bluuebunny/crossref_metadata_2025 huggingface dataset:
Progressive Entity Matching: A Design Space Exploration
Authors: Maciejewski Jakub , Nikoletos Konstantinos , Papadakis George , Velegrakis Yannis
Published: February 2025

Entity Resolution (ER) is typically implemented as a batch task that processes all available data before identifying duplicate records. However, applications with time or computational constraints, e.g., those running in the cloud, require a progressive approach that produces results in a pay-as-you-go fashion. Numerous algorithms have been proposed for Progressive ER in the literature. In this work, we propose a novel framework for Progressive Entity Matching that organizes relevant techniques into four consecutive steps: (i) filtering, which reduces the search space to the most likely candidate matches, (ii) weighting, which associates every pair of candidate matches with a similarity score, (iii) scheduling, which prioritizes the execution of the candidate matches so that the real duplicates precede the non-matching pairs, and (iv) matching, which applies a complex, matching function to the pairs in the order defined by the previous step. We associate each step with existing and novel techniques, illustrating that our framework overall generates a superset of the main existing works in the field. We select the most representative combinations resulting from our framework and fine-tune them over 10 established datasets for Record Linkage and 8 for Deduplication, with our results indicating that our taxonomy yields a wide range of high performing progressive techniques both in terms of effectiveness and time efficiency.
RLER-TTE: An Efficient and Effective Framework for En Route Travel Time Estimation with Reinforcement Learning
Authors: Zheng Zhihan , Yuan Haitao , Chen Minxiao , Wang Shangguang
Published: February 2025

En Route Travel Time Estimation (ER-TTE) aims to learn driving patterns from traveled routes to achieve rapid and accurate real-time predictions. However, existing methods ignore the complexity and dynamism of real-world traffic systems, resulting in significant gaps in efficiency and accuracy in real-time scenarios. Addressing this issue is a critical yet challenging task. This paper proposes a novel framework that redefines the implementation path of ER-TTE to achieve highly efficient and effective predictions. Firstly, we introduce a novel pipeline consisting of a Decision Maker and a Predictor to rectify the inefficient prediction strategies of current methods. The Decision Maker performs efficient real-time decisions to determine whether the high-complexity prediction model in the Predictor needs to be invoked, and the Predictor recalculates the travel time or infers from historical prediction results based on these decisions. Next, to tackle the dynamic and uncertain real-time scenarios, we model the online decision-making problem as a Markov decision process and design an intelligent agent based on reinforcement learning for autonomous decision-making. Moreover, to fully exploit the spatio-temporal correlation between online data and offline data, we meticulously design feature representation and encoding techniques based on the attention mechanism. Finally, to improve the flawed training and evaluation strategies of existing methods, we propose an end-to-end training and evaluation approach, incorporating curriculum learning strategies to manage spatio-temporal data for more advanced training algorithms. Extensive evaluations on three real-world datasets confirm that our method significantly outperforms state-of-the-art solutions in both accuracy and efficiency.
Schema-Based Query Optimisation for Graph Databases
Authors: Sharma Chandan , Genevès Pierre , Gesbert Nils , Layaïda Nabil
Published: February 2025

Recursive graph queries are increasingly popular for extracting information from interconnected data found in various domains such as social networks, life sciences, and business analytics. Graph data often come with schema information that describe how nodes and edges are organized. We propose a type inference mechanism that enriches recursive graph queries with relevant structural information contained in a graph schema. We show that this schema information can be useful in order to improve the performance when evaluating recursive graph queries. Furthermore, we prove that the proposed method is sound and complete, ensuring that the semantics of the query is preserved during the schema-enrichment process.
Shapley Value Estimation based on Differential Matrix
Authors: Pang Junyuan , Pei Jian , Xia Haocheng , Li Xiang , Liu Jinfei
Published: February 2025

The Shapley value has been extensively used in many fields as the unique metric to fairly evaluate player contributions in cooperative settings. Since the exact computation of Shapley values is \#P-hard in the task-agnostic setting, many studies have been developed to utilize the Monte Carlo method for Shapley value estimation. The existing methods estimate the Shapley values directly. In this paper, we explore a novel idea-inferring the Shapley values by estimating the differences between them. Technically, we estimate a differential matrix consisting of pairwise Shapley value differences to reduce the variance of the estimated Shapley values. We develop a least-squares optimization solution to derive the Shapley values from the differential matrix, minimizing the estimator variances. Additionally, we devise a Monte Carlo method for efficient estimation of the differential matrix and introduce two stratified Monte Carlo methods for further variance reduction. Our experimental results on real and synthetic data sets demonstrate the effectiveness and efficiency of the differential-matrix-based sampling approaches.
SNAILS: Schema Naming Assessments for Improved LLM-Based SQL Inference
Authors: Luoma Kyle , Kumar Arun
Published: February 2025

Large Language Models (LLMs) have revolutionized Natural Language to SQL (NL-to-SQL), dominating most NL-to-SQL benchmarks. But LLMs still face limitations due to hallucinations, semantic ambiguity, and lexical mismatches between an NL query and the database schema. Naturally, a lot of work in the ML+DB intersection aims to mitigate such LLM limitations. In this work, we shine the light on a complementary data-centric question:
How should DB schemas evolve in this era of LLMs to boost NL-to-SQL?
The intuition is that more NL-friendly schema identifiers can help LLMs work better with DBs. We dive deeper into this seemingly obvious, but hitherto underexplored and important, connection between schema identifier ''naturalness'' and the behavior of LLM-based NL-to-SQL by creating a new integrated benchmark suite we call SNAILS. SNAILS has 4 novel artifacts: (1) A collection of real-world DB schemas not present in prior NL-to-SQL benchmarks; (2) A set of labeled NL-SQL query pairs on our collection not seen before by public LLMs; (3) A notion of naturalness level for schema identifiers and a novel labeled dataset of modified identifiers; and (4) AI artifacts to automatically modify identifier naturalness. Using SNAILS, we perform a comprehensive empirical evaluation of the impact of schema naturalness on LLM-based NL-to-SQL accuracy, and present a method for improving LLM-based NL-to-SQL with natural views. Our results reveal statistically significant correlations across multiple public LLMs from OpenAI, Meta, and Google on multiple databases using both zero-shot prompting as well as more complex NL-to-SQL workflows: DIN SQL, and CodeS. We present several fine-grained insights and discuss pathways for DB practitioners to better exploit LLMs for NL-to-SQL.
Bursting Flow Query on Large Temporal Flow Networks
Authors: Xu Lyu , Jiang Jiaxin , Choi Byron , Xu Jianliang , He Bingsheng
Published: February 2025

Recently, queries that find bursting patterns in temporal graph data have received increasing research attention. In particular, finding the flow in temporal networks whose flow values are bursting in a time interval has numerous applications, such as detecting the money laundering by the maximum average transfer flow in a transaction graph, and the congestion by the maximum average traffic flow in a road network. Despite its usefulness, there is limited research on querying such a flow pattern. In this paper, we study a novel query of finding
a flow pattern of burstiness
in a temporal flow network. In a nutshell, this query aims to find the
bursting flow f
from a source node to a sink node such that the ratio of
f
's flow value to the time interval length of
f
is maximized. To solve this query, we propose the first solution called BFQ that enumerates all the necessary time intervals and then computes the maximum flow value for each interval. Based on BFQ, we propose an efficient solution called BFQ*, which consists of optimization techniques that incrementally compute the maximum flows without computing the common parts of flows from scratch. The experimental results demonstrate the efficiency of our solutions. A case study on a real world transaction network demonstrates the application of this bursting flow query on detecting abnormal transactions.
DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training
Authors: Liu Renjie , Wang Yichuan , Yan Xiao , Jiang Haitian , Cai Zhenkun , Wang Minjie , Tang Bo , Li Jinyang
Published: February 2025

Graph neural networks (GNNs) are models specialized for graph data and widely used in applications. To train GNNs on large graphs that exceed CPU memory, several systems have been designed to store data on disk and conduct out-of-core processing. However, these systems suffer from either
read amplification
when conducting random reads for node features that are smaller than a disk page, or
degraded model accuracy
by treating the graph as disconnected partitions. To close this gap, we build
DiskGNN
for high I/O efficiency and fast training without model accuracy degradation. The key technique is
offline sampling
, which decouples
graph sampling
from
model computation
. In particular, by conducting graph sampling
beforehand
for multiple mini-batches, DiskGNN acquires the node features that will be accessed during model computation and conducts pre-processing to pack the node features of each mini-batch contiguously on disk to avoid read amplification for computation. Given the feature access information acquired by offline sampling, DiskGNN also adopts designs including
four-level feature store
to fully utilize the memory hierarchy of GPU and CPU to cache hot node features and reduce disk access,
batched packing
to accelerate feature packing during pre-processing, and
pipelined training
to overlap disk access with other operations. We compare DiskGNN with state-of-the-art out-of-core GNN training systems. The results show that DiskGNN has more than 8x speedup over existing systems while matching their best model accuracy. DiskGNN is open-source at https://github.com/Liu-rj/DiskGNN.
Multi-Level Graph Representation Learning Through Predictive Community-based Partitioning
Authors: Lim Bo-Young , Park Jeong-Ha , Lee Kisung , Kwon Hyuk-Yoon
Published: February 2025

Graph representation learning (GRL) aims to map a graph into a low-dimensional vector space while preserving graph topology and node properties. This study proposes a novel GRL model, Multi-Level GRL (simply, ML-GRL), that recursively partitions input graphs by selecting the most appropriate community detection algorithm at each graph or partitioned subgraph. To preserve the relationship between subgraphs, ML-GRL incorporates global graphs that effectively maintain the overall topology. ML-GRL employs a prediction model, which is pre-trained using graph-based features and covers a wide range of graph distributions, to estimate GRL accuracy of each community detection algorithm without partitioning graphs or subgraphs and evaluating them. ML-GRL improves learning accuracy by selecting the most effective community detection algorithm while enhancing learning efficiency from parallel processing of partitioned subgraphs. Through extensive experiments with two different tasks, we demonstrate ML-GRL's superiority over the six representative GRL models in terms of both learning accuracy and efficiency. Specifically, ML-GRL not only improves the accuracy of existing GRL models by 3.68 ~ 47.59% for link prediction and 1.75 ~ 40.90% for node classification but also significantly reduces their running time by 9.63 ~ 62.71% and 7.14 ~ 82.14%, respectively. Our source code is available at https://github.com/pnpy6elp/Multi_Level_GRL.
SPAS: Continuous Release of Data Streams under w-Event Differential Privacy
Authors: Li Xiaochen , Li Tianyu , Cheng Yitian , Gong Chen , Ren Kui , Qin Zhan , Wang Tianhao
Published: February 2025

Continuous release of data streams is frequently used in numerous applications. However, when data is sensitive, this poses privacy risks. To mitigate this risk, efforts have been devoted to devising techniques that satisfy a formal privacy notion called w-event differential privacy. Nevertheless, a recent benchmark reveals that none of the existing works offer a universally effective solution across all types of data streams, making it challenging to select an appropriate scheme for unknown data streams in practical scenarios.

We identify that all existing methods are
heuristic-based
and make
data-independent
decisions. In this paper, we change this landscape by introducing SPAS which is built on
data-dependent
strategies. Specifically, SPAS continuously predicts an optimal publishing strategy within each sliding window that minimizes the error of the released results based on the characteristics of the data stream. Additionally, we develop a weighted sparse vector technique to control data sampling and manage privacy budget consumption following that optimal publishing strategy. Comprehensive experimental evaluations demonstrate the efficacy of SPAS in adapting to diverse one-dimensional and multi-dimensional data streams for both data release and range query tasks. Our code is open-sourced.
Decentralised Privacy-Preserving Power Flow Forecast on Smart Meters
Authors: Nowak Severin , Widmer Fabian , Taylor Jeremy , Raimundo Daniel , Papaemmanouil Antonios
Published: February 2025

The transition toward decarbonisation of energy systems necessitates significant adaptations in distribution networks, challenging the grid planning and operation for Distribution System Operators (DSOs). This paper explores how smart meters can improve DSO capabilities amidst evolving regulatory frameworks and technological landscapes. DSOs face the challenge of accommodating more distributed generation, electrification of loads, and the surge in electric vehicles, all while striving to maintain grid reliability and cost-effectiveness. To meet these demands without compromising data security and privacy, advanced data science techniques are explored, leveraging smart meter data in near real-time.

The paper proposes decentralised algorithms capable of near real-time computation and forecasting of network power flows, revealing congestion issues without centralising consumer data. Field trials conducted in Rolle VD provide concrete evidence of the feasibility and advantages of this approach. They not only offer insights into network conditions but also ensure data privacy. Moreover, the developed decentralised congestion calculation method has shown to deliver results comparable to commonly used centralised algorithms, even when confronted with real-world challenges such as connection issues. Utilizing smart meters with enhanced computing capabilities and Internet of Things (IoT) infrastructure, the proposed method presents a viable alternative to traditional approaches, enabling DSOs to adapt to the evolving energy landscape efficiently.
The plan4.energy Approach for Planning Support for Positive Energy Districts
Authors: Wimmer Yannick , Schwabeneder Daniel , Niederkofler Michael , Valentin Patrizia-Ilda
Published: February 2025

This paper introduces a software solution for planning support of Positive Energy Districts (PEDs), highlighting the challenges of interdisciplinary planning and stakeholder involvement. The development of the software tool,
plan4.energy
, addresses these challenges by providing stakeholders with accessible means to assess project variants and understand key planning levers. Based upon a database of over 300,000 simulation results and a user-friendly interface,
plan4.energy
facilitates informed decision-making for PED planning, enhancing the integration of renewable energy sources and optimizing system-level performance.
Towards a State Explanation Framework in Cyber-Physical Systems
Authors: Schreiberhuber Katrin , Ekaputra Fajar J. , Sabou Marta , Hauer Daniel , Diwold Konrad , Frühwirth Thomas , Steindl Gernot , Schwarzinger Tobias
Published: February 2025

The concept of Explainable Cyber-Physical Systems (ExpCPSs) aims to provide clear explanations for system decisions and actions, which is crucial for understanding, managing, and controlling high-risk Cyber-Physical Systems (CPSs), such as intelligent energy distribution networks or Smart Grids (SGs). Existing approaches for ExpCPSs, however, are typically designed only for specific application areas, limiting their impact. This paper proposes a domain-independent framework for automatically generating system state explanations in a CPS through (i) a unified data model and (ii) novel explanation algorithms to improve systems transparency, interpretability, and trustworthiness. The proposed framework facilitates root cause analysis on CPSs independently of their domains, enabling users to query system states and receive comprehensive explanations. We evaluated our solution in a real-world Electric Vehicle (EV) charging garage use case, highlighting its feasibility and effectiveness.
FoSSaCS 2024: Report of the Program Chairs
Authors: Kobayashi Naoki , Worrell James
Published: February 2025

The 27th edition of the Foundations of Software Science and Computation Structures (FoSSaCS) took place in Luxembourg City as part of the European Joint Conferences on Theory and Practice of Software (ETAPS), held from April 6 to 11, 2024.

The conference received 108 submissions, of which 28 were accepted. Each paper underwent a careful evaluation by at least three members of the program committee, ensuring a high standard of review. Following the rebuttal period, there was a lively PC discussion which culminated in an online PC meeting to finalise the decisions. The chosen papers represented a broad cross-section of foundational topics related to programming and computer systems, including automata, games, logic, verification, concurrency theory, type theory, and categorical semantics.
The Centralization of a Decentralized Video Platform - A First Characterization Of PeerTube
Authors: Polinski Michael , Jo Richard , McAfee Kevin , Bustamante Fabián E.
Published: February 2025

PeerTube is an open-source video sharing platform built as a decentralized alternative to YouTube. With software like Mastodon and Friendica, PeerTube is part of a series of federated social media platforms built partly in response to growing concerns about centralized control and ownership of the incumbent ones. In this paper, we present the first characterization of PeerTube, including its underlying infrastructure and the content being shared on its network. Our findings reveal concerning trends toward centralization that echo patterns observed in other contexts, exacerbated by the limited degree of content replication. PeerTube instances are mostly located in North America and Western Europe, with about 70% hosted in Germany, the USA, and France, and over 50% hosted on the top 7 ***ASes. We also find that over 92% of videos are stored without any redundancy in spite of PeerTube's native support for video redundancy.
Complex Reflection Groups as Differential Galois Groups
Authors: Arreche Carlos E. , Bainbridge Avery , Obert Ben , Ullah Alavi
Published: February 2025

Complex reflection groups comprise a generalization of Weyl groups of semisimple Lie algebras, and even more generally of finite Coxeter groups. They have been heavily studied since their introduction and complete classification in the 1950s by Shephard and Todd, due to their many applications to combinatorics, representation theory, knot theory, and mathematical physics, to name a few examples. For each given complex reflection group G, we explain a new recipe for producing an integrable system of linear differential equations whose differential Galois group is precisely G. We exhibit these systems explicitly for many (low-rank) irreducible complex reflection groups in the Shephard-Todd classification.
Marginal Independence and Partial Set Partitions
Authors: Carrión Francisco Ponce , Sullivant Seth
Published: February 2025

Marginal independence models are often studied by associating a combinatorial structure to describe the independence relations between random variables. For instance, Drton and Richardson [3] used bidirected graphs to develop a family of marginal independence models for discrete random variables, and Boege, Petrovíc, and Sturmfels [1] used simplicial complexes. The marginal independence models of [3] fit more broadly into the class of graphical models [4, 5]. Each of these structures describes a very different family of models, with very little overlap between them. In both cases it had been shown that when we restrict to discrete random variables, we obtain toric models after a linear change of coordinates. However, there are collections of marginal independence statements that cannot be expressed either as graphs or simplicial complexes. This motivates the proposal of a more general combinatorial structure which we use in our treatment of marginal independence.
ITiCSE Announcement
Authors: Barendsen Erik , Paterson Jim , Quille Keith
Published: February 2025

The 30th annual ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) will be held at Radboud University in Nijmegen, Netherlands, from 30 June to 2 July 2025. ITiCSE is a computing education conference held annually, typically in Europe, sponsored by ACM SIGCSE and in collaboration with ACM Europe Council and Informatics Europe.
Information Processing in Biochemical Networks
Authors: Tkačik Gašper , Wolde Pieter Rein ten
Published: February 2025

Living systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has productively engaged with the first two, addressing information flows has been more challenging, with some scattered success in evolutionary theory and a more coherent track record in neuroscience. Nevertheless, interdisciplinary work of the past two decades at the interface of biophysics, quantitative biology, and engineering has led to an emerging mathematical language for describing information flows at the molecular scale. This is where the central processes of life unfold: from detection and transduction of environmental signals to the readout or copying of genetic information and the triggering of adaptive cellular responses. Such processes are coordinated by complex biochemical reaction networks that operate at room temperature, are out of equilibrium, and use low copy numbers of diverse molecular species with limited interaction specificity. Here we review how flows of information through biochemical networks can be formalized using information-theoretic quantities, quantified from data, and computed within various modeling frameworks. Optimization of information flows is presented as a candidate design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation architectures built of individually noisy components.
Targeting Protein–Protein Interactions in Hematologic Malignancies
Authors: Cierpicki Tomasz , Grembecka Jolanta
Published: February 2025

Over the last two decades, there have been extensive efforts to develop small-molecule inhibitors of protein–protein interactions (PPIs) as novel therapeutics for cancer, including hematologic malignancies. Despite the numerous challenges associated with developing PPI inhibitors, a significant number of them have advanced to clinical studies in hematologic patients in recent years. The US Food and Drug Administration approval of the very first PPI inhibitor, venetoclax, demonstrated the real clinical value of blocking protein–protein interfaces. In this review, we discuss the most successful examples of PPI inhibitors that have reached clinical studies in patients with hematologic malignancies. We also describe the challenges of blocking PPIs with small molecules, clinical resistance to such compounds, and the lessons learned from the development of successful PPI inhibitors. Overall, this review highlights the remarkable success and substantial promise of blocking PPIs in hematologic malignancies.
Roles of Cellular Neighborhoods in Hepatocellular Carcinoma Pathogenesis
Authors: Ma Lichun , Li Cherry Caiyi , Wang Xin Wei
Published: February 2025

The development of hepatocellular carcinoma (HCC) involves an intricate interplay among various cell types within the liver. Unraveling the orchestration of these cells, particularly in the context of various etiologies, may hold the key to deciphering the underlying mechanisms of this complex disease. The advancement of single-cell and spatial technologies has revolutionized our ability to determine cellular neighborhoods and understand their crucial roles in disease pathogenesis. In this review, we highlight the current research landscape on cellular neighborhoods in chronic liver disease and HCC, as well as the emerging computational approaches applicable to delineate disease-associated cellular neighborhoods, which may offer insights into the molecular mechanisms underlying HCC pathogenesis and pave the way for effective disease interventions.
Pharmacogenetics: Opportunities for the All of Us Research Program and Other Large Data Sets to Advance the Field
Authors: Empey Philip E. , Karnes Jason H. , Johnson Julie A.
Published: February 2025

Pharmacogenetic variation is common and an established driver of response for many drugs. There has been tremendous progress in pharmacogenetics knowledge over the last 30 years and in clinical implementation of that knowledge over the last 15 years. But there have also been many examples where translation has stalled because of the lack of available data sets for discovery or validation research. The recent availability of data from very large cohorts with linked genetic, electronic health record, and other data promises new opportunities to advance pharmacogenetics research. This review presents the stages from pharmacogenetics discovery to widespread clinical adoption using prominent gene-drug pairs that have been implemented into clinical practice as examples. We discuss the opportunities that the All of Us Research Program and other large biorepositories with genomic and linked electronic health record data present in advancing and accelerating the translation of pharmacogenetics into clinical practice.
G Protein–Coupled Receptor Heteromers in Brain: Functional and Therapeutic Importance in Neuropsychiatric Disorders
Authors: Sun Yalin , Hasbi Ahmed , George Susan R.
Published: February 2025

G protein–coupled receptors (GPCRs) represent the largest family of plasma membrane proteins targeted for therapeutic development. For decades, GPCRs were investigated as monomeric entities during analysis of their pharmacology or signaling and during drug development. However, a considerable body of evidence now indicates that GPCRs function as dimers or higher-order oligomers. Greater acceptance of oligomerization occurred with the recognition that GPCR interactions form heteromeric receptor complexes, which was validated in vivo, often with pharmacologic, signaling, and functional properties distinct from the constituent protomers. GPCR heteromerization is reviewed in the context of brain disorders, with examples illustrating their functional implication in diverse neuropsychiatric and neurodegenerative disorders, making them an enormous unexploited resource for selective pharmacotherapy target identification. The strategies for development of heteromer-selective ligands are discussed as a new opportunity to precisely target the function of a receptor complex with greater specificity, in contrast to the classical ligands targeting individual receptors.
Progress in Multiferroic and Magnetoelectric Materials for Emerging Technologies in Next Generation Sensing Devices
Authors: Dabas Samiksha , Kumar Manish , Singh Dharm Veer , Chaudhary Vishal , Sharma Subhash
Published: February 2025

Multiferroic (MF) and magnetoelectric (ME) materials are highly sought after by researchers in the quest for fabrication, design, and functioning of novel, precise, low-noise next-generation sensing technologies. We provide a succinct perspective on usage of MEs and MFs in sensing applications including antenna, magnetic field and current sensing, bio-magnetic sensing, proximity sensing or motion detection, tactile sensor with robotic arms to measure mechanical properties of target, piezoelectric nanogenerator, magneto-mechano-electrical energy generator device, and gas sensing. The current developments in 2D MFs have also reinvigorated researchers’ interest towards miniaturized electronics and “multiferroic magnonics” for future low power consumption and low noise computing.
Prediction of Temperature Field and Control Method for Heat Damage in Deep Shaft
Authors: Wang Chunlong , Lin Qibin , Lang Xiaodong , Xu Ye , Wang Xi , Fan Yuyun
Published: February 2025

During the excavation of the shaft, the inlet air temperature undergoes seasonal variations and is influenced by geothermal effects and air compression heat. Merely augmenting the inlet air volume fails to mitigate the extreme temperatures encountered at the deep working face. Consequently, the implementation of refrigeration and cooling technologies becomes imperative to manage the heat‐induced issues. To address the high‐temperature challenge during shaft excavation at the Sanshandao Gold Mine, a ventilation system model was developed utilizing Fluent simulation software. This model facilitated the prediction of the temperature field dynamics at the working face, taking into account project progression and seasonal shifts. Through a comprehensive analysis of factors encompassing cooling capacity deterioration, energy consumption for cooling, and the installation and maintenance requirements of refrigeration units across various systems, a surface‐based centralized refrigeration system was devised. Furthermore, a simulation analysis was conducted to evaluate the refrigeration technology, offering valuable technical insights for the calculation of cooling capacity, as well as the selection and application of appropriate refrigeration systems. The results indicated that subsequent to excavating the shaft to a depth of 1600 m, the working face temperature fluctuated with seasonal variations but consistently remained above 28°C. At a depth of 1800 m, the temperature peaked, reaching a maximum of 40.19°C. Following the implementation of the surface centralized refrigeration system, with an inlet air volume of 22.6 m3/s and an inlet air temperature maintained below 10°C, the working face temperature was effectively reduced to below 27°C. This study presents a comprehensive suite of refrigeration and cooling methodologies, encompassing temperature field prediction, refrigeration parameter calculation, simulation analysis of cooling performance, refrigeration system design, and their application in deep shaft excavation. These methods provide a technical foundation for mitigating heat‐induced damage in deep shafts.
Short‐Term Target Maneuvering Trajectory Prediction Using DTW–CNN–LSTM
Authors: Guo Haifeng , Yang Jinyi , Jing Xianyong , Zhang Peng
Published: February 2025

This paper introduces a novel prediction model designed to mitigate the substantial data dependency associated with maneuver trajectory prediction in unmanned combat air vehicles (UCAVs) during air combat. Considering the characteristics of high noise, dynamic complexity, and variable data lengths inherent in short‐range air combat scenarios, we employ dynamic time warping (DTW) to assess the similarity of 3D trajectory data. This approach allows us to identify and select the most analogous historical data, which we then utilize as our training dataset. In pursuit of enhanced precision for online trajectory prediction, we propose an improved convolutional neural network (CNN) that not only offers “after‐zero” information but also incorporates delay compensation mechanisms. Our experimental findings indicate that the proposed prediction model not only satisfies the stringent timeliness requirements but also outperforms benchmark models in terms of prediction accuracy across various operating conditions.
Assessment of the Correlation Between Renal Function Markers and Serum Ferritin Levels in Thalassemia Patients
Authors: Tammadondar Mohammad , Vatankhah Ali , Khatibzade-Nasari Niloufar
Published: February 2025

Background: This study aimed to analyze renal function markers and correlate the results with serum ferritin levels in patients with thalassemia in Bandar Abbas, Iran, in 2020.

Method: A cross‐sectional study was conducted, and patients with a proven hemoglobinopathy diagnosis were included. Fasting blood was obtained to measure creatinine, sodium, potassium, calcium, phosphorus, magnesium, uric acid, and ferritin. In addition, a 24‐h urine specimen was collected to determine the level of urine albumin, creatinine, sodium, potassium, calcium, phosphorus, magnesium, uric acid, and β2‐microglobulin. Fractional excretion of sodium, potassium, calcium, phosphorus, magnesium, and uric acid was also calculated. Spearman’s coefficient of correlation was used to determine the correlations between serum ferritin level and other variables.

Results: Of the sixty‐six patients studied, 3.0% had sickle cell thalassemia, 80.3% had major thalassemia, and 72.7% had intermediate thalassemia. In our study, serum ferritin, 24‐h urine protein, and fractional excretion of sodium, calcium, and urine were higher than the normal range in thalassemia patients. There was a significant correlation between serum ferritin level and fractional excretion of magnesium, calcium, sodium, phosphorus, and uric acid with a negative correlation coefficient (p ≤ 0.05). We also found that the serum ferritin levels strongly correlated with microalbuminuria (p ≤ 0.05).
Comparison of 3D Printing Technology and Artificial Intelligence Assisted in Total Knee Arthroplasty
Authors: Pan Jingbo , Zhang Qi , Kong Gang , Liu Dan , Sun Xiujiang , Zhang Guodong , Zou Yue , Liu Kegui
Published: February 2025

This study aimed to evaluate the efficacy of personalized 3D printing osteotomy guides and artificial intelligence (AI)‐assisted surgical planning in total knee arthroplasty (TKA). A cohort of 60 cases was randomly allocated into two groups: one receiving assistance from personalized 3D printing osteotomy guides and the other benefiting from AI‐assisted surgical planning for the procedure. We compared surgical duration, intraoperative bleeding, postoperative incision drainage, length of hospital stay, surgical accuracy, and postoperative visual analog scale (VAS) and Hospital for Special Surgery (HSS) outcomes between the two groups. Continuous data conforming to a normal distribution were analyzed using independent‐samples t‐tests, while categorical data were assessed with chi‐square tests. The findings of our study indicated that the 3D printing‐assisted TKA (3D‐TKA) group experienced significant lower levels (p < 0.05) of bleeding and drainage (160.1 ± 24.3 mL, 199.5 ± 29.6 mL) compared to the AI‐assisted TKA (AI‐TKA) group (174.7 ± 25.7 mL, 223.8 ± 29.2 mL). Furthermore, the duration of surgery and hospital stay (81.4 ± 8.9 min, 7.7 ± 1.3 day) was significantly longer (p < 0.05) in the 3D‐TKA group than the AI‐TKA group (72.9 ± 10.0 min, 6.8 ± 1.6 day). No significant differences (p > 0.05) were observed in surgical accuracy between the two groups. On the first day postoperation, VAS scores were significantly lower (p = 0.001) in the 3D‐TKA group. In summary, each surgical approach offers distinct benefits. 3D printing primarily enhances patient outcomes, whereas AI assistance tends to favor surgical efficiency.
Dual‐View Deep Learning Model for Accurate Breast Cancer Detection in Mammograms
Authors: Shah Dilawar , Ullah Khan Mohammad Asmat , Abrar Mohammad , Tahir Muhammad
Published: February 2025

Breast cancer (BC) remains a major global health problem designed for early diagnosis and requires innovative solutions. Mammography is the most common method of detecting breast abnormalities, but it is difficult to interpret the mammogram due to the complexities of the breast tissue and tumor characteristics. The EfficientViewNet model is designed to overcome false predictions of BC. The model consists of two pathways designed to analyze breast mass characteristics from craniocaudal (CC) and mediolateral oblique (MLO) views. These pathways comprehensively analyze the characteristics of breast tumors from each view. The proposed study possesses several significant strengths, with a high F1 score and recall of 0.99. It shows the robust discriminatory ability of the proposed model compared to other state‐of‐the‐art models. The study also explored the effects of different learning rates on the model’s training dynamics. It showed that the widely used stepwise reduction strategy of the learning rate played a key role in the convergence and performance of the model. It enabled fast early progress and careful fine‐tuning of the learning rate as the model nears optimum. The model opens the door to achieving a high level of patient outcomes through a very rigorous methodology.
Comprehensive Network Analysis of Lung Cancer Biomarkers Identifying Key Genes Through RNA‐Seq Data and PPI Networks
Authors: Alruily Meshrif , Elbashir Murtada K. , Ezz Mohamed , Aldughayfiq Bader , Alrowaily Majed Abdullah , Allahem Hisham , Mohammed Mohanad , Mostafa Elsayed , Mostafa Ayman Mohamed
Published: February 2025

This study addresses the pressing need for improved lung cancer diagnosis and treatment by leveraging computational methods and omics data analysis. Lung cancer remains a leading cause of cancer‐related deaths globally, highlighting the urgency for more effective diagnostic and therapeutic approaches. Current diagnostic methods, such as imaging and biopsies, suffer from limitations in sensitivity, specificity, and accessibility, often due to factors such as poor data quality, small sample sizes, and variability in data sources. These limitations highlight the necessity for the development of advanced noninvasive techniques. Computational methods utilizing omics data have shown promise in overcoming these challenges by comprehensively understanding the molecular pathways involved in lung cancer. We propose a novel approach that utilizes RNA‐Seq data and employs LASSO regression with attention mechanisms to identify lung cancer biomarkers. Our results demonstrate the effectiveness of this approach in identifying potential biomarkers for lung cancer, including well‐known genes such as TP53, EGFR, KRAS, ALK, and PIK3CA, validating the model’s ability to uncover key genes associated with lung cancer development and progression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed significant associations of the identified genes with critical biological processes and pathways, including protein synthesis, folding, cell adhesion, gene regulation, and immune responses. The PPI network analysis, constructed using the STRING database and Cytoscape application, highlighted a highly interconnected interaction landscape, with central hub genes playing pivotal roles in lung cancer progression. RPSA emerged as a crucial hub gene, consistently identified across different centrality measures. This study sheds light on the potential of computational methods and omics data analysis in improving lung cancer diagnosis and treatment, offering new insights for future research directions and personalized medicine strategies.
Selection of Interspecific Peanut Arachis hypogaea L. Genotypes for Thrips Resistance Using Multivariate Analysis
Authors: Pirotta Melina Zacarelli , Michelotto Marcos Doniseti , José de Godoy Ignácio , Franco Claudenir Facincani , da Silva Souza Jardel , Unêda-Trevisoli Sandra Helena
Published: February 2025

This study leverages multivariate analysis, including principal component analysis (PCA) and cluster analysis, to select peanut genotypes with resistance to thrips and desirable agronomic traits. The focus is on progenies derived from the cross between the cultivar IAC 503 4x and an interspecific synthetic amphidiploid (A. magna x A. cardenasii) 4x. Analyzing F4 generation progenies using Federer’s augmented block scheme with intercalary checks, the study evaluates resistance to thrips based on natural infestation and damage symptoms, alongside agronomic traits indicating proximity to the cultivated variety. The multivariate techniques applied are PCA and hierarchical cluster analysis using Euclidean distance and Ward’s method, and the nonhierarchical K‐means method. PCA identifies two principal components explaining 78.39% of the variance, focusing on pod and grain yield, number of pods and grains, number of thrips, and visual symptom scores. This allows for the discrimination of 24 progenies based on crucial agronomic characteristics. Cluster analysis forms nine groups, with selected progenies clustering together, indicating consistency between multivariate analysis methods. These analyses effectively select segregating progenies from initial generations of peanuts, emphasizing traits related to thrips resistance and production components. The agreement between PCA and cluster analysis results highlights the efficiency of these methods in genotype selection for improved pest resistance and agronomic performance, contributing to the sustainability and economic viability of peanut production.
Understanding Moral Courage From Nurse Leaders’ Perspectives: A Qualitative Exploration
Authors: Huang Mingtao , Ouyang Yanzhen , Huang Qiuyu , Xu Guiru
Published: February 2025

Background: The stance of nurse leaders toward moral courage and encouragement received from them hold significant sway in determining whether nurses can courageously uphold the correct values in their duties and actions.

Aim: The objective of this study is to gain insights into nurse leaders’ perceptions of moral courage in the workplace, with the aim of addressing barriers to the development of moral courage among nurses.

Research Design: This study used descriptive qualitative methods in accordance with the Consolidated Criteria for Reporting Qualitative Studies (COREQ).

Participants and Research Context: This study was conducted in 2024. Participants were selected by purposive sampling. The sample size was determined by data saturation, which was reached with 10 participants. The interviews were conducted through semistructured telephone interviews. Data were analyzed using a deductive method of content analysis.

Ethical Considerations: The ethics committee of Fujian Medical University (No. 196, 2024) provided approval for this study. Verbal consent was obtained before the interview with the participants. All data were processed anonymously and confidentially.

Results: The analysis resulted in 14 subcategories, which were subsequently grouped into 4 categories.

Conclusions: This study emphasizes the essential role of nurse leaders in promoting moral courage within healthcare teams. By fostering an ethical environment and providing support, nurse leaders enable nurses to make ethically sound decisions. Future research could further explore the longitudinal development of moral courage in nursing practice to understand the impact of moral courage on healthcare outcomes.
Staphylococcus epidermidis: Antimicrobial Resistance Profiles of Biofilm‐Forming Isolates From Pediatric Bacteremia in Pakistan
Authors: Nisar Maleeha , Rahman Hazir , Ahmad Saghir , Tabassum Tabassum , Alzahrani Khalid J. , Alzahrani Fuad M. , Alsharif Khalaf F.
Published: February 2025

Background: Staphylococcus epidermidis is an important cause of nosocomial infections in children. The study undertaken identified antibiotic resistance markers among biofilm‐forming S. epidermidis.

Methods: A total of 105 bacteremia‐positive samples from hospitalized children were processed for identification of S. epidermidis using species‐specific rdr gene. Phenotypic antibiotic resistance was checked through Kirby–Bauer disc diffusion method. 96‐well microtiter plate assays and PCR were used for biofilm production and antibiotic‐resistant genes, respectively.

Results: Among 105 clinical isolates, rdr gene was detected in 34 (32.38%) isolates. The rdr detected isolates exhibited biofilm formation (n = 34; 100%). Multidrug‐resistant (MDR) pattern was observed among S. epidermidis, while the frequency of MDR was higher in very strong biofilm‐forming S. epidermidis (n = 18; 52.9%, p ≤ 0.002) as compared to weak biofilm‐forming S. epidermidis (n = 6; 17.6%). All S. epidermidis strains were resistant to cefoxitin, penicillin, and augmentin (n = 34; 100%). High resistance was observed against erythromycin (n = 29; 85.29%) and ciprofloxacin (n = 25; 73.5%). S. epidermidis displayed complete susceptibility (n = 34; 100%) toward vancomycin, tetracycline, and linezolid. Among the S. epidermidis isolates, the methicillin resistance gene (mecA, n = 29; 85.2%, p ≤ 0.000), the erythromycin resistance gene (msrA, n = 19; 55.7%) and the beta‐lactamase resistance gene (blaZ, n = 17; 50%) were detected. Detection of mecA (n = 17; 94.4%), msrA (n = 8; 44.4%) and blaZ (n = 11; 61.1%) significantly (p ≤ 0.0052) correlated with very strong biofilm‐forming S. epidermidis.

Conclusion: Biofilm formation is significantly associated with antibiotic resistance. The study’s result will help to understand the molecular mechanism of antimicrobial resistance in biofilm‐forming S. epidermidis among pediatric patients.
Identification of Damping Ratios of Long‐Span Bridges Using Adaptive Modal Extended Kalman Filter
Authors: Zhang Xiaoxiong , Luo Rongli , He Jia , Hua Xugang , Yang Lun , Peng Xiaobin , Yang Can , Chen Zhengqing
Published: February 2025

Identification of damping ratio is very important for the assessment of service performance of long‐span bridges. In this paper, an adaptive EKF in modal domain, named adaptive modal EKF (AMEKF), is proposed for identifying the damping ratios of long‐span bridges. The dominant modes are selected, and the dimension of the extended state vector is significantly reduced with the aid of modal coordinate and the corresponding modal transformation. Then, the EKF principle is employed for the identification in modal domain. Moreover, an innovation‐based procedure is presented to adaptively adjust the covariance matrix of process noise for the purpose of assuring the parametric identification accuracy. A forgetting factor is employed to put proper weights for the previous and current estimates in each time step. A merit of the proposed approach is that all the damping ratios of the selected modes can be simultaneously identified. The effectiveness of the proposed approach is numerically verified via a long‐span suspension bridge. The dynamic tests on a simply supported overhanging steel beam and an aeroelastic model of some long‐span suspension bridge are further used for the validation. Results show that the proposed approach is capable of identifying damping ratios with acceptable accuracy.