Search Results (4,465)

Assessing the level of circularity in the Hotel, Restaurant and Catering (HoReCa) sector is a significant challenge due to the lack of standardized quantification methods and the absence of structured environmental and material accounting systems, features that are typical of a sector largely composed of micro-enterprises. The technical standard UNI/TS 11820:2024 has developed a set of 71 indicators for the circular economy, structured across six domains (material resources and components; energy and water; waste and emissions; logistics; products and services; and human resources, assets, policies, and sustainability), allowing the assessment of circularity levels in a replicable and comparable manner. The present research measures circularity in a table-service restaurant micro-enterprise, which has voluntarily adopted circular economy practices since its foundation. The purpose is to test the applicability of UNI/TS 11820:2024 in the HoReCa context, improve knowledge about this technical standard, and highlight its strengths and weaknesses from the managerial, methodological and public authorities’ perspective. The overall organization’s circularity score achieved is 31.88%, with performance ranging from 14.40% for “material resources and components” to 56.25% for “human resources, assets and policies”. Although UNI/TS 11820:2024 aims at bridging theoretical and practical gaps towards a harmonized set of measurement tools, sector-specific indicators for the foodservice context remain underrepresented, and public authorities and universities should promote both basic and advanced education in the field of circular economy measurement to support wider adoption. Full article

Accurate identification of arable land, as the foundation of the high-standard farmland construction, impacts the crop layout, accurate management of water and fertilizers, and intelligent control. Due to the 10-m resolution limitation of Sentinel-2 imagery, there is feature overlap within individual pixels of the satellite imagery. This leads to fragmented semantic features during farmland identification, and adjacent plots often appear unclear and intertwined. To address these issues, a Hierarchical Agricultural Segmentation Network (HASNet) was proposed. Built upon the classic encoder-decoder structure, this HASNet model incorporates an expanded feature enhancer (DFE) module to recover weak features and reconstruct cropland features (e.g., edges and shapes) that are obscured by mixed pixels. It also introduces a lightweight strip spatial attention (LSSA) mechanism to capture long-range features unique to farmland. Furthermore, it used a pyramid decoding module (PDM) to refine cropland parcel boundaries. Taking a farm in Xinjiang Uygur Autonomous Region, a semantic segmentation dataset of cultivated land was constructed based on Sentinel-2 imagery. Through accuracy comparisons, visualizations, and inferences, HASNet achieved an MIoU of 88.52% and a Kappa coefficient of 87.82%, outperforming mainstream models such as Unetformer and MPFUnet. Ablation experiments confirmed the effectiveness of the DFE, LSSA, and PDM modules in feature capture and edge refinement. The large-scale image sliding inference experiment prevented the seam effect and demonstrated its practicality. In summary, HASNet provides low-cost technical and theoretical support for the intelligent monitoring of high-standard farmland. Full article

Simulation-based training is increasingly used to support skill acquisition in endoscopic sinus surgery (ESS), although many existing simulators lack objective methods for performance evaluation. This study aimed to assess the face and construct validity of a patient-specific, multi-material 3D-printed sinonasal simulator for ESS using both structured technical scoring and postoperative radiological analysis. Fifteen surgeons with different levels of experience (novices, intermediates, and experts; n = 5 per group) performed a standardized sequence of ESS procedures on identical 3D-printed models, including dacryocystorhinostomy, uncinectomy, maxillary antrostomy, anterior and posterior ethmoidectomy, sphenoidotomy, and frontal sinusotomy (DRAF I). Surgical performance was evaluated on video recordings using a modified Objective Structured Assessment of Technical Skills (OSATS) score. After simulation, each model underwent computed tomography (CT) scanning and a dedicated radiological checklist was applied to assess the adequacy of surgical steps, dimensional parameters of enlarged sinus ostia, and potential procedural complications. Mean OSATS scores differed significantly among expertise levels, with experts achieving higher scores (48.0 ± 1.9) than intermediates (39.4 ± 3.4) and novices (28.4 ± 3.8) (p < 0.01). CT analysis showed a significantly greater extent of ethmoidal cell removal in experts compared with novices (87.5% vs. 57.5%, p = 0.02) and a larger latero-lateral diameter of the frontoethmoidal recess compared with intermediates (p = 0.04). Questionnaire results indicated high perceived educational value, particularly among novices, despite some limitations in haptic realism. While the simulator appears to be a promising tool for ESS training, further studies are required to validate its effectiveness in improving surgical performance. Full article

Against the backdrop of the global advancement of carbon neutrality goals and the energy transition in the building sector, zero-carbon buildings have emerged as pivotal enablers for achieving carbon neutrality in the construction industry. The rule-based scheduling of energy storage systems (ESS) is critical to enhancing energy efficiency and economic performance of buildings. This study takes the Jinan Zero-Carbon Operation Center Project in Shandong Province as the research object, developing a comprehensive technical framework covering the entire process from design to operation, and investigates the rule-based design and ESS scheduling strategies in response to Shandong’s newly implemented seasonal time-of-use (TOU) electricity pricing policy. First, core performance indicators are defined in accordance with national evaluation standards for zero-carbon buildings. Hourly building energy loads and photovoltaic (PV) generation profiles are simulated over a full year, which serves as the basis for determining the optimal PV installed capacity and ESS sizing. Second, an ESS scheduling strategy integrating PV generation forecasting and the seasonal TOU electricity price structure is formulated, with clear charging and discharging logic defined. Finally, the operational and economic performance of different scheduling modes are evaluated and compared through case studies. The results show that the annual PV generation ratio reaches 101.38%, with a self-consumption rate of 73% and a self-sufficiency rate of 72%, all meeting the core requirements for zero-carbon buildings. Compared with the conventional real-time scheduling mode (Mode 1), the proposed optimized mode (Mode 2) that incorporates TOU pricing and PV forecasting achieves an annual operational cost saving of 367,349 CNY, corresponding to a reduction of 47.02%. Distinct seasonal variations in core indicators are also observed: the PV generation ratio is lower in summer and winter but the self-consumption rate is higher, with the opposite trend in spring and autumn. The proposed technical framework and scheduling strategy provide practical guidance for the design and operational optimization of zero-carbon buildings and offer decision-making support for ESS operation under TOU electricity pricing policies. Full article

Grassland fire is one of the major disasters threatening regional ecological security. Its occurrence, development, and spread are closely related to the spatial distribution and coverage of surface vegetation and snow cover across grassland areas. As the primary combustible fuel source, higher vegetation coverage increases fuel load and continuity, thereby directly determining grassland fire danger levels and accelerating fire spread velocity. In contrast, snow cover imposes an indirect regulatory effect on the spatiotemporal pattern of fire danger factors: it lowers surface temperature, raises near-surface humidity, and restricts the germination and growth of herbaceous vegetation in cold seasons, which effectively reduces available combustible materials and weakens regional fire hazard conditions. Therefore, accurately obtaining the coverage status of vegetation (direct combustible fuel factor) and snow cover (indirect fire-regulating factor) in complex grassland scenarios is the essential premise for reliable grassland fire danger monitoring, early warning, disaster prevention and control, and regional ecological management. Aiming at the practical problems in complex grassland scenarios (such as undulating terrain, uneven vegetation growth, large differences in snow depth, and complex lighting conditions), including difficulty in extracting vegetation and snow-covered areas, blurred and confusing boundaries, and low accuracy in coverage calculation, which seriously restrict the technical bottleneck of precise monitoring of grassland fire danger factors, this study takes near-ground images collected by grassland fire danger factor monitoring stations as the core data source, and proposes an improved UNet image segmentation model combined with image segmentation technology and deep learning methods to realize precise extraction of vegetation and snow-covered areas and efficient calculation of coverage in complex scenarios. To improve the model’s feature extraction ability, boundary localization accuracy, and reduce model parameters and computational overhead, the CBAM-ASPP (Convolutional Block Attention Module—Atrous Spatial Pyramid Pooling) module is integrated at the end of the encoding path. The attention mechanism is used to enhance the weight of key features, and the multi-scale receptive field of atrous spatial pyramid pooling is utilized to strengthen the model’s ability to fuse features of vegetation and snow areas of different scales. The residual attention mechanism is introduced in the upsampling stage to effectively alleviate the gradient disappearance problem, improve the model’s ability to accurately locate the boundaries of vegetation and snow areas, and reduce segmentation errors. In the training process, a dynamically weighted hybrid loss function is adopted to dynamically adjust the weights according to the segmentation difficulty of different types of samples during training, optimize the model training effect, and improve the segmentation accuracy and generalization ability. Experiments were conducted using near-ground images of typical complex grassland scenarios as the dataset, and the performance of the proposed model was verified through comparative experiments. The results show that in the vegetation segmentation task, the mean Intersection over Union (mIoU) of the model reaches 84.70%, and the accuracy rate is 91.28%, which are 1.48 and 1.58 percentage points higher than those of the standard UNet model, respectively. In the snow segmentation task, the mIoU of the model reaches 92.74%, and the accuracy rate is 94.19%, which are 2.39 and 2.36 percentage points higher than those of the standard UNet model, respectively. At the same time, the number of parameters of the model is reduced by 12.85% compared with the standard UNet. Also, its comprehensive performance is significantly better than that of mainstream image segmentation models such as FCN, SegNet, and DeepLabv3+. Based on the standardized time-series data retrieved by the optimized segmentation model, this study further constructs a Grassland Fire Risk Index (GFRI) using the Analytic Hierarchy Process (AHP). Pearson correlation verification confirms that the GFRI has an extremely significant positive correlation with historical fire frequency, accurately capturing the seasonal dynamic rhythm of regional grassland fire occurrence. This integrated framework of intelligent segmentation and fire risk quantification provides a reliable technical solution for grassland fire factor monitoring, dynamic fire risk assessment, early warning systems, and refined regional ecological management. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly proposed to improve surgical planning, guidance, and postoperative surveillance. Yet many promising applications remain disconnected from the full surgical pathway and the feasible limitations of clinical deployment. In contrast to prior reviews that primarily catalog AI use cases, this review combines the literature to define the translational pathway—from label design through staged validation to workflow integration—required for clinically deployable computed tomography (CT)-based surgical AI. CT and particularly computed tomography angiography (CTA) are especially usable sources for surgical AI because they provide a standardized three-dimensional anatomic model that is already embedded in many clinical workflows. In autologous breast reconstruction, deep inferior epigastric perforator (DIEP) flap CTA offers an unusually strong model system: the anatomy is discrete, surgeon decisions are actionable, and downstream operative and postoperative outcomes are measurable. These characteristics make DIEP reconstruction suitable not only for technical model development, but also for exacting testing of how CT-based AI should be annotated, validated, displayed, and governed. Methods: This focused narrative review combines evidence across the surgical workflow, spanning preoperative planning and risk stratification, intraoperative support, and postoperative monitoring. Reporting standards, implementation frameworks, governance, and regulatory sources were also considered when directly relevant to clinical deployment. Results: Across the available literature on breast reconstruction with the DIEP flap, preoperative CTA has been associated with reductions in operative time of approximately 54–76 min in individual studies. Semi-automated perforator mapping can reduce review time from 2 to 3 h to approximately 30 min. Intraoperative extended-reality tools and surgeon-facing navigation systems illustrate the importance of the ‘last mile’ of translation, while postoperative monitoring models show how imaging-linked data can support a closed-loop learning system. Across these stages, recurring limits include target mismatch, weak external validation, protocol variability, inconsistent reporting, limited subgroup analysis, and inadequate integration of economic and governance considerations. Conclusions: We argue that the next important step is not a generic autonomous model, but a clinically deployable DIEP-CTA-AI program. The practical blueprint proposed here is staged: structured anatomical labels, separate imaging, surgeons’ decisions, and outcome reference standards, dense intermediate endpoints, retrospective and external validation, reader studies, prospective silent deployment, and workflow-impact assessment. If implemented in this way, DIEP flap CTA can serve as a practical blueprint for CT-based AI translation in surgery more broadly. Full article

Background: Haptic simulation technologies are increasingly integrated into preclinical dental education to support procedural skill development. However, the extent to which haptic simulators improve performance compared to traditional phantom-head-based training remains unclear. Our goal is to systematically evaluate the effectiveness of haptic simulators in operative dentistry training, compared with conventional approaches. Methods: A systematic literature search was conducted in PubMed, Scopus, and Cochrane (2015–2025), complemented by manual searching, to identify studies evaluating virtual reality haptic simulators in preclinical operative dentistry education. The search strategy, structured according to the PICO framework, included preclinical undergraduate dental students, interventions with virtual reality haptic simulators, comparisons with conventional methods, and objective assessment of technical performance. Relevant data were extracted in a standardized manner, and the methodological quality of randomized controlled trials was assessed using RoB 2.0, while non-randomized studies were evaluated using ROBINS-I v2. Results: Of the 66 identified articles, 5 studies were included. The use of virtual reality simulators with haptic feedback in preclinical dental students was associated with increased efficiency in cavity preparation, reflected by reduced execution time and improved learning curves, as well as specific technical gains such as depth control. Overall cavity preparation quality was comparable to that achieved with conventional methods, with virtual reality being well accepted as an effective complementary tool in preclinical operative dentistry education. Conclusions: Haptic simulators appear effective for early preclinical skill development in operative dentistry and may complement traditional instruction. Evidence remains insufficient to confirm superiority over conventional methods or long-term clinical benefit. Higher-quality multicenter randomized trials with standardized performance measures are needed to strengthen the evidence base. Full article

The target price for naval equipment orders is driven by the coupling of multidimensional technical and economic factors, exhibiting typical characteristics such as high dimensionality, strong nonlinearity, multicollinearity, and small-sample fluctuations. Traditional cost estimation methods struggle to achieve high-precision fitting and interpretable decision support. To address these issues, this paper constructs an integrated prediction model that combines Boruta–Lasso two-stage feature selection, grid search-optimized CatBoost, and SHAP interpretability analysis. First, the Boruta algorithm is used for rough screening of feature significance, then Lasso regression is applied for sparse fine screening, effectively eliminating redundant features and significantly mitigating multicollinearity; grid search and five-fold repeated cross-validation are employed to optimize CatBoost hyperparameters, while 10 repeated experiments with random seeds are conducted to verify model generalization robustness. SHAP is used to quantify the marginal contribution of features, revealing nonlinear associations and statistical response transition points between core features and price. This study is based on 33 publicly available real data from main combat vessels, from which 198 modeling samples were generated through interpolation-based small-sample data augmentation. The interpolated samples were only used for data augmentation and were not considered independent empirical samples. All core conclusions were validated on the 33 original real samples, and there are no missing values in the dataset. Experimental results show that the proposed model achieved the best individual results on the test set, with a coefficient of determination of R² = 0.8949, root mean square error RMSE = 0.0554, and mean absolute error MAE = 0.0476. Across 10 repeated robustness experiments, the average results were R² = 0.8828, RMSE = 0.0586, and MAE = 0.0529, with overall performance better than comparison models such as XGBoost, random forest, and standard CatBoost. Ablation experiments validated the effectiveness of the two-stage Boruta–Lasso selection strategy in improving model accuracy and stability. SHAP attribution analysis shows that full-load displacement, number of vertical missile launch cells, number of phased array radars, and combat capability are core features highly correlated with price, all showing significant nonlinear positive correlations and clear statistical response transition points. The dataset in this study has no missing values, is entirely constructed based on publicly traceable data, and does not include confidential information such as internal shipyard costs. The findings reflect statistical associations rather than causal effects. However, the sample size and ship-type coverage are limited, so the model’s applicability is somewhat constrained, and its generalization ability needs to be further verified on larger-scale, multi-ship-type independent datasets. This model combines high prediction accuracy, strong robustness, and good interpretability, providing reliable technical support for ship equipment procurement pricing demonstration, full lifecycle cost management, and scientific procurement decision-making. Full article

Background: Retinal and optic nerve disorders remain major causes of visual morbidity worldwide. Ocular fundus flavoprotein fluorescence (FPF) imaging has emerged as a potential noninvasive biomarker of mitochondrial dysfunction for earlier detection and evaluation of disease severity. Methods: We conducted a Systematic Scoping Review of the diagnostic and correlational utility of quantitative FPF parameters in retinal and optic nerve diseases compared with healthy controls. Following PRISMA-ScR guidelines, we searched MEDLINE, Web of Science, Scopus, and CENTRAL for peer-reviewed human studies available online before 31 December 2025. Results: Seventeen studies were included, encompassing 1914 eyes and 1339 participants, and were predominantly cross-sectional. In healthy eyes, mean macular and optic nerve head FPF intensity were reported as 24.1 ± 12.2 gsu and 30.6 ± 14.6 gsu, respectively. Higher signals were reported in several disorders, including diabetes mellitus (76.0 [67.0–92.0] gsu), neovascular age-related macular degeneration (67.47 ± 17.77 gsu), and retinitis pigmentosa (50.5 ± 12.2 gsu). However, lower, unchanged, or stage-dependent signals were also observed within the same disease categories. Interpretation across studies was limited by substantial heterogeneity in patient selection, disease definitions, imaging protocols, control groups, and FPF outcome metrics. The precise cellular and sublayer origin of the detected signal also remains challenging to determine. Conclusions: Ocular fundus FPF imaging provides promising metabolic insight into retinal and optic nerve diseases. However, current evidence remains heterogeneous and largely cross-sectional, limiting clinical interpretability and generalizability. Longitudinal studies, technical standardization, and multimodal integration are needed to define reproducible disease-specific FPF profiles and improve translational applicability. Full article

Building details are often treated as technical externalities, subordinate to form, image and architectural narrative. Reading details as liminal spaces reverses that hierarchy. The joint concentrates transitions between the inside and outside, public and private, exposure and protection, and these transitions are constructed as intervals, experienced through thickness, reveal, edge condition, shadow, touch, and the small resistances that accompany crossing. The article develops its analysis through archival hand-drawn detail drawings from the Azrieli Architectural Archive. It defines building details as both technical assemblies and threshold devices, points where architecture becomes accountable to perception as well as to climate, labor, regulation, and everyday use. A semiotic reading of large-scale sheets shows how line weight, hatching, notation, and layout encode priorities, marking boundaries between what must be precisely resolved and what may remain adjustable. The archive is treated as a laboratory of “detail families,” recurring junction types such as windows, stairs, and envelope edges that reveal office-specific languages of joining. Two case studies, by the architects Ram Karmi and Arieh Sharon with Eldar Sharon, show how micro-variations in depth, overlap, and edge control tune thresholds, producing perceptual tipping points where comfort can shift into irritation, calm into unease, and openness into vulnerability. Although grounded in a local archive, the argument addresses a broader condition of contemporary practice: standardization and digital production chains can relocate authorship and responsibility away from the joint, precisely where buildings most affect everyday conduct. The paper proposes a liminal literacy of detailing as both a historiographic method and a design ethic aimed at making threshold decisions legible, contestable, and accountable in present-day workflows. Full article

This paper presents a comprehensive review on near-infrared (NIR) spectroscopy applied in food analysis, systematically elaborating its core principles, widespread industrial applications, advanced chemometric strategies, and cutting-edge technological progress. NIR spectroscopy (760–2500 nm), characterized by rapid, non-destructive detection and minimal sample preparation, has been widely implemented in quality evaluation and safety monitoring of grains, meat, fruits and vegetables, dairy, fermented products, tea, coffee, and other processed foods, realizing quantitative analysis of nutrients, freshness assessment, texture prediction, adulteration identification, origin tracing, and rapid preliminary screening of toxin/pesticide residues. A series of chemometric methods, including spectral preprocessing (SNV, MSC, S-G smoothing), feature extraction, and variable selection (CARS, PSO-CMW, ICPA), as well as linear/nonlinear modeling algorithms (PLS, SVM, BP-ANN, fuzzy clustering) significantly boost the accuracy and robustness of spectral analysis. Meanwhile, portable NIR devices and online monitoring systems promote on-site and real-time detection in food supply chains. Despite existing challenges such as calibration transfer, matrix interference, and model generalization, innovations like multimodal data fusion, deep learning integration, and intelligent algorithm optimization offer effective solutions. This review not only summarizes the latest research advances of NIR technology in the food field but also emphasizes its significant advantages as a rapid, non-destructive complementary tool to traditional destructive detection methods, providing theoretical support and technical reference for accelerating the industrial translation and standardized application of NIR spectroscopy, and ultimately safeguarding global food quality and safety. Full article

Down quality is the core evaluation indicator of thermal insulation products, and its grade determination strictly complies with the down content index specified in the national standard GB/T 17685-2016 Feather and Down. Traditional down quality inspection adopts manual sorting and weighing methods, which are plagued by low efficiency, strong subjectivity and high error rates, thereby restricting the intelligent upgrading of the down industry. This study aims to develop an automatic down detection and quantitative grading method conforming to national standards based on deep learning. A down dataset consisting of 632 RGB images is constructed, with each image containing 5–10 individual down samples and covering five categories: mature down clusters, immature down clusters, down filaments, feathers, and yellow-tail down. Three mainstream frameworks including YOLOv8, YOLOv11 and YOLOv26 are trained for performance comparison. Precision, recall, mAP@50 and mAP@50-95 are adopted as evaluation metrics. In addition, this paper proposes a research idea for down content calculation and automatic classification and grading of down quality in accordance with relevant national standards. The experimental results demonstrate that the latest models do not necessarily achieve the optimal performance. The newly released YOLOv26n and YOLOv26m exhibit relatively low accuracy in the down detection task, with mAP@50 values of only 0.98556 and 0.99077, and recall rates of 0.95032 and 0.97848, respectively, failing to outperform their previous-generation counterparts. In contrast, YOLOv11n achieves the best comprehensive performance, with an mAP@50 of 0.99416, a precision of 0.99544, a recall of 0.99722, and an mAP@50-95 of 0.63464. Meanwhile, the model has only 2.58 M parameters, a computational complexity of 6.3 GFLOPs, and a single training time of approximately 6.7 min, achieving an optimal balance between detection accuracy and computational efficiency. All models show the highest detection accuracy for mature down clusters and yellow-tailed down, while slight confusion exists between immature down clusters and down filaments. This study verifies the feasibility of the YOLO series models in down quality inspection in accordance with national standards, and reveals that model architecture iteration does not necessarily lead to performance improvement on specific industrial datasets. The lightweight and robustly designed YOLOv11n presents greater practical value. The intelligent detection scheme proposed in this paper can assist in optimizing the traditional manual quality inspection workflow, alleviating the burden of manual counting and reducing subjective errors. It provides new ideas and technical references for the rapid screening and objective determination of down quality. Furthermore, the proposed research framework for automatic classification and grading of down quality is expected to promote the development of down quality inspection toward standardization, intelligence, and automation in the future. Full article

Background: Surveillance of non-muscle-invasive bladder cancer (NMIBC) relies on cystoscopy and urine cytology, both of which have well-recognised limitations. Molecular urine assays have been developed to reduce the burden of invasive surveillance, yet their real-world clinical utility remains uncertain. Uromonitor^® is a quantitative PCR-based assay targeting hotspot variants in the TERT promoter, FGFR3, and KRAS, which are frequently altered in urothelial carcinoma. We evaluated the performance of Uromonitor^® in routine clinical practice and assessed its technical reproducibility. Methods: Uromonitor^® diagnostic test accuracy was retrospectively calculated from samples from patients undergoing investigation for suspected bladder cancer (n = 64) or surveillance (n = 30) following a prior diagnosis at a tertiary referral centre between 2021 and 2023. Uromonitor^® results were compared with histology where available (n = 49, 52%), or with contemporaneous cystoscopy and urine cytology findings (n = 45, 48%). This pragmatic dual reference standard reflects routine clinical practice but may introduce some heterogeneity in diagnostic accuracy verification. A prospective in-house verification cohort was used to assess inter-laboratory reproducibility. Discordant cases underwent orthogonal next-generation sequencing (NGS) analysis. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated for the Uromonitor^® against the standard of care. Results: Ninety-four patients were included in the clinical performance analysis. Overall sensitivity, specificity, PPV, NPV and overall accuracy for Uromonitor^® were 38%, 88%, 63%, 72% and 70%, respectively. Sensitivity was higher in the diagnostic setting (47%; 95% CI 27.3–68.3%) than during surveillance (23%; 95% CI 8.2–50.2%). Several false-negative cases in the verification cohort harboured variants either detectable by NGS at variant allele frequencies below or slightly above the assay’s limit of detection or variants not covered by the assay hotspot design. Inter-laboratory reproducibility was excellent, with 100% concordance observed in the verification cohort. Conclusions: In a real-world clinical setting, Uromonitor^® demonstrated high specificity but limited sensitivity for detection of bladder cancer, particularly during surveillance. A negative result does not reliably exclude recurrence. Assay sensitivity thresholds and restricted variant coverage appear to be key contributors to false-negative results. These findings highlight the need for cautious clinical integration of Uromonitor^®. It is unclear whether this approach has sufficient sensitivity in surveillance to safely reduce cystoscopy frequency. This underscores the need for further refinement of urine-based molecular assays, including a need for enhanced sensitivity and broader mutational coverage before routine clinical adoption. Full article

Our study examines the growing role of Explainable Artificial Intelligence (XAI) in cancer medical imaging, where transparency and interpretability are essential for trustworthy clinical decision making. Using a PRISMA-guided systematic literature review, 926 records published between 2020 and 2026 were identified from major databases, with 46 studies meeting the inclusion criteria after screening and quality assessment. The review systematically analyzes XAI techniques, model architectures, evaluation approaches, interpretability mechanisms, challenges, and future research directions. The findings show that gradient-based methods, particularly Grad-CAM, dominate the field due to their ease of integration with convolutional neural networks. At the same time, complementary approaches such as LIME, SHAP, and Integrated Gradients provide additional attribution insights. Evaluation practices remain heterogeneous, with a strong reliance on qualitative visual inspection and limited standardized quantitative frameworks. XAI contributes to interpretability primarily through spatial localization, feature attribution, and clinical decision support; however, challenges persist, including instability in explanations, coarse localization, high computational cost, and limited compatibility with transformer-based models. Overall, while XAI enhances transparency in cancer imaging, its clinical reliability remains constrained by methodological and technical limitations. Future work should focus on standardized evaluation, clinician-centered validation, and the development of robust, multimodal, and architecture-aware explainability frameworks. Full article

Introduction: Navigated transcranial magnetic stimulation (nTMS) is an advanced, noninvasive method for stimulation-based functional brain mapping. Its main clinical value in neurosurgery lies in preoperative identification of eloquent cortical areas and the integration of functional information into neuronavigation-based surgical planning. State of the Art: This narrative review with a structured literature search summarizes the historical and technical foundations of TMS/nTMS, but primarily focuses on neurosurgical applications, including motor and language mapping, comparison with functional MRI and direct cortical stimulation, safety considerations, and practical limitations. Broader neurological and therapeutic applications are discussed as contextual extensions rather than as a comprehensive disease-specific review. Clinical Implications: Current evidence is strongest for preoperative motor mapping in patients with tumors located in or near the motor–eloquent cortex. Language mapping, neurological diagnostics, and therapeutic repetitive TMS (rTMS) applications remain more heterogeneous and require careful interpretation according to the level of evidence, protocol standardization, and patient selection. Future Directions: Further multicenter studies, standardized mapping protocols, integration with advanced imaging and tractography, and health-system implementation strategies are needed to define the optimal role of nTMS in personalized neurosurgical and neurological care. Full article