Search Results (3,243)

This study investigates the relationship between lameness, milk composition, and rumination behaviour in dairy cows by leveraging sensor-based data for automated monitoring. Lameness was found to significantly impact both rumination and milk production. Lameness was assessed in 24 multiparous Holstein dairy cows throughout early lactation (up to 100 days postpartum), utilising a 1-to-5 scale. Lameness was found to significantly impact both rumination and milk production. On the day of diagnosis, rumination time decreased by 26.64% compared to the pre-diagnosis period (p < 0.01) and by 26.06% compared to healthy cows, indicating the potential of rumination as an early health indicator. The milk yield on the day of diagnosis was 28.10% lower compared to pre-diagnosis levels (p < 0.01) and 40.46% lower than healthy cows (p < 0.05). These findings suggest that lameness manifests prior to clinical signs, affecting productivity and welfare. Milk composition was also influenced, with lame cows exhibiting altered fat (+0.68%, p < 0.05) and lactose (−2.15%, p < 0.05) content compared to healthy cows. Positive correlations were identified between rumination time and milk yield (r = 0.491, p < 0.001), while negative correlations were observed between milk yield and milk fat, protein, and the fat-to-protein ratio (p < 0.001). Additionally, lameness was associated with elevated somatic cell counts in the milk, although sample size limitations necessitate further validation. This study highlights the critical role of rumination and milk performance metrics in identifying subclinical lameness, emphasising the utility of automated systems in advancing dairy cow welfare and productivity. The findings underscore the importance of early detection and management strategies to mitigate the economic and welfare impacts of lameness in dairy farming. Full article

Recycled concrete aggregate (RCA) has been widely adopted in construction and emerged as a sustainable alternative to conventional natural aggregates in the construction industry. However, the study of holistic perspectives in recent literature is lacking. This review paper aims to provide a comprehensive analysis of RCA, highlighting its properties, applications, and overall sustainability benefits to facilitate the comprehensive points of view of technology, ecology, and economics. This paper explores the manufacturing process of RCA, examines its mechanical and durability characteristics, and investigates its environmental impacts. Furthermore, it delves into the various applications of RCA, such as road construction materials, pavement bases, and concrete materials, considering their life cycle performance and economic considerations. This review reveals that there is a need for systemic data collection that could enable automated concrete mix design. The findings concerning various mix concrete designs suggest that increasing the 1% replacement level reduces the compressive strength by 0.1913% for coarse RCA and 0.2418% for fine RCA. The current critical research gaps are the durability of RCA concrete, feasibility analyses, and the implementation of treatment methods for RCA improvement. An effective life cycle assessment tool and digitalization technologies can be applied to enhance the circular economy, aligning with the United Nations’ sustainable development goals (UN-SDGs). The equivalent mortar volume method used to calculate the RCA concrete mix design, which can contain chemical additives, metakaolin, and fibers, needs further assessment. Full article

This study presents the results of the analyses of 15 structural mortars from the building at civ. 21, level +0 of the Sarno Bath complex in Pompeii. These samples were collected during recent stratigraphic excavations (year 2022) for detailed in-laboratory compositional characterization, aiming to trace the construction phases of the originating walls. The 2022 samples were firstly analyzed via quantitative phase analysis–X-ray powder diffraction. The resulting quantitative mineralogical profiles were then processed alongside those analyzed in previous studies from level +0 structures of the Sarno Baths using multivariate statistical methods, including principal component analysis (PCA) and discriminant analysis, applied to quantitative phase analysis (QPA)–X-ray powder diffraction data (XRPD), to identify and map the construction phases. This approach enabled the correlation of the 2022 samples with previously established construction phases. Polarized-light optical microscopy and scanning electron microscopy (SEM) coupled with energy dispersive X-ray spectroscopy (EDS) were then primarily used for validation purposes. These methods highlighted the compositional differences between samples and revealed significant features related to the use of specific raw materials. These results confirm the reliability of the semi-automated sample processing proposed in this research, adopting discriminant analysis as a machine-learning-based tool for defining construction phases in Pompeian contexts. Full article

The pore structure plays a crucial role in soil systems. It affects a range of processes essential for soil ecological functions, such as the transport and retention of water and nutrients, as well as gas exchanges. The mechanical and hydrological characteristics of soil are predominantly determined by the three-dimensional pore pore-space structure. A precise analysis of pore structure can help specialists understand how these shapes impact plant root activity, leading to better cultivation practices. X-ray computed tomography provides detailed information without destroying the sample. However, manually delineating pore structure and estimating porosity are challenging tasks. This work proposes an automated method for 3D pore segmentation and characterization using convolutional neural networks with attention mechanisms. The method introduces a novel approach that combines attention at both channel and spatial levels, enhancing the segmentation and property estimation, providing valuable insights for a more detailed study of soil conditions. In experiments conducted with a private dataset, the segmentation results achieved mean Dice values of 99.10% ± 0.0004 and mean IoU values of 98.23% ± 0.0008. Additionally, in tests with Phaeozem Albic, the automatic method provided porosity estimates comparable to those obtained by a method based on integral geometry and morphology. Full article

Although Caenorhabditis elegans is commonly used to assess the neurotoxicity of environmental pollutants, studies that explore the intricate biology of its nervous system, particularly those addressing long-term effects and aging in adult worms, are rare. These models offer significant advantages for understanding the full spectrum of neurobiological impacts. Here, we investigated the effects of silica nanomaterials on the γ-aminobutyric acid (GABA) neural system in young to middle-aged nematodes and found a unique degeneration pattern characterized by loss of anterior- and posteriormost GABAergic D-type motor neurons. Four-day-old nematodes were identified as a vulnerable age group, where the pollutant-accelerated neurodegeneration that is typically seen in old C. elegans. Proteomics of 4-day-old C. elegans revealed significant alterations of protein abundance, including the downregulation of proteins such as glutamate dehydrogenase (gdh-1) and glutamate oxaloacetate transaminase (got-1.2), which are essentially involved in GABA metabolic pathways. Consistent with these findings, we demonstrated locomotion deficits in C. elegans exposed to nanoscale silica by establishing a semi-automated behavioral arena. Our setup not only visualizes but also automatically quantifies vulnerabilities at the individual worm level. This novel neurodegeneration model now enables the simulation of real-world pollutant mixtures and environmental conditions, capturing the complexity of the exposome. Full article

Technological advances in the production of printed circuit boards (PCBs) are increasing the number of components inserted on the surface. This has led the electronics industry to seek improvements in their inspection processes, often making it necessary to increase the level of automation on the production line. The use of machine vision for quality inspection within manufacturing processes has increasingly supported decision making in the approval or rejection of products outside of the established quality standards. This study proposes a hybrid smart-vision inspection system with a machine vision concept and vision sensor equipment to verify 24 components and eight screw threads. The goal of this study is to increase automated inspection reliability and reduce non-conformity rates in the manufacturing process on the assembly line of automotive products using machine vision. The system uses a camera to collect real-time images of the assembly fixtures, which are connected to a CMOS color vision sensor. The method is highly accurate in complex industry environments and exhibits specific feasibility and effectiveness. The results indicate high performance in the failure mode defined during this study, obtaining the best inspection performance through a strategy using Vision Builder for automated inspection. This approach reduced the action priority by improving the failure mode and effect analysis (FMEA) method. Full article

Three-dimensional site modeling is an important aspect of Building Information Modeling (BIM), especially in mountainous areas. Accurate site modeling is essential for efficient construction planning and resource allocation. A key issue in site modeling is how to accurately calculate the shape of side-slopes. It involves three sub-problems: geometric representation of side-slopes, determination of fill/cut types, and intersection of side-slopes surface with the terrain surface. To address this, a two-stage method for constructing side-slope models adaptive to terrain is proposed. In the first stage, a marching algorithm along polylines is used to calculate the intersection points of the site boundary polylines with the terrain surface. These intersection points are used to segment the boundary polylines. A rule-based approach is then applied to automatically determine the fill/cut type for each segment. Subsequently, the equations of the side-slopes passing through each segment are derived using geometric principles. In the second stage, a marching algorithm along the plane is used to trace the intersection lines of side-slopes with the terrain. Finally, the side-slopes are rendered with precision by integrating the equations of each segment with the determined intersection lines. The effectiveness of the method is verified through illustrative examples. Algorithm efficiency analysis and 3D modeling illustrations have demonstrated that this method not only boasts accuracy and swift computation but also excels in the level of automation achieved in the modeling process. Full article

This study explores the use of machine learning for the automated classification of the ten most abundant groups of marine organisms (in the size range of 5–12 cm) plus marine snow found in the ecosystem of the U.S. east coast. Images used in this process were collected using a shadowgraph imaging system on a towed, undulating platform capable of collecting continuous imagery over large spatiotemporal scales. As a large quantity (29,818,917) of images was collected, the task of locating and identifying all imaged organisms could not be efficiently achieved by human analysis alone. Several tows of data were collected off the coast of Delaware Bay. The resulting images were then cleaned, segmented into regions of interest (ROIs), and fed through three convolutional neural networks (CNNs): VGG-16, ResNet-50, and a custom model created to find more high-level features in this dataset. These three models were used in a Random Forest Classifier-based ensemble approach to reach the best identification fidelity. The networks were trained on a training set of 187,000 ROIs augmented with random rotations and pixel intensity thresholding to increase data variability and evaluated against two datasets. While the performance of each individual model is examined, the best approach is to use the ensemble, which performed with an F1-score of 98% and an area under the curve (AUC) of 99% on both test datasets while its accuracy, precision, and recall fluctuated between 97% and 98%. Full article

This review analyzes the current practices in the data-driven characterization, design and optimization of disordered nanoporous materials with pore sizes ranging from angstroms (active carbon and polymer membranes for gas separation) to tens of nm (aerogels). While the machine learning (ML)-based prediction and screening of crystalline, ordered porous materials are conducted frequently, materials with disordered porosity receive much less attention, although ML is expected to excel in the field, which is rich with ill-posed problems, non-linear correlations and a large volume of experimental results. For micro- and mesoporous solids (active carbons, mesoporous silica, aerogels, etc.), the obstacles are mostly related to the navigation of the available data with transferrable and easily interpreted features. The majority of published efforts are based on the experimental data obtained in the same work, and the datasets are often very small. Even with limited data, machine learning helps discover non-evident correlations and serves in material design and production optimization. The development of comprehensive databases for micro- and mesoporous materials with low-level structural and sorption characteristics, as well as automated synthesis/characterization protocols, is seen as the direction of efforts for the immediate future. This paper is written in a language readable by a chemist unfamiliar with the data science specifics. Full article

The current paradigm of low-T combustion and autoignition of hydrocarbons is based on the sequential two-step oxygenation of fuel radicals. The key chain-branching occurs when the second oxygenation adduct (OOQOOH) is isomerized releasing an OH radical and a key ketohydroperoxide (KHP) intermediate. The subsequent homolytic dissociation of relatively weak O–O bonds in KHP generates two more radicals in the oxidation chain leading to ignition. Based on the recently introduced intramolecular “catalytic hydrogen atom transfer” mechanism (J. Phys. Chem. 2024, 128, 2169), abbreviated here as I-CHAT, we have identified a novel unimolecular decomposition channel for KHPs to form their classical isomers—enol hydroperoxides (EHP). The uncertainty in the contribution of enols is typically due to the high computed barriers for conventional (“direct”) keto–enol tautomerization. Remarkably, the I-CHAT dramatically reduces such barriers. The novel mechanism can be regarded as an intramolecular version of the intermolecular relay transfer of H-atoms mediated by an external molecule following the general classification of such processes (Catal. Rev.-Sci. Eng. 2014, 56, 403). Here, we present a detailed mechanistic and kinetic analysis of the I-CHAT-facilitated pathways applied to n-hexane, n-heptane, and n-pentane models as prototype molecules for gasoline, diesel, and hybrid rocket fuels. We particularly examined the formation kinetics and subsequent dissociation of the γ-enol-hydroperoxide isomer of the most abundant pentane-derived isomer γ-C5-KHP observed experimentally. To gain molecular-level insight into the I-CHAT catalysis, we have also explored the role of the internal catalyst moieties using truncated models. All applied models demonstrated a significant reduction in the isomerization barriers, primarily due to the decreased ring strain in transition states. In addition, the longer-range and sequential H-migration processes were also identified and illustrated via a combined double keto–enol conversion of heptane-2,6-diketo-4-hydroperoxide as a potential chain-branching model. To assess the possible impact of the I-CHAT channels on global fuel combustion characteristics, we performed a detailed kinetic analysis of the isomerization and decomposition of γ-C5-KHP comparing I-CHAT with key alternative reactions—direct dissociation and Korcek channels. Calculated rate parameters were implemented into a modified version of the n-pentane kinetic model developed earlier using RMG automated model generation tools (ACS Omega, 2023, 8, 4908). Simulations of ignition delay times revealed the significant effect of the new pathways, suggesting an important role of the I-CHAT pathways in the low-T combustion of large alkanes. Full article

Future autonomous vehicles will interact with traffic infrastructure through roadside units (RSUs) directly connected to transportation field cabinets (TFCs). These TFCs must be interconnected to share traffic information, enabling infrastructure-to-infrastructure (I2I) communications that are reliable, synchronous and capable of transmitting vehicle data to the Internet. However, I2I communications present a complex optimization challenge. This study addresses this by proposing the design, implementation, and evaluation of an automated management model for I2I service channels based on multi-agent reinforcement learning (MARL) integrated with deep reinforcement learning (DRL). The proposed models efficiently manage the routing and scheduling of data frames between internet of vehicles (IoV) infrastructure devices through time-sensitive networking (TSN) to ensure real-time synchronous I2I communications. The solution incorporates both a routing model and a scheduling model, evaluated in a simulated shared environment where agents operate within the TSN control plane. Both models are tested for different topologies and background traffic levels. The results demonstrate that the models establish the majority of paths in the scenario, adhering to near-optimal routing and scheduling policies. Recursively, for each individual request to create a service channel, the system establishes online an optimal synchronous path between entities with a limited time budget. In total, 71% of optimal routing paths are established and 97% of optimal schedules are achieved. The approach takes into account the periodic nature of the transmitted data and its robustness through TSN networks, obtaining 99 percent of compliant service requests with flow jitter levels below 100 microseconds for different topologies and different network utility percentages. The proposed solution achieves lower execution delays compared to the iterative ILP approach. Additionally, the solution facilitates the integration of 5G networks for vehicle-to-infrastructure (V2I) communications, which is identified as an area for future exploration. Full article

Aerosol vertical stratification significantly influences the Earth’s radiative balance and particulate-matter-related air quality. Continuous vertically resolved observations remain scarce compared to surface-level and column-integrated measurements. This work presents and makes available a novel, long-term (2016–2022) aerosol dataset derived from continuous (24/7) vertical profile observations from three selected stations (Aosta, Rome, Messina) of the Italian Automated Lidar-Ceilometer (ALC) Network (ALICENET). Using original retrieval methodologies, we derive over 600,000 quality-assured profiles of aerosol properties at the 15 min temporal and 15 metre vertical resolutions. These properties include the particulate matter mass concentration (PM), aerosol extinction and optical depth (AOD), i.e., air quality legislated quantities or essential climate variables. Through original ALICENET algorithms, we also derive long-term aerosol vertical layering data, including the mixed aerosol layer (MAL) and elevated aerosol layers (EALs) heights. Based on this new dataset, we obtain an unprecedented, fine spatiotemporal characterisation of the aerosol vertical distributions in Italy across different geographical settings (Alpine, urban, and coastal) and temporal scales (from sub-hourly to seasonal). Our analysis reveals distinct aerosol daily and annual cycles within the mixed layer and above, reflecting the interplay between site-specific environmental conditions and atmospheric circulations in the Mediterranean region. In the lower troposphere, mixing processes efficiently dilute particles in the major urban area of Rome, while mesoscale circulations act either as removal mechanisms (reducing the PM by up to 35% in Rome) or transport pathways (increasing the loads by up to 50% in Aosta). The MAL exhibits pronounced diurnal variability, reaching maximum (summer) heights of >2 km in Rome, while remaining below 1.4 km and 1 km in the Alpine and coastal sites, respectively. The vertical build-up of the AOD shows marked latitudinal and seasonal variability, with 80% (30%) of the total AOD residing in the first 500 m in Aosta-winter (Messina-summer). The seasonal frequency of the EALs reached 40% of the time (Messina-summer), mainly in the 1.5–4.0 km altitude range. An average (wet) PM > 40 μg m⁻³ is associated with the EALs over Rome and Messina. Notably, 10–40% of the EAL-affected days were also associated with increased PM within the MAL, suggesting the entrainment of the EALs in the mixing layer and thus their impact on the surface air quality. We also integrated ALC observations with relevant, state-of-the-art model reanalysis datasets (ERA5 and CAMS) to support our understanding of the aerosol patterns, related sources, and transport dynamics. This further allowed measurement vs. model intercomparisons and relevant examination of discrepancies. A good agreement (within 10–35%) was found between the ALICENET MAL and the ERA5 boundary layer height. The CAMS PM₁₀ values at the surface level well matched relevant in situ observations, while a statistically significant negative bias of 5–15 μg m⁻³ in the first 2–3 km altitude was found with respect to the ALC PM profiles across all the sites and seasons. Full article

In this work, a highly sensitive, selective, and industrially compatible gas sensor prototype is presented. The sensor utilizes three distributed-feedback quantum cascade lasers (DFB-QCLs), employing wavelength modulation spectroscopy (WMS) for the detection of hydrogen sulfide (H₂S), methane (CH₄), methyl mercaptan (CH₃SH), and carbonyl sulfide (COS) in the spectral regions of 8.0 µm, 7.5 µm, and 4.9 µm, respectively. In addition, field-programmable gate array (FPGA) hardware is used for real-time signal generation, laser driving, signal processing, and handling industrial communication protocols. To comply with on-site safety standards, the QCL sensor prototype is housed in an industrial-grade enclosure and equipped with the necessary safety features to ensure certified operation under ATEX/IECEx regulations for hazardous and explosive environments. The system integrates an automated gas sampling and conditioning module, alongside a purge and pressurization system, with intrinsic safety electronic components, thereby enabling reliable explosion prevention and malfunction protection. Detection limits of approximately 0.3 ppmv for H₂S, 60 ppbv for CH₃SH, and 5 ppbv for COS are demonstrated. Noise-equivalent absorption sensitivity (NEAS) levels for H₂S, CH₃SH, and COS were determined to be 5.93 × 10⁻⁹, 4.65 × 10⁻⁹, and 5.24 × 10⁻¹⁰ cm⁻¹ Hz^−1/2. The suitability of the sensor prototype for simultaneous sulfur species monitoring is demonstrated in process streams of a hydrodesulphurization (HDS) and fluid catalytic cracking (FCC) unit at the project’s industrial partner, OMV AG. Full article

The importance of measuring service quality for business performance has been widely recognized in service marketing literature due to its pivotal influence on customer satisfaction and its long-term impact on customer loyalty. The SERVQUAL model, comprising five dimensions—reliability, assurance, tangibility, empathy, and responsiveness—provides a measurable framework for evaluating the overall customer satisfaction. This study endeavors to ascertain whether all SERVQUAL dimensions carry equal weight in their effect on the overall service quality and to estimate the service quality based on various input features. To achieve this, questions were framed to assess the impact of variables such as gender, age, marital status, highest level of education, and frequency of hotel stays. The importance of each feature relative to the five SERVQUAL dimensions was investigated using machine learning models, specifically, CatBoost and Microsoft Azure Automated Machine Learning (AutoML) studio. This study revealed that both CatBoost and Azure AutoML identified the frequency of hotel stays and age group as the dominant predictors of service quality. Additionally, Azure AutoML highlighted the marital status as a more significant factor, suggesting its potential influence on customer preferences. The comparative modeling results demonstrated a strong alignment between the feature importance derived from CatBoost and Azure AutoML, enabling decision-makers to identify which dimensions are influenced by specific predictors and focus on targeted improvements. Full article

This study examines educators’ perceptions of artificial intelligence (AI) in educational settings, focusing on their familiarity with AI tools, integration into teaching practices, professional development needs, the influence of institutional policies, and impacts on mental health. Survey responses from 353 educators across various levels and countries revealed that 92% of respondents are familiar with AI, utilizing it to enhance teaching efficiency and streamline administrative tasks. Notably, many educators reported students using AI tools like ChatGPT for assignments, prompting adaptations in teaching methods to promote critical thinking and reduce dependency. Some educators saw AI’s potential to reduce stress through automation but others raised concerns about increased anxiety and social isolation from reduced interpersonal interactions. This study highlights a gap in institutional AI policies, leading some educators to establish their own guidelines, particularly for matters such as data privacy and plagiarism. Furthermore, respondents identified a significant need for professional development focused on AI literacy and ethical considerations. This study’s findings suggest the necessity for longitudinal studies to explore the long-term effects of AI on educational outcomes and mental health and underscore the importance of incorporating student perspectives for a thorough understanding of AI’s role in education. Full article