Search Results (3,090)

Improving cyclist safety conditions in the urban context is a key strategy to promote sustainable transport modes and reduce noise and environmental pollution. Recent plans have also addressed this point. In September 2020, the UN General Assembly declared the Decade of Action for Road Safety 2021–2030, aiming to reduce the number of road deaths by at least half. To achieve this task and highlight the risk factor, after collecting and pre-processing cyclist crash data in the city of Rome between 2013 and 2020, Random Forest and Ordered Logistic Regression models are proposed. The crash dataset is also enriched with vehicular speed and flows, and geographical information. A DBSCAN Clustering Analysis is also proposed to identify anomalous areas in the city. The findings show that the presence of cycle paths, the presence of anthropic activities, such as shops, schools, and universities, play a risk mitigation role. Conversely, vehicular speed and heavy vehicles emerge as the main detected risk factors. Finally, spatial analysis indicates that commercial activities reduce cycle path safety due to complex interactions with other road users. Furthermore, historic areas present unique risks driven by pedestrian flows and poor road surfaces, despite low vehicular traffic. Full article

Joint roughness coefficient (JRC) and inclination exert a decisive influence on the stability and safety of rock mass engineering. Simulations of uniaxial compression were conducted on saw-tooth-shaped joint specimens using a calibrated particle flow (PFC2D) model. The specimens contained five JRC values (0, 5, 10, 15, 20) and five joint inclinations (0°, 30°, 45°, 60°, 90°). The results indicate that at joint inclinations of 0° and 90°, JRC has a marginal influence on peak stress and elastic modulus. In contrast, as the inclination increases, the peak stress, peak strain, and elastic modulus collectively exhibit an approximate V-shaped trend. The dominant failure mode observed was a mixed splitting-shear mechanism. The number of cracks at final failure increases with higher JRC values under the same joint inclination. As the joint inclination varied, the distributions of global, tensile, and shear cracks all exhibited similar V-shaped trends. Concurrently, the proportions of different microcrack types demonstrated relative stability throughout the failure process, with tensile and shear failures constituting the dominant microscopic mechanisms. Full article

Background: Pulmonary infarction (PI) is the result of the occlusion of distal pulmonary arteries resulting in damage to downstream lung areas that become ischemic, hemorrhagic, or necrotic, and it is often a complication of an underlying condition such as pulmonary embolism (PE). Since in most of cases it is located peripherally, lung ultrasound (LUS) can be a good evaluation tool. The typical radiological features of PI are well-known; however, there are limited data on its sonographic characteristics and its evolution. Methods: The aim of this study is to evaluate, using LUS, a convenience sample of patients with acute PE with computed tomography (CT) consolidation findings consistent with PI. Patients’ clinical characteristics were collected and LUS findings at baseline and their short-term progression was assessed. LUS was performed within 72 h of PE diagnosis (T0) and repeated after one (T1) and four weeks (T2). Each procedure started with a focused examination of the areas of lesions based on CT findings, followed by an exploration of the other posterior and lateral lung fields. The convex probe was used for initial evaluation integrating LUS evaluation with the linear one was employed for smaller and more superficial lesions and when appropriate. Color Doppler mode was added to study vascularization. Results: From June to October 2023, 14 consecutive patients were enrolled at the Respiratory Unit of the University Hospital of Pisa. The main population characteristics included the absence of respiratory failure and prognostic high-risk PE (100%), the absence of significant comorbidities (79%), and the presence of typical symptoms, such as chest pain (57%) and dyspnea (50%). The average number of consolidations per patient was 1.4 ± 0.6. Follow-up LUS showed the disappearance of some consolidations and some morphological changes in the remaining lesions: the presence of hypoechoic consolidation with a central hyperechoic area (“bubbly consolidation”) was more typical at T1 while the presence of a small pleural effusion often persisted both at T1 and T2. A decrease in wedge/triangular-shaped consolidations was observed (82% at T0, 67% at T1, 24% at T2), as was an increase in elongated shapes, representing a residual pleural thickening over time (9% at T0, 13% at T1, 44% at T2). A reduction in size was also observed by comparing the mean diameter, long axis, and short axis measurements of each consolidation at the three different studied time points: the average of the short axes and the median of the mean diameters showed a statistically significant reduction after four weeks. Additionally, a correlation between lesion size and pleuritic pain was described, although it did not achieve statistical significance. Conclusions: Patients’ clinical characteristics and ultrasound features are consistent with previous studies studying PI at PE diagnosis. Most consolidations detected by LUS change over time regarding size and form, but a minority of them do not differ. LUS is a safe and non-invasive exam that could help to improve patients’ clinical approach in emergency rooms as well as medical and pulmonology settings, clinically contextualized for cases of chest pain and dyspnea. Future studies could expand the morphological study of PI. Full article

The paper presents the development and preliminary evaluation of a two-dimensional (2-D) network of magnetometers for magnetic anomaly detection. The configuration significantly improves over the existing one-dimensional (1-D) architecture, as it enhances the spatial characterization of magnetic anomalies through the simultaneous acquisition of data over an extended area. This leads to a reliable estimation of the target motion parameters. Each sensor node in the network includes a custom-designed electronic system, integrating a biaxial fluxgate magnetometer that operates in null mode. Deep learning models process the raw measurements collected by the magnetometers and extract structured information that enables both automated detection and preliminary target tracking. In the experimental evaluation, a $5\times 5$ array of nodes was deployed over a $12\times 12$ m² area for terrestrial tests, using moving ferromagnetic cylinders as targets. The results confirmed the feasibility of the 2-D configuration and supported its integration into intelligent, real-time surveillance systems for security and underwater monitoring applications. Full article

Licorice (Glycyrrhiza uralensis Fisch.) is a widely used natural sweetener and functional food ingredient. Its sensory profile, nutritional value, and bioactive composition are strongly affected by geographical origin and cultivation mode, particularly the distinction between wild and cultivated resources. Consequently, developing a rapid and robust method for origin traceability is imperative for rigorous quality control and product standardization. This study proposes a non-destructive traceability framework integrating near-infrared (NIR) spectroscopy with a Support Vector Machine (SVM). The method’s validity was rigorously evaluated using a comprehensive dataset collected from China’s three primary production regions—Gansu Province, the Inner Mongolia Autonomous Region, and the Xinjiang Uygur Autonomous Region, encompassing both wild and cultivated resources. Experimental results demonstrated that the proposed framework achieved an overall classification accuracy exceeding 99%. The results show that the proposed method offers a rapid, efficient, and environmentally friendly analytical tool for the quality assessment of licorice, providing a scientific basis for rigorous quality control and standardization in the functional food industry. Full article

In order to solve the problem of false alarms and missed alarms in pipeline monitoring caused by a large amount of noise in the negative pressure wave signal collected by pressure sensors, a new pressure signal denoising method based on the black-winged kite algorithm (BWK) is proposed. First, the variational mode decomposition (VMD) parameters are optimized through BWK. Next, the effective modal components are screened by sample entropy, and the secondary noise reduction of the signal is carried out by using the wavelet thresholding (WT). Finally, the signal is reconstructed to achieve noise reduction. Simulation experiments show that, compared with WT and empirical mode decomposition (EMD), the method proposed in this paper can achieve the best noise reduction effect under both high and low signal-to-noise ratio (SNR) conditions. The method proposed in the paper can achieve the highest SNR of 14.2280 dB, compared to WT’s SNR of 12.6458 dB and EMD’s SNR of 5.5292 dB. To further validate the performance of the algorithm, an experimental platform for simulating pipeline leaks is built. Compared with WT and EMD, the method proposed in this paper also shows the best noise reduction effect. This method provides a high-precision and adaptive solution for leak detection in urban water supply pipelines and has strong engineering application value. Full article

External causes account for over four million deaths globally each year, with psychoactive substance use being a major risk factor. However, the true impact and regional patterns of psychoactive substance use in these deaths remains undefined in Brazil. To address this critical knowledge gap, this pioneering four-city study sought to elucidate the prevalence of alcohol and drug use by external cause victims. We collected postmortem blood from 3577 victims of violent death across four distinct Brazilian cities (Belém, Recife, Vitória, and Curitiba), representing the North, Northeast, Southeast, and South regions, respectively, using a standardized protocol to identify alcohol, illicit drugs, and psychoactive medicines. Analysis revealed a predominantly male cohort (89.7%; 56.0% aged 30 years or more), with homicide as the primary manner of death (67.3%). Over half of the victims (53.0%) tested positive for at least one psychoactive substance prior to death; cocaine (29.6%) and alcohol (27.7%) were most common. Substance use was highest among homicide victims (55.7%), especially cocaine (36.0%), and among self-harm cases (54.6%), which showed elevated benzodiazepine prevalence (20.0%). Substance use patterns varied regionally: alcohol-related deaths were more common in Recife (Northeast), drug-only deaths concentrated in Vitória (Southeast) and Belém (North), and Curitiba (South) showed a higher prevalence of alcohol use versus drug use. This widespread, regionally heterogeneous prevalence underscores the urgent need for targeted, region-specific interventions. By critically linking psychoactive substance use to various modes of violent death, these data provide crucial forensic and public health insights to inform tailored preventive strategies. Full article

The six platinum group elements (PGE) are among the rarest elements in the upper continental crust of the earth. Higher values of PGE have been detected in the upper mantle and in chondrite meteorites. The PGE are siderophile and chalcophile elements and are divided into the following: (1) the Ir subgroup (IPGE) = Os, Ir, and Ru and (2) the Pd subgroup (PPGE) = Rh, Pt, and Pd. The IPGE are more refractory and less chalcophile than the PPGE. High concentrations of PGE led, in rare cases, to the formation of mineral deposits. The PGE are carried in discrete phases, the platinum group minerals (PGM), and are included as trace elements into the structure of base metal sulphides (BM), such as pentlandite, chalcopyrite, pyrite, and pyrrhotite. Similarly to PGE, the PGM are also divided into two main groups, i.e., IPGM composed of Os, Ir, and Ru and PPGM containing Rh, Pt, and Pd. The PGM occur both in mafic and ultramafic rocks and are mainly hosted in stratiform reefs, sulphide-rich lenses, and placer deposits. Presently, there are only 169 valid PGM that represent about 2.7% of all 6176 minerals discovered so far. However, 496 PGM are listed among the valid species that have not yet been officially accepted, while a further 641 are considered as invalid or discredited species. The main reason for the incomplete characterization of PGM resides in their mode of occurrence, i.e., as grains in composite aggregates of a few microns in size, which makes it difficult to determine their crystallography. Among the PGM officially accepted by the IMA, only 13 (8%) were discovered before 1958, the year when the IMA was established. The highest number of PGM was discovered between 1970 and 1979, and 99 PGM have been accepted from 1980 until now. Of the 169 PGM accepted by the IMA, 44% are named in honour of a person, typically a scientist or geologist, and 31% are named after their discovery localities. The nomenclature of 25% of the PGM is based on their chemical composition and/or their physical properties. PGM have been discovered in 25 countries throughout the world, with 64 from Russia, 17 from Canada and South Africa (each), 15 from China, 12 from the USA, 8 from Brazil, 6 from Japan, 5 from Congo, 3 from Finland and Germany (each), 2 from the Dominican Republic, Greenland, Malaysia, and Papua New Guinea each, and only 1 from Argentine, Australia, Bulgaria, Colombia, Czech Republic, England, Ethiopia, Guyana, Mexico, Serbia, and Tanzania each. Most PGM phases contain Pd (82 phases, 48% of all accepted PGM), followed, in decreasing order of abundances, by those of Pt 35 phases (21%), Rh 23 phases (14%), Ir 18 phases (11%), Ru 7 phases (4%), and Os 4 phases (2%). The six PGE forming the PGM are bonded to other elements such as Fe, Ni, Cu, S, As, Te, Bi, Sb, Se, Sn, Hg, Ag, Zn, Si, Pb, Ge, In, Mo, and O. Thirty-two percent of the 169 valid PGM crystallize in the cubic system, 17% are orthorhombic, 16% hexagonal, 14% tetragonal, 11% trigonal, 3% monoclinic, and only 1% triclinic. Some PGM are members of a solid-solution series, which may be complete or contain a miscibility gap, providing information concerning the chemical and physical environment in which the mineral was formed. The refractory IPGM precipitate principally in primitive, high-temperature, mantle-hosted rocks such as podiform and layered chromitites. Being more chalcophile, PPGE are preferentially collected and concentrated in an immiscible sulphide liquid, and, under appropriate conditions, the PPGM can precipitate in a thermal range of about 900–300 °C in the presence of fluids and a progressive increase of oxygen fugacity (fO₂). Thus, a great number of Pt and Pd minerals have been described in Ni-Cu sulphide deposits. Two main genetic models have been proposed for the formation of PGM nuggets: (1) Detrital PGM represent magmatic grains that were mechanically liberated from their primary source by weathering and erosion with or without minor alteration processes, and (2) PGM reprecipitated in the supergene environment through a complex process that comprises solubility, the leaching of PGE from the primary PGM, and variation in Eh-pH and microbial activity. These two models do not exclude each other, and alluvial deposits may contain contributions from both processes. Full article

Rice blast, caused by the fungus Magnaporthe oryzae, is a devastating disease that threatens global food security, causing annual yield losses of 10–30%. Consequently, novel control strategies beyond conventional fungicides are urgently needed. Tetramycin, a polyene macrolide antibiotic, is known for its broad-spectrum antifungal activity. However, the specific mechanisms underlying its efficacy against rice blast remain to be fully elucidated. In this study, we demonstrate that tetramycin confers resistance through a dual mode of action. First, in vitro assays revealed that tetramycin directly inhibits M. oryzae mycelial growth. Second, and more critically, it functions as a potent immune elicitor in Oryza sativa. Transcriptome analysis coupled with physiological assays showed that tetramycin treatment triggers a rapid oxidative burst, characterized by significantly elevated activities of key defense enzymes, including superoxide dismutase, peroxidase, phenylalanine ammonia lyase, and polyphenol oxidase (PPO). This oxidative response is further orchestrated through the simultaneous activation of the jasmonic acid (JA) and salicylic acid (SA) signaling pathways, as evidenced by the distinct upregulation of their respective biosynthetic genes and hormone levels. Collectively, these findings indicate that tetramycin not only acts as a direct fungicide but also primes the rice innate immune system via a synergistic reactive oxygen species-JA-SA signaling network, offering a sustainable strategy for rice blast management. Full article

This study investigates the spatiotemporal characteristics and travel patterns of micro-electric vehicles (micro-EVs) by analyzing real-world trip data collected over three years from shared micro-EV services operating in three regions of South Korea. Individual trips were extracted from GPS-based trajectory data, and a network-based detour ratio was introduced to capture non-linear trip characteristics. In addition, a hierarchical clustering analysis was applied to identify heterogeneous micro-EV trip patterns. The results show that micro-EVs are predominantly used for short-distance urban trips, while a smaller but behaviorally distinct subset of trips demonstrates their capacity to support medium-distance travel under specific functional contexts. The clustering analysis identified six distinct trip pattern groups, ranging from dominant short-distance routine travel to less frequent patterns associated with adverse weather conditions and extreme detouring behavior. Overall, the findings suggest that micro-EVs function as a complementary urban mobility mode, primarily supporting localized travel while selectively accommodating extended-range and specialized trips. From a sustainability perspective, these findings highlight the role of micro-EVs as energy-efficient, low-emission alternatives to conventional passenger vehicles for short- and medium-distance urban trips. By empirically identifying heterogeneous and long-tailed micro-EV travel patterns, this study provides practical insights for sustainable urban mobility design and environmentally responsible transportation policies. Full article

Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques have received rapid advance in recent years, and the Multi-temporal InSAR (MT-InSAR) has been widely applied in various earth observations. Distributed scatterer (DS) InSAR is one of the most advanced MT-InSAR methods, and has overcome the limitation of the lack of enough measurement points in the low coherent regions for traditional methods. While the Joint-Scatterer InSAR (JS-InSAR) is the extension of DS InSAR method, which exploited the overall information of Joint Scatterers to carry out DS identification and phase optimization. And it can avoid the inaccuracy caused by the offset errors between scatterers in complex terrain areas. However, the intensive computation and low efficiency have severely restricted the application of JS-InSAR, especially when dealing with massive and long historical SAR images. As the sequential estimator has proven to successfully improve the efficiency of MT-InAR and obtain near-time deformation time series, in this work, we proposed the sequential-based JS-InSAR (S-JSInSAR) method with flexible batches. This method has adaptively divided large single look complex (SLC) stack into different batches with flexible number and certain overlaps. Then, the JS-InSAR processing is performed on each batch, respectively, and these estimated results are integrated into the final deformation time series based on the connection mode. Thus, S-JSInSAR can efficiently process large InSAR dataset, and mitigate the decorrelation effect caused by long temporal baselines. To demonstrate the effectiveness of the S-JSInSAR, a multi-year of 145 Sentinel-1 ascending SAR images in Tangshan, China, were collected to estimate the long deformation time series. And the results compared with other methods have shown the processing time has substantially decreased without the loss of deformation accuracy, and obtain deformation spatial distribution with more details in local regions, which have well validated the efficiency and reliability of the proposed method. Full article

Titanium dioxide (TiO₂) nano- and submicrometric particles’ widespread use in different sectors raised concerns about human and environmental exposure. The validation of analytical methods is essential to ensure reliability in risk assessment studies. In this study, a single-particle inductively coupled plasma mass spectrometry (spICP-MS) method was validated for the detection, quantification, and dimensional characterization of TiO₂ particles in biological tissues. Tissue samples collected after exposure to TiO₂ particles underwent mild acidic digestion using a HNO₃/H₂O₂ mixture to achieve complete matrix decomposition while preserving particle integrity. The resulting digests were analyzed by ICP-MS operated in single-particle mode to quantify and size TiO₂ particles. Method validation was conducted according to ISO/IEC 17025:2017 and included linearity, repeatability, recovery, and detection limit assessments. The limit of detection for TiO₂ particles was 0.04 µg/g, and 55.7 nm was the size the detection limit. Repeatability was within 0.5–11.5% for both TiO₂ mass concentrations and particle size determination. The validated method was applied to tissues from inhalation-exposed subjects, showing TiO₂ levels of 80 ± 20 µg TiO₂/g and particle number concentrations of 5.0 × 10⁵ ± 1.2 × 10⁵ part. TiO₂/mg. Detected TiO₂ particles’ mean diameter ranged from 230 to 330 nm. The developed and validated spICP-MS method provides robust and sensitive quantification of TiO₂ particles in biological matrices, supporting its use in human biomonitoring and exposure assessment studies. Full article

Cannabis is one of the most common intoxicants used worldwide. Cannabis is widely consumed worldwide and can lead to visual alterations. However, most of the available information on its effects comes from studies conducted in developed countries, while data remain limited in developing regions such as Morocco, despite its significant role in cannabis cultivation. The aim of this study was to explore multiple visual parameters and self-perceived eyesight in cannabis users in Morocco. A cross-sectional study was conducted between March 2022 and April 2023 in Marrakesh, Morocco, in cannabis consumers. Data collection was performed in two phases. First a hetero-administrated questionnaire was used to collect socio-demographics, intoxicant consumption habit information, and eye health information. Then, several visual acuity tests were performed, including a preliminary examination, a visual function assessment, and an eye health assessment. Ninety-five cannabis users participated in this study. The majority were single (62.1%) males (87.4%). All lived in the Marrakesh-Safi region (100%), and most had daily activities such as having a job or being a student (77.9%). Most had vision conditions like astigmatism or myopia (83.4%). The majority had multiple addictions (66.5%), mainly to tobacco (43.7%). Hashish was the main cannabis type used (57.9%), and smoked cannabis was the principal mode of consumption (94.7%). Many had a family history of cannabis addiction (58.9%). Day light sensitivity (66.3%) and appearance of eye symptoms after cannabis use (90.5%) were declared by the majority. In most cases, no impact on far or near vision or vision impairment due to cannabis use were declared. Our results showed that using cannabis could have significant adverse effects on visual functions. Full article

Background/Objectives: Digestive problems are common in the general population and may be influenced by lifestyle, emotional status and diet. This study aimed to estimate the prevalence of digestive problems in Spanish adults and examined associated factors. Methods: Descriptive cross-sectional analysis of anonymized adult microdata from the 2023 Spanish Health Survey was performed. Data were collected using a mixed-mode design (self-administered web questionnaire with interviewer-administered follow-up). Digestive problems were recoded by combining gastric ulcer, constipation, and prescribed use of laxatives, astringent drugs, and stomach medication. Therefore, digestive problems are primarily defined as the presence of gastric ulcers, diarrhea, and/or constipation. Variables included sociodemographic, Body Mass Index (BMI), smoking, alcohol intake, physical activity, Personal Health Questionnaire Depression Scale (PHQ-8), World Health Organization Well Being Index (WHO-5), and macronutrient intake estimated from a Food-Frequency Questionnaire using the Spanish Food Composition Database (BEDCA). Group comparisons and multivariable logistic regression were conducted (95% CI; significance level set at p < 0.05). Results: Of 34,148 participants, 13,518 provided information on digestive problems; among these respondents, 3860 (28.6%) reported having digestive issues. Prevalence ranged from 5.2% to 36.5% among national territories. Higher odds (OR) of digestive problems were associated with age (OR 1.026, 95% CI 1.023–1.029), female sex (OR 1.168, 1.070–1.276), non-smoking (OR 1.240, 1.005–1.531) and ex-smoking (OR 1.447, 1.272–1.647) compared to current smokers, higher PHQ-8 scores (OR 1.040, 1.029–1.051), greater protein intake (OR 1.016, 1.009–1.023), consumption of sweet pastries (OR 1.058, 1.039–1.077), and dairy products (OR 1.027, 1.002–1.053); in contrast, lower odds were associated with higher WHO-5 scores (OR 0.985, 0.982–0.987), total fiber intake (OR 0.968, 0.949–0.987), and legume consumption (OR 0.894, 0.856–0.933). Conclusions: Digestive problems show considerable variability in prevalence among survey-based Spanish sample. Digestive problems were associated with older age, female sex, depressive symptoms, high-protein intake, and higher consumption of sweet pastries and dairy products, whereas higher well-being scores, higher fiber intake and legume consumption were associated with lower odds of digestive problems. Full article

Condition-based maintenance strategies using AI-driven health monitoring have emerged as valuable tools for industrial reliability, yet their implementation remains challenging in industries with limited operational data. Class I induction motors (≤15 kW), which power critical equipment in industries such as grain handling facilities, represent a significant portion of industrial assets but lack established healthy vibration baselines for effective monitoring. A fundamental challenge exists in deploying AI-based health monitoring systems when no historical performance data is available, creating a ’cold-start’ problem that prevents industries from adopting predictive maintenance strategies without costly pilot programs or prolonged data collection periods. This study developed a data-driven health monitoring framework for Class I induction motors that eliminates the dependency on long-term historical trends. Through extensive experimental testing of 98 configurations on new motors, a correlation between vibration amplitude at rotational frequency and motor power rating was established, enabling the creation of a synthetic signal generation algorithm. A robust Health Index (HI) model with integrated diagnostic capabilities was developed using the JPCCED-HI framework, trained on both experimental and synthetically generated healthy vibration data to detect degradation and diagnose common failure modes. The regression analysis revealed a statistically significant relationship between motor power rating and healthy vibration signatures, enabling synthetic generation of baseline data for any Class I motor within the rated range. When implemented at an operational grain silo facility, the HI model successfully detected faulty behavior and accurately diagnosed probable failure modes in equipment with no prior monitoring history, demonstrating that maintenance decisions could be made based on condition data rather than reactive responses to failures. This framework enables immediate deployment of AI-based condition monitoring in industries lacking historical data, eliminating a major barrier to adopting predictive maintenance strategies. The synthetic data generation approach provides a cost-effective solution to the data scarcity problem identified as a critical challenge in industrial AI applications, while the successful industrial implementation validates the feasibility of this approach for small-to-medium industrial facilities. Full article