bluuebunny/crossref_metadata_2025 huggingface dataset:
Nebivolol prevents redox imbalance and attenuates bladder dysfunction induced by cyclophosphamide in mice
Authors: Jesus Carolina P.S. , Pimenta Gustavo F. , de Oliveira Mariana G. , Dourado Thales M.H. , Antunes Edson , Tirapelli Carlos R.
Published: September 2024

Cyclophosphamide (CYP) is combined with cytoprotective agents to minimize its toxicity in the bladder, which is mediated by reactive oxygen species (ROS). Using multiple antioxidant mechanisms, nebivolol protects from oxidative stress in distinctive conditions. We hypothesized that nebivolol would attenuate both molecular and functional alterations induced by CYP in the bladder. Male C57BL/6 were pretreated or not with nebivolol (10 mg/kg/day, gavage), which was given 5 days before a single injection of CYP (300 mg/kg; i.p.). Molecular and functional parameters were assessed at 24 h in the bladder. Nebivolol prevented increases in ROS generation and lipoperoxidation as well as reduction of superoxide dismutase activity induced by CYP. Increased voiding frequency, decreased voiding interval, and reduced bladder capacity were found in CYP-treated mice. These responses were prevented by nebivolol. An augmented number of urinary spots and smaller urinary volumes were detected in CYP-injected mice, and nebivolol partially prevented these responses. The reduction of ROS levels is the primary mechanism by which nebivolol attenuates the deleterious effects of CYP in the bladder. The association of nebivolol with other cytoprotective agents could be an option to prevent CYP-associated oxidative damage to the bladder during chemotherapy.
Perturbative color correlations in double parton scattering
Authors: Blok B. , Mehl J.
Published: September 2024

In this paper, we study the contribution of color correlations to Double Parton Scattering (DPS). We show that there is a specific class of Feynman diagrams related to so-called [Formula: see text] processes when the contribution of these color correlations is not Sudakov suppressed with the transverse scales. The effective absence of Sudakov suppression gives hope that although they are small relative to color singlet correlations, they eventually can be observed.
A randomly generated Majorana neutrino mass matrix using adaptive Monte Carlo method
Authors: Singh Y. Monitar , Singh Mayengbam Kishan , Singh N. Nimai
Published: September 2024

A randomly generated complex symmetric matrix using the adaptive Monte Carlo method is taken as a general form of Majorana neutrino mass matrix, which is diagonalized by the use of eigenvectors. We extract all the neutrino oscillation parameters, i.e. two mass-squared differences ([Formula: see text] and [Formula: see text]), three mixing angles ([Formula: see text], [Formula: see text], [Formula: see text]) and three phases, i.e. one Dirac CP violating phase ([Formula: see text]) and two Majorana phases ([Formula: see text] and [Formula: see text]). The Charge-Parity (CP) violating phases are extracted from the mixing matrix constructed with the eigenvectors of the Hermitian matrix formed by the complex symmetric mass matrix. All the latest neutrino oscillation parameters within 3[Formula: see text] bound are only allowed in Normal Hierarchy (NH) consistent with the latest Planck cosmological upper bound, [Formula: see text][Formula: see text]eV. Three cases of one-zero texture i.e. [Formula: see text], [Formula: see text], [Formula: see text] and two cases of two-zero texture, i.e. [Formula: see text] and [Formula: see text] are allowed in normal hierarchy whereas none of zero texture is allowed in inverted hierarchy. We also study effective neutrino masses [Formula: see text] in tritium beta decay and [Formula: see text] in neutrinoless double beta decay and calculate numerically the allowed range of mass elements of Majorana mass matrix.
Soft synchronous gauge in the perturbative gravity
Authors: Khatsymovsky V. M.
Published: September 2024

An attempt to directly use the synchronous gauge ([Formula: see text]) in perturbative gravity leads to a singularity at [Formula: see text] in the graviton propagator. This is similar to the singularity in the propagator for Yang–Mills fields [Formula: see text] in the temporal gauge ([Formula: see text]). There the singularity was softened, obtaining this gauge as the limit at [Formula: see text] of the gauge [Formula: see text], [Formula: see text]. Then the singularities at [Formula: see text] are replaced by negative powers of [Formula: see text], and thus we bypass these poles in a certain way. Now consider a similar condition on [Formula: see text] in perturbative gravity, which becomes the synchronous gauge at [Formula: see text]. Unlike the Yang–Mills case, the contribution of the Faddeev–Popov ghosts to the effective action is nonzero, and we calculate it. In this calculation, an intermediate regularization is needed, and we assume the discrete structure of the theory at short distances for that. The effect of this contribution is to change the functional integral measure or, for example, to add nonpole terms to the propagator. This contribution vanishes at [Formula: see text]. Thus, we effectively have the synchronous gauge with the resolved singularities at [Formula: see text], where only the physical components [Formula: see text] are active and there is no need to calculate the ghost contribution.
Semantics-Fusion: Radar Semantic Information-Based Radar–Camera Fusion for 3D Object Detection
Authors: Tian Ziran , Huang Xiaohong , Xu Kunqiang , Sun Xujie , Deng Zhenmiao
Published: September 2024

The fusion of millimeter-wave radar and camera for three-dimensional (3D) object detection represents a pivotal technology for autonomous driving, yet it is not without its inherent challenges. First, the radar point cloud contains clutter, which can result in the generation of impure radar features. Second, the radar point cloud is sparse, which presents a challenge in fully extracting the radar features. This can result in the loss of object information, leading to object misdetection, omission, and a reduction in the robustness. To address these issues, a 3D object detection method based on the semantic information of radar features and camera fusion (Semantics-Fusion) is proposed. Initially, the image features are extracted through the centroid detection network, resulting in the generation of a preliminary 3D bounding box for the objects. Subsequently, the radar point cloud is clustered based on the objects’ position and velocity, thereby eliminating irrelevant point cloud and clutter. The clustered radar point cloud is projected onto the image plane, thereby forming a radar 2D pseudo-image. This is then input to the designed 2D convolution module, which enables the full extraction of the semantic information of the radar features. Ultimately, the radar features are fused with the image features, and secondary regression is employed to achieve robust 3D object detection. The performance of our method was evaluated on the nuScenes dataset, achieving a mean average precision (mAP) of 0.325 and a nuScenes detection score (NDS) of 0.462.
TLCSFI: A Pose-Guided Person Re-Identification Method with Two-Level Channel–Spatial Feature Integration
Authors: Huan Ruohong , Zhao Xin , Gao Nan , Chen Peng , Liang Ronghua
Published: September 2024

Person re-identification methods currently encounter challenges in feature learning, primarily due to difficulties in expressing the correlation between local features and integrating global and local features effectively. To address these issues, a pose-guided person re-identification method with Two-Level Channel–Spatial Feature Integration (TLCSFI) is proposed. In TLCSFI, a two-level integration mechanism is implemented. At the first level, TLCSFI integrates the spatial information from local features to generate fine-grained spatial features. At the second level, the fine-grained spatial feature and the coarse-grained channel feature are integrated together to complete channel–spatial feature integration. In the method, a Pose-based Spatial Feature Integration (PSFI) module is introduced to generate the pose union feature, which calculates intra-body affinity to guide the integration of spatial information among local pose feature maps. Then, a Channel and Spatial Union Feature Integration (CSUFI) module is proposed to efficiently integrate the channel information of the global feature and the spatial information of the pose union feature. Two individual networks are designed to extract channel and spatial information, respectively, in CSUFI, which are then weighted and integrated. Experiments are conducted on three publicly available datasets to evaluate TLCSFI, and the experimental results demonstrate its competitive performance.
An Improved YOLOv8 Network for Multi-Object Detection with Large-Scale Differences in Remote Sensing Images
Authors: Li Zhaofei , Zhou Hao , Zhang Yijie , Tao Hongjie , Yu Hongchun
Published: September 2024

Aiming to address the challenges of low object detection precision in remote sensing images due to high background complexity and significant target scale variations, a novel model for large-scale disparate object detection in remote sensing images is proposed based on the modified YOLOv8. The model incorporates a Context Aggregation Module (CAM) with an attention mechanism in the backbone network to exploit contextual information, enabling multi-scale feature fusion for effective small object detection. The Neck network utilizes GSConv modules and employs the Slim-Neck design paradigm to enhance model robustness, making it better suited for detecting objects in high-complexity backgrounds. Furthermore, the model adopts the Wise-IoU as the loss function, incorporating a dynamic nonmonotonic focusing mechanism and a gradient gain allocation strategy to enhance the overall performance of disparate object detection. The experimental results indicate that promising performance improvements in the face of large-scale variations in remote sensing image targets. Specifically, the model achieves mAP values of 71.4% and 90.3% on the DOTA and NWPU VHR-10 datasets, respectively, representing increases of 4.4% and 3.7% compared to the original model. Compared to other typical algorithms, it also has considerable advantages in both comprehensive detection accuracy and detection speed.
Speech Fatigue Recognition Under Small Samples Based on Generative Adversarial Networks and BLSTM
Authors: Chen Shuxi , Qiu Jianlin , Zhang Haifei , Yu Yifan , Chen Hao , Sun Yiyang
Published: September 2024

To address the issue of low accuracy in speech fatigue recognition (SFR) under small samples, a method for small-sample SFR based on generative adversarial networks (GANs) is proposed. First, we enable the generator and discriminator to adversarially train and learn the features of the samples, and use the generator to generate high-quality simulated samples to expand our dataset. Then, we transfer discriminator parameters to fatigue identification network to accelerate network training speed. Furthermore, we use a bidirectional long short-term memory network (BLSTM) to further learn temporal fatigue features and improve the recognition rate of fatigue. 720 speech samples from a self-made Chinese speech database (SUSP-SFD) were chosen for training and testing. The results indicate that compared with traditional SFR methods, like convolutional neural networks (CNNs) and long short-term memory network (LSTM), our method improved the SFR rate by about 2.3–6.7%, verifying the effectiveness of the method.
Machine Learning Clustering Algorithm for Water Environmental Monitoring
Authors: Jiang Hongfen , Gu Junfeng , Xi Haixu , Yu Qian , Wang Xiaoyue , Liu Yijun
Published: September 2024

Machine learning based on genetic algorithms is an important application. The multi-dimensional ordered sample clustering problem is often solved using Fisher’s optimal segmentation method. However, this method has obvious shortcomings when encountering long sample problems due to its high storage requirements during the computation process. Therefore, Fisher’s optimal two-segmentation method is generally used in practical problems instead, which avoids storage problems. But it is prone to local optima. Based on the analysis of the shortcomings of the Fisher optimal segmentation and optimal two-segmentation algorithms, this paper proposes a genetic-based machine learning clustering algorithm, which overcomes the problem of Fisher’s optimal two-segmentation algorithm being prone to local optima and also solves the problem of high storage requirements during the computation process of Fisher’s optimal segmentation method. The application of this algorithm in the optimization system of water environment monitoring points shows that it is effective.
DPDCD: The Intelligent Prediction Strategy for the Course Development of Diabetic Kidney Disease
Authors: Wang Jianfeng , Li Guozhi , Li Jirui , Liu Liang
Published: September 2024

Diabetic nephropathy (DKD) is a severe diabetes complication and a leading cause of mortality. While urine microalbuminuria and eGFR are common markers, some advanced cases may exhibit normal values. The progression from microalbuminuria to overt proteinuria in DKD is gradual and requires comprehensive physiological tests for diagnosis. This study employs deep reinforcement learning to predict DKD progression based on patients’ physiological data, aiming to assist clinical diagnosis and treatment. A multivariate logistic regression algorithm is used to describe the DKD prediction probability. An optimized DKD progression prediction algorithm (DPDCD) based on deep reinforcement learning determines the best model coefficients, enabling accurate prediction of DKD progression using routine clinical data. Experimental results demonstrate that DPDCD outperforms other algorithms in predicting DKD progression, providing valuable support for clinicians.
Node Importance Estimation for Knowledge Graphs Based on Multi-Perspectives Attention Fusion Mechanism
Authors: Ge Kao , Han Qing-Bang
Published: September 2024

The advent of the Internet has led to the emergence of multi-source heterogeneous knowledge graphs, which have become a crucial means of storing and disseminating knowledge. Node importance estimation is a technique that is employed extensively in a number of fields, including recommender systems, intelligent search and resource allocation. This study introduces a Multi-perspective attention mechanism Fusion Algorithm for the mapping of multi-perspective features of knowledge graphs to Node Importance Estimation (MFA-NIE). First, structural embedding features, relational predicate features, and attribute features (both textual and quantitative) are established for the nodes. Subsequently, an enhanced attention mechanism is employed to extract, compress, and fuse these features. The fused hidden layer vector is employed in the design of a key-based attention mechanism, which enables the propagation of messages from neighboring nodes to the source node. This process results in the iterative updating of the source node’s hidden features. Finally, TOPSIS centrality, based on the topology of the source node, is employed to dynamically adjust the mapping between the fused features and node importance. Experiments were conducted on real-world, large-scale, multi-source, heterogeneous knowledge graphs, comparing the MFA-NIE algorithm with traditional and advanced baseline algorithms, including PR, PPR, GENI, RGTN, CLINE, MCRL, and others. The results demonstrate that the MFA-NIE algorithm significantly improves effectiveness and accuracy, showcasing its superiority, versatility, and practical application value.
Constructing a Nondegenerate m-Dimensional Integer-Domain Chaotic Map Model over GF(2n) with Application in PRNG
Authors: Xu Dongya , Liu Hongjun
Published: September 2024

To address the problem of dynamic degradation over a finite-precision platform of chaotic maps and the reversibility of linear chaotic maps, we propose an improved model over GF([Formula: see text]) that is called the nondegenerate m-Dimensional [Formula: see text] Integer-Domain Chaotic Maps (mD-IDCMs). This model incorporates modular exponentiation operation, and is capable of constructing nondegenerate IDCMs of any dimension. Moreover, we prove the irreversibility of mD-IDCM and analyze its chaotic behaviors in terms of positive Lyapunov Exponents (LEs). The results of theoretical analysis show that the proposed mD-IDCM model can obtain the desired positive LEs by appropriately configuring its coefficient matrix. Then, we present two instances, and analyze their LEs, Kolmogorov entropy, Sample entropy, Correlation dimension, and the dynamic analysis indicates that the chaotic map constructed by mD-IDCM has ergodicity within a sufficiently large chaotic range. Finally, we design a Pseudo-Random Number Generator (PRNG) with a key to verify the practicability of the mD-IDCM.
Lie Rota–Baxter operators on the Sweedler algebra H4
Authors: Bardakov Valeriy G. , Nikonov Igor M. , Zhelaybin Viktor N.
Published: September 2024

If A is an associative algebra, then we can define the adjoint Lie algebra [Formula: see text] and Jordan algebra [Formula: see text]. It is easy to see that any associative Rota–Baxter (RB) operator on A induces a Lie and Jordan RB operator on [Formula: see text] and [Formula: see text], respectively. Are there Lie (Jordan) RB operators, which are not associative RB operators? In this paper, we explore these questions for the Sweedler algebra [Formula: see text], which is a 4-dimensional non-commutative Hopf algebra. More precisely, we describe the RB operators on the adjoint Lie algebra [Formula: see text].
MC-biquandles and MC-biquandle coloring quivers
Authors: Choi Seonmi , Nelson Sam
Published: September 2024

We introduce the notion of mc-biquandles, algebraic structures which have possibly distinct biquandle operations at single-component and multi-component crossings. These structures provide computable homset invariants for classical and virtual links. We categorify these homsets to obtain mc-biquandle coloring quivers and define several new link invariants via decategorification from these invariant quivers.
OPTIMIZED ELECTROLESS NICKEL DEPOSITION ON MILD STEEL USING LAWSONE AS COMPLEXING AGENT
Authors: THABASSOOM H. ASIA , FLORENCE J. FELICITA
Published: September 2024

Lawsonia inermis Linn Leaf extract has been evaluated in alkaline solutions for its influence on electroless Ni–P alloy deposition on mild steel. During electroless plating, the effect of experimental processing conditions on deposition rate is investigated. Bath compositions and operational parameters have been optimized. The findings indicate that increasing the volume of additives from 0.5[Formula: see text]mL to 5[Formula: see text]mL improves the inclusion rate of electroless Ni–P alloy coating. In the absence of a complexing agent, additive Lawsone exhibits additional structural features and phases. The deposition rate and the corrosion rate of the Ni–P deposits were estimated, respectively, from the weight loss after a period of time. It is recommended to replace sodium citrate with Lawsone. Lawsone’s effect acting as a complexing agent on Ni–P electroless coating was explored utilizing SEM, EDAX, AFM, FTIR, and XRD, as well as corrosion investigations. Polarization and electrochemical impedance spectroscopy are used to assess the corrosion efficiency.
Dynamic Intelligent Analysis of Single-Column Pier Bridges Considering Vehicle–Bridge Interaction
Authors: Cao Liang , Zhou Hailei , Liu Jiepeng , Frank Chen Y.
Published: September 2024

With its advantages of low engineering cost, minimal space occupancy, and aesthetically pleasing shape, the single-column pier bridge is frequently utilized in urban viaducts and interchange ramps. Due to structural peculiarities, the bridge subjected to eccentric loading, flexural and torsional interaction, and centrifugal force may undergo tipping accidents. To better comprehend the overturning resistance of the bridge, the vibration governing equation of the bridge (idealized as an orthotropic thin plate with point supports in the domain model) is established considering vehicle–bridge interaction. An approximate dynamic analysis formula is subsequently derived via a hybrid method using the grey wolf optimizer. Compared with the outcomes of finite element analysis, the maximal deviation arising from this derived analytical formula is approximately [Formula: see text]% with the greater discrepancy being observed in scenarios featuring minimal support reactions. Compared with the finite element method, the proposed method in this study has higher computational efficiency with precision. This analytical solution is then utilized in the parametric study to scrutinize the influences of span, road surface roughness, and vehicle velocity on a single-column pier bridge under four vehicles. The analysis findings indicate that the proposed approach is reasonably precise, remarkably efficient, and efficacious.
The United Nations Sustainable Development Goal 13 Climate Action and Efforts of UK Banks Toward a Low Carbon Economy?
Authors: L. Harris Wesley , Wonglimpiyarat Jarunee
Published: September 2024

Banks are increasingly motivated to align their business operations with the United Nations Sustainable Development Goals (SDGs) as this can help them attract investors and build a more loyal customer base in the long run. This paper explores whether major UK banks (HSBC Holdings Plc, Lloyds Banking Group Plc, Barclays Plc, Standard Chartered Plc, and NatWest Group Plc) are increasing their involvement in green banking by transitioning away from fossil fuels. The research gathered expert and policymaker views on decarbonization (reducing carbon footprint) at the Scotland Policy Conference UK in 2022 to support its findings. The results reveal slow progress toward a green banking regime, as the banks continued underwriting fossil fuel IPOs and bond issuances, and funding carbon-intensive industries. Their portfolios primarily consist of fossil fuel projects, with implications for insufficient effort toward meeting UN SDGs related to global climate commitments. The study proposes a policy recommendation: governments should introduce regulations requiring banks to develop a minimum percentage of financial products and services designed to support SDG-related activities, fostering a low-carbon economy.
Machine Learning Coffee Price Predictions
Authors: Jin Bingzi , Xu Xiaojie
Published: September 2024

Most market players have found great significance in price projections for basic agricultural commodities for a substantial duration. We look at the daily price of coffee that is released in this research in order to tackle the issue. The analytical sample runs from 2 January 2013 to 10 April 2024, a period of more than 12 years. A significant influence on the business sector comes from the price series under investigation. Specifically, in this particular situation, Gaussian process regression models are developed using Bayesian optimization techniques and cross-validation processes. Thus, this circumstance prompts the development of price forecasting methodologies. Using our empirical forecasting technique, we produce relatively accurate price projections for the out-of-sample assessment period, which runs from 3 January 2022 to 10 April 2024. It was found that price forecasts of coffee had a relative root mean square error of 2.0500%. With the availability of price forecasting models, investors and governments can make educated decisions about the coffee market given that they have access to the required data.
China’s Geopolitical Strategy: Crafting a New World Order
Authors: Ul Mansoor Sheikh Inam
Published: September 2024

In the intricate tapestry of global geopolitics, China’s ascent as a potential preeminent power in a new world order has become a compelling narrative. This paper explores Beijing’s multifaceted rise by delving into the diplomatic, economic, technological, cultural, and environmental dimensions of the nation’s ascent. China’s adept diplomacy, exemplified by its active participation in international organizations, introduces profound implications for global governance and cooperation. Economically, its transformation from an agrarian society to the world’s second-largest economy redefines the dynamics of international trade and development, epitomized by the far-reaching Belt and Road Initiative. China’s technological prowess, particularly in fields like artificial intelligence and 5G technology, reshapes the landscape of innovation, while the influence of its soft power through cultural exports and educational outreach resonates across borders. The ethical and moral dimensions of global governance are at the forefront of this narrative, with questions of universal values and human rights taking center stage. China’s commitment to environmental sustainability and its role in addressing climate change also have profound implications for global environmental protection. Looking ahead, two potential scenarios, a bipolar world order and a multipolar world order, come into focus, each with its own implications for international relations, trade, technology, and environmental cooperation. In this evolving global narrative, international organizations adapt to accommodate shifting power dynamics, emphasizing the need for equitable representation and multilateral collaboration. Ultimately, the paper underscores the interplay of nations in the recalibration of the global order, urging proactive engagement and foresight in navigating the complex terrain of China’s rise and its impact on the future of international relations.
Association of body mass index and perinatal outcomes of women delivering at advanced maternal age
Authors: Yan Guimin , Dong Ying , Yu Xiaojuan , Zhang Lin , Zhao Xin , Zhou Jing , Du Yan , Wang Ling
Published: September 2024

Purpose: To explore the association between body mass index (BMI) and perinatal outcomes of women delivering at advanced maternal age (AMA).

Patients and methods: In this retrospective study, 261 Chinese women aged 35 and above who delivered from January 1st to June 30th, 2021 at Shanghai Putuo Maternity and Infant Health Hospital were enrolled. Information including height, pre-pregnancy and prenatal weight, and maternal and neonatal complications was recorded. Women were classified into the normal group ([Formula: see text][Formula: see text]kg/m2, [Formula: see text], 82.4%) and overweight group (BMI: [Formula: see text][Formula: see text]kg/m2, [Formula: see text], 17.6%) based on their pre-pregnancy BMI. Women were further categorized based on their BMI gains during pregnancy: group A (BMI gain [Formula: see text][Formula: see text]kg/m2) and group B (BMI gain [Formula: see text][Formula: see text]kg/m2). The association of perinatal outcomes and pre-pregnancy BMI was investigated based on BMI gain strata.

Results: Of women in the overweight group, the risk of gestational diabetes mellitus (GDM) and cesarean section (CS) were significantly higher than that of the normal group ([Formula: see text]). Furthermore, we found overweight women had a higher risk of CS when their BMI gain was [Formula: see text][Formula: see text]kg/m2 and a higher incidence of GDM when their BMI gain [Formula: see text][Formula: see text]kg/m2 (both [Formula: see text]).

Conclusion: Adverse perinatal complications that occur in AMA women are associated with overweight and excessive BMI gain during pregnancy. Effective management of weight around pregnancy may reduce adverse outcomes.
Geothermal resources of Hungary: a play-based review
Authors: Nádor Annamária , Lenkey László
Published: September 2024

In this paper we review the geothermal resources of Hungary using the geothermal play concept and its scale-dependent hierarchical categorization. The Pannonian Basin belongs to an extensional domain play type, with two main plays: the porous Neogene basin-fill sediments and the fractured–karstified Paleozoic and Mesozoic basement rocks. Both plays can be divided into hydrostatic and overpressured play segments.

The most widely utilized play level in the hydrostatic segment of the porous Neogene sediment play comprises laterally extended, highly permeable sandstone layers storing low-salinity thermal water with average flow rate of
c.
900 l min
−1
and temperatures between 50 and 100°C in the depth range of 1–2 km.

The other prosperous play level is represented by the fractured–karstified Mesozoic carbonates in the hydrostatic segment of the basement play having elevated temperatures (90–120°C) and several thousand l min
−1
flow rates. The play level related to fractured Paleozoic rocks is less important due to its limited extent. The overpressured segment of the basement play may have high flow rates but it is still an untapped resource as exploitation is limited by high overpressure, high temperatures (170–200°C) and highly saline hot waters.
Understanding the performance of machine learning models from data- to patient-level
Authors: Valeriano Maria Gabriela , Matran-Fernandez Ana , Kiffer Carlos , Lorena Ana Carolina
Published: September 2024

Machine Learning (ML) models have the potential to support decision-making in healthcare by grasping complex patterns within data. However, decisions in this domain are sensitive and require active involvement of domain specialists with deep knowledge of the data. To address this task, clinicians need to understand how predictions are generated so they can provide feedback for model refinement. There is usually a gap in the communication between data scientists and domain specialists that needs to be addressed. Specifically, many ML studies are only concerned with presenting average accuracies over an entire dataset, losing valuable insights that can be obtained at a more fine-grained patient-level analysis of classification performance. In this article, we present a case study aimed at explaining the factors that contribute to specific predictions for individual patients. Our approach takes a data-centric perspective, focusing on the structure of the data and its correlation with ML model performance. We utilize the concept of
Instance Hardness
, which measures the level of difficulty an instance poses in being correctly classified. By selecting the hardest and easiest to classify instances, we analyze and contrast the distributions of specific input features and extract meta-features to describe each instance. Furthermore, we individually examine certain instances, offering valuable insights into why they offer challenges for classification, enabling a better understanding of both the successes and failures of the ML models. This opens up the possibility for discussions between data scientists and domain specialists, supporting collaborative decision-making.
LTP-MMF: Toward Long-Term Provider Max-Min Fairness under Recommendation Feedback Loops
Authors: Xu Chen , Ye Xiaopeng , Xu Jun , Zhang Xiao , Shen Weiran , Wen Ji-Rong
Published: September 2024

Multi-stakeholder recommender systems involve various roles, such as users and providers. Previous work pointed out that max-min fairness (MMF) is a better metric to support weak providers. However, when considering MMF, the features or parameters of these roles vary over time, and how to ensure long-term provider MMF has become a significant challenge. We observed that recommendation feedback loops (RFL) will influence the provider MMF greatly in the long term. RFL means that recommender systems can only receive feedback on exposed items from users and update recommender models incrementally based on this feedback. When utilizing the feedback, the recommender model will regard the unexposed items as negative. In this way, the tail provider will not get the opportunity to be exposed, and its items will always be considered negative samples. Such phenomena will become more and more serious in RFL. To alleviate the problem, this article proposes an online ranking model named Long-Term Provider Max-min Fairness (LTP-MMF). Theoretical analysis shows that the long-term regret of LTP-MMF enjoys a sub-linear bound. Experimental results on three public recommendation benchmarks demonstrated that LTP-MMF can outperform the baselines in the long term.
Interpretable Failure Localization for Microservice Systems Based on Graph Autoencoder
Authors: Sun Yongqian , Lin Zihan , Shi Binpeng , Zhang Shenglin , Ma Shiyu , Jin Pengxiang , Zhong Zhenyu , Pan Lemeng , Guo Yicheng , Pei Dan
Published: September 2024

Accurate and efficient localization of root cause instances in large-scale microservice systems is of paramount importance. Unfortunately, prevailing methods face several limitations. Notably, some recent methods rely on supervised learning which necessitates a substantial amount of labeled data. However, labeling root cause instances is time-consuming and laborious, especially with multiple modalities of data including logs, traces, metrics, and so on. Moreover, some approaches favor deep learning for localization but lack interpretability and continuous improvement mechanisms.

To address the above challenges, we propose
DeepHunt
, a novel root cause localization method based on multimodal data analysis. Firstly,
DeepHunt
introduces root cause score (RCS) by integrating reconstruction errors and failure propagation patterns (upstream–downstream relationships), imparting interpretability to the localization of root causes. Then, it embraces graph autoencoder (GAE) to address the limitation imposed by scarce labeled data. It employs data augmentation to mitigate the adverse effects of insufficient historical training samples. We evaluate
DeepHunt
on two open source datasets, and it outperforms existing methods when facing a zero-label cold start.
DeepHunt
can be further improved by continuously fine-tuning through a feedback mechanism.
Demo2Test: Transfer Testing of Agent in Competitive Environment with Failure Demonstrations
Authors: Chen Jianming , Wang Yawen , Wang Junjie , Xie Xiaofei , Wang Dandan , Wang Qing , Xu Fanjiang
Published: September 2024

The competitive game between agents exists in many critical applications, such as military unmanned aerial vehicles. It is urgent to test these agents to reduce the significant losses caused by their failures. Existing studies mainly are to construct a testing agent that competes with the target agent to induce its failures. These approaches usually focus on a single task, requiring much more time for multi-task testing. However, if the previously tested tasks (source tasks) and the task to be tested (target task) share similar agents or task objectives, the transferable knowledge in source tasks can potentially increase the effectiveness of testing in the target task. We propose Demo2Test for conducting transfer testing of agents in the competitive environment, i.e., leveraging the demonstrations of failure scenarios from the source task to boost the testing effectiveness in the target task. It trains a testing agent with demonstrations and incorporates the action perturbation at key states to balance the number of revealed failures and their diversity. We conduct experiments in the simulated robotics competitive environments of MuJoCo. The results indicate that Demo2Test outperforms the best-performing baseline with improvements ranging from

\(22.38\%\)

to

\(87.98\%\)

, and

\(12.69\%\)

to

\(60.98\%\)

, in terms of the number and diversity of discovered failure scenarios, respectively.
Self-Assembly and Transport Phenomena of Colloids: Confinement and Geometrical Effects
Authors: Solano-Cabrera César O. , Castro-Villarreal Pavel , Moctezuma Rosario E. , Donado Fernando , Conrad Jacinta C. , Castañeda-Priego Ramón
Published: September 2024

AbstractColloidal dispersions exhibit rich equilibrium and nonequilibrium thermodynamic properties, self-assemble into diverse structures at different length scales, and display transport behavior under bulk conditions. In confinement or under geometrical restrictions, new phenomena emerge that have no counterpart when the colloids are embedded in an open, noncurved space. In this review, we focus on the effects of confinement and geometry on the self-assembly and transport of colloids and fluidized granular systems, which serve as model systems. Our goal is to summarize experiments, theoretical approximations and molecular simulations that provide physical insight on the role played by the geometry at the mesoscopic scale. We highlight particular challenges, and show preliminary results based on the covariant Smoluchowski equation, that present promising avenues to study colloidal dynamics in a non-Euclidean geometry.
Mechanisms Underlying Ophiocordyceps Infection and Behavioral Manipulation of Ants: Unique or Ubiquitous?
Authors: van Roosmalen Emmeline , de Bekker Charissa
Published: September 2024

Parasite manipulation of host behavior, as an effective strategy to establish transmission, has evolved multiple times across taxa, including fungi. Major strides have been made to propose molecular mechanisms that underlie manipulative parasite-host interactions including the manipulation of carpenter ant behavior by Ophiocordyceps. This research suggests that the secretion of parasite proteins and light-driven biological rhythms are likely involved in the infection and manipulation biology of Ophiocordyceps and other manipulating parasites. Here, we discuss research on Ophiocordyceps considering findings from other (fungal) parasites that either are relatively closely related (e.g., other insect- and plant-infecting Hypocreales) or also manipulate insect behavior (e.g., Entomophthorales). As such, this review aims to put forward this question: Are the mechanisms behind Ophiocordyceps manipulation and infection unique, or did they convergently evolve? From this discussion, we pose functional hypotheses about the infection biology of Ophiocordyceps that will need to be addressed in future studies.
Cyclic Diguanylate in the Wild: Roles During Plant and Animal Colonization
Authors: Isenberg Ruth Y. , Mandel Mark J.
Published: September 2024

Cyclic diguanylate (c-di-GMP) is a near-ubiquitous signaling molecule that regulates the motility-to-sessility transition in many bacterial species. Among the phenotypes influenced by c-di-GMP are biofilm formation, motility, cell cycle, and virulence. The hallmark phenotypes regulated by c-di-GMP—biofilm formation and motility—are key determinants of host–bacterial interactions. A large body of research has identified the roles of c-di-GMP in regulating phenotypes in culture. While numerous studies have investigated roles for c-di-GMP during the establishment and maintenance of pathogenic host–bacterial associations, considerably less attention has been devoted to defining the roles of c-di-GMP during beneficial and commensal associations. This review describes the known roles of c-di-GMP in regulating phenotypes that contribute to host colonization, with a focus on knowledge gaps and future prospects for examining c-di-GMP during beneficial colonization.
Study on Attenuation Mechanism and Durability Improvement of Platinum-Carbon Catalysts for Proton Exchange Membrane Fuel Cells
Authors: Chen Ligang , Zhao Wei , Ren Yuchen , Liu Zhiyang , Zhang Luosha , An Ziying , Jia Yin , Li Chunji , Xu Min , Zhang Ningyuan , Zhang Yu , Xie Kangjun , Chai Maorong
Published: September 2024

The relationship between Pt particle size and activity and durability of Pt/C electrocatalysts on rotating disk electrodes is established and the results show that Pt/C electrocatalysts with particle size over 3.5 nm have excellent resistance to Pt particle decay and carbon corrosion. Further, the research results on the mechanism of Pt particle decay and carbon corrosion show that the Pt particle attenuation is composed of 80% Ostwald ripening and 20% particle agglomeration, and the carbon corrosion is affected by the catalytic action of Pt particles. Therefore, the above results show that regulating the Pt particle size to 3.5–4.0 nm can improve the durability of Pt/C electrocatalysts on RDE. To verify the accuracy of this conclusion and determine the optimal particle size range in practical application, single cells with 5 × 5 cm2 is assembled to evaluate the performance and durability of cathode Pt/C electrocatalysts under H2/air. The results show that cathode Pt/C electrocatalysts with particle size between 3.5 nm and 3.8 nm have high single cell performance (2.3 A cm−2@0.65 V) and durability (A loss of 15 mV@0.8 A cm−2 after 30000 cycles). These findings reveal the attenuation mechanism of Pt/C electrocatalysts and provide ideas for the development of high-durability Pt-based electrocatalysts for practical applications.
Transformer Inrush Current and Internal Fault Discrimination Using Multitypes of Convolutional Neural Network Techniques
Authors: Athamneh Abedalgany Abedallah , Alqudah Ali Mohammad
Published: September 2024

To maintain the security of transformer differential protection, it is essential to restrain its response to oversetting differential current caused by the inrush current or other switching conditions. This paper presents a new proposed method to discriminate the transformer’s internal fault from the inrush current; the discrimination process is based on a convolutional neural network (CNN) with a combination of the higher order spectral estimations that perform a deep learning classification with high accuracy. This research succeeded in proposing two robust and efficient CNN models; the first one is the 1D CNN, which takes the sole signal without any transformation, while the second model is the 2D CNN, which takes the short‐time Fourier transform of the signal. Both developed models are light and have the minimum number of layers that can achieve a very high performance. The performance of the proposed models is tested using a dataset prepared according to laboratory‐measured transformer parameters. The results are compared with other well‐known methods, and the achievement of high numerical performance evaluation validates the consistency of the proposed methods.
Supplementary Table S6 from A Phase II Placebo-Controlled Study of the Effect and Safety of Nanvuranlat in Patients with Advanced Biliary Tract Cancers Previously Treated by Systemic Chemotherapy
Authors: Furuse Junji , Ikeda Masafumi , Ueno Makoto , Furukawa Masayuki , Morizane Chigusa , Takehara Tetsuo , Nishina Tomohiro , Todaka Akiko , Okano Naohiro , Hara Kazuo , Nakai Yousuke , Ohkawa Kazuyoshi , Sasaki Takashi , Sugimori Kazuya , Yokoyama Naoyuki , Yamamoto Kouji
Published: September 2024

Supplementary Table S6. Treatment-related Adverse events (All grade, ≥Grade 3).
Supplementary Figures S1-7 from Anti–4-1BB×PDL1 Bispecific Antibody Reinvigorates Tumor-Specific Exhausted CD8+ T Cells and Enhances the Efficacy of Anti-PD1 Blockade
Authors: Jeon Seung Hyuck , You Gihoon , Park Junsik , Chung Youseung , Park Kyungjin , Kim Hyunjoo , Jeon Jaehyoung , Kim Youngkwang , Son Woo-Chan , Jeong Da Som , Shin Eui-Cheol , Lee Jung-Yun , Han Dai Hoon , Jung Jaeho , Park Su-Hyung
Published: September 2024

Supplementary Figures S1-7
Supplementary Table S1 from A Phase II Placebo-Controlled Study of the Effect and Safety of Nanvuranlat in Patients with Advanced Biliary Tract Cancers Previously Treated by Systemic Chemotherapy
Authors: Furuse Junji , Ikeda Masafumi , Ueno Makoto , Furukawa Masayuki , Morizane Chigusa , Takehara Tetsuo , Nishina Tomohiro , Todaka Akiko , Okano Naohiro , Hara Kazuo , Nakai Yousuke , Ohkawa Kazuyoshi , Sasaki Takashi , Sugimori Kazuya , Yokoyama Naoyuki , Yamamoto Kouji
Published: September 2024

Supplementary Table S1. Representativeness of Study Participants.
Supplementary Video S1 from Low-Dose Ionizing γ-Radiation Elicits the Extrusion of Neutrophil Extracellular Traps
Authors: Teijeira Alvaro , Garasa Saray , Ochoa Maria C. , Sanchez-Gregorio Sandra , Gomis Gabriel , Luri-Rey Carlos , Martinez-Monge Rafael , Pinci Beatrice , Valencia Karmele , Palencia Belen , Barbés Benigno , Bolaños Elixabet , Azpilikueta Arantza , García-Cardosa Marina , Burguete Javier , Eguren-Santamaría Iñaki , Garate-Soraluze Eneko , Berraondo Pedro , Perez-Gracia Jose L. , de Andrea Carlos E. , Rodriguez-Ruiz Maria E. , Melero Ignacio
Published: September 2024

Supplementary Video S1. Time-lapse confocal microscopy showing the induction of NETosis by low-dose y-irradiation of human neutrophils. 3D reconstruction of a z-stack time-lapse acquisition of pre-stained neutrophils (red) treated with the indicated radiation doses and followed over time by time-lapse confocal microscopy. Extracellular DNA (green) was stained by Sytox Green present in the cell culture media.
Supplementary Figure S4 from A Phase II Placebo-Controlled Study of the Effect and Safety of Nanvuranlat in Patients with Advanced Biliary Tract Cancers Previously Treated by Systemic Chemotherapy
Authors: Furuse Junji , Ikeda Masafumi , Ueno Makoto , Furukawa Masayuki , Morizane Chigusa , Takehara Tetsuo , Nishina Tomohiro , Todaka Akiko , Okano Naohiro , Hara Kazuo , Nakai Yousuke , Ohkawa Kazuyoshi , Sasaki Takashi , Sugimori Kazuya , Yokoyama Naoyuki , Yamamoto Kouji
Published: September 2024

Supplementary Figure S4. Progression-free survival (PFS) in high and low LAT1 groups.
Supplementary Table 1 from Platinum-Based Chemotherapy Induces Opposing Effects on Immunotherapy Response-Related Spatial and Stromal Biomarkers in the Bladder Cancer Microenvironment
Authors: Chelushkin Maksim A. , van Dorp Jeroen , van Wilpe Sandra , Seignette Iris M. , Mellema Jan-Jaap J. , Alkemade Maartje , Gil-Jimenez Alberto , Peters Dennis , Brugman Wim , Stockem Chantal F. , Hooijberg Erik , Broeks Annegien , van Rhijn Bas W.G. , Mertens Laura S. , van der Heijden Antoine G. , Mehra Niven , van Montfoort Maurits L. , Wessels Lodewyk F.A. , Vis Daniel J. , van der Heijden Michiel S.
Published: September 2024

Supplementary Table S1. Representativeness of Study Participants
Supplementary Data 1 from Platinum-Based Chemotherapy Induces Opposing Effects on Immunotherapy Response-Related Spatial and Stromal Biomarkers in the Bladder Cancer Microenvironment
Authors: Chelushkin Maksim A. , van Dorp Jeroen , van Wilpe Sandra , Seignette Iris M. , Mellema Jan-Jaap J. , Alkemade Maartje , Gil-Jimenez Alberto , Peters Dennis , Brugman Wim , Stockem Chantal F. , Hooijberg Erik , Broeks Annegien , van Rhijn Bas W.G. , Mertens Laura S. , van der Heijden Antoine G. , Mehra Niven , van Montfoort Maurits L. , Wessels Lodewyk F.A. , Vis Daniel J. , van der Heijden Michiel S.
Published: September 2024

Processed mIF and IHC data and RNA-seq-based signature scores
Supplemental Figure S5 from Tumor-Localized Interleukin-2 and Interleukin-12 Combine with Radiation Therapy to Safely Potentiate Regression of Advanced Malignant Melanoma in Pet Dogs
Authors: Stinson Jordan A. , Barbosa Matheus Moreno P. , Sheen Allison , Momin Noor , Fink Elizabeth , Hampel Jordan , Selting Kim A. , Kamerer Rebecca L. , Bailey Keith L. , Wittrup Karl D. , Fan Timothy M.
Published: September 2024

Supplemental Figure S5. Biologic antitumor response to cytokine-only treatment cohort.
Source data 1 from Novel ERBB2 Variant Potentially Associated with Resistance against Anti-HER2 Monoclonal Antibody–Based Therapy in ERBB2-Amplified Metastatic Colorectal Cancer
Authors: Iida Naoko , Imai Mitsuho , Okamoto Wataru , Kato Takeshi , Esaki Taito , Kato Ken , Komatsu Yoshito , Yuki Satoshi , Masuishi Toshiki , Nishina Tomohiro , Ebi Hiromichi , Taniguchi Hiroya , Nonomura Norio , Sunakawa Yu , Shiozawa Manabu , Yamazaki Kentaro , Boku Shogen , Bando Hideaki , Shiraishi Yuichi , Kobayashi Maki , Goto Hiroki , Sato Akihiro , Fujii Satoshi , Yoshino Takayuki , Nakamura Yoshiaki
Published: September 2024

Source data
Supplementary Figure S2 from Interim Phase II Results Using Panitumumab-IRDye800CW during Transoral Robotic Surgery in Patients with Oropharyngeal Cancer
Authors: Stone Logan D. , Kasten Benjamin B. , Rao Shilpa , Gonzalez Manuel. L. , Stevens Todd M. , Lin Diana , Carroll William , Greene Benjamin , Moore Lindsay S. , Fuson Andrew , James Sherin , Hartman Yolanda E. , McCammon Susan , Panuganti Bharat , Nabell Lisle M. , Li Yufeng , Li Mei , Bailey Luke , Rosenthal Eben L. , Jeyarajan Harishanker , Thomas Carissa M. , Warram Jason M.
Published: September 2024

Supplementary Figure S2: Additional characterization of bipolar forceps instrument and normal tissue fluorescence signals without the tumor in view.