bluuebunny/crossref_metadata_2025 huggingface dataset:
Neutrino tomography of the earth: the earth total mass, moment of inertia and hydrostatic equilibrium constraints
Authors: Petcov S. T.
Published: September 2024

We investigate the implications of the constraints following from the precise knowledge of the total Earth mass, $$M_\oplus $$

M
⊕

, and moment of inertia, $$I_\oplus $$

I
⊕

, and from the requirement that Earth be in hydrostatic equilibrium (EHE), in the neutrino tomography studies of the Earth density structure. In order to estimate the sensitivity of a given neutrino detector to possible deviations of the inner core (IC), outer core (OC), core (IC + OC) and mantle Earth densities from those obtained using geophysical and seismological data and described by the preliminary reference Earth model (PREM), in the statistical analyses performed within the neutrino tomography studies one typically varies the density of each of these structures. These variations, however, must respect the $$M_\oplus $$

M
⊕

, $$I_\oplus $$

I
⊕

and EHE constraints. Working with PREM average densities we derive the $$M_\oplus $$

M
⊕

, $$I_\oplus $$

I
⊕

and EHE constraints on the possible density variations when one approximates the Earth density structure with (i) three layers – mantle, outer core and inner core, and (ii) four layers – upper mantle, lower mantle, outer core and inner core. We get drastically different results in the two cases.
Currents in celestial CFT
Authors: Ball Adam
Published: September 2024

In this review, we discuss currents in celestial CFT and the consistency of their naïve symmetry algebras. In particular, we study in detail the Jacobi identity and the double residue condition for soft insertions, hard momentum space insertions, and hard celestial insertions. In the latter case, we introduce the notion of a “hard current” in CFT and work through examples in the 2D critical Ising model. The current algebra of hard insertions in pure Einstein gravity is a slight conceptual generalization of the familiar [Formula: see text]-wedge current algebra. We also review branch cut terms in the celestial OPE, which indicate new primary content and were previously missed until recently. We work through an explicit toy example illustrating the mechanism by which such branch cut terms can arise. These branch cut terms prevent a symmetry interpretation but are fully compatible with a consistent OPE.
There’s plenty of room in the middle: The unsung revolution of the renormalization group
Authors: Goldenfeld Nigel
Published: September 2024

The remarkable technical contributions of Michael E. Fisher to statistical physics and the development of the renormalization group are widely known and deeply influential. But less well known is his early and profound appreciation of the way in which renormalization group created a revolution in our understanding of how physics — in fact, all science — is practiced and the concomitant adjustment that needs to be made to our conception of the purpose and philosophy of science. In this chapter, I attempt to redress this imbalance, with examples from Fisher’s writings and my own work. It is my hope that this tribute will help remove some of the confusion that surrounds the scientific usage of minimal models and renormalization group concepts, as well as their limitations, in the ongoing effort to understand emergence in complex systems.
Exact Second-Order Stiffness Matrix of Axial-Loaded Beam–Columns on Winkler Foundation
Authors: Zhao Chuan-Hao , Zhu Yi , Pan Wen-Hao , Luo Yao-Zhi
Published: September 2024

This paper establishes the exact second-order stiffness matrix of axial-loaded beam–columns on Winkler foundation. This Winkler foundation model has been widely used in engineering applications. The equilibrium analysis of an axial-loaded beam–column on Winkler foundation is conducted, and the element flexural deformations and forces are solved exactly from the governing differential equation. Two dimensionless factors for the axial force and foundation spring stiffness are defined, respectively. The exact second-order element stiffness matrix of the axial-loaded beam–column on Winkler foundation is then formulated, showing the relationship between the element-end deformations (translation and rotation angle) and the corresponding forces (shear force and bending moment). Such an exact second-order stiffness matrix may be used for the buckling and second-order solutions with allowance for the use of one element per member for the exact solution. The classical buckling problem of axial-loaded beam–columns on Winkler foundation with typical element-end boundary conditions is then analyzed by the developed matrix stiffness method. By comparing with classical buckling criterion expressions of axial-loaded beam–columns on Winkler foundation with various boundary conditions, it is demonstrated that the derived exact second-order stiffness matrix can be used to obtain the exact buckling solutions systematically.
RESOURCE-CONSTRAINED PRODUCT INNOVATION — AN EVIDENCE-BASED REVIEW AND RESEARCH AGENDA
Authors: SARKAR SOUMODIP , MATEUS SARA
Published: September 2024

Historically, leveraging the right resources has been deemed critical for successful product innovation. However, recent findings reveal that some entrepreneurs and ventures, despite facing resource constraints, can still develop innovative products. This intriguing phenomenon has captured the attention of scholars, resulting in an increasing number of empirical work examining various aspects of resource-constrained product innovation. To consolidate the dispersed knowledge generated by these studies and establish a solid foundation for evidence-based entrepreneurship and management, we conducted a comprehensive review of 53 articles. Through an evidence-based analysis, we construct a framework that encompasses the who-why-how-where-when-what of the phenomenon. This framework delineates the scope of the phenomenon and sheds light on the enabling antecedents and processes undertaken by the actors, thus serving as a foundation for both research and practice. Moreover, we identified significant research gaps on the topic and provided suggestions for future research to focus on underexplored concepts and relationships within the framework.
INNOVATION HUBS IN RURAL AREAS IN GERMANY: A QUALITATIVE ANALYSIS OF ROLES, TYPES, AND IMPACTS
Authors: DUCHEK STEPHANIE , HÖNE SARAH
Published: September 2024

Innovation hubs are a promising new way to foster innovations in rural areas. However, research in this field is still at a preliminary stage. The literature neither provides a clear definition, nor a systematic overview of roles and types of these hubs. To help close this research gap, we first conducted expert interviews with 10 different innovation hubs in rural areas in Germany. Based on our qualitative content analysis, we were able to identify four central roles of such hubs (supporters, knowledge brokers, networkers, and physical experiment spaces) and develop an analysis scheme for evaluating hubs regarding these roles. Second, we applied these findings to a total sample of 45 innovation hubs in rural areas in Germany which enabled us to provide a detailed characterisation and comparison of different types of innovation hubs. Further, we offer first insights into the specific role of these types in regional development as well as requirements for their long-term survival. With these insights, our study provides a useful basis for future research on the concept and impact of innovation hubs as well as the successful management of innovation hubs in practice.
The average number of Goldbach representations and Zero-free regions of the Riemann zeta function
Authors: Billington Keith , Cheng Maddie , Schettler Jordan , Irma Suriajaya Ade
Published: September 2024

In this paper, we prove an unconditional form of Fujii’s formula for the average number of Goldbach representations and show that the error in this formula is determined by a general zero-free region of the Riemann zeta function, and vice versa. In particular, we describe the error in the unconditional formula in terms of the remainder in the Prime Number Theorem which connects the error to zero-free regions of the Riemann zeta function.
2-Adic volume of the homogeneous space of a quadratic form
Authors: Yim Jaehyun
Published: September 2024

In this paper, we provide a formula for the 2-adic volume of the homogeneous space of a quadratic form. This serves as a key ingredient in the density theorem of the unnormalized Tamagawa numbers of projective special orthogonal groups.
A generalization of the Grunwald–Wang theorem for nth powers
Authors: Mishra Bhawesh
Published: September 2024

Let n be a natural number greater than 2 and q be the smallest prime dividing n. We show that a finite subset A of rationals, of cardinality at most q, contains a nth power in [Formula: see text] for almost every prime p if and only if A contains a perfect nth power, barring some exceptions when n is even. This generalizes the Grunwald–Wang theorem for nth powers, from one rational number to finite subsets of rational numbers. We also show that the upper bound q in this generalization is optimal for every n.
On solutions of ∑i=1n1/xi = 1 in integers of the form 2akb
Authors: Louwsma Joel
Published: September 2024

We give an algorithm that produces all solutions of the equation [Formula: see text] in integers of the form [Formula: see text], where k is a fixed positive integer that is not a power of 2, a is an element of [Formula: see text] that can vary from term to term, and b is a nonnegative integer that can vary from term to term. We also completely characterize the pairs [Formula: see text] for which this equation has a nontrivial solution in integers of this form.
New proofs of the septic Rogers–Ramanujan identities
Authors: Rosengren Hjalmar
Published: September 2024

We give new proofs of the 12 Rogers–Ramanujan-type identities due to Rogers and Slater that are traditionally associated with the moduli 7, 14 and 28.
New proofs of theorems on q-orthogonal functions
Authors: Chen Dandan , Liu Zhiguo
Published: September 2024

In this paper, we establish a q-integral formula by using the orthogonality relation, and also provide a new proof of the q-orthogonality relation for the continuous q-ultraspherical polynomials. A new q-beta integral with five parameters is evaluated.
A further common q-generalization of the (B.2) and (C.2) supercongruences of Van Hamme
Authors: Guo Victor J. W.
Published: September 2024

In 2019, the author and Zudilin gave a parametric [Formula: see text]-supercongruence, which is a common generalization of the (B.2) and (C.2) supercongruences of Van Hamme. In this paper, we further give a generalization of this [Formula: see text]-supercongruence. As a corollary, we obtain the following supercongruence: for any prime [Formula: see text], positive integer [Formula: see text], and nonnegative integer [Formula: see text], [Formula: see text]
Hansraj Gupta’s “A technique in partitions” revisited: Congruences, cranks, and polyhedral geometry
Authors: Aniceto Joselyne , Kronholm Brandt
Published: September 2024

We revisit a 1975 result of Gupta on a “new” technique for computing values of [Formula: see text], the function enumerating the partitions of [Formula: see text] whose part sizes come from a finite set [Formula: see text]. We generalize Gupta’s main theorem and then use this result to establish an infinite family of partition congruences. The generating function techniques employed have a direct link to polyhedral geometry, which we then use to prove the existence of combinatorial witnesses, known as “cranks”, for the partition congruences.
A heterogeneous model quantization and similarity matching approach based on structural features for MBSE
Authors: Dong Qi , Tan Jianfei , Zhao Chun
Published: September 2024

With the development of Model-Based Systems Engineering (MBSE), model reuse technology is an important method to improve the modeling efficiency and credibility, and the research difficulties and hotspots in the field of complex system simulation. However, model reuse still faces challenges due to the existence of text structure bias and feature mismatch. To this end, this paper presents a structure-based model similarity quantization algorithm to obtain the similarities among models. In the first stage, the properties of the structure between the models are obtained by the structural vectorization method, and different module data are accounted. In the second stage, weights are assigned to the module data using the AHP–CRITIC assignment method. The experiment is based on the circuit amplifier model as a case study, and the experimental results verify the feasibility of structure-based model similarity quantification.
Adaptive Quotient Filters
Authors: Wen Richard , McCoy Hunter , Tench David , Tagliavini Guido , Bender Michael A. , Conway Alex , Farach-Colton Martin , Johnson Rob , Pandey Prashant
Published: September 2024

Filters trade off accuracy for space and occasionally return false positive matches with a bounded error. Numerous systems use filters in fast memory to avoid performing expensive I/Os to slow storage. A fundamental limitation in traditional filters is that they do not change their representation upon seeing a false positive match. Therefore, the maximum false positive rate is only guaranteed for a single query, not for an arbitrary set of queries. We can improve the filter's performance on a stream of queries, especially on a skewed distribution, if we can adapt after encountering false positives. Adaptive filters, such as telescoping quotient filters and adaptive cuckoo filters, update their representation upon detecting a false positive to avoid repeating the same error in the future. Adaptive filters require an auxiliary structure, typically much larger than the main filter and often residing on slow storage, to facilitate adaptation. However, existing adaptive filters are not practical and have not been adopted in real-world systems for two main reasons. First, they offer weak adaptivity guarantees, meaning that fixing a new false positive can cause a previously fixed false positive to come back. Secondly, the sub-optimal design of the auxiliary structure results in adaptivity overheads so substantial that they can actually diminish overall system performance compared to a traditional filter. In this paper, we design and implement the \sysname, the first practical adaptive filter with minimal adaptivity overhead and strong adaptivity guarantees, which means that the performance and false-positive guarantees continue to hold even for adversarial workloads. The \sysname is based on the state-of-the-art quotient filter design and preserves all the critical features of the quotient filter such as cache efficiency and mergeability. Furthermore, we employ a new auxiliary structure design which results in considerably low adaptivity overhead and makes the \sysname practical in real systems. We evaluate the \sysname by using it to filter queries to an on-disk B-tree database and find no negative impact on insert or query performance compared to traditional filters. Against adversarial workloads, the \sysname preserves system performance, whereas traditional filters incur 2× slowdown from adversaries representing as low as 1% of the workload. Finally, we show that on skewed query workloads, the \sysname can reduce the false-positive rate 100× using negligible (1/1000th of a bit per item) space overhead.
Efficient Approximation Algorithms for Minimum Cost Seed Selection with Probabilistic Coverage Guarantee
Authors: Feng Chen , Chen Xingguang , Guo Qintian , Zhang Fangyuan , Wang Sibo
Published: September 2024

Given a social network
G
, a cost associated with each user, and an influence threshold η, the minimum cost seed selection problem (MCSS) aims to find a set of seeds that minimizes the total cost to reach η users. Existing works are mainly devoted to providing an expected coverage guarantee on reaching η, classified as MCSS-ECG, where their solutions either rely on an impractical influence oracle or cannot attain the expected influence threshold. More importantly, due to the expected coverage guarantee, the actual influence in a campaign may drift from the threshold evidently. Thus, the advertisers would like to request for a probability guarantee of reaching η. This motivates us to further solve the MCSS problem with a probabilistic coverage guarantee, termed MCSS-PCG.

In this paper, we first propose our algorithm CLEAR to solve MCSS-ECG, which reaches the expected influence threshold without any influence oracle or influence shortfall but a practical approximation ratio. However, the ratio involves an unknown term (i.e., the optimal cost). Thus, we further devise the STAR method to derive a lower bound of the optimal cost and then obtain the first explicit approximation ratio for MCSS-ECG. In MCSS-PCG, it is necessary to estimate the probability that the current seeds reach η, to decide when to stop seed selection. To achieve this, we design a new technique named MRR, which provides efficient probability estimation with a theoretical guarantee. With MRR in hand, we propose our algorithm SCORE for MCSS-PCG, whose performance guarantee is derived by measuring the gap between MCSS-ECG and MCSS-PCG, and applying the theoretical results in MCSS-ECG. Finally, extensive experiments demonstrate that our algorithms achieve up to two orders of magnitude speed-up compared to alternatives while meeting the requirement of MCSS-PCG with the smallest cost.
GABoost: Graph Alignment Boosting via Local Optimum Escape
Authors: Liu Wei , Zhang Wei , Zhao Haiyan , Jin Zhi
Published: September 2024

Heterogeneous graphs provide a universal data structure for representing various kinds of structured data in numerous domains. The
graph alignment
problem aims to find the correspondences of vertices in different graphs, playing a fundamental role in many downstream tasks of heterogeneous graph mining. In recent years, many graph alignment methods have been proposed, ranging from
classical optimization methods
,
spectral methods
, to
embedding learning based-methods
. Due to the problem's complexity, the result found by most existing methods is either a
heuristic solution
or a
critical point
in the solution space. In this paper, we propose GABoost, a graph alignment boosting algorithm that takes as input an initial alignment between two heterogeneous graphs and outputs a boosted alignment via an iterative
local-optimum-escape
process. One of the distinctive features of GABoost is that it can be sequentially composed with any graph alignment methods to improve the output of upstream methods. To examine the effectiveness of GABoost, we select 7 upstream methods of graph alignment as well as 6 real-world datasets, and quantitatively investigate the degree to which GABoost boosts these methods. The results show that GABoost improves the alignment accuracy of the 7 upstream methods by 25.25% on average with acceptable time overhead.
Near-Duplicate Text Alignment with One Permutation Hashing
Authors: Peng Zhencan , Zhang Yuheng , Deng Dong
Published: September 2024

This paper studies the near-duplicate text alignment problem under the constraint of Jaccard similarity. Specifically, given a collection of long texts and a short query text, this problem finds all the
subsequences
in each text whose Jaccard similarities to the query are no smaller than a given threshold. Near-duplicate text alignment is computationally intensive. This is because there are O(n
2
) subsequences in a text with
n
tokens. To remedy this issue, a few recent studies propose to first generate the min-hash sketch of every subsequence in each text and then find all the subsequences whose min-hash sketches are similar to that of the query. They introduce the concept of "compact windows" and show that the O(n
2
k) min-hashes in a text with
n
tokens can be losslessly compressed in compact windows using O(nk) space, where
k
is the sketch size. However, the space cost O(nk) is still too high for long texts, especially when the sketch size
k
is large. To address this issue, we propose to use One Permutation Hashing (OPH) to generate the min-hash sketch and introduce the concept of "OPH compact windows". Although the size of each sketch remains the same, which is O(k), we prove that all the O(n
2
k) min-hashes generated by OPH in a text with
n
tokens can be losslessly compressed in OPH compact windows using only O(n+k) space. Note the generation of OPH compact windows does not necessitate the enumeration of the O(n
2
k) min-hashes. Moreover, we develop an algorithm to find all the sketches in a text similar to that of the query directly from OPH compact windows, along with three optimizations.We conduct extensive experiments on three real-world datasets. Empirical results show our proposed algorithms significantly outperformed existing methods in terms of index cost and query latency and scaled well.
Pluto: Sample Selection for Robust Anomaly Detection on Polluted Log Data
Authors: Ma Lei , Cao Lei , VanNostrand Peter M. , Hofmann Dennis M. , Su Yao , Rundensteiner Elke A.
Published: September 2024

Log anomaly detection, critical in identifying system failures and preempting security breaches, finds irregular patterns within large volumes of log data. Modern log anomaly detectors rely on training deep learning models on clean anomaly-free log data. However, such clean log data requires expensive and tedious human labeling. In this paper, we thus propose a robust log anomaly detection framework, PlutoNOSPACE, that automatically selects a clean representative sample subset of the polluted log sequence data to train a Transformer-based anomaly detection model. Pluto features three innovations. First, due to localized concentrations of anomalies inherent in the embedding space of log data, Pluto partitions the sequence embedding space generated by the model into regions that then allow it to identify and discard regions that are highly polluted by our pollution level estimation scheme, based on our pollution quantification via Gaussian mixture modeling. Second, for the remaining more slightly polluted regions, we select samples that maximally purify the eigenvector spectrum, which can be transformed into the NP-hard facility location problem; allowing us to leverage its greedy solution with a (1-(1/e)) approximation guarantee in optimality. Third, by iteratively alternating between the above subset selection, a model re-training on the latest subset, and a subset filtering using dynamic training artifacts generated by the latest model, the data selected is progressively refined. The final sample set is used to retrain the final anomaly detection model. Our experiments on four real-world log benchmark datasets demonstrate that by retaining 77.7% (BGL) to 96.6% (ThunderBird) of the normal sequences while effectively removing 90.3% (BGL) to 100.0% (ThunderBird, HDFS) of the anomalies, Pluto provides a significant absolute F-1 improvement up to 68.86% (2.16% → 71.02%) compared to the state-of-the-art sample selection methods. The implementation of this work is available at https://github.com/LeiMa0324/Pluto-SIGMOD25.
Theoretically and Practically Efficient Maximum Defective Clique Search
Authors: Dai Qiangqiang , Li Ronghua , Cui Donghang , Wang Guoren
Published: September 2024

The study of
k
-defective cliques, defined as induced subgraphs that differ from cliques by at most
k
missing edges, has attracted much attention in graph analysis due to their relevance in various applications, including social network analysis and implicit interaction predictions. However, determining the maximum
k
-defective clique in graphs has been proven to be an NP-hard problem, presenting significant challenges in finding an efficient solution. To address this problem, we develop a theoretically and practically efficient algorithm that leverages newly-designed branch reduction rules and a pivot-based branching technique. Our analysis establishes that the time complexity of the proposed algorithm is bounded by O(mγ
k
n
), where γ
k
is a real value strictly less than 2 (e.g., when k= 1, 2, and 3, γ
k
= 1.466, 1.755, and 1.889, respectively). To our knowledge, this algorithm achieves the best worst-case time complexity to date compared to state-of-the-art solutions. Moreover, to further reduce unnecessary branches, we propose a time-efficient upper bound-based pruning technique, which is obtained by manipulating information such as the number of distinct colors assigned to vertices and the presence of non-neighbors among them. Additionally, we employ an ordering-based heuristic approach as a preprocessing step to improve computational efficiency. Finally, we conduct extensive experiments on a diverse set of over 300 graphs to evaluate the efficiency of the proposed solutions. The results demonstrate that our algorithm achieves a speedup of 3 orders of magnitude over state-of-the-art solutions in processing most of real-world graphs.
Neural-aware Decoupling Fusion based Personalized Federated Learning for Intelligent Sensing
Authors: Gao Yujia , Shen Li , Liu liang , Cao Zijian , Tao Dacheng , Ma Huadong , Kato Nei
Published: September 2024

Personalized federated learning (PFL) is a framework that targets individual models for optimization, providing better privacy and flexibility for clients. However, in challenging intelligent sensing applications, the heterogeneous client’s data distributions make the aggregation of local models in the server unstable or even hard to converge. To deal with the performance degradation caused by the preceding problem, existing PFL methods focus more on how to fine-tune the global model but ignore the impact of the global model fusion algorithm on the results. In this article, we propose a new explainable neural-aware decoupling fusion based PFL framework,
p-FedADF
, to address the preceding challenges. It contains two carefully designed modules. The local decoupling module, deployed on the client, utilizes the architecture disentangle technique to decouple the feature extractors in the client’s local model into sub-network according to data categories. It obtains the inference process of feature extraction for different categories of data by training. The global aggregation module, deployed on the server, aligns the sub-network positions for multiple clients and implements a fine-grained generic feature extractor aggregation. In addition, we provide a mask encoding scheme to reduce the communication overhead of transmitting the sub-network sets between the server and clients. Our p-FedADF obtains 1.6%, 0.2%, 2.3%, and 4.5% improvement on a real-world dataset and three benchmark datasets, compared to state-of-the-art methods.
Iterative Time Series Imputation by Maintaining Dependency Consistency
Authors: Hu Hanwen , Qian Shiyou , Yang Dingyu , Cao Jian , Xue Guangtao
Published: September 2024

Data imputation is crucial in the analysis of incomplete time series, such as forecasting and classification, which involves learning dependencies among the observed values to infer missing ones. As there are no ground truths for missing values, the challenge of time series imputation lies in preventing the model from overfitting to spurious correlations. In this article, we believe that
ensuring dependency consistency
between observed and imputed values in a sequence is paramount for data imputation. Based on this idea, we propose a model called

IR
2
-Net

,

1

which combines an
incomplete representation mechanism (IRM)
with an
iterative reconstruction framework (IRF)
to establish a closed-loop learning-validation imputation paradigm. Firstly, IRM facilitates the representation of dependencies in
incomplete sequences
while preserving their distributions and semantics, effectively preventing the model from capturing spurious correlations. Secondly, IRF enables the model to reconstruct identical complete sequences separately based on imputed and observed values, ensuring that the dependencies of imputed values remain consistent with those of the observed ones. We conduct experiments on four datasets and compare IR
2
-Net with seven state-of-the-art imputation models. The experiment results show that IR
2
-Net outperforms all the baselines by 4.1%–23.4% in terms of accuracy. Moreover, IRF and IRM are two general modules that can be easily integrated into two existing models, significantly enhancing their performance by 18.3%–42.0%.
Layout Congestion Prediction Based on Regression-ViT
Authors: Mo Guiqi , Xia Yimin , Ou Jianhong , Cai Shuting , Xiong Xiaoming
Published: September 2024

To accelerate the back-end design flow of integrated circuit (IC), numerous studies have made exploratory advancements in machine learning (ML) for electronic design automation (EDA). However, most research works are limited to deep learning (DL) models predominantly based on convolutional neural networks, and the models often suffer from poor generalization due to the scarcity of data. In this study, we propose the Double generative adversarial networks (D-GAN) model to enrich the dataset and propose the Regression Vision Transformer (R-ViT) model to predict layout congestion information. Compared with the baseline model, experimental results show improvements of 3.03% and 2.64% in Receiver Operating Characteristic-Area under Curve (ROC-AUC) and Precision-Recall Curve-Area under Curve (PRC-AUC) respectively. To further enhance the prediction accuracy of the model, an adaptive Huber loss function is designed to optimize the training process, resulting in an improvement of up to 11.03% in ROC-AUC compared with the baseline model. Lastly, extended experiments are conducted to study the effects of parameters and convolutional kernel size on performance, which find a better configuration.
Turbulence from an Observer Perspective
Authors: Zaki Tamer A.
Published: September 2024

Turbulence is often studied by tracking its spatiotemporal evolution and analyzing the dynamics of its different scales. The dual to this perspective is that of an observer who starts from measurements, or observations, of turbulence and attempts to identify their back-in-time origin, which is the foundation of data assimilation. This back-in-time search must contend with the action of chaos, which obfuscates the interpretation of the observations. When the available measurements satisfy a critical resolution threshold, the influence of chaos can be entirely mitigated and turbulence can be synchronized to the exact state–space trajectory that generated the observations. The critical threshold offers a new interpretation of the Taylor microscale, one that underscores its causal influence. Below the critical threshold, the origin of measurements becomes less definitive in regions where the flow is inconsequential to the observations. In contrast, flow events that influence the measurements, or are within their domain of dependence, are accurately captured. The implications for our understanding of wall turbulence are explored, starting with the highest density of measurements that entirely tame chaos and proceeding all the way to an isolated measurement of wall stress. The article concludes with a discussion of future opportunities and a call to action.
Understanding the Diversity, Evolution, Ecology, and Applications of Mycoviruses
Authors: Xie Jiatao , Jiang Daohong
Published: September 2024

Mycoviruses are widely distributed among various kinds of fungi. Over the past 10 years, more novel mycoviruses have been discovered with the use of high-throughput sequencing techniques, and research on mycoviruses has made fantastic progress, promoting our understanding of the diversity, classification, evolution, and ecology of the entire virosphere. Mycoviruses affect the biological and ecological functions of their hosts, for example, by suppressing or improving hosts’ virulence and reproduction ability, and subsequently affect the microbiological community where their hosts live; hence, we may develop mycoviruses to regulate the health of environments, plants, animals, and human beings. In this review, we introduce recently discovered mycoviruses from fungi of humans, animals, plants, and environments, and their diversity, evolution, and ecological characteristics. We also present the potential application of mycoviruses by describing the latest progress on using mycoviruses to control plant diseases. Finally, we discuss the main issues facing mycovirus research in the future.
Comparing the Physiological Responses to the 6‐Minute Walk Test, Timed Up and Go Test, and Treadmill Cardiopulmonary Exercise Test
Authors: Alves Eduardo S. , Bellet R. Nicole , Sharma Pramod , Balmain Bryce N. , Aitken Craig , Doering Thomas , Orola Leilani , Green Anita , Paim Tatiana , O’Connor Fergus , Morris Norman R.
Published: September 2024

Purpose: To compare physiological responses during a treadmill cardiopulmonary exercise test (CPX), 6‐minute walk test (6MWT), and timed up and go test (TUGT) in individuals referred for unexplained breathlessness and symptom limited treadmill exercise testing.

Methods: Heart rate (HR), oxygen consumption (V̇O2), carbon dioxide production (V̇CO2), respiratory exchange ratio (RER), minute ventilation (V̇E), systolic blood pressure (SBP), and rating of perceived exertion (RPE) were recorded throughout each test.

Results: Each test demonstrated a significant increase (p < 0.01) in the cardiopulmonary (V̇O2, V̇CO2 and V̇E, RPE, SBP, and HR) and perceptual (RPE) responses from rest to end exercise. The increase in cardiopulmonary and perceptual responses was greatest for the CPX with significantly smaller responses demonstrated during the 6MWT (p < 0.01) and even smaller responses for the TUGT (p < 0.01 vs CPX and 6MWT).

Conclusion: Not surprisingly, the treadmill CPX results is the greatest physiological response in our group. Despite being of short duration, the TUGT results in an increased physiological response.
Accurate Prediction of Electrochemical Degradation Trajectory for Lithium‐Ion Battery Using Self‐Discharge
Authors: Kim Homin , Jung Taeksoo , Jung Jaehyun , Noh Yoojeong , Lee Byeongyong
Published: September 2024

Accurate prediction of battery performance is crucial for timely battery health management. However, it is challenging to forecast precisely battery’s performance due to its intricate degradation mechanisms and variability in the degradation rates. To overcome the challenges, we employed an artificial intelligence (AI)‐driven approach. By training an indicator that reflects well structural degradation of a battery (self‐discharge; SD), a model could predict battery performance metrics with a high accuracy compared to models using direct indicators (e.g., capacity). In a comparative analysis, the self‐discharge model with the couple of bidirectional‐long–short term memory demonstrates outstanding prediction accuracy with a mean absolute percentage error of 0.39% for capacity retention (CR) prediction and a root mean square error of 13.2 cycles for remaining useful life prediction. These findings underscore the significance of incorporating indicators reflecting the internal electrode health of batteries for accurate lifespan prediction.
Consistency and Complementarity Jointly Regularized Subspace Support Vector Data Description for Multimodal Data
Authors: Wang Chuang , Hu Wenjun , Wang Juan , Qian Pengjiang , Wang Shitong
Published: September 2024

The one‐class classification (OCC) problem has always been a popular topic because it is difficult or expensive to obtain abnormal data in many practical applications. Most of OCC methods focused on monomodal data, such as support vector data description (SVDD) and its variants, while we often face multimodal data in reality. The data come from the same task in multimodal learning, and thus, the inherent structures among all modalities should be hold, which is called the consistency principle. However, each modality contains unique information that can be used to repair the incompleteness of other modalities. It is called the complementarity principle. To follow the above two principles, we designed a multimodal graph–regularized term and a sparse projection matrix–regularized term. The former aims to preserve the within‐modal structural and between‐modal relationships, while the latter aims to richly use the complementarity information hidden in multimodal data. Further, we follow the multimodal subspace (MS) SVDD architecture and use two regularized terms to regularize SVDD. Consequently, a novel OCC method for multimodal data is proposed, called the consistency and complementarity jointly regularized subspace SVDD (CCS‐SVDD). Extensive experimental results demonstrate that our approach is more effective and competitive than other algorithms. The source codes are available athttps://github.com/wongchuang/CCS_SVDD.
Enhanced Light‐Gradient Boosting Machine (GBM)‐Based Artificial Intelligence‐Blockchain‐Based Telesurgery in Sixth Generation Communication Using Optimization Concept
Authors: S. Punitha , K. S. Preetha
Published: September 2024

Telesurgery and robotic surgery are two real‐time mission‐critical applications where Artificial Intelligence (AI) has a lot of perspective. In this work, a blockchain‐ and AI‐powered telesurgery system for 6 G communication is suggested, which describes a transparent, safe, self‐managing, and trustworthy structure having massive Ultra‐Reliable Low‐Latency Communication (mURLLC). The condition is categorized using AI methods like Enhanced Light GBM, whose criticality scores range from 0 to 1 (after the predicted output, the criticality score of the corresponding disease is divided into high critical, medium critical, and low critical on the basis of the scores that range from 0 to 1). Here, the parameter tuning in light GBM is performed using the Tasmanian Devil Optimization (TDO) with the consideration of attaining the fitness function and thus referred to as Enhanced Light GBM. This proposed novel Enhanced Light GBM predicts the final output based on the criticality scores. In the future, recent deep learning algorithms can be considered for drone‐assisted telesurgery framework together with the consideration of hybrid optimization algorithms. The proposed Enhanced Light GBM‐TDO model for the drone‐oriented telesurgery framework with respect to prediction accuracy is 3.22%, 3.11%, 1.84%, 3.40%, 2.26%, and 1.15% advanced than Aayush, Habits, BATS, CSIMH, MGA, and heuristic approach, respectively.
Sex Differences in the Associations among Insulin Resistance Indexes with Metabolic Syndrome: A Large Cross‐Sectional Study
Authors: Zhang Wenkang , Chen Chen , Li Mingkang , Yan Gaoliang , Tang Chengchun
Published: September 2024

Purpose. Metabolic syndrome (MetS) is closely related to insulin resistance (IR), and the sex differences have not been fully explored. This study was aimed to investigate the sex differences in the associations among IR indexes with MetS in a large population. Methods. A total of 60,799 participants were enrolled in the current study. MetS was defined using the National Cholesterol Education Program Adult Treatment Panel III criteria. The following IR indexes were evaluated: triglyceride‐glucose (TyG) index, TyG‐waist circumference (TyG‐WC), TyG‐waist to height ratio (TyG‐WHtR), TyG‐body mass index (TyG‐BMI), triglyceride to high‐density lipoprotein cholesterol (TG/HDL‐C), and metabolic score for IR (MetS‐IR). Factors associated with MetS were examined using logistic regressions. The receiver operating characteristic curves were used to evaluate the predictive value of the IR indexes for MetS. Results. The prevalence of MetS was 11.8% (n = 4097) for males and 5.4% (n = 1390) for females and increased with age in both subgroups. The IR index levels of male patients were higher than female patients (all p < 0.001). The IR indexes were independent risk factors for MetS except for TyG‐WHtR and TG/HDL‐C in female patients. TyG had the greatest area under the curve (AUC) (AUC, 0.930; 95% CI, 0.928–0.933; p < 0.001) in the male patients and TyG‐WHtR (AUC, 0.916; 95% CI, 0.913–0.920; p < 0.001) in the female patients. The AUCs of 6 IR indexes combination were 0.960 (95% CI, 0.957–0.962; p < 0.001) and 0.962 (95% CI, 0.959–0.964; p < 0.001), with the sensitivities of 91.29% and 90.94%, the specificities of 88.27% and 89.51% in male and female groups, respectively. Conclusions. The IR index levels are higher in male than female patients. In IR indexes, TyG has the highest AUC in male patients and TyG‐WHtR in female patients. The combination of IR indexes improved diagnostic efficiency compared with a single parameter. Moreover, the IR indexes are independently associated with MetS except for TyG‐WHtR and TG/HDL‐C in female patients. Our findings indicate that the multi‐index association of IR indexes may play a potential role in MetS diagnosis, and understanding the sex differences in risk factors for MetS may help doctors properly implement more individualized prevention strategies.
The Use of Transition Curves in Airport Runway Rapid Exit Taxiways (RETs)
Authors: Ciampa Donato , Diomedi Maurizio , Vuono Pietro , Olita Saverio
Published: September 2024

This paper describes a study result focused on the transition curves used in airport rapid exit taxiways (RETs). Such geometric elements are not currently made explicit by standards, and major international government agencies, such as the International Civil Aviation Organization and the Federal Aviation Administration, are limited to providing some layouts consisting of only constant curvature elements, although the aircrafts speed in RETs is variable. In addition to the clothoid that has unitary shape parameter “n” and the multiparameter clothoids (generalized Cornu spirals), the study analyzes the Bloss curve employment. Regarding the multiparameter clothoids, four schemes with shape parameter equal to 1.25, 1.50, 1.75, and 2.00 were considered, respectively. The proposed curves’ effectiveness was evaluated by analyzing the main geometric and kinematic variables trend, that is, curvature, transversal acceleration, lateral jerk, speed, travel time, longitudinal deceleration, and centrifugal force. To ensure the analysis effectiveness, the study layouts were designed from those used for runways codes 1‐2 and 3‐4 maintaining the main geometric elements dimensions unchanged (length of straight lines, radii of circular curves, exit angles, etc.). The kinematic variables values along the design RETs were calculated “point by point” by MATLAB software to take into account nonuniform motion regimen. The obtained results show that both the Bloss curve and the multiparameter clothoid having a shape factor equal to 2 are capable of better contribution in terms of safety and comfort. Implications in the airport construction field are related to the possibility of using lower deceleration values compared to current standards, that is, allowing higher exit speed for the same deceleration. This allows aircrafts to vacate the runway more quickly and, therefore, to increase runway capacity without taking expensive RETs repositioning.
Sudden Onset of Coma and Fulminant Progression to Brain Death in a 48‐Year‐Old Male With Cerebral Malaria
Authors: Costa Marina , Barbosa Cristiana , Pereira Mauro , Ribeiro Luís , Silveira Pedro
Published: September 2024

Cerebral malaria is the most severe complication of Plasmodium falciparum infection. Left untreated, it is universally fatal. Coma is the clinical hallmark, emerging between the first and third days of fever. Adults typically present with mild cerebral edema, usually with a more favorable prognosis compared to the pediatric population. We present a case of a 48‐year‐old man with a recent travel to Angola who presented comatose on the second day of a febrile illness with clinical signs of cerebral herniation and diffuse cerebral edema and cerebellar tonsil ectopia on cranioencephalic computed tomography. He had a missed diagnosis on a first visit to the emergency department 2 days prior. The diagnosis of cerebral malaria was confirmed after the identification of the parasite in peripheral blood. He was admitted to an intensive care unit; however, progression to brain death was inevitable within a few hours. Malaria affects 5% of the world’s population. In Portugal, it has an incidence of 0.01 in every 1000 inhabitants, and all cases are imported. Despite its rarity in a nonendemic country, its severity alerts to the consideration of this syndrome in the etiologic workup of coma. The early recognition of the diagnosis is of major importance for the establishment of definitive treatment, as its timely administration has a crucial impact on the outcome.
New and Advanced Methods in Detection of Fruit Juice Adulteration: Focusing on Nano‐Biosensor
Authors: Shokri Samira , Soltani Mahya , Mazaheri Yeganeh , Rezagholizade-shirvan Alieh , Mohammadi Mansoureh , Shariatifar Nabi , Zeinali Tayebeh
Published: September 2024

One of the most popular products made from fruit is juice, which is consumed as a snack by people of all ages due to its high content of polyphenols and vitamins. Therefore, the prevalence of adulteration in juices is very high, and ensuring the authenticity and quality of juices is a major concern for those responsible for monitoring food safety for the public. The most common frauds in fruit juices include adding water, sugar, organic acids, and other juices clouding agents and exceeding the permitted amount of sulfites. Having knowledge of the types of fraud in fruit juices and their detection methods is essential for developing more efficient detection methods. Chromatographic techniques, such as high‐performance liquid chromatography (HPLC) and gas chromatography (GC), are commonly used to determine the purity of fruit juices. Among the new methods for detecting counterfeit fruit juice, the development of bio‐nanosensors is particularly notable. These sensors reduce sample volume, analytical time, costs, and solvent consumption and will be discussed further in this study. Therefore, this review will explain the basic analytical approaches and chemometric tools used to assess the authenticity of fruit juice and identify any adulteration in the juice.
Thiol‐Free p‐Type Colloidal Quantum Dot for Efficient Broadband Optoelectronics
Authors: Kim Sol-Hee , Kim Yun-Hoo , Choi Jae-Hwan , Jeong Seoryeon , Kim Dongeon , Yang Minjung , Lee Seo-Young , Kim Jeongeun , Ahn Yongnam , Kim Yong-Hoon , Jung Yujin , Baek Se-Woong
Published: September 2024

Colloidal quantum dots (CQDs) are promising semiconductors for optoelectronic applications owing to their bandgap tunability and solution processability. Historically, ethanedithiol has been widely employed as a surface ligand to form a p‐type CQD layer via a layer‐by‐layer process. However, the limited control of p‐type characteristics reduces the device performance, and the high reactivity of ligand and processing solvents degrade the underlying layer. In this study, a thiol‐free p‐type CQD is demonstrated by creating native p‐type CQDs during the synthesis process. Sulfurization of PbS CQD results in p‐type properties that enable the avoidance of any further thiol‐ligand treatment process. Further, alternative surface passivation of the sulfurized CQD using halide ligand yields a robust, p‐type, morphologically uniform, and trap‐suppressed film. The developed CQD film is then employed as a hole‐transporting layer (HTL) for both broadband solar cells and photodetectors. The resulting CQD devices exhibit improved performance with a 65.7% increase in photovoltaic efficiency and a 1.7‐fold improvement in responsivity for infrared photodetection. The devices also demonstrate higher ambient stability, retaining 85% of the initial performance after 1,000 hr owing to the uniform top HTL morphology, indicating the potential of the new p‐type layer to be utilized in various emerging optoelectronics.