Performance Gap Research Articles

The CoachAI Badminton Environment: A Novel Reinforcement Learning Environment with Realistic Opponents (Student Abstract)

The growing demand for precise sports analysis has been explored to improve athlete performance in various sports (e.g., basketball, soccer). However, existing methods for different sports face challenges in validating strategies in environments due to simple rule-based opponents leading to performance gaps when deployed in real-world matches. In this paper, we propose the CoachAI Badminton Environment, a novel reinforcement learning (RL) environment with realistic opponents for badminton, which serves as a compelling example of a turn-based game. It supports researchers in exploring various RL algorithms with the badminton context by integrating state-of-the-art tactical-forecasting models and real badminton game records. The Badminton Benchmarks are proposed with multiple widely adopted RL algorithms to benchmark the performance of simulating matches against real players. To advance novel algorithms and developments in badminton analytics, we make our environment open-source, enabling researchers to simulate more complex badminton sports scenarios based on this foundation. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI%20Badminton%20Environment.

The CoachAI Badminton Environment: Bridging the Gap between a Reinforcement Learning Environment and Real-World Badminton Games

We present the CoachAI Badminton Environment, a reinforcement learning (RL) environment tailored for AI-driven sports analytics. In contrast to traditional environments using rule-based opponents or simplistic physics-based randomness, our environment integrates authentic opponent AIs and realistic randomness derived from real-world matches data to bridge the performance gap encountered in real-game deployments. This novel feature enables RL agents to seamlessly adapt to genuine scenarios. The CoachAI Badminton Environment empowers researchers to validate strategies in intricate real-world settings, offering: i) Realistic opponent simulation for RL training; ii) Visualizations for evaluation; and iii) Performance benchmarks for assessing agent capabilities. By bridging the RL environment with actual badminton games, our environment is able to advance the discovery of winning strategies for players. Our code is available at https://github.com/wywyWang/CoachAI-Projects/tree/main/Strategic%20Environment.

Convolutional Channel-Wise Competitive Learning for the Forward-Forward Algorithm

The Forward-Forward (FF) Algorithm has been recently proposed to alleviate the issues of backpropagation (BP) commonly used to train deep neural networks. However, its current formulation exhibits limitations such as the generation of negative data, slower convergence, and inadequate performance on complex tasks. In this paper we take the main ideas of FF and improve them by leveraging channel-wise competitive learning in the context of convolutional neural networks for image classification tasks. A layer-wise loss function is introduced that promotes competitive learning and eliminates the need for negative data construction. To enhance both the learning of compositional features and feature space partitioning, a channel-wise feature separator and extractor block is proposed that complements the competitive learning process. Our method outperforms recent FF-based models on image classification tasks, achieving testing errors of 0.58%, 7.69%, 21.89%, and 48.77% on MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 respectively. Our approach bridges the performance gap between FF learning and BP methods, indicating the potential of our proposed approach to learn useful representations in a layer-wise modular fashion, enabling more efficient and flexible learning. Our source code and supplementary material are available at https://github.com/andreaspapac/CwComp.

Learning Encodings for Constructive Neural Combinatorial Optimization Needs to Regret

Deep-reinforcement-learning (DRL) based neural combinatorial optimization (NCO) methods have demonstrated efficiency without relying on the guidance of optimal solutions. As the most mainstream among them, the learning constructive heuristic (LCH) achieves high-quality solutions through a rapid autoregressive solution construction process. However, these LCH-based methods are deficient in convergency, and there is still a performance gap compared to the optimal. Intuitively, learning to regret some steps in the solution construction process is helpful to the training efficiency and network representations. This article proposes a novel regret-based mechanism for an advanced solution construction process. Our method can be applied as a plug-in to any existing LCH-based DRL-NCO method. Experimental results demonstrate the capability of our work to enhance the performance of various NCO models. Results also show that the proposed LCH-Regret outperforms the previous modification methods on several typical combinatorial optimization problems. The code and Supplementary File are available at https://github.com/SunnyR7/LCH-Regret.

Combinatorial Stochastic-Greedy Bandit

We propose a novel combinatorial stochastic-greedy bandit (SGB) algorithm for combinatorial multi-armed bandit problems when no extra information other than the joint reward of the selected set of n arms at each time step t in [T] is observed. SGB adopts an optimized stochastic-explore-then-commit approach and is specifically designed for scenarios with a large set of base arms. Unlike existing methods that explore the entire set of unselected base arms during each selection step, our SGB algorithm samples only an optimized proportion of unselected arms and selects actions from this subset. We prove that our algorithm achieves a (1-1/e)-regret bound of O(n^(1/3) k^(2/3) T^(2/3) log(T)^(2/3)) for monotone stochastic submodular rewards, which outperforms the state-of-the-art in terms of the cardinality constraint k. Furthermore, we empirically evaluate the performance of our algorithm in the context of online constrained social influence maximization. Our results demonstrate that our proposed approach consistently outperforms the other algorithms, increasing the performance gap as k grows.

Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding

Incomplete multi-view clustering becomes an important research problem, since multi-view data with missing values are ubiquitous in real-world applications. Although great efforts have been made for incomplete multi-view clustering, there are still some challenges: 1) most existing methods didn't make full use of multi-view information to deal with missing values; 2) most methods just employ the consistent information within multi-view data but ignore the complementary information; 3) For the existing incomplete multi-view clustering methods, incomplete multi-view representation learning and clustering are treated as independent processes, which leads to performance gap. In this work, we proposed a novel Incomplete Contrastive Multi-View Clustering method with high-confidence guiding (ICMVC). Firstly, we proposed a multi-view consistency relation transfer plus graph convolutional network to tackle missing values problem. Secondly, instance-level attention fusion and high-confidence guiding are proposed to exploit the complementary information while instance-level contrastive learning for latent representation is designed to employ the consistent information. Thirdly, an end-to-end framework is proposed to integrate multi-view missing values handling, multi-view representation learning and clustering assignment for joint optimization. Experiments compared with state-of-the-art approaches demonstrated the effectiveness and superiority of our method. Our code is publicly available at https://github.com/liunian-Jay/ICMVC. The version with supplementary material can be found at http://arxiv.org/abs/2312.08697.

Analyzing the Impact of Personalization on Fairness in Federated Learning for Healthcare.

As machine learning (ML) usage becomes more popular in the healthcare sector, there are also increasing concerns about potential biases and risks such as privacy. One countermeasure is to use federated learning (FL) to support collaborative learning without the need for patient data sharing across different organizations. However, the inherent heterogeneity of data distributions among participating FL parties poses challenges for exploring group fairness in FL. While personalization within FL can handle performance degradation caused by data heterogeneity, its influence on group fairness is not fully investigated. Therefore, the primary focus of this study is to rigorously assess the impact of personalized FL on group fairness in the healthcare domain, offering a comprehensive understanding of how personalized FL affects group fairness in clinical outcomes. We conduct an empirical analysis using two prominent real-world Electronic Health Records (EHR) datasets, namely eICU and MIMIC-IV. Our methodology involves a thorough comparison between personalized FL and two baselines: standalone training, where models are developed independently without FL collaboration, and standard FL, which aims to learn a global model via the FedAvg algorithm. We adopt Ditto as our personalized FL approach, which enables each client in FL to develop its own personalized model through multi-task learning. Our assessment is achieved through a series of evaluations, comparing the predictive performance (i.e., AUROC and AUPRC) and fairness gaps (i.e., EOPP, EOD, and DP) of these methods. Personalized FL demonstrates superior predictive accuracy and fairness over standalone training across both datasets. Nevertheless, in comparison with standard FL, personalized FL shows improved predictive accuracy but does not consistently offer better fairness outcomes. For instance, in the 24-h in-hospital mortality prediction task, personalized FL achieves an average EOD of 27.4% across racial groups in the eICU dataset and 47.8% in MIMIC-IV. In comparison, standard FL records a better EOD of 26.2% for eICU and 42.0% for MIMIC-IV, while standalone training yields significantly worse EOD of 69.4% and 54.7% on these datasets, respectively. Our analysis reveals that personalized FL has the potential to enhance fairness in comparison to standalone training, yet it does not consistently ensure fairness improvements compared to standard FL. Our findings also show that while personalization can improve fairness for more biased hospitals (i.e., hospitals having larger fairness gaps in standalone training), it can exacerbate fairness issues for less biased ones. These insights suggest that the integration of personalized FL with additional strategic designs could be key to simultaneously boosting prediction accuracy and reducing fairness disparities. The findings and opportunities outlined in this paper can inform the research agenda for future studies, to overcome the limitations and further advance health equity research.

Solar System Implementation Performance at Universiti Sains Malaysia (USM)

Universiti Sains Malaysia (USM) has embarked on several solar energy initiatives to promote greener operations and sustainability. However, concerns exist regarding the actual performance of existing systems falling short of their potential. This project delves into the limitations of the current solar systems at USM, focusing on identifying and analyzing factors hindering optimal output. The research aims to understand the performance gaps affecting USM's solar systems comprehensively. By meticulously analyzing historical data, including solar irradiance, temperature, and system output, alongside a thorough study of existing infrastructure and maintenance practices, the project seeks to pinpoint specific bottlenecks. An in-depth interview with USM solar system specialists further assesses the systems' efficiency. This approach allows for a holistic evaluation of current practices and identification of key improvement areas. The ultimate goal is to establish concrete recommendations for optimizing USM's solar systems. These will encompass technical solutions, such as system upgrades, maintenance improvements, and operational adjustments, including data-driven monitoring and dynamic energy management strategies. Through this research, we strive to unlock the full potential of USM's solar infrastructure, enhancing its contribution to campus sustainability while providing valuable insights for wider solar energy implementation across Malaysia.

Design–operation gap caused by parameter variance in HVAC system control sequences: A simulation-based study on energy efficiency and temperature controllability

During the design phase, the annual energy consumption of heating, ventilation, and air-conditioning (HVAC) systems is frequently simplified or fixed under certain assumptions due to uncertain parameters within the control sequence, resulting in a performance gap between the intended and actual performances. This study assessed the impact of six uncertain parameters on system energy consumption and indoor temperature control under various updating scenarios. First, sensitivity analysis was conducted using the one-at-a-time method to identify the influential impact of the single uncertain parameter. Then, mutual impact analysis was conducted to showcase how different parameters affect each other, and mixed impact analysis was applied to evaluate how intended design performance converges to actual operating outcomes. A detailed physical model of a single-zone variable air volume system was developed to enable a comprehensive evaluation across different operational conditions. The results indicate that a 10% change in some parameters can lead to a 20∼30% increase in energy consumption, especially during low-load winter months. Properly evaluating the influence of one parameter is difficult without taking the effects of additional parameters into account, and some combinations of parameters can significantly decrease the system performance. These results underscore the necessity of considering the updating and combination scenarios of different parameters within the control sequences during the design phase.

Abstract 4800: Reducing health disparities for prostate adenocarcinoma by integrating multi-omics data via a multi-modal transfer learning approach

Abstract Prostate adenocarcinoma (PRAD) is the most common subtype of prostate cancer, which is the second leading cause of cancer death in American men. Although significant progress has been made to improve PRAD prognosis, existing studies indicated that Black American men have disproportionately high incidence and mortality rate in prostate cancer compared with non-Hispanic White American men. With artificial intelligence (AI) and machine learning (ML) being increasingly applied to PRAD research and clinical decision-making, PRAD data disparities would introduce bias to AI/ML models and further enhance negative impacts on healthcare towards underrepresented groups. Transfer learning has shown potential to reduce racial disparities in PRAD. However, its performance may be deteriorated due to: (1) its model requires large-scale training samples which are difficult to obtain in clinical settings, and (2) it only uses single-omics data without integrating multi-omics information. To address these concerns, we propose to develop a multi-modal transfer learning model to integrate multi-omics data for reducing health disparities. Specifically, we first investigated two multi-modal ensemble methods, Pearson Correlation Coefficient (PCC) based patient-pairwise similarity, and variational autoencoder (VAE) to integrate different types of omics data. Then, we leveraged a transfer learning model based on domain adaptation to pre-train the model on the majority group (White Americans) and fine-tune the model using the minority group (Black Americans). To further address the imbalanced data among ethnic groups, we explored implementing a data augmentation method, Synthetic Minority Oversampling Technique (SMOTE), to increase the size of minority group data. We evaluated our model on multi-omics data (mRNA, miRNA and methylation) of PRAD from The Cancer Genome Atlas (TCGA) database. Results suggested that our proposed approach achieved better performance of reducing health disparities for Black Americans compared with the mixture model and the independent model as well as the conventional transfer learning model in predicting progression-free interval (PFI) prognosis for PRAD patients. Furthermore, we also demonstrated that SMOTE alleviated the data imbalanced problem and improved the performance of the prognosis classification for ethnic minority groups. In summary, our results demonstrated that our proposed multi-modal transfer learning approach could effectively reduce the performance gap between majority and minority groups in PRAD prognosis, thereby mitigating health disparities. We expect that our proposed multi-model transfer learning framework can be customized and extensible to reduce health disparities in other types of cancer. Citation Format: Lusheng Li, Jieqiong Wang, Shibiao Wan. Reducing health disparities for prostate adenocarcinoma by integrating multi-omics data via a multi-modal transfer learning approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4800.

Knowledge distillation based on projector integration and classifier sharing

Knowledge distillation can transfer the knowledge from the pre-trained teacher model to the student model, thus effectively accomplishing model compression. Previous studies have carefully crafted knowledge representation, targeting loss function design, and distillation location selection, but there have been few studies on the role of classifiers in distillation. Previous experiences have shown that the final classifier of the model has an essential role in making inferences, so this paper attempts to narrow the gap in performance between models by having the student model directly use the classifier of the teacher model for the final inference, which requires an additional projector to help match features of the student encoder with the teacher's classifier. However, a single projector cannot fully align the features, and integrating multiple projectors may result in better performance. Considering the balance between projector size and performance, through experiments, we obtain the size of projectors for different network combinations and propose a simple method for projector integration. In this way, the student model undergoes feature projection and then uses the classifiers of the teacher model for inference, obtaining a similar performance to the teacher model. Through extensive experiments on the CIFAR-100 and Tiny-ImageNet datasets, we show that our approach applies to various teacher–student frameworks simply and effectively.

Advancements in Uncooled Bolometer Technology: Shortwave Infrared Detection via CuFeSe2 Nanocrystal Colloidal Thin Films

Microbolometers have emerged as a cost‐effective alternative to cooled infrared photon detectors, albeit with certain trade‐offs in terms of responsivity (), detectivity (), and response time (). The research in this field is driven by the potential applications in night vision devices, military surveillance, and autonomous vehicles, leading to a growing interest in exploring new materials to bridge the performance gap between cooled photon detectors and uncooled bolometers. This study focuses on the optoelectronic and bolometric characteristics of nanocrystals (NCs) in a colloidal solution. These NCs exhibit a significant change in resistivity (ρ) when subjected to temperature variations ranging from 170 to 400 K. Specifically, the temperature coefficient of resistance (TCR), α, is 1.9% per Kelvin for a room temperature resistivity of 505 Ω cm. Furthermore, the responsivity of NCs is reported to be 0.101 A W−1, and the Hall mobility of the colloidal solution is determined as . Finally, a comprehensive comparison is conducted between the performance metrics of established bolometer materials, such as VOx and a‐Si, and those of colloidal NCs. Based on the results, colloidal NCs are a promising option for future bolometer technology.

Budget Process and Execution: A Case Study on the Underperformance of the Peruvian Health System, 2000-2021.

Health system financing for emerging economies with aging populations and changing epidemiological profiles is an increasing challenge. Peru, as one of the countries with the highest economic growth in this century, provides a good example for analyzing the impact of the budgeting process and the budget execution on the performance of a health system. This article aims to answer how policies and processes are the root causes of the performance gap of the Peruvian health system. We used a case study methodology composed of 17 semistructured interviews with senior national and regional actors conducted between the end of 2021 and the beginning of 2022. Participants were selected with a combination of purposive, convenience, and snowball sampling until reaching saturation at 14 interviews. Participants' answers were grouped according to the topics explored, enabling comparisons and identification of themes. The responses revealed that 4 interconnected influences affect the Peruvian health system. Political instability affects the sustainable development of health policies. The fiscal cycle limits the public health expenditure to a low 3% of the gross domestic product. The budget cycle is based on the low motivation of the Ministry of Health (MOH) to establish a proper budgeting process. The execution cycle represents the results of chronic underinvestment with a lack of professionals, equipment, and data affecting the access to care expressed by a high out-of-pocket share in health expenditure. To escape these cycles, the MOH needs to be able to argue in economic terms for the prioritization of health, showing the economic rationale for investment in health. Taxes need to finance the additional investment within the available fiscal space. The rigidity of the budget law needs to be adapted, and a technical budget that is oriented to the current and future health priorities needs to be elaborated.

A Cross-linked n-Type Conjugated Polymer with Polar Side Chains Enables Ultrafast Pseudocapacitive Energy Storage.

Pseudocapacitors bridge the performance gap between batteries and electric double-layer capacitors by storing energy via a combination of fast surface/near-surface Faradaic redox processes and electrical double-layer capacitance. Organic semiconductors are an emerging class of pseudocapacitive materials that benefit from facile synthetic tunability and mixed ionic-electronic conduction. Reported examples are mostly limited to p-type (electron-donating) conjugated polymers, while n-type (electron-accepting) examples remain comparatively underexplored. This work introduces a new cross-linked n-type conjugated polymer, spiro-NDI-N, strategically designed with polar tertiary amine side chains. This molecular design aims to synergistically increase the electroactive surface area and boost ion transport for efficient ionic-electronic coupling. Spiro-NDI-N demonstrates excellent pseudocapacitive energy storage performance in pH-neutral aqueous electrolytes, with specific capacitance values of up to 532 F g-1 at 5 A g-1 and stable cycling over 5000 cycles. Moreover, it maintains a rate capability of 307 F g-1 at 350 A g-1. The superior pseudocapacitive performance of spiro-NDI-N, compared to strategically designed structural analogues lacking either the cross-linked backbone or polar side chains, validates the essential role of its molecular design elements. More broadly, the design and performance of spiro-NDI-N provide a novel strategy for developing high-performance organic pseudocapacitors.

End-to-end Point Supervised Object Detection with low-level instance features

The use of point-level annotations in Point Supervision Object Detection (PSOD) has gained popularity due to its low labeling cost and the provision of specific location information. Previous studies have often relied on Multiple Instance Learning (MIL) to select and refine a low-quality pseudo box, which heavily relies on category information. This method concentrates on identifying the most distinctive objects in each category within an image. However, without the boundary constraints provided by Ground-Truth (GT), the detector may suffer from instance ambiguity problems, which can significantly deteriorate the model’s performance. To tackle these concerns, we propose an end-to-end object detection model called low-level instance features object detector (LIFOD) to mime low-level instance features information to refine the pseudo box. LIFOD utilizes a Low-level Feature Extractor (LFE) module to exploit low-level instance feature information to combine low-level feature information and category information, thus solving the challenge of instance ambiguity. To further alleviate the problem of local minima, we introduce bounding box regression in LIFOD and implement an end-to-end training strategy for point-supervised models to mitigate local minima. We evaluate our proposed LIFOD method on the public datasets COCO 2014 and 2017 and show that it outperforms the existing image-level and point-level supervised detection methods, and significantly reduces the gap in performance between PSOD and box-level object detection.

SYCL in the edge: performance and energy evaluation for heterogeneous acceleration

Edge computing is essential to handle increasing data volumes and processing capacities. It provides real-time and secure data processing near data sources, like smart devices, alleviating cloud computing energy use, and saving network bandwidth. Specialized accelerators, like GPUs and FPGAs, are vital for low-latency edge computing but the requirements to customized code for different hardware and vendors suppose important compatibility issues. This paper evaluates the potential of SYCL in addressing code portability issues encountered in edge computing. We employed the Polybench suite to compare various SYCL implementations, specifically DPC++ and AdaptiveCpp, with the native solution, CUDA. The disparity between SYCL implementations was negligible, at just 5%. Furthermore, we evaluated SYCL in the context of specific edge computing applications such as video processing using three different optical flow algorithms. The results revealed a slight performance gap of 3% when transitioning from CUDA to SYCL. Upon evaluating energy consumption, the observed difference ranged from ±10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 10\\%$$\\end{document}, depending on the application utilized. These gaps are the price one may need to pay when achieving the ability to successfully run the same code on two distinct edge boards. These findings underscore SYCL’s capacity to increase productivity in terms of development costs and facilitate IoT deployment without being locked into a particular platform or manufacturer.

Enhancing Security in Visible Light Communication: A Tabu-Search-Based Method for Transmitter Selection.

In this paper, we explore the secrecy performance of a visible light communication (VLC) system consisting of distributed light-emitting diodes (LEDs) and multiple users (UEs) randomly positioned within an indoor environment while considering the presence of an eavesdropper. To enhance the confidentiality of the system, we formulate a problem of maximizing the sum secrecy rate for UEs by searching for an optimal LED for each UE. Due to the non-convex and non-continuous nature of this security maximization problem, we propose an LED selection algorithm based on tabu search to avoid getting trapped in local optima and expedite the search process by managing trial vectors from previous iterations. Moreover, we introduce three LED selection strategies with a low computational complexity. The simulation results demonstrate that the proposed algorithm achieves a secrecy performance very close to the global optimal value, with a gap of less than 1%. Additionally, the proposed strategies exhibit a performance gap of 28% compared to the global optimal.

Strategi Penempatan Pegawai Berdasarkan Sistem Merit pada Direktorat Pembinaan dan Pengawasan Tenaga Kesehatan Kementerian Kesehatan

Human Resources have an important role and become the key to success in carrying out activities to achieve the vision and mission of the organization. HR problems in Government Agencies are not only in HR Professionalism. In addition, organizational changes need to be made to face national development and global competition. In organizational changes, employee placement based on qualifications, competencies and performance needs to be done properly so that employee professionalism is maintained. The purpose of this study is to find employee placement strategies based on the merit system at the Directorate of Health Worker Development and Supervision to run optimally. This study used a descriptive method of qualitative approach with data collection using observation, interviews, and documentation. The number of informants in this study was 5 people with data analysis techniques using data reduction, data presentation, and conclusions. The results of this study state that the placement of employees based on the merit system has not run optimally, there are still problems including there are employees who have not been improved in competence, performance appraisals have not been consistently carried out based on organizational and employee performance targets, and have not carried out competency development mapping based on performance and competency gaps. The age factor is still a consideration in employee placement so that the application in the concept of performance achievement has not been carried out optimally. The resulting strategy is by mapping and improving competency development based on competency and performance gaps, conducting objective and measurable performance appraisals based on organizational and employee performance goals, changing organizational culture and implementing talent management in accordance with applicable regulations.

Exploring key factors affecting the ultimate compression capacity of Unbonded Steel-mesh-reinforced Rubber Bearings

To address bearing-sliding damage in highway bridges during seismic events, the Unbonded Steel-mesh-reinforced Rubber Bearing (USRB) was developed. This novel bearing employs flexible, high-strength steel wire meshes as reinforcement, enhancing lateral deformation capacity and reducing bearing-sliding risk. Recent seismic specifications increased the bearings' vertical design load to 30 MPa, where existing bearings with flexible reinforcements (e.g., fiber-reinforced bearings) fail to meet this standard. Hence, for the first time, our study thoroughly investigated the ultimate compression capacity of USRBs and the key factors influencing the capacity. Through ultimate compression tests on full-scale prototypes, we demonstrated that USRBs can comply with heightened seismic requirements, with primary failure attributed to the tensile failure of the steel mesh reinforcement. These tests were complemented by 3D finite element modeling in ANSYS, employing the LINK180 elements for mesh reinforcement and defining the ultimate capacity as the point at which reinforcement stress reached tensile strength. This particular modeling approach, validated against experimental data, can accurately predict the ultimate compression capacity and the force-deformation response of USRBs. A comprehensive parametric analysis, using the validated modeling approach and an advanced statistical method LHS-PRCC, was conducted to identify the key factors influencing the compressive capacity, including bearing geometric parameters (i.e., bearing width, the second shape factor, and rubber layer thickness), steel-mesh configuration characteristics, and steel-mesh material properties. Our findings emphasize the significance of rubber layer thickness, steel mesh weight, and steel wire tensile strength and strain in influencing the USRBs' ultimate compression capacity. Based on these key factors, an empirical equation for the ultimate compression capacity of USRBs was established to guide the optimization design of USRBs. This study not only fills the gap in USRB's performance under extensive compression but also provides a foundation for future optimization of these bearings to enhance their performance in bridge engineering.

Dion-Jacobson Type Lead-Free Double Perovskite with Ultra-Narrow Aromatic Interlayer Spacing for Highly Sensitive and Stable X-ray Detection.

The low-toxic and environmentally friendly 2D lead-free perovskite has made significant progress in the exploration of "green" X-ray detectors. However, the gap in detection performance between them and their lead-based analogues remains a matter of concern that cannot be ignored. To reduce this gap, shortening the interlayer spacing to accelerate the migration and collection of X-ray carriers is a promising strategy. Herein, a Dion-Jacobson (DJ) lead-free double perovskite (4-AP)2 AgBiBr8 (1, 4-AP = 4-amidinopyridine) with an ultra-narrow interlayer spacing of 3.0Å, is constructed by utilizing π-conjugated aromatic spacers. Strikingly, the subsequent enhanced carrier transport and increased crystal density lead to X-ray detectors based on bulk single crystals of 1 with a high sensitivity of 1117.3µCGy-1 cm-2 , superior to the vast majority of similar double perovskites. In particular, the tight connection of the inorganic layers by the divalent cations enhances structural rigidity and stability, further endowing 1 detector with ultralow dark current drift (3.06×10-8 nAcm-1 s-1 V-1 , 80V), excellent multiple cycles switching X-ray irradiation stability, as well as long-term environmental stability (maintains over 94% photoresponse after 90 days). This work brings lead-free double perovskites one step closer to realizing efficient practical green applications.