Optimal Resource Research Articles

Green HRM and Resource Optimization in the Public Sector: A Pathway to Achieving Sustainable Environmental Policy Goals

The study discusses the role of Green Human Resource Management in optimizing resources and achieving environmental policy goals within public sector organizations. In view of increasing pressure to use more sustainable practices, Green HRM emerged as a core determinant in enhancing environmental performance and efficiency in operations. The study examines three influential Green HRM practices, that is, green recruitment, green training, and environmental performance management, in light of assessing the impact of their influence on the consumption of resources, energy, and waste management. A quantitative study was performed by the author based on a survey with the sample size consisting of 200 respondents from 25 public sector organizations. There also seems to be a strong positive relationship found between Green HRM practice and the optimization of resources in particular-through green recruitment and particularly green training. Furthermore, the study indicated that resource optimization served as a mediator between realizing sustainable environmental policy goals and Green HRM. Thus, effective Green HRM practices help an organization decrease its environmental footprint while at the same time improving its operations' efficiency. The study demonstrates that public sector organizations should also use the strategic frameworks designed to set up sustainable long-run sustainability goals with Green HRM practices. This way, research supports literature on Green HRM with its importance and in avenues for potential future research directions towards integrating the practice into the public sector.

Harnessing Artificial Intelligence in Sustainable Tourism in the Post-Pandemic World

The tourism industry around the world is constantly being challenged to adopt sustainability due to environmental imperatives and changed traveler preferences. This paper examines the transformative role that artificial intelligence can take in the post-pandemic era in the development of sustainable tourism. From applications in environmental monitoring, resource optimization, carbon footprint reduction, waste management, and developing personalized sustainable experiences, AI can be seen as a crucial enabler of tourism in tune with ecological goals. Besides, the paper mentions challenges such as costs, technical barriers, and ethical ones, keeping in mind balanced implementation approaches. In any case, AI, from the perspective of sustainable tourism, opens opportunities for the protection of ecosystems and experiences for visitors.

Examination of AI Enhanced Distributed Systems and its Effects on Software Engineering

Distributed systems are groups of independent computers that appear to the system's users as a single coherent system. These systems involve several critical factors, including network communication, concurrency, and fault tolerance, which are vital in software engineering. For example, cloud computing platforms like Amazon Web Services (AWS) use distributed systems to offer scalable, on-demand resources to millions of users worldwide. The introduction of Artificial Intelligence (AI) into software engineering marks a transformative period that reshapes traditional development processes and breaks down the complexity of distributed systems. AI automates routine tasks and simplifies complex processes, serving as a digital collaborator that enables developers to focus on strategic thinking and creativity. One significant advantage of using AI in distributed systems within software engineering is enhanced resource optimization. AI can identify irregularities that might indicate hardware malfunctions and take preventative measures to address them, such as rerouting traffic to healthier servers. However, distributed systems also have challenges, including increased complexity in system design, difficulty ensuring data consistency, and potential security vulnerabilities. The intricate structure of these systems can result in problems with high startup costs, safety risks, and the accuracy of the data provided. Integrating AI into distributed systems offers both significant advantages and disadvantages. This study evaluates how AI impacts the efficiency and security of distributed systems in software engineering. By analyzing the advantages and disadvantages of AI-enhanced distributed systems, we can gain a comprehensive understanding of their overall effectiveness and implications in the field.

Innovative Application of 6G Network Slicing Driven by Artificial Intelligence in the Internet of Vehicles

ABSTRACTThe rapid growth of vehicle networks in the Internet of Vehicles (IoV) needs novel approaches to optimizing network resource allocation and enhancing traffic management. Sixth‐generation (6G) network slicing, when paired with artificial intelligence (AI), has enormous potential in this field. The purpose of this research is to investigate the use of AI‐driven 6G network slicing (NS) for efficient usage of resources and accurate traffic prediction in IoV systems. A unique network design is suggested, combining data‐driven approaches and dynamic network slicing. Data are acquired from vehicular sensors and traffic monitoring systems, and log transformation is used to handle exponential growth patterns like vehicle counts and congestion levels. The Fourier transform (FT) is used to extract frequency‐domain information from traffic data, which allows for the detection of periodic patterns, trends, and anomalies such as vehicle velocity and traffic density. The Dipper Throated Optimized Efficient Elman Neural Network (DTO‐EENN) is used to forecast traffic and optimize resources. This technology allows the system to predict traffic patterns and dynamically alter network slices to ensure optimal resource allocation while reducing latency. The results show that the suggested AI‐driven NS technique increases forecast accuracy and network performance while dramatically reducing congestion levels. The research indicates that AI‐driven 6G based NS offers a solid framework for optimizing IoV performance.

MONITORING AND EVALUATION OF AGRICULTURAL-BASED PROJECT OUTCOMES ON HOUSEHOLD FOOD SECURITY IN MURANG’A COUNTY, KENYA

Objectives: This study investigates the role of monitoring and evaluation (M&E) practices in improving agricultural project outcomes and addressing household food insecurity in Murang’a County, Kenya. Theoretical Framework: Grounded in Change Theory, this study highlights how systematic M&E processes align activities with project goals, promoting accountability, resource optimization, and outcome sustainability. Method: A mixed-methods approach was utilized, collecting quantitative data from 383 respondents through structured questionnaires and qualitative insights via interviews. Regression and correlation analyses were applied to identify significant relationships. Results and Discussion: Findings revealed a significant positive correlation (r = 0.201, p < 0.05) between robust M&E frameworks and food security improvements. Key factors included readiness assessments, accountability, and produce storage. Challenges such as limited funding and inadequate training hindered effectiveness. Research Implications: Policymakers are encouraged to integrate community-inclusive, results-based M&E systems into agricultural projects. Recommendations include capacity building, technological adoption for real-time data, and fostering stakeholder collaboration to ensure sustainability. Originality/Value: This research advances the understanding of M&E’s role in achieving sustainable agricultural outcomes. It provides a replicable model for improving food security in similar rural contexts through effective monitoring frameworks.

Cloud-edge hybrid deep learning framework for scalable IoT resource optimization

In the dynamic environment of the Internet of Things (IoT), edge and cloud computing play critical roles in analysing and storing data from numerous connected devices to produce valuable insights. Efficient resource allocation and workload distribution are vital to ensuring continuous and reliable service in growing IoT ecosystems with increasing data volumes and changing application demands. This study proposes a novel optimisation approach utilising deep learning to tackle these challenges. The integration of Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) offers a practical approach to addressing the dynamic characteristics of IoT applications. The hybrid algorithm's primary characteristic is its capacity to simultaneously fulfil multiple objectives, including reducing response times, enhancing resource efficiency, and decreasing operational costs. DQN facilitates the formulation of optimal resource allocation strategies in intricate and unpredictable environments. PPO enhances policies in continuous action spaces to guarantee reliable performance in real-time, dynamic IoT settings. This method achieves an optimal equilibrium between policy learning and optimisation, rendering it suitable for contemporary IoT systems. This method improves numerous IoT applications, including smart cities, industrial automation, and healthcare. The hybrid DQN-PPO-GNN-RL model addresses bottlenecks by dynamically managing computing and network resources, allowing for efficient operations in low-latency, high-demand environments such as autonomous systems, sensor networks, and real-time monitoring. The use of Graph Neural Networks (GNNs) improves the accuracy of resource representation, while reinforcement learning-based scheduling allows for seamless adaptation to changing workloads. Simulations using real-world IoT data on the iFogSim platform showed significant improvements: task scheduling time was reduced by 21%, operational costs by 17%, and energy consumption by 22%. The method reliably provided equitable resource distribution, with values between 0.93 and 0.99, guaranteeing efficient allocation throughout the network. This hybrid methodology establishes a novel benchmark for scalable, real-time resource management in extensive, data-centric IoT ecosystems, consequently enhancing system performance and operational efficiency.

Semantic-based topic multicast aggregated routing algorithm for resource optimization of pub/sub systems in wireless sensor network

Semantic-based topic multicast aggregated routing algorithm for resource optimization of pub/sub systems in wireless sensor network

Evaluation of Farming System Modules for Improving Profitability of Small Farmers in Coastal Odisha, India

The study evaluated the profitability, sustainability and environmental impact of integrated farming system (IFS) modules, including cropping systems, horticulture, dairy, poultry and fishery in an area of 1.2 ha, in coastal Odisha, India. This research project was part of the sixth production cycle under the All India Coordinated Research Project on IFS and conducted at the Central Research Station, Odisha University of Agriculture and Technology, Bhubaneswar, during the 2022-23 agricultural year. The experiment integrated five major modules: cropping systems (0.32 ha), horticulture (0.31 ha), dairy (2 cross-bred cows), poultry (500 coloured chicken for meat) and fishery (0.40 ha). Productivity was assessed using rice equivalent yield (REY) and economic returns were calculated for each module. Sustainability was evaluated through sustainable yield indices and carbon footprint assessments, while soil quality parameters, such as bulk density, pH, organic carbon and available macronutrients, were monitored over six years. Dairy emerged as the most productive module, yielding the highest REY (15.14 t) and gross return (Rs.3,27,040). Horticulture recorded the highest net return (Rs.1,40,627) and benefit-cost ratio (3.61). The cost of production was mainly influenced by external inputs (52%), followed by labour (33%) and system-recycled inputs (15%). The system generated 399 man-days annually, with over half contributed by dairy. Environmental sustainability was achieved through agroforestry and compost application, making the model carbon-negative. Dairy was the largest greenhouse gas emitter, while horticulture and biomass contributed significantly to carbon sequestration. Over six years, soil quality improved, with reduced bulk density, increased water-holding capacity and enhanced organic carbon levels. Sustainable yield indices for four modules were above 0.6, with dairy showing the highest sustainability. Poultry performance required further improvement to enhance overall system sustainability. In conclusion, the integrated farming system enhanced overall productivity, profitability and environmental sustainability for smallholder farmers in coastal Odisha, showcasing its potential for resource optimization and resilience.

The Role of Remote Sensing and GIS in Sustainable Development and National Resilience

Purpose: This study explores the critical role of Remote Sensing (RS) and Geographic Information Systems (GIS) in supporting Sustainable Development Goals (SDGs) and strengthening national resilience. It aims to highlight the originality and significance of RS and GIS as strategic tools for spatial data collection, analysis, and visualization. Study Design/Methodology/Approach: This research employs a qualitative methodology based on a literature review and case studies. The study evaluates the application of RS and GIS in various sectors, including disaster mitigation, environmental preservation, and resource optimization. Findings: The findings indicate that RS and GIS significantly contribute to SDGs by facilitating actions on climate change, biodiversity conservation, equitable resource distribution, and poverty alleviation. Additionally, RS and GIS strengthen national resilience through economic optimization, ecological disaster mitigation, conflict resolution, and strategic area monitoring. Originality/Value: This study underscores the integrative role of RS and GIS in aligning SDGs and national resilience objectives, offering practical implications for decision-making across multiple sectors. The results provide a foundation for policymakers and practitioners to leverage RS and GIS for holistic and sustainable development.

Integrating Artificial Intelligence in agricultural higher education: Transforming learning and research

The integration of Artificial Intelligence (AI) in agricultural higher education is transforming the landscape of agricultural practices and research. This paper explores the multifaceted applications of AI technologies in the curriculum and pedagogical approaches of agricultural institutions. By enhancing data analysis, predictive modeling, and decision-making processes, AI empowers students and researchers to tackle complex agricultural challenges such as crop management, pest control, and resource optimization. Furthermore, the study examines the implications of AI on student engagement, skill development, and interdisciplinary collaboration. As agricultural sectors increasingly rely on data-driven solutions, the incorporation of AI in higher education not only prepares future professionals with essential competencies but also fosters innovation in sustainable agricultural practices. This article underscores the critical role of AI in shaping the future of agricultural education and its potential to revolutionize the industry.

Satellite Edge Computing for Mobile Multimedia Communications: A Multi-agent Federated Reinforcement Learning Approach

The rapid expansion of satellite mega-constellations has highlighted the potential of satellite edge computing as a promising solution for mobile multimedia communications. While reinforcement learning has been explored in satellite communication systems, significant challenges remain, including high latency and limited resources. This study addresses these challenges by focusing on the joint optimization of communication, computing, and caching resources in satellite edge computing to support mobile multimedia applications. A mixed-integer nonlinear programming (MINLP) problem is formulated with the objective of minimizing the total delay experienced by mobile users, subject to multidimensional resource capacity constraints, which is NP-hard and computationally intractable to solve in polynomial time. To address this complexity, we propose a multi-agent federated reinforcement learning (MAFRL) approach as an efficient solution. In this framework, each satellite operates as an autonomous learning agent equipped with an actor-critic network structure. The proposed MAFRL method demonstrates superior performance, achieving lower delays compared to all baseline approaches. It effectively optimizes delay-sensitive mobile multimedia communications by minimizing total delay and improving task offloading ratios. To the best of the authors’ knowledge, this study is the first to introduce a MAFRL-based approach for resource allocation in satellite edge computing, marking a significant contribution to the field.

An Overview and Criticality of High Availability and Disaster Recovery for SAP HANA

Setting up High Availability and Disaster Recovery for SAP HANA presents critical strategies for ensuring business continuity in enterprise environments. The implementation incorporates sophisticated architectural components for maintaining system operations through multi-layered redundancy and protection mechanisms. SAP HANA deployments leverage scale-out systems with coordinator and worker nodes, while system replication provides robust disaster recovery capabilities. Pacemaker cluster management enables automated failover and resource orchestration, with comprehensive testing frameworks ensuring system reliability. The architecture supports advanced features including active/active replication and tenant-level management, allowing organizations to achieve optimal resource utilization and granular control over high availability settings. These implementations demonstrate significant improvements in system performance, data consistency, and recovery capabilities, resulting in substantial reductions in downtime costs and data loss risks. The combination of strategic redundancy, proactive monitoring, and sophisticated replication mechanisms enables organizations to maintain continuous operations while meeting stringent service level agreements in mission-critical environments.

Toward Enhanced Seed Potato Yield: Ultrasonication Techniques for Sustainable Agricultural Development

The study aimed to explore the potential of ultrasonication techniques in seed potato production as a sustainable agricultural innovation. By improving seed potato efficiency and promoting resource optimization, this research aligns with the goals of sustainable agricultural and rural development, addressing challenges such as food security, environmental preservation, and economic viability in rural farming communities. The study was conducted over three years in the central–eastern region of Poland, utilizing a randomized block design with a split-split-plot approach. The main experimental factor was the cultivation technology, which included (a) an innovative ultrasonic pre-sowing treatment method and (b) a traditional cultivation method without such treatment. The secondary factor was the potato varieties. The ultrasonic treatment of tubers was performed using an ultrasonic tub-type device equipped with piezoelectric transducers. Cultivation technology, potato varieties, and weather conditions had a significant impact on the yield of tubers in the seed potato fraction size, the number of tubers in this fraction, and the multiplication coefficient. Additionally, the genetic characteristics of the studied varieties and random environmental factors significantly influenced the weight of a single seed potato tuber and the number of shoots produced by each plant.

Mathematical optimisation model for searching duplicate string objects in the memory snapshot

The purpose of this paper is to identify the increased memory usage of software appli-cations. The modern software development cycle focuses on functionality and often ignores aspects of optimal resource usage. Limited physical scaling sets an upper limit on the system's capacity to process requests. The presence of unchanged objects with the same information is a sign of increased memory consumption. Avoiding duplicate objects in memory allows for a more rational use of the existing resource and an increase in the amount of information proc-essed. Existing scientific publications focus on the study of memory leakage problems, and limit their attention to excessive memory usage due to the lack of a unified model for finding excessive memory usage. It is worth noting that existing programming templates contain the ‘object pool’ template, but leave the conclusion about the feasibility of its implementation to engineers without providing a mathematical basis. The paper presents the development of a mathematical model for the process of detecting duplicate objects with the immutability prop-erty of the String type in a memory snapshot. Industrial systems that require hundreds of GB of RAM to operate and contain millions of objects in RAM are analysed. Given this scale of data, there is a need to optimise the duplicate detection process. The research method is to analyse memory snapshots of highly loaded systems using the software code developed on .NET technology and the ClrMD library. The memory snapshot reflects the state of the proc-ess under study at a given time, contains all objects, threads and operations performed. The ClrMD library allows you to programmatically examine objects, their types, get field values, and build graphs of relationships between objects. Based on the results of the study, an opti-misation was proposed that allows to speed up the process of finding duplicates several times. The scientific contribution of the study is the creation of a mathematically sound approach that significantly reduces the use of memory resources and optimises computing processes. The practical usefulness of the model is confirmed by the optimisation results achieved through the recommendations, reduced hosting costs (which provides greater cost-effectiveness in the deployment and use of software systems in industrial environments), and increased data processing.

Climate-adaptive optimal water resources management: A multi-sectoral approach for the Munneru river basin, India.

Climate change profoundly affects water resource allocation by disrupting the availability, distribution, and quality of water across various regions. Optimal allocation of water resources represents a comprehensive strategy for water resource management by addressing the intricate connections between water allocation systems and their repercussions on the environment, society, and economy. In this study, an Optimal Water Resources Management (OWRM) framework was developed, focusing on the optimal allocation of water resources and crop planting structures across various sectors. The Munneru river basin, located in the lower Krishna River region of India, was selected as the study area to validate the proposed framework. Five distinct water-demanding sectors-irrigation, domestic, livestock, industrial, and irrigation water requirements for major agricultural seasons-were identified in the study area, and their sectoral water demands were calculated at the basin level. The crop water and irrigation water requirements for various crops were estimated using the CROPWAT tool, while the framework also optimized crop planting structures to maximize returns and resource efficiency. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was applied, with two objectives focused on equity and economic value. Superior solutions were then identified using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The OWRM framework was applied after identifying critical cases of water availability in future periods under climate change scenarios. Through this integrated approach, an average annual increase of 52.6% in agricultural sector returns was achieved for the simulation period (2016-17 to 2022-23). For a condition of providing at least 90% water supply to each sector, the optimal crop patterns led to revenue increases of 136.4%, 59.2%, and 74.7% compared to actual revenues for the water years 2020-21, 2021-22 and 2022-23 respectively. The developed OWRM methodology can be applied to other basins across the world that are impacted by climate change.

A novel graph neural network based approach for influenza-like illness nowcasting: exploring the interplay of temporal, geographical, and functional spatial features

BackgroundAccurate and timely monitoring of influenza prevalence is essential for effective healthcare interventions. This study proposes a graph neural network (GNN)-based method to address the issue of cross-regional connectivity in predicting influenza outbreaks, aiming to achieve real-time and accurate influenza prediction.MethodsWe proposed a GNN-based approach with dual topology processing, capturing both geographical and socio-economic associations among counties/cities. The model inputs consist of weekly matrices of influenza-like illness (ILI) rates at city level, along with geographical topology and functional topology. The model construction involves temporal feature extraction through 1-dimensional gated causal convolution, spatial feature embedding through graph convolution, and additional adjustments to enhance spatiotemporal interaction exploration. Evaluation metrics include four commonly used measures: root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and Pearson correlation (Corr).ResultsOur approach for predicting influenza outbreaks achieves competitive performance on real-world datasets (Corr = 0.8202; RMSE = 0.0017; MAE = 0.0013; MAPE = 0.0966), surpassing established baselines. Notably, our approach exhibits excellent capability in accurately and timely capturing short-term influenza outbreaks during the flu season, outperforming competitors across all evaluation metrics.ConclusionThe incorporation of dual topology processing and the subsequent fusion mechanism allows the model to explore in-depth spatiotemporal feature interactions. Demonstrating superior performance, our approach shows great potential in early detection of flu trends for facilitating public health decisions and resource optimization.

Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models.

Research on Strategies for Promoting the Education Management of Higher vocational College Students’ Sustainable Career Planning Based on the SCCT Theory and SWOT Analysis

Sustainable career planning among higher vocational college students plays a significant role in personal development, career planning, resource optimization, social harmony, and sustainable development. This study investigates the current status, anticipated scenarios, and influencing factors of sustainable career planning among higher vocational students based on Social Cognitive Theory (SCT) and Social Cognitive Career Theory (SCCT). The findings indicate that the level of sustainable career planning among the surveyed students is moderate and is influenced by multiple factors. The study develops educational management strategies to promote sustainable career planning among higher vocational students, utilizing SWOT analysis and focus group discussions. High-level experts evaluate these strategies for their adaptability and feasibility. Based on the consensus of these experts, it was determined that there is agreement on “Strategies for Promoting the Education Management of Higher Vocational College Students’ Sustainable Career Planning Based on the SCCT Theory and SWOT Analysis.

Strategic evaluation of European Inland transport companies for European green deal compliance: A multi-criteria decision making-analysis.

The purpose of this study is to evaluate the readiness of European inland transport companies for the European Green Deal by analyzing the causal relationships between key factors essential for EGD implementation. The research identifies six critical factors: Investment in Green Technologies, Policy Alignment and Compliance, Stakeholder Collaboration, Resource Management, Employee Training and Engagement, and Emission Reduction Initiatives. This research offers the first integrated evaluation of EGD readiness utilizing a combination of fuzzy-DEMATEL, multiple regression analysis, and Structural Equation Modeling aimed at uncovering systemic interactions among the factors in the system. The study revealed Resource Management and Employee Training as significant drivers in enhancing the readiness, highlighting the role of human capital and resource optimization in environmental sustainability. Expert panel, consisting of 19 industry representatives selected through purposive sampling, provided insights on the effective relationships which offer practical strategies for managers to prioritize resource management and employee engagement to achieve Green Deal compliance and guiding more strategic and efficient allocation of resources to enhance alignment with European Green Deal stipulations.

Advances in the selective c-MET kinase inhibitors: Application of fused [5,6]-Bicyclic nitrogen-containing cores for anticancer drug design.

Over the past two decades, small molecules bearing [5,6]-bicyclic nitrogen-containing cores have emerged as one of the most extensively studied structures for the development of selective c-MET kinase inhibitors. Structure-activity relationship (SAR) studies have demonstrated that modifying these cores can significantly impact the biological properties of c-MET inhibitors, including safety/toxicity, potency, and metabolic stability. For example, although c-MET kinase inhibitors containing the [1,2,4]triazolo[4,3-b][1,2,4]triazine scaffold (core P) exhibit high inhibitory potency, they often face challenges due to metabolic stability defects. Alternatively, compounds containing [1,2,3]triazolo[4,5-b]pyrazine (core K) and [1,2,4]triazolo[4,3-b]pyridazine (core I) scaffolds demonstrate lower potency but improved metabolic stability, allowing some of them to progress into clinical trials and even be approved as novel anticancer drugs. Fortunately, X-ray crystallography studies have well elucidated key interactions between [5,6]-bicyclic nitrogen-containing cores and crucial amino acid residues within the c-MET active site. These insights emphasize the significance of π-π stacking interactions with Tyr1230 and hydrogen bonding with Asp1222, providing valuable guidance for the targeted design and optimization of selective c-MET kinase inhibitors. Following the identification/introduction of sixteen distinct [5,6]-bicyclic nitrogen-containing cores (cores A-P) utilized in the design of selective c-MET kinase inhibitors over the past two decades, this manuscript offers a comprehensive review of their roles in drug development of anticancer agents, and describes the various synthesis methods employed. The insights presented herein can serve as a resource for better structural optimization of c-MET kinase inhibitors in the future research.