Search Results (1,602)

Accurate mapping of wetland vegetation is essential for ecological monitoring and conservation, yet it remains challenging due to the spatial heterogeneity of wetlands, the scarcity of ground-truth data, and the spread of invasive species. Invasive plants alter native vegetation patterns, making their early detection critical for preserving ecosystem integrity. This study proposes a novel framework that integrates self-supervised learning (SSL), supervised segmentation, and multi-sensor data fusion to enhance vegetation classification in the Bayinbuluke Alpine Wetland, China. High-resolution satellite imagery from PlanetScope-3 and Jilin-1 was fused, and SSL methods—including BYOL, DINO, and MoCo v3—were employed to learn transferable feature representations without extensive labeled data. The results show that SSL methods exhibit consistent variations in classification performance, while multi-sensor fusion significantly improves the detection of rare and fragmented vegetation patches and enables the early identification of invasive species. Overall, the proposed SSL–fusion strategy reduces reliance on labor-intensive field data collection and provides a scalable, high-precision solution for wetland monitoring and invasive species management. Full article

Australia consumes approximately 29 million m³ of concrete each year with an estimated embodied carbon (EC) of 12 Mt CO_2e. High consumption of concrete makes it critical for successful decarbonization to support the achievement of ‘Net Zero 2050’ objectives of the Australian construction industry. Portland cement (PC) constitutes only 12–15% of the concrete mix but is responsible for approximately 90% of concrete’s EC. This necessitates reducing the PC in concrete with supplementary cementitious materials (SCMs) or using alternative binders such as geopolymer concrete. Concrete mixes including a combination of PC and SCMs as a binder have lower embodied carbon (EC) than those with only PC and are termed as low-carbon concrete (LCC). SCM addition to a concrete mix not only reduces EC but also enhances its mechanical and durability properties. Fly ash (FA) and granulated ground blast furnace slag (GGBFS) are the most used SCMs in Australia. It is noted that other SCMs such as limestone, metakaolin or calcinated clay, Delithiated Beta Spodumene (DBS) or lithium slag, etc., are being trialed. This technical paper presents a methodology that enables selecting LCCs with various degrees of SCMs for various elements of bridge structure without compromising their functional performance. The proposed methodology includes controls that need to be applied during the design/selection process of LCC, from material quality control to concrete mix design to EC evaluation for every element of a bridge, to minimize the overall carbon footprint of a bridge. Typical properties of LCC with FA and GGBFS as binary and ternary blends are also included for preliminary design of a fit-for-purpose LCC. An example for a bridge located in the B2 exposure classification zone (exposed to both carbonation on chloride ingress deterioration mechanisms) has also been included to test the methodology, which demonstrates that EC of the bridge may be reduced by up to 53% by use of the proposed methodology. Full article

The flat peach, an important commercial crop in the 143rd Regiment of Shihezi, China, is overwintered using plastic film mulching. Flat peaches are cultivated to boost the local temperate rural economy. The development of accurate maps of the spatial distribution of flat peach plantations is crucial for the intelligent management of economic orchards. This study evaluated the performance of pixel-based and object-based random forest algorithms for mapping flat peaches using the GF-1 image acquired during the overwintering period. A total of 45 variables, including spectral bands, vegetation indices, and texture, were used as input features. To assess the importance of different features on classification accuracy, the five different sets of variables (5, 15, 25, and 35 input variables and all 45 variables) were classified using pixel/object-based classification methods. Results of the feature optimization suggested that vegetation indices played a key role in the study, and the mean and variance of Gray-Level Co-occurrence Matrix (GLCM) texture features were important variables for distinguishing flat peach orchards. The object-based classification method was superior to the pixel-based classification method with statistically significant differences. The optimal performance was achieved by the object-based method using 25 input variables, with an overall accuracy of 94.47% and a Kappa coefficient of 0.9273. Furthermore, there were no statistically significant differences between the image-derived flat peach cultivated area and the statistical yearbook data. The result indicated that high-resolution images based on the overwintering period can successfully achieve the mapping of flat peach planting areas, which will provide a useful reference for temperate lands with similar agricultural management. Full article

Acute myeloid leukemia (AML) comprises 15−20% of pediatric leukemia and 35% of adult leukemia cases, requiring insights into prognostic factors of this disease to be an important aspect of diagnosis and treatment. A mutational profile of patients with AML is a crucial predictor of their outcome. Discernment of present mutations, co-mutation combinations, and variations in the mutations in a single gene requires proper research and analysis to determine their impact on a patient’s prognosis. Common and infrequent mutations are continuously investigated and analyzed in different patient cohorts, bringing new insights that lead to changes in classifications, treatments, and diagnoses. For instance, mutations in NPM1, FLT3, and DNMT3A, three frequent driver mutations, have high incident rates with differing prognoses and treatments in pediatric and adult patients. AML patients with MECOM face particularly dire outcomes, as well as those with ASXL1 and TP53, making their mutational analysis crucial for review in developing a prognosis. Full article

The breach of an interspecies barrier by RNA viruses has facilitated the emergence of lethal hCoVs, particularly SARS-CoV-2, resulting in significant socioeconomic setbacks and public health risks globally in recent years. Moreover, the high evolutionary plasticity of hCoVs has led to the continuous emergence of diverse variants, complicating clinical management and public health responses. Studying the evolutionary trajectory of hCoVs, which provides a molecular roadmap for understanding viruses’ adaptation, tissue tropism, spread, virulence, and immune evasion, is crucial for addressing the challenges of zoonotic spillover of viruses. Tracing the evolutionary trajectory of lethal hCoVs provides essential genomic insights required for risk stratification, variant/sub-variant classification, preparedness for outbreaks and pandemics, and the identification of critical viral elements for vaccine and therapeutic development. Therefore, this review examines the evolutionary landscape of the three known lethal hCoVs, presenting a focused narrative on SARS-CoV-2 variants under monitoring (VUMs) as of May 2025. Using advanced bioinformatics approaches and data visualization, the review highlights key spike protein substitutions, particularly within the receptor-binding domain (RBD), which drive transmissibility, immune escape, and potential resistance to therapeutics. The article highlights the importance of real-time genomic surveillance and intervention strategies in mitigating emerging variant/sub-variant risks within the ongoing COVID-19 landscape. Full article

This study aimed to assess the presence of microplastics (MPs) in an urban river (Gari, Lazio, Italy) using case-building caddisfly larvae as potential bioindicators. Results from the benthic faunal assemblage (STAR_ICMi = 0.797) revealed the presence of a rich and well-diversified macroinvertebrate community, thus reflecting a suitable ecological status. Of 279 caddisfly cases collected, 26% contained small plastic particles of various shapes and colours, while 542 MP items per m² were found in their substrate. Polyvinyl chloride (PVC) and Polyethylene terephthalate (PET) were the most abundant polymers identified by FT-IR analysis found in the Gari River, while the co-presence of lower-density polymers such as polystyrene (PS) and polyethylene (PE) or polypropylene (PP) reflects the contribution of multiple factors controlling MP deposition. The most abundant MPs were of secondary origin, as evidenced by the Carbonyl Index and the predominant shape. Despite the amounts of MPs found in the Gari River, their ecological and chemical status has been classified as “good” during the monitoring campaigns. These results highlight the need to further investigate the environmental impacts of MPs to implement water quality classification indices. Full article

Background/Objectives: Mandibular asymmetry is a common morphological alteration in orthodontics and orthognathic surgery, generally diagnosed with panoramic radiographs despite their limitations. Automated processing systems offer a promising alternative for improving its detection and analysis. The aim of this study was to develop a pilot computational model to detect and measure mandibular asymmetry in the body and ramus by analyzing anatomical distances in digital panoramic radiographs of adults. Methods: This was a descriptive observational pilot study, carried out on 30 digital panoramic radiographs of young adult patients (15 men, 15 women). Three craniometric points (Condylion, Gonion and Gnathion) were used as references landmarks. An algorithm was implemented in Python^® (v3.12) with OpenCV to extract anatomical coordinates and calculate Euclidean distances (Go-Gn, Co-Go) from pixels to millimeters. Data were statistically analyzed in SPSS (v23.0) using normality tests, paired t-tests, Wilcoxon tests, and Mann–Whitney U tests (p < 0.05). Results: No significant differences were observed in mandibular lengths by sex, with men having greater lengths in both the body (80.63 mm vs. 73.86 mm) and the ramus (55.82 mm vs. 49.15 mm). In addition, significant differences were found in total mandibular ramus measurements (p = 0.023). A classification of asymmetry by severity was proposed (mild: ≤3 mm, moderate: 3–6 mm, severe: >6 mm), with mild asymmetries being the most frequently found. The model showed reliable processing capacity. Conclusions: This pilot study shows the feasibility of using Python for automated measurement of mandibular asymmetry in panoramic radiographs and highlights its future potential for neural network integration and diagnostic-epidemiological use. Full article

Identifying tobacco smoke exposure in indoor environments is critical for public health, especially in vulnerable populations. In this study, we developed and validated a machine learning model to detect smoking households based on indoor air quality (IAQ) data collected using low-cost sensors. A dataset of 129 homes in Spain and Austria was analyzed, with variables including PM_2.5, PM₁, CO₂, temperature, humidity, and total VOCs. The final model, based on the XGBoost algorithm, achieved near-perfect household-level classification (100% accuracy in the test set and AUC = 0.96 in external validation). Analysis of PM_2.5 temporal profiles in representative households helped interpret model performance and highlighted cases where model predictions revealed inconsistencies in self-reported smoking status. These findings support the use of sensor-based approaches for behavioral inference and exposure assessment in residential settings. The proposed method could be extended to other indoor pollution sources and may contribute to risk communication, health-oriented interventions, and policy development, provided that ethical principles such as transparency and informed consent are upheld. Full article

Farmland ecosystems present complex pest–predator co-occurrence patterns, posing significant challenges for image-based multitarget recognition and ecological modeling in sensor-driven computer vision tasks. To address these issues, this study introduces a tree-guided Transformer framework enhanced with a knowledge-augmented co-attention mechanism, enabling effective feature extraction from sensor-acquired images. A hierarchical ecological taxonomy (Phylum–Family Species) guides prompt-driven semantic reasoning, while an ecological knowledge graph enriches visual representations by embedding co-occurrence priors. A multimodal dataset containing 60 pest and predator categories with annotated images and semantic descriptions was constructed for evaluation. Experimental results demonstrate that the proposed method achieves 90.4% precision, 86.7% recall, and 88.5% F1-score in image classification, along with 82.3% hierarchical accuracy. In detection tasks, it attains 91.6% precision and 86.3% mAP@50, with 80.5% co-occurrence accuracy. For hierarchical reasoning and knowledge-enhanced tasks, F1-scores reach 88.5% and 89.7%, respectively. These results highlight the framework’s strong capability in extracting structured, semantically aligned image features under real-world sensor conditions, offering an interpretable and generalizable approach for intelligent agricultural monitoring. Full article

Background: The miniaturization of ureterorenoscopes increasingly enables atraumatic primary ureteroscopy, without ureteral dilation or presenting. This study aims to evaluate the feasibility and safety of primary ureteroscopy using the HU30M (6.3 Fr, HugeMed, Shenzhen HugeMed Medical Technical Development Co., Ltd., Shenzhen, China), the smallest currently available ureteroscope. Methods: We analyzed consecutive patients in whom primary ureteroscopy using the HU30M was performed or attempted, using prospectively collected in-hospital and 30-day follow-up data for retrospective evaluation. The primary outcome was the success rate of primary ostial intubation. Secondary outcomes included the stone-free rate (SFR) in patients with urolithiasis, incidence of in-hospital complications (Clavien–Dindo classification) and 30-day emergency readmission. Additionally, we conducted a propensity score-matched comparative analysis of the HU30M versus a contemporary 7.5 Fr digital single-use ureteroscope (PUSEN PU3033AH, Zhuhai Pusen Medical Technology Co., Ltd., Jinhua, China). Results: Between January and April 2025, primary ureteroscopy using the HU30M was performed or attempted in 34 patients, including four bilateral procedures. Primary ureteroscopy was defined as ureteroscopic access without prior stenting or dilation. Indications were diagnostic evaluation in 15 patients (44%), uretreroscopic stone treatment in 10 patients (29%) and endoscopic combined intrarenal surgery (ECIRS) in 9 patients (27%). Successful primary ostial intubation was achieved in 36 of 38 renal units (95%). Among urolithiasis cases, SFR was 17/19 (90%) in-hospital complications were limited to postoperative fever in two patients (6%) and no procedure-related 30-day emergency readmission occurred. In matched analyses, HU30M demonstrated significantly shorter operative times compared with the 7.5 Fr ureteroscope, while postoperative hemoglobin drop, inflammatory parameters and renal function were comparable. Conclusions: Primary ureteroscopy with HU30M is feasible and safe across diverse indications, achieving high success of atraumatic ostial access. Comparative analyses suggest procedural efficiency advantages and overall safety comparable to the current digital single-use ureteroscope standard. Full article

Advances in multimodal artificial intelligence enable new sensor-inspired approaches to lie detection by combining behavioral perception with generative reasoning. This study presents a deception detection framework that integrates deep video and audio processing with large language models guided by chain-of-thought (CoT) prompting. We interpret neural architectures such as ViViT (for video) and HuBERT (for speech) as digital behavioral sensors that extract implicit emotional and cognitive cues, including micro-expressions, vocal stress, and timing irregularities. We further incorporate a GPT-5-based prompt-level fusion approach for video–language–emotion alignment and zero-shot inference. This method jointly processes visual frames, textual transcripts, and emotion recognition outputs, enabling the system to generate interpretable deception hypotheses without any task-specific fine-tuning. Facial expressions are treated as high-resolution affective signals captured via visual sensors, while audio encodes prosodic markers of stress. Our experimental setup is based on the DOLOS dataset, which provides high-quality multimodal recordings of deceptive and truthful behavior. We also evaluate a continual learning setup that transfers emotional understanding to deception classification. Results indicate that multimodal fusion and CoT-based reasoning increase classification accuracy and interpretability. The proposed system bridges the gap between raw behavioral data and semantic inference, laying a foundation for AI-driven lie detection with interpretable sensor analogues. Full article

The paper presents the application of high-speed flame imaging combined with convolutional neural networks (CNNs) for determining different states of biomass–coal co-combustion in terms of thermal power and excess air coefficient. The experimental setup and methodology used in a laboratory-scale co-combustion system are described, highlighting tests conducted across nine defined operational variants. The performance of several state-of-the-art CNN architectures was examined, focusing particularly on those achieving the highest classification metrics and exploring the dependence of input image resolution and applying a transfer learning paradigm. By benchmarking various CNNs on a large, diverse image dataset without preprocessing, the research advances intelligent, automated control systems for improved stability, efficiency, and emissions control, bridging advanced visual diagnostics with real-time industrial applications. The summary includes recommendations and potential directions for further research related to the use of image data and machine learning techniques in industry. Full article

The development of positron emission tomography (PET) tracers targeting α-synuclein (α-syn) aggregates is critical for the early diagnosis, differential classification, and therapeutic monitoring of synucleinopathies such as Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy. Despite recent advances, challenges including the low abundance of α-syn aggregates (10–50× lower than amyloid-beta (Aβ) or Tau), structural heterogeneity (e.g., flat fibrils in PD vs. cylindrical forms in DLB), co-pathology with Aβ/Tau, and poor metabolic stability have hindered PET tracer development for this target. To optimize our previously reported pyridothiophene-based radiotracer, [¹⁸F]asyn-44, we present the synthesis and evaluation of novel S,N-heterocyclic scaffold derivatives for α-syn. A library of 49 compounds was synthesized, with 8 potent derivatives (LMD-006, LMD-022, LMD-029, LMD-044, LMD-045, LMD-046, LMD-051, and LMD-052) demonstrating equilibrium inhibition constants (K_i) of 6–16 nM in PD brain homogenates, all of which are amenable for radiolabeling with fluorine-18. This work advances the molecular toolkit for synucleinopathies and provides a roadmap for overcoming barriers in PET tracer development, with lead compounds that can be considered for biomarker-guided clinical trials and targeted therapies. Full article

Objectives: Periapical periodontitis, which is a periodontal dysfunction, is a current clinical problem. Due to the frequency of occurrence and the adverse effects they cause, they are considered a social disease. They require detailed diagnostics to implement appropriate treatment. Mathematical calculations based on data obtained from radiological images used in routine clinical practice may help differentiate the forms of periodontitis. This study aimed to evaluate the areas affected by periodontitis in comparison to the healthy tissues of the periapical area. Methods: The study analyzed texture components using the gray-level co-occurrence matrix (GLCM) and the gray-level run-length matrix (GRLM) on an orthopantomogram (OPG) from 50 patients with clinically confirmed periodontitis treated at the Department of Maxillofacial and Plastic Surgery, University of Bialystok. Texture analysis was performed on defined regions of interest (ROIs) to distinguish diseased from healthy tissues. We employed four classification algorithms to assess model performance. Results: The data set included 50 patients, with 76 cases of periodontitis and 50 healthy ROIs. The reference standard was clinical diagnosis confirmed by two specialist doctors. The best-performing algorithm achieved an AUC of 98%. Conclusions: The obtained results showed significant statistical differences in the inflamed regions compared to the control, which may aid in diagnosing and selecting the treatment method for periodontitis. 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