The ocean’s mixed layer depth (MLD) plays an important role in understanding climate dynamics, especially during extreme weather occurrences like hurricanes. This study investigates the effects of Hurricane Katrina (2005) on the MLD in the Gulf of Mexico, using the Delft3D modeling system. By integrating hydrodynamics and wave dynamics modules, we simulate the ocean’s response to extreme weather, focusing on temperature, salinity and MLD variations. Our analysis reveals significant cooling and mixing induced by Katrina, resulting in spatial and temporal fluctuations in temperature (~± 4°C) and salinity (~± 2.5 ppt). The MLD is estimated using a simple threshold method, revealing a substantial deepening to ~120 m on August 29th-30th during Hurricane Katrina in the middle of northern Gulf of Mexico, compared to an average MLD of ~20-40 m during pre-storm conditions. It took about 18-days to recover to ~84% of the pre-storm level after Katrina. Compared to the stand-alone FLOW model, the coupled FLOW+WAVE model yields a deeper MLD of ~5%. The MLD recovery and wave effect on the MLD provide insights from various scientific, environmental and operational perspectives, offering a valuable basis for ocean management, planning and applications, particularly during extreme weather events.

We consider an alternative hypothesis of crater formation -- through an electric discharge machining (EDM) of the celestial bodies' surfaces. The source of the energy and the current itself may lie in the plasma infalling on a celestial body (from an extremely strong solar outburst or a nearby supernova). This mechanism may have been even more important than impact cratering in the early Solar System, with potential implications to the history of the dynamics of the System itself and solar activity in particular. In this light, the conclusion that shocked and metamorphosed minerals found within crater sites are evidence of hypervelocity impacts might be reconsidered. There are ambiguities that remain among sources of mineralogical formations. This study is ongoing with details of investigations supporting a new approach to crater formation not yet exhausted. Apart from mineralogical analysis and theoretical considerations regarding the large scale crater formation, we present a description of experimental studies of EDM acting at low pressures. Experimental results are made with crude setups and could easily be replicated on a larger scale with valuable research potential, as there is a well understood relation between the crater size and the energy needed for its production.

Local food has continued to become more popular in both the home and as featured ingredients in all segments of food service. Numerous studies have explored the motivating factors for consumer purchasing motivators. However, no known study has explored if there is a correlation between ego and intent to buy local foods. This study adopted a modified version of the Theory of Trying and surveyed a sample of 237 food service buying agents in South Carolina to examine their perceptions of buying local. Results suggest that ego is a predictor of trying to purchase local foods for restaurants. It was also found that the modified model is an acceptable model for predicting buyer intent. These findings support the majority of the studies looking at ego-centric behaviors with green purchases and could potentially help local food suppliers to better market their products to foodservice establishments. This study could also act as the foundation for future studies to explore the full extent of ego’s motivational effect on purchasing intentions in the food service industry.

The achievement of size uniformity and monodispersity in perovskite quantum dots (QDs) requires the implementation of precise temperature control and the establishment of optimal reaction conditions. Nevertheless, the accurate control of a range of reaction variables represents a considerable challenge. This study addresses the aforementioned challenge by employing manganese (Mn) doping to achieve size uniformity in CsPbBr₃ perovskite QDs without the necessity for precise control of the reaction conditions. By optimizing the Mn:Pb ratio, it is possible to successfully dope CsPbBr₃ QDs with appropriate concentrations of Mn²⁺ and achieve a uniform size distribution. The spectroscopic measurements on single QDs indicate that the appropriate Mn²⁺ concentrations can result in a narrower spectral linewidth, a longer photoluminescence (PL) lifetime, and a reduced biexciton Auger recombination rate, thus positively affecting the PL properties. This study not only simplifies the size control of perovskite QDs, but also demonstrates the potential of Mn-doped CsPbBr₃ QDs for narrow linewidth light-emitting diode applications.

Having sustained, over the course of more than two decades, record-breaking natural catastrophe losses, American insurers and reinsurers are justifiably questioning the potential linkage between anthropogenic climate change and extreme weather. Here, we explore issues pertaining to this linkage, looking at both the likely short-term implications for the insurance industry, as well as potential longer-term impacts on financial performance and corporate resilience. We begin our discussion with an overview of the implications that climate change is likely to have on the industry, especially as it relates to how catastrophic risks are construed, assessed, and managed. We then present the rudiments of an econometric analysis that explores the financial resilience of the property/casualty (P/C) industry in the face of both natural and man-made catastrophes. In this analysis, we explore the profitability consequences of several illustrative scenarios involving large-scale losses from extreme weather—specifically, a sequence of storms like those striking the U.S. in 2004—and a scenario that explores the prospect of a Katrina-scale storm in combination with a mass terror attack on the scale of 9/11. At systemic levels of aggregation, our analysis suggests a high degree of macro-resilience for the insurance industry. Moreover, we find that insurer resilience is higher for larger impacts, considering both the speed of recovery, as well as the inverse of the area under the unaffected system profile. We conclude with a summary of our findings and a closing commentary that explores the potential implications of these results for P/C insurers moving forward.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

This paper presents an analysis of future precipitation patterns over the Lake Victoria Basin using bias-corrected CMIP6 model projections. A mean increase of about 5% in mean annual (ANN) and seasonal [March-May (MAM), June-August (JJA), and October-December (OND)] precipitation climatology is expected over the domain by mid-century (2040-2069). The changes intensify towards the end of the century (2070-2099) with an increase in mean precipitation of about 16% (ANN), 10% (MAM), and 18% (OND) expected, relative to the 1985-2014 baseline period. Additionally, the mean daily precipitation intensity (SDII), the maximum 5-day precipitation values (RX5Day), and the heavy precipitation events, represented by the width of the right tail distribution of precipitation (99p-90p) show an increase of 16%, 29%, and 47%, respectively, by the end of the century. The projected changes have a substantial implication for the region - which is already experiencing conflicts over water and water-related resources.

We report on ground-based measurements of the atmospheric electric field (Ez= -Potential Gradient (PG)) and current density (Jz) that were conducted at two locations in Israel. One is the Emilio Segre cosmic ray station located on Mt. Hermon (34.45 N, 2020 m AMSL) located in northern Israel near the Syrian-Lebanon border and at the other at the Wise astronomical observatory in the Negev desert highland plateau of southern Israel (31.18 N, 870 m AMSL). We searched for possible effects of strong, short-term solar events on the potential gradient and the vertical current density, as disruption to the Global Electric Circuit are often observed following strong solar events. The first case study (St. Patrick Day, 17 March 2015) was classified as the strongest event of 2015. The second case study (8 Sep 2017) was categorized as the strongest event of 2017 and one of the twenty strongest events on record to date. The results show that the electrical parameters measured at ground level at both stations were not affected during the two massive proton events and the ensuing geomagnetic storms. The magnetospheric shielding in lower latitudes is strong enough to shield against flux of energetic particles from solar events, obscuring any impact that may be noticeable above the local daily variations induced by local meteorological conditions (aerosol concentrations, clouds, high humidity, and wind speed) which were investigated as well.

Advancements in Machine Learning techniques, availability of more data-sets, and increased computing power have enabled a significant growth in a number research areas. Predicting, detecting and classifying complex events in earth systems which by nature are difficult to model is one of such areas. In this work, we investigate the application of different machine learning techniques for detecting and identifying extreme rainfall events in a sub-catchment within Pangani River Basin, found in Northern Tanzania. Identification and prediction of extreme rainfall event is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify a rain condition in the selected sub-catchment, we use data from five weather stations which have been labeled for the whole sub-catchment. In order to assess which Machine Learning technique suits better for rainfall identification, we apply five different algorithms in a historical dataset for the period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, reporting Random Forest and XGBoost as the ones with best overall performance. However, since the class distribution is imbalanced, the generic Multi-layer Perceptron performs best when identifying the heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the Pangani River Basin

This work focuses on the analysis of the performance of satellite-based precipitation products for monitoring extreme rainfall events. Five precipitation products are inter-compared and evaluated in capturing indices of extreme rainfall events during 1998-2019 considering four indices of extreme rainfall. Satellite products show a variable performance, which in general indicates that the occurrence and amount of rainfall of extreme events can be both underestimated or overestimated by the datasets in a systematic way throughout the country. Also, products that consider the use of ground truth data have the best performance.

Mountain systems within the Mediterranean region, e.g. the Pyrenees, are very sensitive to climate change. In the present study, we quantified the magnitude of extreme precipitation events and the number of days with torrential precipitation (daily precipitation ≥ 100 mm) in all the rain gauges available in the Pyrenees for the 1981-2015 period, analyzing the contribution of the synoptic scale in this type of events. The easternmost (under the Mediterranean influence) and north-westernmost (under Atlantic influence) areas of the Pyrenees registered the highest number of torrential events. The heaviest events are expected in the eastern part, i.e. 400 mm day-1 for a return period of 200 years. Northerly advections over the Iberian Peninsula, which present a low zonal index, i.e. im-plying a stronger meridional component, give rise to torrential events over the western Pyrenees; and easterly advections favour extreme precipitation over the eastern Pyrenees. The air mass travels a long way, from the east coast of North America, bringing heavy rainfall to the western Pyrenees. In the case of the torrential events over the eastern Pyrenees, the air mass causing the events in these areas is very short and originates in the Mediterranean Basin. The NAO index has no influence upon the occurrence of torrential events in the Pyrenees, but these events are closely related to certain Mediterranean teleconnections such as the WeMO

We propose and experimentally realize a scheme of the optical nonreciprocity (ONR) using degenerate two-level atoms embedded in an optical ring cavity. For the degenerate transition Fg=4→Fe=3, we first study the cavity-transmission property in different coupling field, and verify that within the strong-coupling regime, the single-dark-state peak formed by electromagnetically induced transparency (EIT) shows ONR. Qualitative theoretical analysis shows that the stable ground-state Zeeman coherence is necessary for intracavity EIT. However, due to the frequency degeneracy, the large Rabi frequency of counter-propagating coupling field can reduce the number of effective atoms and weaken the strong-coupling of the atom-cavity, which determines that ONR based on intracavity EIT occurs only at weaker probe intensity. Furthermore, the ONR transmission with high contrast, linewidth-narrowed and broadband is experimentally demonstrated.

Climate change poses new challenges for agricultural production as an increase in temperature also brings an increase in frequency and intensity of droughts. We study the occurrences of droughts in recent years as well as their effect on agricultural yields in combination with other factors such as winter frost. Furthermore we run experiments that aim to predict future drought events in order to provide insights whether current agricultural products will remain viable long-term and to indicate short-term drought risks. Our study area consists of the South-Moravian region in the Czech Republic and Saxony in Germany with the timeframe ranging from 1990 − 2022. For the effect on agricultural yields we use statistical methods such as regression and correlation. We train a Long-Short Term Memory (LSTM) neural net on data for the Standardised Precipitation-Evapotranspiration Index (SPEI) in order to predict future events. Our results indicate that the increased severity of droughts in recent years didn’t yet cause yield losses in our researched area. Our predictions with the LSTM look promising, but will need more data for future use-cases in order to increase accuracy and robustness.

Based on the daily observation data of 2254 meteorological stations in China from 1961 to 2021 after homogenization and correction, the extreme value analysis method that combines relative threshold and absolute threshold was used to identify extreme climate events in the mainland of China, namely extreme high temperature, extreme low temperature, extreme drought, extreme precipitation and extreme typhoon events. The frequency and linear variation trends of individual extreme events in the past 60 years were investigated. On this basis, the multi extreme events composite risk grade index (MXCI) was constructed, which can objectively reflect the comprehensive characteristics of multi extreme climate events in different regions. The results show that the high value areas of MXCI were mainly located in Southeast China and Southwest China. The MXCI presented a decreasing trend in most of the western and southeastern regions of China, while an increasing trend from Southwest China to the south of Northeast China, due to the frequent occurrence of extreme high temperature, extreme drought and other extreme events in these regions.

This research is focused on two key areas. The first is mapping the 2022 flood in the Red River of the North near Grafton, North Dakota, US, and the second is evaluating the scour potential of the Grafton Bridge. Local scour of bridge piers can cause hydraulic structures such as bridge piers and abutments to fail during floods, making it a crucial area of investigation. To collect bathymetry and discharge data during low and high flow conditions, including a flood event with a 16.5-year return period in 2022, an Autonomous Surface Vehicle (ASV) incorporated with LiDAR DEM (Digital Elevation Model) data obtained from the US Geological Survey (USGS) National Map was used. Flood mapping and evaluation of local scour around the bridge pier were conducted using the HEC-RAS 6.0.0 software, which utilizes the Colorado State University method as a default equation. This research demonstrates the potential of ASVs in collecting critical data and LiDAR DEM data is an efficient method for flood mapping and determining scour potential, as it integrates bathymetry, flow velocity, and flood prediction.

Portugal Mainland has hundreds of thousands of people living in the Atlantic coastal zone, with numerous high economic value activities and a high number of infrastructures that must be protected from natural coastal hazard, namely extreme storms and sea level rise (SLR). In the context of climate change adaptation strategies, a reliable and accurate assessment of the physical vulnerability to SLR is crucial. This study is a contribution to the implementation of flooding standards imposed by the European Directive 2007/60/EC, which requires each member state to assess the risk associated to SLR and floods caused by extreme events. Therefore, coastal hazard in the Continental Atlantic coast of Portugal Mainland was evaluated for 2025, 2050 and 2100 in the whole coastal extension with different sea level scenarios for different extreme event return periods and due to SLR. A coastal flooding probabilistic map was produced based on the developed methodology using Geographic Information Systems (GIS) technology. The Extreme Flood Hazard Index (EFHI) was determined on flood probabilistic bases through five probability intervals of 20% of amplitude. For a given SLR scenario, the EFHI is expressed, on the probabilistic flooding maps for an extreme tidal maximum level, by five hazard classes ranging from 1 (Very Low) to 5 (Extreme).

A portable laser photoacoustic sensor based on a Field-Programmable Gate Array (FPGA) is reported for methane detection. A tunable DFB diode laser in the 1654 nm wavelength range is used as an excitation source. The photoacoustic signal processing was implemented by a FPGA device. A small resonant photoacoustic cell is designed. The minimum detection limit (1σ) of 10 ppm for methane (CH₄) is demonstrated.

We propose novel spectrometer designs that aim to enhance the measured spectral range 1 of ions on a finite-sized detector. In contrast to the traditional devices that use uniform magnetic 2 field, in which the deflection of particles increases inversely proportional to their momentum, in a 3 gradient magnetic field, the deflection of particles will be decreased due to the reduction of the 4 magnetic field along their propagation. Low energy ions are particularly impacted, as they are 5 deflected less and are more likely to reach the detector compared to being driven out of it while 6 using a uniform magnetic field. By the means of a gradient magnetic field, the nonlinear dispersion 7 of ions in a homogeneous magnetic field can approach a nearly linear dispersion. Nonetheless, 8 the dispersion of low energy ions in a homogeneous magnetic field remains unnecessarily high. 9 In this article, we explore linear, quadratic, and higher-order field profiles in comparison to the 10 homogeneous field case. We discuss the employed methodology and present simulation results of 11 the spectrometer with an extended ion spectral range, focusing on the minimum detectable energy 12 (energy dynamic range) and energy resolution

In order to correct attenuated millimeter-wavelength (Ka-band) radar data and address the problem of instability, an attenuation correction methodology (attenuation correction with variation trend constraint; VTC) was developed. Using synchronous observation conditions and multi-band radars, the VTC method adopts the variation trends of reflectivity in X-band radar data captured with wavelet transform as a constraint to adjust reflectivity factors of millimeter-wavelength radar. The correction was evaluated by comparing reflectivities obtained by millimeter-wavelength cloud radar and X-band weather radar. Experiments showed that attenuation was a major contributory factor in the different reflectivities of the two radars when relatively intense echoes exist, and the attenuation correction developed in this study significantly improved data quality for millimeter-wavelength radar. Reflectivity differences between the two radars were reduced and reflectivity correlations were enhanced. Errors caused by attenuation were eliminated, while variation details in the reflectivity factors were retained. The VTC method is superior to the bin-by-bin method in terms of correction amplitude and can be used for attenuation correction of shorter wavelength radar assisted by longer wavelength radar data.

Deployments of real-world object-detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, a conditioning method is investigated. The method aims to guide the training loop of thermal object detection systems by leveraging an auxiliary branch to predict the weather, while directly or indirectly conditioning the baseline detection system. Leveraging such an approach to train detection networks does not necessarily improve the performance of native architectures, however, it can be observed that conditioned networks manage to extract a signal from thermal images that guides the network to detect objects that baseline models miss. As the extracted signal appears to be quite noisy and very challenging to regress accurately, further work is needed to identify an ideal optimization vector.

Heavy rains result in significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended periods of intense precipitation. Accurate rainfall prediction is crucial in nations such as Bangladesh, where agriculture is the predominant field of occupation. The efficiency of machine learning methods is enhanced by the nonlinearity of rainfall, surpassing the effectiveness of other approaches. This study proposes the novel combination of rainfall occurrence prediction, rainfall amount prediction, and daily average temperature prediction. This research implements machine learning techniques and an ensemble-based classifier to predict rainfall occurrence, as well as machine learning regressor models and an ensemble-based regressor to predict the rainfall amount and daily average temperature, using the Bangladesh Weather Dataset. The ensemble classifier demonstrated an accuracy of 83.41% and a recall of 78.17%, exhibiting the best performance in predicting when it will rain, but its precision was the lowest, at 51.16%. The ensemble regression model outperformed the base models, including linear regression, random forest, and support vector regression in rainfall amount prediction, with the lowest mean absolute error of 0.36 and root mean squared error of 0.90. Additionally, this model provided the most precise daily average temperature prediction results with the lowest mean absolute error of 0.42 and root mean squared error of 0.54, highlighting its superiority over the other regression models in forecasting temperature. Ensemble approaches consistently exhibit superior task performance metrics.

The understanding of weather and climate extremes provides academics, decision makers, international development agencies, nongovernmental organizations and civil society the necessary information for monitoring and giving early warning to prevent or minimize the risks associated with weather related hazards. Different researches were carried out to provide vital information that will further enhance the assessment of vulnerability and its impacts. Lack of proper understanding of weather and climate extremes was realized to be responsible for the huge and devastating losses that could have being averted or minimized over the past decades. Different countries and institutions have put in place a number of ways to increase sensitization and awareness of weather extremes. This became necessary in order to reduce the losses associated with these extremes both on local and regional scales.

This research addresses the strategic issue of why key decision-makers fail to foresee potential extreme ‘black swan’ events. Following a review of the literature, a conceptual framework is developed that identifies two types of organisational blindness that are reflected in Tetlock’s hedgehog cognitive thinking style, being the oversimplification of uncertainty (e.g., inductive biases) and an unquestioned, top-down, reference narrative. This framework is tested using a case study approach and qualitative analysis of secondary data sources available from the Royal Commission of Inquiry and other published reports following the 2010 methane explosion at the Pike River Coal Ltd’s mine (Pike) in New Zealand, that killed 29 miners and caused the loss of all funds invested. The results indicate that the combined effect of both blindness’s meant that Pike’s collective intelligence was limited, and for the three key decision-makers at the Pike River mine, some type of extreme ‘black swan’ event was apparently inevitable. This research provides theoretical and practical contributions to the analysis of business and public policy decision-making under uncertainty.

Climate change is a crucial issue of the 21st century, leading to more frequent and severe extreme precipitation events globally. These events result in significant social and economic disruptions, including flooding, loss of life, and damage to infrastructure. Projections suggest that extreme rainfall will intensify in the latter half of the century, underscoring the need for accurate and timely forecasting. Despite advancements in meteorological and climate models that offer high accuracy for various weather parameters, these models still struggle to detect extreme values, particularly for precipitation. This research examines the sensitivity of extreme precipitation events to temperature, based on the Clausius-Clapeyron relationship, focusing on Thessaloniki, Greece. It also evaluates the effectiveness of reanalysis data in identifying extreme precipitation and explores how rainfall-temperature relationships can enhance prediction accuracy. The findings are vital for improving the estimation of extreme rainfall events and informing the design of flood-resilient infrastructure.

Greenhouse gas (GHG) emission-induced climate change, particularly occurring since the mid-20th century has been considerably affecting short-term weather conditions, such as increasing weather variability, and incidence of extreme weather-related events. Milk production is sensitive to such changes. In this study, we use spatial panel econometric models, Spatial Error Model (SEM) and Spatial Durbin Model (SDM), with a panel dataset at the state-level varying over seasons, to estimate the relationship between weather indicators and milk productivity, in an effort to reduce the bias of omitted climatic variables that can be time-varying and spatially correlated and cannot be directly captured by conventional panel data models. We find an inverse U-shaped effect of summer heat stress on milk production per cow (MPC), indicating that milk production reacts positively to a low-level increase in summer heat stress, and then MPC declines as heat stress continues increasing beyond a threshold value of 72. Additionally, fall precipitation exhibits an inverse U-shaped effect on MPC, showing that milk yield increases at a decreasing rate until fall precipitation rises to 14 inches and then over that threshold milk yield declines at an increasing rate. We also find that, relative to conventional panel data models, spatial panel econometric models could improve prediction performance by leading to smaller in-sample and out-sample root mean squared errors. Our study contributes to the literature by exploring the feasibility of promising spatial panel models and resulting in estimating weather influences on milk productivity with high model predicting performance.

Extreme climates such as droughts and hot extremes, may result in marked damage to crop yields and threaten regional and global food security. Maize is a major grain crop in Liaoning Province, severely affected by drought and hot events. The objective of this study is to advance an approach to estimate the risk of crop yield reduction under the compound dry-hot events (CDHE) in Liaoning Province. Results show that the ratio of stations experiencing compound dry-hot events during the twelfth leaf and maturity stage tended to increase on the time scale. On a spatial scale, the frequency of compound dry-hot events in central Liaoning Province was relatively low during the whole and various reproductive periods of maize. In addition to the twelfth leaf, western Liaoning Province is prone to compound dry-hot events. The high-risk area for the occurrence of the compound dry-hot events in maize is Chaoyang City, located in the western part of Liaoning Province, and the cities in the low-risk area account for about 64.3% of the total number of cities in Liaoning Province, mainly in the central and northern parts of Liaoning Province. Generally, this study emphasizes focusing on the impacts of compound dry-hot events on agricultural production and provides an index to assess the risk of compound dry-hot events on maize production.

In this research, the extreme SuperHyperNotion, namely, extreme SuperHyperGirth, is up. $E_1$ and $E_3$ are some empty extreme SuperHyperEdges but $E_2$ is a loop extreme SuperHyperEdge and $E_4$ is an extreme SuperHyperEdge. Thus in the terms of extreme SuperHyperNeighbor, there's only one extreme SuperHyperEdge, namely, $E_4.$ The extreme SuperHyperVertex, $V_3$ is extreme isolated means that there's no extreme SuperHyperEdge has it as an extreme endpoint. Thus the extreme SuperHyperVertex, $V_3,$ is excluded in every given extreme SuperHyperGirth. $ \mathcal{C}(NSHG)=\{E_i\}~\text{is an extreme SuperHyperGirth.} \ \ \mathcal{C}(NSHG)=jz^i~\text{is an extreme SuperHyperGirth SuperHyperPolynomial.} \ \ \mathcal{C}(NSHG)=\{V_i\}~\text{is an extreme R-SuperHyperGirth.} \ \ \mathcal{C}(NSHG)=jz^I~{\small\text{is an extreme R-SuperHyperGirth SuperHyperPolynomial.}} $ The following extreme SuperHyperSet of extreme SuperHyperEdges[SuperHyperVertices] is the extreme type-SuperHyperSet of the extreme SuperHyperGirth. The extreme SuperHyperSet of extreme SuperHyperEdges[SuperHyperVertices], is the extreme type-SuperHyperSet of the extreme SuperHyperGirth. The extreme SuperHyperSet of the extreme SuperHyperEdges[SuperHyperVertices], is an extreme SuperHyperGirth $\mathcal{C}(ESHG)$ for an extreme SuperHyperGraph $ESHG:(V,E)$ is an extreme type-SuperHyperSet with the maximum extreme cardinality of an extreme SuperHyperSet $S$ of extreme SuperHyperEdges[SuperHyperVertices] such that there's only one extreme consecutive sequence of the extreme SuperHyperVertices and the extreme SuperHyperEdges form only one extreme SuperHyperCycle. There are not only four extreme SuperHyperVertices inside the intended extreme SuperHyperSet. Thus the non-obvious extreme SuperHyperGirth isn't up. The obvious simple extreme type-SuperHyperSet called the extreme SuperHyperGirth is an extreme SuperHyperSet includes only less than four extreme SuperHyperVertices. But the extreme SuperHyperSet of the extreme SuperHyperEdges[SuperHyperVertices], doesn't have less than four SuperHyperVertices inside the intended extreme SuperHyperSet. Thus the non-obvious simple extreme type-SuperHyperSet of the extreme SuperHyperGirth isn't up. To sum them up, the extreme SuperHyperSet of the extreme SuperHyperEdges[SuperHyperVertices], isn't the non-obvious simple extreme type-SuperHyperSet of the extreme SuperHyperGirth. Since the extreme SuperHyperSet of the extreme SuperHyperEdges[SuperHyperVertices], is an extreme SuperHyperGirth $\mathcal{C}(ESHG)$ for an extreme SuperHyperGraph $ESHG:(V,E)$ is the extreme SuperHyperSet $S$ of extreme SuperHyperVertices[SuperHyperEdges] such that there's only one extreme consecutive extreme sequence of extreme SuperHyperVertices and extreme SuperHyperEdges form only one extreme SuperHyperCycle given by that extreme type-SuperHyperSet called the extreme SuperHyperGirth and it's an extreme SuperHyperGirth . Since it 's the maximum extreme cardinality of an extreme SuperHyperSet $S$ of extreme SuperHyperEdges[SuperHyperVertices] such that there's only one extreme consecutive extreme sequence of extreme SuperHyperVertices and extreme SuperHyperEdges form only one extreme SuperHyperCycle. There are only less than four extreme SuperHyperVertices inside the intended extreme SuperHyperSet, thus the obvious extreme SuperHyperGirth, is up. The obvious simple extreme type-SuperHyperSet of the extreme SuperHyperGirth, is: ,is the extreme SuperHyperSet, is: does includes only less than four SuperHyperVertices in a connected extreme SuperHyperGraph $ESHG:(V,E).$ It's interesting to mention that the only simple extreme type-SuperHyperSet called the extreme SuperHyperGirth amid those obvious[non-obvious] simple extreme type-SuperHyperSets called the neutrosophic SuperHyperGirth , is only and only. A basic familiarity with extreme SuperHyperGirth theory, SuperHyperGraphs, and extreme SuperHyperGraphs theory are proposed.

We describe a forecast system based on the Weather Research and Forecasting (WRF) model for the prediction of high-impact weather events affecting power utilities, particularly conditions conducive to wildfires. The system was developed for Pacific Gas and Electric Corporation (PG&E) to operationally forecast conditions in Northern and Central California. It was established in 2014 and has been periodically improved. In the current configuration, WRF forecasts are routinely performed multiple times each day on a 2-km grid, and the results are used as input to wildfire fuel moisture, fire probability, and wildfire spread models. Components of the system include a stochastically perturbed ensemble; an annually updated reanalysis covering more than 30 years; and validation against PG&E’s extensive mesonet and other observing networks. We describe the development and validation of the next operational edition. Configurations tested include irrigation triggered by crop growing season and an ensemble-based approach with intelligently sub-selected Global Ensemble Forecast System (GEFS) members. The latter approach allows for enhanced uncertainty sampling in WRF forecasts and can greatly improve WRF forecasts when calibrated by observations.

Extreme overvalued beliefs (EOB) are rigidly-held, shared beliefs that are the motive behind most acts of terrorism and mass shootings. EOBs are differentiated from delusions and obsessions. The concept of overvalued idea was first described by Wernicke and later applied to terrorism by McHugh. Our group of forensic psychiatrists (Rahman, Resnick, Harry) refined the definition as an aid in the differential diagnosis seen in acts of violence. The form and content of EOBs is discussed as well as group effects, conformity and obedience to authority. Religious cults such as: The People’s Temple, Heaven’s Gate, Aum Shinrikyo, Islamic State (ISIS) and conspiracy beliefs such as assassinations, moon-hoax and vaccine-induced autism beliefs are discussed using this construct. Finally, some concluding thoughts on countering violent extremism, including its online presence is discussed utilizing information learned from online eating disorder and consumer experience.

Fish kills in Southeast Asia are frequently associated with harmful algal blooms (HABs) and cause significant ecological and economic impacts. This paper serves as a review of fish kill events with focus on those related to HABs within the region. It examines the causative algal species, known mechanisms of fish mortality, and socio-economic consequences. Fish kills have been reported across multiple countries within Southeast Asia, with notable hotspots in the Philippines, Malaysia, Singapore, Indonesia, and Thailand. The common harmful microalgal species span a diverse group including dinoflagellates (Karenia spp., Karlodinium spp., Margalefidinium polykrikoides, Noctiluca scintillans), raphidophytes (Chattonella spp.), diatoms (Skeletonema spp. and Chaetoceros spp.), and cyanobacteria (Trichodesmium spp.). These microalgae lead to fish kills through mechanisms such as hypoxia, physical gill damage, and ichthyotoxin production. Freshwater fish kills linked to HABs have also been documented for the Philippines, but there is no or limited information for the region. Our review highlights the widespread and recurring nature of fish kills, their impact on fisheries and aquaculture, and challenges in managing and mitigating their effects. There are efforts at enhancing management and mitigation using clay and early-warning systems. However, it is essential to further improve monitoring efforts, the development and deployment of early-warning systems, and viable and holistic mitigation strategies to protect the region’s aquatic resources and dependent communities, especially as aquaculture and coastal development are increasing concurrent with a changing climate that can exacerbate the risks of fish kills and HABs in Southeast Asia.

Studying models of healthy aging and exceptional longevity is crucial to understand a possible longevity signature as most of them show resistance to age-related diseases. In particular, semi- and supercentenarians are a highly selected group, having survived significant adversities, including the Spanish Flu and COVID-19 pandemics, indicating distinctive immune system characteristics. This paper analyse the inflammatory scores (INFLA-score, Systemic Inflammation Response Index (SIRI)) and Aging-Related Immune Phenotype (ARIP) indicators calculated from the dataset of the DESIGN project, including 249 participants aged 19-111 years, trying to understand the immune-inflammatory (IMFLAM) role to achieve longevity. Statistical analyses, including linear regression and one-way ANOVA, were performed to explore the correlations between these parameters and age. Both INFLA-score and SIRI showed a significant increase with age. However, no statistical difference is observed when analysing the values for semi- and supercentenarians with those for other age groups, whose values are similar to that of adults and less than younger centenariansRegarding ARIP values, it is noteworthy that when comparing the CD8 Naive/Effector scores between groups, no significant differences were observed between the semi- and supercentenarian group and the other groups. These results support the idea that the control of IMFLAM response can promote extreme longevity.

In a recent paper, we extended previous studies on solar-terrestrial relationships by investigating the origin of the March 2012 heatwave in northeast USA. In the present study we check the possible relationship of solar activity with the early March 2012 bad weather in northeast Thrace, Greece. To this end, we examined data from various remote sensing instrumentation monitoring the Sun (SDO satellite), the Interplanetary Space (ACE satellite), the Earth’s magnetosphere (Earth based measurements, NOAA-19 satellite), the top of the clouds (TERRA and AQUA satellites) and the near ground atmosphere. Our comparative data analysis suggests that: (i) the winter-like weather (rainfall, fast winds, decreased temperature) in Thrace started on 6 March 2012, on the same day as the heatwave starting in USA, (ii) during the March 2012 winter-like event in Thrace (6-15 March), the ACE satellite recorded enhanced fluxes of solar energetic particles (SEPs), while SOHO and PAMELA recorded solar protons at very high energies (> 500 MeV), (iii) Thrace experienced particularly intense cyclonic circulation, during periods of magnetic storms on 8 - 10 and 12-13 March, which occurred after the arrival at ACE of two interplanetary shock waves, on March 8 and March 11, respectively, (iv) at the beginning of the two above mentioned periods large atmospheric electric fields were recorded, with values ranging between ~-2000V/m - ~1800V/m on 8 March, (v) the winter-like 8-10 March 2012 weather occurred after the detection of the main SEP event related with a coronal mass ejection released in the interplanetary space as a result of intense solar flare activity observed by SDO on 7 March 2012, (vi) the 8-10 March weather was related with a deep drop of ~630C in the cloud top temperature measured by MODIS/TERRA, which favors strong precipitation. Finally, we analyzed data from the electric power network in Thrace (~410N) and we found, for the first time sudden voltage changes of ~3.5kV in the electric grid in Greece, during the decay phase of the March 2012 superstorm. We discuss the winter-like March 2012 event in Thrace in terms of solar cosmic ray influence on the low troposphere mediated by positive NAO. Finally, we infer that the novel finding of the geomagnetic effects on the electric power grid in Thrace may open a new window into space weather applications research.

The spectra and global distributions of the X-ray emissions generated by the solar wind charge-exchange (SWCX) process in the terrestrial magnetosheath are investigated based on a global hybrid model and a global geocoronal hydrogen model. The solar wind O6+ ions which are the primary charge state for oxygen ions in solar wind are considered. The line emissivity of the charge-exchange born O5+ ions are calculated by Spectral Analysis System for Astrophysical and Lab oratory (SASAL). It is found that the emission lines from the O5+ ranges from 105.607 to 118.291 eV with the strong line at 107.047 eV. We then simulated the magnetosheath X-ray emission intensity distributions with a virtual camera at two positions of north pole and dusk at six stages during the passing of a perpendicular interplanetary shock combined with a tangential discontinuity structure through the Earth’s magnetosphere. During this process, the X-ray emission intensity increases with time, and the maximum value is 27.11 keV cm-2 s-1 sr-1 on the dayside, which is 4.5 times that before the solar wind structure reaches the Earth. A clear shock structure can be seen in the magnetosheath and moves earthward. The maximum emission intensity seen at dusk is always higher than that seen at north pole.

The extraocular muscles (EOMs) possess unique characteristics that set them apart from other skeletal muscles. These muscles, responsible for eye movements, exhibit remarkable resistance to various muscular dystrophies and aging, presenting a significant contrast to the vulnerability of skeletal muscles to these conditions. In this review, we delve into the cellular and molecular underpinnings of the distinct properties of EOMs. We explore their structural complexity, highlighting differences in fiber types, innervation patterns, and developmental origins. Notably, EOM fibers express a diverse array of myosin heavy chain isoforms, retaining embryonic forms into adulthood. Moreover, their motor innervation is characterized by a high ratio of nerve fibers to muscle fibers and the presence of unique neuromuscular junctions. These features contribute to the specialized functions of EOMs, including rapid and precise eye movements. Understanding the mechanisms behind the resilience of EOMs to disease and aging may offer insights into potential therapeutic strategies for treating muscular dystrophies and myopathies affecting other skeletal muscles.

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

Underreporting is the main limitation of the spontaneous reporting systems. This study aims to reduce underreporting enhancing public participation in vaccinovigilance through an SMS-based reporting system integrated with electronic immunization registries (IRs). Using the VigiVax software, parents of vaccinated children aged 2 years or younger received a single SMS inquiry about adverse events following immunization (AEFI). Responses were collected, validated by health operators and integrated with the information on IRs. AEFI reports were automatically submitted to the Italian Pharmacovigilance system. Among 254,160 SMS messages sent, corre-sponding to 451,656 administered doses (AD), 71,643 responses were collected (28.2% response rate), 21,231of them (8.3%) reporting an AEFI. After seriousness assessment based on clinical criteria, 50 reports (0,24%) were classified as serious. Among these, causality assessment identi-fied 31 reports at least potentially related to the vaccination (RR: 6.86/100,000 AD). Febrile seizures following MMRV vaccination accounted for 11 of these 31 cases, with an incidence of 32 per 100,000 AD. No fatal outcomes were reported. Our findings support the highly favorable risk profile of pediatric vaccinations and the possibility to improve spontaneous reporting through the integration of digital technologies.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

Background/Objectives: Monoclonal antibodies (mAbs) have revolutionized multiple myeloma (MM) treatment. However, post-marketing data on their neuropsychiatric safety is limited. This study aimed to evaluate neuropsychiatric adverse events (AEs) related to mAbs used for MM through a retrospective pharmacovigilance analysis using the Food and Drug Administration (FDA) Adverse Events Reporting System (FAERS) database. Methods: Individual case safety reports (ICSRs) from 2015 to 2023 with at least one neuropsy-chiatric AE and one of the MM-approved mAbs as the suspect drug (i.e., daratumumab, elo-tuzumab, isatuximab, belantamab mafodotin, teclistamab, elranatamab, and talquentamab) were analyzed using descriptive and disproportionality approaches. Results: Unknown signals of disproportionate reporting (SDR) included cerebral infarction for daratumumab (n = 45; reporting odds ratio (ROR) = 2.39, 95% confidence interval (CI) = 1.79-3.21; information component (IC) = 1.54, IC025-IC075 = 1.05-1.9), elotuzumab (25; 7.61, 5.13-11.28; 3.03, 2.37-3.51), and isatuximab (10; 2.56, 1.38-4.76; 1.67, 0.59-2.4); mental status changes for daratumumab (40; 2.66, 1.95-3.63; 1.67, 1.14-2.04) and belantamab mafodotin (10; 4.23, 2.28-7.88; 2.3, 1.22-3.03); altered state of consciousness for daratumumab (32; 1.97, 1.39-2.78; 1.32, 0.73-1.74) and belantamab mafodotin (6; 2.35, 1.05-5.23; 1.6, 0.19-2.52); Guil-lain-Barre syndrome (GBS) for daratumumab (23; 6.42, 4.26-9.69; 2.81, 2.11-3.3), isatuximab (8; 10.72, 5.35-21.48; 3.57, 2.35-4.37), and elotuzumab (3; 4.74, 1.53-14.7; 2.59, 0.52-3.8); and orthos-tatic intolerance for daratumumab (10; 12.54, 6.71-23.43; 3.75, 2.67-4.48) and elontuzumab (4; 28.31, 10.58-75.73; 5 , 3.24-6.08). Conclusions: Our analysis highlighted several previously unacknowledged SDRs for MM-approved mAbs. Given the complex and not entirely understood etiology of some neuropsy-chiatric AEs, including GBS, further investigations are necessary.

The landscape of clinical management for metastatic melanoma (MM) and other solid tumors has been modernized by the advent of immune checkpoint inhibitors (ICI), including programmed cell death-1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4) inhibitors. While these agents demonstrate efficacy in suppressing tumor growth, they also lead to immune-related adverse events (irAEs), resulting in exacerbation of autoimmune diseases such as Rheumatoid arthritis (RA), Ulcerative colitis (UC), and Crohn’s disease (CD). The immune checkpoint inhibitors offer promising advancements in the treatment of melanoma and other cancers, but they also present significant challenges related to irAEs and autoimmune diseases. Ongoing research is crucial to better understand these challenges and develop strategies for mitigating adverse effects while maximizing therapeutic benefits. In this manuscript, we addressed this challenge using network-based approaches by constructing and analyzing molecular and signaling networks associated with tumor-immune cross-talk.. Our analysis revealed that IL6 is the key regulator responsible for irAEs during ICI therapies. Furthermore, we conducted an integrative network and molecular-level analysis, including virtual screening, of drug libraries, such as the Collection of Open Natural Products (COCONUT) and the Zinc15 FDA-approved library, to identify potential IL-6 inhibitors.. Subsequently, the compound amprenavir was identified as the best molecule that may disrupt essential interactions between IL6 and IL6R responsible for initiating signaling cascades underlying irAEs in ICI therapies.

A joint analysis of solar wind, geomagnetic field, and earthquake catalog data showed that before the catastrophic M7.8 and M7.5 Kahramanmaras earthquake sequence on February 6, 2023, the closed strong magnetic storm occurred on November 7, 2022, SYM/H=-117 nT. Storm started at 08:04 UT. At this time, the high latitudinal part of Turkey's longitudinal region of future epicenters was located under the polar cusp, where the solar wind plasma would directly access the Earth's environment. The time delay between storm onset and earthquake occurrence was ~91 days. We analyzed all seven strong (M7+) earthquakes from 1967 to 2020 to verify the initial findings. A similar pattern has been revealed for all events. The time delay between magnetic storm onset and earthquake occurrence varies from some days to some months. To continue these investigations, a retrospective analysis of seismic and other geophysical parameters just after preceded geomagnetic storms in the epicenter areas is desirable.

Weather forecast has a big impact on the global economy, accurate and timely weather forecast is required by all, it affects many aspects of human livelihood and lifestyle, it also plays a critical role in decision making for severe weather management and for primary and secondary sectors like agriculture, transportation, tourism, and industry as they rely on good weather conditions for production and operations. The erratic and uncertain complex nature of the weather makes traditional weather forecasting tedious and a challenging task, traditional weather forecast involves applying technology and scientific knowledge on numerical weather prediction (NWP), and weather radar to solve complex mathematical equations to obtain forecasts based on current weather conditions. These traditional processes utilize expensive, complex physical and computational power to produce forecasts, which can be inaccurate and have various catastrophic impacts on society. In this research, a machine learning-based weather forecasting model was proposed, the model was implemented using 4 classifier algorithms which include Random Forest classifier, Decision Tree Algorithm, Gaussian Naïve Bayes model, and Gradient Boosting Classifier, these algorithms were trained using a publicly available dataset from Kaggle for the city of Seattle for the period 2012 to 2015. The model’s performance was evaluated; the Gaussian Naive Bayes algorithm proved to be the best performing algorithm with a predictive accuracy of 84.153 %.

In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.

With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.

We identified six natural "control knobs" for global weather and climate, which clearly regulate, increase and decrease global temperatures, thus performing a true temperature "control" on Earth. Identified control knobs act as Earth's orbital forcing. We present a detailed Earth orbital model, from where the analysis proceeds. Based on our orbital model, we compare meteorological data from all over the globe, in order to detect orbital forcing fingerprints. Ninety temperature graphs and bar charts demonstrate the results. Empirical meteorological data covers the Pacific ocean, the Atlantic ocean, Arctic and Antarctic sea ice extent, and presents land based meteorological measurements from all continents for the year 2020, as well as multi-decadal data from global daily temperature datasets. A supplementary file provides another 100 additional graphs of weather data and temperature charts. The global orbital "temperature control" is clearly observable in all graphs and charts. Until today, orbital forcing has not been integrated in weather models and CMIP6 climate models.It is concluded that the proposed new orbital forcing should be part of weather and climate forecasting models.

In every economy, Stock markets are part of the key elements the build it up. A few decades ago, there has been a significant change in Ghana stock market returns (GSE). Our study examines the statistical and economic significance of investor sentiment, based on weather conditions/changes, on stock market returns. OLS models, assisted by unit root tests were employed in analyzing the data obtained from the Ghana stock exchange platform from 2000 to 2017. From our literature review, we discovered that investors’ perceptions play a central role in finalizing the direction of stock market returns. Regarding our empirical results, we tested whether weather variations influence the investment decisions of investors; we discovered that temperature and cloud cover significantly influences stock market returns. This is because of mood changes is associated with weather conditions variations. However, sunshine per our regression coefficient shows a statistically insignificant impact on investors’ investment choices. Precipitation to a large extend influence stock market activities further affecting its results negatively as our regression results depicted. We concluded stock brokerage firms, companies, and investors (foreign/local) must incorporate weather changes/effects when strategizing about their investment outcomes.

Previous studies have acknowledged the impact of weather changes on retail uncertainty. They primarily focus on understanding how weather conditions affect offline consumer behavior and aiming to develop effective marketing strategies. However, there is little research on the complex impact of weather on online shopping behavior. To bridge this gap, we conduct a study with a sample of 261 consumers from China with shopping experience in Community Retail Shops (CRSs). We utilize the S-O-R model and theories including meteorological emotional effect theory, emotional coherence and meteorological psychology to propose and validate models elucidating the relationship between weather and consumers' online shopping behavior in CRS. Our findings reveal that weather conditions affect consumers' spending patterns and purchase diversity, mediating by consumers' emotion and risk aversion, when they comfortably shop online at home. Furthermore, employing the fsQCA model, we identify the critical path through which weather conditions and consumer types influence risk aversion awareness. The results provide management implications for retailers to develop online marketing strategies for different consumer types.

Weather-related risks in aviation are one of the main causes of accidents worldwide. This article aims to analyze aviation risks resulting from adverse weather conditions, using the case study of the Pucallpa accident that occurred on August 23, 2005, in eastern Peru. In this analysis, we have determined the relationship between meteorological information and aviation risks, presented a matrix of risks associated with the most predominant meteorological variables at Pucallpa airport, and drawn up three maps of meteorological risks indicating the month of the year in which their incidence is most relevant. Finally, we conclude that it is essential to emphasize the importance of considering the prevailing weather conditions at Pucallpa airport to help mitigate the potential risks that may occur during different phases of the flight.

— An extreme bandgap Al0.64Ga0.36N quantum channel HEMT with Al0.87Ga0.13N top and back barriers grown by MOCVD over bulk AlN substrate demonstrated a critical breakdown field of 11.37 MV/cm, higher than 9.8 MV/cm expected for the channel Al0.64Ga0.36N material. We show that the fraction of this increase is due to the quantization of the 2D electron gas. The polarization field maintains electron quantization in the quantum channel even at low sheet densities in contrast to conventional HEMT designs. An additional increase in the breakdown field is due to quantum-enabled real space transfer of energetic electrons into high-Al barrier layers in high electric fields. These results show the advantages of quantum channel design for achieving record high breakdown voltages and enabling superior power HEMT devices.

This paper studies pandemic viruses that spread during the period (1759–2020) according to solar activity cycles. Our findings and results include the following: (1) The severity of a pandemic correlates negatively with the strength of solar activity; (2) Pandemic viruses are classified into three types based on their compatibility with solar activity associations. Most of them spread through the quiet Sun, where viruses survive better in cold and rainy weather, and in stable geomagnetic fields without strong disturbances; (3) The emergence of new strains of influenza viruses was manifested in two ways. First, the annual epidemics due to antigenic drift. Second, pandemics recur every 1–12 solar cycles (about 11–120 years) due to viral reassortment of new subtypes, which results in antigenic shifts; (4) Pandemic viruses have two groups according to their recurring period: first, recurring in nine solar cycles; second, recurring in twelve solar cycles. Furthermore, we reassort pandemic viruses from their previous spread in the same periodic classification. Moreover, we derive a periodicity formula for each subtype of the pandemic virus as a spread date.

A time integration scheme based on semi-Lagrangian advection and Laplace transform adjustment has been implemented in a baroclinic primitive equation model. The semi-Lagrangian scheme makes it possible to use large time steps. However, errors arising from the semi-implicit scheme increase with the time step size. In contrast, the errors using the Laplace transform adjustment remain relatively small for typical time steps used with semi-Lagrangian advection. Numerical experiments confirm the superior performance of the Laplace transform scheme relative to the semi-implicit reference model. The algorithmic complexity of the scheme is comparable to the reference model, making it computationally competitive, and indicating its potential for integrating weather and climate prediction models.

Living organisms with developed endocrine systems react in a complex way to environmental changes. Changing atmospheric pressure causes different blood pressures or hormone levels; carcinogenic radiation modifies the structure of DNA. Charged particles and ions act as neurotransmitters and block certain types of protein channels and receptors. A high concentration of carbon dioxide has an indirect effect on both blood pressure and neuron activity. The bioelectric nature of living tissues highlights the complexity of the connection between the dynamic physical environment and biological systems. Recent results from studies on the interactions and connections mentioned above are reviewed in this paper.

In the past two decades the world experienced an exceptional number of unprecedented extreme weather events, some causing major human suffering and economic damage, such as the March 2012 heat event, which was called “Meteorological March Madness.” From the beginning of space era a correlation of solar flares with pressure changes in atmosphere within 2–3 days or even less was reported. In this study we wanted to test the possible relation of highly warm weather events in North-East America with Solar Energetic Particle (SEP) events. For this reason we compared ground temperatures T_M in Madison, Wisconsin, with energetic particle fluxes P measured by the EPAM instrument onboard the ACE spacecraft. In particular, we elaborated case events and the results of a statistical study of the SEP events related with the largest (Dst ≤ −150nT) Coronal Mass Ejection (CME)-induced geomagnetic storms, between with the years 1997–2015. The most striking result of our statistical analysis is a very significant positive correlation between the highest temperature increase_. ΔT_M and the time duration of the temperature increase ΔT_M (r = 0.8, p <0.001) at “winter times” ( r = 0.5, p , 0.01 for the whole sample of 26 SEP examined events). The time response of T_M to P was found to be in general short (a few days), but in the case of March 2015, during a gradual P8 increase, a cross-correlation test indicated highest c.c. within 1 day (p < 0.05). The March 2012 “meteorological anomaly” was elaborated in the case of South-East Europe, where, beside a period of strong winds and rainfall (6-13.3.2012), intense precipitation in North-East Greece (Alexandroupoli) were found to be correlated with distinct high energy flux enhancements. A rough theoretical interpretation is discussed for the space—atmospheric extreme weather relationship we found. However, much work should be done to achieve early warning of space weather dependent extreme meteorological events. Such future advances in understanding the relationships between space weather and extreme atmospheric events would improve atmospheric models and help people’s safety, health and life.

Accurate seasonal weather forecasts are vital across a broad spectrum of applications, bearing significant environmental and socioeconomic implications. This importance renders the subject a matter of primary interest to a wide range of stakeholders, including the general public, agricultural sector, emergency responders, financial institutions, and policy strategists. The need for precision in long-term predictive models necessitates the development of innovative methodologies. These methodologies should be capable of deciphering atmospheric patterns and mechanisms with detail, especially at a local level, without the resource constraints that dynamical downscaling imposes. In response to this expanding demand, this study presents a novel solution, combining a stochastic deep learning methodology, specifically Generative Adversarial Networks (GANs), with a Digital Elevation Model (DEM). The cornerstone of the proposed model is the enhancement of gridded temperature fields from seasonal forecasts. The area of focus was the Hellenic region, wherein the spatial resolution is amplified from a coarse 1° x 1° grid to an impressively detailed 0.1° x 0.1° grid. This offers a transformative perspective for interpreting and employing this crucial meteorological data. The results suggest that the downscaled fields adequately approximate the actual spatial distribution, although the predicted values tend to slightly overestimate and in some cases underestimate the original ones. This study underlines the potential of this approach to significantly enhance the resolution and utility of weather forecasts, thereby contributing to a variety of sectors dependent on reliable meteorological data.

Traffic Inspection (TI) work in this article is positioned as a specific module of road traffic with its primary function oriented towards monitoring and controlling safe traffic and the execution of significant events within a particular geographic area. Exploratory research on the significance of event execution in simple, complicated and complex traffic flow and process situations is related to the activities of monitoring and controlling functional states and performance of categorical variables, which include objects and locations of road infrastructure, communication infrastructure and networks of traffic inspection resources. It is emphasized that the words “work” and “traffic” have the semantic status as synonyms (in one world language), which is explained in the design of the Agent-based model of the complexity of content and contextual structure of TI work at the singular and plural level with 12 points of interest (POI) in the thematic research. An Event Execution Log (EEL) was created for on-site data collection with eight variables, seven of which are independent (event type, activities, objects, locations, host, duration-period and periodicity of the event) and one dependent (significance of the event) variable. The structured data set includes 10,994 input-output vectors in 970 categories collected in the EEL created by 32 human agents (traffic inspectors) over a 30-day period. An algorithmic presentation of the methodological research procedure for preprocessing and final data processing in the ensemble of machine learning models for classification and selection of TI tasks is given. Data cleaning was performed on the available dataset to increase data consistency for further processing. Vector elimination was carried out based on the Location variable, such that the total number of vectors equals the number of unique categories of this variable, which is 636. The main result of this research is the Classification modeling of the significance of events in TI work based on machine learning techniques and the Stacking ensemble. The created machine learning models for event significance classification modeling have high accuracy values. To evaluate the performance metrics of the Stacking ensemble of models, the confusion matrix, Precision, Recall, ND F1-score are used.

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM-IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Run Final (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001-2020. The Asfezari gridded precipitation data which are developed using a dense of ground-based observation were utilized as the reference dataset. The findings indicate that the IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All the three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggest that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

This paper considers the problem of predicting earthquakes. It uses a small amount of information to create a descriptive key that can be used as a footprint to describe an event. A frequency grid clusters events that occurred at the same time and then the algorithm averages the history of these events over preceding days, in particular the gaps when the events did not occur. The gaps are measured for the clustered events only and can be used to create a description that is quite unique. Results suggest that seismic events can in fact be traced using this key and subsequently recognised again, if the same conditions reoccur. They also suggest that force direction may be more important than magnitude, for this type of earthquake. Greek and USA datasets have been looked at and the prediction accuracy can be 70% or better. The author therefore suggests that this is an interesting method that deserves attention.

In Eucalyptus plantations, coppice rotations often yield less than initial rotations. The TECHS project (Tolerance of Eucalyptus Clones to Hydric, Thermal and Biotic Stresses) studied short rotation coppicing across a 3,000 km gradient. The main objective of this work was to compare the survival, sprouting and initial growth of Eucalyptus clones managed, and to examine factors that might influence the productivity of the coppice rotation: climate, genotypes and stocking. Eight of the TECHS sites spread from latitudes 6o S to 30o S were included in the coppice study, with 17 genotypes at each site. The initial rotation had been planted at a 3 x 3 m spacing, and also in a spacing trial at densities from 450 to 14,000 trees ha-1. Six months after harvesting the initial Eucalyptus rotation, average survival was 88%, with tropical clones showing over twice the sprouting biomass (6.7 vs 2.9 Mg ha-1) and four times the woody biomass compared to subtropical clones (4.7 vs 1.1 Mg ha-1). Greater initial water deficits had stronger sprouting and growth. Clones with higher belowground carbon allocation in the initial rotation performed better in coppicing, and precipitation became more influential after 12 months. Drought and spacing trial significantly affected growth.

A 3-month (June-August) regime of the year 2002 summer rainfall (JJA2002) was simulated with 30 physics combinations using the Weather Research and Forecasting (WRF) model at 12-km horizontal grid resolution. The objective is to examine summer rainfall sensitivity to parameterization of microphysical, convective, and boundary layer processes and identify the best possible combination of parameterization options that perform relatively better in simulating the spatial and temporal distribution of summer rains over Ethiopia. The WRF simulated rainfall was evaluated against station data and satellite rainfall products (CHIRPS and ENACTS) using mean absolute error, Pearson and Pattern correlation coefficients (PCC), pattern correlation, and error in a number of rainy days as evaluation metrics. Summer rainfall is found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All simulations captured the spatial distribution of mean seasonal precipitation with PCC ranging from 0.89-0.94. However, all simulations overestimated precipitation amount and number of rainy days. Out of the 30, the simulations that use a combination of Grell-3D cumulus scheme, ACM2 boundary layer, Lin Microphysics, Dudhai shortwave radiation, and RRTM longwave radiation scheme ranked the top and provided the most realistic simulation in terms of amount and spatio-temporal distribution of summer rainfall.

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, road objects etc. In this paper, we explore the use of focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective to help address data imbalance. In addition, we explore the attention mechanism for pixel based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperforms current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.

Automated Guided Vehicles are self-guided vehicles used in manufacturing and warehouses for material transport. They play a vital role in just-in-time material delivery, ensuring smooth production lines. However, disruptions in their fixed routes can lead to efficiency losses. This paper studies unexpected disruptions in mobile robot routes, assessing the costs of rectification using the E* analysis. The study highlights the significant additional cost of such disruptions, underscoring the importance of their consideration in AGV route planning to enhance system resilience.

Cardiovascular diseases (CVDs) represent the leading cause of global mortality with 1,7 million deaths a year. One of the alternative systems to drug therapy to minimize the risk of CVDs is represented by alpha-linolenic acid (ALA), an essential fatty acid of the Omega-3 series, known for its cholesterol-lowering effect. The main purpose of this review is to analyze the effects of ALA and investigate the relevant n-6/n-3 ratio in order to maintain functionally beneficial effects. Concerning the lipid-lowering preventive effects, ALA may favorably affect the values of LDL-C and triglycerides in either adult and pediatric populations and it has a positive and protective effect against hypertension, suggesting that ALA is an useful diet-related antihypertensive compound. According to the 2009 EFSA statement, dietary ALA may contribute to reducing the risk of CVDs, thanks to anti-hypertensive, anti-atherosclerotic and cardio-protective effects.

In this study an adjusting post-processing approach is implemented for improving intra-hourly forecasts of solar power and ramp events of PV solar power systems at different locations in the United States. This study also serves as an out-of-sample test to evaluate the performance of the adjusting approach with different locations and timescales. Thus, various individual intra-hourly forecasts of solar power are combined and adjusted by applying the adjusting approach. Both point and probabilistic forecasts of solar power are included. After that, solar power ramp event forecasting by the adjusting approach is carried out.

Extreme heat is a climate hazard that has been linked with high morbidity and mortality. The way extreme heat events are felt in the population varies depending on a variety of factors, such as age, employment status, living conditions, air-conditioning, housing conditions, habits, behaviors, and other socio-demographic parameters. To quantify and locate vulnerabilities of populations to extreme heat, we analyze socio-economic data at the NUTS3 level for the national case study of Greece and at the census tract level for the local case study of the city of Athens. The target variable for this study is defined as the average daily mortality during heatwaves per 100,000 individuals and a methodology is developed for constructing this variable, based on socio-economic data available in public databases. Independent variables were selected based on their contribution to socioeconomic vulnerability; they include the percentage of elderly, retired, unemployed, renting, living alone, residing in smaller houses, living in older houses, and immigrants from developing countries. An ensemble gradient boosted tree model was employed for this study to obtain feature importance metrics that was used to construct a composite index of socioeconomic heat vulnerability. The socioeconomic heat vulnerability index (SHVI) was calculated for each prefecture in Greece and for each census tract in the city of Athens, Greece. The unique feature of this SHVI is that it can be applied to any geographical resolution using the same methodology and produces a result that is not only quantifiable, but also allows comparison between vulnerability scores of different regions. This application aimed to map the SHVI of both prefecture and city, to examine the significance of scale, to identify vulnerability hotspots, and rank the most vulnerable areas, which are prioritized by authorities for interventions that protect public health.

During public health events, Active Mobility (walking and cycling) plays an important role in securing residents' travel. To enhance the resilience of urban transportation, this article integrates the travel environment elements for walking and cycling and constructs a travel environment for Active Mobility evaluation index system. This article uses survey data from the main urban area of Harbin in the context of the COVID-19 pandemic in May 2022, and combines the Gradient Boosting Decision Tree (GBDT) and Impact-Asymmetry Analysis (IAA) methods to analyze the satisfaction of the travel environment for Active Mobility. It proposes strategies for optimizing the travel environment for Active Mobility, ensuring residents' travel during public health events, guiding them to adopt Active Mobility, and improving urban transportation resilience. This research shows that Active Mobility can enhance urban transportation resilience. In addition, there are differences in resident demand for walking and cycling environments in the travel environment for Active Mobility. Residents are more concerned about the comfort of the walking environment, while they prioritize the convenience of the cycling environment.

It is argued that all physical knowledge ultimately stems from observation and that the simplest possible observation is that an event has happened at a certain space-time location . Considering historic experiments, which had been groundbreaking in the evolution of our modern ideas of matter on the atomic, nuclear and elementary particle scales, it is shown that such experiments produce as outputs macroscopically observable events which represent binary decisions with regard to simple alternatives and which accumulate in the course of time into spatio-temporal patterns whose forms allow decisions to be taken concerning conceivable alternatives of explanation. Working towards elucidating the physical and information-theoretic characteristics of those elementary pieces of information we arrive at the conclusion that - unlike the events of relativity which are mere 4-tuples of spacetime coordinates - the events of observation are pieces of information endowed with the properties of physical action, spacetime extension, and Lorentz invariance. Another important aspect is that such events are produced at the expense of irreversibly dissipating energy which can be stated in Landauer diction in the form that more energy needs to be spent in the erasure of events than had previously been invested in their initiation.

Area covered: These drugs, particularly calcineurin inhibitors, have very narrow therapeutic windows, i.e., they have numerous drug-related side effects. Herein, we focus on the most frequent immunosuppressive drug-related side effects that we encounter in kidney-transplant recipients: namely nephrotoxicity, post-transplant diabetes mellitus, leukopenia, anemia, dyslipidemia, mouth ulcers, hypertension, and viral reactivations (cytomegalovirus and BK virus). However, other therapies that are used after kidney transplantation, e.g., valcancyclovir, may also contribute to adverse events such as leukopenia. For each side-effect, we suggest how it can be prevented and how to treat it when present.

Shared e-scooters are gaining popularity as a solution for first and last-mile connectivity in urban areas. This study conducted in Doha, Qatar, aimed to understand how weather and land use patterns affect e-scooter usage, utilizing various datasets. Given the novelty of micromobility systems in Qatar and their sensitivity to the local hot and humid climate, the study employed a comprehensive analytical approach. Analysis revealed that e-scooter demand spikes on weekends, particularly in the afternoon. Influential land use factors include proximity to universities and employment centers, while a higher presence of Qatari females in an area correlates with reduced usage, pointing to cultural influences. Weather conditions, especially humidity and extreme temperatures, significantly impact e-scooter demand. The study employed the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for trip clustering and the Random Forest (RF) algorithm to model trip counts, considering temperature and humidity. Insights showed humidity as a critical predictor of hourly e-scooter trip counts. These findings underscore the importance of considering weather, land use, the relationship between land use characteristics and weather variations, and finally cultural factors in optimizing e-scooter services. By integrating data analysis and machine learning, the study offers valuable insights for enhancing urban mobility and transportation planning in GCC countries, promoting sustainable urban mobility.

Coastal marine ecosystems worldwide, like Florida Bay, are increasingly affected by tide alteration and anthropogenic disturbances which affect water quality imbalance leading to algal blooms. Increased bloom persistence is a serious threat due to the long-lasting impact on processes like carbon cycle and services like species presence. However, exploring eco-environmental feedback patterns of algal blooms remains challenging and poorly investigated, also due to the paucity of data. Florida Bay, taken as an epitome, has long experienced algal blooms in its central and western regions, and in 2006 an unprecedented bloom occurred in the eastern habitats. We analyzed the occurrence of blooms from three perspectives: (1) the spatial spreading networks of chlorophyll-a (CHLa) that pinpoints source and unbalanced habitats; (2) the fluctuations of water quality factors pre- and post-bloom outbreaks to in assess environmental impacts of ecological dysbiosis and target prevention and control of algal of blooms; and (3) the biogeochemical-spreading network topological co-evolution to quantify ecosystemic stability and ecological shift likelihood in long-term. Here, we propose Transfer Entropy (TE) to quantify dynamical interactions between spatial areas and biogeochemical factors (ecosystem connectome) underpinning bloom emergence and spreading, as well as environmental effects; the Pareto principle is defined for identifying the salient eco-environmental interactions of CHLa. We quantified the spatial dynamics of algal blooms, and thus obtained areas in need for ecological monitoring and potential bloom control. Results show that algal blooms are increasingly persistent over space with long-term negative effects on water quality factor. A dichotomy is reported between spatial ecological corridors of spreading and biogeochemical networks as well as divergence from the optimal eco-organization: randomization of the former due to nutrients' overload and temperature increase leads to scale-free CHLa spreading and extreme outbreaks a posteriori. Subsequently this increases bloom persistence, turbidity and salinity with potentially strong ecological effects on highly biodiverse and vulnerable areas such as tidal flats and marshes. Algal blooms are important ecosystem regulators of nutrient cycles; however, beyond limit chlorophyll-a outbreaks cause aquatic species mortality due to their effect on water turbidity, nutrient balance (nitrogen and phosphorus in particular), salinity and temperature. Beyond compromising local environmental quality, socio-ecological services are also compromised at large scales, yet ecological assessment models like the one presented are in need of application in subtropical and tropical bays worldwide.

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect and analyze meteorological measurements. This paper presents the results of our analysis, demonstrating the effectiveness of citizen science in generating valuable environmental data. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. Our findings underscore the potential of citizen science to augment traditional monitoring efforts and inform decision-making processes in environmental research and management. The paper discusses the methodology employed, including data collection and statistical analysis techniques. Results highlight the reliability and accuracy of citizen-generated data, as well as the strengths of the MeteoTracker platform. The implications of our findings for environmental research, policy development, and public engagement are discussed, along with recommendations for enhancing the effectiveness of citizen science initiatives in environmental monitoring.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is a challenging task suffering from dynamic objects and continuous environmental changes. The issue gets worse due to interrupting the quality of perception by adverse weather like snow, rain, fog, night light, sand storm, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights were collected from training on the individual datasets, their merged version, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the above-mentioned datasets and their subsets. The evaluation revealed that using custom datasets for training significantly improves the detection performance compared to the YOLOv8 base weights. And using more images through the feature-related data merging technique steadily increases the object detection performance.

The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multi-class UAV's classification performance using single-shot object detection algorithms YOLOv5 and YOLOv7. The transfer learning is employed for performance improvement and rapid training with improved results. We customized a multi-class dataset containing multi-rotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the integration of transfer learning has achieved good results, with an overall best average-classification precision of 94\%, an average recall of 93.1\%, a mAP$@$0.5 average of 95.3\%, and an average F1 score of 92.33\%. The dataset and code are available as an open source: https://github.com/ZeeshanKaleem/YOLOV5-Large-vs-YOLOV7.git

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

The paper deals with the problem of choosing the best O&M strategy for wind power plants. Current maintenance theory considers just production opportunities and minimizes the maintenance costs, but with the liberalization of the electricity market also the electricity price has become an important variable to take into account in the O&M scheduling. Another important variables that is often neglected by the existing maintenance theory is the weather condition. This paper proposes a new strategy that takes into account the electricity price and weather conditions, improves the expected profit of the systems, and reduce the overall maintenance and logistic costs. The maintenance schedule is formalized as an optimization problem where the discounted cumulative profit of a wind generation portfolio in a fixed-time horizon (e.g. two years ahead), subject to the technologically-derived maintenance time constraints is optimized. Both the theoretical and computational aspects of the proposed O&M strategy are discussed. Results show that taking into account market and weather opportunities in the design of the maintenance strategy, it is possible to achieve a more complete scheduling for a given set of wind power plants.

Introduction Delayed intra-operative neuro-monitoring (IOM) events can be defined as loss of responses, after baseline traces were present upon completion of instrumentation and deformity correction. This study reports this subgroup of events, assesses their incidence and evaluates potential causative factors. Methods We reviewed 1881 consecutive patients who underwent spinal deformity correction between 2011 and 2021. Surgeries were performed using a standardised anaesthetic regimen and multimodal IOM protocol. Results 44 patients (2.4%) had an IOM event. 9 patients (6F:3M) had a true positive delayed event after posterior segmental pedicle screw instrumentation. Mean age was 12.5 (2-17) years with mean thoracic Cobb angle of 78° (51-94°). Mean estimated blood loss was 28%. Titanium beam shaped rods were utilised in 3 patients and 5.5mm titanium rods in 5 patients. Motor evoked potentials were lost on average 18 (10-30) minutes after completion of instrumentation. 7 patients had bilateral loss and 2 patients had unilateral loss of MEP traces. Sensory evoked potential amplitude was preserved in all cases. There was a transient latency shift in one patient. MEPs returned within 20 minutes of rod release. The rod was re-contoured to incorporate more scoliosis in 1 patient. 2 patients had beam rods exchanged to 5.5mm titanium rods. 6 patients had rod removal with retention of pedicle screws and staged completion of instrumentation at mean 5 (1-7) days later. No patients had a postoperative neurological deficit. Conclusion Delayed IOM loss represented 15% of all events. Increased cord tension and hypo-perfusion were considered likely mechanisms. Rod release restored MEPs in all cases. The timing of events emphasises the importance of continued monitoring for minimum 30 minutes after completion of spinal instrumentation or until wound closure.

On review of neuroscience literature extreme capsule considered as a whiter matter tract. Nevertheless it is not clear that extreme capsule itself is a association fiber pathway or is o bottleneck for passing other association fiber. By a systematic search with investigating anatomical position, dissection, connectivity and cognitive role of extreme capsule it can be argued that extreme capsule probably is a bottleneck for passing uncinated fasciculus (UF) and inferior fronto – occipital fasciculus(IFOF), And its different role of language processing is duo to different tract that passing it.

Reconstruction of fine-scale information from sparse data is often needed in practical fluid dynamics where the sensors are typically sparse and yet, one may need to learn the underlying flow structures or inform predictions through assimilation into data-driven models. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches encode the physics into an underlying sparse basis space that spans the manifold to generate well-posedness. To achieve this, one commonly uses generic orthogonal Fourier basis or data specific proper orthogonal decomposition (POD) basis to reconstruct from sparse sensor information at chosen locations. Such a reconstruction problem is well-posed as long as the sensor locations are incoherent and can sample the key physical mechanisms. The resulting inverse problem is easily solved using $l_2$ minimization or if necessary, sparsity promoting $l_1$ minimization. Given the proliferation of machine learning and the need for robust reconstruction frameworks in the face of dynamically evolving flows, we explore in this study the suitability of non-orthogonal basis obtained from Extreme Learning Machine (ELM) auto-encoders for sparse reconstruction. In particular, we assess the interplay between sensor quantity and sensor placement for a given system dimension for accurate reconstruction of canonical fluid flows in comparison to POD-based reconstruction.

Event-based vision is an emerging field of computer vision that offers unique properties such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution, object detection and tracking approach, that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per each detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze the tracking performance at different temporal rates. Our approach shows promising results with minimal performance deterioration at higher temporal resolutions (48 – 384 Hz) when compared with the baseline frame-based performance at 24 Hz.

Parkinson-like events are side effects that may persist for an extended period even after drug administration is discontinued. Although these events can be triggered by various drugs, the mechanisms underlying their diverse symptoms remain largely unclear. To investigate this, we used the Japanese Adverse Drug Event Reporting Database, which is maintained by the Phar-maceuticals and Medical Devices Agency, to analyze the risk factors associated with Parkinson-like events along with associated drug trends and characteristics. Our findings indicate that similar to　Parkinson’s disease, age-related differences affect the onset of these events, with older individuals being more susceptible. Hierarchical clustering and principal component analysis revealed that the mechanisms triggering these Parkinson-like events are consistent across reports, suggesting a common underlying cause. However, even with a consistent mechanism, side effects can vary depending on the site of action. These insights underline the importance of swift identification of the drugs suspected of causing these events and implementation of measures to reduce their side effects.

This article analyses entropy changes triggered by specific events in deterministic and indeterministic systems. Article considers a simple model consisting of water in a cuvette, an ice cube in the device above the cuvette and a random number generator (RNG) that controls the probability of dropping the ice into water. Article introduces the entropic potential Z(T, A) of an event A occurred in a system R at the moment Т₀, which describes the influence of the event A to the entropy of the system R in the future (for the moments T>Т₀). The entropic potential of an event Z(T,A) can be calculated as the difference between the mathematical expectations of entropy of the system R for the moment T (T>Т₀) made immediately before and immediately after the event A as Z(T, A) = Ŝ_T(Т₀+dT) - Ŝ_T(Т₀-dT). Article also presents examples of calculations of the entropic potentials of events in indeterministic systems with different probabilities of events. Since real-life systems are mostly indeterministic, the entropic potentials of events in real-life usually have non-zero values. The entropic potentials of the events "useful" for the system are negative, and entropic potentials of the events "harmful" for the system are positive.

Background: Recent infectious disease outbreaks have brought increased attention to strengthening the capacity to prevent, detect, and respond to natural biological threats. However, deliberate biological events also represent a significant global threat that have received relatively little attention. The Biological Weapons Convention provides a foundation for the response to deliberate biological events, but the political mechanisms to respond to and recover from such an event are poorly defined. Methods: We performed an analysis of the epidemiological timeline, the international policies triggered as a notional deliberate biological event unfolds, and the corresponding stakeholders and mandates assigned by each mandate. Findings: The results of this analysis identify a significant gap in both policy and stakeholder mandates: there is no single policy nor stakeholder mandate for leading and coordinating the response activities associated with a deliberate biological event. These results were visualized using an open source web-based tool published at https://dbe.talusanalytics.com. Interpretation: While there are organizations and stakeholders responsible for roles in leading security or public health response, these roles are non-overlapping and are led by organizations not with limited interaction outside such events. The lack of mandates highlights a gap in the mechanisms available to coordinate response and a gap in guidance for managing the response. The results of the analysis corroborate anecdotal evidence from stakeholder meetings and highlight a critical need and gap in deliberate biological response policy.

In this paper, an extensive study on the operation, control and performance of a hybrid photovoltaic and wind turbine (PV-WT) system is presented. The study includes the operation of the hybrid systems under different weather conditions which are reflected on a change in the wind speed and irradiance. Various loading conditions are investigated such a change in active and reactive power demands for static and dynamic loads. The performance of the hybrid system is examined via MATLAB/SIMULINK simulations.

Background and objectives: In the current context, the 2021 presidential elections in Peru distance from the social objective, not being objectively represented, that is why we analyze their validity, we determine the distances between the parties if they are extreme, the correspondences with the departments and their prospects, in the surveys we propose which departments influence the results. Methods: We use a mixed methodology, qualitative analysis, it will be multidimensional with the support of statistical methods and programs such as R Studio, worddj, Gephi, and Iramuteq. The quantitative analysis will be through factor analysis, correspondence and discriminant analysis to the data of the election Results and conclusions: The textual analysis mentions that there are dimensions such as the social issue, the results of the surveys and democracy that are far apart, regarding the electoral issue. This inculcates to work both on the part of the organisms that carry out these processes, as well as the initiative of the candidates, and the media. Regarding the quantitative analysis, it is detailed that the representative parties must be greater than 4.0 million voters, to make representative parties at the national level. The classification regarding the percentage of votes is given in three groups, highlighting the independence of the Peru Libre political party in these . In the correspondence analysis we can detail that both Fuerza Popular and Peru Libre are extremist parties. The vast majority of Peruvian citizens have an ideological tendency, intermediate between both parties. The prospective projections with a cross section give a victory to the Peru Libre party. In the discriminant analysis, the sample for the surveys focuses on 7 departments that does not include the Peruvian capital.

Weather radar plays a crucial role in the monitoring of severe convective weather. However, due to its limited detection range, it cannot conduct effective monitoring in remote offshore areas. Therefore, this paper utilized the Unet++ to establish a model for retrieving radar composite reflectivity based on Himawari-9 satellite datasets. In the process of comparative analysis, we found that both satellite and radar data exhibit significant diurnal cycles, but there are notable differences in their variation characteristics. To address this, we established 4 comparative models to test the influence of latitude and diurnal cycles on the inversion results. The results show that adding the distribution map of the minimum cloud-top brightness temperature at the corresponding time in the model can effectively reduce the root mean square error (RMSE) of the model and enhance the accuracy of severe convective weather monitoring. This conclusion can be applied in the future to enhance and improve satellite quantitative precipitation estimation.

Introduction: The COVID-19 pandemic highlighted the necessity for rapid, efficient testing at large-scale events. Developing rapid and efficient testing methods was critical, especially for events with a high risk of virus spread. The queueing theory offers a powerful tool to manage the flow of people through testing centers. This study uses the M/M/c model to examine how different floor plan configurations impact the efficiency of RADT testing centers. Aim of the study: This research applies queueing theory to optimize the Rapid Antigen Diagnostic Test (RADT) processes at mass gatherings. The study uses the M/M/c queueing model to evaluate the dynamics of RADT centers configured in U-shaped and straight-line layouts. The primary goal is to assess the feasibility of using RADTs efficiently at events and mass gathering entrances, aiming to enhance throughput and minimize wait times. Methodology: The study involved 500 healthy participants, managed by medical staff across a U-shaped and a straight floor plans. Agile management techniques were employed to enhance operational efficiency. The process stages included queue number issuance, registration, sample collection, sample mixing, and results dissemination. Results: Both floor plans enabled the completion of RADT screening in approximately 2 minutes per participant. The U-shaped layout slightly outperformed the straight-line setup in efficiency, demonstrating its effectiveness in optimizing the testing process. Conclusion: This study confirms the feasibility of implementing Rapid Antigen Diagnostic Test (RADT) processes at mass gatherings. It was found that the U-shaped floor plan configuration is particularly effective, indicating its potential suitability for future mass testing scenarios.

This paper examines the impact of random sample sizes on the extreme value theory of competing risks, a significant area in finance and environmental science. We capture limit distributions of two extreme types under random sampling sizes, known as accelerated mixed l-max and p-max stable type distributions. The study presents results for both maxima and minima in competing risks scenarios, addressing cases of independent and non-independent random sample sizes. Numerical examples validate our theoretical findings, demonstrating the applicability of our approach to various random sample size distributions, including time-shifted Poisson or binomial, geometric, and negative binomial distributions.

Portugal, in line with the European Union, is aiming for carbon neutrality by 2050 (Net 1 Zero), which implies a transition to sustainable energy sources. Climate change is all too evident, extreme weather periods are occurring in a cyclical manner, with greater brevity to such an extent that the grid operator must deal with production scenarios where it can no longer rely on hydroelectric production given the recurring drought situation. This situation increases dependence on thermal production using natural gas and imports. This has significant economic implications. Portugal has exploited its onshore wind potential, reaching an installed capacity of 5.671 MW by 2022. However, the expansion of onshore wind energy is limited to reinforcing existing infrastructure. To overcome these challenges, it is necessary to expand the exploitation of offshore wind potential that is already underway. This article proposes the location of offshore wind production platforms along the Portuguese coast. This allows for an analysis of offshore production and its optimization according to the minimum cost per MWh in the face of extreme scenarios, i.e., in periods of extreme drought where hydroelectric production capacity is practically non-existent. The model is fed by using market price indications and the amount of energy needed for the following day. Using forecast data, the model adapts offshore wind production for the following day according to the minimization of the average market price. The results of the simulations allow us to conclude that despite the high cost of offshore technology (in deep waters), in extreme climate scenarios, it enables cost reduction and a clear decrease in imports.

Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both models’ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD model’s fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.

Power disaggregation aims at determining the appliance-by-appliance electricity consumption leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models have been proven remarkable results on energy disaggregation. Nevertheless, those procedures have various weaknesses: there is a scalability problem as the number of devices to observe raises and the algorithmic complexity of the inference step is severe. DNN architectures, such as Convolutional Neural Networks, have demonstrated to be a viable solution to deal with FHMMs shortcomings. Nonetheless, there are two significant limitations: a complicated and time-consuming training system based on back-propagation has to be employed to estimates the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome those limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. Experiment evaluation has been conducted using the UK-DALE corpus. We find that the suggested approach achieves similar performances to recently proposed ANN-based methods and outperforms FHMMs. Besides, our solution generalises well to unseen houses.

As a model for economic and ecological systems, replicator dynamics represents a basic form of agent competition for finite resources. Here, we investigate the effects of stochastic resetting in this kind of processes. Random reset events abruptly lead individual resources to a small value from which dynamics must start anew. Numerical results show that resource distribution over the population of competing agents develops highly nonuniform profiles, exhibiting clustering and fluctuations with anomalous dependence on the population size. This non-standard statistical behavior jeopardizes an analytical treatment based on mean-field assumptions. We propose alternative simplified analytical approaches which provide a stylized description of entropy evolution for the clustered distribution of resources and explain the unusually slow decrease of fluctuations.

A large amount of data is produced every day by stakeholders of the Air Traffic Management (ATM) system, in particular airline operators, airports, and air navigation service provider (ANSP). Most data is kept private for many reasons, including commercial and security concerns. More than data, shared information is precious, as it leverages intelligent decision-making support tools designed to smooth daily operations. We present a framework to detect, identify and characterise anomalies in past aircraft trajectory data. It is based on an open source of ADS-B based aircraft trajectories, and extracted information can benefit a wide range of stakeholders: Air Traffic Control (ATC) training centres could play more realistic simulations; ANSP may improve capacity indicators; academics improve safety models and risk estimations; and commercial stakeholders, like airlines and airports, may use such information to improve short-term predictions and optimise their operations. The technique is based on autoencoding artificial neural networks applied on flows of trajectories, which provide a useful reading grid associating cluster analysis with quantified level of abnormality. In particular, we find that the highest anomaly scores correspond to poor weather conditions, whereas anomalies with a lower score relate to ATC tactical actions.

Background: A personalized approach to occupational medicine allows specialists to 1 prevent professional hazards such as stress-related depression and anxiety in extreme work environments. Objective: we aim to detect genetic markers of low resilience to stress. Methods: The study cohort included 97 elite athletes and 167 special forces personnel. The research team collected buccal mucosa samples and examined psychological status with the Hospital Anxiety and Depression Scale (HADS). We assessed 35 variants within selected genes that are most often associated with low resilience to stress, anxiety, and depression. Fisher’s Exact test was used to determine nonrandom associations between scores in the HADS scale and the genetic variants. We also trained machine learning models to predict score values from genotyping findings and ranked genetic biomarkers according to their predictive power. Results: High-risk depression profiles included C/T genotype of MTHFR C677T and A/C variant of MTHFR A1298C. Susceptibility to anxiety was associated with several polymorphisms regulating neuroactive substances, immune response, and coagulation. The ML models accurately detect depression or anxiety levels with MAE/ROV of 17.69±1.35 and 17.86±2.09% respectively. Conclusions: The study findings justify a polymorphic nature of anxiety and confirm the immune system's involvement in regulating stress response.

The thermomechanical and viscoelastic properties of a glass fiber polyethylene terephthalate glycol (GF/PETG) continuous unidirectional (UD) tape were investigated using differential scanning calorimetry (DSC), thermo-mechanical analysis (TMA), and dynamic mechanical analysis (DMA). The study identified five operational conditions based on Army Regulation 70-38 Standard. The DSC results revealed a glass transition temperature of 78.0 ± 0.3°C, guiding the selection of temperatures for TMA and DMA tests. TMA provided the coefficient of thermal expansion in three principal directions, consistent with known values for PETG and GF materials. DMA tests, including strain sweep, temperature ramp, frequency sweep, creep, and stress relaxation, defined the linear viscoelastic region and temperature-dependent properties of the material. The frequency sweep indicated an increase in modulus with rising frequency, identifying several natural frequency modes. Creep and stress relaxation tests showed time-dependent behavior, with strain increasing under higher loads and stress decreasing over time for all tested input values. Viscoelastic models fitted to the data yielded R² values of 0.99, demonstrating good agreement. The study successfully measured thermo-mechanical and viscoelastic properties across various conditions, providing insights into how temperature influences the material's mechanical response under extreme conditions.

The primary purpose of the study was the determination of the spatial day-to-day variability of extreme temperatures in Poland and the dependency of large temperature changes on atmospheric circulation in accordance with the Grosswetterlagen (GWL) classification. The goal was implemented based on data from 1966 to 2015, made available by the Institute of Meteorology and Water Management – National Research Institute. Day-to-day changes in maximum and minimum temperatures were designated with a rate of ≥ 6 °C (large) and with a rate of ≥ 12 °C (very large) and their spatial distribution was presented. Finally, the analysis of the dependency of considerable temperature changes on atmospheric circulation in accordance with the Grosswetterlagen (GWL) classification was conducted. The obtained results showed that in Poland in the period 1966-2015 the number of large changes in both Tmax and Tmin slightly increased, although the fluctuations show spatial variability. Large changes in Tmax occur more frequently in spring, and in Tmin in winter. Large changes in Tmax and Tmin are mainly recorded during cyclonic circulation, however, the anticyclonic circulation types favour especially large decreases in Tmin.

Construction of Pontoons is based on multiple elements, dimensions and weight. The study has addressed about how the industry of the floating reinforcement concrete precast (pontoons) installs in the factory with the combinations of utility, electricity services, and Internet service. The pontoon bridges are successfully installed in the road for transport or sea for shops. The installation process for pontoons is successfully attempted in a balanced situation above surface of the sea to the resistant of floating precast (pontoons) to any ambient effects such as weather conditions, the movement of the waves or any others effects. The findings elaborate that it is not just a military solution. Pontoon installation can significantly serve for civil purposes.

The present study analyzes students' weather-related hazard and risk literacy in Greece, a climate change hotspot region in the Eastern Mediterranean. In this context, we examine the students' level in two core literacy variables, namely knowledge and competency. In addition, we explore how knowledge, attitudes and socio-demographic variables influence students' competencies related to weather and climate risk assessment and adaptability. A questionnaire-based survey was conducted on 474 students aged 12-16. The regression results showed that knowledge significantly affects the level of competency. Self-belief and confidence in science were the most influential among the attitudinal variables. We conclude by discussing educational and behavioural issues highlighted as essential to address with targeted policies and measures in formal education complemented by non-formal educational activities.

Adverse events associated with opioid use in palliative care have been extensively studied. However, predicting the occurrence of adverse events based on the specific opioid used remains unclear. This study aimed to comprehensively analyze the adverse events caused by µ receptor stimulation of opioids approved in Japan and investigate the tendencies of adverse event occur-rence among different opioids.We utilized the FDA Adverse Event Reporting System (FAERS) database to extract reported adverse events of opioids approved in Japan. Cluster analysis was performed on reporting odds ratios (RORs) of adverse event names among opioids to visualize relationships between opioids and adverse events, facilitating a comparative study of their clas-sifications.We calculated the RORs of adverse events for the target opioids. Based on these RORs, we performed a cluster analysis, which resulted in the classification of 11 target opioids into five distinct groups. we were able to comprehensively compare and examine the relationships between opioids and adverse events. This analysis helps in understanding and managing the risks and benefits of each drug in palliative care settings. By analyzing relationships between opioids and adverse events, clinicians can make informed decisions about opioid selection, dosage, and monitoring to maximize patient safety and comfort.

Estimation of the potential consequences from events occurring downstream of a dam are part of the risk assessment needed during the installation phase of a new dam. In case of specific Natural or Man Made ongoing or prospected events, it may be also important to carry out fast computations that can provide information on the areas at risk either because the original design analyses are not available or because the parameters needed are different. This study is aimed to develop a procedure that strongly facilitates the preparation of the input deck and the derivation of the output quantities to allow a fast analysis of a dam break event using a shallow water model, NAMI DANCE, as the analysis tool.

Walnut trees are grown worldwide for their edible fruits of high nutritional value. To address climate change, researchers have studied walnut phenology to create cultivars adapted to warmer climates. The standardisation of walnut phenological observations is needed and the Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale is the most accurate and internationally accepted for this purpose. Here, the principal growth stages (PGS) of Persian walnut (Juglans regia L.) are described using stages from a previously available alphanumerical scale. This standardised phenological scale describes Persian walnut growth from the dormant vegetative state through reproductive budding to senescence. This phenological scale is expected to increase the efficiency of walnut phenological monitoring. Fifty-seven stages were used to describe the life cycle of Persian walnut in this BBCH scale. Of these 57 stages, 3 stages are dedicated to seed germination (PGS-0), 4 stages to bud development (PGS-0), 7 stages to leaf development (PGS-1), 4 stages to stem elongation (PGS-3), 8 stages to inflorescence emergence (PGS-5), 5 stages to male flowering (PGS-6), 5 stages to female flowering (PGS-6), 5 stages to fruit development (PGS-7), 12 stages to fruit ripening (PGS-8) and 4 stages to leaf senescence (PGS-9).