Search Results (271)

The cation/H⁺ exchanger (CHX) gene family plays a vital role in maintaining K⁺/Na⁺ homeostasis in plants, yet its functional characterization in pepper (Capsicum annuum L.) remains largely unexplored. To elucidate the potential roles of CHX genes in stress adaptation and development in pepper, a genome-wide identification and systematic analysis of this gene family was performed. Using a combination of Hidden Markov Model (HMM) searches, phylogenetic reconstruction, conserved motif and promoter analysis, and expression profiling across tissues and under multiple stress conditions, a total of 23 CaCHX genes were identified, which are unevenly distributed across 10 chromosomes and classified into 6 phylogenetic subfamilies. Expression profiling revealed that most CaCHX genes were highly expressed in flowers, suggesting their potential involvement in reproductive development, while only CaCHX12 and CaCHX17 were detected in leaves. Under treatments such as abscisic acid (ABA), gibberellic acid (GA), NaCl, and jasmonic acid (JA), CaCHX1, CaCHX20, and CaCHX23 exhibited distinct temporal expression patterns, suggesting their involvement in hormone-mediated stress responses. This study provides the first comprehensive genomic and transcriptomic overview of the CHX family in pepper, offering novel insights into its regulatory roles in flower development and stress tolerance and, thus supplying valuable genetic resources for molecular breeding aimed at enhancing pepper resilience. Full article

Soil erosion is a significant challenge to the environment, ecology, and economy, and areas that undergo fast land use change and climate change are the most affected. This research evaluates the effects that climate change and Land-Use/Land-Cover (LULC) change have, separately and together, on soil loss and sediment retention in the Lam Phra Phloeng (LPP) watershed, Thailand. The InVEST Sediment Delivery Ratio (SDR) model was applied under the Shared from Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), using projected LULC for 2050 and 2100. The Cellular Automata–Markov (CA–Markov) model has been utilized to generate future land use/land cover (LULC) scenarios demonstrating how land changes over spatial and temporal scale. Results show a marked decline in sediment retention and a rise in soil loss, especially under high-emission scenarios and cropland expansion. By 2100, cropland soil loss increased by 57.35%, while forest cover—a key determinant of sediment retention—declined from 45.41% in 2020 to 22.19%. When climate and land-use changes are considered together, they have a much greater effect on sediment loss, especially in cropland and built-up areas. These results highlight the vital role that forest conservation and adaptive land management, e.g., afforestation and sustainable agriculture, play in ensuring the continued availability of clean water in watersheds and in erosion control. The research provides policy-makers with real-life scenarios to draw on when sketching integrated watershed management plans aimed at reducing the negative effects of land use and climate change on soil stability and water resources in the LPP watershed. Full article

Assessing the runoff response to land use and climate change in karst basins is essential for sustainable water resources management and for advancing the understanding of basin-scale hydrometeorological processes. This study applied the SWAT model integrated with CA-Markov–based land use projections and CMIP6 climate data under the SSP245 (medium emissions) and SSP585 (high emissions) scenarios to conduct multi-scenario simulations, evaluating the impacts of these changes and projecting future runoff in the Wujiang River source region. The results indicate that (1) the SWAT model performed satisfactorily in simulating hydrological processes in this karst basin, with R² and NSE values during calibration and validation reaching at least 0.75 and 0.7, respectively—furthermore, the PBIAS absolute values were below 10% during both calibration and validation; (2) runoff variations under four land use scenarios from 2000 to 2015 showed limited deviation from the baseline; (3) more pronounced runoff alterations were observed under extreme land use scenarios when compared to grassland-dominated conditions; (4) future climate scenarios SSP245 (medium emissions) and SSP585 (high emissions) consistently project a decreasing trend in runoff; and (5) combined scenario analyses reveal that climate change acts as the dominant factor driving runoff reduction in karst basins. These findings improve the mechanistic understanding of karst hydrological processes under global change, and the methodology established here holds potential for extension to other karst regions, thereby supporting strategic water resources planning. Full article

Cooperative path planning for unmanned aerial vehicle (UAV) swarms has attracted considerable research attention, yet it remains challenging in complex, uncertain environments. To tackle this problem, we model the cooperative path planning task as a heterogeneous decentralized Markov Decision Process (MDP), emphasizing the symmetry-inspired role assignment between leader and wingmen UAVs, which ensures balanced and coordinated behaviors in dynamic settings. We address the problem using a Multi-Agent Soft Actor-Critic (MASAC) framework enhanced with a symmetry-aware reward mechanism designed to optimize multiple cooperative objectives: simultaneous arrival, formation topology preservation, dynamic obstacle avoidance, trajectory smoothness, and inter-agent collision avoidance. This design promotes behavioral symmetry among agents, enhancing both coordination efficiency and system robustness. Simulation results demonstrate that our method achieves efficient swarm coordination and reliable obstacle avoidance. Quantitative evaluations show that our MASAC-CA algorithm achieves a Mission Success Rate (MSR) of 99.0% with 2–5 wingmen, representing approximately 13% improvement over baseline MASAC, while maintaining Formation Keeping Rates (FKR) of 59.68–26.29% across different swarm sizes. The method also reduces collisions to near zero in cluttered environments while keeping flight duration, path length, and energy consumption at levels comparable to baseline algorithms. Finally, the proposed model’s robustness and effectiveness are validated in complex uncertain environments, underscoring the value of symmetry principles in multi-agent system design. Full article

Land use and land cover (LULC) changes significantly shape urban environments and directly impact ecological and socioeconomic systems. This study aims to explore these interconnections by employing the Cellular Automata–Markov (CA–Markov) model to assess and predict LULC dynamics in Arkansas. Historical LULC datasets from 2001 to 2021, obtained from the National Land Cover Database, were simplified from 11 into 5 classes to facilitate analysis and effectively map transitions. The model was validated by predicting LULC for 2016 and 2021 and comparing the predictions with the real maps, achieving an overall accuracy of approximately 91.9%, using model validation metrics, including precision, recall, F1-score, and Kappa Coefficient, and highlighting the strength of the predictions. Predictions for 2026 and 2031 reveal a continuous increase in built-up areas at the expense of vegetation cover, underscoring ongoing urbanization trends. Specifically, built-up areas are projected to increase from 28.39% in 2021 to 30.15% in 2031, while vegetation cover is expected to decline from 49.30% to 47.48%. This research demonstrates the utility of the CA–Markov model in simulating urban growth patterns and provides actionable insights into sustainable urban planning and land management strategies. Full article

Endemic ectotherms in high-altitude regions face dual threats from climate change and human activities, yet quantifiable indicators to disentangle these stressors remain limited. We developed a novel multi-scenario framework to disentangle the independent and synergistic impacts of climate change and anthropogenic landscape change on the habitat suitability of the Tibetan hot-spring snake (Thermophis baileyi) across the Tibetan Plateau. Our analysis was based on field survey data and species occurrence records, utilizing the species distribution model and the CA–Markov model. We identified temperature seasonality (41.8% contribution) as the primary environmental factor influencing its distribution, followed by precipitation of the coldest quarter (15.1%) and land cover (13.8%). The results showed that moderate climate warming would benefit the survival of the species, with a 24.03–38.55% gain in high-suitability habitat (HSH) area under climate change-only scenarios. However, extreme warming (exceeding SSP5-8.5) would surpass the thermal tolerance threshold of T. baileyi, reducing its HSH and triggering a northward shift in its distribution centroid. Landscape change reduced the HSH (5.98% reduction under land cover change-only scenario), and attenuated climate-driven gains by 4.99–11.31% under combined climate–landscape change scenarios. In addition, only one-fifth of the current HSH was covered by national natural reserves. Synergistic anthropogenic pressures critically offset climate benefits, demonstrating the need for integrated conservation strategies to address the challenges posed by both extreme climate warming and land cover change threats to mitigate future habitat degradation. The quantification of climate–land cover change impacts on T. baileyi offers critical insights for high-altitude ectotherm distributions under global changes and evidence-based conservation planning. Full article

Rapid urbanization has increased pressure on ecosystems, posing serious risks to environmental quality and sustainable development. Understanding the spatiotemporal dynamics and driving mechanisms of Fractional Vegetation Cover (FVC), a key indicator of ecological health, is essential for advancing high-quality regional development and ecological civilization. In this study, Normalized Difference Vegetation Index (NDVI), meteorological, and socio-economic data from 2000 to 2022 were used to analyze the changes and driving forces of FVC in the Beijing–Tianjin–Hebei (BTH) urban cluster using a pixel dichotomy model and Partial Least Square–Structural Equation Modeling (PLS–SEM). The CA-Markov model was applied to predict future FVC patterns under different scenarios. The results show that FVC in the BTH increased from 0.462 to 0.576 between 2000 and 2022. However, this positive trend was accompanied by pronounced spatial differences: FVC increased significantly in the northwestern mountains, while it declined in urban built-up areas. PLS–SEM analysis further indicated that climate factors were the main drivers of FVC growth (0.903), whereas socioeconomic (−0.469) and topographic (−0.260) factors exerted limiting effects. Compared with 2022, FVC declined to varying degrees under all scenarios. Notably, the ecological protection scenario resulted in far less FVC degradation than the inertial development and economic priority scenarios. These findings provide scientific support for spatial planning and emphasize the importance of ecological protection policies in sustaining vegetation and promoting long-term sustainable development. Full article

Optimizing the spatial pattern of water conservation services (WCSs) is essential for enhancing regional water retention and promoting sustainable water resource management. The Saihanba region, a critical ecological barrier in northern China, has experienced severe degradation due to historical over-logging, leading to weakened WCS functions. This study used remote sensing techniques to interpret land use/land cover change (LULC) and combined it with meteorological and basic ecological data to assess changes in WCS capacity in the Saihanba region, China, under multiple 2035 scenarios using CA-Markov and Bayesian network models. The Bayesian belief network identified priority areas for spatial optimization. Results showed the following: (1) The spatial distribution patterns of WCSs showed a strong dependence on land-use types, with both forest and grassland areas demonstrating superior water conservation capacity compared to other land cover categories; (2) although total WCS capacity varied across scenarios, spatial distribution remained consistent—high-value zones were mainly in the south and central-east, while lower values occurred in the west; and (3) WCS areas were categorized into key optimization, ecological protection, and general management zones. Notably, the Sandaohekou Forest Farm and the western Qiancengban Forest Farm emerged as critical areas requiring urgent optimization. These findings offer practical guidance for spatial planning, ecological protection, and water resource governance, supporting long-term WCS sustainability in the region. The study also contributes to cleaner production strategies by aligning ecosystem service management with sustainable development goals. Full article

Urbanization, characterized by population growth and socioeconomic development, is a major driving factor of land use land cover (LULC) change. A spatio-temporal understanding of land cover change is crucial, as it provides essential insights into the pattern of urban development. This study conducted a longitudinal analysis of LULC change in order to evaluate the tradeoffs of urban growth and sustainability challenges in the Himalayan region. Landsat time-series satellite imagery from 1988 to 2024 were analyzed for two major cities in Nepal—Kathmandu metropolitan city (KMC) and Pokhara metropolitan city (PMC). The LULC classification was conducted using a machine learning support vector machine (SVM) approach. For this study period, our analysis showed that KMC and PMC witnessed urban growth of over 400% and 250%, respectively. In the next step, LULC change and urban expansion patterns were predicted based on the urban development indicator using the Cellular Automata Markov chain (CA-Markov) model for the years 2040 and 2056. Based on the CA-Markov chain analysis, the projected expansion areas of the urban area for the two future years are 282.39 km² and 337.37 km² for Kathmandu, and 93.17 km² and 114.15 km² for PMC, respectively. The model was verified using several Kappa variables (K-location, K-standard, and K-no). Based on the LULC trends, the majority of urban expansion in both the study areas has occurred at the expense of prime farmlands, which raises grave concern over the sustainability of the food supply to feed an ever-increasing urban population. This haphazard urban sprawl poses a significant challenge for future planning and highlights the urgent need for effective strategies to ensure sustainable urban growth, especially in restoring local food supply to alleviate over-reliance on long-distance transport of agro-produce in high-altitude mountain regions. The alternative planning of sustainable urban growth could involve adequate consideration for urban farming and community gardening as an integral part of the urban fabric, both at the household and city infrastructure levels. Full article

The purpose of this study is to analyze the temporal and spatial characteristics of urban expansion and its influencing factors in Shaanxi Province, China, as well as simulate future land use and predict the situation and development stage of urban expansion. An understanding of these factors is conducive to the coordinated development of the population, resources, and the economy; the optimization of the urban spatial layout; and the high-quality development of Shaanxi Province. Research methods: With IDRISI Selva17 and the expansion intensity index, the CA–Markov model was adopted to simulate and predict the land use type based on the land use data of Shaanxi Province from 2000 to 2020. The urban built-up areas in Shaanxi Province have been continuously expanding in the past 30 years, especially since 2010, when expansion slightly accelerated, and the expansion intensity changed, first rising and then falling. The Kappa index is as high as 0.70, which further confirms the accuracy of the land use spatial evolution prediction by the CA–Markov model. By combining the urban expansion index with the simulation model, this paper provides an in-depth analysis of the internal relationship between the historical evolution of and future trends in construction land expansion because of the high-quality coordinated development of Shaanxi Province and extends the research perspective with creative ideas. Full article

Understanding the drivers of water yield (WY) changes in ecologically sensitive, data-scarce watersheds is crucial for sustainable management, particularly in the context of accelerating forest expansion and urbanization. This study focuses on the upper Yellow River Basin (UYRB), a critical headwater region that supplies 60% of the Yellow River’s flow and is undergoing rapid land use transitions from 1990 to 2100. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the Future Land-Use Simulation (FLUS) model, we quantify historical (1990–2020) and projected (2025–2100) WY dynamics under three SSP scenarios (SSP126, SSP370, and SSP585). InVEST, a spatially explicit ecohydrological model based on the Budyko framework, estimates WY by balancing precipitation and evapotranspiration. The FLUS model combines cellular automata (CA) with an artificial neural network (ANN)-based suitability evaluation and Markov chain-derived transition probabilities to simulate land-use change under multiple scenarios. Results show that WY increased significantly during the historical period (1990–2020), primarily driven by increased precipitation, with climate change accounting for 94% and land-use change for 6% of the total variation in WY. Under future scenarios (SSP126, SSP370, and SSP585), WY is projected to increase to 217 mm, 206 mm, and 201 mm, respectively. Meanwhile, the influence of land-use change is expected to diminish, with its contribution decreasing to 9.1%, 5.7%, and 3.1% under SSP126, SSP370, and SSP585, respectively. This decrease reflects the increasing strength of climate signals (especially extreme precipitation and evaporative demand), which masks the hydrological impacts of land-use transitions. These findings highlight the dominant role of climate change, the scenario-dependent effects of land-use change, and the urgent need for integrated climate–land management strategies in forest-urbanizing watersheds. Full article

Indonesia faces significant challenges in meeting food security targets due to rapid agricultural land loss, with approximately 1.22 million hectares of rice fields converted between 1990 and 2022. Therefore, this study developed a prediction model for the loss of rice fields by 2030, incorporating land productivity attributes, specifically rice cropping intensity/RCI, using geospatial technology—a novel method with a resolution of approximately 10 m for quantifying ecosystem service (ES) impacts. Land use/land cover data from Landsat images (2013, 2020, 2024) were classified using the Random Forest algorithm on Google Earth Engine. The prediction model was developed using a Multi-Layer Perceptron Neural Network and Markov Cellular Automata (MLP-NN Markov-CA) algorithms. Additionally, time series Sentinel-1A satellite imagery was processed using K-means and a hierarchical clustering analysis to map rice fields and their RCI. The validation process confirmed high model robustness, with an MLP-NN Markov-CA accuracy and Kappa coefficient of 83.90% and 0.91, respectively. The present study, which was conducted in Indramayu Regency (West Java), predicted that 1602.73 hectares of paddy fields would be lost within 2020–2030, specifically 980.54 hectares (61.18%) and 622.19 hectares (38.82%) with 2 RCI and 1 RCI, respectively. This land conversion directly threatens ES, resulting in a projected loss of 83,697.95 tons of rice production, which indicates a critical degradation of service provisioning. The findings provide actionable insights for land use planning to reduce agricultural land conversion while outlining the urgency of safeguarding ES values. The adopted method is applicable to regions with similar characteristics. Full article

Salt concentration monitoring is crucial for industrial process control and wastewater management, yet existing methods often lack real-time capability or require invasive sampling. This paper presents a novel RFID wireless sensing system for noninvasive solution concentration monitoring, combining physical modeling with advanced estimation algorithms. By combining the Cole–Cole model and the slit cylindrical capacitor (SCC) model, the system establishes physics-based state-space models to characterize concentration-dependent RFID signal variations. The concentration dynamics are modeled as a hidden Markov process and tracked using an adaptive extended Kalman filter (AEKF). The AEKF algorithm avoids computationally expensive inversion of complex observation equations while automatically adjusting noise covariance matrices via innovation sequence. Experimental results demonstrate a mean relative error (MRE) of 2.8% for CaCl₂ solution across 2–10 g/L concentrations. Within the experimentally validated optimal range (2–8 g/L CaCl₂), the system maintains MRE below 3% under artificially introduced measurement noise, confirming its strong robustness. Compared with baseline approaches, the proposed AEKF algorithm shows improved performance in both accuracy and computational efficiency. Full article

This study focuses on assessing the physical growth of cities and the land-cover changes resulting from it, which play a crucial role in understanding the environmental impacts and managing phenomena such as the Daytime Urban Surface Heat Island Intensity (DSUHII). Predicting the trends of these changes for the future provides valuable insights for urban planning and mitigating thermal effects in arid environments. This research aims to evaluate the spatial and temporal changes in the intensity of urban surface heat islands in cities under different climatic conditions, resulting from land-cover changes in the past, and to predict future trends. For this purpose, Landsat satellite data products, including Surface Reflectance with a 30-m resolution and Land Surface Temperature (LST) originally at a 100 (120)-meter resolution for Landsat 8 (Landsat 5) (resampled to 30 m for compatibility), along with a database of underlying criteria affecting urban growth, were used to analyze land-cover and LST changes. The land-cover classification was carried out using the Support Vector Machine (SVM) algorithm, and its accuracy was assessed. Spatial and temporal changes in LST and land-cover classes were quantified using cross-tabulation models and subtraction operators. Subsequently, the impact of land-cover changes on LST in different climates was analyzed, and the trends of land-cover and DUSHII changes were simulated for the future using the CA–Markov model. The results showed that in the humid climate (Babol and Rasht), built-up areas increased by over 100% from 1990 to 2023 and are projected to grow further by 2055, while green spaces significantly decreased. In the cold–dry climate (Mashhad), urban development increased dramatically, and green spaces nearly halved. In the hot–dry climate (Yazd and Kerman), built-up areas tripled, and the reduction of green spaces will continue. Additionally, in cities with hot and dry climates, a significant area of barren land was converted into built-up areas, and this trend is predicted to continue in the future. DSUHII in Babol increased from 2.5 °C in 1990 to 5.4 °C in 2023 and is projected to rise to 7.8 °C by 2055. In Rasht, this value increased from 2.9 °C to 5.5 °C, and is expected to reach 7.6 °C. In Mashhad, the DSUHII was negative, decreasing from −1.1 °C in 1990 to −1.5 °C in 2023, and is projected to decline to −1.9 °C by 2055. In Yazd, DSUHII also remained negative, decreasing from −2.5 °C in 1990 to −3.3 °C in 2023, with an expected drop to −6.4 °C by 2055. Similarly, in Kerman, the intensity of DSUHII decreased from −2.8 °C to −5.1 °C, and it is expected to reach −7.1 °C by 2055. Overall, the conclusions highlight that in humid climates, DSUHII has significantly increased, while green spaces have decreased. In moderate, cold, and dry climates, a gradual reduction in DSUHII is observed. In the hot–dry climate, the most substantial decrease in DSUHII is evident, indicating the varying impacts of land-cover changes on DSUHII across these regions. Full article

Machine-to-machine (M2M) communications are becoming the most important factor shaping network traffic. However, traditional controls developed for human-generated traffic are not able to cope with new demands. Thus, hybrid MAC protocols have been proposed to make use of the combined advantages of contention and reservation. Most of them are based on a contention stage (where a variant of CSMA/CA or ALOHA is used) followed by a reservation stage (e.g., TDMA or FDMA). In this paper, we introduce 2CA-R², a hybrid MAC protocol for M2M communications intended to be used in the device domain. What distinguishes this proposal is that the contention stage is controlled by a conflict–resolution algorithm known as Adaptive-2C. The protocol was evaluated using a model based on a Markov chain and computer simulations. Its performance was compared with DCF, the MAC technique used in IEEE802.11 standards. Our results show significant improvements over DCF in various metrics of network performance across different traffic situations. We also evaluated the time the protocol takes to resolve an access conflict, and we observed substantial improvements in the number of stations that can be served with the same network resource (in some cases, around a 40% improvement). Full article