Search Results (5,427)

Grain filling is a vital factor influencing both rice grain yield and quality, yet its underlying mechanisms remain poorly understood. In this study, we perform a functional analysis of the grain-filling defective mutant pex1 in rice. pex1 plants produce seeds that are floury, thick-branched, and exhibit a significantly slower grain-filling rate compared to the wild type. Further analysis reveals that the pex1 mutants accumulated more starch in the pericarp but exhibited a defect in starch accumulation in the endosperm during grain filling, indicating an impaired transport of photosynthetic products from the pericarp to the endosperm. Cells within the nucellar projection in the pex1 mutant appear irregular and loose loosely arranged, consistent with defective transfer of assimilates. Expression analysis reveals a downregulation of key grain-filling genes during the filling phase in the pex1 mutant compared to the wild type, which correlates with the reduced grain-filling rate. Subcellular localization suggests that OsPEX1 is associated with the endoplasmic reticulum. Our findings demonstrate that OsPEX1 plays a crucial role in grain filling. Full article

With the advancement of 3D sensing technologies, point clouds have become a key data format in the construction industry, supporting tasks such as as-built verification and BIM integration. However, robust and accurate edge detection from unstructured point cloud data remains a critical challenge, particularly in architectural environments characterized by structured geometry and variable noise conditions. This study presents a comparative evaluation of two classical edge detection algorithms—Random Sample Consensus (RANSAC) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)—applied to terrestrial laser-scanned point cloud data of eight rectangular structural columns. After preprocessing with the Statistical Outlier Removal (SOR) algorithm, the algorithms were evaluated using four performance criteria: edge detection quality, BIM-based geometric accuracy (via Cloud-to-Cloud distance), robustness to noise, and density-based performance. Results show that RANSAC consistently achieved higher geometric fidelity and stable detection across varying conditions, while DBSCAN showed greater resilience to residual noise and flexibility under low-density scenarios. Although DBSCAN occasionally outperformed RANSAC in local accuracy, it tended to over-segment edges in high-density regions. These findings underscore the importance of selecting algorithms based on data characteristics and project goals. This study establishes a reproducible framework for classical edge detection in architectural point cloud processing and supports future integration with BIM-based quality control systems. Full article

Islands and island communities are often perceived as homogenous in mainstream discourse. While many islands share characteristics, such as smallness or isolation, these are experienced differently across and within island contexts and intersect with spatial, socio-cultural, political, and economic landscapes. The concept of islandness is developed to both understand shared island characteristics and their differences across places, communities, and situations. This makes islandness highly relevant to discussions of green transitions as it highlights the need to examine the diverse, intersecting, and local realities that might interfere with green citizenship. However, analytical approaches to islandness are limited, with few spatial, scalable, and transferable frameworks available. This paper argues that spatial microsimulation offers a productive way to engage with islandness using the case of climate change and environmental attitudes across Iceland. We present the SimIceland model, developed within the EU-funded project PHOENIX: The Rise of Citizens’ Voices for a Greener Europe. The model is developed to better understand how Iceland’s citizens’ feel about climate change by taking socio-cultural, environmental, and different geographical administrative regions into account. Through a simple example of an analytical demonstration, we show how this model can support a deeper understanding of islandness in the specific context of climate attitudes in Iceland. Furthermore, we discuss how the model can contribute to public participation initiatives. The model and data are open access, and we conclude by inviting further developments and the use of spatial microsimulation to explore islandness, green citizenship, and participatory approaches to sustainability in island contexts. Full article

This paper presents the research outcomes of the Golden Seal project, which addresses the omnipresent issue of plastic pollution in coastal areas while enhancing their touristic value through the deployment of Internet of Things (IoT) technologies integrated into a gamified recycling framework. The developed system employs an IoT-enabled Wireless Sensor Network (WSN) to systematically collect, transmit, and analyze environmental data. A centralized, cloud-based platform supports real-time monitoring and data integration from Unmanned Aerial and Surface Vehicles (UAV and USV) equipped with sensors and high-resolution cameras. The system also introduces the Beach Cleanliness Index (BCI), a composite indicator that integrates quantitative environmental metrics with user-generated feedback to assess coastal cleanliness in real time. A key innovation of the project’s architecture is the incorporation of a Serious Game (SG), designed to foster public awareness and encourage active participation by local communities and municipal authorities in sustainable waste management practices. Pilot implementations were conducted at selected sites characterized by high tourism activity and accessibility. The results demonstrated the system’s effectiveness in detecting and classifying plastic waste in both coastal and terrestrial settings, while also validating the potential of the Golden Seal initiative to promote sustainable tourism and support marine ecosystem protection. Full article

The Akkuyu Nuclear Power Plant (NPP) is framed as a flagship of Türkiye’s national low-carbon transition. This study examines how domestic economic actors perceive the project’s socio-economic and environmental impacts, and how those perceptions align with—or diverge from—official assessments and the United Nations Sustainable Development Goals. Using a qualitative phenomenological approach, the research draws on 28 semi-structured interviews with members of the Silifke Chamber of Commerce and Industry Council. This lens captures how locally embedded businesses read the project’s risks and rewards in real time. Four themes stand out. First, respondents see a clear economic uptick—but one that feels time-bound and vulnerable to the project cycle. Second, many feel excluded from decision-making; as a result, their support remains conditional rather than open-ended. Third, participants describe environmental signals as ambiguous, paired with genuine ecological concern. Fourth, skepticism about governance intertwines with sovereignty anxieties, particularly around foreign ownership and control. Overall, while short-term economic benefits are widely acknowledged, support is tempered by procedural exclusion, environmental worry, and distrust of foreign control. Conceptually, the study contributes to energy-justice scholarship by elevating sovereignty as an additional dimension of justice and by highlighting the link between being shut out of processes and perceiving higher environmental risk. Policy implications follow directly: create robust, domestic communication channels; strengthen participatory governance so local actors have a real voice; and embed nuclear projects within regional development strategies so economic gains are durable and broadly shared. Full article

This paper outlines the implementation and core results of a combined archaeological, historical, and ethnographic study of the histories of well construction and water management among Boran, Gabra, and Rendille pastoralists in arid and semi-arid areas of Northern Kenya and Southern Ethiopia. Co-developed with representatives from different local communities from the outset, this project sought to document the spatial distribution of different types of hand-dug wells found across the study areas, their associated oral histories and, if possible, establish through archaeological means their likely date of initial construction. Concurrent with addressing these academic objectives, this project aimed to train a cohort of local heritage stewards in archaeological, historical, and ethnographic data collection and interpretation, equipping them with the necessary skills to monitor sites of heritage value and further record additional elements of the tangible and intangible heritage of the study areas. This paper discusses the archaeological work that the community trainees participated in, the strategies developed with them to create wider awareness of this heritage, and its implications for identifying ways to ”weather” climate change in the future. Full article

The challenges of climate change require in-depth attention and targeted strategies for specific sectors, such as energy and mining. Within the mining sector, climate change imposes constraints on the sustainable extraction of minerals, thereby heightening the importance of several minerals in addressing these challenges. Chile emerges as a pivotal nation due to its substantial reserves of copper, lithium, cobalt, nickel, and graphite, which are essential for energy transition and decarbonization processes. Consequently, Chile must foster gradual processes to establish competitive advantages based on technological and innovative capabilities, thus projecting a competitive and sustainable mining industry. This endeavor should be accompanied by enhancements in policies and instruments to guide development, expanding local value creation. This study examines the global challenges faced by the mining sector in the context of the energy transition and evaluates Chile’s response through an assessment of public policies for mining development. It provides an analysis of the scope of various public policy instruments to establish the link between international agreements and development opportunities, subsequently proposing a series of indicators to assess policy progress. To this end, the Environmental Observatory of Mining Projects is developing indicators to evaluate compliance with these policies. In addressing the nation’s challenges related to green and sustainable mining, 20 indicators have been developed in collaboration with civil society and public and private stakeholders through a design thinking process. These indicators enable the evaluation of aspects such as air quality, water quality, and the surface area affected by tailings, among others. The initial section of the document outlines the global challenges in achieving the carbon neutrality goals set by the IPCC. The subsequent section elaborates on the theoretical framework of the research, addressing theories of economic development and sustainability, public policy approaches considered in recent years, as well as the governance of mining development, with an emphasis on its capacity to articulate industrial policies, promote environmental sustainability, and foster technological innovation. The third section details the research methodology and framework of the study. This study examines how Chile’s mining policies align with the global energy transition. Amid growing demand for critical minerals, climate change, and decarbonization, Chile faces both opportunities and socio-environmental risks. Addressing these challenges requires integrated sustainability strategies and an active state role to ensure inclusive, environmentally responsible, and innovation-driven mining development. Full article

Climate change significantly affects hydrological processes in forest ecosystems, particularly in sensitive coastal areas such as the Croatan National Forest (CNF) in North Carolina. Accurate projections of future water yield are essential for managing agriculture, forestry, and natural ecosystems. This study investigates the potential impacts of climate change on water yield using a combination of statistical downscaling and machine learning. Two downscaling methods, a Statistical DownScaling Model (SDSM) and Multivariate Adaptive Constructed Analogs (MACA), were evaluated, with the SDSM providing superior performance for local climate conditions. To improve precipitation input accuracy, twenty ensemble scenarios were generated using the SDSM, and various machine learning algorithms were applied to identify the optimal ensemble. Among these, the Extreme Gradient Boosting (XGBoost) algorithm exhibited the lowest error and was selected for producing high-quality precipitation time series. This methodology is integrated into the MIDAS (Machine Learning-Based Integration of Downscaled Projections for Accurate Simulation) approach, which leverages machine learning to enhance climate input precision and reduce uncertainty in hydrological modeling. Water yield was simulated over the period 1961–2060, combining observed and projected climate data to capture both historical trends and future changes. The results show that combining statistical downscaling with machine learning algorithms can help improve the accuracy of water yield projections under climate change and be useful for water resource planning, forest management, and climate adaptation. Full article

Deterministic sea wave reconstruction techniques are critical for enhancing maritime safety and disaster warnings. Coherent radar remote sensing captures sea surface velocity information to enable more precise wave reconstruction. Existing difference matrix methods address rank-deficient systems through artificial boundary processing, which distorts local hydrodynamic characteristics and propagates errors to global features, thereby limiting the accuracy and stability of reconstructions. To resolve this limitation, this study proposes a physics-constrained deterministic wave reconstruction methodology. We introduce the Data-Anchored Projection model for the differential matrix, extracting hydrodynamic constraints directly from radar backscatter data. This approach achieves stable solutions for rank-deficient systems without artificial boundaries. The model’s performance was rigorously validated through both simulated and real-sea experiments. The simulation results demonstrate a minimum 13% accuracy improvement over conventional methods and high stability under various sea states and at different range resolutions. In a real-sea trial under sea states 3 to 5, reconstruction errors remained below 10%, with consistent stability observed across varying sea states. Full article

The article presents an analysis of current (during the period 1985–2022) and projected (during the period 2025–2099) changes in the hydrological regime of the Buktyrma, Yesil, and Zhaiyk river basins in Kazakhstan under the conditions of global climate change. This study is based on the integration of data from General Circulation Models (GCMs) of the sixth phase of the CMIP6 project, socio-economic development scenarios SSP2-4.5 and SSP5-8.5, as well as the results of hydrological modelling using the SWIM model. The studies were carried out with an integrated approach to hydrological change assessment, taking into account scenario modelling, uncertainty analysis and the use of bias correction methods for climate data. A calculation method was used to analyse the intra-annual distribution of runoff, taking into account climate change. Detailed forecasts of changes in runoff and intra-annual water distribution up to the end of the 21st century for key water bodies in Kazakhstan were obtained. While the projections of river flow and hydrological parameters under CMIP6 scenarios are actively pursued worldwide, few studies have explicitly focused on forecasting intra-annual flow distribution in Central Asia, calculated using a methodology appropriate for this region and using CMIP6 ensemble scenarios. There have been studies on changes in the intra-annual distribution of runoff for individual river basins or local areas, but for the historical period, there have also been studies on modelling runoff forecasts using CMIP6 climate models, but have been very few systematic publications on the distribution of predicted intra-annual runoff in Central Asia, and this issue has not been fully studied. The projections suggest an intensification of flow seasonality (1), earlier flood peaks (2), reduced summer discharges (3) and an increased likelihood of extreme hydrological events under future climatic conditions. Changes in the seasonal structure of river flow in Central Asia are caused by both climatic factors—temperature, precipitation and glacier degradation—and significant anthropogenic influences, including irrigation and water management structures. These changes directly affect the risks of flooding and water shortages, as well as the adaptive capacity of water management systems. Given the high level of water management challenges and interregional conflicts over water use, the intra-annual distribution of runoff is important for long-term planning, the development of adaptation measures, and the formulation of public policy on sustainable water management in the face of growing climate challenges. This is critically important for water, agricultural, energy, and environmental planning in a region that already faces annual water management challenges and conflicts due to the uneven seasonal distribution of resources. Full article

Local economic development (LED) strategies at the district level—such as sub-city or neighbourhood initiatives—play a crucial role in fostering sustainable and inclusive urban growth. This study explores the critical factors influencing LED and urban regeneration at the district scale, emphasising the integration of sustainability, digital technologies, inclusivity, energy efficiency, community engagement, and innovation into strategic planning. To prioritise these CFs, a tailored survey was distributed among a group of 13 city experts from European cities, involved in research projects focused on district-level quality-of-life enhancements through building retrofits, urban space interventions, energy community promotion, and technological deployment. By focusing on the district level, this research highlights the importance of tailoring strategies to local contexts and leveraging the unique characteristics of each neighbourhood. The findings reveal the need for local governments to enhance the capacity of administrative staff to engage citizens and direct external support for development projects. The normative recommendations derived from this study are specifically grounded in district-level research and practice, ensuring their applicability to sub-city areas. This paper concludes that a context-specific and collaborative approach is essential for achieving equitable and sustainable economic development at the district level. Full article

Urbanization has emerged as one of the most significant global challenges and opportunities of the 21st century, driven by a complex interplay of dynamic processes. In Ethiopia, cities have undergone rapid expansion in recent decades, largely due to state-led economic reforms and infrastructure development. This study aims to investigate the spatiotemporal dynamics, driving forces, and future projections of urban expansion along the Ethio–Djibouti trade corridor, with a focus on Dire Dawa City in eastern Ethiopia. Landsat imagery from 1993, 2003, 2013, and 2023 was utilized to detect land use and land cover (LULC) changes and analyze urban growth patterns. Additionally, maps illustrating the city’s demographic, economic, and topographic characteristics were developed to identify the key driving factors behind land conversion and urban expansion. The spatial matrix and landscape expansion index were employed to examine the spatial patterns of urban growth. Furthermore, the study applied the Multi-Layer Perceptron–Markov Chain (MLP–MC) model to simulate future LULC changes and urban expansion. The results indicate that the built-up area in Dire Dawa has increased significantly over the past three decades, growing from 6.21 km² in 1993 to 21.54 km² in 2023. This urban growth is predominantly characterized by edge expansion, reflecting a pattern of unidirectional, unsustainable development that has consumed large areas of agricultural land. The analysis shows that socioeconomic development and population growth have had a greater influence on LULC conversion and urban expansion than physical factors. Based on these identified drivers, the study projected land conversion and simulated urban expansion for the years 2043 and 2064. The findings underscore the urgent need for context-sensitive urban growth strategies that harmonize local realities with national development policies and the Sustainable Development Goals. Full article

Online laboratories have emerged as a viable alternative for providing hands-on experience to engineering students, especially in fields related to computer, software, and electrical engineering. In particular, remote laboratories enable users to interact in real time with physical hardware via the internet. However, current remote laboratory systems often restrict access to a single user per session, limiting broader participation. Embedded systems laboratory activities have traditionally relied on in-person instruction and direct interaction with hardware, requiring significant time for code development, compilation, and hardware testing. Students typically spend an important portion of each session coding and compiling programs, with the remaining time dedicated to hardware implementation, data collection, and report preparation. This paper proposes a remote laboratory implementation that optimizes remote laboratory stations’ availability, allowing users to lock the system only during the project debugging and testing phases while freeing the remote laboratory station for other users during the code development phase. The implementation presented here was developed for a microprocessor laboratory course. It enables users to code the solution in their preferred local or remote environments, then upload the resulting source code to the remote laboratory hardware for cross-compiling, execution, and testing. This approach enhances usability, scalability, and accessibility while preserving the core benefits of hands-on experimentation and collaboration in online embedded systems education. Full article

This review presents a critical analysis of Japan’s hydrogen strategy, focusing on the broader context of its decarbonization efforts. Japan aims to achieve carbon neutrality by 2050, with intermediate targets including 3 million tons of hydrogen use by 2030 and 20 million tons by 2050. Unlike countries with abundant domestic renewables, Japan’s approach emphasizes hydrogen imports and advanced storage technologies, driven by limited local renewable capacity. This review not only synthesizes policy and project-level developments but also critically evaluates Japan’s hydrogen roadmap by examining its alignment with global trends, technology maturity, and infrastructure scalability. The review integrates recent policy updates, infrastructure developments, and pilot project results, providing insights into value chain modeling, cost reduction strategies, and demand forecasting. Three policy conclusions emerge. First, Japan’s geography justifies an import-reliant pathway, but it heightens exposure to price, standards, and supply-chain risk; diversification across LH₂ and ammonia with robust certification and offtake mechanisms is essential. Second, near-term deployment is most credible in industrial feedstocks (steel, ammonia, methanol) and the maritime sector, while refueling rollout lags materially behind plan and should be recalibrated. Third, cost competitiveness hinges less on electrolyzer CAPEX than on electricity price, liquefaction, transport; policy should prioritize bankable offtake, grid-connected renewables and transmission, and targeted CAPEX support for import terminals, bunkering, and cracking. Japan’s experience offers a pathway in the global hydrogen transition, particularly for countries facing similar geographic and energy limitations. By analyzing both the progress and the limitations of Japan’s hydrogen roadmap, this study contributes to understanding diverse national strategies in the rapidly changing state of implementation of clean energy. Full article

Wetlands are an important part of coastal ecosystems but are under increasing pressure from climate change-induced sea-level rise and flooding, in addition to development pressures associated with increasing human populations. The change in tidal events and their intensity due to sea-level rise is also reshaping and challenging the vitality of existing wetland systems, requiring more intensive localized studies to identify future-focused restoration and conservation strategies. To support this endeavor, this study utilizes tide gauge datasets from the Australian Bureau of Meteorology (BOM) for maximum sea-level (Hmax) prediction and Landsat Collection surface reflectance datasets obtained from the United States Geological Survey (USGS) database to detect and project patterns of change in the Maroochy River floodplain of Queensland, Australia. This study developed an efficient hybrid deep learning model combining a Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNNBiLSTM) architecture for the prediction of maximum sea-level and tidal events. The proposed model significantly outperformed three benchmark models (Multiple Linear Regression (MLR), Support Vector Regression (SVR), and CatBoost) in achieving a high correlation coefficient (r = 0.9748) for maximum sea-level prediction. To further address the increasing frequency and intensity of tidal events linked to sea-level rise, a CNNBiLSTM classification model was also developed, achieving 96.72% accuracy in predicting extreme tidal occurrences. This study identified a significant positive linear increase in sea-level rise of 0.016 m/year between 2014 and 2024. Wetland change detection using Landsat imagery along the Maroochy River floodplain also identified a substantial vegetation loss of 395.64 hectares from 2009 to 2023. These findings highlight the strong potential of integrating deep learning and remote sensing for improved prediction and assessment of sea-level extremes and coastal ecosystem changes. The study outcomes provide valuable insights for informing not only conservation and restoration activities but also for providing localized projections of future change necessary for the progression of effective climate adaptation and mitigation strategies. Full article