Monitoring Of Changes Research Articles

Classification of green tea and quantitative prediction of catechin content based on indicator displacement array sensors

Green tea, renowned for its health benefits and cultural significance, exhibits variations in quality and biochemical composition across different grades and varieties. Traditional quality assessment methods are often labour-intensive and time-consuming, rendering them impractical for rapid analysis. This study employed a sensitive indicator displacement array sensor along with supervised learning algorithms—Extreme learning machine, Support vector machine, Random forest, and Back propagation neural network—to accurately classify green tea varieties and grades, as well as predict catechin content. Sensor components were initially screened based on their responsiveness to epigallocatechin gallate, and subsequently developed using specific indicator-receptor combinations: Pyrocatechol Violet/Fe2+, Alizarin Red S/Phenylboronic Acid, and Bromopyrogallol Red/3-Nitrophenylboronic Acid. Validation was performed through monitoring changes in RGB values and UV–visible spectroscopy. Discrimination among Anji white tea grades was assessed using principal component analysis and further refined through supervised algorithms, achieving high accuracy rates, especially with Random forest and Extreme learning machine models, which excelled in both calibration and prediction sets. These methodologies were extended to classify ten green tea varieties, demonstrating robustness and high accuracy in a multiclass setting. Independent validation confirmed the models’ efficacy in-class differentiation, with most achieving an accuracy of over 90%. Moreover, the quantitative prediction of catechin content was enhanced using nonlinear models, specifically Support vector regression optimized with the Particle swarm optimization algorithm. These models exhibited superior performance compared to linear models, particularly in effectively predicting individual catechin levels. These findings highlight the potential of combining sensor technologies with supervised learning algorithms to enhance tea quality assessment and categorization, establishing a robust framework for future applications in tea quality control.

MATNet: Multilevel attention-based transformers for change detection in remote sensing images

Remote sensing image change detection is crucial for natural disaster monitoring and land use change. As the resolution increases, the scenes covered by remote sensing images become more complex, and traditional methods have difficulties in extracting detailed information. With the development of deep learning, the field of change detection has new opportunities. However, existing algorithms mainly focus on the difference analysis between bi-temporal images, while ignoring the semantic information between images, resulting in the global and local information not being able to interact effectively. In this paper, we introduce a new transformer-based multilevel attention network (MATNet), which is capable of extracting multilevel features of global and local information, enabling information interaction and fusion, and thus modeling the global context more effectively. Specifically, we extract multilevel semantic features through the Transformer encoder, and utilize the Feature Enhancement Module (FEM) to perform feature summing and differencing on the multilevel features in order to better extract the local detail information, and thus better detect the changes in small regions. In addition, we employ a multilevel attention decoder (MAD) to obtain information in spatial and spectral dimensions, which can effectively fuse global and local information. In experiments, our method performs excellently on CDD, DSIFN-CD, LEVIR-CD, and SYSU-CD datasets, with F1 scores and OA reaching 95.67%∕87.75%∕90.94%∕86.82% and 98.95%∕95.93%∕99.11%∕90.53% respectively.

Non-invasive monitoring of adrenocortical activity in the Gould’s wattled bat (Chalinolobus gouldii)

Although bats are the second most species-rich mammalian order, very little is known about their endocrine physiology. Glucocorticoids (GCs) are commonly associated with the stress response, but also modulate vital physiological functions which help animals adapt to their environment. Understanding normal patterns of adrenocortical activity can provide valuable insights into a species’ fitness. Non-invasive hormone monitoring via faecal samples provides an integrated measure of adrenocortical activity while minimising stress on the animal but must be properly validated to ensure reliable results. The goal of this study was to validate an enzyme immunoassay for monitoring faecal glucocorticoid metabolites (FGMs) in a common Australian insectivorous bat species, the Gould’s wattled bat (Chalinolobus gouldii). We compared the performance of five assays for monitoring changes in FGMs following capture and transfer of C.gouldii from the wild to captivity. Four of the five assays detected a significant increase in FGMs following capture, but the magnitude of the increase and consistency across individuals differed considerably. We selected the UVM-69a assay as the best performing assay to then describe normative patterns of adrenocortical activity in the species. Males had higher FGM levels than females, and juveniles had higher FGM levels than adults. Individuals with poorer body condition had higher FGM levels. We also demonstrate seasonal patterns of FGMs with higher levels in March and April corresponding with reproductive up-regulation and lower levels in May and November. Our study is the first of its kind to examine adrenocortical activity in an Australian insectivorous bat and provides a valuable tool for studying this species. Understanding adrenal function in common species such as C.gouldii can shed light on the physiological mechanisms facilitating survival and success in changing environments.

Entrepreneurial ecosystem pillars and performance of SMEs in the manufacturing sector of Uganda

Purpose The purpose of this paper was to determine which entrepreneurial ecosystem pillars matter most in enhancing the performance of SMEs in the manufacturing sector of Uganda. Design/methodology/approach Descriptive cross-sectional survey of 310 SMEs in manufacturing sector were sampled. Stratified and simple random sampling techniques were employed because of the population’s homogenous characteristics. Data was collected using a Self-Administered Questionnaire and analyzed using SPSS and AMOS version 23. Findings The results indicate both institutional arrangements and resource endowments significantly influence performance of small and medium enterprises (SMEs) in the manufacturing sector of Uganda. However, institutional arrangements have a stronger predictive power on performance of SMEs in the manufacturing sector of Uganda as compared to resource endowments. Research limitations/implications The data was cross-sectional in nature thus limiting monitoring changes in the performance of SMEs in the manufacturing sector over a long period of time. Besides, the study concentrated on SMEs in the manufacturing sector which is just subset of the industrial sector leaving other sectors like trade and services. Originality/value An empirical study on entrepreneurial ecosystem pillars in a strategic and important sector – SMEs manufacturing sector, at a micro-level, and being done in Uganda is a contribution to existing literature. This is because, most entrepreneurial ecosystem studies are largely conceptual and are normally done at macro and meso-levels targeting SMEs generally and mostly in developed countries which have completely different business environment compared with developing countries.

Real-Time estimation of internal and solar heat gains in buildings using deep learning

This paper presents real-time monitoring of dynamic internal and solar heat gains using programmable low-cost cameras and deep learning techniques. For monitoring changes in occupancy, equipment, lighting, and window status in real time, a convolutional neural network (CNN)-based multi-head classification model was developed and trained with High Dynamic Range (HDR) images, collected using a low-cost fisheye camera in offices. A Python-based region of interest (ROI) generation program was developed to predefine the target areas for monitoring. The classification heads of the model were trained from scratch using a small office image dataset first; then, they were fine-tuned using another HDR image dataset collected in a larger open-plan office with more complex scenes, to evaluate the transferability of the model. The weighted mean classification precision and recall results showed that the developed model could classify the detailed status of each target heat gain (occupants, equipment, lighting, windows) in the predefined areas of the scene with great performance. Finally, to evaluate the impact of real-time monitoring of heat gains on energy demand, the open plan office space used for the experimental dataset collection was modeled using EnergyPlus software using (i) commonly assumed fixed schedules for occupancy, equipment and lighting and (ii) real-time monitored dynamic schedules for internal and solar gain components under the same weather conditions. The results showed that recommended fixed schedules may lead to significant errors in estimated internal and solar gains. The largest discrepancy was noted for occupancy and equipment usage, but other categories also showed both underestimation and overestimation of thermal load components. Implementing reliable and continuous monitoring of dynamic internal and solar heat gains is important for efficient demand-driven HVAC control.

The role of hydrological measuring devices in monitoring changes in the flow rate of the Boudraa River during the year 2023 (sub-watershed of Oued M’hasser, Morocco).

Abstract Tracking the water flow and surface runoff is of utmost importance in light of the climate changes experienced globally and specifically in Morocco. In recent years, Morocco has been facing a significant water deficit due to declining rainfall and prolonged drought periods, which affects various water uses. As the Atlas and Rif regions serve as the natural water reservoirs for Morocco, it has become essential to track the water flow and valleys in these areas to study their hydrological behavior. This understanding is crucial for determining their functioning mechanisms and making appropriate decisions to ensure the sustainability of these resources. In this context, we have monitored the changes in the flow of Ain Boudraa in the basin of Wadi Mhasser in the Central High Atlas. We have studied the impact of climatic factors on the water flow and water levels, employing a methodology that involves statistical analysis of precipitation data from the nearby station, along with conducting hydrological measurements of the flow and construction a calibration curve.

Distinctive water bodies surrounding lakes: An effective indicator for drought monitoring and assessment

The response mechanisms of surface water to drought and the potential and performance of water body changes in drought monitoring and assessment remain insufficiently elucidated. We take the Poyang Lake Basin, which suffers from drastic water body changes and frequent droughts, as a representative. Through integrating multisource data to extract long-term precise water bodies, we construct standardized water body area/number anomaly indices (SAAI/SNAI). Subsequently, we quantify the correlation and time lag response between SAAI, SNAI and drought. Our findings reveal that overall, water body area and number in the Poyang Lake Basin all exhibit a significant correlation with typical drought indices. Among them, the large water body area has a higher correlation coefficient with drought, making it a more effective indicator for drought monitoring. Water body changes of Poyang Lake surrounding area offer a more precise reflection of drought conditions than other sub-basins and exhibit greater sensitivity to drought. Meanwhile, water body area changes in Poyang Lake surrounding area lag behind meteorological drought by approximately half a month. And the basin’s water body area can typically respond with changes to some prolonged and severe hydrological droughts about 1 to 2 months in advance. We propose an integrated mechanism for drought monitoring and assessment informed by these conclusions and use actual drought events for qualitative validation. The results indicate that it is possible to assess water body drought conditions for the next approximately half month based on current meteorological drought conditions. Also, combining water body area changes with meteorological drought severity can aid in evaluating hydrological drought conditions for the following about 1–2 months. We extend our investigation to explore the universality of these phenomenon across eight shallow lakes globally characterized by significant water fluctuations. The results reveal that the water body changes in most of these lakes exhibit good potential for drought monitoring and assessment, and present a strong consistency with deep soil moisture, allowing for the accurate reflection of deep soil drought conditions. The lag response patterns between water bodies and drought in Dongting Lake, Hongze Lake, and Tonlé Sap Lake—also situated in the monsoon climate zone—are more similar to those observed in Poyang Lake. These distinctive lake water bodies can serve as an innovative indicator for drought monitoring and assessment, providing potential support for endeavors in drought prevention and mitigation.

Survey techniques and impact mitigation for the Endangered northern quoll (Dasyurus hallucatus) in the semi-arid landscapes of the Pilbara

Improvements in survey techniques for threatened species gives quantifiable confidence about their presence or absence at a given location, enhancing our understanding of patterns of distribution and abundance. This is particularly important for legislatively protected threatened species that may be at risk of disturbance. Survey techniques vary in detection confidence, resource investment, and invasive impacts to individuals. We review the appropriate applications of techniques in surveying for the endangered northern quoll (Dasyurus hallucatus), including the effort required to be 95% confident of detecting presence and monitoring change in population trends in the Pilbara bioregion. The outlined protocols indicate best practice for effective and efficient northern quoll monitoring while protecting the welfare of the animals being monitored, and are relevant to Environmental Protection and Biodiversity Conservation Act requirements. We also provide suggestions to mitigate impacts on animals and habitat, and describe future directions and emerging techniques for the monitoring of northern quolls and other endangered species. This information is targeted at those interested in monitoring northern quolls in a field setting, including researchers, environmental consultants, Traditional Owners, and land managers.

Remote Sensing, Artificial Intelligence and Smart Agriculture Technology Trends of the Future

The efficient and sustainable operation of the agricultural sector has become increasingly important in light of the transformations brought about by the third and fourth industrial revolutions. Population growth, increasing food demand, rising input costs, and environmental pressures necessitate innovative approaches not only to ensure food security but also to mitigate the effects of climate change. The European Union (EU) emphasizes the role of digital technologies in supporting agricultural productivity and resilience by promoting a bio-based economy. Strategies such as Farm to Fork (F2F) initiative aim to reduce pesticide and nutrient inputs, thus preserving biodiversity and supporting ecosystem health. Artificial intelligence (AI) and predictive analytics, along with connected sensors, offer opportunities to optimize water and nutrient usage and increase crop yields. By utilizing AI, combining remote sensing technologies, and monitoring changes in land use, it is possible to reduce environmental risks associated with agricultural practices. Although there are challenges such as high investment costs and data control for the integration of digital technologies, ongoing research and development efforts promise to overcome these obstacles. In conclusion, the integration of digital technologies into agriculture presents unique opportunities to address urgent issues and achieve sustainability goals. This review discusses the applicability of fundamental technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Precision Agriculture (PA), and Machine Learning (ML) in making agriculture more efficient and sustainable, by enabling the perception, monitoring, collection, analysis, and extraction of meaningful insights from agricultural data.

Innovative study of the fetal heart in monochorionic multiple births

Introduction. Fetal echocardiography is currently one of the main methods of prenatal diagnosis. However, in recent years, attention has shifted towards the use of ultrasound to assess fetal myocardial function.The aim of this study is compare global longitudinal deformation of the myocardium of the left (LV) and right ventricles (RV) of the heart in fetuses with uncomplicated singleton pregnancy, in fetuses from monochorionic twins with transfusion syndrome, selective growth retardation and in uncomplicated monochorionic multiple pregnancy.Material and methods. A prospective cohort study was conducted which included 60 pregnant patients: Group I — patients with an uncomplicated singleton pregnancy (n=25); Group II — 35 patients with monochorionic diamniotic twins, whose pregnancy in 10 cases was complicated by feto-fetal transfusion syndrome (stages I-III according to Quintero R.A.), in 10 cases — selective growth retardation of the second fetus and in 15 cases monochorionic twins without complications. The following parameters were determined: global longitudinal deformation (GLD) of the myocardium of the ventricles of fetal hearts using the speckle tracking method of ejection fraction (EF), end-diastolic and end-systolic volumes of the ventricles of the heart.Results. Significant differences were established in GLD of RV and LV between singleton and monochorionic multiple pregnancies (LV: -21.2 ± 2.03 versus -23.41 ± 0 .25, p = 0.003); RV: — 20.3 ± 2.5 versus -23.3 ± 2.5, p = 0.013). Significant differences were revealed in GLD of the LV and RV in recipient fetuses before and after laser coagulation of placental anastomoses (LPCA) [LV in recipients before LCPA (- 20.4 ± 2.98) versus LV recipient after LCPA (-24.2 ± 3.3), p = 0.018], [RV in recipients before LKPA (- 20.4 ± 2.8) vs. RV in recipients after laser coagulation of placental anastomoses (-23.7 ± 3.4, p = 0.012].Conclusion. Assessment of the cardiac function of fetuses from monochorionic twins using the echocardiographic speckle tracking technique makes it possible to identify early changes in fetal myocardial function in twins from monochorionic diamniotic twins, as well as to carry out reliable testing monitoring changes in cardiac function over time.

A light-addressable potentiometric sensor-based extracellular calcium dynamic monitoring and imaging platform for cellular calcium channel drug evaluation

Disruption and dysregulation of cellular calcium channel function can lead to diseases such as ischemic stroke, heart failure, and arrhythmias. Corresponding calcium channel drugs typically require preliminary efficacy evaluations using in vitro models such as cells and simulated tissues before clinical testing. However, traditional detection and evaluation methods often encounter challenges in long-term continuous monitoring and lack calcium specificity. In this study, a dynamic monitoring system based on ion-sensitive membranes for light-addressable potentiometric sensor (LAPS) was developed to meet the demand for monitoring changes in extracellular calcium ion (Ca2+) concentration in live cells. The effects of Ca2+ channel agonists and blockers on 2D and 3D HL-1 cells were investigated, with changes in extracellular Ca2+ concentration reflecting cellular calcium metabolism, facilitating drug evaluation. Additionally, calcium imaging technology with optical addressing capability complemented the LAPS system's ability to perceive 3D cell morphology, enhancing its drug evaluation capabilities. This work provides a novel, label-free, specific, and stable technique for monitoring cellular calcium metabolism. It achieves both continuous monitoring at single points and custom sensing area calcium imaging, holding significant implications for drug screening and disease treatment related to human calcium homeostasis.

Explicit Formulas of Average Run Length for Triple Moving Average Control Chart to Monitor Changes in Mean Parameter of Normal and Non-normal Process

In this research, a formula is presented for calculating the average run length of a triple moving average (TMA) control chart, which is used to measure control chart performance and determine control chart width. To apply appropriately to data in various processes, a TMA control chart is used to detect small changes in a process by finding the average value of the data over a specified period of time (w). However, determining measure control chart performance and control chart width is difficult because the formula for calculation is not created. The performance of the control charts is measured using the standard normal distribution probability properties and compared with the moving average (MA) control charts and the double moving average (DMA) control charts. The analysis results show that TMA is very effective at detecting small changes, when w is set to have a small value. The study determines that the data within the process are normally and non-normally distributed. Detecting changes on real data by TMA charts has the best detection performance compared to MA and DMA.

Prevalence and Determinants of Burnout Syndrome: A Cross-sectional Study among Healthcare Providers in Akuapem North Municipality of Ghana

Aim: This study examines the prevalence and factors associated with burnout in healthcare providers in Akuapem North Municipality, Ghana. Methods: A descriptive cross-sectional survey was conducted among 277 healthcare workers using a structured questionnaire between February and March 2024. The Maslach Burnout Inventory (MBI) assessed EE, DP, and PA. Work-related and organizational factors were analyzed using Pearson chi-square tests and multiple regression analysis to identify key predictors of burnout. Statistical significance was set at p < .05. Data were analyzed using SPSS Version 19. Results: Emotional exhaustion was high, with 15.5% of participants experiencing extreme fatigue. Depersonalization levels were high for 48.0% of respondents, while 74.0% reported low personal achievement. Chi-square tests indicated that marital status significantly impacted emotional exhaustion [χ²(8) = 17.561, p = .025], and profession was significantly related to depersonalization [χ²(2) = 22.201, p < .001]. Regression analysis revealed that work-related factors, such as overall comfort (β = 5.978, p < .001) and support (β = 4.416, p < .001), were significant predictors of burnout. Organizational factors like fairness (β = 2.563, p < .001) and work-life balance (β = 7.294, p < .001) also had significant effects. Conclusion: Burnout is prevalent among healthcare providers in Akuapem North Municipality, with notable levels of emotional exhaustion, depersonalization, and reduced personal achievement. Work-related and organizational factors are key contributors. Recommendations: Targeted interventions, including improved support systems and organizational fairness, are recommended to address burnout effectively. Future research should evaluate targeted interventions addressing these factors and monitor changes in burnout over time. Additionally, it should investigate the interactions between individual and organizational factors across various healthcare settings.

The Kunming-Montreal Global Biodiversity Framework needs headline indicators that can actually monitor forest integrity

Abstract Intact native forests under negligible large-scale human pressures (i.e. high-integrity forests) are critical for biodiversity conservation. However, high-integrity forests are declining worldwide due to deforestation and forest degradation. Recognizing the importance of high-integrity ecosystems (including forests), the Kunming-Montreal Global Biodiversity Framework (GBF) has directly included the maintenance and restoration of ecosystem integrity, in addition to ecosystem extent, in its goals and targets. Yet, the headline indicators identified to help nations monitor forest ecosystems and their integrity can currently track changes only in (1) forest cover or extent, and (2) the risk of ecosystem collapse using the IUCN Red List of Ecosystems (RLE). These headline indicators are unlikely to facilitate the monitoring of forest integrity for two reasons. First, focusing on forest cover not only misses the impacts of anthropogenic degradation on forests but can also fail to detect the effect of positive management actions in enhancing forest integrity. Second, the risk of ecosystem collapse as measured by the ordinal RLE index (from Least Concern to Critically Endangered) makes it unlikely that changes to the continuum of forest integrity over space and time would be reported by nations. Importantly, forest ecosystems in many biodiverse African and Asian nations remain unassessed with the RLE. As such, many nations will likely resort to monitoring forest cover alone and therefore inadequately report progress against forest integrity goals and targets. We concur that monitoring changes in forest cover and the risk of ecosystem collapse are indeed vital aspects of conservation monitoring. Yet, they are insufficient for the specific purpose of tracking progress against crucial ecosystem integrity components of the GBF’s goals. We discuss the pitfalls of merely monitoring forest cover, a likely outcome with the current headline indicators. Augmenting forest cover monitoring with indicators that capture change in absolute area along the continuum of forest integrity would help monitor progress toward achieving area-based targets related to both integrity and extent of global forests.

Algorithm for constructing unmeasured logging curves (pseudo-logging) in wells of developed fields

In many cases, the diagrams of some logs in an old well of producing fields are not recorded for some reason or another. Currently, the importance of reassessing the efficiency of such fields is obvious, while the absence of some log curves causes great difficulties. In particular very few studies with acoustic logging (AL) have been carried out in old wells. On the other hand, in 1960-80, among the studies carried out in wells, preference was given to various types of electrical logging (EL), neutron-gamma logging (NGL), and gamma ray logging (GRL). To overcome such difficulties, it is necessary to create diagrams of unmeasured well logs or pseudo-logs. Nowadays, there are methods for constructing pseudolog curves. For example, the method of statistical analysis is widely used. However, in most cases, their implementation is very complex and requires a lot of work. We have developed a new method to construct pseudo-log curves. At first, the propagation velocity of elastic waves in rocks is studied based on seismic survey (SS) and AL data, which are performed presently on the investigation field. Based on this information, objects of interest to the oil industry are selected, their geometric dimensions are determined, the contour of oil and gas saturation is assessed, areas with abnormally high reservoir pressure are studied, the process of accumulation deposition is re-examined, and the preparation of an optimal well network diagram for the effective exploitation of the field is determined. Further, based on the long-ago measured curves of electrical loggings and neutron-gamma logging, we obtain the pseudo-log AL curve in old boreholes and the data SS. The proposed method was tested in 48 wells in the Bahar field (Azerbaijan). The method has shown high efficiency, its reliability is confirmed by independent data. The results of the method help in solving several geological problems: monitoring changes in petrophysical quantities of rocks between wells during development; study of well formations located below the design depth; recalculation of hydrocarbon reserves; determination of the location for drilling new wells, etc

Continuous Monitoring of Forests in Wetland Ecosystems with Remote Sensing and Probability Sampling

With the drastic reduction in wetland areas, it is essential to conduct an annual monitoring of the biomass or carbon content of wetland ecosystems to support international initiatives and agreements focused on sustainable development, climate change, and carbon equity. Forests in wetland ecosystems play a crucial role in carbon sequestration; however, the monitoring of small, fragmented forest components in wetlands remains insufficient, leading to an underestimation of their ecological and carbon sequestration functions. This study utilizes a model-assisted (MA) estimator, a monitoring procedure that is asymptotically design-unbiased and incorporates remote sensing, to assess the status and trends in the above-ground biomass (AGB) of forest components in wetlands, while also proposing a method of optimizing the sample size to enable continuous monitoring. Based on the population of the forest component of Baiyangdian wetland, major findings indicate that: (1) neglecting the forest component of Baiyangdian wetland will lead to an underestimation of the total aboveground biomass by 224.34 t/ha and 243.64 t/ha in the years 2022 and 2023, respectively; (2) in either year-specific monitoring or interannual change monitoring, the MA estimator is more cost-effective than the expansion estimator, a comparable procedure that relies solely on field observations; (3) the method used to optimize sample size can effectively tackle the cost-related concerns of subsequent continuous monitoring. Overall, the neglect of forest components is inevitably bound to give rise to an underestimation of wetlands, and use of an MA estimator and optimizing the sample size could effectively address the cost issue in continuous monitoring. This holds significant importance when developing management strategies to prevent the further degradation of wetland ecological functions and carbon sink capabilities.

Forest Change Monitoring Based on Block Instance Sampling and Homomorphic Hypothesis Margin Evaluation

Forests play a crucial role in maintaining the integrity of natural ecosystems. Accurate mapping of windfall damages following storms is essential for effective post-disaster management. While remote sensing image classification offers substantial advantages over ground surveys for monitoring changes in forests, it encounters several challenges. Firstly, training samples in classification algorithms are typically selected through pixel-based random sampling or manual regional sampling. This approach struggles with accurately modeling complex patterns in high-resolution images and often results in redundant samples. Secondly, the limited availability of labeled samples compromises the classification accuracy when they are divided into training and test sets. To address these issues, two innovative approaches are proposed in this paper. The first is a new sample selection method which combines block-based sampling with spatial features extracted by single or multiple windows. Second, a new evaluation criterion is proposed by using the homomorphic hypothesis margin map with out-of-bag (OOB) accuracy. The former can not only assess the confidence level of each pixel category but also make regional boundaries clearer, and the latter can replace the test set so that all samples can be used for change detection. The experimental results show that the OOB accuracy obtained by spatial features with whole block sampling was 7.2% higher than that obtained by spectral features with pixel-based sampling and 2–3% higher than that for block center sampling, of which the highest value reached 98.8%. Additionally, the feasibility of identifying storm-damaged forests using only post-storm images has been demonstrated.

Airline business models as complex systems: assessing component interdependencies through interpretive structural modelling

PurposeThis paper explores the intricate interdependencies among core components of airline business models (BMs). In the airline industry, where BMs are complex systems, a successful BM requires an orchestrated configuration of various components. However, there is a paucity of research in BM literature pertaining to the interrelationships among key components of airline BMs.Design/methodology/approachEmploying interpretive structural modelling, we gathered input from experts in Iran to assess the driving power and dependency of elements within airline BMs.FindingsOur findings highlight the significance of operating environment conditions and competitive market dynamics as pivotal external components shaping the foundational structure. Value proposition, customer relationship management, and process monitoring are crucial linkage components that drive power and dependency. Notably, capturing value is positioned with the highest dependency.Practical implicationsWe utilised the ISM technique to visualize interdependencies within airline business models, aiding strategic decision-making. Our findings suggest aligning business and operational strategies with market needs ensures effective value creation and capture, maintaining competitive advantage in the airline industry. In addition, our research reveals critical factors affecting value creation and capture, emphasising monitoring the operating environment and competitive market, and strategically managing value propositions and customer relationship initiatives in the airline industry. We advise adapting business models to external changes for sustained growth and recommend regular monitoring of industry trends and customer expectations.Originality/valueFramed within complexity theory, these insights offer valuable perspectives on identifying and situating critical BM components in the airline industry. The practical implications derived from this study serve as strategic tools for airline managers and potential investors to optimise the design of their airline BMs.

Spatiotemporal evolution and driving factors analysis of fractional vegetation coverage in the arid region of northwest China

The arid region of northwest China (ARNC) is the most ecologically fragile region in China, and is characterized by harsh natural conditions, severe soil erosion, and poor soil fertility. Understanding long-term vegetation changes in this region is critical for effective environmental monitoring and climate change adaptation. Fractional vegetation coverage (FVC) is a key parameter for characterizing the ecological conditions of the ARNC. However, the reliance on low-resolution FVC and NDVI data due to the lack of medium-resolution data has limited our understanding of the environmental dynamics in this region. Therefore, this study addressed this gap by utilizing Landsat data to generate FVC data, enabling a detailed investigation of the spatial-temporal variations and driving factors of vegetation in the ARNC from 2000 to 2020. The results indicated the following: (1) The FVC was generally low, with an average of 0.191. The FVC was greater in the northwest and lower in the southeast in terms of spatial distribution features. The trend of FVC change in ARNC showed significant spatial variability, with degradation outweighing improvement. (2) The coefficient of variation of FVC was 0.377, indicating significant temporal fluctuations, with more stable conditions in the northwest than in the southeast. (3) The spatial differentiation of the FVC in ARNC was primarily driven by land cover types, evapotranspiration, and precipitation, with explanatory powers exceeding 30 % each. This study is significant because it provides a comprehensive understanding of vegetation dynamics in one of China's most vulnerable regions, offering critical insights for ecological restoration, desertification control, and sustainable development. The findings underscore the importance of targeted ecological governance to address the challenges posed by environmental degradation in the ARNC.

Connected Dominating Set Construction Using the Hunger Games Search Optimisation Algorithm in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) play a significant role in monitoring changes in the physical environment, relying on a subset of designated nodes known as the Connected Dominating Set (CDS) to facilitate communication and serve as the network's virtual backbone for routing and clustering. Despite the various algorithms for CDS construction, the challenges persist in terms of construction cost and scalability. In this context, this paper introduces the utilisation of the Hunger Games Search Optimisation Algorithm (HGSOA) for Connected Dominating Set Construction in Wireless Sensor Networks (HGSOA-CDS-WSN). The primary intention of this paper is “to develop a technique capable of constructing small-sized CDS with reduced construction costs, particularly suitable for uniformly or randomly distributed nodes”. The proposed HGSOA-CDS-WSN method is implemented in MATLAB based on various metrics. The results are compared with those of existing techniques, such as the Parallel Structure from Motion for UAV Pictures using Weighted CDS (PSfM-CDS-WSN), Polyhedra Associated with Locating-Dominating, Open Locating-Dominating, and Locating Total-Dominating Sets in Graphs (PAL-CDS-WSN), and Sustainable Maintenance of Connected Dominating Set by Solar Energy Harvesting for Internet of Things Networks (SHE-CDS-WSN). The proposed technique achieves lower delay by 13.24%, 25.64%, and 9.96%, and higher throughput by 18.34%, 24.55%, and 23.04% compared to the existing methods. This systematic evaluation underscores the efficacy and superiority of the proposed HGSOA-CDS-WSN technique in terms of connected dominating set size, construction time, energy efficiency, network stability, and overall performance, highlighting its potential to advance wireless sensor network applications.