Search Results (929)

Laboratory test results for vehicle emissions, fuel economy, and driving range often fail to reflect real-world performance, undermining the effectiveness of sustainability policies and consumer guidance. This study provides the first integrated national assessment of real-world emissions and range outcomes for passenger vehicles in Australia. Using Portable Emissions Measurement Systems (PEMS) data from 114 petrol, diesel, hybrid, and battery-electric vehicles (BEVs) tested by the Australian Automobile Association (AAA), the analysis compares laboratory-certified values against on-road results and benchmarks them with international datasets from Europe and China. Real-world CO₂ emissions were, on average, 6.9% higher than laboratory ratings for petrol vehicles and 3.2% higher for diesel vehicles. Many diesel models exceeded Euro 6 NO_x limits by several multiples, while hybrids exhibited inconsistent CO₂ reductions under urban conditions. BEVs also displayed measurable divergence: real-world energy consumption was 1–20% higher than laboratory ratings, resulting in an average 16% reduction in effective driving range relative to WLTP values. These outcomes reveal a consistent tendency toward overstated laboratory performance across powertrains, highlighting systemic shortcomings in certification test cycles. The findings have direct implications for greenhouse gas mitigation, urban air quality, and consumer energy efficiency and support Australia’s active transition to WLTP and Euro 6 standards, institutionalisation of real-world testing, and inclusion of verified real-world energy use and range data in consumer labelling to enhance transparency and policy effectiveness. Full article

This paper explores the feasibility of using acoustic wave propagation, particularly in the ultrasonic range, as a solution for data transmission in environments where traditional radio frequency (RF) communication is ineffective due to signal attenuation—such as in liquids or dense media like metal or stone. Leveraging GNU Radio and commercially available audio hardware, a low-cost, SDR (Software Defined Radio) system was developed to transmit data blocks (e.g., images, text, and audio) through various substances. The system employs BFSK (Binary Frequency Shift Keying) and BPSK (Binary Phase Shift Keying), operates at ultrasonic frequencies (typically 40 kHz), and has performance validated under real-world conditions, including water, viscous substances, and flammable liquids such as hydrocarbon fuels. Experimental results demonstrate reliable, continuous communication at Nyquist–Shannon sampling rates, with effective demodulation and file reconstruction. The methodology builds on concepts originally developed for Ad Hoc Sensor Networks in shipping containers, extending their applicability to submerged and RF-hostile environments. The modularity and flexibility of the GNU Radio platform allow for rapid adaptation across different media and deployment contexts. This work provides a reproducible and scalable communication solution for scenarios where RF transmission is impractical, offering potential applications in underwater sensing, industrial monitoring, railways, and enclosed infrastructure diagnostics. Across controlled laboratory experiments, the system achieved 100% successful reconstruction of transmitted image files up to 100 kB and sustained packet delivery success exceeding 98% under stable coupling conditions. Full article

This research presents a comprehensive evaluation of semi-elastic polyurethane adhesives used for bonding wooden flooring, with a particular focus on both domestic (oak) and exotic hardwood species (teak, iroko, wenge, merbau). Given the increasing interest in sustainable construction practices and the growing use of diverse wood species in flooring systems, this study aimed to assess the mechanical, morphological, and surface properties of adhesive joints under both standard laboratory and thermally aged conditions. Mechanical testing was conducted according to PN-EN ISO 17178 standards and included shear and tensile strength measurements on wood–wood and wood–concrete assemblies. Specimens were evaluated in multiple aging conditions, simulating real-world application environments. Shear strength increased post-aging, with the most notable improvement observed in wenge (21.2%). Tensile strength between wooden lamellas and concrete substrates remained stable or slightly decreased (up to 18.8% in wenge), yet all values stayed above the 1 MPa minimum requirement, confirming structural reliability. Surface properties of the wood species were characterized through contact angle measurements and 3D optical roughness analysis. Teak exhibited the highest contact angle (74.9°) and the greatest surface roughness, contributing to mechanical interlocking despite its low surface energy. Oak and iroko showed high wettability and balanced roughness, supporting strong adhesion. Scanning electron microscopy (SEM) revealed stable adhesive penetration across all species and aging conditions, with no signs of delamination or interfacial failure. The study confirms the suitability of polyurethane adhesives for durable, long-lasting bonding in engineered and solid wood flooring systems, even when using extractive-rich or dimensionally sensitive tropical species. The results emphasize the critical role of surface morphology, wood anatomy, and adhesive compatibility in achieving optimal bond performance. These findings contribute to improved material selection and application strategies in flooring technology. Future research should focus on bio-based adhesive alternatives, chemical surface modification techniques, and in-service performance under cyclic loading and humidity variations to support the development of eco-efficient and resilient flooring systems. Full article

This dataset presents a real-world lipidomics resource for developing and benchmarking quality control methods, batch effect detection algorithms, and data validation workflows. The data originates from a cross-sectional clinical study of psoriatic arthritis (PsA) patients (n = 81) and healthy controls (n = 26), matched for age, sex, and body mass index, which was collected at a tertiary university rheumatology center. Subtle laboratory irregularities were detected only through advanced unsupervised analysis, after passing conventional quality control and standard analytical methods. Blood samples were processed using standardized protocols and analyzed using high-resolution and tandem mass spectrometry platforms. Both targeted and untargeted lipid assays captured lipids of several classes (including carnitines, ceramides, glycerophospholipids, sphingolipids, glycerolipids, fatty acids, sterols and esters, endocannabinoids). The dataset is organized into four comma-separated value (CSV) files: (1) Box–Cox-transformed and imputed lipidomics values; (2) outlier-cleaned and imputed values on the original scale; (3) metadata including clinical classifications, biological sex, and batch information for all assay types and control sample processing dates; and (4) a variable-level description file (readme.csv). The 292 lipid variables are named according to LIPID MAPS classification and standardized nomenclature. Complete batch documentation and FAIR-compliant data structure make this dataset valuable for testing the robustness of analytical pipelines and quality control in lipidomics and related omics fields. This unique dataset does not compete with larger lipidomics quality control datasets for comparisons of results but provides a unique, real-life lipidomics dataset displaying traces of the laboratory sample processing schedule, which can be used to challenge quality control frameworks. Full article

The H2IOSC project aims to establish a federated cluster of European distributed research infrastructures involved in the humanities and cultural heritage sectors, with operating nodes across Italy. Through four key RIs—DARIAH-IT, CLARIN, OPERAS, and E-RIHS—the project promotes collaboration among researchers with interdisciplinary expertise. Within this framework, DIGILAB functions as the digital access platform for the Italian node of E-RIHS. Conceived as a socio-technical infrastructure for the Heritage Science community, DIGILAB is designed to manage heterogeneous data and metadata through advanced knowledge graph representations. The platform adheres to the FAIR principles and supports the complete data lifecycle, enabling the development and maintenance of Heritage Digital Twins. DIGILAB integrates diverse categories of information related to cultural sites and objects, encompassing historical and artistic datasets, diagnostic analyses, 3D models, and real-time monitoring data. This monitoring capability is achieved through the deployment of cutting-edge Internet of Things (IoT) technologies and large-scale Wireless Sensor Networks (WSNs). As part of DIGILAB, we developed SENNSE (v1.0), a fully open hardware/software platform dedicated to environmental and structural monitoring. SENNSE allows the remote, real-time observation and control of cultural heritage sites (collecting microclimatic parameters such as temperature, humidity, noise levels) and of cultural objects (collecting object-specific data including vibrations, light intensity, and ultraviolet radiation). The visualization and analytical tools integrated within SENNSE transform these datasets into actionable insights, thereby supporting advanced research and conservation strategies within the Cultural Heritage domain. In the following sections, we provide a detailed description of the SENNSE platform, outlining its hardware components and software modules, and discussing its benefits. Furthermore, we illustrate its application through two representative use cases: one conducted in a controlled laboratory environment and another implemented in a real-world heritage context, exemplified by the “Biblioteca Bernardini” in Lecce, Italy. Full article

Indoor environmental comfort plays a central role in occupants’ well-being, learning outcomes, and productivity, especially in educational buildings characterized by high occupancy variability and diverse activities. This paper presents a real-world dataset collected at the Cesena Campus of the University of Bologna, aimed at supporting occupant-centric comfort analysis and prediction in classrooms and laboratories. The dataset integrates continuous environmental measurements, such as temperature, humidity, noise, air pressure, and CO₂ concentration, with subjective comfort feedback gathered from students during regular lectures. Data were collected using permanently installed ceiling sensors and additional control sensors placed near occupants, enabling both longitudinal monitoring and validation analyses. Furthermore, the dataset includes both repeated comfort perception reports and a one-time comfort definition phase capturing individual relevance weights for different comfort dimensions. By combining objective and subjective data in realistic academic settings, the dataset provides a valuable resource for developing, benchmarking, and validating data-driven models for smart campus applications, indoor comfort prediction, and human-centered building analytics. Full article

Plastic recycling represents an essential element of strategies aimed at lowering global carbon emissions while supporting a circular plastics economy. However, the effectiveness of current plastic sorting systems remains limited by data scarcity, spectral variability, and the complexity of real world waste streams. This study introduces a CNN-based polymer classification framework that integrates physics-informed spectral simulation, adaptive data augmentation, and Bayesian hyperparameter optimization to enable robust classification under data limited conditions. Our framework combines near-infrared (NIR) spectral data from technical scale measurements with synthetically generated spectra. With only 100 measured spectra per polymer, the proposed framework achieves average balanced accuracies of 0.9739 in multi-target polymer classification tasks. By using technical scale spectral data, this study bridges the gap between laboratory model development and real sorting conditions. Full article

Background/Objectives: Intraductal papillary mucinous neoplasms (IPMN) are the most common pancreatic cystic lesions and are established precancerous entities. Side-branch IPMN (SB-IPMN) is the most prevalent subtype and generally carries a low risk of malignant transformation. The revised 2024 Kyoto guidelines define management and surveillance strategies based on high-risk stigmata and worrisome features; however, real-life adherence to these recommendations remains variable. To compare real-world management of SB-IPMN at a tertiary medical center with Kyoto guideline-based recommendations using an AIgo-based decision-support tool. Methods: SB-IPMN cases were retrospectively analyzed. An algorithm implementing the Kyoto guidelines was used to generate recommended management strategies based on imaging, clinical, and laboratory data, and these recommendations were compared with actual clinical decisions. Long-term clinical and radiological follow-up data were collected, including development of pancreatic ductal adenocarcinoma (PDAC). Results: A total of 368 patients (69% male; median age 69.5 years) were followed for a median of 48.5 months radiologically and 64 months clinically. Median cyst size at presentation was 10 (6–14) mm. Only 58 patients (15.8%) were managed in accordance with the Kyoto guidelines; most underwent more intensive surveillance (60.3%), while 23.9% received less intensive monitoring (p = 0.04). Larger cyst size (>2 cm) was associated with higher concordance with current guidelines. Younger patients, including all patients under 50 years of age, were more frequently over-surveilled. Over-surveillance resulted in an excess of 0.42 MRI/MRCP examinations per patient-year. Only one PDAC case occurred, arising after more than five years of cyst stability. Conclusions: Fewer than 20% of patients with SB-IPMN were managed according to Kyoto guidelines. Over-surveillance was common, particularly in younger patients, without apparent oncologic benefit. AIgo-based decision-support tools may help standardize care and optimize resource utilization. Full article

The increasing demand for reliability and safety in naval and offshore structures has accelerated the adoption of advanced Structural Health Monitoring (SHM) techniques. Among them, acoustic methods—ranging from passive acoustic emission monitoring to guided ultrasonic waves—have demonstrated exceptional potential for early detection, localization, and characterization of structural damage under harsh marine environments. This review provides a comprehensive and critical synthesis of the state-of-the-art in acoustic-based SHM applied to ships, submarines, offshore platforms, and floating renewable energy systems. Special emphasis is placed on the comparative performance of different acoustic techniques, their integration with numerical modeling and data-driven methods, and their suitability for real-world deployment considering hydrodynamic, operational, and environmental constraints. By bridging current achievements with future challenges, the paper highlights research gaps and outlines key directions to accelerate the transition of acoustic SHM technologies from laboratory studies to widespread industrial applications. This review aspires to serve as a reference work for both academic researchers and practitioners, consolidating knowledge and inspiring innovation in the field. Full article

Plastic has transitioned rapidly from a revolutionary material to a global environmental concern, primarily due to mismanagement. Synthetic polymers have quickly gained widespread use due to their versatility, durability, and affordability. However, the properties making plastic indispensable contribute to its permanence in the environment, where it breaks down into microplastics—tiny particles that are typically classified in the size range from 0.1 μm to 5 mm. These particles can now be found in all ecosystems, including the oceans, soil, atmosphere, and within living organisms, raising global concerns about their long-term environmental and health impacts. This review critically examines the current status and potential for identifying, analyzing, and mitigating microplastic pollution. In this paper, we particularly focus on the destructive and non-destructive analytical methods used for microplastic identification and characterization, examining their technical capabilities and limitations, the challenges in maintaining sample integrity, and the reliability of their quantification methods. In addition, the review addresses microplastic removal strategies, from laboratory procedures to real-world applications, examining barriers to implementation and the limited availability of existing solutions. Finally, the review highlights the urgent need for standardized protocols, regulatory frameworks, and interdisciplinary collaboration to address the multifaceted nature of microplastic pollution. Full article

Background/Objectives: Accurate prognostic assessment remains crucial in metastatic renal cell carcinoma (mRCC), especially as treatment options have expanded beyond vascular endothelial growth factor (VEGF)-targeted therapies to include immune checkpoint inhibitors (ICIs) and ICI–TKI combinations. The widely used IMDC classification shows important limitations in the modern therapeutic era, highlighting the need for complementary prognostic tools. In this context, the Meet-URO and CANLPH scores—incorporating clinical, inflammatory, and nutritional markers—have emerged as promising alternatives. To evaluate and compare the prognostic performance of the Meet-URO and CANLPH scoring systems in a real-world mRCC cohort predominantly treated with first-line tyrosine kinase inhibitor (TKI) monotherapy due to limited access to ICI-based combinations. Methods: This retrospective single-center study included 112 patients with mRCC. The Meet-URO score was calculated for all patients, while the CANLPH score was assessed in 56 patients with complete laboratory data. CAR, NLR, and PHR were computed using baseline pre-treatment measurements. Overall survival (OS) and progression-free survival (PFS), the latter defined exclusively for first-line therapy, were estimated using the Kaplan–Meier method. Correlations between inflammatory markers and survival outcomes were analyzed using Spearman’s rho. Results: Meet-URO demonstrated clear prognostic stratification across all five categories, with the most favorable outcomes in score group 2 and progressively poorer OS and PFS in higher-risk groups. CANLPH also showed meaningful survival discrimination, with the highest inflammatory group (score 3) exhibiting markedly reduced OS and PFS. CAR was the strongest individual predictor of survival, while NLR and PHR showed weaker associations. Conclusions: Both Meet-URO and CANLPH provide strong, complementary prognostic information in mRCC, even in a cohort largely treated with TKI monotherapy. Their integration into routine risk assessment may enhance clinical decision-making, particularly in resource-limited settings. Full article

Dengue and chikungunya are endemic arboviral diseases in many low- and middle-income countries, often co-circulating and presenting with overlapping symptoms that hinder early diagnosis. Timely differentiation is critical, especially in resource-limited settings where laboratory testing is unavailable. We developed and evaluated machine-learning (ML)- and deep-learning (DL) models to classify dengue, chikungunya, and discarded cases using a large-scale, real-world dataset of over 6.7 million entries from Brazil (2013–2020). After applying the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance, we trained six ML models and one artificial neural network (ANN) using only demographic, clinical, and comorbidity features. The Random Forest model achieved strong multi-class classification performance (Recall: 0.9288, the Area Under the Curve (AUC): 0.9865). The ANN model excelled in identifying chikungunya cases (Recall: 0.9986, AUC: 0.9283), suggesting its suitability for rapid screening. External validation confirmed the generalizability of our models, particularly for distinguishing discarded cases. Our models demonstrate high-accuracy in differentiating dengue and chikungunya using routinely collected clinical and epidemiological data. This work supports the development of Artificial Intelligence-powered decision-support tools to assist frontline healthcare workers in under-resourced settings and aligns with the One Health approach to improving surveillance and diagnosis of neglected tropical diseases. Full article

Ethanol stability is an important indicator for evaluating the quality and heat-processing suitability of raw milk. Traditional ethanol stability testing relies on destructive laboratory procedures, which are not suitable for large-scale industrial use. In contrast, parameters such as protein, fat, lactose and other basic compositional indicators are already measured routinely in dairy plants through sensor-based or spectroscopic systems. This provides the basis for developing a non-destructive soft sensing approach for ethanol stability. In this study, a soft sensing model was developed to predict ethanol stability from commonly monitored raw-milk intake indicators. An autoencoder was used to examine feature correlations and select variables with stronger relevance to ethanol stability. TabNet was then applied to build the classification model, and a TabDDPM-based data generation method was introduced to address class imbalance in the dataset. The proposed model was trained and tested using three years of industrial raw-milk intake data from a dairy company. It achieved an accuracy of 92.57% and a recall of 90.26% for identifying ethanol-unstable samples. These results demonstrate the model’s strong potential for practical engineering applications in real-world dairy quality monitoring. Full article

Agitation-related movements in intensive care units (ICUs), such as unintended tube removal and bed exit attempts, pose significant risks to patient safety. The wearable inertial measurement units (IMUs) offer a potential means of capturing such movements. However, the technical feasibility of discriminating ICU-relevant agitation motion patterns from multi-site IMU data remains insufficiently established. To evaluate the technical feasibility of using a convolutional neural network (CNN) applied to multi-site IMU signals to discriminate predefined ICU-relevant agitation-related motion patterns under controlled experimental conditions. Fifteen healthy volunteers performed six scripted movements designed to emulate ICU-relevant agitation-related behaviors while wearing seven IMU sensors on the limbs and waist. A CNN comprising three convolutional layers with kernel sizes of 3, 5, and 7 was trained using 1-s windows extracted from 8-s trials and evaluated using leave-one-subject-out cross-validation. The performance was summarized using macro-averaged accuracy, sensitivity, specificity, precision, and F1 score. Across 135 independent training runs, the CNN achieved a median macro-averaged accuracy of 77.0%, sensitivity of 77.0%, specificity of 95.4%, and F1 score of 77.4%. These results indicate stable window-level discrimination of the predefined motion classes under standardized conditions. This proof-of-concept study demonstrates that multi-site IMU signals combined with CNN-based modeling can technically discriminate ICU-relevant agitation-related motion patterns in a controlled laboratory setting. Although these findings do not establish clinical validity in ICU patients, they provide a methodological foundation for future studies aimed at patient-level validation and real-world critical care deployment. Full article

Numerous road accidents involving vulnerable road users result from their insufficient visibility to drivers. To increase the appeal of warning clothing and motivate consumers to use it, particularly in non-professional settings, an innovative high-visibility vest with an active lighting system (ALS) and phosphorescent elements was developed. The effectiveness of the vest’s visibility-enhancing elements was assessed by examining two factors: the intensity of the light emitted by the phosphorescent tapes and the luminance of the optical fibers in the ALS. Studies have shown that thermal-transfer phosphorescent tapes are approximately 42% more effective in terms of luminescence than sewn-on tapes. The ALS demonstrated high durability, withstanding up to 15 washing cycles at 40 °C in a mild process. The luminance of optical fibers decreases significantly with increasing distance from the light source (LED). The difference between the luminance at the light source and at the end of the 1 m optical fiber was about 6 cd/m², representing approximately 68% of the maximum luminance value. This finding can assist in designing luminous clothing. Tests in real-world conditions in a tunnel have shown that the ALS allows the visibility of vest user to be increased to over 430 m, which is a 67% increase compared to retroreflective tapes. Laboratory performance testing confirmed the high acceptability of the vest model, including its aesthetics, by potential users. Full article