Management Layer Research Articles

Innovative Integration Strategies for Platform-as-a-Service in Large-Scale Environments

This article presents a comprehensive examination of innovative integration strategies for Platform-as-a-Service (PaaS) in large-scale environments, addressing the complex challenges faced by modern enterprises in cloud computing. We explore the evolution of PaaS solutions and delve into the technical intricacies of system integration, focusing on microservices architectures, cloud-native solutions, and distributed systems. The article highlights critical issues such as data synchronization, API management, and maintaining high availability across diverse subsystems. Through in-depth case studies of large enterprise PaaS integration and hybrid cloud implementation, we derive valuable insights and best practices. The article introduces a novel framework for seamless PaaS integration, encompassing infrastructure abstraction, service orchestration, data integration, and API management layers. Furthermore, we discuss emerging approaches to enhance scalability and reliability, including autonomous scaling and multi-cloud load balancing. The article also evaluates innovative tools and technologies aimed at improving interoperability, such as GraphQL for API integration and AI-powered integration assistants. By analyzing future trends like edge computing integration and quantum-resistant security, this article provides architects and developers with crucial insights for navigating the evolving PaaS landscape, emphasizing the importance of continuous learning and cross-functional collaboration in achieving successful large-scale PaaS integrations.

Single-Phase Multilevel Inverter Design with Low Voltage Stress and Optimized Control Model

The research explicitly examines the power control of a single-phase renewable energy sources battery energy storage system (RES-BESS)mixed-distributing network designed for domestic usage. The system utilizes a cascaded H-bridge (CHB) conversion architecture as its grid connection. The CHB uses a hierarchical power management design consisting of a single centralized regulator for the top layer and several dispersed regulators for the bottom layer. The higher stratum produces the reference signals that need to be monitored. Under optimal circumstances, the CHB should operate efficiently with all solar panels at their Maximum Power Point (MPP) and with an electrical factor of 1. However, several functional limitations prevent this from always being feasible, such as limits related to voltage and current, biased shadowing, and State of Charge (SOC) incompatibilities. Hence, a novel optimization technique is introduced to take into account these impacts directly and calculate a collection of reference parameters to be monitored. This approach aims to maximize the long-term viability of the structure while ensuring compliance with the given limitations. The optimization method in this structure incorporates many functional needs, prioritizing them based on the specific operating circumstances. The decentralized management layer utilizes traditional feedback-controlling methods to monitor the best referencing. Experimental findings are conducted to verify the best method under normal conditions for operation and evaluate the efficacy of the suggested CHB arrangement with the complete control technique.

Estimating soil erosion in response to land use/cover change around Ghibe III hydroelectric dam, Southern Ethiopia

Soil erosion is a hazard in every part of the world. The small-scale farmers often grapple with low agricultural production and food insecurity. This study was conducted to estimate soil loss in response to land use/cover change in 1990, 2000, 2010, and 2020 around Ghibe hydroelectric III dam in Southern Ethiopia. The Revised Universal Soil Loss Equation (RUSLE) model was applied using a geographic information system (GIS). Digital elevation model with 30 m to determine topographic factor and supportive practice (P) factor, rainfall from the National Meteorological Institute to calculate the erosivity (R) factor, a digital soil map of the world for erodibility (K) factor, and Landsat 5TM images of 1990, 2000, 2010, and the Landsat 8 OLI of 2020 to determine trends of land use/cover change and the cover management (C) factor were employed. The raster layers of topography, cover management, rainfall erosivity, soil erodibility, and conservation techniques were processed and multiplied using the GIS platform. The overall accuracy of supervised classification was 89.89. The results showed that the percentage of cropland and built-up areas increased by 11.93 and 32.44%, respectively throughout the three study periods. Conversely, the proportion of forest land, grassland, bare land, and bushland declined by 8.2, 9.3, 10.13, 8.6%, respectively. The average annual soil loss rates increased from 30.95 t ha−1 yr−1 in 1990 to 43.85 t ha−1 yr−1in 2020. This is significantly higher than the maximum threshold rate of erosion for Ethiopian highlands (11 t ha−1 yr−1). The local government officers, non-governmental organizations, and farmers who are trained should strengthen watershed management techniques.

Security Evaluation Framework for Virtualised Environments

With the expansion of cloud computing, virtual environments remain susceptible to sophisticated security threats, such as hypervisor vulnerabilities, VM escapes, inter-VM attacks, DoS attacks, and malware injections. Hence, ensuring that virtualised environments are secure, has become increasingly crucial. These threats are particularly affecting as many enterprises which have shifted to online services and remote work. This paper examines the existing frameworks which have been designed to address the existing challenges, their gaps and strengths; and proposes an enhanced security framework that can help to mitigate these threats. The framework was developed following an iterative process encompassing several stages. It has the hypervisor layer, virtual machine layer, network layer, management layer, and monitoring and response layer components. The framework aims to enhance detection capabilities, reduce response times, minimise system performance impact, and lower false positive rates while optimising resource utilisation by providing a practical and effective approach to securing virtualised infrastructures, thereby ensuring the resilience and reliability of cloud-based services.

Multiple levels of crypto-fiduciary security unforgeable stamp

The paper introduces the concept of a multi-level crypto-fiduciary security system, focusing on the development and implementation of an unforgeable stamp mechanism. This security architecture integrates cryptographic techniques with fiduciary principles to create a robust authentication framework that ensures data integrity, confidentiality, and trust in transactions. The unforgeable stamp serves as a cryptographic seal that is tamper-proof, traceable, and resistant to counterfeiting or duplication, providing enhanced protection against fraud and unauthorized access. The proposed system leverages multiple layers of encryption, key management, and blockchain technology to achieve a secure and verifiable method of transaction validation. This work aims to address critical security challenges in financial, governmental, and other sensitive applications, where trust and data authenticity are paramount.

Distributed robust [formula omitted]asso-[formula omitted] based on [formula omitted]ash optimization for smart grid: Guaranteed robustness and stability

The integration of variable renewable energy supplies into smart grid energy management poses several obstacles to system operation. An efficient solution for resource management is essential to ensuring reliable operation. This research presents distributed robust Lasso-model predictive control (D − RLMPC) as a way to handle energy problems in a multi-layer and multi-time frame optimization method. The D − RLMPC is a hierarchical system that integrates a centralized supervisory management (SM) layer for long-term optimization with a distributed coordination management (CM) layer for short-term adaptation to high power fluctuations. The higher layer, known as the SM, is responsible for providing the grid operator with specific operating plans and offering guidance to the bottom layer, known as the CM. The CM is responsible for coordinating the interaction between the centralized optimization goals and the physical power system layer. Furthermore, a distributed extended Kalman filter (DEKF) is used to ascertain the inter-dependencies among subsystems. Next, an iterative approach based on Nash optimization is proposed to get the globally optimum solution of the whole system in a partly distributed manner. The simulation results demonstrate the effectiveness of the proposed control approach, which combines the advantages of centralized and distributed control to provide a comprehensive solution for the grid operating issue. To verify and assess the effectiveness of the suggested approach, the acquired outcomes are compared to those of the centralized robust, distributed robust, and distributedMPC approaches. The simulation findings confirm the practicality of using the suggested system to manage future smart grid assets.

Vapor-feed direct methanol fuel cells using pure methanol

This work optimizes the performance of the direct methanol fuel cell (DMFC) to increase its efficiency and strengthen its validity in portable power generation. Specifically, this work focuses on optimizing vapor-feed supply techniques and incorporating water management layers (WMLs) to analyze their effect on methanol crossover. The significance of the vapor-feed supply technique is to enhance the reaction kinetics of the methanol oxidation reaction (MOR) and enable the use of pure methanol (MeOH) as a fuel. Pure methanol is the ideal fuel for the DMFC as it has the highest possible energy density compared to dilute concentrations. However, use of pure methanol is hindered by methanol crossover, which is regarded as the largest technical barrier to commercializing DMFCs. This study measured methanol crossover through a CO2 sensor attached to the cathode outlet and added hydrophobic WMLs to the cathode to alleviate the methanol crossover. The hydrophobic WMLs increased the mass transfer resistance to generate a pressure gradient that encourages water backflow for use in both the proton exchange membrane (PEM) and anode reactions. The influence of vapor flow rate and fuel concentration will also be explored to show their impact on performance and methanol crossover. Likewise, long-term consumption and durability tests were conducted with and without a WML to dictate the WML’s superior fuel efficiency, total efficiency, energy density, and reduced methanol crossover using pure methanol. The addition of the WML increased the energy density of the vapor feed DMFC, using pure methanol, from 705.9 Wh kgMeOH-1 to 867.7 Wh kgMeOH-1 and lowered the crossover current density by 14.8 % when discharged at a constant 200 mA cm−2.

Bi-Layer Model Predictive Control strategy for techno-economic operation of grid-connected microgrids

Effective management of renewable energy sources (RESs) alongside energy storage systems is crucial for maintaining stability and enhancing the economic performance of a grid-connected microgrid (MG). To address shortcomings in existing energy management strategies, which often overlook essential elements such as voltage support, battery degradation management, and uncertainties in load demand and solar generation, this study proposes a Bi-Layer Strategy for optimizing the operation of Battery Energy Storage Systems (BESS) within grid-connected MGs. The proposed approach maximizes economic returns, minimizes battery degradation, and provides reactive power support to maintain voltage stability. The method consists of the Energy Management Layer (EML) and the Control Layer (CL). The EML adopts a Model Predictive Control (MPC) framework to optimize the BESS power output over a 24-h rolling horizon based on forecasted solar generation, load demand, and electricity prices. The CL translates the optimized hourly setpoints from the EML into real-time operational commands, ensuring that the BESS adapts to actual grid conditions and maintains stable operation. Simulations conducted on a test MG, comprising 150 households, 500 kW rooftop solar PV, and a 300 kWh Li-Ion BESS demonstrate the strategy’s effectiveness under both fixed-rate and Time-of-Use (ToU) pricing schemes. The strategy reduces costs, mitigates battery degradation, and enhances voltage stability by optimizing BESS operations. Compared to a similar method in the literature, it reduces battery degradation by 24%, leading to net savings of $10,000 AUD, while the compared strategy resulted in a $5000 AUD loss. Real-time validation using the OPAL-RT platform further confirms the strategy’s effectiveness in optimizing BESS operations under real-world conditions.

Integrating IoT and visual question answering in smart cities: Enhancing educational outcomes

Emerging as a paradigmatic shift in urban development, smart cities harness the potential of advanced information and communication technologies to seamlessly integrate urban functions, optimize resource allocation, and improve the effectiveness of city management. Within the domain of smart education, the imperative application of Visual Question Answering (VQA) technology encounters significant limitations at the prevailing stage, particularly the absence of a robust Internet of Things (IoT) framework and the inadequate incorporation of large pre-trained language models (LLMs) within contemporary smart education paradigms, especially in addressing zero-shot VQA scenarios, which pose considerable challenges. In response to these constraints, this paper introduces an IoT-based smart city framework that is designed to refine the functionality and efficacy of educational systems. This framework is delineated into four cardinal layers: the data collection layer, data transmission layer, data management layer, and application layer. Furthermore, we introduce the innovative TeachVQA methodology at the application layer, synergizing VQA technology with extensive pre-trained language models, thereby considerably enhancing the dissemination and assimilation of educational content. Evaluative metrics in the VQAv2 and OKVQA datasets substantiate that the TeachVQA methodology not only outperforms existing VQA approaches, but also underscores its profound potential and practical relevance in the educational sector.

From Bottom-Up Towards a Completely Decentralized Autonomous Electric Grid Based on the Concept of a Decentralized Autonomous Substation

The idea of a decentralized electric grid has shifted from being a concept to a reality. The growing integration of distributed energy resources (DERs) has transformed the traditional centralized electric grid into a decentralized one. However, while most efforts to manage and optimize this decentralization focus on the electrical infrastructure layer, the operational and control layer, as well as the data management layer, have received less attention. Current electric grids rely on centralized control centers (CCCs) that serve as the electric grid’s brain, where operators monitor, control, and manage the entire grid infrastructure. Hence, any disruption caused by a cyberattack or a natural event, disconnecting the CCC, could have numerous negative effects on grid operations, including socioeconomic impacts, equipment damage, market repercussions, and blackouts. This article introduces the idea of a fully decentralized electric grid that leverages autonomous smart substations and blockchain integration for decentralized data management and control. The aim is to propose a blockchain-enabled decentralized electric grid model and its potential impact on energy markets, sustainability, and resilience. The model presented underlines the transformative potential of decentralized autonomous grids in revolutionizing energy systems for better operability, management, and flexibility.

A hierarchical energy management strategy for DC microgrid hybrid energy storage systems based on fractional-order sliding mode controller

A hierarchical energy management strategy (EMS) for a fuel cell (FC)-supercapacitor (SC)‑lithium battery hybrid energy storage system (HESS), based on a fractional-order sliding mode controller (FOSMC), is proposed to address the nonlinear behavior of low-voltage direct current (DC) microgrid HESS. To fully exploit the energy of the SC, the system management layer divides the EMS into maximum power operation mode and state machine control algorithm operation mode. For the first time in equipment control layer, a FOSMC, more suitable for handling nonlinear characteristics, is employed, and its stability is evaluated using the Lyapunov method. Finally, a 48 V HESS experimental platform is constructed for validation, demonstrating that the proposed EMS enhances the stability, response speed, and robustness of the HESS. Compared to conventional sliding mode controller (SMC) and integral sliding mode controller (ISMC), the adoption of a FOSMC reduces the steady-state error of the bus voltage by at least 9.1 % and increases the response speed by at least 11.1 %, while exhibiting good robustness to external disturbances and uncertainties in controller parameters. This fills the current research gap in parameter robustness validation.

A conceptual framework for integrating volunteers in emergency response planning and optimization assisted by decision support systems

In disaster response, the overwhelming amount of time-sensitive information and response options, combined with the dynamic nature of disasters, makes decision-making challenging for emergency service providers. Furthermore, it is often not economically feasible for countries to maintain a large number of full-time emergency responders. As such, many countries rely heavily on volunteer emergency responders during major disasters. This means that the success of disaster response often hinges on the efficient use of this volunteer workforce. We propose a framework for a Decision Support System (DSS) designed to optimize the use of volunteers by emergency services. This framework includes the data management layer, integrating necessary inputs and information; the analytical layer, which serves as the system's processing core; the user interface layer; and the decision-making layer. We argue that, while significant academic focus has been on the analytical layer, practical implementation requires the integration of all four components. Additionally, we emphasize the need for coordination with a broad spectrum of stakeholders involved in data provision, decision-making, and resource deployment for operationalizing this DSS. We also explore and analyze existing methodologies for developing the analytical layers, the requirements of these models, and the current methodological gaps. The proposed framework establishes a clear roadmap for adopting emergency response approaches that are human-centric, but at the same time, effectively utilize advancements in modeling, optimization, machine learning, and data integration.

Safety considerations for cryogenic hydrogen circulation system in China spallation neutron source phase II project

Cryogenic hydrogen circulation system is a critical infrastructure of the China Spallation Neutron Source phase II project. Safety considerations related to hydrogen are paramount for the stable operation of the system. This study focuses on the cryogenic hydrogen circulation system within the context of the China Spallation Neutron Source phase II project and conducts a detailed investigation of hydrogen safety issues throughout the process. The operational modes and fault occurrence mitigation strategies of the cryogenic hydrogen circulation system are analyzed and discussed. Additionally, employing centralized layout, zoning management, and multiple barrier layers as guiding concepts for hydrogen safety design, the study addresses physical and interlocking control designs concerning hydrogen distribution, hydrogen venting, vacuum, hydrogen circulation pressure control, and hydrogen leakage. Drawing upon the successful experiences of the first-phase project, the hydrogen safety design of the cryogenic hydrogen circulation system in the China Spallation Neutron Source phase II project provides valuable insights for the development and optimization of hydrogen safety designs across various industrial domains.

Avoiding future surprises after acute shocks: long-term flood risk lessons catalysed by the 2021 summer flood in the Netherlands

In summer 2021, the Dutch Province of Limburg faced an acute high-water event caused by two days of heavy rainfall, resulting in €383 million worth of damage. Afterwards, various policy actors organised a number of evaluations. This study analyses these evaluations and the long-term lessons drawn from this flood event. Dealing with climate change requires insights into how lessons from acute shocks such as floods can stimulate the anticipation of climate change and solutions that can cope with future extremes. This study therefore applies policy learning theory—single, double and triple loop learning—to different layers of flood safety to analyse the extent to which governments prepare for future events and how.The results show that pre-2021 water and flood risk management policies—our point of comparison to derive what lessons emerge after the 2021 flood—focused mostly on flood prevention (layer 1) and from 1996 onwards with the start of the Room for the River programme also on spatial planning (layer 2), without paying much attention to crisis management (layer 3). Based on the 2021 flood, the official evaluation committee recommends adding two more layers to the multilayer flood safety approach: increasing water awareness (layer 0 as a base layer for the other layers) and focusing on climate-robust recovery after floods (layer 4) to improve long-term robustness. Based on our research, triple-loop learning as the deepest type of learning appeared in the form of applying the Room for the River approach to the tributary brooks systems. To prepare for the future, policy evaluations recommend improving stress tests to better deal with climate change. Futures scenarios and visions for the future are also recommended. Creating future visions can help to steer changes in existing land use and re-think areas to build or to use for water retention, for example. Governments are advised to strengthen their anticipatory capacities as part of their crisis management response and recovery systems. Furthermore, governments will need to apply the different layers for multilayer safety in an integrated way to ensure that regions are able to prepare for future floods.

The Structural Equation Modeling of the Relationship Between Insight and Crisis Management Mediated by Passive Defense: A Case Study of Isfahan Police Department, Iran

Background and objective Increasing insight, law enforcement knowledge, and using active and passive defense mechanisms have a significant role in crisis management, and many disasters can be solved with these mechanisms in the society. The current research aims to provide a structural equation modeling (SEM) of the relationship between insight and crisis management mediated by passive defense mechanism. Method This is a descriptive survey using SEM method. The study population consists of the employees of Isfahan police department (FAA) in Isfahan, Iran, who had participated in the provincial insight courses (n=240). The sample size was determined using Cochran’s formula. By using simple random sampling method, 148 eligible employees were selected, all of whom were second-level managers. The standard crisis management questionnaires of Jahanbakhsh et al. (2015), a researcher-made insight scale with 14 items, and a researcher-made questionnaire of passive defense with 30 items were used to collect data. To analyze the data and test the hypotheses, correlation test, stepwise multiple regression analysis, and SEM were used in SPSS software, version 20 and Smart PLS software, version 3. Results The findings showed that insight and its components together with passive defense had a significant relationship with crisis management. The variable of passive defense also had a significant mediating role in the relationship between insight and crisis management. Conclusion increasing insight is one of the important factors in improving the quality of crisis management along with paying attention to the dimensions and components of passive defense. One of the strengths of this study was the attention to the hidden layers of crisis management in the performance of police missions. By understanding the concept of passive defense, the relationship between insight and crisis management can be understood and it can be used to prevent and deal with internal and external threats.

A RSBU-LSTM network for radio frequency fingerprint identification relying on multiple features

Radio frequency fingerprint identification (RFFI) can distinguish highly similar wireless communication devices to protect physical layer security and improve the security of wireless networks effectively, which has been widely used for spectrum management and physical layer secure communication. However, most RFFI methods show a degradation of performance under low signal-to-noise ratio (SNR) environments. In this paper, we propose a RSBU-LSTM network relying on multiple features to improve the identification accuracy with low SNR. Firstly, we use multiple features of in-phase (I), quadrature (Q), and phase as inputs. Then, we use multiple Residual Shrinkage Building Units (RSBUs) to extract the correlation features within the cycle of signals and preserve as many features as possible in low SNR environments. Finally, we use the long short-term memory (LSTM) to extract the relevant features of the signals of non-adjacent cycles. The experimental results show that the proposed network can effectively complete RFFI in low SNR environments and show better performance than other models used for comparison.

Improvement of the secure routing model in software-configured networks

Currently, the deployment of such network architectures as Software-Defined Networking (SDN) is facing new cyber security threats that require the development and research of new specialized solutions to increase the level of network security. Despite its high openness and programmability, the SDN architecture replaces the traditional network, but it increases the number of potential network attacks, which leads to new security problems. The growing interest in SDN and the widespread deployment of software-configured networks of various types allow identifying their shortcomings in the process of combating cyber security threats. Obviously, security issues are closely related to the characteristics of SDN networks themselves. Furthermore, security issues in SDN can be divided based on three layers: data plane, control plane, and application plane. At the same time, devices of different SDN levels can be among the objects of attacks. Therefore, according to the multilayer architecture of SDN, security threats can be classified at the data transmission, management and application layers. For its part, the data plane consists of switches and other network devices and is mainly responsible for data processing, forwarding, discarding, and collecting statistics. The data plane functions on the basis of flow rules provided by the network controller. While the main causes of security problems are the SDN architecture itself, external malicious attacks, insufficient access control and encryption tools. Today, an important place in the complex of means of increasing network security, including SDN networks, is given to routing protocols, which require the systematic and coordinated interaction of a number of network elements at the same time – SDN switches and network controllers during the formation (calculation) of paths and flow rules, along which the required level of security must be ensured according to selected indicators or criteria. The paper analyzes how to modify route metrics in such a way that the resulting model acquires the properties of secure QoS routing. It is shown that the improvement of the model and the choice of the route should be chosen taking into account the basic metrics of the criticality of vulnerabilities and the bandwidth of the communication channels that make up this route.

Maize plant growth period identification based on MobileNet and design of growth control system

To address the current inefficiencies and subjective nature of manual observation in maize cultivation, with the aim of achieving high efficiency and productivity, this study focused on the DeMaya D3 maize variety. It proposes a maize growth stage recognition method based on the MobileNet model, which is a lightweight convolutional neural network architecture. The method was tested and achieved recognition accuracies of 0.98, 0.96, 0.92, 0.85, and 0.97 for different growth stages, respectively. Additionally, a maize growth prediction model was developed. Based on data collected from experimental plots regarding maize plant height and stem diameter, the Prophet model and an optimized version of the Prophet model were used to forecast maize growth trends. The Prophet model is an open-source tool for time series forecasting. Comparative analysis was conducted between the predictions of the original Prophet model and the optimized version. The relative errors of the Prophet model predictions were 0.85%, 2.11%, and 0.79%, while those of the optimized Prophet model were 0.76%, 0.47%, and 0.71%. Compared to the Prophet model, the optimized model reduced errors by 0.09%, 1.64%, and 0.08%, respectively. The maize plant growth control system was designed to obtain the information through the collection layer. The decision-making layer judged the soil nutrient absorption and growth status. Finally, the management layer controlled water and fertilizer.

Integrating Tradition and Science: A Holistic Approach to Lower Back Pain Management with Yoga and Neurotherapy

Introduction: Low back pain (LBP) is a widespread global health issue affecting 40 to 80% of adults. Conventional treatments like medications, physical therapy, and surgery often provide only temporary relief and can lead to reinjury and persistent disability. This has sparked growing interest in non-conventional approaches for long-term solutions. This review investigates the potential of integrating ancient traditions and modern scientific findings, specifically Yoga and Neurotherapy, to manage LBP effectively. Literature Review: Existing literature highlights the limitations of conventional LBP treatments and the rising interest in holistic, long-term solutions. Yoga is well-documented for enhancing physical function, promoting relaxation, and improving mind-body awareness. Neurotherapy focuses on regulating the nervous system to modulate pain. By synthesizing various studies, this review presents an integrated approach that leverages the strengths of both Yoga and Neurotherapy for LBP management. Materials and Methods: To explore the effectiveness of Yoga and Neurotherapy in LBP management, a systematic analysis of scientific literature was conducted. Databases such as PubMed, Medline, and Google Scholar were searched using keywords like "Yoga," "Neurotherapy," "Low Back Pain," and "integrative treatment." Relevant articles were selected based on their contributions to understanding the mechanisms, effectiveness, and clinical applications of these therapies. Results: The analysis revealed that both Yoga and Neurotherapy offer substantial benefits for LBP sufferers. Yoga enhances physical function, promotes relaxation, and improves mind-body awareness, contributing to long-term relief. Neurotherapy regulates the nervous system to modulate pain, providing an additional layer of management. Combining these approaches creates a synergistic effect, improving patient outcomes more effectively than either treatment alone. Conclusion: Integrating Yoga and Neurotherapy provides a comprehensive, holistic solution for managing LBP, addressing the physical, mental, and neurological dimensions of pain. This review highlights the importance of such integrated strategies and suggests future research should focus on optimizing these approaches to enhance patient outcomes and provide long-term relief from LBP.

Research on Fuzzy Evaluation of Land Environmental Security in Three Provinces of Northeast China

Among the world’s most important three chief black lands, China’s northeast region is a vital hub for the commercial extraction of grain, undertaking the task of grain reserves and special transfers. Utilizing the provincial data of the three northeastern provinces from 2010 to 2021, and using existing documents and records as essential elements, we structured a land security appraisal system with five criterion layers and 21 indicator layers of pressure, state, impact, response, and management. The three provinces in the northeast were evaluated for the security of their land resources using the entropy-dependent weight-TOPSIS pattern. The study findings indicate that: (1) In general, land resource security of the three northeastern provinces shows a pattern of decline followed by a gradual increase from 2012 to 2021, and there is a steady improvement in the level of land’s ecological stability; (2) There are differences in the ecological stability of the soil across cities and provinces, with Heilongjiang Province having the best land security status and Liaoning Province having the worst; (3) The amount of soil erosion in tiny watersheds is the indicator that has the biggest influence on land ecological stability in the three provinces in the northeast; (4) The response layer is the criterion layer that has the strongest correlation with land ecological safety.