Miracle-dz/UniFAR-FTUs huggingface dataset:
Bioacoustic IoT Sensors as Next-Generation Tools for Monitoring: Counting Flying Insects through Buzz

Simple Summary The decline in global biodiversity due to habitat loss, climate change, and human activities necessitates effective monitoring. Flying insects, such as pollinators, which are crucial for ecosystem functions, require a thorough assessment. While informative, conventional methods, including hand netting, transect surveys, and pan traps, are labour-intensive or offer snapshots of limited scope. This study conducts a comparative analysis of traditional and innovative methods involving the recording and analysis of buzzing sounds emitted by flying insects. Insect abundance, reported as counts for traditional methods gathered by pan traps, and average buzzes per hour, measured by acoustic IoT (Internet of Things) sensors, were correlated. This finding suggests that acoustic automatic monitoring shows the potential to circumvent the limitations of conventional methods, emphasising continuous, non-intrusive monitoring for robust data acquisition. The development of passive detection devices will pave the way to implement new, sustainable approaches to enhance conservation efforts and ecosystem management, advancing our understanding of flying insect dynamics amidst global biodiversity challenges. Abstract The global loss of biodiversity is an urgent concern requiring the implementation of effective monitoring. Flying insects, such as pollinators, are vital for ecosystems, and establishing their population dynamics has become essential in conservation biology. Traditional monitoring methods are labour-intensive and show time constraints. In this work, we explore the use of bioacoustic sensors for monitoring flying insects. Data collected at four Italian farms using traditional monitoring methods, such as hand netting and pan traps, and bioacoustic sensors were compared. The results showed a positive correlation between the average number of buzzes per hour and insect abundance measured by traditional methods, primarily by pan traps. Intraday and long-term analysis performed on buzzes revealed temperature-related patterns of insect activity. Passive acoustic monitoring proved to be effective in estimating flying insect abundance, while further development of the algorithm is required to correctly identify insect taxa. Overall, innovative technologies, such as bioacoustic sensors, do not replace the expertise and data quality provided by professionals, but they offer unprecedented opportunities to ease insect monitoring to support conservation biodiversity efforts.
Initialization shock in the ocean circulation reduces skill in decadal predictions of the North Atlantic subpolar gyre

Due to large northward heat transport, the Atlantic meridional overturning circulation (AMOC) strongly affects the climate of various regions. Its internal variability has been shown to be predictable decades ahead within climate models, providing the hope that synchronizing ocean circulation with observations can improve decadal predictions, notably of the North Atlantic subpolar gyre (SPG). Climate predictions require a starting point which is a reconstruction of the past climate. This is usually performed with data assimilation methods that blend available observations and climate model states together. There is no unique method to derive the initial conditions. Moreover, this can be performed using full-field observations or their anomalies superimposed on the model's climatology to avoid strong drifts in predictions. How critical ocean circulation drifts are for prediction skill has not been assessed yet. We analyze this possible connection using the dataset of 12 decadal prediction systems from the World Meteorological Organization Lead Centre for Annual-to-Decadal Climate Prediction. We find a variety of initial AMOC errors within the predictions related to a dynamically imbalanced ocean states leading to strongly displaced or multiple maxima in the overturning structures. This likely results in a blend of what is known as model drift and initial shock. We identify that the AMOC initialization influences the quality of the SPG predictions. When predictions show a large initial error in their AMOC, they usually have low skill for predicting internal variability of the SPG for a time horizon of 6-10 years. Full-field initialized predictions with low AMOC drift show better SPG skill than those with a large AMOC drift. Nevertheless, while the anomaly-initialized predictions do not experience large drifts, they show low SPG skill when skill also present in historical runs is removed using a residual correlation metric. Thus, reducing initial shock and model biases for the ocean circulation in prediction systems might help to improve their prediction for the SPG beyond 5 years. Climate predictions could also benefit from quality-check procedure for assimilation/initialization because currently the research groups only reveal the problems in initialization once the set of predictions has been completed, which is an expensive effort.
Wind Power Applications of eXplainable Artificial Intelligence Techniques

The worldwide growth of wind power capacity underscores the need for more extensive data utilization in critical areas such as Operation & Maintenance, condition monitoring, and forecasting. However, the widespread use of Machine Learning in wind power carries the risk of excessive reliance on complex black-box models. To address this, our study focuses on developing an eXplainable Artificial Intelligence (XAI) framework for multivariate wind power problems. The workflow involves a Sequential Features Selection algorithm to identify suitable variables for regression. Shapley coefficients are then computed to estimate each feature’s impact on the output, revealing hidden patterns and relationships. The framework effectiveness is validated through two substantial test cases: multivariate power curve analysis for turbine condition monitoring and ultra-short-term forecasting for wind farm operations. The outcomes underscore the algorithm’s proficiency in fine-tuning feature selection and providing comprehensive explanations for output behaviors. By adopting this XAI framework, wind power researchers and practitioners can navigate multivariate problems with enhanced interpretability, developing transparent models for specific applications within the wind power domain.
Predicting Blood Glucose Levels with Machine Learning Techniques

We explored the effectiveness of machine learning algorithms in predicting blood glucose levels based on various input features, including demographic data, lifestyle factors, and physiological measurements using a dataset obtained from the OhioT1DM database. In particular, the dataset comprises data from 12 individuals with type 1 diabetes, spanning the years 2018 and 2020. The data for each individual is a comprehensive set of 22 features collected from devices, wearable sensors, and self-reports, capturing various lifestyle, and physiological aspects such as eating dairy, steps, heart rate, skin temperature, air temperature, and galvanic skin response (GSR). Machine learning algorithms, including Long short-term memory (LSTM) and Support Vector Regression (SVR) models, with historical data from 5, 10, 15, and 20 steps were employed to develop models to predict blood glucose levels at 30 and 60 minutes ahead. The accuracy of the predicted horizons was evaluated using three metrics, namely, MAE, MAPE, and RMSE to compare the performance of LSTM and SVR for each patient ID. To train and test the predictive models, the data for each individual in the dataset was split into training and testing sets. This approach ensured that the model learned from one individual's data and was evaluated on unseen data from the same individual. This individual-based training and testing procedure allowed for personalized predictions and accounts for inter-individual variations in diabetes management. The experimental results indicated that SVR performed better for the 30-min prediction, while LSTM performed better for the 60-min prediction. Among the patient IDs, ID596 achieved the best prediction results in terms of MAE, and RMSE, with values of 10.41 mg/dl MAE and 15.15 mg/dl RMSE at the 30-min prediction, and 18.26 mg/dl MAE, and 24.99 mg/dl RMSE at the 60-min prediction. Additionally, ID570 achieved the best prediction results in terms of MAPE, with values of 7.2% at the 30-min, and 13.3% at the 60-min prediction.
Weakening and moistening of the summertime Saharan heat low through convective cold pools from the Atlas Mountains

The West African Monsoon (WAM) and its representation in numerical models is heavily influenced by the Saharan heat low (SHL), a low‐pressure system driven by radiative heating over the central Sahara and ventilated by the cold and moist inflow from adjacent oceans. It has recently been shown that a significant part of the southerly moisture flux into the SHL originates from convective cold pools over the Sahel. These density currents driven by evaporation of rain are largely missing in models with parameterized convection. This crucial issue has been hypothesized to contribute to the inability of many climate models to reproduce the variability of the WAM. Observations from the Atlas Mountains, located at the northern flank of the SHL, indicate frequent convection and cold‐pool generation during boreal summer, often during episodes of multiple days. This study is the first to analyze impacts of such convective periods on the SHL, based on simulations of two example cases using the Weather Research and Forecast (WRF) model at convection‐permitting resolution. Sensitivity experiments with artificially removed cold pools, lower resolutions, and parameterizations are conducted. Results indicate that cold pools lead to increases in surface pressure of more than 1 hPa and significant moisture transports into the desert over several days. This moisture affects radiative heating and thus the energy balance of the SHL. Together with studies focusing on the Sahel, this work emphasizes the need for improved parameterization schemes for deep convection in order to produce more reliable climate projections for the WAM.
HF signatures of powerful lightning recorded on DEMETER

[1] Emissions in the HF range (1.7 – 3 MHz) are observed at the times of powerful lightning on the low-altitude satellite DEMETER. At � 700 km, wave activity observed on the E field spectrograms recorded on DEMETER during nighttime is mainly dominated by upgoing 0+ whistlers and higher-order dispersed whistlers. Over a period of 30 months, 130 events with HF emissions at frequency � 2 MHz have been observed at the time of intense 0+ whistlers on VLF spectrograms. A global map of the distribution of events indicates that they do not occur above regions of most thunderstorm activity such as the upper part of South America or the middle of Africa. This lack of occurrence above these two near-equatorial regions is consistent with the high value of the critical frequency of the F layer which prevents the transionospheric propagation of the HF component of the lightning pulses up to 700 km. The time, location, intensity, and polarity of the lightning discharges related to a subset of these HF events are determined above the North American region with the National Lightning Detection Network. As most of the events are recorded in Survey Mode when full resolution of the data is not available, the neural network on board DEMETER is used to determine the times of the 0+ whistlers recorded by the satellite with a time accuracy of � 0.1 s. The impulsive HF events correspond to intense lightning discharges occurring in regions immediately below the satellite. It is assumed that the propagation of these HF pulses up to the altitude of the satellite is favored by local ionospheric heating due to high thunderstorm activities.
Reliability-based Topology Optimization of Continuum Structure

: The reliability-based topology optimization method of continuous structures is investigated considering structural applied loads and the geometry description. Firstly, based on the solid isotropic material with penalization approach, the deterministic mathematical model of topology optimization is developed, in which minimization of the compliance is taken as objective function and the volume is taken as constraint function; Secondly, using the relation between failure probability and reliability index, the mathematical model of reliability topology optimization based on the reliability index constraint is established. Reliability index probabilistic constraint problem considering uncertainties is solved using first order reliability method, in which the reliability index constraints are transformed into random variables, and then the modified random variables are used as deterministic variables to carry on the deterministic topology optimization; Finally, several numerical examples are simulated to show that reliability-based topology optimization yields structures that are more reliable than those produced by deterministic topology optimization and also certificate the validity of the proposed method.
Reliability-based topology optimization for multi-material structures

: In order to prevent unexpected failures and potential catastrophic damage in components or systems, uncertainties in loads and material properties are taken into account during optimization. This is achieved through the use of Reliability-Based Topology Optimization (RBTO), which combines reliability analysis with optimization to produce engineering solutions with a satisfactory level of reliability. Complex building structures are often comprised of multiple materials and require diverse performance characteristics. Using a single material may not be feasible in these cases. Multi-material RBTO offers a solution by allowing for the optimized spatial distribution of various materials to meet diverse design objectives. This paper presents an investigation into multi-material RBTO that takes into account uncertainties in loads and material properties, as well as the behavior and characteristics of different candidate materials. The optimization algorithm used in this approach relies on gradient calculations of objective and constraint functions to efficiently converge to optimal solutions. The proposed RBTO framework is evaluated on structural optimization problems, and the results show that it can improve performance while satisfying probabilistic constraints accurately, thus increasing the efficiency and effectiveness of optimal design identification
A New Source for Land Cover Change Validation: Wal-Mart from Space

We introduce an event data set of the location and opening dates for 3,043 Wal-Mart stores as a means for validating land cover change-related products at medium (30 m) to coarse (1 km) resolutions throughout the conterminous United States (US). As validation data, these Wal-Mart stores and distribution centers share several favorable attributes, including construction atop a diverse array of vegetated environments, wide dispersion across the entire country, building and parking lot footprints that measure between 100 m and 500 m on a side, and construction dates that span much of the remote sensing record (1964-2005). To generate the data set, we geo-coded the full Wal-Mart store address list, combined these locations with a listing of Wal-Mart store opening dates, and geo-located the building footprints of 30 Wal-Mart stores using cost-free high-resolution (4 m) imagery available from internet search engines. Twenty-five stores constructed in North Carolina and Virginia between 1987 and 2002 served to validate a single scene (WRS2 p16 r035, 180 km per side) of the new Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) product - a 28.5 m resolution forest disturbance map which is in production for the conterminous US. Disturbance events were clearly discernable in the LEDAPS beta product at all 25 of the validation sites. In addition, we selected five Wal-Mart sites constructed between 2000-2005 in Maine, North Carolina, Oklahoma, and California to validate the University of Maryland? s 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index 16-day time series (MOD44C). These five construction events are evident in the time series. At a Wal-Mart distribution center in Gordonsville, Virginia, a similar construction signature is present at 1 km resolution for the MOD13A2 enhanced vegetation index 16-day time series. These results demonstrate a new approach for validating land cover change related products by combining an unusual disturbance event data set with free high-resolution internet-based images.
Promoting Intratumoral Drug Accumulation by Bio-Membrane Regulated Active Targeting for Tumor Photothermal Therapy

Introduction Insufficient tumor permeability and inadequate nanoparticle retention continue to be significant limitations in the efficacy of anti-tumor drug therapy. Numerous studies have focused on enhancing tumor perfusion by improvement of tumor-induced endothelial leakage, often known as the enhanced permeability and retention (EPR) effect. However, these approaches have produced suboptimal therapeutic outcomes and have been associated with significant side effects. Therefore, in this study, we prepared tumor cell membrane-coated gold nanorods (GNR@TM) to enhance drug delivery in tumors through homogeneous targeting of tumor cell membranes and in situ real-time photo-controlled therapy. Methods Here, we fabricated GNR@TM, and characterized it using various techniques including Ultraviolet-Visible (UV-Vis) spectrophotometer, particle size analysis, potential measurement, and transmission electron microscopy (TEM). The cellular uptake and cytotoxicity of GNR@TM were analyzed by flow cytometry, confocal laser scanning microscopy (CLSM), TEM, CCK8 assay and live/dead staining. Tissue drug distribution was determined by inductively coupled plasma mass spectrometry (ICP-MS) and immunofluorescence staining. Furthermore, to evaluate the therapeutic effect, mice bearing MB49 tumors were intravenously administered with GNR@TM. Subsequently, near-infrared (NIR) laser therapy was performed, and the mice’s tumor growth and body weight were monitored. Results The tumor cell membrane coating endowed GNR@TM with extended circulation time in vivo and homotypic targeting to tumor, thereby enhancing the accumulation of GNR@TM within tumors. Upon 780 nm laser, GNR@TM exhibited excellent photothermal conversion capability, leading to increased tumor vascular leakage. This magnification of the EPR effect induced by NIR laser further increased the accumulation of GNR@TM at the tumor site, demonstrating strong antitumor effects in vivo. Conclusion In this study, we successfully developed a NIR-triggered nanomedicine that increased drug accumulation in tumor through photo-controlled therapy and homotypic targeting of the tumor cell membrane. GNR@TM has been demonstrated effective suppression of tumor growth, excellent biocompatibility, and significant potential for clinical applications.
A modeling study of the response of Asian summertime climate to the largest geologic forcings of the past 50 Ma

The modern Asian summer monsoon is known to be affected by the modern continental geometry, orography, atmospheric composition, interglacial climate state, and orbital configuration. All of these factors, however, have undergone substantial changes since the Indian and Asian continents collided 50 million years ago. Within the framework of one general circulation model we evaluate the relative importance of each of these factors for the spatial patterns of summertime climate fields and precipitation‐weighted 18O. We find that the continental‐scale structure of the monsoon circulation remains robust, but there are important impacts on local environmental conditions. The largest differences in climate are associated with changes in orbital configuration and topography because of how those factors influence local gradients in the summer climate variables. Finally, model calculations are consistent with the modern understanding of monsoon dynamics in which the strength of the circulation and the distribution of rainfall are tightly coupled to the spatial patterns of low‐level moist static energy and upper tropospheric temperature.
Mathematisches Forschungsinstitut Oberwolfach Mini-workshop: Topology of Closed One-forms and Cohomology Jumping Loci

The purpose of this workshop was to bring together researchers from the two different fields mentioned in the title, and to create more interaction and connections between these fields. Among the topics which appear in both subjects are Lusternik-Schnirelmann category, Bieri-Neumann-Strebel invariants and a spectral sequence introduced by Farber and Novikov.
Mathematisches Forschungsinstitut Oberwolfach Mini-workshop: Random Trees, Information and Algorithms

The subject of this Mini-Workshop is the probabilistic analysis of random tree models that originate from applications in Computer Science. Emphasis is put on their connections to algorithms and information theory. Trees with a stochastic growth dynamic appear in Computer Science as data structures, in the context of coding schemes as well as connected to fundamental algorithms such as sorting, searching and selecting. The focus of this Mini-Workshop is on probabilistic and analytic techniques that have been developed recently in the asymptotic analysis of random trees such as martingale methods, connections to branching random walks, the contraction method, the method of moments as well as various techniques based on generating functions.
A systematic review and meta-analysis

Background:We aimed to evaluate the prognostic significance of high expression of the miR-200 family of microRNAs in bladder cancer. Methods: Studies on the correlation between the miR-200 family and prognosis in patients with bladder cancer were searched in databases. Combined hazard ratios (HRs) were calculated based on HRs and 95% confidence intervals (CIs) for overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS). Cochranes Q test and the I statistic were utilized to assess heterogeneity across the included studies. Potential publication bias was analyzed by Begg and Egger tests. The meta-analysis was conducted using RevMan 5.3 and Stata SE12.0. Results: Data from a total of 1150 patients from 8 studies were extracted. The meta-analysis revealed that high expression of the miR-200 family was correlated with better OS (pooled hazard ratio: 0.50, 95% confidence interval: 0.40–0.62), CSS (pooled hazard ratio: 0.36, 95% confidence interval: 0.22–0.59) and RFS (pooled hazard ratio: 0.48, 95% confidence interval: 0.36–0.65). Both Begg test and Egger test verified no publication bias within the included cohorts. Conclusion: The high expression of the miR-200 family is strongly associated with better prognosis in bladder cancer patients, which will improve bladder cancer management in clinical practice. Abbreviations: CIs = confidence intervals, CSS = cancer-specific survival, CTCs = circulating tumor cells, EMT = epithelialmesenchymal transition, HRs = hazard ratios, LMR = lymphocyte/monocyte ratio, mGPS = modified Glasgow Prognostic Score, NLR = neutrophil/lymphocyte ratio, NMIBC = non-muscle-invasive bladder cancer, NOS = Newcastle-Ottawa Scale, OS = overall survival, PLR = platelet/lymphocyte ratio, RFS = recurrence-free survival, ZEB = zinc finger E-box-binding homeobox.