Search Results (1,068)

The latest climate change predictions indicate that the sea level will accelerate in the coming decades as a direct consequence of global warming. This is expected to seriously threaten low-lying coastal areas worldwide, resulting in severe coastal flooding with significant socio-economic impacts, leading to the loss of coastal settlements, exploitable land, and natural ecosystems. The main objective of this study is to provide a first-order preliminary estimation of potential inundation extents along the northern coastline of the Amvrakikos Gulf, a deltaic complex formed by the Arachthos, Louros, and Vouvos rivers in Western Greece, resulting from long-term sea-level rise induced by climate change, using the integrated Bathtub and Hydraulic Connectivity (HC) inundation method. A 2 m resolution Digital Elevation Model (DEM) was used, along with local long-term sea-level projections, for the years 2050 and 2100. Additionally, subsidence rates due to the compaction of deltaic sediments were taken into account. To assess the area’s proneness to inundation caused or enhanced by sea-level rise, the extent of each land cover type, the Natura 2000 Network protected area, the settlements, the total length of the road network, and the cultural assets located within the inundation zones under each climate change scenario were considered. The analysis revealed that under the optimistic SSP1-1.9 scenario of the Intergovernmental Panel on Climate Change (IPCC), areas of 40.81 km² (min 20.34 km², max 63.55 km²) and 69.10 km² (min 41.75 km², max 88.02 km²) could potentially be inundated by 2050 and 2100, respectively. Under the pessimistic SSP5-8.5 scenario, the inundation zone expands to 42.56 km² (min 37.05 km², max 66.31 km²) by 2050 and 84.55 km² (min 67.54 km², max 116.86 km²) by 2100, affecting a significant portion of ecologically valuable wetlands and water bodies within the Natura 2000 protected area. Specifically, the inundated Natura 2000 area is projected to range from 37.77 km² (min 20.30 km², max 46.82 km²) by 2050 to 50.74 km² (min 38.71 km², max 62.84 km²) by 2100 under the SSP1-1.9 scenario, and from 39.34 km² (min 34.53 km², max 49.09 km²) by 2050 to 60.48 km² (min 49.73 km², max 82.5 km²) by 2100 under the SSP5-8.5 scenario. Four settlements with a total population of approximately 800 people, as well as 32 economic facilities most of which operate in the secondary and tertiary sectors and are small to medium-sized economic units, such as olive mills, farms, gas stations, spare parts stores, construction companies, and food service establishments, are expected to experience significant exposure to coastal flooding and operational disruptions in the near future due to sea-level rise. Full article

Image-based geo-localization is a challenging task that aims to determine the geographic location of a ground-level query image captured by an Unmanned Ground Vehicle (UGV) by matching it to geo-tagged nadir-view (top-down) images from an Unmanned Aerial Vehicle (UAV) stored in a reference database. The challenge comes from the perspective inconsistency between matched objects. In this work, we propose a novel metric learning scheme for hard exemplar mining to improve the performance of cross-view geo-localization. Specifically, we introduce a Dynamic Online Cross-Batch (DOCB) hard exemplar mining scheme that solves the problem of the lack of hard exemplars in mini-batches in the middle and late stages of training, which leads to training stagnation. It mines cross-batch hard negative exemplars according to the current network state and reloads them into the network to make the gradient of negative exemplars participating in back-propagation. Since the feature representation of cross-batch negative examples adapts to the current network state, the triplet loss calculation becomes more accurate. Compared with methods only considering the gradient of anchors and positives, adding the gradient of negative exemplars helps us to obtain the correct gradient direction. Therefore, our DOCB scheme can better guide the network to learn valuable metric information. Moreover, we design a simple Siamese-like network called multi-scale feature aggregation (MSFA), which can generate multi-scale feature aggregation by learning and fusing multiple local spatial embeddings. The experimental results demonstrate that our DOCB scheme and MSFA network achieve an accuracy of 95.78% on the CVUSA dataset and 86.34% on the CVACT_val dataset, which outperforms those of other existing methods in the field. Full article

The retail sector is the most demand-sensitive echelon in the supply chain, where non-moving items accumulate and become dead stock. Existing inventory management studies focus on fast-moving products and income generation. This paper focuses on dead stock management and proposes a mixed strategy solution using a pure integer non-linear programming model that minimizes the dead stock management cost of a retail chain operator. The number of products and volume of product-related data in a retail chain system require big data analysis to ensure sustainable inventory practices that reduce waste generated from dead stock inventory. Through hypothetical data sets, the 3-store, 10-product run showed that discount percentage, expected sales success probability of a product in a store location, and disposition of unsold products were the main drivers of the decisions made by the model. The most significant cost contributors arising from these decisions were the unrecovered product cost (UPC), disposed product cost (PC), and salvage value from the successful sale of dead stock. Inventory managers must balance the effect on these cost components when they choose the strategies to use in managing dead stock. Full article

Transition to electric vehicles has accelerated in diverse consumer sectors all over the world. Electric School Buses (ESBs) are a particular area of interest due to their environmental and financial potential benefits, including Vehicle-to-Grid (V2G) synergies. Storing electricity in times of lower demand to supply the grid at optimal times can provide significant sustainability benefits, among them a reduction in new generation capacity and financial revenue for battery owners. ESBs, with their high-capacity batteries, have significant potential to supply the grid in V2G systems. There are more than half a million school buses in the US, with a wide geographical distribution, which have significant idle times during school days and holidays. This presents very attractive investment possibilities, providing school districts with additional revenue and supplying local communities with sustainable electricity at high-demand times. This study develops a framework to financially evaluate sustainability of ESB V2G schemes in the US. It applies data analytics, GIS, and Business Intelligence to integrate and assess publicly available data to provide stakeholders with decision-making tools in selecting optimal locations and operation times for these projects. Results indicate that revenue for these projects is significant in most schools, with some locations generating very high revenue potential. Geospatial analysis for most locations and time frames indicates very promising results, with schools potentially receiving significant income from these systems. The framework provides, therefore, relevant information for stakeholders to make sustainable decisions on the development of these projects. Full article

Large language models and retrieval-augmented generation (RAG) are widely applied in knowledge question-answering tasks. However, in knowledge-intensive domains such as agriculture, hallucination and insufficient retrieval accuracy remain challenging. To address these issues, we propose Sem-RAG, a corn planting knowledge question-answering algorithm based on fine-grained semantic retrieval enhancement. Unlike standard NaiveRAG, which retrieves only fixed-length text chunks, and GraphRAG, which relies solely on graph node connections, Sem-RAG introduces a dual-store retrieval mechanism. It constructs both a surface semantic store (chunk-level embeddings) and a fine-grained semantic store derived from Leiden-based community summaries. These community summaries do not merely shorten contexts; instead, they provide thematic-level semantic aggregation across document chunks, thereby enhancing semantic coverage and reducing noise. During retrieval, user queries are matched against the surface store to locate relevant chunks and simultaneously linked to corresponding thematic summaries in the fine-grained store, ensuring that both local details and higher-level associations are leveraged. We evaluated Sem-RAG on the corn knowledge question-answering dataset CornData. The algorithm achieved Answer-C, Answer-R, and CR scores of 94.6%, 84.6%, and 70.4%, respectively, which were 2.6%, 1.7%, and 1.6% higher than those of traditional NaiveRAG. These results demonstrate that Sem-RAG materially improves the quality and reliability of agricultural knowledge Q&A by combining dual-store retrieval with community-level semantic aggregation. Full article

In the event of power outages caused by natural disasters, accidents, or other emergencies, outdoor emergency lighting systems play a critical role in providing illumination to maintain spatial orientation, facilitate evacuation procedures, and help individuals avoid hazardous areas or locate safe shelters. Compared to traditional lighting technologies, LED-based outdoor emergency lighting offers several advantages, including compact size, long operational lifespan, low energy consumption, high safety, resistance to breakage, and the absence of chemical residue or pollution. These characteristics align with contemporary trends in environmental sustainability and energy efficiency. This study proposes a novel LED driver circuit architecture for outdoor emergency lighting applications. The primary circuit topology is based on an improved buck-boost converter integrated with a flyback converter, forming a hybrid buck-boost-flyback configuration. The proposed circuit is capable of recycling the energy stored in the transformer’s leakage inductance, thereby enhancing overall power conversion efficiency. A 12 W (20 V/0.6 A) prototype LED driver circuit was designed and implemented to validate the performance of the proposed system. Experimental measurements, including waveform analysis and efficiency evaluation, demonstrate that the driver circuit achieves a high efficiency exceeding 91%. These results confirm the practical feasibility and effectiveness of the proposed electronic driver for LED-based outdoor emergency lighting applications. Full article

In response to the IMO’s decarbonisation strategy, hydrogen—especially green hydrogen—becomes a promising alternative fuel in shipping. This article provides a comparative analysis of two hydrogen propulsion technologies suitable for a service vessel (SOV) operating in offshore wind farms: hydrogen fuel cells and hydrogen-powered internal combustion engines. This study focuses on the use of liquid hydrogen (LH₂) stored in cryogenic tanks and fuel cells as an alternative to the previously considered solution based on compressed hydrogen (CH₂) stored in high-pressure cylinders (700 bar) and internal combustion engines. The research aims to examine the feasibility of a fully hydrogen-powered SOV energy system. The analyses showed that the use of liquefied hydrogen in SOVs leads to the threefold reduction in tank volume (1001 m³ LH₂ vs. 3198 m³ CH₂) and the weight of the storage system (243 t vs. 647 t). Despite this, neither of the technologies provides the expected 2-week autonomy of SOVs. LH₂ storage allows for a maximum of 10 days of operation, which is still an improvement over the CH₂ gas variant (3 days). The main reason for this is that hydrogen tanks can only be located on the open deck. Although hydrogen fuel cells take up on average 13.7% more space than internal combustion engines, they are lower (by an average of 24.3%) and weigh less (by an average of 50.6%), and their modular design facilitates optimal arrangement in the engine room. In addition, the elimination of the exhaust system and lubrication simplifies the engine room layout, reducing its weight and space requirements. Most importantly, however, the use of fuel cells eliminates exhaust gas emissions into the atmosphere. Full article

The primary objective of this study is to assess the techno-economic feasibility of an innovative solar energy generation system with a rainwater collection feature to generate electrical energy and meet irrigation needs in agriculture. The proposed system is designed for an agricultural area (Gonyeli, North Cyprus) with high solar potential and limited rainfall. In the present study, global rainfall datasets are utilized to assess the potential of rainwater harvesting at the selected site. Due to the lack of the measured rainfall data at the selected site, the accuracy of rainfall of nine global reanalysis and analysis datasets (CHIRPS, CFSR, ERA5-LAND, ERA5, ERA5-AG, MERRA2, NOAA CPC CMORPH, NOAA CPC DAILY GLOBAL, and TerraClimate) are evaluated by using data from ground-based observations collected from the Meteorological Department located in Lefkoşa, Northern Cyprus from 1981 to 2023. The results demonstrate that ERA5 outperformed the other datasets, yielding a high R-squared value along with a low mean absolute error (MAE) and root mean square error (RMSE). Based on the best dataset, the potential of the rainwater harvesting system is estimated by analyzing the monthly and seasonal rainfall patterns utilizing 65 different probability distribution functions for the first time. Three goodness-of-fit tests are utilized to identify the best-fit probability distribution. The results show that the Johnson and Wakeby SB distributions outperform the other models in terms of fitting accuracy. Additionally, the results indicate that the rainwater harvesting system could supply between 31% and 38% of the building’s annual irrigation water demand (204 m³/year) based on average daily rainfall and between 285% and 346% based on maximum daily rainfall. Accordingly, the system might be able to collect a lot more water than is needed for irrigation, possibly producing an excess that could be stored for non-potable uses during periods of heavy rainfall. Furthermore, the techno-economic feasibility of the proposed system is evaluated using RETScreen software (version 9.1, 2023). The results show that household energy needs can be met by the proposed photovoltaic system, and the excess energy is transferred to the grid. Furthermore, the cash flow indicates that the investor can expect a return on investment from the proposed PV system within 2.4 years. Consequently, the findings demonstrate the significance of this system for promoting resource sustainability and climate change adaptation. Besides, the developed system can also help reduce environmental impact and enhance resilience in areas that rely on water and electricity. Full article

Electronic devices with polyurethane electronic packaging have been stored in Chinese tropical marine atmosphere environments for 10 years. The long-term natural aging mechanism was studied by comparing the appearance inspection, molecular structure, elemental content, and chemical functional groups of the surface and interior of polyurethane electronic potting. The results indicated that, despite evident chemical aging and physical changes in the encapsulant material, it continued to effectively protect the internal electronic devices, maintaining their performance within an acceptable range. The interior polyurethane potting of electronic devices was white, but the surface turned yellow with noticeable color change. On the surface, the content of tolylene diisocyanate was greatly decreased. The peak heights of the internal carbamate groups located at 1708 cm⁻¹ and 1529 cm⁻¹ were significantly higher than those at the surface. In addition, the internal C element content for the carbamate group at 289.5 eV was higher than that of the surface. It can be inferred that, under ambient temperature and trace oxygen conditions, the urethane groups on the polyurethane electronic potting surface undergo aging reactions. These groups slowly oxidize into the quinoid structure of the chromophore, causing the surface to turn yellow. Despite this discoloration, the potting still protects electronic devices. Therefore, polyurethane electronic potting is ideal for the long-term sealed storage of electronic devices. Full article

Autonomous ground vehicles (AGVs) are of major importance in exploration missions since they perform difficult tasks in changing or harmful environments. Mapping and exploration is crucial in hazardous areas, or areas inaccessible to humans, demanding autonomous navigation. This paper proposes a lightweight, low-cost AGV platform, which will be used in resource-constrained situations and aimed at scenarios like exploration missions (e.g., cave interiors, biohazard environments, or fire-stricken buildings) where there are serious security threats to humans. The proposed system relies on simple ultrasonic sensors when navigating and applied traversal algorithms (e.g., BFS, DFS, or $A^{*}$) during path planning. Since on-board microcomputers have limited memory, the traversal data and direction decisions are stored in a file located on an SD card, which supports long-term, energy-saving navigation and risk-free backtracking. A fish-eye camera set on a servo motor captures three photos ordered from left to right and stores them on the SD card for further off-line processing, integrating each frame into a low-frame-rate video. Moreover, when the battery level falls below 50%, the exploration path does not extend further and the AGV returns to the base station, thus combining a secure backtracking procedure with energy-efficient decisions. The resultant platform is low-cost, modular, and efficient at augmenting; thus it is suitable for exploring missions with applications in search and rescue, educational robotics, and real-time applications in low-infrastructure environments. Full article

Currently, supermarket-run shopping centers, known as “Big Marts,” monitor sales information for every single item in order to predict potential customer demand and update inventory management. Anomalies and general trends are commonly discovered through data warehouse mining using a range of machine learning techniques, and businesses such as Big Marts can use the obtained data to forecast future sales volumes. Compared to other research publications, this one forecasted sales with higher accuracy using machine learning models including KNN (K Nearest Neighbors), Naïve Bayes, and Random Forest. To adapt the proposed business model to anticipated outcomes, the sales forecast is based on Big Mart sales for various stores. Using different machine learning methods, the data that is produced may then be used to predict potential sales volumes for retailers such as Big Marts. The projected cost of the suggested system includes the following identifiers: price, outlet, and outlet location. In order to facilitate data-driven decision-making in retail operations and help Big Marts optimize their business models and effectively satisfy anticipated demand, this study emphasizes the importance of incorporating cutting-edge machine learning approaches. Full article

This study presents a comparative analysis of the physicochemical, microbiological, and microstructural characteristics of feed products subjected to electron beam irradiation using the ILU-10 accelerator, as well as non-irradiated samples stored under suboptimal conditions. Feed product samples were obtained from a flour and feed manufacturing facility located in the city of Semey, Abai region. For each type of feed product, 20 samples were irradiated at doses of 3, 6, and 9 kGy, while another 20 samples served as a non-irradiated control group. All samples were stored in conditions that were unfavorable for long-term preservation. After one month, microbiological parameters, mycotoxin content, changes in acidity (pH), and microstructure were assessed using standard analytical methods. This study revealed a significant reduction in the number of microorganisms and preservation of key physicochemical properties of the feed products after one month of storage when irradiated at a dose of 9 kGy. These findings suggest that electron beam treatment can serve as an effective method for feed preservation in cases where storage conditions do not meet regulatory standards. However, further research is required to explore alternative approaches and gain a deeper understanding of the potential applications of electron beam technology in compound feed storage systems. Full article

This paper presents a flexible approach to a multipurpose, heterogeneous archive and data management system model that merges the robustness of legacy grid-based technologies with modern cloud and edge computing paradigms. It leverages innovations driven by big data, IoT, AI, and machine learning to create an adaptive data storage and processing framework. In today’s digital age, where data are the new intangible gold, the “gold rush” lies in managing and storing massive datasets effectively—especially when these data serve governmental or commercial purposes, raising concerns about privacy and data misuse by third-party aggregators. Astronomical data, in particular, require this same thoughtful approach. Scientific discovery increasingly depends on efficient extraction and processing of large datasets. Distributed archival models, unlike centralized warehouses, offer scalability by allowing data to be accessed and processed across locations via cloud services. Incorporating edge computing further enables real-time access with reduced latency. Major astronomical projects must also avoid common single points of failure (SPOFs), often resulting from suboptimal technological choices driven by collaboration politics or In-Kind Contributions (IKCs). These missteps can hinder innovation and long-term project success. The principal goal of this work is to outline best practices in archival and data management projects—from policy development and task planning to use-case definition and implementation. Only after these steps can a coherent selection of hardware, software, or virtual environments be made. The proposed model—CTAARCHS (Cloud-based Technologies for Astronomical Archiving Research Contents and Handling Systems)—is an open-source, multidisciplinary platform supporting big data needs in astronomy. It promotes broad institutional collaboration, offering code repositories and sample data for immediate use. Full article

Domestic water heaters traditionally use natural gas or electric resistance to heat stored water. A gas water heater relies on a non-renewable resource, while an electric water heater might rely on electricity generated by a non-renewable resource. This study analyzes the performance of an electric water heater featuring a novel heating element design based on a positive temperature coefficient (PTC) material powered directly by solar photovoltaic (PV) modules in a northern latitude installation. The project analyzes the operation of two different design temperatures of the PTC heating elements (50 °C, and 70 °C) when fed by three solar PV panels during the spring in the high-latitude location of Anchorage, Alaska (61.2° N). Our results show that both design temperatures of the PTC heating elements are able to achieve self-regulation at a sufficient and safe operating temperature for a domestic use case. Analysis of water heater performance directly connected to PV power showed that the PTC-equipped water heater had a limited period of heating when sufficient solar irradiance is available. Because of this, restrictive use of the water heater might be necessary during periods of non-daylight hours to preserve hot water in an insulated tank. However, this PV-to-PTC setup could be effectively used in industrial, commercial, and research settings. Full article

Green methane has been deemed as a low CO₂ emission gas. The cost to produce green methane varies considerably by location and technologies (USD 15/GJ to USD 60/GJ). Although green methane has higher price than the average price of market natural gas in Australia (USD 11–40/GJ between 2019 and 2023), it is currently significantly lower than the production cost for green hydrogen, with the levelized cost of hydrogen (LCOH) at USD 6.6/kg. Green methane production can utilise different processing steps. Separation processes require energy to separate CO₂, with the remaining issue of safely storing the captured CO₂ or venting it to the atmosphere. Direct catalytic biogas methanation (e-methane) does not require the separation of CO₂ but converts CO₂ together with CH₄ to a purer stream of CH₄, converting the CO₂ to an energy product_. E-methane consequently can be considered as an alternative energy carrier to store off-peak electricity from the grid, commonly called power-to-gas technology (P2G). Furthermore, injecting green methane into gas pipelines does not require significant gas infrastructure upgrading and has no upper limit, as it is compatible with natural gas. Here we review the status of biogas and direct green methane production from biogas around the world and assess technologies that are used to produce green methane via separation or direct catalytic conversion. We evaluate their techno-economic assessment results, with a particular focus on e-methane, identifying the opportunity as a pathway to supply low-emission gas with the perspective of a future e-methane industry within Australia. Full article