcome the methodological limitations of existing ones. Full article

Defect detection on Printed Circuit Boards (PCBs) constitutes a pivotal component of the quality control system in electronics manufacturing. However, owing to the intricate circuitry structures on PCB surfaces and the characteristics of defects—specifically their minute scale, irregular morphology, and susceptibility to background texture interference—existing generic deep learning models frequently fail to achieve an optimal equilibrium between detection accuracy and inference speed. To address these challenges, this study proposes MDEB-YOLO, a lightweight real-time detection network tailored for PCB micro-defects. First, to enhance the model’s perceptual capability regarding subtle geometric variations along conductive line edges, we designed the Efficient Multi-scale Deformable Attention (EMDA) module within the backbone network. By integrating parallel cross-spatial channel learning with deformable offset networks, this module achieves adaptive extraction of irregular concave–convex defect features while effectively suppressing background noise. Second, to mitigate feature loss of micro-defects during multi-scale transformations, a Bidirectional Residual Multi-scale Feature Pyramid Network (BRM-FPN) is proposed. Utilizing bidirectional weighted paths and residual attention mechanisms, this network facilitates the efficient fusion of multi-view features, significantly enhancing the representation of small targets. Finally, the detection head is reconstructed based on grouped convolution strategies to design the Lightweight Grouped Convolution Head (LGC-Head), which substantially reduces parameter volume and computational complexity while maintaining feature discriminability. The validation results on the PKU-Market-PCB dataset demonstrate that MDEB-YOLO achieves a mean Average Precision (mAP) of 95.9%, an inference speed of 80.6 FPS, and a parameter count of merely 7.11 M. Compared to baseline models, the mAP is improved by 1.5%, while inference speed and parameter efficiency are optimized by 26.5% and 24.5%, respectively; notably, detection accuracy for challenging mouse bite and spur defects increased by 3.7% and 4.0%, respectively. The experimental results confirm that the proposed method outperforms state-of-the-art approaches in both detection accuracy and real-time performance, possessing significant value for industrial applications. Full article

The severity of viral myocarditis in humans and mice is variable between individuals. Numerous observations demonstrate the influence of host genetics on disease course, but few genes have actually been identified to have such properties. In past work, mouse strains that are sensitive or resistant to severe disease were used to map the viral myocarditis susceptibility locus Vms1. Here, we demonstrate that Tnni3k, one of the genes within the Vms1 locus, influences the severity of disease following inoculation with coxsackievirus CVB3. Compared to disease-resistant C57BL/6J wild-type mice, strain-matched Tnni3k knockout mice showed higher cardiac inflammation and, in particular, a greater infiltration of macrophages into the heart. Long-term damage associated with viral infection was comparable in mice of both genotypes. Use of a second mouse line engineered with a point mutation to encode a kinase-dead version of Tnni3k showed the same elevated inflammation as the full null. These results identify Tnni3k and its kinase activity as being protective in modulating the acute phase of inflammatory response to CVB3 infection in the heart. Full article

Background/Objectives: In an attempt to combine the benefits of the Holmium:YAG (Ho:YAG) laser and Thulium Fiber Laser (TFL), the “Magneto” mode lowers the peak power of the Ho:YAG laser, generating longer duration pulses. The purpose of this study is to compare the effect of the standard virtual basket (VB) Ho:YAG laser, Magneto Ho:YAG laser and TFL on soft tissue in an ex vivo model. Methods: Two renal units from a female pig were used for the current experiment. Sixteen distinct areas were defined. Each area included three parallel lines, which were made with the three different laser technologies. The VB Ho:YAG laser was used for the first line and the Ho:YAG laser in the “Magneto mode” was used to generate the second line, while the third line was performed with a TFL in short pulse mode. The same laser settings (1 J/10 Hz/10 W) and the same fiber diameter (200 μm) were used for all three laser incisions. The same surgeon performed all incisions with a standardized and repeatable technique, controlling hand speed and distance of laser fiber from kidney surface using the stabilization setup. Sections of the selected areas produced distinct paraffin blocks, each one containing three parallel laser lines. Two independent pathologists evaluated the incision depth, incision width, coagulation depth and carbonization effect of the three different lasers. Results: Although the incision depth and the carbonization effect were comparable between the three lasers, incision width and coagulation depth showed a statistically significant difference. Median incision width was 1.17 (1.04, 1.99) mm for the VB Ho:YAG laser, 1.05 (0.89, 1.50) mm for the Magneto Ho:YAG laser and 0.82 (0.65, 0.88) mm for the TFL (p = 0.001). The coagulation depth was 0.49 (0.41, 0.56) mm for the VB Ho:YAG laser, 0.51 (0.39, 0.59) mm for the Magneto Ho:YAG laser and 0.18 (0.17, 0.23) mm for the TFL (p < 0.001). During post hoc analysis for the three comparisons, the differences between the VB Ho:YAG laser and TFL and between the Magneto Ho:YAG laser and TFL were statistically significant for both parameters. Conclusions: Both the VB and Magneto Ho:YAG lasers produced laser incisions with statistically significant greater incision width and coagulation depth than the TFL on the ex vivo model. Overall, the Magneto Ho:YAG laser was associated with the greatest median coagulation depth. Post Hoc Man–Whitney tests for the three comparisons revealed statistically significant differences only between the VB Ho:YAG laser and TFL and between the Magneto Ho:YAG laser and TFL. This finding could potentially be translated into better haemostasis during endourological soft tissue surgery. The implementation of additional studies, both experimental and clinical ones, is of outmost importance to draw safer conclusions. Full article

A recent phylogenetic reconstruction based on molecular genetic data revealed the presence of six well-supported clades within the freshwater fish family Nemacheilidae (Burmese, Eastern, Indochinese, Northern, Southern and Sundaic Clade). Species with the present generic name Schistura were found in three of the six major clades. Since the type species of Schistura belongs to the Southern Clade, species in other major clades can no longer be considered as members of the genus Schistura. We here review the generic status of ‘Schistura’ species in the Burmese Clade. ‘Schistura’ savona represents an isolated lineage, for which the genus Acoura is available, previously a synonym of Schistura. A second group comprises two species, ‘Schistura’ kohchangensis and the undescribed ‘Schistura’ cf. kohchangensis. We propose Pogonoschistura as a new generic name for these two species and name the undescribed species. Pogonoschistura differs from all other genera within the Burmese Clade by the combination of the following character states: caudal fin slightly emarginate to emarginate; adult males with a suborbital flap and a suborbital groove; dorsal fin with 7½ or 8½ branched rays; lips not covered by papillae; anus closer to anal-fin base than to pelvic-fin base; and lateral line incomplete, but exceeding vertical through pelvic-fin origin. Pogonoschistura pawaiensis, new species, is distinguished from its congener by having 9+8 branched rays in caudal fin, 7½ branched rays in dorsal fin and a distinct head pigmentation pattern. Full article

This study conducts a landslide deformation assessment in Tianshui, Gansu Province, on the Chinese Loess Plateau, utilizing the Small Baseline Subset InSAR (SBAS-InSAR) method integrated with velocity direction conversion and Z-score clustering. The Chinese Loess Plateau is one of the most landslide-prone regions in China due to frequent rains, strong topographical gradients and severe soil erosion. By constructing subsets of interferograms, SBAS-InSAR can mitigate the influence of decorrelation to a certain extent, making it a highly effective technique for monitoring regional surface deformation and identifying landslides. To overcome the limitations of the satellite’s one-dimensional Line-of-Sight (LOS) measurements and the challenge of distinguishing true landslide signals from noise, two optimization strategies were implemented. First, LOS velocities were projected onto the local steepest slope direction, assuming translational movement parallel to the slope. Second, a Z-score clustering algorithm was employed to aggregate measurement points with consistent kinematic signatures, enhancing identification robustness, with a slight trade-off in spatial completeness. Based on 205 Sentinel-1 Single-Look Complex (SLC) images acquired from 2014 to 2024, the integrated workflow identified 69 “active, very slow” and 63 “active, extremely slow” landslides. These results were validated through high-resolution historical optical imagery. Time series analysis reveals that creep deformation in this region is highly sensitive to seasonal rainfall patterns. This study demonstrates that the SBAS-InSAR post-processing framework provides a cost-effective, millimeter-scale solution for updating landslide inventories and supporting regional risk management and early warning systems in loess-covered terrains, with the exception of densely forested areas. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

Prostate cancer (PCa) is the most common male cancer and the second leading cause of cancer death in men. Androgen deprivation therapy (ADT) has been widely used as the first-line treatment for PCa. However, most PCa will progress to castration-resistant PCa (CRPC) that resists ADT 1 to 3 years after the treatment. Steroidogenesis from cholesterol is one of the mechanisms leading to ADT resistance. In PCa cells, low-density lipoprotein (LDL) mediated uptake is the major venue to acquire cholesterol. However, the mechanism of regulating this process is not fully understood. Fibroblast growth factor receptor 1 (FGFR1) is a receptor tyrosine kinase (RTK) that is ectopically expressed in PCa cells and promotes PCa progression by activating downstream signaling pathways. To comprehensively determine the roles of FGFR1 in PCa, we generated FGFR1-null DU145 cells and compared the transcriptomes of FGFR1-null and wild-type cells. We found that ablation of FGFR1 reduced the expression of genes promoting LDL uptake and de novo synthesis of cholesterol, thereby reducing the overall cholesterol pool in PCa cells. Detailed mechanistic studies further revealed that FGFR1 boosted the activation of sterol regulatory element-binding protein 2 (SREBP2) through ERK-dependent phosphorylation and cleavage, which, in turn, increased the expression of low-density lipoprotein receptor (LDLR) and enzymes involved in de novo cholesterol synthesis. Furthermore, in silico analyses demonstrated that high expression of FGFR1 was associated with high LDLR expression and clinicopathological features in PCa. Collectively, our data unveiled a previously unrecognized therapeutic avenue for CRPC by targeting FGFR1-driven cholesterol uptake and de novo synthesis. Full article

Emerging landslides and severe floods highlight the urgent need to analyse and support predictive models and early warning systems. Soil moisture is a crucial parameter and it can now be determined from space with a resolution of a few tens of meters, potentially leading to the continuous global monitoring of landslide risk. We address this issue by determining the volumetric water content (VWC) of a testbed in Southern Italy (bare soil with significant flood and landslide hazard) through the comparison of two different satellite observations on the same day. In the first observation (Sentinel-1 mission of the European Space Agency, C-band Synthetic Aperture Radar (SAR)), the back-scattered radar signal is used to determine the VWC from the dielectric constant in the microwave range, using a time-series approach to calibrate the algorithm. In the second observation (hyperspectral PRISMA mission of the Italian Space Agency), the short-wave infrared (SWIR) reflectance spectra are used to calculate the VWC from the spectral weight of a vibrational absorption line of liquid water (wavelengths 1800–1950 nm). As the main result, we obtained a Pearson’s correlation coefficient of $0.4$ between the VWC values measured with the two techniques and a separate ground-truth confirmation of absolute VWC values in the range of 0.10–0.30 within $\pm 0.05$. This overlap validates that both SAR and hyperspectral data can be well calibrated and mapped with 30 m ground resolution, given the absence of artifacts or anomalies in this particular testbed (e.g., vegetation canopy or cloud presence). If hyperspectral data in the SWIR range become more broadly available in the future, our systematic procedure to synchronise these two technologies in both space and time can be further adapted to cross-validate the global high-resolution soil moisture dataset. Ultimately, multi-mission data integration could lead to quasi-real-time hydrogeological risk monitoring from space. Full article

Curling presents a challenging continuous-control problem in which shot outcomes depend on long-horizon interactions between complex physical dynamics, strategic intent, and opponent responses. Despite recent progress in applying reinforcement learning (RL) to games and sports, curling lacks a unified environment that jointly supports stable, rule-consistent simulation, structured state abstraction, and scalable agent training. To address this gap, we introduce a comprehensive learning framework for curling AI, consisting of a full-sized simulation environment, a task-aligned Markov decision process (MDP) formulation, and a two-phase training strategy designed for stable long-horizon optimization. First, we propose a novel MDP formulation that incorporates stone configuration, game context, and dynamic scoring factors, enabling an RL agent to reason simultaneously about physical feasibility and strategic desirability. Second, we present a two-phase curriculum learning procedure that significantly improves sample efficiency: Phase 1 trains the agent to master delivery mechanics by rewarding accurate placement around the tee line, while Phase 2 transitions to strategic learning with score-based rewards that encourage offensive and defensive planning. This staged training stabilizes policy learning and reduces the difficulty of direct exploration in the full curling action space. We integrate this MDP and training procedure into a unified Curling RL Framework, built upon a custom simulator designed for stability, reproducibility, and efficient RL training and a self-play mechanism tailored for strategic decision-making. Agent policies are optimized using Soft Actor–Critic (SAC), an entropy-regularized off-policy algorithm designed for continuous control. As a case study, we compare the learned agent’s shot patterns with elite match records from the men’s division of the Le Gruyère AOP European Curling Championships 2023, using 6512 extracted shot images. Experimental results demonstrate that the proposed framework learns diverse, human-like curling shots and outperforms ablated variants across both learning curves and head-to-head evaluations. Beyond curling, our framework provides a principled template for developing RL agents in physics-driven, strategy-intensive sports environments. Full article