Search Results (5,489)

Why are rock art sites found in certain places and not others? Can locational or environmental variables inform an understanding of the function and meaning of the art? How can we move beyond observed patterning in spatial associations to a credible explanation of such meanings and ensure that we are not confusing correlation with causation? And what variables were most relevant in influencing site locational choices? These and related problems, whether recognized or not, are the subtext of the last three decades of rock art site distributional and landscape studies. They are now especially important to resolve given the need for accurate predictive modeling due to the rapid transformation of certain regions from undeveloped rural areas into rural industrial landscapes. Partly with this problem in mind, Whitley developed a descriptive model that provides an explanation for the location of Native Californian rock art in the Mojave Desert. It identifies the variables most relevant to site locations based on ethnographic Indigenous ontological beliefs about the landscape. These concern the geographical distribution of supernatural power and its association with certain landforms, natural phenomena and cultural features. His analysis further demonstrated that this model can account for two unusually large concentrations of sites and motifs: the Coso Range petroglyphs and the Carrizo Plain pictographs. But unanswered was the question of whether the model is applicable more widely, especially to smaller sites and localities made by different cultural groups. We documented and analyzed three petroglyph localities with seven small petroglyph sites in the southern Mojave Desert, California, to test this model. These sites are attributed to the Takic-speaking Cahuilla and Serrano tribes. Our study revealed a good fit between the expected natural and cultural variables associated with rock art site locations, with the number of such variables present at any given locale potentially correlated with the size of the individual sites. In addition to the research value of these results, this suggests that the model may be useful in the predictive modeling of rock art site locations for heritage management purposes. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a global problem with antibiotic consumption considered a key modifiable factor for the development of AMR. Long-term care (LTC) facilities have been identified as potential reservoirs for Escherichia coli (E. coli) resistance due to high rates of urinary tract infection (UTI) and high levels of antibiotic consumption among residents. However, while the relationship between these two factors is well accepted, little is known about the possible temporal relationship between these. This study explores trends in E. coli resistance and antibiotic consumption in LTC focused on potential temporal relationships between antibiotic utilization and AMR. Methods: A retrospective, longitudinal, and ecological analysis was conducted between 31 May 2016 and 31 December 2018. The primary outcomes were the monthly prevalence of E. coli AMR in urine isolates and the monthly percentage of residents using an antibiotic recommended for the management of UTI in national treatment guidelines (amoxicillin, amoxicillin with clavulanic acid, cefalexin, norfloxacin, and trimethoprim). Results: During the study period, 10,835 urine E. coli isolates were tested, and 3219 residents received one or more medicines and were included in the medicines dataset. Over one-quarter were resistant to at least one of the target antibiotics (23.3%). For most antibiotics, the temporal relationship between AMR and antibiotic utilization was unclear; however, potential patterns were observed for both trimethoprim and amoxicillin with clavulanic acid. Trimethoprim showed a temporal decrease in both AMR and utilization, while amoxicillin with clavulanic acid showed a lag time of approximately four months between utilization and resistance. Conclusions: The dynamic nature of AMR demonstrated in this study highlights the need for more up-to-date local surveillance to inform antibiotic choice in this setting. Full article

To address the problem of overlooking target movement characteristics and historical activity patterns in conventional path estimation methods, we propose a method based on the principle of multi-source spatio-temporal data fusion. It integrates optical image data with navigation and positioning data and improves the A* algorithm. While seeking the shortest path, the algorithm prioritizes points within hotspot areas to achieve accurate target path estimation. The algorithm extracts hotspot areas using spatial analysis methods such as kernel density analysis and uses them as the basis for path estimation. Through many simulation experiments, it is verified that the proposed improved the A* algorithm is more consistent with the actual path than the traditional A* algorithm. Full article

This study introduces a multimodal sentiment analysis system to assess and recognize human pain sentiments within an Internet of Things (IoT)-enabled healthcare framework. This system integrates facial expressions and speech-audio recordings to evaluate human pain intensity levels. This integration aims to enhance the recognition system’s performance and enable a more accurate assessment of pain intensity. Such a multimodal approach supports improved decision making in real-time patient care, addressing limitations inherent in unimodal systems for measuring pain sentiment. So, the primary contribution of this work lies in developing a multimodal pain sentiment analysis system that integrates the outcomes of image-based and audio-based pain sentiment analysis models. The system implementation contains five key phases. The first phase focuses on detecting the facial region from a video sequence, a crucial step for extracting facial patterns indicative of pain. In the second phase, the system extracts discriminant and divergent features from the facial region using deep learning techniques, utilizing some convolutional neural network (CNN) architectures, which are further refined through transfer learning and fine-tuning of parameters, alongside fusion techniques aimed at optimizing the model’s performance. The third phase performs the speech-audio recording preprocessing; the extraction of significant features is then performed through conventional methods followed by using the deep learning model to generate divergent features to recognize audio-based pain sentiments in the fourth phase. The final phase combines the outcomes from both image-based and audio-based pain sentiment analysis systems, improving the overall performance of the multimodal system. This fusion enables the system to accurately predict pain levels, including ‘high pain’, ‘mild pain’, and ‘no pain’. The performance of the proposed system is tested with the three image-based databases such as a 2D Face Set Database with Pain Expression, the UNBC-McMaster database (based on shoulder pain), and the BioVid database (based on heat pain), along with the VIVAE database for the audio-based dataset. Extensive experiments were performed using these datasets. Finally, the proposed system achieved accuracies of 76.23%, 84.27%, and 38.04% for two, three, and five pain classes, respectively, on the 2D Face Set Database with Pain Expression, UNBC, and BioVid datasets. The VIVAE audio-based system recorded a peak performance of 97.56% and 98.32% accuracy for varying training–testing protocols. These performances were compared with some state-of-the-art methods that show the superiority of the proposed system. By combining the outputs of both deep learning frameworks on image and audio datasets, the proposed multimodal pain sentiment analysis system achieves accuracies of 99.31% for the two-class, 99.54% for the three-class, and 87.41% for the five-class pain problems. Full article

We present a comprehensive comparison of different Markov chain Monte Carlo (MCMC) sampling methods, evaluating their performance on both standard test problems and cosmological parameter estimation. Our analysis includes traditional Metropolis–Hastings MCMC, Hamiltonian Monte Carlo (HMC), slice sampling, nested sampling as implemented in dynesty, and PolyChord. We examine samplers through multiple metrics including runtime, memory usage, effective sample size, and parameter accuracy, testing their scaling with dimension and response to different probability distributions. While all samplers perform well with simple Gaussian distributions, we find that HMC and nested sampling show advantages for more complex distributions typical of cosmological problems. Traditional MCMC and slice sampling become less efficient in higher dimensions, while nested methods maintain accuracy but at higher computational cost. In cosmological applications using BAO data, we observe similar patterns, with particular challenges arising from parameter degeneracies and poorly constrained parameters. Full article

Colorectal cancer (CRC) is a significant worldwide health problem due to its high prevalence, mortality rates, and frequent diagnosis at advanced stages. While diagnostic and therapeutic approaches have evolved, the underlying mechanisms driving CRC initiation and progression are not yet fully understood. Early detection is critical for improving patient survival, as initial cancer stages often exhibit epigenetic changes—such as DNA methylation—that regulate gene expression and tumor progression. Identifying DNA methylation patterns and key survival-related genes in CRC could thus enhance diagnostic accuracy and extend patient lifespans. In this study, we apply two of our recently developed methods for identifying differential methylation and analyzing survival using a sparse, finite mixture of accelerated failure time regression models, focusing on key genes and pathways in CRC datasets. Our approach outperforms two other leading methods, yielding robust findings and identifying novel differentially methylated cytosines. We found that CRC patient survival time follows a two-component mixture regression model, where genes CDH11, EPB41L3, and DOCK2 are active in the more aggressive form of CRC, whereas TMEM215, PPP1R14A, GPR158, and NAPSB are active in the less aggressive form. Full article

Background: Prader–Willi Syndrome (PWS) is a rare, genetic, multi-systemic disorder. Its main characteristics are muscular hypotonia, behavioral problems, intellectual disability, endocrine deficiencies, hyperphagia, and a high risk of morbid obesity and related comorbidities. This study aimed to investigate the rate of comorbidity, prescription of endocrine medications, and mortality in individuals with PWS compared to the general population. Methods: The association between PWS and outcomes were investigated in a matched cohort study of individuals born in the period of 1930–2018 with data from Swedish national health and welfare registers. Each individual was matched with 50 non-PWS comparisons. The associations between PWS, outcomes and prescribed endocrine medications were estimated through Cox proportional hazard models, presented as Hazard Ratios (HR) with 95% Confidence Intervals (CIs). Results: Among 360 individuals (53% men) with PWS, 16% had diabetes mellitus, 6% heart failure, 4% vein thrombosis, 2% atrial fibrillation, 2% coronary heart disease, and 1% pulmonary embolism. Individuals with PWS had an increased rate of heart failure (HR: 23.85; 95% CI: 14.09–40.38), diabetes mellitus (HR: 17.49; 95% CI: 12.87–23.74), vein thrombosis (HR: 10.44; 95% CI: 5.69–19.13), pulmonary embolism (HR: 5.77; 95% CI: 2.27–14.67), atrial fibrillation (HR: 5.19; 95% CI: 2.48–10.86), and coronary heart disease (HR: 3.46; 95% CI: 1.50–7.97) compared to non-PWS individuals. Somatotropin was prescribed in 63%, antidiabetics in 18%, and thyroid hormones in 16% of the PWS individuals (<1%, 2%, and 3%, respectively, in non-PWS individuals). The rate of mortality was fifteen times higher in PWS than in non-PWS, with a mean age at death of 42 years. Conclusions: The rates of diabetes mellitus and cardiovascular comorbidities were higher in individuals with PWS. As expected, the prescription of somatotropin was high, but the endocrine prescription pattern also reflected the high prevalence of diabetes mellitus and thyroid illness. Although the mean age at death was older than previously reported, a higher awareness and intensified efforts to avoid obesity, as well as the prevention and early treatment of cardiovascular and endocrine comorbidity, are crucial aims in the care of people with PWS. Full article

The world prevalence of the two types of authorized and fraudulent transactions makes it difficult to distinguish between the two operations. The small percentage of fraudulent transactions, in turn, gives rise to the class imbalance problem. Hence, an adequately robust fraud detection mechanism must exist for tax systems to avoid their collapse. It has become significantly difficult to obtain any dataset, specifically a tax return dataset, because of the rising importance of privacy in a society where people generally feel squeamish about sharing personal information. Because of this, we arrive at the decision to synthesize our dataset by employing publicly available data, as well as enhance them through Correlational Generative Adversarial Networks (CGANs) and the Synthetic Minority Oversampling Technique (SMOTE). The proposed method includes a preprocessing stage to denoise the data and identify anomalies, outliers, and dimensionality reduction. Then the data have undergone enhancement using the SMOTE and the proposed CGAN techniques. A unique encoder design has been proposed, which serves the purpose of exposing the hidden patterns among legitimate and fraudulent records. This research found anomalous deductions, income inconsistencies, recurrent transaction manipulations, and irregular filing practices that distinguish fraudulent from valid tax records. These patterns are identified by encoder-based feature extraction and synthetic data augmentation. Several machine learning classifiers, along with a voting ensemble technique, have been used both with and without data augmentation. Experimental results have shown that the proposed Soft-Voting technique outperformed the original without an ensemble method. Full article

Background: The Mediterranean Diet is recognized as one of the healthiest dietary patterns; however, in recent years, a decline in adherence has been observed in Mediterranean countries. University students represent a particularly vulnerable population, as starting university introduces new influences and responsibilities that directly impact their lifestyle and health. Objective: Analyze adherence to the Mediterranean Diet among university students and its association with other lifestyle habits and mental and physical health indicators. Methods: A cross-sectional study was conducted with a sample of 1268 students (23.65 ± 7.84 years) from a university in northern Spain between November 2020 and March 2021. An online questionnaire was administered to assess Mediterranean Diet adherence along with variables such as perceived stress, self-esteem, life satisfaction, suicidal behavior, emotional and behavioral problems, emotional intelligence, physical activity, sedentary behavior, alcohol consumption, and compulsive internet use. Results: 29.26% of students had high adherence to the Mediterranean Diet. Regression analysis indicated that high adherence was associated with higher levels of emotional intelligence, as well as lower levels of suicidal ideation, emotional problems, and compulsive internet use. Conclusions: The associations found between Mediterranean Diet and other indicators and lifestyle habits highlight the need for interdisciplinary promotion strategies within the university ecosystem. Full article

Reduced body movement and weakened musculoskeletal function as a result of aging increase the risk of falls and serious physical injuries requiring medical attention. To solve this problem, a fall prevention algorithm using an acceleration sensor has been developed, and research is being conducted to enable continuous monitoring using a Holter electrocardiograph. In this study, we implemented a multi-input model that can detect and classify movements, including falls, utilizing the baroreflex characteristics of the heart’s potential energy changes due to movement, measured with an electrocardiogram with a three-axis acceleration sensor and a Holter electrocardiograph. Patterns were identified from the various movement characteristics of acceleration sensor data using a deep learning model consisting of CNN-LSTM, and heart rate variability (HRV) data were analyzed using a wide learning model to provide additional weight values for fall classification. Finally, a multi-input model using wide and deep learning was proposed to enhance the accuracy of fall classification. The results show that the HRV increased in fall case except in two motion types, while it decreased when standing up from a chair, indicating the application of the baroreflex characteristics reflecting the heart’s potential energy. Compared to the classification model using conventional HRV and ACC, a higher accuracy was achieved in the multi-input model using ACC-HRV data, and a precision, recall, and F1 score of 0.91 was measured, indicating improved performance. This is expected to have a positive impact on fall prevention by improving the accuracy of fall classification in the elderly for 15 different movements. Full article

Most unmanned aerial vehicles (UAVs) nowadays engage in coverage missions using coverage paths with simple patterns that are limited in two-dimensional space, ignoring the elevation features of mountainous terrain intentionally, resulting in losing details and increasing the coverage path density and the UAV energy consumption. To address these problems, a coverage path planning method based on the DH (Diameter–Height) model for mountainous terrain is proposed. Firstly, a DH model is proposed for simplifying the 3D map construction of mountainous terrain. Secondly, in virtue of the DH model, the 3D map is partitioned into sub-regions. The equal-interval spiral ascent path and parallel circular coverage path are generated for covering the mountainous terrain with conformal coverage effects. A coverage path connection method with trajectory minimization and obstacle avoidance is proposed in the final path connection stage. The simulation experiments have demonstrated the feasibility and effectiveness of the proposed method and verified that the generated coverage path has the advantages of conformal coverage effects, trajectory minimization, and obstacle avoidance. The comparative experiment has also demonstrated the advantages of the proposed method in terms of UAV energy savings. Full article

Lakes are a crucial component of inland water bodies, and changes in their water levels serve as key indicators of global climate change. Traditional methods of lake water level monitoring rely heavily on hydrological stations, but there are problems such as regional representativeness, data stability, and high maintenance costs. The satellite altimeter is an essential tool in lake research, with the Synthetic Aperture Radar (SAR) altimeter offering a high spatial resolution. This enables precise and quantitative observations of lake water levels on a large scale. In this study, we used Sentinel-3A SAR Radar Altimeter (SRAL) data to establish a more reasonable lake height inversion algorithm for satellite-derived lake heights. Subsequently, using this technology, a systematic analysis study was conducted with Qinghai Lake as the case study area. By employing regional filtering, threshold filtering, and altimeter range filtering techniques, we obtained effective satellite altimeter height measurements of the lake surface height. To enhance the accuracy of the data, we combined these measurements with GPS buoy-based geoid data from Qinghai Lake, normalizing lake surface height data from different periods and locations to a fixed reference point. A dataset based on SAR altimeter data was then constructed to track lake surface height changes in Qinghai Lake. Using data from the Sentinel-3A altimeter’s 067 pass over Qinghai Lake, which has spanned 96 cycles since its launch in 2016, we analyzed over seven years of lake surface height variations. The results show that the lake surface height exhibits distinct seasonal patterns, peaking in September and October and reaching its lowest levels in April and May. From 2016 to 2023, Qinghai Lake showed a general upward trend, with an increase of 2.41 m in lake surface height, corresponding to a rate of 30.0 cm per year. Specifically, from 2016 to 2020, the lake surface height rose at a rate of 47.2 cm per year, while from 2020 to 2022, the height remained relatively stable. Full article

The use of Neural Networks in quantum many-body theory has undergone a formidable rise in recent years. Among the many possible applications, their pattern recognition power can be utilized when dealing with the study of equilibrium phase diagrams. Learning by Confusion has emerged as an interesting and unbiased scheme within this context. This technique involves systematically reassigning labels to the data in various ways, followed by training and testing the Neural Network. While random labeling results in low accuracy, the method reveals a peak in accuracy when the data are correctly and meaningfully partitioned, even if the correct labeling is initially unknown. Here, we propose a generalization of this confusion scheme for systems with more than two phases, for which it was originally proposed. Our construction relies on the use of a slightly different Neural Network: from a binary classifier, we move to a ternary one, which is more suitable to detect systems exhibiting three phases. After introducing this construction, we test it on free and interacting Kitaev chains and on the one-dimensional Extended Hubbard model, consistently achieving results that are compatible with previous works. Our work opens the way to wider use of Learning by Confusion, demonstrating once more the usefulness of Machine Learning to address quantum many-body problems. Full article

The permeability of a coal seam is a crucial factor in coal seam gas extraction. Poor permeability of coal seams can lead to difficulties in over-pumping as well as high gas emissions after mining. This issue is particularly prominent when mining extra-thick coal seams with hard roofs, and it is the major problem that restricts the safe and efficient mining of coal seams. In the context of extra-thick coal seam mining in the Datong mine area, field investigation into the gas emission patterns of the working face reveals that the volume of gas emissions correlates closely with variations in working face pressure, demonstrating a high degree of consistency. The mechanism of irregular gas emission was analyzed, and the influence law of roof breakage on gas emission was obtained. It was found that roof breakage will aggravate gas emission. As a result, an integrated control technology involving “ground fracturing + gas extraction” was innovatively proposed. Based on the characteristics of ground fracture network, the mechanism of pressure relief and permeability enhancement of fractured wells and the characteristics of full time and space extraction were analyzed. Using the 8101 and 8204 working faces of the Tashan Coal Mine as a case study, the results demonstrated that vertical well fracturing of the 8101 working face enabled gas extraction 150 m ahead, with an accelerated increase in gas concentration within a 40 m range. Similarly, the horizontal well of the 8204 working face served as a drainage well after fracturing. Gas concentration at the mining position 50 m away from the horizontal well increased rapidly, and the gas extraction rate stabilized at approximately 30 m³/min. The approach effectively mitigated the problem of uneven gas emission caused by gas accumulation and roof fractures in the working face. Ground fracturing not only reduced the area and intensity of stress concentration in the advanced coal body but also enhanced gas emission. Furthermore, the fracturing well served as a gas drainage well, improving the control and achieving positive application results. Full article

It is unclear whether patterns of alcohol consumption are associated with sleep disturbance. We aimed to investigate the relationship between comprehensive alcohol-related factors and insomnia symptoms, as well as sleep quality, among adult women in Japan. Responses to an online cross-sectional survey were gathered from 12,000 women. The survey items included demographic characteristics, alcohol consumption (Alcohol Use Disorders Identification Test, nightcaps, years of drinking), sleep-related factors (sleep duration, insomnia symptoms, sleep quality), lifestyle-related factors, and mental health. Binary logistic regression was used to investigate the relationship between alcohol consumption and both insomnia symptoms and sleep quality. A total of 10,233 women were included in the final analysis. The results revealed that several alcohol-related behaviors, including the consumption of nightcaps and years of drinking, were significantly associated with insomnia symptoms and poor sleep quality. Specifically, nightcaps were significantly associated with all types of insomnia symptoms and poor sleep quality, with a higher odds ratio than other alcohol-related items. Our findings suggest that specific alcohol-related behaviors, particularly the consumption of nightcaps, are associated with insomnia symptoms and poor sleep quality among women. Intervention programs for alcohol consumption should be provided to prevent sleep problems among women. Full article