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Graphene oxide (GO) has attracted significant attention due to its unique optical properties and tunable surface chemistry, making it an excellent platform for optical sensing applications. Combining GO with metallic nanoparticles allows for the fabrication of highly sensitive surface-enhanced Raman scattering (SERS) substrates for analytical purposes. Here, we report our research on chemical strategies to decorate GO paper with Au nanostars (AuNSs) for the SERS detection of methylene blue, a water pollutant model. Several preparative approaches were employed to evaluate their sensitivity to detect MB molecules, including polyelectrolytes, distinct graphene-based materials, and the deposition method of the AuNSs. [ABSTRACT FROM AUTHOR]

Nuclear power plays a significant role in fulfilling the energy needs of Pakistan and its share in the total energy mix has increased from 4.7% to 8.8% in the past seven years. As per the Pakistan energy outlook report (2021–2030), this share is hypothesized to increase to 10.82% by the year 2030, which will alleviate the energy shortage problem and, at same time, reduce carbon emissions. Like all thermal power plants, it is also necessary for nuclear plants to operate at optimum efficiency. This study is based on the thermodynamic analysis of the conventional side of an advanced HPR-1000 (PWR) nuclear power plant. In this paper, a comparison of indigenously developed model results is made, with vendor-provided sea water temperatures and power curves for year-long sea water temperature variation. Firstly, a computational model is developed using Engineering Equation Solver (EES) software to evaluate the performance of the secondary side of the plant and is validated based on the designer-provided heat balance analysis for full power mode. Then, the condenser heat balance is performed for different cooling medium inlet temperatures and terminal temperature differences to study the relationship of condenser performance, thermal efficiency, and output power. Initial results reveal that sea water temperature varies at the condenser inlet from 5 to 35 °C, the power output of the unit decreases by 54 MW, and the thermodynamic efficiency drops by 1.79%. Thus, this paper highlights the impact of sea water temperature on plant performance and the need to devise more effective techniques to approach the plant's optimum efficiency. [ABSTRACT FROM AUTHOR]

This paper describes all the characteristics of the Guatemalan earthquake catalog and how it has evolved. Over 64,483 earthquakes are included in this paper distributed in some areas of El Salvador, Mexico, Honduras, and Belize, but mainly in Guatemala. Regularly, the earthquake catalogs improve their characteristics over time, however, this is not the case for the catalog of Guatemala. Although earthquake detection improved with the establishment of the national seismic network operated by the National Institute of Seismology, Vulcanology, Meteorology and Hydrology (INSIVUMEH) in 1977, the catalog has not kept a favorable evolution over time. This has led to problems with earthquake detection, large location errors, increasing magnitude of completeness, and others that are going to be discussed later in this paper. [ABSTRACT FROM AUTHOR]

In this work, we focused on Electrospray (ES) and Ultrasonic Spray Coating (USC) as two promising and innovative fabrication techniques for the optimisation of carbon electrode surfaces to be employed in Bio-Electrochemical Systems. We performed, on commercial carbon paper electrodes, controlled depositions of a nanostructured layer containing PEO and PEDOT:PSS. We then employed electron microscopy and Raman spectroscopy to characterise the morphology and superficial uniformity of the so-obtained electrodes. Together with electrochemical characterisations and experiments in bio-electrochemical devices, we demonstrated how ES and USC represent promising techniques for the optimisation of carbon electrodes' surfaces, obtained with the deposition of a conductive nanostructured layer. [ABSTRACT FROM AUTHOR]

Return periods (Ts) are used to estimate the interval of time between natural hazard occurrences of a certain size and assess the risks associated with hydrological occurrences, climate extremes, structural failures and seismicity. Despite Ts being widely used, they are characterized by strong misconceptions and ambiguities. Moreover, although they have been successfully used when discussing storm surges, high tides and extreme precipitation, concerns arise from their use in assessing probabilities of future global mean sea level rise (SLR). Most papers discuss SLR return periods considering storm surges or high tides and not SLR itself, as a separate and unique hazard. Sea level rise due to storm surges or tides is regional and temporary and differs from the global SLR, which is a long lasting and slow phenomenon. This paper discusses these misconceptions and misuses of return periods in assessing flood risk and the probability of sea level rise at the global level and suggests a method for assessing likelihoods of climate change risks that can be widely accepted and commonly used by all stakeholders and decision makers for all types of climate hazards. [ABSTRACT FROM AUTHOR]

By combining blockchain with physical tracking technologies, raw materials can potentially be traced throughout their global supply chains. Physical tracking technologies enable observing how raw materials move within the physical world, whereas blockchain translates these events into the digital world with an immutable record. This paper presents a taxonomy of different physical tracking technologies and examines if and how the combination of these technologies render raw material supply chains more transparent. Although academic literature highlights the theoretical benefits of combining these transformative technologies, large scale projects are still in their early stages. Following a brief literature review, this paper leverages an empirical approach to classify different tracking technologies, their fields of application and limitations, as well as how these technologies can enable supply chain transparency. Obviously, there is no single technology that can fulfil all requirements along complex supply chains. However, the relevant combination of respective technologies can help bridge gaps by increasing transparency within supply chains. [ABSTRACT FROM AUTHOR]

The Hapke model has been widely used in the field of soil remote sensing. However, the latest development of the Hapke model (i.e., Hapke-HSR model) adopted a simple hypothesis to consider the influence of the soil moisture content (SMC), which brought great difficulties to SMC parameter inversion. This paper presents a method to improve the Hapke model using the improved multilayer radiative transfer model of soil reflectance (MARMIT-2), which can effectively improve the ability of the Hapke-HSR model to characterize the variation in the SMC. Finally, we used the soil database to comprehensively verify the ability of the improved Hapke model. The results show that the improved Hapke can effectively characterize the spectral characteristics of soil and show a higher fitting accuracy (RMSE = 0.009) compared with the Hapke-HSR model (RMSE = 0.031), especially at a high SMC (≥30%). Therefore, the improved Hapke model can better understand soil physical properties and improve the inversion accuracy of soil–vegetation physical parameters, which can be used to enhance agricultural water use efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a meticulous exploration of advanced machine learning techniques for precise forest type classification using multi-temporal remote sensing data within a woodland environment. The study comprehensively evaluates a diverse range of models, ranging from advanced (ensemble) machine learning (ML) methods to several finely tuned support vector machine (SVM) variants, with a specific focus on Bayesian-optimized SVM with a radial basis function (RBF) kernel. Our findings highlight the robust performance of the Bayesian-optimized SVM, achieving a high accuracy of up to 94.27% and average precision and recall of 94.46% and 94.27%, respectively. Notably, this accuracy aligns with the levels attained by acclaimed ensemble techniques such as random forest and CatBoost while also surpassing those of XGBoost and LightGBM. These results highlight the potential of these methodologies to significantly enhance forest type mapping accuracy compared to traditional (linear) SVM and black-box neural networks. This, in turn, can enable the reliable identification and quantification of key services, including carbon storage and erosion protection, intrinsic to the forest ecosystem. The findings of our comparative study emphasize the profound impact of employing and fine-tuning ML approaches in the realm of remote sensing-based environmental analysis. [ABSTRACT FROM AUTHOR]

Lignin, a paper and pulp industry waste product, has attracted significant attention in recent years as a promising sustainable material for high-end energy applications. Herein, we examine lignin as a potential material for ionic thermoelectric hydrogels and carbon-based materials. Optimized lignin-derived hydrogels demonstrate a remarkable Seebeck coefficient of 3.63 mV/K when subjected to an axial temperature gradient. Furthermore, synthesized lignin-based porous carbon materials exhibit exceptional performance as supercapacitor electrodes, with a superior specific capacitance of 56.3 F/g at 0.5 A/g. Lignin-based hydrogels and porous carbon electrodes offer a promising path towards the development of lignin-derived ionic thermoelectric supercapacitors. [ABSTRACT FROM AUTHOR]

This paper surveys the historical indigenous water-harvesting and water-supply systems in the Levant, focusing on Syrian lands, where they are numerous and well-preserved. It also deals with the important hydraulic works recently carried out to exploit the area in terms of agriculture to preserve, supply and collect water. Furthermore, the paper's scope is to evaluate the feasibility of rehabilitating ancient hydraulic structures, either by maintaining the old systems or by implementing new projects. [ABSTRACT FROM AUTHOR]

The potential for the generation of pico- and micro-hydropower through hydraulic energy recovery has been demonstrated across many sectors of the water-energy-food nexus, often termed hidden hydropower. The potential to recover energy from hidden hydropower in water supply, crop production, food processing, and energy production has been demonstrated via numerous in-depth case studies, regional assessments, and physical experiments. This paper presents a holistic overview of the potential role and impacts of micro-hydropower energy recovery on the water-energy-food nexus in the context of climate change. The paper comprises a review and synthesis of the available literature. The paper outlines the potential impacts of hidden hydropower on cost of water supply, energy and food production, considering also the potential impacts on crop yield and food supply. Policy and technological barriers to the exploitation of hidden hydropower resources within the nexus are outlined and recommendations to overcome these are provided. The results of this investigation highlight the potential of micro-hydropower energy recovery in water systems to reduce energy consumption by 0.005–3.7% across various sectors and regions, with consequent impacts in the operating and consumer costs of food, water and energy, as well as on the CO2 emissions of these activities. This hidden hydropower has the potential to ease the pressures of the water-energy-food nexus and is an important element of the route towards sustainability within the nexus. [ABSTRACT FROM AUTHOR]

Nowadays, most industrial solid waste, agriculture waste, and natural minerals are piled up, which not only occupies land resources, but also has a dangerous impact on the environment. The responsible management and recycling of these materials can have significant environmental benefits, while also providing a means of accessing raw materials for the creation of geopolymers. Geopolymers are new, environment-friendly, cementless materials and alternative construction materials to ordinary Portland cement. They not only have excellent mechanical properties, but also have valuable properties, e.g., high-temperature resistance. This paper summarizes the feasibility studies in terms of base materials and properties of today's developing geopolymers. This paper also highlights the significance of developing sustainable materials for civil engineering. [ABSTRACT FROM AUTHOR]

The application of curved Insulating Glass Units (IGUs) in facades has emerged as a novel solution to meet aesthetic and energy performance objectives. Due to improved stiffness, curved IGUs cannot equalise internal and atmospheric pressure changes by pillowing, as flat IGUs do. Thus, the climatic loads in curved IGUs may be several times higher than those in flat units. This paper deals with experiments and numerical simulations of cylindrically curved IGUs. It presents the results of an experimental campaign designed to collect data that validate the numerical model of a curved IGU developed within this study. The model is sequentially used to analyse a case study that compares the resulting internal pressure, displacement, and stress in glass in a flat and curved IGU subjected to the characteristic climatic actions given by DIN18008-1. [ABSTRACT FROM AUTHOR]

The feedback from international practice has shown that the installation of photovoltaic (PV) parks on abandoned dumps is one of the best environmental and economical solutions. This paper discusses the site selection criteria for the installation of PV systems on dumps. When installing the PV systems, different environmental, technical, and economic criteria should be respected. The ground movement and combustion hazards in the dumps appear to be the main constraints and limitations for the installation of photovoltaic panels. This paper presents several examples of the installation of photovoltaic panels on coal-lignite dumps in France. However, the number of projects is still very limited compared to the identified potential. [ABSTRACT FROM AUTHOR]

Machine learning is constantly gaining ground in the mining industry. Machine learning-based systems take advantage of the computing power of personal, embedded and cloud systems of today to rapidly build models of real processes, something that would have been impossible or extremely time-consuming a couple of decades ago. The widespread access to the internet and the availability of cheap and powerful cloud computing systems led to the development and acceptance of tools to automate resource modelling processes or optimise mine scheduling, using machine learning methodologies. The domain modelling system discussed in this paper, called DomainMCF, has been developed by Maptek, using artificial neural network technology. In the application presented in this paper, DomainMCF is used to model the spatial distribution of marble quality categorical parameters, and the results are combined to produce a final marble quality classification using drillhole and quarry face samples from an operational marble quarry in NE Greece. DomainMCF was made available for this study as a cloud processing service through an early access program for individuals or companies interested in testing its capabilities and suitability in various modelling scenarios and geological settings. The resulting marble product classifications are compared with those produced by the already established classification system that is based on a more conventional estimation method. The produced results show that DomainMCF can be effectively applied to the modelling of marble quality spatial distribution and similar domaining problems. [ABSTRACT FROM AUTHOR]

This paper presents a discussion on Quebec's pegmatite lithium resources and potential markets. It also evaluates the opportunities of lithium battery production for electric vehicles (EV) in the province while reducing greenhouse gas emissions. The paper shows that mining Quebec's lithium ore deposits would be sufficient to satisfy the province's lithium demand and also for exporting abroad lithium-ion batteries. By considering only the projects whose final product is LMH or LCE, Quebec would be able to produce between 10 and 21 million lithium-ion batteries for electric vehicles with a greenhouse gas emissions footprint of only 43% of the international average value due to Quebec's hydro power. Finally, considering Quebec's lithium mining project economics, the increased future demand for lithium would render Quebec's lithium pegmatite projects competitive compared with those reported for brine projects. [ABSTRACT FROM AUTHOR]

Greece is gifted with geologic features that promote geothermal heat flow. Geothermal energy exploration began in the late 60s, culminating in the first geothermal energy law in 1984 and the introduction of geothermal energy as a mineral resource under the amendment of the Greek Mining Code. Since then, low- and high-temperature geothermal activities followed their separate ways, with a modest utilization of the energy product in the primary sector (agriculture, aquaculture) and attempts for electricity production stalled since the mid-1990s. The adoption of green policies by both the EU and Greece, the acceptance of global warming as an existing threat, the adhesion to CO2 reduction goals, energy efficiency and the application on renewable energy solutions as means to combat the increase in global temperature have led to an increasing interest in the utilization of the geothermal energy applications. This paper presents the new legal framework for geothermal energy established by Law 4602/2019, as introduced by the Greek Ministry of Environment and Energy, Directorate-General for Mineral Raw Materials and discusses its scope and goals set by the implementation of its provisions. The paper offers a roadmap to successfully test those new policies and regulatory provisions and, finally, it maps the interfaces of stakeholders and geothermal industry in an attempt to highlight the steps of the necessary administrative procedures towards the facilitation of viable geothermal projects. [ABSTRACT FROM AUTHOR]

Recycling plant-based materials for various applications not only reduces the harm to the environment but also presents an excellent green source for nanomaterial synthesis. Being chiral and biodegradable makes cellulose, which is an organic polymer, an economic and easy-to-access plant-derived green material. Cellulose can be synthesized into nanostructures for a vast array of high-demand applications, such as drug delivery; biomedicines, which includes "biosensors and diagnostics"; medical implants; skin tissue healing; wastewater treatment; touch screen technology; electronic skin; human–machine interfaces; flexible devices; energy storage devices; clothes; packaging; and cosmetics. The daily newspapers that are delivered to our homes can be one of the best sources of cellulose for us. Our work in this study concentrated on removing nanocrystalline cellulose from newspapers. To begin, we deinked the newspapers and then the deinked pulp was transformed into its nanostructures, or nanocrystalline cellulose, to achieve a high aspect ratio, on the one hand, using chemicals like NaOH, thiourea, etc., and on the other side, via a mechanical process. We used a variety of characterization techniques, including scanning electron microscopy to study morphological properties, X-ray diffraction, and dynamic light scattering for dimensional analysis, Fourier transforms infrared spectroscopy for thermogravimetric analysis, and others, to confirm that the synthesized materials had achieved the intended outcomes. A high aspect ratio enables us to create surfaces with a huge surface area with very little synthetic material. The final product, which was created by synthesis, has been discovered to have features that are identical to those of nanocrystalline cellulose, which is available for purchase in the market for use in laboratory purposes. To make nanocomposites, this nanocrystalline cellulose can be combined with various organic and inorganic polymers, which can be further used as a base material for energy storage devices. In this paper, we compared our materials at different time durations used in synthesis. [ABSTRACT FROM AUTHOR]

This paper proposes a model for the dependence of heat capacity of thin metal films on the temperature and on the number of atomic layers in these films directly. Model representations are based on the principles of statistical physics for solids and concepts of the distribution of principal quantum numbers in the system of oscillators distributed in solids at high temperatures, i.e., Bose–Einstein distribution. The calculations were performed based on the comparison of the Helmholtz free energy values for the various configurations of films and the number of layers in them. The main tool for the model implementation was the formation and further calculation of the partition function, being an expression of the distribution of principal quantum numbers in the complex system of a thin film. Calculations showed the existence of the optimal film thickness at which the maximum heat capacity was achieved. The calculations were performed based on a comparison of the values of the Helmholtz free energy for different film configurations and the number of layers in them. The main tool for implementing the model was the formation and further calculation of the partition function, which was an expression of the distribution of principal quantum numbers in the complex system of a thin film. The calculation results show the presence of a 15–20% increase in the heat capacity of thin films, corresponding to 400–600 atomic layers and the Dulong–Petit law, i.e., the comparison of exceeding heat capacity values with bulk objects for a certain temperature range. The heat capacity reaches the highest values in thin films of 30–50 atomic layers in thickness and exceeds the value of 3R by ~2.0 times. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) are an evolving class of crystalline porous materials made of organic linkers and metallic nodes. The rich chemistry of MOFs allows them to have an almost infinite number of possible structures. Consequently, they have been of great interest because of their highly tunable properties and unique features, such as their high porosity, high surface area, structural stability, structural diversity, and tailorability. These enable MOFs to be a flexible catalytic platform for photocatalytic applications. Thus, this paper discusses the opportunities of MOFs for use in catalysis. In particular, the use of metal-organic frameworks as a photocatalyst is briefly discussed. Specifically, MOFs can be used as a photocatalyst for carbon dioxide reduction (CO2RR), nitrogen reduction reactions (NRRs), and water-splitting reactions (HERs and ORRs). However, using MOFs as catalytic platforms has some challenges that must be addressed to achieve commercialization. Therefore, this paper also discusses some prospects of designing MOFs for their specific catalytic applications to improve their catalytic properties and enhance selectivity. More importantly, an outlook is also provided on how MOF catalysts can further be developed to enable other catalytic reactions. Overall, MOFs have great potential as a photocatalytic material, provided they are uniquely designed to suit their intended applications. [ABSTRACT FROM AUTHOR]

The predominant climatological wind regime in summer over the Greek Seas and especially over the Aegean Sea is undoubtedly the Etesian winds system, very well known since the time of the ancient Greeks, who first identified and described its main characteristics. The Etesian winds have been under continuous and intensive research since early 1900s, and numerous papers have been published, which have revealed all the secrets associated with the physical mechanisms responsible for their creation and maintenance. The ordinary synoptic situation, which is closely associated with the appearance of spells of Etesian winds outbreaks over the Aegean Sea, refers to cases in which the Subtropical Jet Stream (SJS) is situated over the Greek mainland. Then the frontal surfaces associated with the Polar Jet Stream (PJS), or a polar jet streak, passing through Greece from the north can cause severe weather in northern Greece as far south as Larisa (39.39° N, 22.26° E). Precipitation does not usually occur south of Larisa, because the SJS constitutes a barrier to the southward extension of the upper half of the frontal surface. In this case, cold advection occurs in the lower troposphere, resulting in a drop in temperatures even in southern Greece, due to the establishment of an Etesian winds outbreak. Thereafter, these north-easterly winds persist for a long time, weakening gradually, following the variation of the pressure gradient due to the combination of the mobile dynamic anticyclone positioned over the Balkans, after the passage of the cold front and the permanent Cyprus surface low. The main goal of this article is to investigate how much the case of a time period around 9 July 2022 differs from the conceptual model mentioned above, as deep convection occurred over all of Greece over three successive days, breaking the Etesian winds regime and defeating the low tropospheric stability usually accompanying this wind regime. [ABSTRACT FROM AUTHOR]

Weather prediction is a key aspect of today's society and its activities. Accurate predictions are crucial for efficiently organizing human activities, particularly in the agricultural, transportation and energy sectors. In this paper, two deep neural networks, designed based on a long short-term memory architecture, are developed to predict the occurrence of rainfall events and the respective amount of rainfall on the island of Nisyros in the south Aegean Sea. Two deep neural networks are developed, serving two different learning tasks. The first network acts as a classifier that assesses whether it is going to rain or not and, sequentially, the second network performs a regression task, quantifying the anticipated amount of rainfall. The performance of such prediction models is highly dependent on input sequences. Among others, the lookback time window shapes those input sequences by determining the number of past time steps to be taken into account. The ideal time window for the classifier involves 24 time steps (i.e., 4 h), resulting in increased accuracy levels exceeding 96.45%. The predictions of the regression model, which has the same lookback time window, feature low errors, measured as 6.635 and 1.411 mm using the mean-square-error and mean-absolute-error indices, respectively. [ABSTRACT FROM AUTHOR]

Being comfortable and healthy in one's home is of the utmost importance and a contributing factor to the quality of their life, and it is greatly affected by the air quality of the residence. Therefore, the subject of this paper centres around the indoor air quality (IAQ) of a standard household in central Athens and how these components influence human health. The air quality assessment is conducted using particulate matter (PM) on-site continuous measurements taken from low-cost sensors (PurpleAir) installed both inside and outside the house. Focusing especially on PM2.5 data from September 2021 to September 2022, the Air Quality Index (AQI) and the number of exceedances are evaluated in detail, investigating the association between indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

This paper investigates the potential impacts of climate change and anthropogenic activities on the transboundary river of Aoos/Vjosa, flowing through Greece and Albania, and proposes strategies for its protection. The paper analyzes the current state of the river and its ecosystems, identifies the key drivers of change, and examines the potential impacts of future climate scenarios and human activities. Using climate data and GIS mapping, the study highlights the importance of sustainable development and cross-border cooperation to ensure the long-term health and sustainability of the river and its resources. [ABSTRACT FROM AUTHOR]

Climate-change-induced environmental impacts has an especially strong influence on water resources. Declining water availability not only results in droughts, but is also responsible for decreasing the quality of water in water-scarce regions, such as the South Caucasus. Armed conflict between Armenia and Azerbaijan in 2020 occurred during the recent major war in the South Caucasus. In this paper, I ask how will intensifying climate change in the region affect the current political situation? Is there any chance of multilateral cooperation for water management? [ABSTRACT FROM AUTHOR]

This paper explores the planned resettlement process and the social impact induced by the Tha Htay Hydropower Project in Myanmar. Through the empirical case study, the status of direct impact on the resettled people, changes in their livelihood, policy orientation towards practical limitations, practical consequences of resettlement, and inadequate preparation for resettlement implementation were studied as verifiable evidence. In this paper, the survey data were compiled from the responses of resettled people from three resettled villages: Maewa, Payit and Yegauk, which were interpreted as the real condition of the project-affected people because a higher degree of their dissatisfaction negatively affected the project, whereas a higher degree of satisfaction led to positive effects. In this paper, a quantitative method was used to analyze the planned resettlement and its social impact. As a survey descriptive design, a simple random sampling method was utilized to collect samples from the target population with a contribution of structured questionnaires. The collected data were presented by mean and standard deviation to decide the real situation of the resettlement project. The study found that details of social impact were considered to carry out when the construction of the hydropower project began. Then, the policy constraint in the resettlement process was verified, which led to the insufficient preparation and implementation of the resettlement. To meet the future development requirements of the planned resettlement process with fewer social impacts on hydropower development projects, practical contributions and policy recommendations are made for the compensation of farmland, where the people's livelihoods are land-based, and additional livelihood packages. [ABSTRACT FROM AUTHOR]

This study presents the provisional, regulating and cultural ecosystem services that can be delivered by the newly constructed multi-purpose reservoir of Lake Karla located in a water-scarce agricultural area in central Greece. The present short paper takes advantage of literature data and outputs produced from a dynamic GIS hydrologic and management model of the study area with SWAT that simulated hydrology, reservoir operation, irrigation practices and crop production. The paper highlights the net provisional services that the local agricultural society can gain from the full operation of Karla and the additional benefits arising, such as flood control, biodiversity maintenance, aesthetic improvement and touristic opportunities. [ABSTRACT FROM AUTHOR]

Nowadays, most industrial solid waste, agriculture waste, and natural minerals are piled up, which not only occupies land resources, but also has a dangerous impact on the environment. The responsible management and recycling of these materials can have significant environmental benefits, while also providing a means of accessing raw materials for the creation of geopolymers. Geopolymers are new, environment-friendly, cementless materials and alternative construction materials to ordinary Portland cement. They not only have excellent mechanical properties, but also have valuable properties, e.g., high-temperature resistance. This paper summarizes the feasibility studies in terms of base materials and properties of today's developing geopolymers. This paper also highlights the significance of developing sustainable materials for civil engineering. [ABSTRACT FROM AUTHOR]

The application of curved Insulating Glass Units (IGUs) in facades has emerged as a novel solution to meet aesthetic and energy performance objectives. Due to improved stiffness, curved IGUs cannot equalise internal and atmospheric pressure changes by pillowing, as flat IGUs do. Thus, the climatic loads in curved IGUs may be several times higher than those in flat units. This paper deals with experiments and numerical simulations of cylindrically curved IGUs. It presents the results of an experimental campaign designed to collect data that validate the numerical model of a curved IGU developed within this study. The model is sequentially used to analyse a case study that compares the resulting internal pressure, displacement, and stress in glass in a flat and curved IGU subjected to the characteristic climatic actions given by DIN18008-1. [ABSTRACT FROM AUTHOR]

During the last few decades, a prominent example of extreme weather events in the Indian Ocean region has been cyclonic storms. In this paper, annual variations of different categories of tropical cyclonic storms over the Bay of Bengal (BOB) and Arabian Sea (ARS) are analyzed. The analysis reveals that the total number of cyclones (TNC) has increased at a high rate (the gradient being + 1.67 per year) and that there are more CSs over the BOB than over the ARS. The rate of increase in CSs over the Arabian Sea is more than that over the Bay of Bengal. Furthermore, two interesting features were noted: (i) monsoons tend to prohibit the formation of CSs; (ii) cyclonic storms (CSs) increased with the increase in Global Sea Surface Temperature (GSST) during said period. An attempt was also made to find out the influence of solar activity on these extreme weather events. Keeping in mind that the sunspot number (SSN) is an indicator of the strength of solar effects, it was found that, most of the time, a high SSN value was associated with a small number of total cyclones (CSs). A high SSN (>90) and number of cyclones showed ahigh correlation coefficient (0.78). The significance was at the 99.99% level, while the correlation coefficient (CC) of cyclones with time was 0.53, and with SSN < 60, it was 0.095. Thus, it appears that although CS frequency is increasing with time, the influence of sunspots is such that it basically opposes the formation of cyclones provided the SSN exceeds a certain critical value (roughly 90). [ABSTRACT FROM AUTHOR]

The optical and radiative properties of aerosols are governed by their types. In this paper, the optical and radiative properties of different aerosol types in Wuhan, China, have been inverted and investigated using the collected PM2.5 samples. The results show that PM2.5 (average mass concentration about 31.25 μg/m3) is mainly contributed by sulfate (SO4) and organic carbon (OC) (22% and 52%, respectively), while aerosol optical depth (AOD, average about 0.28) is mainly contributed by SO4 and black carbon (EC) (22% and 19%, respectively). SO4 and nitrate (NO3) have a negative radiative forcing at the top of the atmosphere (TOA) and a cooling effect, while OC and EC have a positive radiative forcing and a heating effect. Moreover, EC has the most significant effect on the radiative forcing in Wuhan, contributing up to 61% and 73% at the bottom of the atmosphere (BOA) and atmosphere (ATM), respectively, while contributing up to 75% to the atmospheric heating rate (about 1~2 K/day). [ABSTRACT FROM AUTHOR]

Air pollution caused by biomass burning is the main environmental concern in Mozambique. This paper investigates the effect of biomass combustion on air quality, focusing on the sources of pollution, pollutants released, and health and environmental consequences. The substantial reliance on biomass for cooking, heating, and energy generation causes high levels of air pollution from the combustion of wood, agricultural residues, and charcoal. During biomass burning, particulate matter (PM), carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), and hazardous air pollutants (HAPs) are emitted, resulting in health hazards and contributing to climate change. Mozambique can considerably improve air quality, safeguard public health, and contribute to sustainable development by using cleaner cooking technology, supporting sustainable biomass management practices, and raising awareness. [ABSTRACT FROM AUTHOR]

The coastal areas of Chile are undergoing major transformations associated with changes in the frequency and intensity of coastal storms, alterations in the rainfall regime with a megadrought of more than 10 years, and variations in ocean currents, which have generated a series of impacts related to beach erosion, the alteration of coastal wetlands and the effects on coastal cities. Faced with this problem, it is necessary to incorporate multiple monitoring methodologies that are complementary to existing ones, and that promote the incorporation of communities as fundamental axes in data collection. In this paper, we present the CoastSnap initiative implemented in Chile, whose results have been notorious, despite the short implementation time. Up to July 2023, the communities had shared 350 photographs that have allowed for analysis of the variability of the beaches, allowing for the quantification of variations on average up to 45 m wide in some of their beaches. [ABSTRACT FROM AUTHOR]

The statistical characteristics of combined lidar and radiometric measurements obtained from satellite lidar CALIOP and ground-based sun-radiometer stations were used as input datasets to retrieve the altitude profiles of aerosol parameters (LRS-C technique). The signal-to-noise ratio of the input satellite lidar signals increased when averaging over a large array of measured data. An algorithm and software package for processing the input dataset of the LRS-C sounding of atmospheric aerosol in regions with medium and low aerosol loads was developed. This paper presents the results of studying long-term changes in the concentration profiles of aerosol modes in regions of East Europe (AERONET site Minsk, 53.92° N, 27.60° E) and East Antarctic (AERONET site Vechernaya Hill, 67.66° S, 46.16° E). [ABSTRACT FROM AUTHOR]

Super-resolution (SR) is a well-established technique used to enhance the resolution of low-resolution images. In this paper, we introduce a novel approach for the super-resolution of Sentinel-2 10 m RGB images using higher-resolution Venus 5 m RGB images. The proposed method takes advantage of a modified SRResNet network, integrates perceptual loss based on the VGG network, and incorporates a learning rate decay strategy for improved performance. By leveraging higher-resolution VENµS 5 m RGB images as reference images, this approach aims to generate high-quality super-resolved images of Sentinel-2 10 m RGB images. The modified SRResNet network was designed to capture and learn underlying patterns and details present in Venus images, enabling it to effectively enhance the resolution of Sentinel-2 images. In addition, the inclusion of perceptual loss based on the VGG network helps preserve important visual features and maintain the overall image quality. The learning rate decay strategy ensures the network converges to an optimal solution by gradually reducing the learning rate during the training process. Our research contributes to the field of super-resolution by offering a novel approach specifically tailored for enhancing the resolution of Sentinel-2 10 m RGB images using Venus 5 m RGB images. The proposed methodology has the potential to benefit various applications, such as remote sensing, land cover analysis, and environmental monitoring, where high-resolution imagery is crucial for accurate and detailed analysis. In summary, our approach presents a promising solution for the super-resolution of Sentinel-2 10 m RGB images, providing an effective means to obtain higher-resolution imagery by leveraging the complementary information from Venus 5 m RGB images. We used the SEN2VENµS dataset for this research. The SEN2VENµS dataset comprises cloud-free surface reflectance patches obtained from Sentinel-2 imagery. Notably, these patches are accompanied by corresponding reference surface reflectance patches captured at a remarkable 5 m resolution by the VENµS Micro-Satellite on the same acquisition day. To assess the effectiveness of the proposed approach, we evaluated it using widely used metrics such as the mean squared error (MSE), the peak signal-to-noise ratio (PSNR), and the structural similarity index (SSIM). These metrics provided quantitative measurements of the quality and fidelity of the super-resolved images. Experimental results demonstrate the effectiveness of our proposed approach in achieving improved super-resolution performance compared to existing methods. As an example, our method achieved a PSNR of 35.70 and a SSIM of 0.94 on the training dataset, outperforming the bicubic interpolation method, which yielded a PSNR of 29.53 and a SSIM of 0.92. On the validation dataset, our approach achieved a PSNR of 40.3809 and a SSIM of 0.98, while the bicubic interpolation method achieved a PSNR of 34.26 and a SSIM of 0.94. Finally, on the test dataset, our approach achieved a PSNR of 29.8231 and a SSIM of 0.90, whereas the bicubic interpolation method yielded a PSNR of 26.99 and a SSIM of 0.85. The evaluation based on MSE, PSNR, and SSIM metrics showcases the enhanced visual quality, increased image resolution, and improved similarity to the reference Venus images. [ABSTRACT FROM AUTHOR]

eXplainable Artificial Intelligence (XAI) has emerged as an essential requirement when dealing with mission-critical applications, ensuring transparency and interpretability of the employed black box AI models. The significance of XAI spans various domains, from healthcare to finance, where understanding the decision-making process of deep learning algorithms is essential. Most AI-based computer vision models are often black boxes; hence, providing the explainability of deep neural networks in image processing is crucial for their wide adoption and deployment in medical image analysis, autonomous driving, and remote sensing applications. Existing XAI methods aim to provide insights about the methodology used by the black-box model in making decisions by highlighting the most relevant regions within the input image that contribute to the model's prediction. Recently, several XAI methods for image classification tasks have been introduced. In contrast, image segmentation has received comparatively less attention in the context of explainability, although it is a fundamental task in computer vision applications, especially in remote sensing. Only some research proposes gradient-based XAI algorithms for image segmentation. This paper adapts the recent gradient-free Sobol XAI method for semantic segmentation. To measure the performance of the Sobol method for segmentation, we propose a quantitative XAI evaluation method based on a learnable noise model. The main objective of this model is to induce noise on the explanation maps, where a higher induced noise signifies low accuracy and vice versa. A benchmark analysis is conducted to evaluate and compare the performances of three XAI methods, Seg-Grad-CAM, Seg-Grad-CAM++ and Seg-Sobol, using the proposed noise-based evaluation technique. This constitutes the first attempt to run and evaluate XAI methods using high-resolution satellite images. Our code is publicly available at GitHub. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) change is a major driver of environmental change in District Bahawalnagar, Punjab. LULC change can lead to changes in soil quality, water availability, and climate, all of which can affect crop yields. LULC change can also lead to the loss of agricultural land, forest land, water bodies, and an increment in urban land that causes climate change and affects the agricultural sector. The study area showed that in the last thirty years, the population increased, built-up land increased, and agricultural land dropped by 30%. The present status of knowledge is reviewed in this paper on the impact of LULC on agricultural production in District Bahawalnagar. The conversion of agricultural land to urban development in District Bahawalnagar has led to a decline in crop yields of an average of 10%. The production of wheat and rice, the two major crops grown in District Bahawalnagar, is influenced by LULC changes. This study also found that the loss of agricultural land has resulted in an increase in soil salinity, which has further reduced crop yields. The detrimental effects of LULC change on agricultural output in District Bahawalnagar can be mitigated by adopting sustainable land management practices. These practices include reforestation, conservation agriculture, and water conservation. The government of Pakistan can also play a role in mitigating the negative impacts of LULC change on agricultural production by developing and implementing land use plans that protect agricultural land from conversion to other uses. More research is required to fully comprehend the effects of LULC and develop effective management strategies. However, LULC is a major challenge that must be addressed if we are to ensure food security in the future. [ABSTRACT FROM AUTHOR]

The Amery Ice Shelf is a major source of sea ice, whose production is linked to the global climate. In 2019, a collapse event occurred in the Amery Ice Shelf; sea ice production before and during this collapse needs to be studied. In this study, polynyas in the Amery Ice Shelf were identified according to ice thickness, and sea ice production was obtained by calculating the heat flux during winter (March–October) in 2013–2020. It was found that the sea ice production in the polynyas fluctuated greatly, and the maximum annual ice production occurred in 2018, which reached 225.4 km3. As for the collapse event in 2019, it is assumed that it may have exacerbated the volatility and instability of sea ice production. [ABSTRACT FROM AUTHOR]

This paper presents a technique for mapping the vegetation cover of mountainous areas based on seasonal satellite data from Landsat-OLI 8, using information about vegetation growth conditions. This mapping is based on the creation of a layer of relatively homogeneous areas in terms of relief and climate. Training samples for the classification of images were formed within these areas. Satellite images were classified using the maximum likelihood method. The created map reflects the spatial distribution of 9 classes of forest vegetation and 10 classes of non-forest vegetation. [ABSTRACT FROM AUTHOR]

Accurate computations of the radiance in the gaseous absorption bands typically require fine wavelength steps. In this paper, a fast technique for computing a radiance spectrum in the wavelength region of 400–2500 nm is proposed. Our approach draws inspiration from the established k-correlation distribution model, with k denoting the absorption coefficient. However, our method expands upon this by considering both the direct transmittance and the scattering coefficient as predictors. At selected spectral points, the full radiative transfer simulations are performed, and the mathematical relation between a predictor and the radiance is established. Then, the radiance is restored on a fine wavelength grid. This approach can be used to enhance the accuracy of the convolved spectrum computations based on precomputed approximately monochromatic lookup tables and reduce the size of lookup tables. Numerical analysis demonstrates the method's applicability to scenarios characterized by aerosol optical thicknesses not exceeding two. [ABSTRACT FROM AUTHOR]

Live Fuel Moisture Content (LFMC) describes the amount of water present in any type of vegetation and helps quantify the amount of fuel available in a wildfire. In this paper, a multivariate linear regression model was built to estimate the LFMC of the weighted average of all shrub-type species present, using the fraction of canopy cover (FCC) of each forest species as weights. Sample training was conducted with field data obtained during the fire season of the years 2019, 2020 and 2021 in 15 plots of a Mediterranean area where vegetation composed of the shrub-type species dominates. Different spectral indices extracted from Sentinel-2 together with the mean surface temperature, the accumulated precipitation and the seasonal parameters were considered as predictors. The results were compared with the extrapolation of another model trained with field data collected in the year 2019. [ABSTRACT FROM AUTHOR]

Methane is the second most important anthropogenic greenhouse gas, whose emissions need to be mitigated to curb global warming. There is a large uncertainty about its point source, but thanks to a new generation of high-spatial-resolution satellites, this situation is changing drastically, revealing thousands of emission point sources worldwide. In this paper, several hotspot areas are mapped, looking for methane emission point sources with different types of high-resolution satellites. Our results demonstrate the potential of satellite remote sensing to reveal methane emission point sources in different scenarios. [ABSTRACT FROM AUTHOR]

To achieve safety, accessibility, and technical compatibility in the operation of rail systems, technical specifications for interoperability are adopted to be complied with. This paper presents a study of the essential interoperability requirement ʺStructure Gaugeʺ within the framework of the regulations applicable to new, upgraded, or renewed infrastructure in the rail system within the European Union. [ABSTRACT FROM AUTHOR]

The paper presents the results relying on 12 experimental M2 water profiles observed in a flume with emergent stems in a square arrangement. The authors used a recently proposed approach to determine the drag coefficients in the flow direction. Since these showed a behavior difficult to interpret, the authors first computed for each profile the best value of the Manning coefficient for the profile simulation and then the drag coefficients. With the help of a classical dimensional analysis, a regression equation was found to predict the drag coefficients, and these were used to simulate the observed profiles with good results. [ABSTRACT FROM AUTHOR]

The digitalisation of water supply systems is essential to support crucial activities, ranging from the hydraulic modelling and calibration of distribution systems to the optimal planning of renewal measures. Digital Twins is the current challenge that aims to join accurate hydraulic models with Artificial Intelligence (AI) tools that employ a faithful digital copy of the original system and smart infrastructures. Thus, the aqueduct Digital Twin model is intended to be the core of future integrated water management systems to support efficient and sustainable management. In this paper, a process of implementing a digital model for a mountain aqueduct with its associated applications is proposed. [ABSTRACT FROM AUTHOR]

In this paper, EXTRASTAR software (EXTRemes Abacus for STAtistical Regionalization) is illustrated, which provides a general framework for extreme value analysis, specifically for Annual Maxima (AM) time series. The proposed methodology represents a useful and quick approach to carry out onsite and regional statistical investigations of extreme events, which is also suitable for climate change scenarios. EXTRASTAR was tested with the AM time series at daily resolutions for the entire Italian rain gauge network by implementing EV1 (Extreme Value Type I), GEV (Generalized Extreme Value) and TCEV (Two-Components Extreme Value) probability distributions. [ABSTRACT FROM AUTHOR]

Water distribution systems (WDSs) are considered inherently vulnerable to intentional and accidental contamination. The installation of a water quality sensor system (WQSS) represents a valid strategy for securing WDSs against contamination. This paper proposes a novel topological-based methodological approach for defining a suitable WQSS, by also addressing both the computational intractability of the problem as the size of the WDS increases and the assumption that sensors are wrongly modelled and positioned on WDS nodes, rather than along system pipes. The method is tested on the WDS of Parete (Italy) and the results demonstrate the significant detection efficiency of the defined WQSS. [ABSTRACT FROM AUTHOR]

Water is a multi-purpose resource, and almost all of these purposes are of essential importance for our contemporary society. The most important water use nowadays is in food and energy production, and these two uses are essential for the development of civilization. The products of the food and energy industries are valued on the market. In the case of sufficiency of both food and energy supplies, it is possible to evaluate alternative uses of water by a combination of the economic models utilized in the food and energy industries. This paper aims to investigate the alternative use of water in plant and energy production by modeling the relation between them. The additional investigation is based on including a consideration of land as consolidated and non-consolidated area. [ABSTRACT FROM AUTHOR]

Access to clean water is vital to human health, communities' development, and economic prosperity. Nowadays, more than 10% of the global population lacks access to clean water. This has created the so-called "water crisis" and set, as a key goal for communities, the protection and optimization of water usage. In this direction, technological concepts, like internet-of-things (IoT) and artificial intelligence (AI) assisted recommendation, which enables real-time monitoring and efficient exploitation of water resources, have been identified as fundamental pillars of the solution. This inspired the design, development, and testing of several breakthrough concepts in this domain; however, to the best of our knowledge, none of them lies in real-time intelligent exploitation of mixing clean and recycled water for crops irrigation. Motivated by this, in this paper, we present a holistic next-generation IoT approach, namely AUGEIAS, for optimal clean and treated wastewater usage in precision agriculture. In more detail, we present AUGEIAS architecture and explain its features and functionalities. Moreover, the AUGEIAS intelligent mechanisms that allow accurate crops water demand and weather prediction, as well as optimization, are documented. Finally, the front end of AUGEIAS platform is presented. [ABSTRACT FROM AUTHOR]

In the EU Drinking Water Directive (EU DWD) 2020/2184, the approach of the "water safety plan", as suggested by the WHO, has been individuated as the correct tool for water utilities to ensure safe, drinkable water. The ADRIATIC-IONIAN Interreg project, MUHA—Multi-Hazard Framework for water related risks management, has become the necessity to effectively link different aspects of the water related risks management in an improved response system, integrating the functions of analysis, forecasting, and incident command systems. This paper aims to describe the rationale of the MUHA project and present some of the main outcomes. [ABSTRACT FROM AUTHOR]