Search Results (978)

The Educational Competition Optimizer (ECO) formulates search as a three-stage didactic process—primary, secondary and tertiary learning—but the original framework suffers from scarce information exchange, sluggish late-stage convergence and an unstable exploration–exploitation ratio. We present EECO, which introduces three synergistic mechanisms: a regenerative population strategy that uses the covariance matrix of elite solutions to maintain diversity, a Powell mechanism that accelerates exploitation within promising regions, and a trend-driven update that adaptively balances exploration and exploitation. EECO was evaluated on the 29 benchmark functions of CEC-2017 and nine real-world constrained engineering problems. Results show that EECO delivers higher solution accuracy and markedly smaller standard deviations than eight recent algorithms, including EDECO, ISGTOA, APSM-jSO, LSHADE-SPACMA, EOSMA, GLSRIME, EPSCA, and ESLPSO. Across the entire experimental battery, EECO consistently occupied the first place in the Friedman hierarchy: it attained average ranks of 2.138 in 10-D, 1.438 in 30-D, 1.207 in 50-D, and 1.345 in 100-D CEC-2017 benchmarks, together with 1.722 on the nine real-world engineering problems, corroborating its superior and dimension-scalable performance. The Wilcoxon rank sum test confirms the statistical significance of these improvements. With its remarkable convergence accuracy and reliable stability, EECO emerges as a promising variant of the ECO algorithm. Full article

In recent years, the global energy crisis, concerns about energy security and grid parity, and the pressure to develop policies for reducing the environmental impact of anthropogenic activities have accelerated investments in renewable energy. A growing body of literature applies the real options approach (ROA) to renewable energy projects, recognizing its value in capturing irreversibility and flexibility under uncertainty. The present work provides a detailed state-of-the-art analysis on the adoption of real options to evaluate mixes of energy technologies for power generation, with a special emphasis on investments in hydropower and solar photovoltaics. The objective is to assess current applications, identify knowledge gaps, and outline priorities for advancing decision-making tools in this domain. We performed a systematic literature review following the PRISMA protocol, identifying 38 papers from the Scopus database up to February 2024. Eligible studies were peer-reviewed articles in English applying the ROA to power generation, following a technology selection process; policy evaluation or research and development studies were excluded. The selected papers were analyzed to identify trends over time and space, adopted energy technology, types of real options with valuation methods, and sources of uncertainty. The present paper also discusses the main findings and emerging gaps, providing an overview of hybrid renewable energy systems. Our analysis suggests that, despite the significant advances achieved in this area, further research is needed to exploit the potential of the ROA in investment decisions for combined renewable energy technologies, especially in cases where internal uncertainty and community perspectives need to be explicitly considered. By linking the ROA to the challenges of mixed renewable energy projects, this study enhances understanding of investment decision-making under uncertainty and identifies pathways toward more robust and adaptive project evaluation. Full article

This paper presents a novel method for locally estimating vehicle density on highways based on vehicle-to-vehicle (V2V) communication, a communication mode within intelligent transport systems (ITSs), enabled via IEEE 802.11p and 3GPP C-V2X technologies. Awareness messages (AMs), such as basic safety messages (BSMs, SAE J2735) and cooperative awareness messages (CAMs, ETSI EN 302 637-2), are periodically broadcast by vehicles and can be leveraged to sense the presence of nearby vehicles. Unlike existing approaches that directly combine the number of sensed vehicles with measured packet reception ratio (PRR) of the AM, our method accounts for the deviations in PRR caused by imperfect channel conditions. To address this, we estimate the actual packet reception probability (PRP)–distance curve by exploiting its inherent downward trend along with multiple measured PRR points. From this curve, two metrics are introduced: node awareness probability (NAP) and average awareness ratio (AAR), the latter representing the ratio of sensed vehicles to the total number of vehicles. The real density is then estimated using the number of sensed vehicles and AAR, mitigating the underestimation issues common in V2V-based methods. Simulation results across densities ranging from 0.02 vehs/m to 0.28 vehs/m demonstrate that our method improves estimation accuracy by up to 37% at an actual density of 0.28 vehs/m, compared with methods relying solely on received AMs, without introducing additional communication overhead. Additionally, we demonstrate a practical application where the basic safety message (BSM) transmission rate is dynamically adjusted based on the estimated density, thereby improving traffic management efficiency. Full article

Insider threats remain a persistent challenge in cybersecurity, as malicious or negligent insiders exploit legitimate access to compromise systems and data. This study presents a bibliometric review of 325 peer-reviewed publications from 2015 to 2025 to examine how machine learning (ML) and deep learning (DL) techniques for insider threat detection have evolved. The analysis investigates temporal publication trends, influential authors, international collaboration networks, thematic shifts, and algorithmic preferences. Results show a steady rise in research output and a transition from traditional ML models, such as decision trees and random forests, toward advanced DL methods, including long short-term memory (LSTM) networks, autoencoders, and hybrid ML–DL frameworks. Co-authorship mapping highlights China, India, and the United States as leading contributors, while keyword analysis underscores the increasing focus on behavior-based and eXplainable AI models. Symmetry emerges as a central theme, reflected in balancing detection accuracy with computational efficiency, and minimizing false positives while avoiding false negatives. The study recommends adaptive hybrid architectures, particularly Bidirectional LSTM–Variational Auto-Encoder (BiLSTM-VAE) models with eXplainable AI, as promising solutions that restore symmetry between detection accuracy and transparency, strengthening both technical performance and organizational trust. Full article

Over the past several decades, there has been an accelerating trend to ever more accurate quantum sensors: sensors of time intervals (i.e., atomic clocks), sensors of magnetic fields (i.e., quantum magnetometers), and sensors of inertial motions (i.e., atom interferometers), to name just a few. With this trend has come a renewed interest in the problem of quantum mechanical measurement (i.e., collapse of the wavefunction), and though there have been many attempts to resolve the problem, there is still no wholly accepted resolution. Here, we discuss a little-explored path for resolving the issue that exploits wavefunction phase. To illustrate this path’s potential, we consider the notion of “eigenphase” sets that are disjoint among orthogonal eigenvectors. Wavefunction collapse then occurs because of constructive/destructive interference when a classical measuring device “phase-locks” to an incoming wavefunction. While the present work examines one method for exploiting wavefunction phase, its primary purpose is to more generally re-focus attention on wavefunction phase as a means for resolving the measurement problem that avoids many other solutions’ problematic aspects. Full article

In open-network environments of smart buildings and urban infrastructure, abnormal traffic from security and energy monitoring systems is critical for operational safety and decision reliability. We can develop malware that exploits building automation protocols to simulate attacks involving the falsification or modification of chiller controller commands, thereby endangering the entire network infrastructure. Intrusion detection systems rely on abundant labeled abnormal traffic data to detect attack patterns, improving network system reliability. However, transmitting such data faces two major challenges: single-feature representations fail to capture comprehensive traffic features, limiting the information representation for artificial intelligence (AI)-based detection models, and unconcealed abnormal traffic is easily intercepted by firewalls or intrusion detection systems, hindering cross-departmental sharing. Existing methods struggle to balance feature integrity and transmission stealth, often sacrificing one for the other or relying on easily detectable spatial-domain steganography. To address these gaps, we propose a multi-channel imaging hiding method that reconstructs abnormal traffic into multi-channel images by combining three mappings to generate grayscale images that depict traffic state transitions, dynamic trends, and internal similarity, respectively. These images are combined to enhance feature representation and embedded into frequency-domain adversarial examples, enabling evasion of security devices while preserving traffic integrity. Experimental results demonstrate that our method captures richer information than single-representation approaches, achieving a PSNR of 44.5 dB (a 6.0 dB improvement over existing methods) and an SSIM of 0.97. The high-fidelity reconstructions enabled by these gains facilitate the secure and efficient sharing of abnormal traffic data, thereby enhancing AI-driven security in smart buildings. Full article

In recent years, the availability of automated change detection algorithms in Google Earth Engine has permitted the cloud-based processing of large quantities of satellite imagery. Models such as the Continuous Change Detection and Classification (CCDC), CCDC-Spectral Mixture Analysis (CCDC-SMA), and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) allow users to exploit decades of Earth Observations (EOs), leveraging the Landsat archive and data from other sensors to detect disturbances in forest ecosystems. Despite the wide adoption of these methods, robust documentation, and a growing community of users, little research has systematically detailed their tuning process in mangrove environments. This work aims to identify the best practices for applying these models to monitor changes within mangrove forest cover, which has been declining gradually in Belize the last several decades. Partnering directly with the Belizean Forest Department, our team developed a replicable, efficient methodology to annually update the country’s mangrove extent, employing EO-based change detection. We ran a series of model variations in both CCDC-SMA and LandTrendr to identify the parameterizations best suited to identifying change in Belizean mangroves. Applying the best performing model run to the starting 2017 mangrove extent, we estimated a total loss of 540 hectares in mangrove coverage by 2024. Overall accuracy across thirty variations in model runs of LandTrendr and CCDC-SMA ranged from 0.67 to 0.75. While CCDC-SMA generally detected more disturbances and had higher precision for true changes, LandTrendr runs tended to have higher recall. Our results suggest LandTrendr offered more flexibility in balancing precision and recall for true changes compared to CCDC-SMA, due to its greater variety of adjustable parameters. Full article

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning. Full article

In karst regions (KRs), unique surface morphology and irrational human exploitation have led to increasingly prominent issues such as land fragmentation and rocky desertification. Understanding the spatiotemporal evolution of cultivated land (CL) in these areas is of great significance for supporting regional socioeconomic development, food security, and ecological sustainability. This study focuses on Guilin, combining GIS spatial analysis with methods including kernel density analysis, dynamic degree, spatial transfer matrix, and a Geodetector to examine the spatiotemporal distribution characteristics, evolution trends, and driving factors of land use based on five-phase of land use data from 2000 to 2020. The results show that: (1) over the past two decades, land use in Guilin has been dominated by CL and forest land, with CL exhibiting a spatial pattern of more in the east and south, and less in the west and north; (2) the CL transfer-out rate exceeded the transfer-in rate, mainly shifting to construction land and forest land; (3) the overall density of CL showed a declining trend, with a relatively stable spatial pattern; and (4) driving factor analysis indicates that the spatiotemporal changes in CL are jointly influenced by multiple factors, with natural factors exerting a stronger influence than socio-economic factors. Among them, the interaction between elevation and temperature had the greatest impact and served as the dominant factor. Although GDP and population were not dominant individually, their explanatory power and sensitivity increased significantly when interacting with other factors, making them key sensitive factors. The results can provide a scientific reference for the protection and rational utilization of CL resources in KR. Full article

Tactical production–distribution planning in paper manufacturing involves hierarchical decision-making under hybrid uncertainty, where aleatory randomness (demand fluctuations, machine variations) and epistemic uncertainty (expert judgments, market trends) simultaneously affect operations. Existing approaches fail to address the bi-level nature under hybrid uncertainty, treating production and distribution decisions independently or using single-paradigm uncertainty models. This research develops a bi-level dependent-chance goal programming framework based on uncertain random theory, where the upper level optimizes distribution decisions while the lower level handles production decisions. The framework exploits structural symmetries through machine interchangeability, symmetric transportation routes, and temporal symmetry, incorporating symmetry-breaking constraints to eliminate redundant solutions. A hybrid intelligent algorithm (HIA) integrates uncertain random simulation with a Reinforcement-Learning-enhanced Arithmetic Optimization Algorithm (RL-AOA) for bi-level coordination, where Q-learning enables adaptive parameter tuning. The RL component utilizes symmetric state representations to maintain solution quality across symmetric transformations. Computational experiments demonstrate HIA’s superiority over standard metaheuristics, achieving 3.2–7.8% solution quality improvement and 18.5% computational time reduction. Symmetry exploitation reduces search space by approximately 35%. The framework provides probability-based performance metrics with optimal confidence levels (0.82–0.87), offering 2.8–4.5% annual cost savings potential. Full article

Linear motor electromagnetic energy regenerative suspension (LMEERS), integrating dual functionalities of energy regeneration and active control, possesses the potential to overcome the performance limitations inherent in existing suspension architectures. Research on key technologies for LMEERS aligns with the contemporary automotive development theme of “enhanced comfort, improved safety, and optimized energy efficiency”. This paper reviews the research progress of the configuration design, performance optimization, functionality switching criterion identification, and top-layer control strategies of LMEERS. Regarding configuration design, a systematic summary is provided for the design schemes of fundamental configuration and the technical features of three composite configurations. In the aspect of performance optimization, the specific approaches and their effectiveness in enhancing LMEERS comprehensive characteristics are analyzed. Concerning functionality switching criterion identification, the operating principles and performance differences among various estimation methods in identifying road surface information are discussed. For top-layer control strategies, the characteristics and applicability of various control methods in exploiting the dual functionalities of LMEERS are summarized. Future developments in LMEERS are anticipated to trend towards integration, lightweighting, standardization, intellectualization, and multi-mode operation. This review provides a theoretical reference for the design optimization and technological innovation of LMEERS, contributing to the advancement of automotive chassis systems in terms of electrification, intellectualization, and energy conservation. Full article

The aim of conversion is to reveal the potential of non-functioning buildings for transformation—i.e., to design a new life for them. A large number of original and presently non-functioning industrial production buildings are connected to mining activity. The subject of this study and area of investigation are selected mining networks from the second half of the 20th century in the Rudňany settlement, which is located in the Spiš region. The aim of the research is to form a process algorithm for the reuse of areas and objects of mining activity and to highlight the cultural values, constructional substance, and preconditions for their further development. Part of the investigation comprises proposals for a new functional usage of the structures that will encompass the complex historical ground-points of the locality and include its historical roots and trends in the region’s social development and community. The quantitative and qualitative research is based on an analysis of the values of such structures based on traditional mining activity, accompanied by landscape research of the specific locality. The results are presented in the form of case studies oriented towards the identification and evaluation of the attributes of change for novel exploitation of the mining sites. The conclusion of the research is composed of an evaluation and interpretation feasibility study pointing out suitable solutions and preconditions for the sustainability of the converted mining structures as parts of open concepts for mining theme parks. Full article

Water scarcity severely restricts the sustainable development of water-stressed regions like Hebei Province. A scientific assessment of water resource carrying capacity (WRCC) is essential. However, single-weighting methods often lead to biased results. To address this limitation, we propose a combined weighting model that integrates the Entropy Weight Method (EWM), Projection Pursuit (PP), and CRITIC. To support this model, we developed a multi-dimensional, long-term WRCC evaluation dataset covering 11 prefecture-level cities in Hebei Province over 24 years (2000–2023). This approach simultaneously considers data dispersion, inter-indicator conflict, and structural features. It ensures that a more balanced weighting scheme is obtained. The traditional TOPSIS model was further improved through Grey Relational Analysis (GRA), which enhanced the discriminatory power and stability of WRCC assessment. The findings were as follows: (1) From 2000 to 2023, the WRCC in Hebei Province showed a fluctuating upward trend and a “high-north, low-south” spatial gradient. (2) Obstacle analysis revealed a vicious cycle of “resource scarcity–structural conflict–ecological deficit”. This cycle is caused by excessive exploitation of groundwater and low efficiency of industrial water use. The combined weighting–GRA–TOPSIS model offers a reliable WRCC diagnostic tool. The results indicate the core barriers to water use in Hebei and provide targeted policy ideas for sustainable development. Full article

This work presents a proof of concept including simulation and experimental validations of acoustic gas sensor prototypes for trace CO₂ detection up to 1 ppm. For the detection of lower gas concentrations especially, the dependency of acoustic resonances on the molecular weights and, consequently, the speed of sound of the gas mixture, is exploited. We explored two resonator types: a cylindrical acoustic resonator and a Helmholtz resonator intrinsic to the MEMS microphone’s geometry. Both systems utilized mass flow controllers (MFCs) for precise gas mixing and were also modeled in COMSOL Multiphysics 6.2 to simulate resonance shifts based on thermodynamic properties of binary gas mixtures, in this case, N₂-CO₂. We performed experimental tracking using Zurich Instruments MFIA, with high-resolution frequency shifts observed in µHz and mHz ranges in both setups. A compact and geometry-independent nature of MEMS-based Helmholtz tracking showed clear potential for scalable sensor designs. Multiple experimental trials confirmed the reproducibility and stability of both configurations, thus providing a robust basis for statistical validation and system reliability assessment. The good simulation experiment agreement, especially in frequency shift trends and gas density, supports the method’s viability for scalable environmental and industrial gas sensing applications. This resonance tracking system offers high sensitivity and flexibility, allowing selective detection of low CO₂ concentrations down to 1 ppm. By further exploiting both external and intrinsic acoustic resonances, the system enables highly sensitive, multi-modal sensing with minimal hardware modifications. At microscopic scales, gas detection is influenced by ambient factors like temperature and humidity, which are monitored here in a laboratory setting via NDIR sensors. A key challenge is that different gas mixtures with similar sound speeds can cause indistinguishable frequency shifts. To address this, machine learning-based multivariate gas analysis can be employed. This would, in addition to the acoustic properties of the gases as one of the variables, also consider other gas-specific variables such as absorption, molecular properties, and spectroscopic signatures, reducing cross-sensitivity and improving selectivity. This multivariate sensing approach holds potential for future application and validation with more critical gas species. Full article

Background and Objective: The intricate connection between daily behaviours and health necessitates robust monitoring, particularly with the advent of Internet of Things (IoT) systems. This study introduces an innovative approach that exploits the synergy of information from various IoT sources to assess the alignment of behavioural routines with health guidelines. The goal is to improve the readability of behaviour models and provide actionable insights for healthcare professionals. Method: We integrate data from ambient sensors, smartphones, and wearable devices to acquire daily behavioural routines by employing process mining (PM) techniques to generate interpretable behaviour models. These routines are grouped according to compliance with health guidelines, and a clustering method is used to identify similarities in behaviours and key characteristics within each cluster. Results: Applied to an elderly care case study, our approach categorised days into three physical activity levels (Insufficient, Sufficient, Desirable) based on daily step thresholds. The integration of multi-source data revealed behavioural variations not detectable through single-source monitoring. We demonstrated that the proposed visualisations in calendar and timeline views aid health experts in understanding patient behaviours, enabling longitudinal monitoring and clearer interpretation of behavioural trends and precise interventions. Notably, the approach facilitates early detection of behaviour changes during contextual events (e.g., COVID-19 lockdown and Ramadan), which are available in our dataset. Conclusions: By enhancing interpretability and linking behaviour to health guidelines, this work signifies a promising path for behavioural analysis and discovering variations to empower smart healthcare, offering insights into patient health, personalised interventions, and healthier routines through continuous monitoring with IoT-driven data analysis. Full article