Update Scheme Research Articles

Adaptive dynamic self-learning grey wolf optimization algorithm for solving global optimization problems and engineering problems.

The grey wolf optimization algorithm (GWO) is a new metaheuristic algorithm. The GWO has the advantages of simple structure, few parameters to adjust, and high efficiency, and has been applied in various optimization problems. However, the orginal GWO search process is guided entirely by the best three wolves, resulting in low population diversity, susceptibility to local optima, slow convergence rate, and imbalance in development and exploration. In order to address these shortcomings, this paper proposes an adaptive dynamic self-learning grey wolf optimization algorithm (ASGWO). First, the convergence factor was segmented and nonlinearized to balance the global search and local search of the algorithm and improve the convergence rate. Second, the wolves in the original GWO approach the leader in a straight line, which is too simple and ignores a lot of information on the path. Therefore, a dynamic logarithmic spiral that nonlinearly decreases with the number of iterations was introduced to expand the search range of the algorithm in the early stage and enhance local development in the later stage. Then, the fixed step size in the original GWO can lead to algorithm oscillations and an inability to escape local optima. A dynamic self-learning step size was designed to help the algorithm escape from local optima and prevent oscillations by reasonably learning the current evolution success rate and iteration count. Finally, the original GWO has low population diversity, which makes the algorithm highly susceptible to becoming trapped in local optima. A novel position update strategy was proposed, using the global optimum and randomly generated positions as learning samples, and dynamically controlling the influence of learning samples to increase population diversity and avoid premature convergence of the algorithm. Through comparison with traditional algorithms, such as GWO, PSO, WOA, and the new variant algorithms EOGWO and SOGWO on 23 classical test functions, ASGWO can effectively improve the convergence accuracy and convergence speed, and has a strong ability to escape from local optima. In addition, ASGWO also has good performance in engineering problems (gear train problem, ressure vessel problem, car crashworthiness problem) and feature selection.

Edge Intelligence-driven Joint Offloading and Resource Allocation for Future 6G Industrial Internet of Things

The 6G will undergo an unprecedented transformation to revolutionize the wireless system evolution from connected things to connected intelligence. Additionally, data scattered around the industrial environments can be collected for the sake of enabling intelligent operations. In this paper, the promising multi-access edge computing (MEC) service is introduced into the IIoT system to assist the computation offloading and resource allocation for different compelling applications. Moreover, relying on defining a total cost function as a weighted sum of task delay and energy consumption, a novel deep reinforcement learning (DRL) based framework is proposed to jointly optimize task offloading and resource allocation. More specifically, the task offloading is decomposed with the aid of the new isotone action generation technique (IAGT) and adaptive action aggregation update strategy (3AUS) based on the proposed DRL framework, and the initial problem can be transformed into a convex optimization problem to solve the resource allocation for each IIoT device. Additionally, we periodically renovate the offloading policy in the DRL framework so that our proposed DRL-based decision-making algorithm can beneficially adapt to different network environments. Finally, extensive simulation demonstrate that our proposed algorithm for each IIoT device can obtain quasi-optimal system performance compared with some conventional baseline algorithms

ЗАБЕЗПЕЧЕННЯ ФІНАНСОВОЇ СТАБІЛЬНОСТІ ПІДПРИЄМСТВА ЗА УМОВ ЗМІН

The article explores the challenges of sustainable and secure economic development at the macro level, considering them as the foundation for ensuring the economic security of enterprises. Special attention is given to the directions for improving the functioning of enterprises through the utilization of contemporary methods and conditions for ensuring economic security.The article examines the challenges of sustainable and secure economic develop-ment at the macro level, considering them as the foundation for ensuring the economic security of water transport enterprises. The identified key issues in economic security and formulate macro-level development questions aimed at ensuring the stability and effectiveness of the long-term functioning of enterprises. Methods of ensuring economic security:-development and implementation of effective risk management strategies;-enhancement of financial stability of the enterprise;-implementation of modern monitoring and analysis systems for economic indicators;-tnsuring robust information security;-implementation of measures to strengthen the legal status of the enterprise;-regular training of personnel in the field of security and crisis management.Indicators of ensuring economic security. Conclusions on ensuring and managing the economic security of the enterprise in an unstable economy.The need for a systematic approach: In the conditions of an unstable economy, it is critically important to manage the economic security of the enterprise systematically, taking into account numerous external and internal fac-tors.Active risk management. Effective risk management plays a key role in ensuring economic security. Enterprises should develop and regularly update strategies for minimizing risks in accordance with changing economic conditions.Flexibility and adaptation. Flexibility in responding to changes in the economic environment and the ability to adapt become critical qualities for successful enterprises. Economic security requires constant monitoring and a quick response to new challenges.Innovation and technological upgrades. Investments in innovation and techno-logical upgrades contribute to strengthening the competitiveness of the enterprise, making it more resilient to economic shocks.

STRATEGIC PRINCIPLES OF CULTURAL POLICY OF UKRAINE IN THE CONTEXT OF THE COURSE OF EUROPEAN INTEGRATION

The purpose of the research deals with proving the need for the formation of strategic foundations of cultural policy in Ukraine for the conditions of military aggression and the post-war period, taking into account the requirements of the state course of European integration. The current state of the cultural sphere in Ukrainian society is demonstrated to be characterised by at least the following factors: the destruction of a large number of infrastructure facilities of cultural institutions due to military operations, a significant reduction in the financing of culture from state and local budgets and the requirement to learn European practices in the implementation of cultural policy as a result of Ukraine's acquisition of the status of an EU member. Given these circumstances, the political class and the administrative establishment of Ukraine are faced with the task of creating a renewed cultural policy that would be able to overcome the losses and traumas of the war period, ensure cultural development in the post-war period, aimed at forming a stable identity of Ukrainians. Hence, the task arises through the means of culture of forming the European component of Ukrainian identity, since the condition for the success of the course of European integration is the need to master the values on which the European community is built. It is established that an updated strategy plan for cultural development is required to address these issues. The necessity of modernization of the legislative base of cultural policy is shown, in particular, the approval of decentralization of the functioning of government bodies, strengthening support for culture from the state and municipal budgets, creating a legislative base for public-private partnership in the cultural sphere, strengthening the information component of the cultural process.

СТРАТЕГИЯ ПРОСТРАНСТВЕННОГО РАЗВИТИЯ РОССИИ: ВЕКТОРЫ ОБНОВЛЕНИЯ

The large-scale geopolitical and geo-economic changes inherent in the modern world order, complemented by prolonged economic, technological, and demographic trends, are projected onto the Russian space, correcting both its external contour and internal architecture. In this context, the discourse on targets, directions, and ways of activating the territorial socio-economic dynamics of the Russian Federation has revived, and the development of an updated strategy for its spatial development has been reinvigorated. Based on the tools of socio-geographical analysis, the study aims to identify the most important problem areas characteristic of modern Russia, dilemmas, alternatives, and priorities in the field of its spatial development, as well as to substantiate on this basis a conceptual approach to updating the key provisions of the current Spatial Development Strategy of the Russian Federation for the Period up to 2025. The paper notes the internal inconsistency of this document (the prevalence of orientation toward ‘alignment’ within the agglocentric model, the desire to combine centripetal trends with the search for formats to counteract the ‘compression’ of the developed space, etc.) and its simultaneous transitivity, susceptibility to innovation (‘municipalization’ of approaches, consideration of the ‘geopolitics factor’, etc.). The paper emphasizes the need to modify the Strategy by focusing on the exogenous determinants of Russia's spatial development (including the formation of ‘Greater Eurasia’), as well as its scientific, technological, and defense-industrial factors. We substantiate the reasonableness of a more consistent shift in emphasis to the stimulation of interregional and inter-municipal integration, to mutually supportive co-development of regions and municipalities within a single market, transport, logistics, and information space based on the balance of ‘Moscow-centrism’ with the development of other growth poles, expansion, and ‘interlayering’ of their spheres of influence.

Q-ADER: An Effective Q-Learning for Recommendation With Diminishing Action Space.

Deep reinforcement learning (RL) has been widely applied to personalized recommender systems (PRSs) as they can capture user preferences progressively. Among RL-based techniques, deep Q-network (DQN) stands out as the most popular choice due to its simple update strategy and superior performance. Typically, many recommendation scenarios are accompanied by the diminishing action space setting, where the available action space will gradually decrease to avoid recommending duplicate items. However, existing DQN-based recommender systems inherently grapple with a discrepancy between the fixed full action space inherent in the Q-network and the diminishing available action space during recommendation. This article elucidates how this discrepancy induces an issue termed action diminishing error in the vanilla temporal difference (TD) operator. Due to this discrepancy, standard DQN methods prove impractical for learning accurate value estimates, rendering them ineffective in the context of diminishing action space. To mitigate this issue, we propose the Q-learning-based action diminishing error reduction (Q-ADER) algorithm to modify the value estimate error at each step. In practice, Q-ADER augments the standard TD learning with an error reduction term which is straightforward to implement on top of the existing DQN algorithms. Experiments are conducted on four real-world datasets to verify the effectiveness of our proposed algorithm.

A multi-factor intelligent biologic search algorithm for closed-area fiber optic network planning

Fiber optic network planning is a critical aspect in numerous intelligent park construction projects. This study addresses the issue of fiber routing allocation within these enclosed parks, which necessitate substantial real-time communication during their operation. To optimize route allocation efficiency and account for line costs, we propose a Multi-factor Intelligent Biologic Search Algorithm (MIBSA). This algorithm operates within a hierarchical framework and progresses through three stages of enhancement. The foundational model utilizes an enhanced Lifelong Planning A* (LPA*) algorithm to delineate the initial path and non-uniformly allocate the primary pheromone of the ant colony algorithm. The upper tier integrates an improved Ant Colony Optimization (ACO) model, incorporating various factors into the pheromone update strategy for multi-objective fiber optic network planning. Simultaneously, this algorithm introduces a batch pheromone boosting mechanism and a momentum method to expedite the convergence process. Experimental findings highlight that MIBSA surpasses the conventional ACO method in solving fiber optic network planning problems, yielding an average comprehensive cost reduction of 16.42% and 7.82% under the environmental map of the objective problem and the general environmental map test, respectively.

Contrastive Correlation Preserving Replay for Online Continual Learning

Online Continual Learning (OCL), as a core step towards achieving human-level intelligence, aims to incrementally learn and accumulate novel concepts from streaming data that can be seen only once, while alleviating catastrophic forgetting on previously acquired knowledge. Under this mode, the model needs to learn new classes or tasks in an online manner, and the data distribution may change over time. Moreover, task boundaries and identities are not available during training and evaluation. To balance the stability and plasticity of networks, in this work, we propose a replay-based framework for OCL, named Contrastive Correlation Preserving Replay (CCPR), which focuses on not only instances but also correlations between multiple instances. Specifically, besides the previous raw samples, the corresponding representations are stored in the memory and used to construct correlations for the past and the current model. To better capture correlation and higher-order dependencies, we maximize the low bound of mutual information between the past correlation and the current correlation by leveraging contrastive objectives. Furthermore, to improve the performance, we propose a new memory update strategy, which simultaneously encourages the balance and diversity of samples within the memory. With limited memory slots, it allows less redundant and more representative samples for later replay. We conduct extensive evaluations on several popular CL datasets, and experiments show that our method consistently outperforms the state-of-the-art methods and can effectively consolidate knowledge to alleviate forgetting.

ЗАБЕЗПЕЧЕННЯ ФІНАНСОВОЇ СТАБІЛЬНОСТІ ПІДПРИЄМСТВА ЗА УМОВ ЗМІН

The article explores the challenges of sustainable and secure economic development at the macro level, considering them as the foundation for ensuring the economic security of enterprises. Special attention is given to the directions for improving the functioning of enterprises through the utilization of contemporary methods and conditions for ensuring economic security.The article examines the challenges of sustainable and secure economic develop-ment at the macro level, considering them as the foundation for ensuring the economic security of water transport enterprises. The identified key issues in economic security and formulate macro-level development questions aimed at ensuring the stability and effectiveness of the long-term functioning of enterprises. Methods of ensuring economic security:-development and implementation of effective risk management strategies;-enhancement of financial stability of the enterprise;-implementation of modern monitoring and analysis systems for economic indicators;-tnsuring robust information security;-implementation of measures to strengthen the legal status of the enterprise;-regular training of personnel in the field of security and crisis management.Indicators of ensuring economic security. Conclusions on ensuring and managing the economic security of the enterprise in an unstable economy.The need for a systematic approach: In the conditions of an unstable economy, it is critically important to manage the economic security of the enterprise systematically, taking into account numerous external and internal fac-tors.Active risk management. Effective risk management plays a key role in ensuring economic security. Enterprises should develop and regularly update strategies for minimizing risks in accordance with changing economic conditions.Flexibility and adaptation. Flexibility in responding to changes in the economic environment and the ability to adapt become critical qualities for successful enterprises. Economic security requires constant monitoring and a quick response to new challenges.Innovation and technological upgrades. Investments in innovation and techno-logical upgrades contribute to strengthening the competitiveness of the enterprise, making it more resilient to economic shocks.

Foolmix: Strengthen the Transferability of Adversarial Examples by Dual-Blending and Direction Update Strategy

Foolmix: Strengthen the Transferability of Adversarial Examples by Dual-Blending and Direction Update Strategy

Fuzzy Levenberg-Marquardt damping factor update strategy and Tikhonov regularization applied to a coupled conduction-radiation inverse heat transfer problem

The recently proposed Fuzzy Levenberg-Marquardt Damping Factor Updating Strategy (FLM) is used along with Tikhonov Regularization (TR) in order to solve a coupled conduction-radiation function estimation inverse problem. FLM consists of updating the Levenberg-Marquardt (LM) damping factor with the assist of Fuzzy Logic. To test the FLM algorithm combined with TR, a conduction-radiation problem is addressed, and it serves as test bed to the numerical experiments, where the objective is to obtain estimatives of parameters, such as the spatially variable scattering albedo, the thermal conductivity and the optical thickness, simultaneously, which is often performed separately. Two main test cases are presented, one considering only the radiation problem, used as benchmark, in order to investigate noise levels and hyperparameters. The second main test case deals with the coupled conduction-radiation heat transfer problem for different functional forms of the spatially variable scattering albedo. For all presented numerical experiments, good estimates were achieved for the sought functions and parameters. These results demonstrate that the methodology presented in this work, through the combination of the Fuzzy LM dampting factor updating strategy and the Tikhonov regularization scheme, may provide valuable tools for inverse heat transfer problems.

A dynamic incentive mechanism for data sharing in manufacturing industry

Data sharing is a critical component in a blockchain traceability platform. Therefore, creating a reasonable incentive mechanism to ensure that all enterprises participate in data sharing is vital for blockchain platforms. Currently, many researchers employ evolutionary game theory to analyze problems related to data sharing. However, evolutionary game theory typically assumes that the population composed of enterprises is mixed uniformly. Enterprises in the manufacturing industry are not uniformly mixed, as they tend to have specific connections with each other due to the size of enterprises and volume of business. Therefore, a networked evolutionary game is introduced to solve this problem. Firstly, an incentive model for enterprises sharing data is established. Then, a scale-free network is employed to simulate the connections between enterprises. To comprehensively consider the individual and group benefits of enterprises in the game, this study designs a strategy update rule for networked evolutionary game based on Discrete Particle Swarm Optimization and Variable Neighborhood Descent algorithm. To tackle the challenge of determining reasonable incentive values in networked evolutionary games, this study proposes a dynamic incentive mechanism based on the Q-Learning algorithm. Finally, the experiments indicate that this method can successfully facilitate the stable involvement of enterprises in data sharing.

The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023.

The 2023 update of the Chinese Society of Clinical Oncology (CSCO) Clinical Guidelines for Gastric Cancer focuses on standardizing cancer diagnosis and treatment in China, reflecting the latest advancements in evidence-based medicine, healthcare resource availability, and precision medicine. These updates address the differences in epidemiological characteristics, clinicopathological features, tumor biology, treatment patterns, and drug selections between Eastern and Western gastric cancer patients. Key revisions include a structured template for imaging diagnosis reports, updated standards for molecular marker testing in pathological diagnosis, and an elevated recommendation for neoadjuvant chemotherapy in stage III gastric cancer. For advanced metastatic gastric cancer, the guidelines introduce new recommendations for immunotherapy, anti-angiogenic therapy and targeted drugs, along with updated management strategies for human epidermal growth factor receptor 2 (HER2)-positive and deficient DNA mismatch repair (dMMR)/microsatellite instability-high (MSI-H) patients. Additionally, the guidelines offer detailed screening recommendations for hereditary gastric cancer and an appendix listing drug treatment regimens for various stages of gastric cancer. The 2023 CSCO Clinical Guidelines for Gastric Cancer updates are based on both Chinese and international clinical research and expert consensus to enhance their applicability and relevance in clinical practice, particularly in the heterogeneous healthcare landscape of China, while maintaining a commitment to scientific rigor, impartiality, and timely revisions.

Surgeons’ perspectives for a future obstetric fistula classification system: exploring the key parameters

Background There is currently no unified classification scheme for accurately describing vesico-vaginal fistulas, thereby limiting communication between surgeons and the ability to compare research. Many factors related to surgical failure or success have not been explored, and surgeons currently do not universally use the same classifications. The objective of this study is to determine which factors are the most important to include in a future obstetric fistula classification scheme. Methods Members of the International Society of Obstetric Fistula Surgeons were surveyed using a non-validated questionnaire to explore their experience and factors they think were most important to include in an updated comprehensive obstetric fistula classification scheme. Based on the surgeons’ response, an average ranking of each determinant of obstetric fistula classification system was computed. Results The most critical factors that surgeons found necessary to include in an updated fistula classification system are the bladder size (88.9%, n=16, rank=8.53), degree of fibrosis of the vagina (83.3%, n=15, rank=8.12), degree of urethral damage (88.9%, n=16, rank=9.34), location of the fistula (100%, n=18, rank=9.22), urethral length (94.4%, n=17, rank=9.06), and whether the fistula is circumferential or not (94.4%, n=17, rank=9.18). The least important factors were how long the patient had had a fistula (11.1%, n=2, rank=2.28) and intraoperative complications (22.2%, n=4, rank=4.59). For most factors important in repair, there was a non-significant correlation between experience level and ratings from 0-10. Scores for urethral length showed a significant correlation between increased surgical experience and higher ratings of importance. Conclusions Three most commonly used classification schemes by Goh, Waaldjik, and the World Health Organization (WHO) only capture some important factors determined by the surveyed surgeons. Given the inconsistency between what experienced and expert surgeons deem to be important determinants for the classification of obstetric fistula and what is currently included in classification systems, we call for constructing a new, validated classification system.

3D multi-object tracking with boosting data association and improved trajectory management mechanism

In the multi-object tracking (MOT) algorithm based on the tracking-by-detection paradigm, matching accuracy between detection and prediction, robustness to occlusion and consistency between trajectory and ground truth are three emphasized issues. In this study, we propose a tracking-by-detection-based online 3D MOT framework, which produces 3D bounding boxes with identity, position, and shape in real time from point clouds provided in each sequential frame. Specifically, first, to increase the correct matching rate, we propose a 3D efficient affinity (3D-EAFF) that combines position correlation and geometric features to optimize data association between predictions and actual detections. Second, to reduce the identity switches caused by occlusion, a trajectory deletion strategy is proposed by using multiple distance thresholds to judge occlusion as well as setting a decay coefficient to eliminate prediction error accumulation. Third, to enhance the smoothness of the trajectory, a trajectory update strategy based on the position relationship between detection and prediction is proposed. By reallocating weights between measurements and prior estimates for Kalman filter updates, we aim to achieve a more optimal dynamic balance. The experimental results on two representative 3D MOT benchmarks NuScenes and KITTI show that our method effectively improves the tracking accuracy, and its robustness to occlusion also reaches competitive performance.

A fast interpretable adaptive meta-learning enhanced deep learning framework for diagnosis of diabetic retinopathy

Gradient-based meta-learning algorithms offer promising solutions to the challenge of swift adaptation to new tasks, especially when faced with limited sample data. One pivotal concern in few-shot classification tasks is striking the right balance between interpretability and accuracy within the meta-learning framework. This study introduces a novel methodology titled Fast, Interpretable, and Adaptive meta-Learning based on Logistic Regression (FIAML-LR). Distinctively, FIAML-LR employs logistic regression to craft a meta-network in the inner loop. This design facilitates faster generation of the learning rate and weight attenuation coefficient, enhancing the interpretability of meta-learning for new task adaptation. An adaptable parameter update strategy is also embedded, initializing with a broader hyperparameter adjustment scope and fine-tuning progressively throughout the experiment. Experimental evidence reveals that, when implemented on a 4-CONV architecture, FIAML-LR not only bolsters the model's interpretability but also amplifies its accuracy for few-shot classification tasks. A focused investigation on the diabetic retinopathy dataset demonstrated that FIAML-LR, even with limited data, could boost classification accuracy by a significant 14.28% against the benchmark model. This heightened accuracy could aid medical professionals in more precisely diagnosing diabetic retinopathy.

Characterization of Verona Integron-Encoded Metallo-β-Lactamase-Type Carbapenemase-Producing Escherichia coli Isolates Collected over a 16-Year Period in Bolzano (Northern Italy).

Multidrug-resistant Escherichia coli, particularly carbapenemase producers, are a major source of concern. This study aims to investigate the long-term epidemiology of Verona integron-encoded metallo-β-lactamase (VIM)-producing E. coli in the health district of Bolzano, Northern Italy, by examining the phenotypic and genotypic characteristics of 26 isolates obtained during 2005-2020. Isolates were identified with matrix-assisted laser desorption/ionization time-of-flight, susceptibility testing was by Vitek 2, Sensititre, and Etest; carbapenemase activity was confirmed by Etest and Carbapenemase Inactivation Method (CIM) test; and the VIM-antigen was identified by the NG-Test CARBA 5. Genome sequencing was performed on an Illumina MiSeq platform. Carbapenem minimum inhibitory concentrations varied across methodologies, and overall category agreement between phenotypic methods was low. All 23 sequenced isolates contained blaVIM-1. Eleven (47.8%) isolates belonged to the clonal lineage ST131, with fimH30 being the most common subclone. In Bolzano ST131-fimH30 was present as early as 2005. While the ST131 clonal lineage predominated for the first 10 years, various clonal lineages were present, especially in subsequent years, indicating the concurrent circulation of multiple clonal lineages. Future efforts should focus on the implementation of surveillance methods, including genomic analysis, as well as the use of updated infection control strategies and antibiotic stewardship programs to prevent the spread of these carbapenem-resistant strains.

Riparian vegetation plastic monitoring: A harmonized protocol for sampling macrolitter in vegetated riverine habitats

Many studies highlighted that rivers transported land-based plastics to the sea. However, most of the litter remains stuck in the fluvial ecosystem, also blocked by vegetation. To date, research on riverine macrolitter focused on floating and riverbank monitoring, thus methods to sample riverbank and floating litter have been developed. Concerning rivers, few recent studies highlighted the role of riparian vegetation in entrapping plastics. Given that vegetation represents a large part of riverine ecosystems and that the dynamics of plastics entrapped by vegetation are neglected, it appears pivotal to study in more detail how vegetation contributes to plastic retention. However, as current protocols and guidelines considered only floating and riverbank plastics without providing standardized and updated strategies to monitor litter in vegetation, here we aimed to develop a new standardized protocol and tools to assess plastics in vegetation. Specifically, we focused on unveiling the three-tridimensional structure of vegetation in relation to plastic occurrence, while considering seasonal and hydromorphological aspects. To investigate the trapping effect of vegetation, we developed a three-dimensional vegetation structure index (3DVI) related to plastics. The 3DVI index considers plant structure (i.e., number of branches) and diversity (i.e., species). To test the 3DVI, we conducted an in-situ case study in central Italy. We found that both primary and secondary riparian vegetation blocked plastic litter. In detail, 3DVI correlated with the number of plastics, highlighting that the densest and most diverse communities trap more plastics. Furthermore, we provided for the first time the assessment of seasonality for the macroplastic entrapment by riparian vegetation and a preliminary quantification of wind-blown plastics. Our results should be of interest to promote the development of standardized and harmonized monitoring strategies for riparian habitat management and conservation.

IMPACT OF TRANSFORMATIONAL LEADERSHIP AND MOTIVATIONAL FACTORS ON TEACHERS’ PERFORMANCE AT SECONDARY SCHOOL LEVEL

Leadership plays a vital role for the development of performance of teachers through motivation. The majorobjective of this research was to find out the impact of transformational leadership and motivational factors on jobperformance. The population of this study was secondary school teachers in Lahore. The population of this researchwas public secondary school teachers whereas, 289 respondents were selected as sample which was selectedthrough simple random sampling technique. For this study, standardized questionnaires were administered tocollected the primary data through survey method. Statistical Package for Social Sciences (SPSS) was computed toanalyze the objectives of this study. The findings of this study revealed that transformational leadership andmotivational factors had moderate correlation with job performance and have moderate effect on job performance.Moreover, transformational leadership had more significant effect on job performance as compare to motivationalfactors. This research is considered as stepping stone for the adaptation of supportive leadership style according tothe situation through updated strategies and techniques.

A UAV Path Planning Method in Three-Dimensional Space Based on a Hybrid Gray Wolf Optimization Algorithm

Path planning, which is needed to obtain collision-free optimal paths in complex environments, is one key step within unmanned aerial vehicle (UAV) systems with various applications, such as agricultural production, target tracking, and environmental monitoring. A new hybrid gray wolf optimization algorithm—SSGWO—is proposed to plan paths for UAVs under three-dimensional agricultural environments in this paper. A nonlinear convergence factor based on trigonometric functions is used to balance local search and global search. A new relative-distance fitness adaptation strategy is created to increase the convergence speed of the SSGWO. Integrating the simulated annealing (SA) algorithm, an alternative position update strategy based on SA is proposed to improve the search process with diverse capabilities. Finally, a B-spline curve is introduced into a smooth path to ensure the path’s feasibility. The simulation results show that the SSGWO algorithm has better convergence accuracy and stability, and can obtain higher-quality paths in a three-dimensional environment, compared with GWO, MGWO, IGWO, and SOGWO.