Dynamic Changes In Environment Research Articles

On the phylogenetic position of Rhinolophus sakejiensis (Chiroptera: Rhinolophidae)

Abstract The Sakeji horseshoe bat Rhinolophus sakejiensis is an endemic to south-central Africa. Until recently, it remained known only from the type series of three specimens collected in Nchila Wildlife Reserve, north-western Zambia. The phylogenetic position of R. sakejiensis was evaluated based on morphologic traits and it was suggested that it represents a part of the ‘clivosus-complex’ within the Rhinolophus ferrumequinum clade of the genus. In this study, we provide an insight into the phylogenetic position of R. sakejiensis employing molecular data, using a newly discovered specimen that originates from the identical area as the type series. We assessed the genetic relationships of R. sakejiensis using the mitochondrial cytochrome b gene. This analysis revealed this species to be phylogenetically distinct from morphologically similar congeners of the capensis, fumigatus, and ferrumequinum species groups. In fact, it does not belong to any recognised species group but rather represents a distinct evolutionary lineage – along with another African relic bat, Rhinolophus horaceki. The divergence time of R. sakejiensis from R. horaceki, its closest relative, was estimated to have occurred in the Middle Miocene, roughly at 15.3 Ma, a period of dynamic environmental changes that may have led to widely separated occurrences of various relic bat lineages.

Leveraging Deep Learning in Remote Sensing: A Novel Approach for Agricultural Greenhouse Detection and Innovation Management

Innovation management plays a pivotal role in harnessing advanced technologies to drive progress across diverse fields. In this context, integrating deep learning models within remote sensing technologies presents transformative potential for monitoring, change detection, analysis, and decision-making in fields such as agriculture, urban planning, and environmental studies. This study examines the role of sophisticated deep learning approaches in analyzing high-resolution satellite imagery to improve the detection of agricultural greenhouses. Using MMSegmentation (DeepLabv3Plus) with multispectral data at 0.7-meter resolution, the research addresses the limitations of traditional methods by substantially enhancing detection accuracy and efficiency. To address data scarcity and increase model robustness, advanced data augmentation techniques—such as rotations, scaling, and flipping - expand dataset diversity, fostering adaptability and performance under diverse conditions. The study also assesses the impact of environmental factors, including seasonal variations and weather, on model performance. Suggested improvements include expanding the dataset to encompass a wider variety of greenhouse types and conditions, incorporating high-resolution or hyperspectral imagery for finer details, and building multi-temporal datasets to capture dynamic environmental changes. The findings underscore the importance of advanced innovation management in enhancing remote sensing applications, offering actionable insights for agricultural management in Albania and similar regions. This research contributes to the broader field of innovation management by showcasing how deep learning can revolutionize practical applications in agriculture

A Bayesian-optimized 1D CNN-based outlier detection approach for wireless sensor networks

Wireless sensor networks (WSNs) have recently emerged as a critical technology in various applications, including industrial automation, building monitoring, and military. However, the data generated by these networks are often prone to outliers, which can compromise sensor data quality and reliability. Detecting outliers is paramount to ensure proper network functioning. Traditional detection techniques pose several challenges, such as weak adaptability to the increasing complexity and dynamic environmental changes, limited accuracy, and higher computation costs. To address these challenges, this paper proposes an optimized one-dimensional convolutional neural networks (1D CNN)-based outlier detection approach for WSNs. This approach comprises two key modules: a predictor module and an outlier detector. The predictor module employs a 1D CNN model to forecast forthcoming sensor measurements based on historical data. Bayesian optimization is used to enhance the 1D CNN model’s accuracy. The outlier detector identifies outliers based on the Euclidean distance between the predicted measurements and their corresponding actual values. Experiments on synthetic and real-world datasets reveal that our proposed approach outperforms other existing deep learning-based frameworks in terms of accuracy, F1 score, and false alarm rates.

Uncovering the Catalysts of Sustainable Business Performance: Digital Orientation, Entrepreneurial Competency, and Strategic Change

ABSTRACTThis research investigates the complex relationships between digital orientation, entrepreneurial competency, and strategic change, and their impact on sustainable business performance. It also explores how sustainable competitive advantage and flexible resources moderate the link between strategic change and sustainable performance outcomes in dynamic business environments. The authors collected survey data from 349 small and medium‐sized enterprises (SMEs) to test the proposed model. They then used partial least squares structural equation modelling (PLS‐SEM) with SmartPLS 4 software to analyze the relationships between the variables. The findings reveal that digital orientation strongly drives strategic change, which in turn strongly enhances sustainable business performance. Digital orientation also directly leads to sustainable performance. Strategic change mediates the relationship between digital orientation and sustainable performance, while entrepreneurial competency partially mediates the effects of digital orientation. Competitive advantage and flexible resources moderately strengthen the link between strategic change and sustainable performance. This study provides novel insights into the complex interplay between digital orientation, entrepreneurial competency, and strategic change, and how these factors jointly contribute to sustainable business performance. The findings also uncover the moderating roles of competitive advantage and flexible resources, offering practical guidance for managers seeking to enhance long‐term organizational success in dynamic environments.

Some methodological aspects of Ukraine’s foreign criminal and legal policy

The article examines the key methodological aspects of Ukraine’s foreign criminal and legal policy. In modern conditions of globalization, crime is increasingly becoming transnational in nature, which requires effective interaction of the state with international institutions. The author analyzes the role of scientific methods, such as dialectical, comparative law, historical, formal-legal, statistical and sociological, in the study of this issue. Foreign criminal and legal policy is considered as a multifaceted system that combines legal, political and international aspects to combat crime. The importance of harmonizing national legislation with international standards is emphasized, as well as the need for a critical review of traditional methodological approaches to ensure effective integration into the international legal space. The importance of methodology for the study of legal phenomena, its influence on the formation and implementation of foreign criminal and legal policy as a component of the general legal policy of the state is revealed. The author emphasizes the need to revise traditional research methods, which are based mainly on a formal-legal approach that does not take into account dynamic changes in the global legal environment. It is noted that improving the methodological tools will contribute to the harmonization of the national legal system with international standards, strengthening the international authority of Ukraine, as well as increasing the effectiveness of the fight against crime. The results of the study are aimed at improving the mechanisms of international cooperation of Ukraine, adapting the legal system to modern challenges and ensuring the sustainable development of the rule of law. Thus, the study of methodological aspects of Ukraine’s foreign criminal law policy not only deepens the scientific understanding of this area, but also forms the foundation for creating an effective strategy for international interaction adapted to the modern challenges of globalization. Particular attention is paid to the importance of implementing independent methodological reflection as a means of adapting national legal policy to international standards. Emphasis is placed on the importance of integrating criminal law norms of Ukraine into the international legal space. The author emphasizes that deepening methodological knowledge and developing new approaches will ensure the effective coexistence of national and international legal systems, contributing to their harmonization.

Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces.

This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot's terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings). The results demonstrate that the proposed framework provides the robot with an enhanced capability to perceive and interpret its environment and adapt to dynamic environment changes. This paper contributes to the advancement of robotic navigation and path-planning systems by providing a scalable and efficient framework for environment analysis. The integration of various point cloud processing techniques into a single architecture not only improves computational efficiency but also enhances the robot's interaction with its environment, making it more capable of operating in complex, hazardous, unstructured settings.

MODELLING AND SIMULATION OF A PATH-PLANNING MOBILE ROBOT FOR SUSTAINABLE AGRICULTURAL APPLICATIONS

This study presents a simulation of a path-planning mobile robot navigation system for agricultural applications, integrating the A* algorithm and reinforcement learning techniques. The system aims to create a robust and efficient system capable of autonomously navigating complex agrarian environments. The A* algorithm is used for initial path planning, while reinforcement learning refines the path in real-time to handle dynamic obstacles and environmental changes. The simulation environment, developed using ROS and Gazebo, replicates a realistic agricultural field with varying terrains and obstacles. The results show that the path efficiency of the A* Algorithm is 120m, Reinforcement Learning is 115m, and the Hybrid Approach is 110, with time taking 45, 42, and 40 seconds respectively. Also, the obstacle avoidance metric shows that the hybrid approach has fewer collisions during the simulation, with a 98% obstacle avoidance rate compared with the reinforcement learning of 95% and the A* algorithm of 90%. For the adaptability to changing conditions metric for A* Algorithm, Reinforcement Learning, and Hybrid Approach, the performance under varied terrain is 80%, 85%, and 90%, respectively. The hybrid approach of A* and reinforcement learning significantly enhances the robot's navigation capabilities, demonstrating superior performance across all evaluated metrics. This research contributes to the advancement of autonomous agricultural robots, providing a foundation for future development and field trials. Integrating advanced path-planning algorithms in agricultural robotics holds significant potential for improving productivity and efficiency in farming operations.

ЭКОНОМИКА ЗНАНИЙ: РАЗВИТИЕ КОМПЕТЕНЦИЙ ПЕРСОНАЛА

In the context of the transition to a knowledge economy, human capital is be-coming a key strategic resource that determines the competitiveness of organizations. The article discusses modern approaches and tools for developing personnel competencies necessary for adaptation to dynamic changes in the business environment. Particular attention is paid to the introduction of digital learning technologies, personalization of educational programs and inter-disciplinary development of employees. Examples of successful practices in this area are ana-lyzed, the importance of partnership between business, government and educational institutions is emphasized. The results obtained can be useful for developing effective human capital man-agement strategies in the context of the knowledge economy.

Achieving Logistics Firm Performance Through High-Performance Work System (HPWS) and e-HRM Capabilities: The Moderating Role of Digital Talent Acquisition

The rising global competition, post-pandemic crisis, and dynamic changes in the business environment have created anxiety and fear among employees resulting in less productivity at the workplace. Consequently, logistics firms are striving to design strategic business models that encourage employees to keep working during turbulent environments and achieve firm goals. To address this issue, the current study integrates a high-performance work system and e-HRM and examines employee work engagement behavior. Moreover, the moderating effect of digital talent acquisition is tested between employee work engagement and logistic firm performance. Hypotheses were tested through empirical data. Findings revealed that high-performance work systems and e-HRM practices explained substantial variance in employee work engagement. Similarly, work engagement and digital talent acquisition have explained a large variance in logistic firm performance. Moreover, motivation capability is the most influential factor due to large effect size [Formula: see text] towards employee work engagement. This study is original as it integrates e-HRM and high-performance work systems toward employee work engagement. In addition to that testing the moderating impact of digital talent acquisition between work engagement and firm performance makes this research unique and valuable.

High-quality reference genome of predatory mite Neoseiulus californicus McGregor (Acari: Phytoseiidae) provides insights into its biological traits and potential RNAi off-target effects.

Neoseiulus californicus is a predatory mite that can control various spider mites and other small arthropods. Despite its acknowledged effectiveness in the natural enemy market, a crucial knowledge gap exists in understanding the genomic features related to its predatory traits and adaptation. With the increasing emphasis on modern pest management strategies and dynamic environmental changes in plant production trends, constructing a reliable genomic resource for N. californicus becomes imperative. In this study, we provided a high-quality genome assembly and annotation of N. californicus, with a size of 188.43 Mb and 12 946 predicted genes. We identified genomic features and traits related to its detoxification, stress response, sensory system, mobility, secretory toxins, digestive enzymes and horizontally transferred genes (HGTs) by comparative genomics. We also predicted the potential off-target effects of double-stranded RNA (dsRNA) that could be used to control spider mites, based on public data and the N. californicus genome. Our study provided a valuable genomic resource for a commercialized predatory mite, offering useful insights for the design of integrated pest management (IPM) strategies in the new era. Further studies are needed to explore the functional roles of key gene families in predatory mites, as well as their interactions with their prey and IPM strategies. © 2024 Society of Chemical Industry.

Simulating water and salt changes in the root zone of salt-alkali fragrant pear and the selection of the optimal surface drip irrigation mode.

Faced with the increasingly serious problem of water scarcity, developing precise irrigation strategies for crops in saline alkali land can effectively reduce the negative effects of low water resource utilization. Using a model to simulate the dynamic changes in soil water and salt environment in the root zone of fragrant pear trees in saline alkali land, and verifying them from a production practice perspective with comprehensive benefits as the goal, can optimize the irrigation amount and irrigation technology elements of saline alkali fruit trees, broaden the comprehensive evaluation perspective of decision-makers, and have important significance for improving the yield and production efficiency of forestry and fruit industry in arid and semi-arid areas worldwide. In this study, a two-year field experiment based on three irrigation levels (3000, 3750, and 4500 m3·ha-1) and four emitter discharge rates (1, 2, 3, and 4 L·h-1) was conducted in Xinjiang, China. The root zone soil water content (SWC) and soil salinity content (SSC) dynamics were simulated during the fertility period of fragrant pear using the numerical model HYDRUS-2D and field data. The results showed that the R2, root mean squared error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE) of the HYDRUS-2D simulated soil water content (SWC) (soil salinity content SSC) reached 0.89-0.97 (0.91-0.97), 0.02-0.16 cm3·cm-3 (0.22-1.54 g·kg-1), and 0.76-0.95 (0.68-0.96), respectively, indicating the strong performance of the model. A positive correlation was observed between the irrigation amount and soil infiltration depth. Moderately increasing irrigation amount could effectively leach soil salinity at a depth of 80-100 cm and maintain a water and salt environment in the main root zone of 0-80 cm, benefiting the growth and development of the main root system of fragrant pear, as well as the yield and quality of above-ground fruits. The irrigation amount and emitter discharge were optimized and quantified based on multi-objective optimization methods, normalization processing, and spatial analysis methods to maximize yield, fruit weight, soluble solids, and net profits. When the yield, fruit weight, soluble solids, and net profits simultaneously reached 90% of their maximum value, the irrigation amount and emitter discharge ranges were 4274-4297 m3·ha-1 and 3.79-3.88 L·h-1, respectively. Our study provides new insights into regulating soil water and salt environmental factors in the saline fragrant pear root zone and assessing the impact of soil water and salt management under precision irrigation strategies, and profoundly influences decision-making for irrigation of forest fruits in saline arid zones based on a production practice perspective.

Phenotypic consequences of logarithmic signaling in MAPK stress response.

How cells respond to dynamic environmental changes is crucial for understanding fundamental biological processes and cell physiology. In this study, we developed an experimental and quantitative analytical framework to explore how dynamic stress gradients that change over time regulate cellular volume, signaling activation, and growth phenotypes. Our findings reveal that gradual stress conditions substantially enhance cell growth compared to conventional acute stress. This growth advantage correlates with a minimal reduction in cell volume dependent on the dynamic of stress. We explain the growth phenotype with our finding of a logarithmic signal transduction mechanism in the yeast mitogen-activated protein kinase (MAPK) osmotic stress response pathway. These insights into the interplay between gradual environments, cell volume change, dynamic cell signaling, and growth, advance our understanding of fundamental cellular processes in gradual stress environments.

Adapting to Changes: A Novel Framework for Continual Machine Learning in Industrial Applications

This paper is dedicated to solving the problem of concept drift in industrial plants using artificial intelligence methods. For this purpose, methodological approaches and procedures are considered and analyzed. Based on the findings, reference architectures were developed at different abstraction levels that can be used in an industrial environment and enable continuous machine learning. Continuous machine learning offers the possibility of adapting to dynamic changes in the production environment, which are reflected in constantly changing data sets. Through a combination of machine learning techniques, a novel and practical framework for continuous learning, also known as lifelong learning, is presented. The integration of problem-focused machine learning methods is advancing in production, e.g., predictive maintenance, process optimization or fault detection. Thereby, fully or semi-automated adaptations to changing environments requiring continuous improvements are less often explored, although practical use cases often require adaptive capabilities as the physical data distribution may change over time. In this paper, the application was continuously improved based on case studies and empirical results, and finally validated with a quality assurance application. Various methods and approaches for detecting concept and data deviations, retraining, packaging and model updating had to be investigated, which led to the question of what a real industry-oriented implementation could look like. The result is a reference architecture that can run on cloud and edge computing resources. This reference architecture is validated in real-world application in the parquet production sector, proving its feasibility and efficiency.

Transformer-based probabilistic demand forecasting with adaptive online learning

Demand forecasting is crucial for the operation and planning of the energy and power industry. Accurate demand forecasting can assist decision-makers in reducing operational or planning costs while optimizing supply chains and resource utilization. Due to various uncertainties and dynamically changing environments, traditional offline deterministic forecasting methods may face difficulty in forecasting demands accurately and be unable to effectively quantify the uncertainties in forecasts. To adapt to dynamic environmental changes, several online learning methods have been proposed, which require large computational resources. To overcome this limitation, this paper proposes a Transformer-based probabilistic demand forecasting with an adaptive online learning algorithm. More specifically, we design a probabilistic decoder for the Transformer to quantify forecast uncertainty. Furthermore, an adaptive online learning algorithm is employed to selectively update parameters based on an adaptive update strategy, reducing computational burden while allowing for timely adaptation to dynamic environmental changes. The performance of the proposed method is evaluated on multiple benchmark datasets and real electricity demand data from fused magnesium furnace (FMF). The results show that the proposed method exhibits superior accuracy in both deterministic and probabilistic forecasting scenarios. Finally, through ablation experiments, the effectiveness of the proposed adaptive online probabilistic forecasting algorithm is validated.

LIO-SAM++: A Lidar-Inertial Semantic SLAM with Association Optimization and Keyframe Selection.

Current lidar-inertial SLAM algorithms mainly rely on the geometric features of the lidar for point cloud alignment. The issue of incorrect feature association arises because the matching process is susceptible to influences such as dynamic objects, occlusion, and environmental changes. To address this issue, we present a lidar-inertial SLAM system based on the LIO-SAM framework, combining semantic and geometric constraints for association optimization and keyframe selection. Specifically, we mitigate the impact of erroneous matching points on pose estimation by comparing the consistency of normal vectors in the surrounding region. Additionally, we incorporate semantic information to establish semantic constraints, further enhancing matching accuracy. Furthermore, we propose an adaptive selection strategy based on semantic differences between frames to improve the reliability of keyframe generation. Experimental results on the KITTI dataset indicate that, compared to other systems, the accuracy of the pose estimation has significantly improved.

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

Aquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species.

Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with Kalman filter

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Kalman Filter, is employed to handle uncertainties and enhance computational efficiency. The fusion process integrates data from sensors monitoring parameters like chlorophyll concentration, surface water elevation, temperature, and precipitation, producing Markov features to capture temporal transitions and environmental dynamics. Data synchronization and fusion are achieved through recursive KF methods, enabling real-time adaptive management in response to environmental fluctuations such as seasonal changes, precipitation (6–18%), and evaporation rates (1.2–11.9 mm/day). Over a 30-day evaluation period, the model accurately predicted chlorophyll concentrations, reaching 128 mg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mg}/\\hbox {m}^{3}$$\\end{document} in mid-level inflow regions (3.6 m water elevation) compared to 86 mg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {mg}/\\hbox {m}^{3}$$\\end{document} in extreme inflow areas (5.5 m). The integration of Markov feature extraction and eigenvalue estimation enhanced prediction stability and sensitivity, with the KF maintaining computational efficiency at 7.8 ms per computation cycle. The model’s accuracy was validated by achieving a residual error of less than 0.05 with minimal noise interference. Overall, the system provides a resilient and precise framework for real-time lake water quality assessment, capable of handling multi-parameter uncertainties and dynamic environmental changes, thereby supporting informed decision-making for aquatic ecosystem management.

Support Vector Machines With Uncertainty Option and Incremental Sampling for Kriging

ABSTRACTThis paper presents a novel approach to pollution assessment by investigating support vector machines (SVM) with an uncertainty option to overcome the limitations of traditional kriging. While kriging is a major tool for geostatistical modelling, allowing to estimate the distribution of contaminants in a region from a small set of samples, it does not allow to extract also the uncertainty map. An uncertainty map is of great interest, as it allows to identify regions of high uncertainty where one should sample in order to reduce high level of uncertainties. In this paper, we propose two variants of the SVM with an uncertainty option, each using a different hinge loss to improve the accuracy and efficiency. These losses allow to estimate different levels of contaminations, as well as uncertainty, such as the three levels: positive, uncertain and negative, namely for pollution estimation: high‐pollution, uncertain and low‐pollution. In addition to the exploration of SVM variants, we propose an innovative active sample selection strategy based on the uncertainty criterion. This strategy is designed to systematically reduce uncertainties in pollution assessment, thus providing adaptability to dynamic environmental changes. An incremental SVM with an uncertainty option is introduced to further optimise the sample selection process. Furthermore, the decision‐making process is refined through the introduction of a novel three‐hinge loss. The corresponding optimization problem and its resolution allow for a more nuanced contamination assessment with multiple levels of estimation, providing a valuable tool for characterising contamination levels with increased granularity. Extensive experiments on synthetic and real data validate the proposed methodology. Synthetic data simulations assess the quality of the approach, while real data from a two‐dimensional porosity measurement demonstrate practical applicability. This research contributes to the advancement of pollution assessment methodologies, providing an adaptable solution for environmental monitoring.

Adaptive control for refrigeration via online identification

Vapor compression refrigeration systems (VCRS) occupy a crucial position in modern society and in the field of thermal sciences. However, the operation of VCRS is subjected to both external disturbances (dynamic-changing environment) and inherent system characteristics (coupling or nonlinear features), leading to issues like reduced refrigeration efficiency and significant fluctuations in cooling capacity. To address these challenges and enhance the adaptability of VCRS in dynamically changing environments, this study establishes a dynamic simulation model for VCRS based on the Switched Moving-Boundary method. The impact of external environmental disturbances on refrigeration performance is investigated, and continuous online identification methods are employed to elucidate its internal coupling characteristics and nonlinear features. The adaptive temperature control method is introduced, benefiting from the developed recursive least squares method with a forgetting factor for online identification, achieving precise model identification which facilities the real-time parameters tuning of adaptive controller. The results indicate that the hybrid paradigm of online identification and adaptive control algorithm not only effectively handles various disturbances but also reduces overshoot and IAE by 2–3 orders of magnitude compared to traditional PID controllers. Adaptive PID control maintains overshoot in the 10–4 order of magnitude and IAE in the 10–5 order of magnitude.

التوجه الريادي و أثره في بناء المنظمات المتعلمة

The study aimed to explore the impact of the entrepreneurial orientation (innovative, risk-taking, proactive, competitive aggressiveness) on building learning organizations with their dimensions (personal mastery, mental models, shared vision, team learning, and systemic thinking) in Yemeni universities represented by Aden University and the University of Technology and Science. The study relied on the descriptive analytical approach, and data was collected through a questionnaire distributed to a sample of faculty members at Sana'a University and the University of Technology and Science, totaling 320 individuals. Data were analyzed using the SPSS statistical software. The findings revealed that the entrepreneurial orientation in Yemeni universities significantly influences the establishment of learning organizations. The study indicated the need for Yemeni universities to enhance entrepreneurial orientations by adopting innovative and proactive approaches to achieve competitive advantages in light of dynamic environmental changes. The results showed the importance of building learning organizations to improve the performance of Yemeni universities and enhance their role in achieving sustainable development by providing qualified graduates capable of integrating into the labor market and contributing to knowledge and innovation. The study recommends that Yemeni universities continue developing entrepreneurial orientations (innovative, risk-taking, and proactive) and enhance them to build learning organizations, which are essential for improving university performance and achieving excellence in education.