Search Results (721)

Background/Objectives: Conventional cancer screening relies heavily on imaging and invasive procedures, leading to high false-positive rates and limited uptake, while leaving several high-mortality cancers without routine screening options. This study evaluated a protein-based multi-cancer early detection (MCED) test designed to detect five high-burden cancers with high sensitivity, specificity, and tissue-of-origin (TOO) accuracy. Methods: Serum from 141 patients with confirmed breast, lung, colorectal, ovarian, or pancreatic cancer and 119 healthy controls was analyzed using a 16-parameter protein biomarker panel. The assay measured extracellular protein kinase A (xPKA) activity, additional kinase activities, and cancer-associated antibodies (IgG, IgM). A supervised, rule-based classification framework was developed for cancer detection and TOO assignment. Results: The MCED test achieved 100% sensitivity across all five cancer types and 97% overall specificity, with 98% TOO accuracy. Importantly, 100% of Stage I cancers were detected. Cancer specificities ranged from 96.6% (breast) to 100% (ovarian, pancreatic, and colorectal). Conclusions: This protein-based MCED approach demonstrates exceptional performance for multi-cancer detection and TOO identification, including robust early-stage detection, and may reduce the downstream diagnostic burden relative to the existing system. Full article

Digital Mobility outcomes can serve as objective biomarkers of health, but their validation in populations with multiple long-term conditions (MLTCs) based on wrist-worn devices remains unexplored. We refined, improved, and introduced novel algorithms, specifically tailored and adapted for (i) gait sequence detection, (ii) initial contact identification, and (iii) stride length estimation from a single wrist-worn device. Validation was performed using data from 28 participants with co-occurring MLTCs performing a 2.5 h real-world monitoring session. Reference data from an established multi-sensor system were used to assess algorithm performance across diverse gait patterns of co-occurring MLTCs. Twenty-eight participants (mean age 70.4 years, 43% females) had a median of three co-occurring MLTCs. Among six gait sequence detection methods, improved versions of the Kheirkhahan algorithm performed best (accuracy = 0.92, specificity = 0.96). For initial contact detection (nine methods tested), Shin’s algorithm achieved the highest performance index (0.85) followed by McCamley (0.84). Stride length estimation was most accurate using novel approaches based on the Weinberg method (performance index > 0.70). The proposed fine-tuned algorithms, the newly developed adaptive variants, and the foot-length augmented versions demonstrated robust performance, surpassing many existing methods and addressing the complexity of gait patterns in MLTCs. These findings enable scalable, real-time mobility monitoring in complex clinical populations using accessible wearable technology. Full article

Given the projection of the impact of climate change and the uncertainty caused by geopolitical volatility, minimising emissions has become an urgent priority for the shipping industry. In this context, the aim of the present study is the calculation and estimation of emissions generated by ship operations within a maritime transportation network, as well as the identification of the optimal route that minimises both emissions and travel time. Emission estimation is carried out using methodologies and assumptions from the Fourth IMO GHG Study. The decision-making, along with the optimisation process, is performed through backward dynamic programming, following a multi-objective optimisation framework. Specifically, the analysis is carried out on both a theoretical and a realistic network. In both cases, various scenarios are examined, including different approaches to vessel speed, some of which incorporate probabilistic speed distributions, as well as scenarios involving uncertainty regarding port availability. Additionally, the resilience of the network is examined, focusing on the additional burden in terms of emissions and travel time when a port is unexpectedly unavailable and a route adjustment is required. The calculations and optimisation are carried out using Excel and the @Risk software by Palisade, with the latter enabling the incorporation of probability distributions and the execution of Monte Carlo simulations. Full article

Detecting citrus Huanglongbing (HLB) in orchard environments is particularly challenging due to multi-scale targets and occlusions due to clustering, which manifest as complex and variable backgrounds, targets ranging from distant single leaves to nearby full canopies, and frequent instances where symptomatic leaves are hidden behind others, all significantly hindering accurate detection. To overcome these challenges, this study introduces a novel citrus object detection model, Multi-Scale Huanglongbing DETR (MSHLB-DETR), developed on the basis of an improved Real-Time DEtection TRansformer (RT-DETR). The model significantly enhances detection accuracy and efficiency for HLB under complex orchard conditions. To address the issue of small target feature loss in leaf detection, a new efficient transformer module called Smart Disease Recognition for Citrus Huanglongbing with Multi-scale (SDRM) is introduced. SDRM includes a space-to-depth (SPD) module and inverted residual mobile block (IRMB), which facilitate deep interaction between local and global features and significantly improve the computational efficiency of the transformer. Additionally, the transformer encoder incorporates a Context-Guided Block (CGBlock) for contextual feature learning. To evaluate the proposed model under complex background conditions, a dataset of 4367 images was collected from diverse orchard scenes, preprocessed, and divided into training, validation, and testing subsets. The experimental results demonstrate that the proposed MSHLB-DETR achieved the best detection performance on the test set, with an ${\mathrm{mAP}}_{50}$ of 96.0%, surpassing other state-of-the-art models of similar scale. Compared to the original RT-DETR, the proposed model increased ${\mathrm{mAP}}_{50}$ by 15.8%, reduced Params by 7.5%, and decreased GFLOPs by 5.2%. This study reveals the critical importance of developing efficient multi-scale detection techniques for the accurate identification of citrus Huanglongbing in complex real-time monitoring scenarios. The proposed algorithm is expected to provide valuable references and new insights for the precise and timely detection of citrus Huanglongbing. Full article

To improve the control accuracy and address the parameter disturbance issues of joint-driven permanent magnet synchronous motors in intelligent manufacturing, this paper proposes an improved deadbeat predictive current predictive control (DPCC) scheme that eliminates dead zones. This scheme establishes a multi-parameter identification model based on the error equation of the d-q axis predicted current, which improves the problem of not being able to identify all parameters caused by insufficient input signals. It also implements decoupling compensation for the coupling between the d-q axis inductance, stator resistance, and magnetic flux linkage. To meet the anticipated control objectives and account for external disturbances, a low-complexity specified performance tracking controller (LCSPC) based on output target error signals has been designed. This mitigates output delay issues arising from nonlinear components during motor operation. Finally, simulation analysis and experimental testing demonstrate that the proposed control scheme achieves high identification accuracy with minimal delay, thus meeting the transient control performance requirements for motors in intelligent manufacturing processes. Full article

Objectives: This study aimed to evaluate the reproducibility of artificial intelligence (AI) in identifying cephalometric landmarks, comparing its performance with manual tracing by an experienced orthodontist. Methods: A high-quality lateral cephalogram of a 26-year-old female patient, meeting strict inclusion criteria, was selected. Eighteen cephalometric landmarks were identified using the WebCeph software (version 1500) in three experimental settings: AI tracing without image modification (AInocut), AI tracing with image modification (AI-cut), and manual tracing by an orthodontic expert. Each evaluator repeated the procedure 10 times on the same image. X and Y coordinates were recorded, and reproducibility was assessed using the coefficient of variation (CV) and centroid distance analysis. Statistical comparisons were performed using one-way ANOVA and Bonferroni post hoc tests, with significance set at p < 0.05. Results: AInocut achieved the highest reproducibility, showing the lowest mean CV values. Both AI methods demonstrated greater consistency than manual tracing, particularly for landmarks such as Menton (Me) and Pogonion (Pog). Gonion (Go) showed the highest variability across all groups. Significant differences were found for the Posterior Nasal Spine (PNS) point (p = 0.001), where AI outperformed manual tracing. Variability was generally higher along the X-axis than the Y-axis. Conclusions: AI demonstrated superior reproducibility in cephalometric landmark identification compared to manual tracing by an experienced operator. While certain points showed high consistency, others—particularly PNS and Go—remained challenging. These findings support AI as a reliable adjunct in digital cephalometry, although the use of a single radiograph limits generalizability. Broader, multi-image studies are needed to confirm clinical applicability. Full article

Background/Objectives: Antibiotic detection in honey is challenging due to the complexity of this product, the typically low levels of residues, and the absence of Maximum Residue Levels (MRLs) for beehive products. The use of antibiotics in apiculture poses potential risks to human health, including antimicrobial resistance and toxic effects. Reliable, sensitive, and selective analytical methods are essential to ensure food safety and enable accurate monitoring of antibiotic contamination in honey. This study aimed to validate a multi-analyte procedure in accordance with the parameters established in Commission Implementing Regulation (EU) 2021/808 for the identification and quantification of antibiotics, including tetracyclines, lincosamides, quinolones, macrolides, β-lactams, sulfonamides, and diaminopyrimidines. Methods: An extraction protocol was developed using 0.1% formic acid in ACN:H₂O (80:20, v/v), followed by a modified QuEChERS with the addition of 1 g NaCl and 2 g MgSO₄. The extracts were analyzed by UHPLC-TOF-MS. Results: The method, validated under CIR (EU) 2021/808, demonstrated robust performance, with recoveries ranging from 80.1% to 117.6%, repeatability between 0.5% and 32.2%, reproducibility between 2.3% and 31.6%, and determination coefficients (R²) ranging from 0.9429 to 0.9982. Validation was achieved for 15 antibiotic residues, with CCβ from 3 to 15 μg·kg⁻¹, LODs between 0.09 and 6.19 μg·kg⁻¹, and LOQs between 0.29 and 18.77 μg·kg⁻¹. Application to 10 commercial Portuguese honey revealed no detectable levels of the target antibiotics. Conclusions: The combination of a simplified extraction with UHPLC-TOF-MS provides a reliable approach for the determination of antibiotics in honey. This validated method represents a valuable tool for food safety monitoring and risk assessment of apiculture practices. Full article

Background/Objectives: Paspalum nicorae Parodi is a native subtropical grass species with promising agronomic attributes, such as persistence, drought and cold tolerance, and rapid establishment. However, the species remains underutilized in breeding programs due to the absence of well-characterized germplasm and limited studies on its genetic variability and agronomic potential. This study aimed to estimate genetic parameters, predict genotypic values, and identify superior ecotypes with desirable forage traits, integrating stability and adaptability analyses. Methods: A total of 84 ecotypes were evaluated over three consecutive years for twelve morphological and forage-related traits. Genetic parameters, genotypic values, and selection gains were estimated using mixed models (REML/BLUP). Stability was assessed through harmonic means of genotypic performance, and the multi-trait genotype–ideotype distance index (MGIDI) was applied to identify ecotypes with balanced performance across traits. Results: Substantial genetic variability was detected for most traits, particularly those related to biomass accumulation, such as total dry matter, the number of tillers, fresh matter, and leaf dry matter. These traits exhibited medium to high heritability and strong potential for selection. Ecotype N3.10 consistently showed superior performance across productivity traits while other ecotypes, such as N4.14 and N1.09, stood out for quality-related attributes and cold tolerance, respectively. The application of the MGIDI index enabled the identification of 17 ecotypes with balanced multi-trait performance, supporting the simultaneous selection for productivity, quality, and adaptability. Comparisons with P. notatum suggest that P. nicorae harbors competitive genetic potential, despite its lower level of domestication. Conclusions: The integration of REML/BLUP analyses, stability parameters, and ideotype-based multi-trait selection provided a robust framework for identifying elite P. nicorae ecotypes. These findings reinforce the strategic importance of this species as a valuable genetic resource for the development of adapted and productive forage cultivars in subtropical environments. Full article

The subject of this paper is the computational homogenisation and identification of heterogeneous materials in the form of auxetic structures made of materials with nonlinear characteristics. It is assumed that some of the material and topological parameters of the auxetic structures are uncertain and are modelled as interval numbers. Directed interval arithmetic is used to minimise the width of the resulting intervals. The finite element method is employed to solve the boundary value problem, and artificial neural network response surfaces are utilised to reduce the computational effort. In order to solve the identification task, the Pareto approach is adopted, and a multi-objective evolutionary algorithm is used as the global optimisation method. The results obtained from computational homogenisation under uncertainty demonstrate the efficacy of the proposed methodology in capturing material behaviour, thereby underscoring the significance of incorporating uncertainty into material properties. The identification results demonstrate the successful identification of material parameters at the microscopic scale from macroscopic data involving the interval description of the process of deformation of auxetic structures in a nonlinear regime. Full article

Accurate and robust power quality disturbance (PQD) classification is critical for modern electrical grids, particularly in noisy environments. This study presents a comprehensive comparative evaluation of machine learning (ML) and deep learning (DL) models for automatic PQD identification. The models evaluated include Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), k-Nearest Neighbors (kNN), Gradient Boosting (GB), and Dense Neural Networks (DNN). For experimentation, a hybrid dataset, comprising both synthetic and real signals, was used to assess model performance. The robustness of the models was evaluated by systematically introducing Gaussian noise across a wide range of Signal-to-Noise Ratios (SNRs). A central objective was to directly benchmark the practical implementation and performance of these models across two widely used platforms: MATLAB R2024a and Python 3.11. Results show that ML models achieve high accuracies, exceeding $95\%$ at an SNR of 10 dB. DL models exhibited remarkable stability, maintaining $97\%$ accuracy for SNRs above 10 dB. However, their performance degraded significantly at lower SNRs, revealing specific confusion patterns. The analysis underscores the importance of multi-domain feature extraction and adaptive preprocessing for achieving resilient PQD classification. This research provides valuable insights and a practical guide for implementing and optimizing robust PQD classification systems in real-world, noisy scenarios. Full article

Difference in Differences (DiD) is a useful statistical technique employed by researchers to estimate the effects of exogenous events on the outcome of some response variables in random samples of treated units (i.e., units exposed to the event) ideally drawn from an infinite population. The term “effect” should be understood as the discrepancy between the post-event realisation of the response and the hypothetical realisation of that same outcome for the same treated units in the absence of the event. This theoretical discrepancy is clearly unobservable. To circumvent the implicit missing variable problem, DiD methods utilise the realisations of the response variable observed in comparable random samples of untreated units. The latter are samples of units drawn from the same population, but they are not exposed to the event under investigation. They function as the control or comparison group and serve as proxies for the non-existent untreated realisations of the responses in treated units during post-treatment periods. In summary, the DiD model posits that, in the absence of intervention and under specific conditions, treated units would exhibit behaviours that are indistinguishable from those of control or untreated units during the post-treatment periods. For the purpose of estimation, the method employs a combination of before–after and treatment–control group comparisons. The event that affects the response variables is referred to as “treatment.” However, it could also be referred to as “causal factor” to emphasise that, in the DiD approach, the objective is not to estimate a mere statistical association among variables. This review introduces the DiD techniques for researchers in economics, public policy, health research, management, environmental analysis, and other fields. It commences with the rudimentary methods employed to estimate the so-called Average Treatment Effect upon Treated (ATET) in a two-period and two-group case and subsequently addresses numerous issues that arise in a multi-unit and multi-period context. A particular focus is placed on the statistical assumptions necessary for a precise delineation of the identification process of the cause–effect relationship in the multi-period case. These assumptions include the parallel trend hypothesis, the no-anticipation assumption, and the SUTVA assumption. In the multi-period case, both the homogeneous and heterogeneous scenarios are taken into consideration. The homogeneous scenario refers to the situation in which the treated units are initially treated in the same periods. In contrast, the heterogeneous scenario involves the treatment of treated units in different periods. A portion of the presentation will be allocated to the developments associated with the DiD techniques that can be employed in the context of data clustering or spatio-temporal dependence. The present review includes a concise exposition of some policy-oriented papers that incorporate applications of DiD. The areas of focus encompass income taxation, migration, regulation, and environmental management. Full article

Background: Childhood obesity has reached epidemic levels in developed countries and is an emerging concern in developing regions. Children with excess weight are more likely to maintain this condition over time into adulthood and face a higher risk of developing metabolic disorders such as type 2 diabetes, hypertension, metabolic dysfunction-associated liver disease, and dyslipidemia. Early identification of obesity risk is, therefore, a key public health challenge. Methods: This is an observational, prospective, single-center cohort pilot study in 66 mother–infant dyads recruited at the Gynecology and Obstetrics Service of the Virgen de la Arrixaca University Hospital (Murcia, Spain). The primary objective is to identify early-life, non-invasive biomarkers associated with increased adiposity by integrating multi-omics approaches and analyzing maternal–infant interactions. Pregnant women will be enrolled during the third trimester and will undergo a baseline visit at 38 weeks of gestation for clinical and anthropometric assessment. Buccal swabs and fecal samples will be collected at baseline and in the peripartum period for epigenetic (DNA methylation), metagenomic, and metabolomic analyses. Infants will be evaluated at birth and followed at 6 months, 1 year, 2 years, and 3 years. Each visit will include detailed anthropometric measurements, along with collection of buccal swabs and fecal samples for multi-omics profiling. Conclusions: This multidisciplinary study aims to assess how maternal factors influence infant epigenetic and microbial patterns, and their relation to adiposity development. Early identification of such biomarkers may guide personalized prevention strategies and reduce the long-term burden of obesity-related comorbidities. Full article

Background/Objectives: Carbapenem-resistant Gram-negative bacilli (CR-GNB) pose a growing challenge to public health worldwide due to limited treatment options. This cross-sectional study investigated the characteristics of CR-GNB isolated from clinical specimens in Lagos, Nigeria. Methods: Gram-negative bacilli (GNB) and clinical data were obtained from three multi-specialist private hospitals between March and June 2023. The GNB were identified using the Analytical Profile Index (API) and investigated for CR-GNB by disk diffusion. Antimicrobial resistance patterns and carbapenemase gene data for presumptive carbapenemase-producing Gram-negative bacilli (CP-GNB) were analyzed using Vitek-2 and polymerase chain reaction (PCR). Results: Of 317 GNB, 29.0% (n = 92) were CR-GNB. Significantly higher numbers of CR-GNB were reported from the intensive care unit and oncology department (p = 0.009). Of all CR-GNB, 17 isolates (18.5%) were classified as presumptive CP-GNB. In this subgroup, resistance rates of ampicillin/sulbactam (100.0%) and trimethoprim/sulfamethoxazole (100.0%) were highest. Ten (10) CP-GNB were confirmed, representing 3.15% of all GNB tested. Seven isolates of New Delhi Metallo-β-lactamase (bla_NDM) were found among P. aeruginosa, K. pneumoniae, E. coli, and A. baumannii. The bla_NDM was identified in strains classified as extensively drug-resistant (XDR) and pandrug-resistant. Conversely, the bla_KPC was detected solely in multidrug-resistant and XDR strains. Conclusions: Emerging CR-GNB, specifically CP-GNB, in Nigeria emphasize the need for specific therapeutic management of infected patients. Antimicrobial stewardship and long-term surveillance efforts must be implemented in healthcare settings, as well as improved, accelerated microorganism identification techniques. Full article

Open-mouth panting in broiler chickens is a visible and critical indicator of heat stress and compromised welfare. However, detecting this behavior in densely populated cages is challenging due to the small size of the target and frequent occlusions and cluttered backgrounds. To overcome these issues, we proposed an enhanced object detection method based on the lightweight YOLOv8n framework, incorporating four key improvements. First, we add a dedicated P2 detection head to improve the recognition of small targets. Second, a space-to-depth grouped convolution module (SGConv) is introduced to capture fine-grained texture and edge features crucial for panting identification. Third, a bidirectional feature pyramid network (BIFPN) merges multi-scale feature maps for richer representations. Finally, a squeeze-and-excitation (SE) channel attention mechanism emphasizes mouth-related cues while suppressing irrelevant background noise. We trained and evaluated the method on a comprehensive, full-cycle broiler panting dataset covering all growth stages. Experimental results show that our method significantly outperforms baseline YOLO models, achieving 0.92 mAP@50 (independent test set) and 0.927 mAP@50 (leakage-free retraining), confirming strong generalizability while maintaining real-time performance. The initial evaluation had data partitioning limitations; method generalizability is now dually validated through both independent testing and rigorous split-then-augment retraining. This approach provides a practical tool for intelligent broiler welfare monitoring and heat stress management, contributing to improved environmental control and animal well-being. Full article

Background/Objectives: Childhood trauma significantly hinders the developmental and academic outcomes of learners, particularly in under-resourced schools such as those in Limpopo province, South Africa. Teachers in these settings often face challenges in supporting trauma-exposed learners due to a lack of knowledge, training, and appropriate resources. Addressing this gap requires the development of structured, trauma-informed educational support systems. Methods: This study forms the final phase of a multi-phase research project aimed at developing a trauma-informed curriculum for primary school teachers. A multi-phase mixed method design was adopted across four phases: (1) a global scoping review to identify effective trauma-informed interventions; (2) empirical interviews with primary school teachers, trauma center managers, clinical psychologists, and social workers to understand local needs and experiences; (3) development of a conceptual framework grounded in theoretical and empirical findings; and (4) curriculum development guided by El Sawi’s curriculum design model. The curriculum was validated using structured questionnaires with a panel of stakeholders including educators, mental health professionals, and curriculum experts. Results: The study identified critical issues, including teachers’ limited understanding of childhood trauma, lack of standardized training, and inadequate classroom strategies. Key curriculum components were developed to address these gaps, including modules on the nature of trauma, early identification of symptoms, trauma-informed teaching practices, and collaboration with mental health professionals. Validation results indicated strong agreement on the curriculum’s clarity, relevance, and potential impact. Conclusions: The developed trauma-informed curriculum provides primary school teachers in Limpopo with the knowledge, tools, and confidence to support trauma-exposed learners. It emphasizes early identification, responsive classroom strategies, and inter-professional collaboration. This curriculum has the potential to enhance learning environments and promote better educational and psychosocial outcomes for trauma-affected learners. Full article