Search Results (1,534)

Purpose: To investigate regional changes in tear film quality associated with orthokeratology (Ortho-K) lens wear using high-resolution spatial mapping and to evaluate the potential of artificial intelligence (AI) models in anticipating these changes. Methods: This study analysed tear film quality in 92 Ortho-K wearers divided into three groups based on lens wear duration (10–29 days, 30–90 days, and ≥91 days). Placido-based topographer measurement was used to generate regional tear film maps before and after treatment. A custom MATLAB pipeline enabled regional comparisons and statistical mapping. A feedforward neural network was trained to forecast local tear film quality using spatial data. Results: Single-value global mean metrics showed minimal changes in tear film quality across groups. However, regional mean mapping revealed significant mid-peripheral and peripheral deterioration over time, particularly in nasal and temporal corneal zones. These changes were often overlooked by global averaging and remained invisible through tear film breakup time (TBUT) measurements. The AI model predicted spatial tear quality with high accuracy (R ≥ 0.9 in testing), capturing nuanced regional variations. Conclusions: The regional analysis uncovers subtle, clinically relevant tear film disruptions caused by Ortho-K lens wear, particularly in peripheral areas. These insights challenge the adequacy of traditional single-value global mean assessments. The AI model demonstrates the potential for non-invasive, predictive evaluation of tear stability, supporting more personalised and effective Ortho-K care. Full article

Background/Objectives: Sarcoidosis is a granulomatous disorder of unknown etiology that can affect multiple organs, including the oral cavity. This study aimed to compare the clinical and demographic characteristics of sarcoidosis cases with and without bone involvement in the jaw. Methods: A systematic review of the case reports and case series of sarcoidosis in the oral cavity between 1943 to 2024 were analyzed. Variables assessed included age, sex, presenting symptoms, duration of symptoms, diagnosis methodology, treatment approaches, and outcomes. Results: A total of 59 studies reporting 77 patients were included, with a mean age of 43.3 yrs. Female predominance was noted in both, bone-involved (61.5%) and non-bone-involvement cases (72.5%). Patients with bone involvement often presented with localized symptoms such as loose teeth (34.6%), bone loss (69.2%), and nasal obstruction (15.4%), whereas non-bone-involvement cases frequently exhibited soft tissue manifestations, like swelling (38%) and bleeding (14%). Treatment typically involved surgical intervention and steroid therapy in both groups, with favorable outcomes achieved in most cases. Conclusions: This systematic review presents the most extensive analysis of oral sarcoidosis. Oral sarcoidosis presents as two distinct clinical entities based on bone involvement. Soft tissue lesions often serve as an early diagnostic clue for systemic disease, while bony manifestations suggest a later, more destructive complication. Recognizing this dichotomy is crucial for dentists and clinicians to ensure timely diagnosis and appropriate referral, and this underscores the oral cavity’s critical role as an indicator of systemic illness and mandates a multidisciplinary management strategy. Full article

This paper provides a comprehensive overview of the application of fractional calculus in modern optimization methods, with a focus on its impact in artificial intelligence (AI) and computational science. We examine how fractional-order derivatives have been integrated into traditional methodologies, including gradient descent, least mean squares algorithms, particle swarm optimization, and evolutionary methods. These modifications leverage the intrinsic memory and nonlocal features of fractional operators to enhance convergence, increase resilience in high-dimensional and non-linear environments, and achieve a better trade-off between exploration and exploitation. A systematic and chronological analysis of algorithmic developments from 2017 to 2025 is presented, together with representative pseudocode formulations and application cases spanning neural networks, adaptive filtering, control, and computer vision. Special attention is given to advances in variable- and adaptive-order formulations, hybrid models, and distributed optimization frameworks, which highlight the versatility of fractional-order methods in addressing complex optimization challenges in AI-driven and computational settings. Despite these benefits, persistent issues remain regarding computational overhead, parameter selection, and rigorous convergence analysis. This review aims to establish both a conceptual foundation and a practical reference for researchers seeking to apply fractional calculus in the development of next-generation optimization algorithms. Full article

The rapid and reliable acquisition of canopy-related metrics is essential for improving decision support in viticultural management, particularly when monitoring individual vines for targeted interventions. This study presents a spatially explicit workflow that integrates Uncrewed Aerial System (UAS) imagery, 3D point-cloud analysis, and Object-Based Image Analysis (OBIA) to detect and monitor individual grapevines throughout the growing season. Vines are identified directly from 3D point clouds without the need for prior training data or predefined row structures, achieving a mean Euclidean distance of 10.7 cm to the reference points. The OBIA framework segments vine vegetation based on spectral and geometric features without requiring pre-clipping or manual masking. All non-vine elements—including soil, grass, and infrastructure—are automatically excluded, and detailed canopy masks are created for each plant. Vegetation indices are computed exclusively from vine canopy objects, ensuring that soil signals and internal canopy gaps do not bias the results. This enables accurate per-vine assessment of vigour. NDRE values were calculated at three phenological stages—flowering, veraison, and harvest—and analyzed using Local Indicators of Spatial Association (LISA) to detect spatial clusters and outliers. In contrast to value-based clustering methods, LISA accounts for spatial continuity and neighborhood effects, allowing the detection of stable low-vigour zones, expanding high-vigour clusters, and early identification of isolated stressed vines. A strong correlation (R² = 0.73) between per-vine NDRE values and actual yield demonstrates that NDRE-derived vigour reliably reflects vine productivity. The method provides a transferable, data-driven framework for site-specific vineyard management, enabling timely interventions at the individual plant level before stress propagates spatially. Full article

In this paper, we research multi-robot coverage path planning (MCPP), which generates paths for agents to visit all target areas or points. This problem is common in various fields, such as agriculture, rescue, 3D scanning, and data collection. Algorithms to solve MCPP are generally categorized into online and offline methods. Online methods work in an unknown area, while offline methods generate a path for the known. Recently, offline MCPP has been researched through various approaches, such as graph clustering, DARP, genetic algorithms, and deep learning models. However, many previous algorithms can only be applied on grid-like environments. Therefore, this study introduces an offline MCPP algorithm that applies graph-adapted K-means and spanning tree coverage for robust operation in non-grid-structure maps such as road networks. To achieve this, we modify a cost function based on the travel distance by adjusting the referenced clustering algorithm. Moreover, we apply bipartite graph matching to reflect the initial positions of agents. We also introduce a cluster-level graph to alleviate local minima during clustering updates. We compare the proposed algorithm with existing methods in a grid environment to validate its stability, and evaluation on a road network digital twin validates its robustness across most environments. Full article

Geological modeling is generally based on deterministic models, which provide a single representation of reality. Probabilistic modeling is more appropriate when quantifying or understanding the uncertainty associated with a parameter of interest as it considers several equally probable geological scenarios. The object of this study is to quantify the uncertainty in the estimation of the minerals in the Punta Alegre gypsum deposit, by applying a new method based on the simple normal equation geostatistical simulation technique. The Punta Alegre gypsum deposit is a sedimentary deposit of clastic origin, formed by the complex redeposition of salts, gypsum and other sediments. To carry out this research, 50 equiprobable scenarios were simulated, reproducing overburden, gypsum series (different types of gypsum) and intercalated non-mineral lithologies (limestone and other rocks) in a network of nodes measuring 5 × 5 × 5 m, using a training image, composites and prior probability maps as input data. As a result of scaling the previously simulated geological units, three-dimensional models of volume proportions and estimation error for gypsum were obtained for panels measuring 10 × 10 × 5 m. The quantification of the uncertainty of the gypsum volume, determined by the root mean square error, established that the volume estimation error is small at a global scale (6.51%), given that there is no significant variation when comparing the deterministic model with the gypsum proportion model obtained from the 50 simulated scenarios. Conversely, at the local scale, there is a significant variation in gypsum volume of 42% in the 10 × 10 × 5 m panels with a future impact on recoverable mining resources, given the uncertainty at a local scale, which will cause an increase in mining dilution due to the inclusion of non-mineral lithologies within the extracted mineral that will be sent to the processing plant. On the other hand, it will cause changes in the mining company’s plan in areas where there are panels that were previously accounted for by the deterministic model as minerals and are not actually exploitable. Full article

Background/Objectives: Metallic surgical implants are commonly used in Orthopaedic surgery. There is a paucity of the literature on patient perspectives and awareness regarding their use. This study aims to investigate patients’ self-perceived awareness, knowledge and concerns toward metallic implant usage in Orthopaedic surgery, in order to provide a tailored and efficient means of pre-operative counselling. Methods: A single-centred, cross-sectional questionnaire-based study was performed in a single tertiary centre in Singapore. Patients between 21 and 75 years who were on follow-up with an Orthopaedic surgeon were recruited from the Orthopaedic specialist outpatient clinic. This questionnaire consisted of three main parts. Firstly, patients were asked to grade their self-perceived knowledge on metallic implants on a Likert scale of 1 to 5. The second part included questions designed to determine the level of knowledge of patients on metallic implants. For the third part, patients were asked to grade how comfortable they were with having metal implants in their bodies on a Likert scale of 0 (Strongly Disagree) to 4 (Strongly Agree). Results: A total of 100 patients were recruited, with 56 males and 44 females. The majority of the patients were Chinese (59%), and 32% had tertiary education. The self-perceived awareness regarding metallic implants was low with a median score of 3 (IQR 1–9) (1—unaware, 10—fully aware). There was no significant difference between younger and older patients (>50 years) or between patients of different educational levels. In total, 17% of the participants stated that they preferred to use non-metallic implants. The most significant concerns were surgical costs (51%), post-operation discomfort (50%) and potential rejection of metallic implants (50%). Conclusions: There is a poor level of self-reported awareness on metallic implants and a lack of knowledge regarding the use of metallic implants in Orthopaedic procedures among patients in our local population. The top concerns regarding the usage of metallic implants were cost, adverse reaction to metal and persistent discomfort. A significant percentage of patients prefer to use non-metallic implant options if available. This highlights the need for tailored pre-operative counselling for the provision of information and to address patients’ concerns accurately. Full article

To address the challenge of effective tracking of weather-induced power fluctuation trends in daytime PV power forecasting, this paper proposes a joint forecasting framework oriented to weather classification. For the weather classification module, a spectral clustering method incorporating multivariate feature fusion-based evaluation is introduced to address the limitation that conventional clustering models fail to effectively identify power fluctuations caused by dynamic weather variations. Simultaneously, to tackle non-stationary fluctuations and local abrupt changes in PV power forecasting, a non-stationary Transformer-BiLSTM model optimised using the Differentiated Creative Search (DCS) algorithm (DCS-NsT-BiLSTM)is proposed. This model enables the co-optimisation of global and local features under diverse weather patterns. The proposed method takes into consideration the climatic typology of PV power plants, thereby overcoming the insensitivity of traditional clustering models to high-dimensional non-stationary data. Furthermore, the approach utilises the novel intelligent optimisation algorithm DCS to update the key hyperparameters of the forecasting model, which in turn enhances the accuracy of day-ahead PV power generation forecasting. Applied to a photovoltaic power station in Jilin Province, China, this method reduced the mean root mean square error by 4.63% across various weather conditions, effectively validating the proposed methodology. Full article

Introduction. Cervical artery dissection (CAD) is a rare condition, being one of the leading causes of stroke in patients under the age of 45, with a reported prevalence of up to 20%. The management of CAD remains controversial due to its rarity and the lack of large-scale randomized controlled trials. The aim of this study was to report the long-term outcomes of CAD in a real-world setting. Methods. This retrospective, observational, single-center study included patients diagnosed with CAD between 2010 and 2019 (approval number: 153/2015/U/Oss/AOUBo). Clinical presentation, risk factors, and medical therapies were prospectively analyzed. Management strategies included both medical and interventional approaches. Follow-up consisted of annual clinical visits and carotid duplex ultrasound (DUS), with telephone interviews every six months. The primary endpoint was defined by the overall long-term stroke/death rate and in relation to the type of medical treatment, localization of the dissection and clinical manifestations. Results. A total of 62 patients were included, predominantly male (65%) with a mean age of 58 (±2) years. Thirteen dissections (21%) were trauma-related. CAD locations included the common carotid artery in 6 cases (10%), extracranial internal carotid artery in 29 (46%), intracranial internal carotid artery in 9 (14%), and vertebral artery in 16 (25%). One patient (2%) had dissections in both the extracranial internal carotid and vertebral arteries, and another (2%) in both the vertebral and basilar arteries. Bilateral dissections were observed in 5 patients (8%). Ischemic manifestations occurred in 43 patients (68%): 10 transient ischemic attacks (16%), 17 minor strokes (27%), and 16 major strokes (25%), with ischemic lesions on cerebral CT in 31 cases (72%). Fifty-eight (93%) patients were treated medically (anticoagulants and/or antiplatelets), while 4 patients (7%) underwent surgical or endovascular intervention. The mean follow-up was 81 ± 35 months. During this period, 2 patients (4%) experienced stroke and 15 (24%) died. The estimated 10-year survival rate was 71%, and the 10-year stroke/death-free survival rate was 70%. Among medically treated patients, the 10-year stroke/death-free survival was 86% for those on anticoagulation and 67% for those on antiplatelet therapy (p = 0.1). Patients presenting with ischemic symptoms had a lower estimated 10-year stroke/death-free survival rate compared to those with non-ischemic presentations (61% vs. 69%, p = 0.7). Patients with dissection of the common carotid artery had a significantly lower estimated 10-year stroke/death-free survival rate (25%), compared to dissections in other cervical arteries (p = 0.001). Conclusions. In this real-world, single-center experience, cervical artery dissection was associated with a favorable long-term prognosis in most cases, especially among patients managed conservatively with medical therapy. Stroke and mortality rates were relatively low during extended follow-up. Although no statistically significant difference was observed between anticoagulation and antiplatelet therapy, the trend favored anticoagulation for stroke/death-free survival. Patients with CCA dissections had significantly worse 10-year stroke/death-free survival compared to those with dissections in other cervical arteries. Full article

Discrimination between normal (fresh/non-frozen) and frozen-thawed beef is crucial for ensuring food safety. This paper proposed a novel, non-destructive and real-time you only look once for normal and frozen-thawed beef discrimination (YOLO-NF) model using deep learning techniques. The simple, parameter-free attention module (SimAM) and the squeeze and excitation (SE) attention mechanism were introduced to enhance the model’s performance. A total of 1200 beef samples were used, with their images captured by a charge-coupled device (CCD) camera. In the model development, specifically, the training set comprised 3888 images after data augmentation, while the validation set and test set each included 216 original images. Experimental results on the test set showed that the YOLO-NF model achieved precision, recall, F1-Score and mean average precision (mAP) of 95.5%, 95.2%, 95.3% and 98.6%, respectively, significantly outperforming YOLOv7, YOLOv5 and YOLOv8 models. Additionally, gradient-weighted class activation mapping (Grad-CAM) was adopted to interpret the model’s decision basis. Moreover, the model was deployed on the web interface for user convenience, and the discrimination time on the local server was 0.94 s per image, satisfying the requirements for real-time processing. This study provides a promising technique for high-performance and rapid meat quality assessment in food safety monitoring systems. Full article

We present a unified framework for fuzzy statistical convergence of Grünwald–Letnikov (GL) fractional differences in Bag–Samanta fuzzy normed linear spaces, addressing memory effects and nonlocality inherent to fractional-order models. Theoretically, we establish the uniqueness, linearity, and invariance of fuzzy statistical limits and prove a Cauchy characterization: fuzzy statistical convergence implies fuzzy statistical Cauchyness, while the converse holds in fuzzy-complete spaces (and in the completion, otherwise). We further develop an inclusion theory linking fuzzy strong Cesàro summability—including weighted means—to fuzzy statistical convergence. Via the discrete Q-operator, all statements transfer verbatim between nabla-left and delta-right GL forms, clarifying the binomial GL↔discrete Riemann–Liouville correspondence. Beyond structure, we propose density-based residual diagnostics for GL discretizations of fractional initial-value problems: when GL residuals are fuzzy statistically negligible, trajectories exhibit Ulam–Hyers-type robustness in the fuzzy topology. We also formulate a fuzzy Korovkin-type approximation principle under GL smoothing: Cesàro control on the test set $\{1,x,x^2\}$ propagates to arbitrary targets, yielding fuzzy statistical convergence for positive-operator sequences. Worked examples and an engineering-style case study (thermal balance with memory and bursty disturbances) illustrate how the diagnostics certify robustness of GL numerical schemes under sparse spikes and imprecise data. Full article

Few-Shot Object Detection (FSOD) aims to identify new object categories with a limited amount of labeled data, which holds broad application prospects in real-life scenarios. Previous approaches usually ignore attention to critical information, which leads to the generation of low-quality prototypes and suboptimal performance in few-shot scenarios. To overcome the defect, an improved FSOD network is proposed in this paper, which mimics the human visual attention mechanism by emphasizing areas that are semantically important and rich in spatial information. Specifically, an Importance-Weighted Local Adaptive Prototype module is first introduced, which highlights key local features of support samples, and more expressive class prototypes are generated by assigning greater weights to salient regions so that generalization ability is effectively enhanced under few-shot settings. Secondly, an Imbalanced Diversity Sampling module is utilized to select diverse and challenging negative sample prototypes, which enhances inter-class separability and reduces confusion among visually similar categories. Moreover, a Weighted Non-Linear Fusion module is designed to integrate various forms of feature interaction. The contributions of the feature interactions are modulated by learnable importance weights, which improve the effect of feature fusion. Extensive experiments on PASCAL VOC and MS COCO benchmarks validate the effectiveness of our method. The experimental results reflect the fact that the mean average precision from our method is improved by 2.84% on the PASCAL VOC dataset compared with Fine-Grained Prototypes Distillation (FPD), and the AP from our method surpasses the recent FPD baseline by 0.8% and 1.8% on the MS COCO dataset, respectively. Full article

The direct current resistivity method holds advantages such as rapid, efficient, and automatic data acquisition. It is an important geophysical exploration technology for monitoring dynamic changes in subsurface geology. However, this method has such issues as volume effect and non-uniqueness in inversion. To meet the demand for high-resolution direct current resistivity inversion of dynamic geological models characterized by discontinuous changes, this study proposed a cross-gradient constrained time-lapse inversion method, thereby enhancing inversion imaging accuracy. A cross-gradient constraint term between models was incorporated into the objective function of time-lapse inversion to constrain the structural consistency and highlight local resistivity changes. This method avoided excessively smooth imaging as often caused by over-reliance on a reference model in time-lapse inversion, thereby significantly improving both the spatial resolution and quantitative accuracy of direct current resistivity monitoring inversion images. Numerical examples confirmed that the proposed method delivers higher inversion imaging accuracy in identifying dynamic resistivity changes, evidenced by a substantially lower normalized mean-square error (MSE). Furthermore, physical model experiments and a case study confirmed the stability of this method under actual monitoring conditions. The proposed method provides a more precise and effective inversion imaging technique for refined monitoring of dynamic changes in subsurface geologic bodies. Full article

Objective: The high expression of prostate-specific membrane antigen (PSMA) associated with neovascularization in non-prostatic malignant tumors and metastatic lesions has been documented in many studies. By taking advantage of the absence of PSMA-related background activity in brain tissue, in recent years, PSMA has been used for the imaging of glial tumors, especially for postoperative follow-up. The aim of this prospective study was to investigate the diagnostic value of ⁶⁸Ga-PSMA-11 PET/CT by comparing ⁶⁸Ga-PSMA-11 PET/CT, ¹⁸F-FDG PET/CT, and MRI findings in patients with brain metastases (BM). Materials and Method: In this prospective study, 27 cases, 11 female and 16 male, with a mean age of 59.48 ± 12.21 years, were included. Patients diagnosed with BM on ¹⁸F-FDG PET/CT or CT/MRI at initial diagnosis or in the follow-up period were included in the study. PET findings of BM lesions obtained from ¹⁸F-FDG and ⁶⁸Ga-PSMA-11 PET/CT imaging, demographic characteristics, histopathological data of the primary foci, and other clinical features were evaluated together. Results: Twenty-four (89%) patients were included in the study for restaging, two (7%) patients for local recurrence assessment, and one (4%) patient for local recurrence and suspicion of additional lesions. The indications for ¹⁸F-FDG PET/CT were breast carcinoma for 37% (n:10), followed by lung carcinoma for 26% (n:7), colorectal adenocarcinoma for 14% (n:4), squamous cell larynx carcinoma for 7% (n:2), gastric signet ring cell carcinoma for 4% (n:1), pancreatic NET3 for 4% (n:1), thyroid papillary carcinoma for 4% (n:1), and malignant melanoma for 4% (n:1). In total, 26/27 included patients had PSMA-positive brain metastases but only one patient had PSMA-negative brain metastases with ⁶⁸Ga-PSMA-11 PET/CT imaging. This patient was followed with a diagnosis of primary larynx squamous carcinoma and had a mass suspected of brain metastases. Further tests and an MRI revealed that the lesion in this patient was a hemorrhagic metastasis. The smallest metastatic focus on ⁶⁸Ga-PSMA-11 PET/CT imaging was 0.22 cm, also confirmed by MRI (range: 0.22–2.81 cm). The mean ± SD SUVmax of the BM lesions was 17.9 ± 8.6 and 6.8 ± 5.2 on ¹⁸F-FDG PET/CT and ⁶⁸Ga-PSMA-11 PET/CT imaging, respectively. Metastatic foci that could not be detected by ¹⁸F-FDG PET/CT imaging were successfully detected with ⁶⁸Ga-PSMA-11 PET/CT imaging in 11 cases (42%). The distribution and number of metastatic lesions observed on cranial MRI and ⁶⁸Ga-PSMA-11 PET/CT were compatible with each other for all patients. Immunohistochemical staining was performed in the primary tumor of 10 (38%) cases, and positive IHC staining with PSMA was detected in 5 patients. In addition, positive IHC staining with PSMA was detected in all of the four surgically excised brain metastatic tumor foci. Conclusions: In this study,⁶⁸Ga-PSMA-11 PET/CT appears to be superior to ¹⁸F-FDG in detecting BM from various tumors, largely due to its high expression associated with neovascularization and the absence of PSMA expression in normal brain parenchyma. This lack of physiological uptake in healthy brain tissue provides excellent tumor-to-background contrast, further supporting the advantage of ⁶⁸Ga-PSMA-11 over ¹⁸F-FDG for BM imaging. However, larger studies are required to confirm these findings, particularly through comparisons across tumor types and histopathological subgroups, integrating PSMA immunohistochemistry (IHC) scores with ⁶⁸Ga-PSMA-11 uptake levels. Beyond its diagnostic potential, our results may also inform PSMA-targeted therapeutic strategies, offering new perspectives for the management of patients with brain metastases from diverse primary tumors. Full article

Background/Objectives: Dementia is a major public health challenge, with age as its primary non-modifiable risk factor. Several modifiable conditions, such as hypertension, diabetes, and depression, have been identified as potential targets for prevention. The aim is to describe the methodology and preliminary results of a study that will be conducted within the Italian National Health Service (INHS), designed to assess the impact of hypertension, diabetes, depression, and their interactions on the onset of dementia. Methods: This population-based cohort study, part of the PREV-ITA-DEM project, was conducted using a Common Data Model (CDM) approach across five Italian regions and cities participating in the NeuroEpiNet network. Individuals aged ≥ 50 years without prior diagnoses of dementia, depression, diabetes, or hypertension were followed from cohort entry (2011–2013) until dementia diagnosis, death, emigration, or study end (2019–2022). Exposures were time-dependent and defined using validated algorithms applied to Healthcare Utilization Databases (HUDs). Associations between chronic conditions and dementia risk will be estimated using competing risks regression models adjusted for confounders. Results: The final cohort comprised more than 3 million individuals, with a mean baseline age of 63–65 years and a female proportion of 52–55%. On 1 January 2011, the prevalence of individuals aged ≥ 50 years with dementia ranged from 8.7 to 14.7 per 1000 population. A harmonized methodological framework based on a CDM was developed and implemented across all sites, incorporating a shared protocol, standardized local databases, and uniform analytic scripts, and the results will be pooled using meta-analytic techniques. Conclusions: Preliminary findings confirm the feasibility of a standardized, multi-regional CDM approach and the potential for HUDs to support large-scale dementia prevention studies in real-world settings. Full article