In struggling against invasive species ravaging riverscape ecosystems, gaps in dispersal pathway knowledge and fragmented approaches across scales have long stalled effective riparian management worldwide. To reduce these limitations and enhance invasion management strategies, selecting appropriate alien species as models for in-depth pathway analysis is essential. Alternanthera philoxeroides (Mart.) Griseb. (alligator weed) emerges as an exemplary model species, boasting an invasion record of around 120 years spanning five continents worldwide, supported by genetic evidence of repeated introductions. In addition, the clonal reproduction of A. philoxeroides supports swift establishment, while its amphibious versatility allows occupation of varied riparian environments, with spread driven by natural water-mediated dispersal (hydrochory) and human-related vectors at multiple scales. Thus, leveraging A. philoxeroides, this review proposes a comprehensive multi-scale framework, which integrates monitoring with remote sensing, environmental DNA, Internet of Things, and crowdsourcing for real-time detection. Also, the framework can further integrate, e.g., MaxEnt (Maximum Entropy Model) for climatic suitability and mechanistic simulations of hydrodynamics and human-mediated dispersal to forecast invasion risks. Furthermore, decision-support systems developed from the framework can optimize controls like herbicides and biocontrol, managing uncertainties adaptively. At the global scale, the dispersal paradigm can employ AI-driven knowledge graphs for genetic attribution, multilayer networks, and causal inference to trace pathways and identify disruptions. Based on the premise that our multi-scale framework can bridge invasion ecology with riverscape management using A. philoxeroides as a model, we contend that the implementation of the proposed framework tackles core challenges, such as sampling biases, shifting environmental dynamics, eco–evolutionary interactions using stratified sampling, and adaptive online algorithms. This methodology is purposed to offer scalable tools for other aquatic invasives, evolving management from reactive measures to proactive, network-based approaches that effectively interrupt dispersal routes.

We present H-NGPCA, a hierarchical clustering algorithm for data streams that integrates an adaptive unit number growth and local dimensionality control. Unlike existing algorithm, H-NGPCA combines the characteristics of centroid-based, model-based and hierarchical clustering. H-NGPCA builds a hierarchical structure of local Principal Component Analysis (PCA) units, where each unit is a hyper-ellipsoid whose shape is updated by a neural network-based online PCA method. The re-positioning of each unit is handled by Neural Gas, a centroid-based clustering algorithm. In the hierarchical tree structure, new units are created in a branch if suggested by a splitting criterion. In addition, each unit determines its own dimensionality based on the data represented by the unit. In extensive benchmarks, H-NGPCA not only surpasses all competing online algorithms with adaptive unit numbers but also achieves competitive performance with state-of-the-art offline methods, reaching an average NMI = 0.87 and CI = 0.26. This demonstrates that H-NGPCA achieves both online adaptability and offline-level accuracy.

The study investigated perpetuation of false narratives and political manipulation through fake news, misinformation, disinformation and computational propaganda in digital ecosystem of Pakistan in 2025. The study critically reviewed role of automation, online algorithms and coordinated human networks curtailing cyber security in political spheres. The viral political news stories of social media have been selected for content analysis employing purposive sampling technique. Exerting theoretical foundations from Framing theory and Propaganda model the study analyzed the key propaganda techniques, frames, patterns and major themes employed for shaping political discourses of Pakistani public. The findings suggested that fake AI driven technologies such as online algorithms, computational propaganda techniques, automation and intensify political polarization and serve as tool for production, consumption and dissemination of fake news and false narratives targeted to frame false narratives through manipulative propaganda techniques of political issues and news stories. The study highlighted urgent need to develop AI powered cyber security frameworks for detection of fake news and control information bias in order to enhance credibility and trustworthiness in digital spaces.

Objective.To develop an online quality control (QC) system for accurate assessment of dwell position and dwell time in high-dose-rate (HDR) brachytherapy, and to investigate the potential of neural networks so as to improve the robustness and stability of the proposed system.Approach.An integrated framework was constructed using a Basler high-speed camera (144 fps, 1920 × 1200 pixels), custom illumination, and dedicated software. Experiments were conducted with a GammaMedPlus iX afterloader equipped with a stepping192Irsource, testing various step sizes (0.2 cm, 0.5 cm, 1.0 cm) and dwell times (2.0 s, 3.0 s, 10.0 s). The core online algorithm employed a frame-difference method for source tracking, while offline analysis evaluated the RT-DETRv2 neural network for source localization.Main results.The system achieved high spatial resolution (0.083 mm) and temporal resolution (7.0 ms). Primarily due to guidewire bending, positional deviations remained below 0.1 cm and increased with guidewire length. After position correction, the positional deviation was reduced to about 0.01 cm. Dwell time deviations were within 10.0 ms. RT-DETRv2 demonstrated outstanding localization accuracy (91% of predictions within 0.26 mm) in various conditions. However, its processing latency of 0.35 s per image makes it unsuitable for online monitoring but well-suited for offline or auxiliary verification in this system.Significance.This work presented a technically feasible online QC method for HDR brachytherapy that enabled precise verification of source delivery parameters. Moreover, the successful application of deep learning for source detection established a foundation for future enhancements in reliability and automation of the proposed QC system.

The article presents a comprehensive interdisciplinary analysis of the phenomenon of «expand-ed identity», a new paradigm of selfconstruction that has emerged in the context of global digitalization of social life. The research synthesizes approaches from sociology, psychology, and philosophy to study how online platforms, social networks, and algorithms not only become tools of self-expression, but actively form the core of a modern person's personality. The article explores the dual nature of this phenomenon: its potential for creating new forms of social connection and at the same time the sys-temic risks it generates for mental health, authenticity and social integrity of a person

This paper develops the first online algorithms for estimating the spectral density function — a fundamental object of interest in time series analysis — that satisfies the three core requirements of streaming inference: fixed memory, fixed computational complexity, and temporal adaptivity. Our method builds on the concept of forgetting factors, allowing the estimator to adapt to gradual or abrupt changes in the data-generating process without prior knowledge of its dynamics. We introduce a novel online forgetting-factor periodogram and show that, under stationarity, it asymptotically recovers the properties of its offline counterpart. Leveraging this, we construct an online Whittle estimator, and further develop an adaptive online spectral estimator that dynamically tunes its forgetting factor using the Whittle likelihood as a loss. Through extensive simulation studies and an application to ocean drifter velocity data, we demonstrate the method’s ability to track time-varying spectral properties in real-time with strong empirical performance.

Historical inference attacks pose a critical privacy threat in mobile edge computing (MEC), where adversaries exploit long-term task and location patterns to infer users’ sensitive information. To address this challenge, we propose a privacy-preserving task scheduling framework that adaptively balances privacy protection and system performance under dynamic vehicular environments. First, we introduce a dynamic privacy-aware adaptation mechanism that adjusts privacy levels in real time according to vehicle mobility and network dynamics. Second, we design a dual-algorithm framework composed of two complementary solutions: a Markov Approximation-Based Online Algorithm (MAOA) that achieves near-optimal scheduling with provable convergence, and a Privacy-Aware Deep Q-Network (PAT-DQN) algorithm that leverages deep reinforcement learning to enhance adaptability and long-term decision-making. Extensive simulations demonstrate that our proposed methods effectively mitigate privacy leakage while maintaining high task completion rates and low energy consumption. In particular, PAT-DQN achieves up to 14.2% lower privacy loss and 19% fewer handovers than MAOA in high-mobility scenarios, showing superior adaptability and convergence performance.

This paper introduces an online interval-valued convex optimization problem wherein the objective functions are intervals of real numbers and change in each iteration. Using gradients of the involved objective functions, we present a gradient-based online algorithm for solving the proposed online problem that achieves sublinear regret bounds O ( T ) . Examples are given to illustrate the results.

Optimal Algorithms for Online Age-of-Information Optimization in Energy Harvesting Systems

In collaboration with VolunteerMatch (VM)—the world’s largest online platform for connecting volunteers with volunteering opportunities—we designed and implemented a new display ranking algorithm. VM’s original ranking algorithm was intended to maximize efficiency (i.e., the total number of connections), but as a consequence, it repeatedly displayed the same few opportunities at the top of its ranking, effectively limiting access to volunteers for the other opportunities. To incorporate VM’s desire for equity (defined as the weekly number of opportunities with at least one connection) along with efficiency, we propose a modeling framework for online display ranking in settings where it is important to manage the trade-off between the total number of connections and the equitable allocation of these connections. We take an adversarial approach in evaluating the performance of online algorithms and show that a class of algorithms that applies a penalty to opportunities after each connection provides a strong (and, in certain regimes, optimal) performance guarantee. Inspired by our theoretical results yet mindful of practical considerations on VM’s platform, we propose SmartSort, a simple score-based ranking algorithm that enjoys comparable guarantees in many regimes. We implemented SmartSort in two experiments, covering Dallas–Fort Worth and all of Southern California. Using a difference-in-differences analysis, we find that the implementation of SmartSort led to an estimated 8% increase in the weekly average number of opportunities with at least one connection (consistent across both experiments) without any significant decrease in the total number of connections. If SmartSort has a similar distributional effect on a national scale, an additional 30,000 connections every year will go to opportunities that would have otherwise lacked access to volunteers. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03838 .

ABSTRACT We consider the problem of sequential (online) estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that certain CUSUM‐type statistics attain the minimax optimal rates for localizing the change point. Our minimax analysis unveils an interesting phase transition from a jump (discontinuity in function values) to a kink (a change in slope). Specifically, for a jump, the minimax rate is of order , whereas for a kink it scales as , given that the sampling rate is of order . We further introduce an online algorithm based on these detectors, which optimally identifies both a jump and a kink, and is able to distinguish between them. Notably, the algorithm operates with constant computational complexity and requires only constant memory per incoming sample. Finally, we evaluate the empirical performance of our method on both simulated and real‐world data sets. An implementation is available in the R package FLOC on GitHub.

In this article, we introduce an over-time variant of the well-known prophet inequality with i.i.d. random variables. Instead of stopping with one realized value at some point in the process, we decide for each step how long we select the value. Then we cannot select another value until this period is over. The goal is to maximize the expectation of the sum of selected values. We describe the structure of the optimal stopping rule and give upper and lower bounds on the prophet inequality. In online algorithms terminology, this corresponds to bounds on the competitive ratio of an online algorithm. We give a surprisingly simple algorithm with a single threshold that results in a prophet inequality of ≈ 0.396 for all input lengths n . Additionally, as our main result, we present a more advanced algorithm resulting in a prophet inequality of ≈ 0.598 when the number of steps tends to infinity. We complement our results by an upper bound that shows that the best possible prophet inequality is at most 1/φ ≈ 0.618, where φ denotes the golden ratio.

With the development of cities, traffic congestion has become a common problem, which seriously affects the efficiency of motorway transport. This study proposed an improved ML-YOLO video data extraction model based on You Only Look Once (YOLOv8n) combined with the Deep Simple Online and real-time tracking (DeepSORT) algorithm, to classify the obtained Traffic Performance Index (TPI) into different congestion levels by extracting traffic flow parameters in real-time and combining with the K-means clustering algorithm. The Long Short-Term Memory Dropout (LSTM-Dropout) model and the emergency lane opening model were used to implement the road congestion warning successfully. The practicality and stability of the model were also verified by calculating the relative error between the predicted traffic flow parameters and the extracted parameters through the LSTM time series model. According to the model results, emergency lanes are opened when the motorway traffic TPI exceeds 0.17 and closed when below 0.17. This study provided a reasonable theoretical basis for motorway traffic managers to decide whether or not to open the emergency lane, effectively relieved motorway road congestion, improved efficiency of road traffic, and had important practical value and significance in reality.

HTM-CDFK: An Online Industrial Control Anomaly Detection Algorithm Based on Hierarchical Time Memory

EA-OSPGB: Multiple robots dynamic online algorithm for solving full coverage path planning of multiple robots in unknown terrain environments

We study three levels in a hierarchy of nondeterminism: A nondeterministic automaton $\mathcal{A}$ is determinizable by pruning (DBP) if we can obtain a deterministic automaton equivalent to $\mathcal{A}$ by removing some of its transitions. Then, $\mathcal{A}$ is history deterministic (HD) if its nondeterministic choices can be resolved in a way that only depends on the past. Finally, $\mathcal{A}$ is semantically deterministic (SD) if different nondeterministic choices in $\mathcal{A}$ lead to equivalent states. Some applications of automata in formal methods require deterministic automata, yet in fact can use automata with some level of nondeterminism. For example, DBP automata are useful in the analysis of online algorithms, and HD automata are useful in synthesis and control. For automata on finite words, the three levels in the hierarchy coincide. We study the hierarchy for Büchi, co-Büchi, and weak automata on infinite words. We show that the hierarchy is strict, study the expressive power of the different levels in it, as well as the complexity of deciding the membership of a language in a given level. Finally, we describe a probability-based analysis of the hierarchy, which relates the level of nondeterminism with the probability that a random run on a word in the language is accepting. We relate the latter to nondeterministic automata that can be used when reasoning about probabilistic systems.

The prophet inequality is a cornerstone of online decision making, comparing a sequential decision maker to a prophet who knows all outcomes in advance. In “Trading Prophets,” J. Correa, A. Cristi, P. Dütting, M. Hajiaghayi, J. Olkowski, and K. Schewior initiate the study of buy-and-sell prophet inequalities. Here, an online algorithm observes a sequence of prices, one after the other, to trade an item. At each time step, the algorithm can decide to buy and pay the current price if it does not already hold the item, or it can decide to sell and collect the current price as a reward if it holds the item. The authors identify settings where a constant-factor approximation to the all-knowing prophet benchmark can be achieved. Interestingly, these conditions differ from those required for standard prophet inequalities. Specifically, they show that no constant-factor inequality exists for arbitrary independent prices. In contrast, they prove that a constant factor is achievable when independent prices arrive in a random order.

Motivated by a realworld practice of a China Post sortation center, this study considers the deployment of Internet of Things (IoT) technology to improve the efficiency of a human–robot hybrid sortation system. In this system, IoT technology enables responsive adjustment of the manual capacity to alleviate the congestion effect of increasing parcel flow on robotic sorting efficiency. We begin with a predictive analysis of a real‐life dataset to quantify the congestion effect on robotic sorting efficiency, and examine the nonstationary behavior of the parcel flow process. Subsequently, we design an online algorithm using IoT advance information (which refers to advance observation of some parcel flows before their actual arrivals) for efficiently adjusting the manual capacity. We develop theoretical guarantee for the effectiveness of the online algorithm by bounding its regret, and also demonstrate that the long‐term expected regret rate is zero under mild conditions. Via extensive simulation experiments, we find that our online algorithm outperforms the China Post sortation center's current policy and conventional Markov dynamic program in terms of computational efficiency and solution quality. The simulation results also demonstrate that the value of IoT technology to the sortation center can be significant, and shed insights on IoT investment by revealing the diminishing return of expanding the horizon of IoT advance information.

Abstract In several modern applications, data are generated continuously over time, such as data generated from virtual learning platforms. We assume data are collected and analyzed sequentially, in batches. Since traditional or offline methods can be extremely slow, an online method for Bayesian model averaging (BMA) has been recently proposed in the literature. Inspired by the literature on renewable estimation, this work developed an online Bayesian method for generalized linear models (GLMs) that reduces storage and computational demands dramatically compared to traditional methods for BMA. The method works very well when the number of models is small. It can also work reasonably well in moderately large model spaces. For the latter case, the method relies on a screening stage to identify important models in the first several batches via offline methods. Thereafter, the model space remains fixed in all subsequent batches. In the post-screening stage, online updates are made to the model specific parameters, for models selected in the screening stage. For larger model spaces, the chance of missing important models in the screening stage is more likely. This necessitates the development of a method, which permits the model space to be updated as new batches of data arrive. In this article, we develop an online Bayesian model selection method for logistic regression, where the selected models can potentially change throughout the data collection process. We use simulation studies to show that our new method can outperform the previous method. Furthermore, we describe scenarios under which the gain from our new method is expected to be small. We revisit the traffic crash data analyzed in the previous work, and illustrate that our new model selection method can have better performance for variable selection.

501 Background: Targeted treatment options have significantly improved outcomes for patients with chronic lymphocytic leukemia (CLL), but oncology healthcare professionals (HCPs) who provide care for fewer numbers of patients with CLL are challenged to remain current with continuously changing treatment paradigms. To assess ongoing educational needs, we analyzed the treatment choices made by HCPs in an online survey for select cases of poor-prognosis CLL and compared these with the choices of experts in CLL treatment for the same clinical scenarios. Methods: As part of a CME program, HCPs could access an online algorithm to enter specific characteristics of a CLL case scenario and receive treatment recommendations from 5 US-based CLL experts. Experts made recommendations in July 2024 for 146 possible case patients built into the algorithm. After entering their case characteristics, HCPs were asked to enter their planned treatment prior to viewing the expert recommendations. We compared expert and HCP treatment choices across various case scenarios that were entered into the algorithm, with a focus on previously untreated CLL with del(17p)/mutated TP53 or R/R CLL. Results: Between August 2024 and February 2025, 346 cases were entered into the algorithm by 174 HCPs. Among responding HCPs, 38% were based in the UK/EU, 24% in the US, and 10% in Asia; 62% treated ≤10 patients with CLL per month and 49% sought recommendations for a specific patient in their practice. Among 231 cases of previously untreated CLL entered, 91 (39%) sought recommendations for del(17p)/mutated TP53 disease. For cases with no major comorbidities and previously untreated CLL with del(17p)/mutated TP53 , all experts recommended acalabrutinib or zanubrutinib; in 19 cases submitted by HCPs practicing in locations where second-generation BTK inhibitors (BTKi) are approved for CLL (including the US, EU, and Japan), 58% planned 1 of these treatments for this case scenario, 17% planned ibrutinib-based therapy, and 11% planned venetoclax-based therapy. Among case scenarios requiring third-line therapy after progressing with 2 regimens including a covalent BTKi and venetoclax plus an anti-CD20 Ab (with progression <2 years after completing treatment), all experts would recommend a noncovalent BTKi (eg, pirtobrutinib), with 1 expert indicating they would also consider CAR T-cell therapy. For the same case scenario among 6 US-based HCPs, 1 planned pirtobrutinib and 3 planned CAR T-cell therapy. Overall, after reviewing expert recommendations, 61% of HCP respondents whose planned treatment differed from the experts indicated that they would change their approach, and 20% indicated barriers to implementing the recommendations. Conclusions: Our findings provide insight into potential educational needs among HCPs who manage patients with CLL, suggesting that targeted expert-led education could benefit these HCPs.