bluuebunny/crossref_metadata_2025 huggingface dataset:
InTime: Towards Performance Predictability In Byzantine Fault Tolerant Proof-of-Stake Consensus
Authors: Sun Weijie , Xu Zihuan , Ni Wangze , Chen Lei
Published: February 2025

Performance predictability, ensuring low latency variability, is crucial for the reliability and efficiency of blockchain consensus. Byzantine Fault Tolerant Proof-of-Stake (BFT-PoS) consensus aims to achieve stable transaction processing latency by scheduling block generation at consistent intervals. However, BFT-PoS's incentive mechanisms grant all transaction tips to the block proposer, which can be exploited by delaying proposals to gain extra Maximal Extractable Value (MEV) rewards, thus undermining performance predictability. Existing solutions impose penalties for delays but lack a standard for measuring the extra rewards from delays or fail in malicious environments.

This paper introduces InTime, a novel approach to safeguard performance predictability in BFT-PoS by economically motivating timely block proposals. We first introduce the
untimely MEV ratio
, a reliable metric to measure the extra rewards gained from proposal delays, facilitating our countermeasures against deliberate delays. Furthermore, we propose the
arrival rate incentive
(ARI), aligning rewards with transaction arrival timing among nodes to reduce potential MEV manipulation. To make ARI robust against malicious behaviors, we establish a
committee time witness
(CTW) workflow to accurately gather and verify transaction arrival times. Extensive experiments demonstrate that InTime can effectively reduce latency variability by up to 95.9%.
On Graph Representation for Attributed Hypergraph Clustering
Authors: Feng Zijin , Qiao Miao , Piao Chengzhi , Cheng Hong
Published: February 2025

Attributed Hypergraph Clustering (AHC) aims at partitioning a hypergraph into clusters such that nodes in the same cluster are close to each other with both high connectedness and homogeneous attributes. Existing AHC methods are all based on matrix factorization which may incur a substantial computation cost; more importantly, they inherently require a prior knowledge of the number of clusters as an input which, if inaccurately estimated, shall lead to a significant deterioration in the clustering quality. In this paper, we propose Attributed Hypergraph Representation for Clustering (AHRC), a cluster-number-free hypergraph clustering consisting of an effective integration of the hypergraph topology and node attributes for hypergraph representation, a multi-hop modularity function for optimization, and a hypergraph sparsification for scalable computation. AHRC achieves cutting-edge clustering quality and efficiency: compared to the state-of-the-art (SOTA) AHC method on 10 real hypergraphs, AHRC obtains an average of 20% higher F-measure, 24% higher ARI, 26% higher Jaccard Similarity, 10% higher Purity, and runs 5.5× faster. As a byproduct, the intermediate result of graph representation dramatically boosts the clustering quality of SOTA contrastive-learning-based hypergraph clustering methods, showing the generality of our graph representation.
Sequoia: An Accessible and Extensible Framework for Privacy-Preserving Machine Learning over Distributed Data
Authors: Xu Kaiqiang , Chai Di , Zhang Junxue , Lai Fan , Chen Kai
Published: February 2025

Privacy-preserving machine learning (PPML) algorithms use secure computation protocols to allow multiple data parties to collaboratively train machine learning (ML) models while maintaining their data confidentiality. However, current PPML frameworks couple secure protocols with ML models in PPML algorithm implementations, making it challenging for non-experts to develop and optimize PPML applications, limiting their accessibility and performance.

We propose Sequoia, a novel PPML framework that decouples ML models and secure protocols to optimize the development and execution of PPML applications across data parties. Sequoia offers JAX-compatible APIs for users to program their ML models, while using a compiler-executor architecture to automatically apply PPML algorithms and system optimizations for model execution over distributed data. The compiler in Sequoia incorporates cross-party PPML processes into user-defined ML models by transparently adding computation, encryption, and communication steps with extensible policies, and the executor efficiently schedules code execution across multiple data parties, considering data dependencies and device heterogeneity.

Compared to existing PPML frameworks, Sequoia requires 64%-92% fewer lines of code for users to implement the same PPML algorithms, and achieves 88% speedup of training throughput in horizontal PPML.
Tribase: A Vector Data Query Engine for Reliable and Lossless Pruning Compression using Triangle Inequalities
Authors: Xu Qian , Yang Juan , Zhang Feng , Pan Junda , Chen Kang , Shen Youren , Zhou Amelie Chi , Du Xiaoyong
Published: February 2025

Approximate Nearest Neighbor Search (ANNS) is a critical problem in vector databases. Cluster-based index is utilized to narrow the search scope of ANNS, thereby accelerating the search process. Due to its scalability, it is widely employed in real-world vector search systems. However, existing cluster-based indexes often suffer from coarse granularity, requiring query vectors to compute distances with vectors of varying quality, thus increasing query complexity. Existing work aim to represent vectors with minimal cost, such as using product quantization (PQ) or linear transformations, to speed up ANNS. However, these approaches do not address the coarse granularity inherent in cluster-based index. In this paper, we present an efficient vector data query engine to enhance the granularity of cluster-based index by carefully subdividing clusters using diverse distance metrics. Building on this refined index, we introduce techniques that leverage triangle inequalities to develop highly optimized and distinct search strategies for clusters and vectors of varying qualities, thereby reducing the overhead of ANNS. Extensive experiments demonstrate that our method significantly outperforms existing in-memory cluster-based indexing algorithms, achieving up to an impressive 10× speedup and a pruning ratio exceeding 99.4%.
PhD Thesis: Neuroevolution Trajectory Networks (NTNs)
Authors: Sarti Stefano
Published: February 2025

Neuroevolution refers to using evolutionary computation to design and optimize neural networks. This thesis entitled Neuroevolution trajectory networks: illuminating the evolution of artificial neural networks, focuses on explainability in neuroevolution. Several neuroevolution algorithms and frameworks have been proposed, which underscores the rising need for methods to evaluate and understand them and their optimisation dynamics.
Lost Gems: ICGA 1985: Proceedings of an International Conference on Genetic Algorithms and their Applications
Authors: Langdon W. B.
Published: February 2025

In 1999 the first Genetic and Evolutionary Computation Conference (GECCO) [1] was founded as a "recombination" of the annual Genetic Programming (GP) conference [2] and the biannual International Conference on Genetic Algorithms (ICGA), which had been started in 1985 and was held every two years in the USA.
Don't Touch the Power Line - A Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity Distribution System
Authors: Kaltenpoth Sascha , Müller Oliver
Published: February 2025

As a possible solution to the demographic change and the resulting knowledge loss due to retirements in the Energy sector, this study aimed to develop a generic pipeline to implement and evaluate proof-of-concepts (PoCs) for LLM-based assistance systems in new domains. Our pipeline contains an LLM-based data generation strategy based on documents, a retrieval-augmented generation (RAG) architecture utilizing prompting techniques on existing German LLMs, and an LLM-based automatic evaluation strategy. We leverage our pipeline to evaluate five LLMs using data from a German DSO. We found that the Llama3 and the Mistral model are appropriately aligned for the task. We plan to pilot the RAG architecture in the DSO's infrastructure for future research and continuously research improvements using the generated human demonstrations.
Practical Application of Energy-Related Use Case Methodology and SGAM Framework: A Study of User Perspectives and Challenges
Authors: Kuchenbuch René , Nale Pranit , Niemann Laura , Werth Oliver , Sauer Jürgen
Published: February 2025

The IEC 62559 Use Case Methodology and the Smart Grid Architecture Model Framework (SGAM) play a crucial role in energy-related national and European research and innovation projects, facilitating a shared understanding among stakeholders in ICT-based Smart Grid solutions. However, their adoption presents challenges due to the complexity and diverse expert backgrounds involved, which can lead to ambiguous IEC 62559 Use Cases and SGAM Models, and consequently, potentially wrong decisions in the implementation by inaccurate descriptions or models. This research identifies the challenges faced by IEC 62559 Use Cases and SGAM Model modelers, as well as their working processes utilizing the toolchain. To achieve this, a survey was conducted among experts experienced in modeling IEC 62559 Use Cases and SGAM Models, followed by qualitative content analysis of the collected data. The findings enhance the usage of the standard/framework and toolchain by improving the understanding of creators' challenges. The results aim to enhance both energy-related and methodological research projects by leveraging modelers' insights into project conditions and processes. This includes ensuring that required information are available on creating, establishing collaborative structures for SGAM modeling, and advancing the maturity of the Use Case Management Repository. The insights support the development of future qualitative SGAM Models and IEC 62559 Use Case descriptions, thereby raising awareness of potential issues.
The Impact of Redispatch on Grid and Storage Expansion Planning in the German Energy System
Authors: Büttner Clara , Müller Ulf
Published: February 2025

In light of the necessity for a cost-effective transition to a new energy system, it has become increasingly important to consider the integration of redispatch measures as a potential option in long-term grid planning. This could help to avoid the need for significant expansion of the grid and storage facilities. In this paper, we present a novel method for the consecutive optimization of the sector-coupled electricity system of the year 2035 at bidding zone level, as well as on a 300 bus model of the German extra high and high voltage grid. Therefore, redispatch can be explicitly co-optimized in conjunction with grid and storage expansion. The results demonstrate that redispatch measures can markedly reduce the necessity for grid and storage expansion, ranging from 76% to 78% in the German grid. By employing only 21% of the optimal annual redispatch volume, 77% of the potential investment cost savings can already be captured.
Forecasting Renewable Energy at European Markets
Authors: Rim Hun , Kardos Juraj , Schenk Olaf
Published: February 2025

The ambitious energy targets, accelerated by the recent energy crisis, are driving the increase of renewable energy share in gross energy consump- tion. However, the intermittent and seasonal nature of renewable energy sources presents challenges in predicting their production capacity. The abil- ity to accurately forecast the evolution of renewable energy's stake in the dynamic and ever-evolving energy market is a critical component in the decision making process of policy makers, and market participants alike. This project aims to explore and evaluate the performance of well-established forecasting methods in anticipating the trends of individual renewable en- ergy components, ultimately contributing to the fostering of a balanced, sustainable, and reliable energy market in the EU. The primary focus is to assess auto-regressive forecasting methods and advanced models incorporat- ing moving-average, exploit seasonality of time series data, or those utilising the correlation with exogenous variables. The results are presented for data considering recent history of the most significant energy component at the European energy markets.
A Platform Ecosystem Providing New Data For The Energy Transition
Authors: Duchon Markus , Matar Jessy , Shoyari Mahsa Faraji , Perzylo Alexander , Kessler Ingmar , Buchenberg Patrick , Kuhn Philipp , Hamacher Thomas , Schlachter Thorsten , Süss Wolfgang , Thinh Nguyen Xuan , Salari Haniyeh Ebrahimi , Latko Jasmin , Xu Minsheng , Shamovich Maxim , Schlütter Dominik , Frisch Jérôme , Rustagi Kushagar , Kraft Markus , Ayasse Carolin , Steinke Florian , Metzger Michael , Kuper Laura
Published: February 2025

There is a great need for high-quality and comprehensive data in the energy sector. This data is collected and preprocessed at considerable expense and is not only required for research, but also by planning offices and other industries in connection with planning activities, such as the creation of municipal heat planning. The NEED ecosystem will accelerate these processes establishing an efficient, robust, and scalable energy data ecosystem. Heterogeneous energy-related data sources will be brought together and automatically linked consistently across different sectors as well as temporal and spatial levels. In this context, existing data sources will not be replaced but rather integrated into the NEED ecosystem as dedicated sources including a semantic description on how to utilize them. In addition to conventional data sources from the various planning levels, we envision a quality assessment scheme based on the FAIR criteria. In reality, we are often faced with missing data, too. To close this gap we explore data-driven, model-driven, AI-based, and tool-driven generation of synthetic data. These heterogeneous data sources will be interlinked using ontology modules which will be represented in a knowledge graph. Via a semantic API, queries will be generated to identify the required data sources, which will be orchestrated to provide the data needed. This will enable researchers, planners, and others including their tools to interact with the NEED ecosystem, while a tool proxy will be able to translate the resulting data into proprietary formats, required by some tools to operate. The NEED ecosystem is planned to be a robust, easy-to-maintain, and flexible infrastructure to enhance planning energy measures at different spatial levels and with different time horizons. We envision to evaluate our NEED approach for the transparent provision of data by integrating relevant data sources as microservices, definition and analysis of application scenarios in the planning domain, as well as the integration of various tools for different planning purposes. With these elements, we will be able to quantify the efficiency of data procurement and demonstrate the functionality of the approach using practical use cases.
Empowering Energy Communities and P2P Energy Sharing: A Novel End-to-End Ecosystem for Planning, Deployment, and Operation
Authors: Kashyap Shievam , Schaffer Christoph , Fischer Tobias , Zauner Michael , Fischer Franz , Kurz Marc , Hartner Georg , Grünberger Stefan , Hödl Oliver
Published: February 2025

The widespread adoption of renewable energy sources has necessitated innovative solutions to address their inherent intermittency and facilitate energy democracy through Energy Communities (ECs). However, the successful planning, deployment, and operation of ECs face multifaceted socio-technical challenges. This publication presents a novel end-to-end ecosystem designed to foster the planning, deployment, and operation of ECs and peer-to-peer (P2P) energy relations. The ecosystem empowers stakeholders by addressing various challenges associated with the formation and management of ECs. A user-centric mobile application facilitates the identification and engagement of prospective EC members, evaluation of feasibility, and negotiation of operational, business, and financial models. The application also enables users to monitor the performance of their ECs or P2P energy-sharing relationships. An Energy Community Marketplace serves as a platform for interactions between feasible clusters and EC operators, acting as a broker to finalize the EC operator and establish connections with users' near real-time data access. The ecosystem inherently includes historical and real-time data integration based on user consent and GDPR best practices. The proposed ecosystem streamlines entire EC lifecycle, fostering active user participation, data-driven decision-making, and seamless integration of diverse stakeholders, ultimately supporting the energy transition and democratization goals.
A Digital Twin Platform Applied to Hydrogen Electrolyzers
Authors: Singh Amit Kumar , Wibbeke Jelke , Raeiszadeh Amin , Huxoll Nils , Brand Michael
Published: February 2025

Digital Twin (DT) technology emerges as a promising solution for addressing the challenges in electrolyzer operation, resulting from scalability issues, component degradation, and limited lifespan. This paper presents a service-oriented DT architecture and platform applied to an industrial-scale hydrogen electrolyzer. By effectively mimicking the behavior of the physical object, the DT enhances predictive diagnosis and supervision of its operation. Initially, a generic DT framework is realized, followed by a demonstration of its application using an electrolyzer use case. The approach combines digital the object with services, facilitating comprehensive monitoring, fostering proactive maintenance, and performance enhancements. Condition monitoring and anomaly detection strategies are implemented to enhance real-time supervision. The overarching objective of this platform is to facilitate the creation of scalable, extendable, interoperable, and dynamic DTs for electrolyzers, thereby advancing their performance and efficiency in hydrogen production systems. Moreover, the DT architecture is platform-agnostic, allowing for the seamless extension to other use cases.
Peer-to-Peer Communication Using Enhanced German Smart Meter Gateway Infrastructure
Authors: Niehs Eike , Essers Julien , Rothert Johannes , Engel Bernd
Published: February 2025

Power grids are rapidly moving towards so-called smart grids. Smart grids describe the networking and control of different actors and components in the energy system using information and communication technology. Households are evolving from consumers to prosumers, as they no longer just consume electricity, but can also produce energy and feed it into the public grid. Additionally, coupling mobility and heating sectors, as well as the increasing number of battery storage systems, further increase the system complexity. The efficient monitoring and control of such systems requires the use of energy management systems. Recently, the German government accelerated the rollout of smart metering systems with the Act to Restart the Digitization of the Energy Transition [8]. The smart meter gateway (SMGW) was introduced to collect metering data within the local metrological network (LMN) and, to establish communication between controllable local systems (CLS) in the home area network (HAN) with external market participants (EMP) in the wide area network (WAN). Using smart metering systems for energy management purposes may potentially increase their value. However, the coordination of and communication between energy management systems using SMGW can be challenging.
The Genomics Revolution Drives a New Era in Entomology
Authors: Li Fei , Wang Xianhui , Zhou Xin
Published: February 2025

Thanks to the fast development of sequencing techniques and bioinformatics tools, sequencing the genome of an insect species for specific research purposes has become an increasingly popular practice. Insect genomes not only provide sets of gene sequences but also represent a change in focus from reductionism to systemic biology in the field of entomology. Using insect genomes, researchers are able to identify and study the functions of all members of a gene family, pathway, or gene network associated with a trait of interest. Comparative genomics studies provide new insights into insect evolution, addressing long-lasting controversies in taxonomy. It is also now feasible to uncover the genetic basis of important traits by identifying variants using genome resequencing data of individual insects, followed by genome-wide association analysis. Here, we review the current progress in insect genome sequencing projects and the application of insect genomes in uncovering the phylogenetic relationships between insects and unraveling the mechanisms of important life-history traits. We also summarize the challenges in genome data sharing and possible solutions. Finally, we provide guidance for fully and deeply mining insect genome data.
Pathogen Binding and Entry: Molecular Interactions with the Insect Gut
Authors: Bonning Bryony C.
Published: February 2025

The point of entry for the majority of arthropod pathogens and arthropod-vectored pathogens of plant, animal, and human health importance is the arthropod midgut. Pathogen interaction with the midgut therefore represents a primary target for intervention to prevent pathogen infection and transmission. Despite this key role in pathogen invasion, relatively little is known of the specific molecular interactions between pathogens and the surface of the arthropod gut epithelium, with few pathogen receptors having been definitively identified. This article provides an overview of pathogen molecular interactions in the arthropod midgut, with a focus on gut surface proteins that mediate pathogen entry, and highlights recent methodological advances that facilitate the identification of pathogen receptor proteins.
Associations of circulating T-cell subsets with endothelial function: the Multi-Ethnic Study of Atherosclerosis
Authors: DeConne Theodore M. , Sitlani Colleen M. , Decker Kevin P. , Delaney Joseph A. , Psaty Bruce M. , Doyle Margaret F. , Buzkova Petra , Landay Alan L. , Huber Sally A. , Hughes Timothy M. , Herrington David , Ding Jingzhong , Olson Nels C.
Published: February 2025

Background: Endothelial dysfunction has emerged as a risk factor for many age-related diseases such as cardiovascular disease and Alzheimer’s disease and related dementias. T-lymphocytes (T-cells) have been identified as important regulators of endothelial function in multiple murine models, and pro-inflammatory and senescent T-cell subsets have been associated with endothelial dysfunction in middle-aged adults with hypertension. However, there is little data on the relationships between T-cell subsets and endothelial function in large, multi-ethnic, population-based cohorts free from cardiovascular diseases. Therefore, the purpose of this study was to determine whether T-cell subsets were associated with endothelial function in participants of the Multi-Ethnic Study of Atherosclerosis (MESA).

Methods: Endothelial function was assessed using flow-mediated dilation (FMD) of the brachial artery by duplex ultrasound at the baseline exam. Baseline peripheral blood T-cell subsets were measured using flow cytometry (N=968). Two analyses were employed. The primary analysis examined associations of Th1 (CD4+ interferon-γ+ (IFN-γ+)) and CD4+CD28−CD57+ T-cells, specified as a priori hypotheses, with FMD using multivariable linear regression. Secondary analyses examined associations between 27 additional immune cell populations with FMD.

Results: Th1 and CD4+CD28−CD57+ T-cells were not associated with FMD. In secondary analyses, a 1-SD higher value of pan CD4+ and pan CD8+ T-cells were associated with lower and higher FMD, respectively.

Conclusions: These results may suggest regulation of endothelial function by T-cells in pre-clinical models is conserved in humans. The findings warrant additional longitudinal human studies with greater T-cell phenotyping to further understand the influence of CD4+ and CD8+ T-cell balance on endothelial function.
Application of Random Forest for Identification of an Appropriate Model for Predicting Meteorological Drought
Authors: Hussain Anwar , Niaz Rizwan , Al-Rezami A. Y. , Mohamed Omer Adam , S. Al-Duais Fuad , M. A. Almazah Mohammed
Published: February 2025

This research aims to find the best model for predicting the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI) in the future. The study estimates SPI and SPEI at different time scales, ranging from 1 to 48 months. To predict drought, Random Forest (RF) models are used based on lag times of 1–12 months for the estimated drought indices (SPI and SPEI). Accuracy and error metrics like Nash–Sutcliffe efficiency (NSE), root‐mean‐square error (RMSE), producer accuracy (PA), user accuracy (UA), and Choen’s kappa are used to assess the models. The NSE values for the SPI at varying time scales (1, 3, 6, 9, 12, and 48 months) indicate that Bahawalpur, Rawalpindi, Murree, and Sargodha stations have the highest NSE values of 0.1148, 0.5868, 0.8302, 0.9196, 0.9516, 0.9801, and 0.9845, respectively. Similarly, the RMSE values for SPI at these time scales show that Bahawalpur, Rawalpindi, Murree, and Sargodha stations have the lowest RMSE values of 0.6187, 0.6094, 0.4091, 0.2865, 0.2275, 0.1594, and 0.1106, respectively. The NSE and variance explained for SPI and SPEI at a 1‐month time scale were found to be poor, but they improved as the time scale increased. On the other hand, the RMSE values for SPI and SPEI at a 1‐month time scale were found to be high but decreased with longer time scales. The stations that exhibit the highest values of the NSE for the SPEI at various time scales (1, 3, 6, 9, 12, and 48 months) are Rawalpindi, Jhelum, Murree, Mianwali, Rawalpindi, and Sargodha, respectively. These stations have NSE values of 0.0784, 0.6074, 0.8353, 0.9225, 0.9542, 0.9760, and 0.9896, respectively. Similarly, the stations with the lowest RMSE values for SPEI at these time scales are Sargodha, Murree, Murree, Murree, Murree, and Sargodha, with RMSE values of 1.002, 0.5909, 0.3993, 0.2626, 0.2132, 0.1546, and 0.0941, respectively. The analysis reveals a distinct pattern indicating that stations situated at higher elevations exhibit a more pronounced correlation between the SPI and SPEI indices in comparison to stations at lower elevations. Notably, Murree, Jhelum, Sialkot, and Rawalpindi demonstrate a statistically significant and strong correlation between the SPI and SPEI. Overall, the results show that SPEI is a better drought index for classifying and monitoring meteorological drought in stations with lower elevations. However, in stations with higher elevations, the selected indices provide similar information, but with some differences.
LC‐MS/MS Triple Quadrupole Multiresidue Analysis of Pesticides From Soils of Vegetable Farms: A Report From Ethiopia
Authors: Feleke Hirut Tsegaye , Belete Tigist Yimer
Published: February 2025

The current need to produce marketable vegetables entails increasing pressure to use agricultural pesticides. However, information on the type of pesticide residues, the concentration in the soil, and their prolonged impacts is lacking in developing countries such as Ethiopia. Therefore, the present work was conducted to identify the types of pesticide residues and measure their concentration in the soil. Surface soil samples were collected from vegetable‐growing farmlands. The types of pesticide residues and their concentrations in the samples were analyzed using LC‐MS/MS triple quadrant with an ESI ion source. The physicochemical properties of the soils were also determined following appropriate procedures. The analytical method was accurate with a percentage recovery of 71%–120% and acceptable with correlation coefficients greater than 0.992. The detection limits and quantifications were within the range of 0.3–1 and 1–3.3 μg/kg, respectively. A total of ten different pesticide residues were found, of which diazinon, dimethoate, malathion, metalaxyl, and profenofos were found in all samples. Profenofos and metalaxyl were the pesticide residues found at high concentrations (116.16 and 106.03 μg/kg, respectively). The textural classes of the samples were clay‐loam with high acidity, which might be associated with the intensive application of pesticides. The results revealed that different pesticide residues were present in the soil, some of which are persistent and hazardous to health and the environment. Authorities must take appropriate measures to reduce the impact on health and ensure sustainable soil productivity in the area.
Determining the Initiation Threshold of Underground Road Network Construction in High‐Intensity Development Areas: A New Methodology Considering Resilience
Authors: Chen Yue , Xu Sudong , Cao Zhan , Zhu Zhenjun , He Zhanpeng
Published: February 2025

Assessing the resilience of road networks in high‐intensity urban development areas is crucial for ensuring sustainable urban growth in the face of increasing traffic demands. Underground road networks are a key solution to alleviating surface traffic congestion and optimizing urban spatial utilization. By enhancing shared mobility on the surface, these networks contribute to reducing vehicle emissions and mitigating environmental pollution. This study explores the optimal conditions for initiating underground road network construction in high‐density development areas. Using a spatiotemporal consumption model, the research calculates the maximum traffic capacity of the network while considering land use characteristics to assess traffic demand. The resilience of these road networks is evaluated through structural indicators that measure their resistance to damage and overall stability. The findings indicate that when the demand‐to‐capacity ratio of the road network ranges from 0.865 to 0.870, the existing road capacity becomes inadequate, necessitating the construction of underground networks to alleviate surface congestion. This study provides both theoretical guidance and practical insights for the planning and development of underground road networks, along with strategies to improve surface environmental quality.
Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels
Authors: Moctezuma Luis Alfredo , Molinas Marta , Abe Takashi
Published: February 2025

Research suggests that dreams play a role in the regulation of emotional processing and memory consolidation; electroencephalography (EEG) is useful for studying them, but manual annotation is time‐consuming and prone to bias. This study was aimed at developing an EEG‐based machine learning (ML) model to automatically identify dream and dreamless states in sleep. We extracted features from EEG data using common spatial patterns (CSPs) and the discrete wavelet transform (DWT) and used them to classify EEG signals into dream and dreamless states using ML models. To determine the most informative channels for classification, we used the permutation‐based channel selection method and the nondominated sorting genetic algorithm II (NSGA‐II). We evaluated our proposal using a public dataset that is part of the DREAM project, which was collected from 58 EEG channels during rapid eye movement (REM) and non‐REM sleep, while 28 subjects reported dream or dreamless experiences. We achieved accuracies greater than 0.85 to distinguish dream and dreamless states using CSP‐based feature extraction combined with k‐nearest neighbors (KNN), as well as through multiple combinations of EEG channels identified by channel selection methods. Our findings suggest that as few as 8–10 EEG channels may be sufficient for dream recognition. Excluding one subject at a time during model training revealed challenges in generalizing the models to unseen subjects. Channel selection methods have proven to be effective in selecting relevant subsets of EEG channels to classify dreams and dreamless experiences. Our results demonstrate the feasibility of automatic dream detection and highlight the need to improve ML generalization.
Potential Mechanisms of Lactate Dehydrogenase and Bovine Serum Albumin Proteins as Antioxidants: A Mixed Experimental–Computational Study
Authors: Ye Jing , Bounds Amy , Crumpton Madeline , Long Mallory , McDonough Haley , Srikhirisawan Isabella , Gao Shanzhen
Published: February 2025

Proteins have shown varying degrees of antioxidant activity. This study examined the potential mechanisms of interactions between proteins and radicals using chemical kinetics and computational methods. The study quantified the antioxidant activity of lactate dehydrogenase (LDH) and bovine serum albumin (BSA) through Trolox equivalent antioxidant capacity (TEAC) and oxygen radical absorbance capacity (ORAC) assays. BSA was about seven times and LDH 12 times more potent as antioxidants for 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonate) (ABTS•−) than they were for peroxyl radicals. According to the evaluation of Trolox equivalents (TE) of 20 proteinogenic amino acids, tryptophan (with a TE value of 101 μmol TE/μmol) exhibited the highest antioxidant activity for ABTS•−, followed by tyrosine (38.7 μmol TE/μmol) and cysteine (30.5 μmol TE/μmol), lysine (0.193 μmol TE/μmol), arginine (0.0325 μmol TE/μmol), valine (0.0280 μmol TE/μmol), histidine (0.00689 μmol TE/μmol), and leucine (0.00560 μmol TE/μmol). The EC50 showed a similar order with a swap between valine and histidine. The antioxidant activity of the amino acids and proteins was temperature dependent. The rate laws, activation energy, and pre‐exponential factor A of these reactions provided information on the reaction mechanisms, i.e., a biomolecular elementary step for the reaction of ABTS•− with amino acids tryptophan, tyrosine, cysteine, or protein LDH, and a more complicated mechanism for BSA. The presence of –NH– or hydroxyl groups on aromatic rings enhanced the antioxidant ability of tryptophan and tyrosine. LDH’s antioxidant activity did not affect its enzymatic activity, indicating that the radical reaction likely happened on the protein’s surface without significantly altering its conformation. The molecular modeling and visualization showed potential reaction sites on the proteins’ accessible tryptophan and tyrosine residues. However, the mere surface exposure of tryptophan and tyrosine does not guarantee their antioxidant activities.
A Rare Case of Intraoperative Anaphylaxis Secondary to Central Venous Catheter Placement: A Case Report
Authors: Ali Yusuf , Gonzalez Andres , Pallan John , Bergbower Emily Anne Smith
Published: February 2025

Central venous catheters (CVCs) are often used intraoperatively for large volume resuscitation and the administration of vasoactive medications. Many of these catheters are impregnated with antimicrobials to prevent infection but independent reports of anaphylaxis directly following catheter placement have been increasing. Here, we report the case of a severe episode of anaphylaxis attributed to a chlorhexidine‐coated CVC in a middle‐aged man who presented for an L5‐S1 Anterior Lumbar Interbody Fusion (ALIF) revision due to hardware failure.
Awake Thoracotomy in an Obese Case of Cardiac Tamponade: A Case Report
Authors: Itmaiza Malek , Bael Peter , Isayed Talha , Abufarah Ali , Frijat Islam , Hamamdeh Majdi
Published: February 2025

Introduction: Awake thoracic surgery is an old procedure still being used to this day for various indications, utilizing many different techniques for local analgesia.

Case Presentation: We described the case of a 60‐year‐old obese heavy smoker, with significant respiratory and cardiac morbidity, who presented with signs of cardiac tamponade secondary to hypothyroidism. Our patient was treated with a pericardial window, using serratus anterior and parasternal blocks.

Discussion: We presented the first case to our knowledge that employed parasternal and serratus anterior blocks as local analgesia for an awake thoracotomy, we also discussed different techniques, indications, benefits, and complications.
Varying Spectrum of Frosted Branch Angiitis: A Curated Constellation of Cases
Authors: Sarkar Avik Dey , Muthukrishnan V. , Kannan Naresh Babu , Goswami Ananya
Published: February 2025

Purpose: The purpose of this study is to elucidate varying etiology and presentations of frosted branch angiitis (FBA), a rare immune‐mediated retinal vasculitis.

Methods: In this case series, four curated cases confined to varying spectrums of FBA have been described. Detailed fundoscopic documentation with pertinent imaging was performed in all the cases. The cases were managed adequately as per protocol and retrospectively analyzed.

Results: Our first case is unique as it has been rarely reported in extant ophthalmic literature. The case is presented as progressive outer retinal necrosis in cytomegalovirus (CMV) retinitis accompanied by FBA. The second case is a Kleiner Type 3 FBA with viral prodrome. Our third case is the fourth reported case in literature of FBA following penetrating trauma. This case is instrumental in confirming the hypothesis of Type 3 hypersensitivity reaction in the slow deposition of immune complexes in the periarteriolar region as a pathophysiology for FBA. The fourth case is the first reported case of coexisting bilateral flecked retina and FBA. It also had an unusual association with tuberculosis mimicking tubercular vasculitis.

Conclusion: This gamut of unique cases may contribute to a better understanding of the variability of clinical presentation of FBA in a more detailed manner for future reference.
Radiology Report Annotation Using Generative Large Language Models: Comparative Analysis
Authors: Altalla’ Bayan , Ahmad Ashraf , Bitar Layla , Al-Bssol Mohammed , Al Omari Amal , Sultan Iyad
Published: February 2025

Recent advancements in large language models (LLMs), particularly GPT‐3.5 and GPT‐4, have sparked significant interest in their application within the medical field. This research offers a detailed comparative analysis of the abilities of GPT‐3.5 and GPT‐4 in the context of annotating radiology reports and generating impressions from chest computed tomography (CT) scans. The primary objective is to use these models to assist healthcare professionals in handling routine documentation tasks. Employing methods such as in‐context learning (ICL) and retrieval‐augmented generation (RAG), the study focused on generating impression sections from radiological findings. Comprehensive evaluation was applied using a variety of metrics, including recall‐oriented understudy for gisting evaluation (ROUGE) for n‐gram analysis, Instructor Similarity for contextual similarity, and BERTScore for semantic similarity, to assess the performance of these models. The study shows distinct performance differences between GPT‐3.5 and GPT‐4 across both zero‐shot and few‐shot learning scenarios. It was observed that certain prompts significantly influenced the performance outcomes, with specific prompts leading to more accurate impressions. The RAG method achieved a superior BERTScore of 0.92, showcasing its ability to generate semantically rich and contextually accurate impressions. In contrast, GPT‐3.5 and GPT‐4 excel in preserving language tone, with Instructor Similarity scores of approximately 0.92 across scenarios, underscoring the importance of prompt design in effective summarization tasks. The findings of this research emphasize the critical role of prompt design in optimizing model efficacy and point to the significant potential for further exploration in prompt engineering. Moreover, the study advocates for the standardized integration of such advanced LLMs in healthcare practices, highlighting their potential to enhance the efficiency and accuracy of medical documentation.
Spanish Registry of Pressure‐Controlled Intermittent Coronary Sinus Occlusion (PiCSO) Balloon: Initial Experience and Short‐Term Clinical Outcomes
Authors: Martínez Maldonado Erick Dante , Jiménez Díaz Victor Alfonso , Bastos Fernández Guillermo , Vidal Calés Pablo , Brugaletta Salvatore , Juan Salvadores Pablo , Íñiguez Romo Andrés
Published: February 2025

Objectives: The mortality of ST elevation myocardial infarction (STEMI) has plateaued in the last decade, with a 30‐day mortality of 3%‐4%. Previous studies in patients with anterior STEMI treated by primary angioplasty and assisted with the pressure‐controlled intermittent coronary sinus occlusion (PiCSO) system have shown a modest but significant absolute reduction of the infarct zone.

Methods: Prospective, observational study were carried out in two hospitals in Spain. The study included patients with acute coronary syndrome and patients with high‐risk percutaneous coronary intervention (PCI). PiCSO therapy was provided throughout the PCI (after achieving thrombolysis in myocardial infarction [TIMI] flow two or three in STEMI cases), with a minimum duration of 20 min from stent implantation until the completion of the procedure.

Results: A total of 22 cases were performed. In 20 cases, PiCSO therapy could be successfully applied, and in 2 cases, it was not possible due to the inability to access the coronary sinus. In 95.45%, the PCI was performed in a single coronary artery. Baseline TIMI flow was 0‐1 in > 60% of patients, with final TIMI 3 flow achieved in > 95%. There were no complications secondary to the use of the PiCSO system. At 6‐month clinical follow‐up, 100% of the patients were alive with significant improvement in functional class and angina status.

Conclusions: The use of the PiCSO device during ACS was safe and may offer potential benefits to patients susceptible to reperfusion injury or in whom relevant distal microembolization is anticipated during primary angioplasty or high‐risk PCI. More data are needed to validate its widespread clinical use.
The Mediating Role of Psychological Capital and Work Engagement in the Relationship Between Well‐Being and Turnover Intention Among Nurses in China
Authors: Liu Di , Zou Mingyang , Ma Yuanshuo , Xie Yujin , Zhang Wenlin , Sun Caihong , Gao Yuan , Shi Lei , Cui Yanze
Published: February 2025

Background: Turnover intentions among nurses pose a significant challenge for healthcare systems globally. While numerous studies have explored the association between nurses’ well‐being and turnover intention, the underlying mechanisms remain insufficiently understood. Addressing the interactions among psychological capital, work engagement, well‐being and turnover intention is therefore essential.

Aim: This study examined the chain‐mediating effects of psychological capital and work engagement on the relationship between well‐being and turnover intention. It aimed to identify the mechanisms influencing turnover intention tendencies among nursing staff and to propose strategies for stabilising nursing teams.

Methods: A multistage random sampling approach was adopted across 21 hospitals in three provinces: Zhejiang (Eastern China), Heilongjiang (Central China) and Chongqing (Western China). Data were collected using the 5‐item World Health Organization Well‐Being Index, the Psychological Capital Questionnaire, the Utrecht Work Engagement Scale and the Turnover Intention Scale. Harman’s single‐factor test was employed to detect common method bias, and chain mediation analysis was conducted using the SPSS PROCESS macro.

Results: Significant correlations were found between well‐being, psychological capital, work engagement and turnover intention (p < 0.001). Well‐being directly influenced turnover intention (effect = −0.1227, 95% CI = −0.1421, −0.1033). Work engagement partially mediated the relationship between well‐being and turnover intention (effect = −0.0813, 95% CI = −0.0944, −0.0687). In addition, psychological capital and work engagement jointly mediated the relationship (effect = −0.0817, 95% CI = −0.0940, −0.0701).

Conclusion: Nurses’ well‐being influences turnover intention via the serial mediation of psychological capital and work engagement. Hospital managers and governments should implement multifaceted interventions to mitigate turnover intentions and enhance the stability of nursing teams.

Implications for Nursing Management: The findings highlight a potential pathway linking nurses’ well‐being to turnover intention. Nursing managers can adopt targeted interventions addressing this pathway to reduce turnover intention rates and ensure team stability.
Characterization of Plasma Cell-Free DNA Variants as of Tumor or Clonal Hematopoiesis Origin in 16,812 Advanced Cancer Patients
Authors: Magee Daniel , Domenyuk Valeriy , Abraham Jim , Perdigones Borderias Nieves , Swensen Jeff , Solipuram Praveena , Ayanambakkam Adanma , Mehdi Raja , Challagalla Jagathi , Heath Elisabeth , Landsverk Megan , Jurkiewicz Magdalena , Shimkus Brian , Pinto Ian , Patterson Daniel , Hsiehchen David , Koya Supriya , Somer Bradley , Velez Michel , Shields Anthony F. , Cultrera Jennifer , Ribeiro Jennifer R. , Hahn-Lowry Robert , Sledge George W. , Oberley Matthew , Radovich Milan , Spetzler David
Published: February 2025

Purpose:

Plasma-based liquid biopsy tests can detect tumor-specific genetic alterations and offer many advantages that complement tissue-based comprehensive genomic profiling. However, age-related clonal hematopoiesis (CH) mutations can confound liquid biopsy results and potentially lead to incorrect therapy choice.

Experimental Design:

We assessed the landscape of 16,812 liquid profiles across 49 cancer types using the Caris Assure assay, a whole-exome and whole-transcriptome next-generation sequencing workflow that independently sequences both plasma-derived cell-free total nucleic acids and the white blood cell DNA and RNA from the buffy coat. The variant source was identified algorithmically by comparing plasma and buffy coat variant frequency and read quality metrics.

Results:

Of 16,812 patients, 42.3% presented at least one CH variant among reportable clinical genes. We found 39% of BRCA2 variants to be of CH origin, as well as 37.9% of CHEK2, 27.4% of BRCA1, 20.1% of ATM, 7.3% of NRAS, 5.8% of BRAF, 2.1% of EGFR, 2.1% of KRAS, and 18.5% of TP53. For patients aged 65 to 69 years, the median proportion of CH variant classification was 20%, whereas it was 33% for patients aged 70 to 74 years, 33% for ages 75 to 79 years, and 50% for ages 80+ years. We found high rates of CH detected in what would be otherwise druggable targets in many cancer types typically treated with PARP inhibitors, including breast, female genital tract, ovarian, pancreatic, prostate, and endometrial cancers.

Conclusions:

This large study highlights the need for thorough CH classification during liquid biopsy to appropriately recommend therapies, especially PARP inhibitors.