Search Results (4,857)

Microseismic monitoring is extensively applied in hydraulic fracturing and mineral extraction, with accurate event localization being a critical component. Recently, deep learning approaches have shown promise for microseismic event localization; however, most of these supervised methods depend on large, labeled datasets, which are costly and challenging to acquire. To mitigate this issue, we propose a semi-supervised approach based on a residual convolutional autoencoder (RCAE) for automated microseismic localization, designed to leverage limited labeled data effectively and improve source localization accuracy even with small sample sizes. Our method employs pre-training by masking and reconstructing unlabeled seismic records, while integrating residual connections within the encoder to enhance feature extraction from seismic signals. This enables high localization accuracy with minimal labeled data, resulting in significant cost savings. Experimental results indicate that our method surpasses purely supervised approaches on both a 2D salt dome model and a 3D homogeneous half-space model, validating its effectiveness in microseismic localization. Further comparisons with baseline models highlight the method’s advantages, providing an innovative solution for improving cost-efficiency in practical applications. Full article

Urban morphology plays a decisive role in regulating microclimate and outdoor thermal comfort in arid cities, where extreme heat and intense solar radiation amplify thermal stress. This study examines the influence of four contrasting urban fabrics in Béchar (Algerian Sahara): the vernacular Ksar, the regular-grid colonial fabric, a modern large-scale residential estate, and low-density detached housing, on local microclimatic conditions. An integrated methodological framework is adopted, combining qualitative morphological analysis, quantitative indicators including density, porosity, height-to-width ratio, and sky view factor, in situ microclimatic measurements, and high-resolution ENVI-met simulations performed for the hottest summer day. Results show that compact urban forms, characterized by low sky view factor values, markedly reduce radiative exposure and improve thermal performance. The vernacular Ksar, exhibiting the lowest SVF, records the lowest mean radiant temperature (approximately 45 °C) and the most favorable average comfort conditions (PMV = 3.77; UTCI = 38.37 °C), representing a reduction of about 3 °C, while its high-thermal-inertia earthen materials ensure effective nocturnal thermal recovery (PMV ≈ 1.06; UTCI = 27.8 °C at 06:00). In contrast, more open modern fabrics, including the colonial grid, large-scale estates, and low-density housing, experience higher thermal stress, reflecting vulnerability to solar exposure and limited thermal inertia. Validation against field measurements confirms model reliability. These findings highlight the continued relevance of vernacular bioclimatic principles for sustainable urban design in arid climates. Full article

Background: Opioids and nonsteroidal anti-inflammatory drugs (NSAIDs) for clavicle fracture pain management carry significant adverse effect and allergic reaction risks. This study assessed ultrasound-guided nerve block (USNB) efficacy for acute clavicle fracture pain in emergency department (ED) patients, providing an alternative to NSAIDs and opioids with fewer adverse effects. Methods: This retrospective, single-center observational study was conducted in accordance with Methods of Medical Record Review Studies in Emergency Medicine Research guidelines. Adult patients (≥20 years) who presented to the ED with traumatic clavicle fractures between 1 January 2015 and 30 November 2023 were included. Of the 343 eligible patients, 12 received ultrasound-guided nerve blocks (USNB) and 331 received standard care. To improve exchangeability, 1:10 matching with replacement was performed according to patients’ characteristics, such as age, sex, initial pain score, and comorbidities. The primary outcome was pain relief, assessed via the pain intensity difference (PID) on the Numerical Rating Scale within 360 min post-intervention. Meaningful pain relief was defined as a PID ≥ 4. Secondary outcomes included rescue opioid use, ED length of stay, hospital length of stay, and USNB-associated complications, such as vascular puncture, nerve injury, or local anesthetic systemic toxicity. Data were analyzed using time-course, time-to-event (time to meaningful pain relief), and linear regression analyses. Results: A total of 12 patients in the USNB group and 85 matched patients in the standard care group were analyzed after baseline characteristics matching with replacement. Compared to standard care, USNB was associated with significantly greater pain relief (p < 0.001). In the time-to-event analysis, USNB led to a 3.41-fold faster achievement of meaningful pain relief compared with that achieved with standard care (HR = 3.41; 95% CI, 1.47–7.90; p = 0.004). No significant differences were observed between groups in rescue opioid use, ED length of stay, or hospital length of stay. No USNB-associated complication developed in the USNB group. Conclusions: In patients with traumatic clavicle fractures, USNB provides more rapid and sustained pain relief than standard analgesic care in the ED, without increasing the ED length of stay. Large prospective studies are needed to confirm these findings. Full article

Turkey is located on an active seismic belt, making the accurate determination of soil properties and earthquake effects essential for safe and reliable structural design. This study investigates the influence of ground improvement on the dynamic behavior of the soil at the construction site of the 950-bed Aydın City Hospital. Evaluations were carried out in terms of the dominant period, local site class and spectral characteristics to assess the effectiveness of the improvement applications. For this purpose, field tests conducted before the improvement were repeated afterward and the obtained data were compared. Local site classes were determined for both unimproved and improved soil conditions based on the relevant seismic code provisions. Furthermore, using site-specific data, nonlinear time-history analyses were performed and site-specific response spectra were obtained for 11 earthquake records at DD-1 and DD-2 seismic hazard levels (return periods of 475 and 2475 years). These spectra were then compared with the corresponding design spectra. The analyses revealed that ground improvement significantly affects not only the bearing capacity and liquefaction potential but also the dynamic behavior, dominant period and local site class of the soil. Full article

There exists an east–west trending albitite (breccia) zone, approximately 400 km in length, closely related to gold mineralization, in Devonian strata in the South Qinling tectonic belt. The genesis and formation age of these albitite (breccia) are of great significance for understanding gold enrichment mechanisms and guiding future exploration. Past studies have mainly focused on the Fengxian–Taibai area in the western segment of the albitite (breccia) zone, whereas the eastern segment remains significantly understudied. In this study, a systematic field investigation, as well as petrology, geochemistry, and accessory-mineral geochronology studies were conducted on albitites and albitite breccias in the Shangnan area, the eastern segment of the albitite (breccia) zone. The results show that the albitites are interlayered with or occur as lenses within Devonian clastic rocks. The albitite breccias are mostly enclosed in albitite and Devonian strata, and the clasts within are subangular, uniform in type, and exhibit minimal displacement. Both albitites and albitite breccias exhibit similar trace-element characteristics and detrital zircon age spectra to those of Devonian clastic rocks. Abundant hydrothermal monazites with U–Pb ages ranging from 260 to 252 Ma are present in both albitites and albitite breccias but absent in Devonian clastic rocks. Collectively, these results indicate that the albitites in the Shangnan area are of hydrothermal metasomatic origin, while the albitite breccias record hydraulic fracturing and cementation, and both are products of the same fluid activity event in the Late Permian. We propose that albitite (breccia) zones in the South Qinling tectonic belt were formed under distinct tectonic settings during different evolution stages of the Late Paleozoic Mianlüe Ocean. Specifically, the albitites (breccias) in the Shangnan area are products of thorough metasomatism, local fracturing, and cementation of Devonian clastic rocks by mixed fluids, which ascended along the Fengzhen–Shanyang Fault coeval with the emplacement of magmatic rocks related to subduction of the Mianlüe Ocean. In contrast, the albitite breccias in the Fengxian–Taibai area are the result of fluid activity during the transition from regional compression to extension after the closure of the Mianlüe Ocean. Full article

Background: Herpes zoster (HZ) represents a substantial public health concern among aging populations, yet regional variability in clinical patterns and risk determinants remains insufficiently documented. In southeastern Romania, epidemiological data are limited, and the combined influence of demographic, behavioral, and metabolic factors on disease severity has not been systematically evaluated. Methods: We performed a retrospective observational study including 100 consecutive patients diagnosed with HZ between 2019 and 2023 in a dermatology department in southeastern Romania. Demographic characteristics, lifestyle behaviors, anthropometric status, clinical manifestations, and outcomes were extracted from medical records. Associations between categorical variables were assessed using Chi-square tests and Cramer’s V, while interaction patterns were explored through log-linear modeling. Heatmaps were generated in Python (version 3.10) using the Matplotlib library (version 3.7.1) to visualize distribution patterns and subgroup relationships. Results: The cohort showed a marked age dependence, with 77% of cases occurring in individuals ≥ 60 years, consistent with immunosenescence-driven reactivation. Women represented 59% of cases, and 84.7% of female patients were postmenopausal. Urban residents predominated (91%). Vesicular eruption (84%) and acute pain (79%) were the most frequent symptoms. Localized HZ was observed in 81% of cases, while ophthalmic involvement (11%) and disseminated forms (8%) were less common. Lifestyle factors significantly influenced clinical severity: smokers, alcohol consumers, and sedentary individuals exhibited higher proportions of postherpetic neuralgia (PHN) and ocular complications (p < 0.001). Overweight and obese patients demonstrated a higher burden of PHN, suggesting a role for metabolic inflammation, although BMI was not associated with incidence. No significant association between age category and complication type was detected, likely due to small subgroup sizes despite a clear descriptive trend toward increased severity with advanced age. Conclusions: These findings support a multifactorial model of HZ severity in southeastern Romania, shaped by age, lifestyle behaviors, hormonal status, and metabolic risk. While incidence patterns align with international data, the strong impact of modifiable factors on complication rates highlights the need for targeted prevention and individualized risk assessment. Results offer a regional perspective that may inform future multicenter investigations. Full article

Background and Objective: The accurate detection of sleep stages and disorders in older adults is essential for the effective diagnosis and treatment of sleep disorders affecting millions worldwide. Although Polysomnography (PSG) remains the primary method for monitoring sleep in medical settings, it is costly and time-consuming. Recent automated models have not fully explored and effectively fused the sleep features that are essential to identify sleep stages and disorders. This study proposes a novel automated model for detecting sleep stages and disorders in older adults by analyzing PSG recordings. PSG data include multiple channels, and the use of our proposed advanced methods reveals the potential correlations and complementary features across EEG, EOG, and EMG signals. Methods: In this study, we employed three novel advanced architectures, (1) CNNs, (2) CNNs with Bi-LSTM, and (3) CNNs with a transformer encoder, for the automatic classification of sleep stages and disorders using multichannel PSG data. The CNN extracts local features from RGB spectrogram images of EEG, EOG, and EMG signals individually, followed by an appropriate column-wise feature fusion block. The Bi-LSTM and transformer encoder are then used to learn and capture intra-epoch feature transition rules and dependencies. A residual connection is also applied to preserve the characteristics of the original joint feature maps and prevent gradient vanishing. Results: The experimental results in the CAP sleep database demonstrated that our proposed CNN with transformer encoder method outperformed standalone CNN, CNN with Bi-LSTM, and other advanced state-of-the-art methods in sleep stages and disorders classification. It achieves an accuracy of 95.2%, Cohen’s kappa of 93.6%, MF1 of 91.3%, and MGm of 95% for sleep staging, and an accuracy of 99.3%, Cohen’s kappa of 99.1%, MF1 of 99.2%, and MGm of 99.6% for disorder detection. Our model also achieves superior performance to other state-of-the-art approaches in the classification of N1, a stage known for its classification difficulty. Conclusions: To the best of our knowledge, we are the first group going beyond the standard to investigate and innovate a model architecture which is accurate and robust for classifying sleep stages and disorders in the elderly for both patient and non-patient subjects. Given its high performance, our method has the potential to be integrated and deployed into clinical routine care settings. Full article

Cultural practices such as Catalonia’s correfocs (fire parades) represent a vibrant expression of intangible heritage. Outdoor activities are conditioned by weather and threatened by climate change. This study analyses the long-term evolution of night-time thermal conditions during correfoc festivals performed in six Catalan towns located on the coast and in the pre-coastal region from 1950 to 2023, using reanalysis-based indicators of air temperature, humidity, and perceived heat as a first exploratory step prior to incorporating in situ meteorological records. Specifically, the Heat Index (HI) and the Universal Thermal Climate Index (UTCI) were computed for the typical event window (21:00–23:00 local time) to assess changes in human thermal comfort. Results reveal a clear and statistically significant warming trend in most pre-coastal locations—particularly Reus, El Vendrell, and Vilafranca—while coastal cities such as Barcelona exhibit weaker or non-significant changes, likely due to maritime moderation. The frequency and intensity of positive temperature anomalies have increased since the 1990s, with a growing proportion of events falling into “caution” or “moderate heat stress” categories under HI and UTCI classifications. These findings demonstrate that correfocs are now celebrated under markedly warmer night-time conditions than in the mid-twentieth century, implying a tangible rise in thermal discomfort and potential safety risks for participants. By integrating climatic and cultural perspectives, this research shows that rising night-time heat can constrain attendance, participation conditions, and event scheduling for correfocs, thereby directly exposing weather-sensitive form of intangible cultural heritage to climate risks. It therefore underscores the need for climate adaptation frameworks and to promote context-specific strategies to sustain these community-based traditions under ongoing Mediterranean warming. Full article

Due to its abundance, bird cherry–oat aphid is the most important vector in Poland of the complex of viruses causing barley yellow dwarf virus (BYDV). These viruses infect all cereals. During the growing season, cereal plants are exposed to many species of agrophages, which can limit their growth, development and yield. As observed for many years, global warming contributes to changes in the development of many organisms. Aphids (Aphidoidea), which are among the most important pests of agricultural crops, respond very dynamically to these changes. Under favorable conditions, their populations can increase several-fold within a few days. The bird cherry–oat aphid (Rhopalosiphum padi L.) is a dioecious species that undergoes a seasonal host shift during its life cycle. Its primary hosts are trees and shrubs (Prunus padus L.), while secondary hosts include cereals and various grass species. R. padi feeds directly on bird cherry tree, reducing its ornamental value, and on cereals, where it contributes to yields losses. The species can also damage plants indirectly by transmitting harmful viruses. Indirect damage is generally more serious than direct feeding injury. Monitoring aphid flights with a Johnson suction trap (JST) is useful for plant protection, which enables early detection of their presence in the air and then on cereal crops. To provide early detection of R. padi migrations and to study the dynamics of abundance, flights were monitored in 2020–2024 with Johnson suction traps at two localities: Winna Góra (Greater Poland Province) and Sośnicowice (Silesia Province). The aim of the research conducted in 2020–2024 was to study the dynamics of the bird cherry–oat aphid (Rhopalosiphum padi L.) population in relation to meteorological conditions as recorded by a Johnson suction trap. Over five years of research, a total of 129,638 R. padi individuals were captured using a Johnson suction trap at two locations (60,426 in Winna Góra and 69,212 in Sośnicowice). In Winna Góra, the annual counts were as follows: 5766 in 2020, 6498 in 2021, 36,452 in 2022, 5598 in 2023, and 6112 in 2024. In Sośnicowice, the numbers were as follows: 6954 in 2020, 9159 in 2021, 49,120 in 2022, 3855 in 2023, and 124 in 2024. The year 2022 was particularly notable for the exceptionally high abundance of R. padi, especially in the autumn. Monitoring crops for the presence of pests is the basis of integrated plant protection. Climate change, modern cultivation technologies, and increasing restrictions on chemical control are the main factors contributing to the development and spread of aphids. Therefore, measures based on monitoring the level of threat and searching for control solutions are necessary. Full article

Polystigma amygdalinum the causal agent of Red Leaf Blotch (RLB), is responsible for one of the most important foliar diseases affecting almond [Prunus dulcis (Miller) D.A. Webb] in the Mediterranean Basin and the Middle East. The study is aimed at improving knowledge on RLB epidemiology and the role of environmental conditions in disease development. Field monitoring was conducted from 2022 to 2025 in three almond orchards located in Apulia (southern Italy) and characterized by different microclimatic conditions. A total of 39 cultivars, including Apulian local germplasm and international cultivars (‘Belona’, ‘Genco’, ‘Guara’, ‘Ferragnès’, ‘Filippo Ceo’, ‘Lauranne^® Avijor’, ‘Soleta’, and ‘Supernova’), were evaluated. Symptoms occurred from late spring to summer, resulting particularly severe on ‘Guara’ and ‘Lauranne^® Avijor’, whereas ‘Belona’, ‘Ferragnès’, ‘Genco’, and ‘Supernova’ exhibited the highest tolerance. To our knowledge, this is also the first report of RLB tolerance by ‘Filippo Ceo’, ‘Ficarazza’, ‘Centopezze’, and ‘Rachele piccola’ representing potential genetic resources for breeding programs. Moreover, these findings reinforced previous observations proving that RLB was less severe on medium-late and late cultivars. Disease incidence varied significantly among sites and years and was strongly associated with increased rainfall, higher relative humidity, and mild temperatures recorded in November, influencing disease occurrence in the following growing season. P. amygdalinum was consistently detected by qPCR in all RLB-affected tissues and, in some cases, from mixed early RLB + Pseudomonas-like symptoms. From some leaves with early RLB symptoms, P. amygdalinum was also successfully isolated in pure culture. Overall, our results provide clear evidence that P. amygdalinum is the sole fungal pathogen consistently associated with typical RLB symptoms in Apulia (southern Italy) and highlight important cultivar-dependent differences. Its frequent molecular detection in leaves showing atypical or mixed symptoms suggests unresolved epidemiological aspects requiring further investigation. Full article

Oilseed flax (Linum usitatissimum, L.) is an important specialty oilseed crop cultivated in arid and semi-arid regions, where timely, accurate yield prediction is crucial for regional oilseed security and agricultural decision-making. To address the lack of robust county-level yield prediction models for oilseed flax, this study proposes a CNN–Informer hybrid framework that integrates convolutional neural networks (CNNs) with the Informer architecture to model multi-source spatio-temporal data. Unlike conventional Transformer-based approaches, the proposed framework combines CNN-based local temporal feature extraction with the ProbSparse attention mechanism of Informer, enabling the efficient modeling of long-range temporal dependencies across multiple years while reducing the computational burden of attention-based time-series modeling. The model incorporates multi-source inputs, including remote sensing indices (NDVI, EVI, SAVI, KNDVI), TerraClimate meteorological variables, soil properties, and historical yield records. Comprehensive experiments conducted at the county level in Gansu Province, China, demonstrate that the CNN–Informer model consistently outperforms representative machine learning and deep learning baselines (Transformer, Informer, LSTM, and XGBoost), achieving an average performance of R² = 0.82, RMSE = 0.31 t/ha, MAE = 0.21 t/ha, and MAPE = 10.33%. Results from feature ablation and historical yield window analyses reveal that a three-year historical yield window yields optimal performance, with remote sensing features contributing most strongly to predictive accuracy, while meteorological and soil variables enhance spatial adaptability under heterogeneous environmental conditions. Model robustness was further verified through fivefold county-based spatial cross-validation, indicating stable performance and strong generalization capability in unseen regions. Overall, the proposed CNN–Informer framework provides a reliable and interpretable solution for county-level oilseed flax yield prediction and offers practical insights for precision management of specialty crops in arid and semi-arid regions. Full article

Due to changing weather conditions, productivity needs to be enhanced and resources must be used more efficiently in agriculture. Precision agriculture relies on systems that can gather real-time environmental data to address these issues. However, the high cost of commercial weather stations often limits their adoption in rural areas. This study introduces a low-cost weather station designed for precision agriculture applications. The system consists of three main modules. The first module is the weather station, which gathers data on temperature, relative humidity, barometric pressure, solar radiation, wind speed and direction, and precipitation. It then transmits this data via LoRa communication to the local console module. This console receives the data, displays it on a screen, and sends it through Wi-Fi to the cloud server module. The cloud server presents the information via an interactive interface and is responsible for storing, processing, and analyzing the data records collected. The system was installed in the municipality of Ojocaliente, Zacatecas, Mexico, where performance and validation tests were conducted over a one-month period using sensors and reference measurements to evaluate the accuracy and stability of the data. The results showed high operational reliability and a strong correlation between the recorded values and the reference data. This confirms that the proposed solution provides a scalable, low-cost, and reliable alternative for environmental monitoring in precision agriculture. Full article

Background: Iron-deficient anemia (IDA) in pregnant women is a significant health issue globally. Oral iron supplementation is the primary treatment for IDA during pregnancy. For women who do not respond to or cannot tolerate oral iron treatment, intravenous (IV) iron preparations may offer a viable therapeutic option in the third trimester of pregnancy. Ferric carboxymaltose (FCM; Ferinject^®) is an IV iron preparation that allows rapid administration of high single doses of iron with a favorable safety profile. This study evaluated the potential impact of FCM therapy on fetal well-being by recording cardiotocography (CTG) before, during, and after iron infusions. Materials and Methods: We examined 105 women with IDA in the third trimester of pregnancy. During the initial evaluation, each patient was assessed for complete blood count, iron metabolism, B12, folates, hemoglobinopathies, CRP, kidney and liver function, and glucose levels. Each subject received intravenous ferric carboxymaltose (FCM), 500 mg. The study focused on the maternal and fetal safety of FCM infusion. The primary endpoint for maternal safety was the observation of adverse effects of iron infusion. For fetal safety, the primary endpoint was the assessment of CTG. Results: We considered 105 women, comprising 101 singleton and 4 twin pregnancies. The median hemoglobin (Hb) at initial observation was 95 g/L and 117 g/L post-therapy. Regarding maternal safety, side effects were observed during or after FCM infusion in four subjects; three cases involved local symptoms, while one case included nausea and skin rash. Concerning fetal safety, 100% of the cardiotocography records were deemed “normal” using the Dawes–Redman criteria. Conclusions: In conclusion, FCM proved effective in treating anemia in this clinically complex population of pregnant women in the third trimester and appeared safe in this cohort, though larger prospective studies are warranted. Full article

Antibiotics are persistent organic pollutants that pose a serious problem for water resources, ultimately having a detrimental effect on human and animal health. The most important aspect of controlling and preventing the spread of antibiotics and their degradation products is continuous screening and monitoring of environmental samples. Optical sensing technologies represent a large group of sensors that allow short-term detection of antibiotics in non-laboratory settings. This article reviews the advances in optical sensing systems (colorimetric, fluorescent, surface-enhanced Raman spectra-based, surface plasmon resonance-based, localized surface plasmon resonance-based, photonic crystal-based, fiber optic, molecularly imprinted polymer-based and electro-optical platforms) for the detection of antibacterial drugs in water. Special attention is paid to the evaluation of the analytic characteristics of optical sensors for the analysis of antibiotics. Particular attention is paid to electro-optical sensing and to the unique possibility of its use in antibiotic determination. Potential strategies are considered for amplifying the recorded signals and improving the performance of sensor systems. The main trends in optical sensing for antibiotic analysis and the prospects for the commercial application of optical sensors are described. Full article

Crowd counting is a critical computer vision task with significant applications in public security and smart city systems. While deep learning has markedly improved accuracy, persistent challenges include extreme scale variations, severe occlusion, and complex background clutter. To address these issues, we propose a novel Hybrid Multi-Scale Transformer-CNN U-shaped Network (HMSTUNet). Our key contributions are: a hybrid architecture integrating a Multi-Scale Vision Transformer (MSViT) for capturing long-range dependencies and a Dynamic Convolutional Attention Block (DCAB) for modeling local density patterns; and a U-shaped encoder–decoder with skip connections for effective multi-level feature fusion. Extensive evaluations on five public benchmarks show that HMSTUNet achieves the best Mean Absolute Error (MAE) on all five datasets and the best Mean Squared Error (MSE) on three. It sets new state-of-the-art records, attaining MAE/MSE of 49.1/77.8 on SHA, 6.2/10.3 on SHB, 142.1/192.7 on UCF_CC_50, 77.9/132.5 on UCF-QNRF, and 43.2/119.6 on NWPU-Crowd. These results demonstrate the model’s strong robustness and generalization capability. Full article