2026 July - 106 articles
Long-running large language model (LLM) agents accumulate memory across many sessions, yet most agent runtimes store it as flat text. We present MEMTIER, a tiered memory architecture and consolidation framework for an open-source agent runtime and st...
Exploratory lag-resolved correlation analysis is widely used when potential dependence between concurrently recorded signals may be delayed, transient, or weak. In autocorrelated time series, however, scanning across candidate lags creates a multiple...
Remote sensing image object detection is a core task in computer vision, which plays a vital role in intelligent transportation, port monitoring, and infrastructure management. However, rotated dense objects in remote sensing scenes suffer from sever...
Early detection of blade structural degradation is critical for wind turbine reliability, as unplanned failures contribute to approximately 25% of operational downtime. Traditional blade condition monitoring methods based on distributed vibration, st...
The super-resolution (SR) of Magnetic Resonance Imaging (MRI) is essential for utilizing clinical scans with limited resolution, noise, and anisotropic sampling, such as multi-modal brain tumor imaging. In this work, we propose a Tri-Representation h...
Energy, conceived as an essential element for economic and social progress, underpins the manufacture of products, transport, the provision of basic needs and, consequently, the improvement of society’s well-being. Within this framework, three-...
Accurate forecasting of country-level temperature anomalies is increasingly important for climate monitoring, policy planning, and environmental risk assessment. However, the trade-off between predictive performance, model complexity, and computation...
Recommender systems face fundamental challenges, including extreme data sparsity and noisy rating observations. We propose L21RSVD, a robust latent factor model that employs L21 norm regularization within a Singular Value Decomposition framework to m...
Temporal super-resolution (SR) aims to reconstruct a high-resolution signal from a low-resolution observation. When hardware limits force low sampling rates, this problem becomes critical for non-stationary signals with abrupt transients and rapid sp...
Reliable state assessment of railway traction power transformers is challenged by heterogeneous operating environments, measurement disturbances, coupled gas-generation mechanisms, and uneven fault-sample distributions. Conventional dissolved gas ana...
Mitigating delayed delivery risks is a critical imperative for e-commerce platforms to prevent financial losses and optimize customer lifetime value. However, extracting actionable intelligence from heterogeneous tabular logistics data remains a pers...
This work deals with the boundary stabilization problem of a flexible beam system subject to actuator saturation and external disturbances. Firstly, for the case of actuator input saturation, by introducing a dead-zone function and constructing an au...
The preconditioned Crank–Nicolson (pCN) Markov chain Monte Carlo method is a standard tool for high-dimensional Bayesian inverse problems because its proposals preserve the prior and its performance degrades gracefully under mesh refinement. Ye...
Accurate stock price forecasting remains a challenging problem due to the noisy, nonlinear, and non-stationary characteristics of financial time series. Although recent advances in deep learning have improved predictive capabilities, most prior studi...
Rule-based classifiers are often preferred over other types of learners due to the transparent mode in which decisions are made. Each decision rule includes in its premise conditions on attributes. When they are satisfied, the conclusion part of the...
After more than a century, factor analysis remains one of the most popular tools in applied statistics. Exploratory factor analysis tends to be driven by iterated principal axis factorization. Confirmatory factor analysis tends to rely on maximum lik...
Decarbonising municipal solid waste (MSW) is a defining algorithmic challenge for smart cities: waste-to-energy (WtE), composting and material-recovery investments must be committed years ahead under deep uncertainty about household source-separation...
The doubly fed induction generator (DFIG) has become one of the most widely adopted technologies in modern wind energy conversion systems due to its high efficiency, flexible operation, and capability to operate under variable wind-speed conditions....
A new method for finding the global extremum of functions of many variables under interval constraints is proposed. The method simulates the process of foraging for edible mushrooms in a forest by a group of mushroom pickers. The algorithm includes a...
Motor imagery-based brain–computer interfaces (BCIs) have attracted significant attention due to their potential applications in assistive technologies, neurorehabilitation, and wearable human–machine interaction systems. However, practic...
Software vulnerabilities pose a severe threat to system security and stability. To reinforce the hierarchical structure of source code, enhance the distinctiveness of embedded features, and reduce feature confusion, we propose CADetect, a vulnerabili...
The field of competitive strategy has attracted growing academic interest in recent years; however, the intellectual framework and thematic evolution of this research area remain fragmented. This study aims to systematically map the evolution of comp...
Although urban bus fleets increasingly generate HVAC operational data through onboard sensors and telematics platforms, their practical use for maintenance decision-making remains limited. In real-world conditions, these data are rarely accompanied b...
Data Envelopment Analysis (DEA) has emerged as a major non-parametric technique for measuring efficiency in sustainability and environmental economics because it can handle multiple inputs and outputs without making explicit functional assumptions. D...
Cervical cancer is the fourth most frequent malignancy in women globally. Pap smear screening is important for early cancer detection, but manual smear analysis is time-consuming, labor-intensive and error-prone for diagnosis. Such issues in resource...
Existing image super-resolution methods still suffer from limitations in edge-structure restoration, high-frequency texture preservation, and artifact suppression, which may lead to blurred contours and unnatural textures. To address these issues, th...
Noisy, expensive, gradient-free optimisers—simulated annealing chief among them—almost never report how confident one should be in the configuration they return, and reinforcement-learning hyperparameter tuning, where the noise is large a...
The Winner-Takes-All (WTA) training paradigm inherently prioritises global trajectory accuracy. For long-tail samples, where terminal prediction is inherently difficult, this prioritisation leads to degraded Final Displacement Error (FDE). This asymm...
Surface electromyography (sEMG) is widely used to evaluate neuromuscular activity; however, objective and reproducible methodologies for assessing signal quality in embedded acquisition systems remain limited. This study presents a multichannel embed...
In this study, a high-order Galerkin–Numerov approach is presented to solve the transient vibration problem of anisotropic Kirchhoff plates supported by a uniform Vlasov foundation. A discretization of the governing fourth-order plate equation...
To address the high non-stationarity of COVID-19 pandemic time-series data and the severe error accumulation issue in long-horizon forecasting, a spatiotemporal two-branch multi-step forecasting model named CFC-FLCA is proposed. This model integrates...
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In this work, we introduce and study a generalization for r-uniform hypergraphs of complement-reducible graphs, the so-called co-graphs. The operations for r-join-hypergraphs are the binary disjoint union of two given r-join-hypergraphs and the r-nar...
Existing metaheuristics for the permutation flow-shop scheduling problem primarily explore the search space directly. This study presents a new decomposition technique that, unlike those methods, repeatedly solves one-machine subproblems and extends...
The structural and positional analysis of planar linkage mechanisms containing higher-class Assur groups requires the generation and solution of systems of algebraic kinematic equations. This paper presents algorithms for the automated analysis of su...
Determining the Remaining Useful Life (RUL) in roller bearings is of utmost importance in rotary machinery. Knowing the present state and acting before a failure occurs is the most important aspect in industrial setups. This research presents an effe...
Wireless Rechargeable Sensor Networks (WRSNs) rely on Wireless Charging Vehicles (WCVs) to replenish sensor nodes under limited mission budgets. Existing semi-on-demand strategies mainly emphasize node selection and service ordering, while charging d...
Urban unmanned aerial vehicle (UAV) logistics systems require routing algorithms that can balance delivery efficiency and operational safety under complex physical and environmental constraints. However, existing multi-objective routing methods often...
Hesitant fuzzy linguistic sets (HFLSs) combine natural language expressions with membership information in a hesitant manner, thereby breaking the barriers between fuzzy evaluation and natural language-based decision-making. Hence, they have become a...
Post-disaster debris detection is important for rapid damage assessment, emergency response, and recovery planning. However, debris objects in aerial imagery are often fragmented, irregularly shaped, partially occluded, and visually confused with sha...
The Colonel Blotto game, formulated by Émile Borel, involves players allocating limited resources to multiple “battlefields” simultaneously, with the winner being the one who allocates more resources to each battlefield. Computatio...
This study introduces the ’Black-Backed Jackal Optimization’ (BBJO), a nature-inspired meta-heuristic algorithm designed for complex, non-linear, and high-dimensional search spaces. The fundamental mathematical model of BBJO relies on the...
This paper presents a unified comparative study of various controllers, including proportional–integral–derivative (PID), Fractional-Order PID (FOPID), Internal Model Control (IMC) controllers, and Tilt–Integral–Derivative (TI...
To address the challenges in maize leaf disease detection, including large variation in lesion scales, weak texture of small targets, strong background interference, limited recall ability for blurred lesions, and computational redundancy of conventi...
Dried droplet imaging has become an established approach for analyzing complex fluid systems as evaporation patterns preserve physicochemical and structural information. However, spatial variability of these patterns limits the effectiveness of conve...
Accurate assessment of radiographic bone loss (RBL) is essential for periodontal diagnosis and staging; however, manual measurement from dental radiographs is labor-intensive, time-consuming and subject to inter- and intra-examiner variability. Exist...
To address the problems of slow convergence, long planned paths, and excessive turning points in unmanned surface vehicle (USV) path planning under complex environments, this paper proposes a path planning method based on an Enhanced Gold Rush Optimi...
The classification of malicious network-traffic is critical to cybersecurity. However, to the best of our knowledge, no previous studies have performed a comparative analysis of supervised algorithms for classifying malicious traffic, specifically wi...
Complex three-dimensional path planning requires a planner to generate collision-free, short, and smooth paths within limited computation time, but traditional RRT-based methods often suffer from unguided sampling, repeated expansion failures in dens...
Exact probabilistic inference in Bayesian Networks (BNs) becomes increasingly expensive as network size and structural complexity grow, limiting its applicability in time-sensitive decision-support systems. This study presents a hybrid inference fram...
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