Background: Epidermal growth factor receptor (EGFR) exon 20 insertion (ex20ins)-mutant non-small cell lung cancer (NSCLC) is characterized by limited sensitivity to standard-dose EGFR tyrosine kinase inhibitors (EGFR-TKIs) and historically poor clinical outcomes. Although agents such as amivantamab and other targeted therapies have expanded treatment options, access barriers and marked variant-specific heterogeneity remain major challenges. Emerging evidence suggests that dose-escalated third-generation EGFR-TKIs may provide clinical benefit in selected ex20ins subtypes, yet real-world data are scarce. Case presentation: , with low PD-L1 expression. In the setting of limited access to amivantamab at diagnosis and preliminary evidence supporting intensified EGFR inhibition in certain "near-loop" ex20ins variants, the patient received off-label high-dose osimertinib 160 mg once daily as first-line therapy. She achieved a durable partial response with manageable Grade 1 skin and nail toxicities and no dose reductions. Following disease progression, and after multidisciplinary discussion and informed consent, she was switched to off-label furmonertinib 240 mg once daily, resulting in additional disease control. Sequential high-dose osimertinib followed by high-dose furmonertinib yielded an overall survival of approximately 37 months. Literature and trend overview: To contextualize this case, we conducted a targeted narrative review and a descriptive bibliometric overview using CiteSpace (2000-2023) based on Web of Science Core Collection records. This analysis demonstrated a growing global research focus on third-generation EGFR-TKIs and variant-specific treatment strategies for EGFR ex20ins-mutant NSCLC, supporting the rationale underpinning the therapeutic approach adopted in this report. Conclusion: Sequential high-dose third-generation EGFR-TKIs may offer clinically meaningful benefit in selected EGFR ex20ins cases; however, this strategy remains non-standard and should be regarded as hypothesis-generating, warranting further prospective evaluation.

The rapid expansion of impervious surfaces in urban environments has significantly increased surface runoff and flood risk. Detention basins, implemented as part of Sustainable Urban Drainage Systems (SUDSs), are widely adopted worldwide to control peak discharges and mitigate recurrent flooding. In this study, an explicit flood routing model is applied to simulate the hydraulic behaviour of an urban detention reservoir, offering a computationally efficient alternative to traditional implicit numerical schemes by avoiding iterative solution procedures. In parallel, twenty-eight machine learning (ML) models are evaluated to estimate the percentage reduction in peak discharge required to comply with local regulatory constraints. The proposed framework integrates explicit hydrological routing with data-driven modelling to support decision-making during the design of detention systems. The methodology is applied to an urban catchment in Cartagena, Colombia, comparing an uncontrolled inflow hydrograph (without SUDSs) with an attenuated outflow hydrograph produced by the detention basin. The results demonstrate a substantial reduction in peak discharge and a delay in the time to peak, fully complying with Colombian regulations that require a minimum attenuation of 30%. Among the evaluated ML models, Squared Exponential Gaussian Process Regression achieved the best performance, yielding coefficient of determination (R2) values of 0.999 in both the validation and test sets. The findings confirm the potential of machine learning techniques to quantify peak-flow reduction requirements accurately and to support the planning and design of detention reservoirs in urban environments. The proposed approach constitutes a practical, efficient, and replicable tool for sustainable urban drainage design since the results of this research can be used to design detention pond systems employing ML tools.

Fusarium crown rot (FCR) caused by Fusarium species adversely affects wheat production worldwide. The present study investigated the distribution and diversity of Fusarium spp. collected from wheat samples in 12 regions of Anhui Province, China, in 2020, 2022, and 2024. A total of nine Fusarium species were identified from 1,099 isolates based on morphological and molecular identification. The dominant pathogen of FCR gradually changed from F. graminearum to F. pseudograminearum with time and from regions of Anhui Province. Pathogenicity assays indicated that all Fusarium species might induce FCR in wheat seedlings; however, F. culmorum was the most pathogenic, followed by F. pseudograminearum and F. graminearum. The knowledge regarding fungicide combinations used to control FCR is largely limited. Hence, the control effects of pyraclostrobin and prothioconazole against F. pseudograminearum were evaluated, both individually and in combination. The results showed that F. pseudograminearum is sensitive to prothioconazole and pyraclostrobin, with average EC 50 values of 0.611 and 1.345 μg/ml, respectively. Additionally, the co-formulation (1:1) was more effective than prothioconazole alone. The results of the seed treatment experiment also revealed that the combination of prothioconazole and pyraclostrobin had greater control effects (80.34%) and yields (7,892.35 kg/ha) in the field than did the combination of prothioconazole or pyraclostrobin alone. Thus, the present study is the first to monitor the distribution pattern of Fusarium spp. in Anhui Province and report a novel combination of the triazole prothioconazole and pyraclostrobin for FCR control in wheat to ensure sustainable agriculture.

A traditional goal of science and environmental communication, including climate communication, has been to encourage disinterested or uninformed audiences to pay more attention to the world around them and to shift disinterest and apathy toward positive engagement with nature and proenvironment lifestyles. We conducted an empirical investigation of audience responses to key aspects of the world scientists' "2024 State of the Climate Report: Perilous Times on Planet Earth," focusing on whether the language of this article manages to sway readers to rethink their attitudes toward climate change. Across many variations, the textual prompts we gave to readers did <i>not</i> overwhelmingly move the needle of public attitudes regarding climate change, suggesting that political affiliation and ideologies may be a much stronger indicator of public actions and attitudes than exposure to scientific information. Regarding climate change, we seem to be living in a time of information saturation and ideological entrenchment.

ABSTRACT Urban and remote communities face persistent challenges associated with centralized power grids. This study investigates the potential of hybrid renewable energy systems with integrated storage solutions as a decentralized alternative to improve efficiency and resilience. The research combines a comprehensive theoretical review of configurations, focusing on hydro pumped storage and hydrogen storage, with the development and application of an optimization model for a remote community exposed to desertification risks. Generation and surplus forecasts were used to evaluate storage strategies, and a multicriteria optimization algorithm was applied to enhance demand–supply matching. The base‐case system consists of 13.5 kW of hydropower, 20 kW of solar PV, and 6 kW of wind capacity, assessed over a 35‐year project lifetime. Results indicate that solar and hydropower contribute approximately 90% of total generation, achieving a levelized cost of energy of 0.05 €/kWh, an internal rate of return of 17.5%, and a payback period of 5 years. The system can export up to 42 000 kWh annually to the grid or nearby users. Additionally, the integration of green hydrogen provides further flexibility, resulting in a levelized cost of hydrogen of 3.5 €/kg, an IRR of 10%, and a 14‐year payback period.

ABSTRACT In this paper, the observer design problem is investigated for a class of nonlinear systems with periodic disturbances. An iterative learning observer (ILO) is constructed to handle the periodic disturbances. Within this ILO, a variable is employed to estimate the periodic disturbances using the iterative learning technique, thereby ensuring satisfactory accuracy in state estimation. To conserve communication resources, an event‐triggered mechanism is utilized to determine the release of measurements to the ILO. The objective of this paper is to design the ILO such that the estimation error system is exponentially ultimately bounded, with the ultimate bound ensured to be independent of the periodic disturbances. First, sufficient conditions are provided to guarantee the ultimate boundedness of the estimation error system. Then, the ILO gains are computed by solving certain linear matrix inequalities. Finally, the effectiveness of the proposed ILO design scheme is validated through a simulation example.

This research introduces a novel methodology that combines Building Information Modelling (BIM) and Economic Multi-Criteria Decision-Making (EMCDM) with Neural Networks to optimize hybrid renewable energy systems in small communities. Its core aim is to improve sustainability, technical performance, and financial vokiability through integrated modelling and decision-making. The approach is applied to a hydropower site, evaluating five Scenarios (IDs 1–5) under a Community and Industry model. Financial benchmarks include a 10% Minimum Required Return and a 7-year payback period. ID3—hydropower, solar, and wind—proves most effective, with ANPV of €10,905 (wet) and €4501 (dry), and ROI of 155%/64%. Its ROIA/MRA Index peaks at 539%, and Payback/N ratios remain within acceptable limits (55%/96%). LCOE stays stable in average conditions (0.042–0.046 €/kWh), rising in dry years (0.07–0.10 €/kWh). Profitability differences primarily stem from demand and curtailment, rather than production costs. The NARX neural network reliably models SS% values from renewable inputs with low error across scenarios. The integrated BIM–EMCDM framework ensures transparent, sustainable, and risk-balanced energy system decisions for long-term autonomy.

Distributed optimization, as a key technology for collaborative intelligence in multiagent systems, has been widely applied in sensor networks, deep learning, and smart grids. Although numerous effective algorithms have been proposed, classical methods typically rely on idealized assumptions, such as accurate objective information, perfect communication channels, and trustworthy system environments. However, these assumptions are frequently violated in real-world applications. To bridge the gap between theory and practice, distributed optimization under information constraints has emerged as a research focus. This survey provides a systematic overview of recent advances in this field. We categorize information constraints based on their origin into three primary types: i) observational constraints, including stochastic objectives, online optimization, and zeroth-order methods; ii) communication constraints, such as random network topologies, delays, asynchronous updates, and communication-efficient strategies; and iii) system-level constraints, encompassing privacy preservation and Byzantine-resilient optimization. This survey reviews the research progress and challenges associated with each constraint category. Furthermore, we use two representative case studies to analyze the practical application of these algorithms and the origins of information constraints in real-world problems. Finally, we explore promising future research directions.

Game theory has emerged as a fundamental framework for modeling and analyzing strategic interactions and decision-making among multiple agents, and has witnessed rapidly growing impact in cyber–physical systems over the past decade. Its integration with dynamic systems has driven major theoretical and technological advances in a wide range of applications, including smart grids, autonomous driving, robotic swarms, and networked control systems. In particular, distributed games in dynamic systems and their equilibrium learning mechanisms have attracted increasing attention due to their scalability, lightweight information exchange, and real-time implementability. This article provides a comprehensive survey of distributed games in dynamic systems, where agents interact only with local neighbors while collectively achieving global equilibrium and stability. First, the foundational theories of distributed dynamic games under three representative classes of systems: linear dynamic systems, nonlinear dynamic systems, and uncertain dynamic systems, are presented. Then, state-of-the-art distributed equilibrium learning and control methods are reviewed, including gradient-based dynamics, payoff-based learning, best-response dynamics, and learning-based approaches. To demonstrate the practical relevance and impact of distributed games in dynamic systems, representative application domains are discussed in detail. Finally, several promising future research directions are outlined, highlighting open challenges at the intersection of distributed games, learning, and dynamic systems.

This paper studies a distributed online optimization problem over partially Free-In and Free-Out (FIFO) networks, in which a set of unfixed agents cooperate to minimize the sum of a group of time-varying functions over a time horizon. To be specific, the agents are divided into static agents and dynamic agents. The static agents are those who remain in the network during the whole time horizon, while the dynamic agents are allowed to join and leave the network freely. Based on the dual averaging technique, two novel distributed algorithms are developed to address the distributed optimization problem in such a dynamic environment. In the case where agents can distinguish whether their out-neighbors are dynamic agents or static agents, a weighting matrix based algorithm is developed. In the case where the identities of out-neighbors are unavailable, a gradient-storage based algorithm is developed, which has higher communication and local storage requirements than the weighting matrix based algorithm. By assuming that each dynamic agent has at least one in-neighbor and one out-neighbor who are static agents, the running average regret is shown to be upper bounded by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(\frac{1}{\sqrt{\mathit {T}}})$</tex-math></inline-formula> under suitable step-sizes, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathit {T}$</tex-math></inline-formula> is the time horizon. A simulation study on binary classification is given to verify the effectiveness of the developed algorithms. In addition, a numerical example is used to highlight the advantages of the proposed methods over the distributed sub-gradient decent method.

Crises faced by regional or national governments are usually caused by either natural or human disasters or by the actions of terrorists or other nations. Decision making in the face of a crisis is difficult because the context typically is complex, and decision makers often have insufficient time or information to thoughtfully make decisions that will manage the crisis well. As a result, they often rely on brief discussions and past experiences that omit key dimensions of the crisis, which results in selection of an inferior response alternative. This article describes concepts and procedures to guide the initial phases of planning for crises so that if and when a crisis occurs, a proactively developed framework provides a sound foundation for quickly structuring decisions and implementing more detailed analyses in advance of taking specific crisis-response actions. Case-study examples are used to illustrate three main elements of our suggested approach: identifying the main dimensions of the decisions to be faced, articulating the objectives that are to be achieved, and generating a set of potentially desirable alternatives to best achieve these objectives. Although the decision analysis and behavioral science methods that we rely on are not novel, proactive structuring of crisis decisions that likely will need to be made quickly has been given only limited attention by analysts and decision makers. Improved recognition of the importance of decision-focused proactive planning should result in better decisions when a crisis occurs, leading to a reduction in the adverse consequences for countries and their citizens. Funding: This work was supported by Ben Delo and Longview Philanthropy.

Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10−5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10−8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10−8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.

Identifying Internet-facing industrial control system (ICS) devices is important for asset inventory, vulnerability assessment, exposure measurement, and security monitoring. This dataset release supports research on network traffic fingerprinting for Internet-facing ICS devices under realistic measurement conditions. The dataset is constructed from Internet-scale ICS service discovery followed by protocol-specific probing across three commonly deployed ICS protocols: Modbus/TCP, EtherNet/IP, and S7comm. It contains anonymized network traffic, scanning logs, device information records, and protocol-specific scanner code used to document the measurement logic. The release covers 13,002 responsive Internet-facing ICS endpoints, including 2,205 labeled endpoints spanning 20 vendors, 8 device types, and 165 device models. The measurements are organized across three collections and 60 scanning rounds for each protocol. The dataset captures Internet-facing measurement characteristics that are rarely represented in testbed datasets, including long-tailed label distributions, response variability across scanning rounds, temporal variation, and scanner-location effects. These characteristics support empirical studies of device fingerprint generation, vendor/type/model identification, and robustness to Internet-facing measurement variability. All released IP addresses are anonymized using a consistent prefix-preserving transformation, allowing cross-file linkage across device information, packet captures, and scanning logs without exposing the original public endpoints.