Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid seismic protection of cable‐stayed bridges

This paper proposes a hybrid control strategy combining passive and semi‐active control systems for seismic protection of cable‐stayed bridges. The efficacy of this control strategy is verified by examining the ASCE first‐generation benchmark problem for a seismically excited cable‐stayed bridge, which employs a three‐dimensional linearized evaluation bridge model as a testbed structure. Herein, conventional lead–rubber bearings are introduced as base isolation devices, and semi‐active dampers (e.g., variable orifice damper, controllable fluid damper, etc.) are considered as supplemental damping devices. For the semi‐active dampers, a clipped‐optimal control algorithm, shown to perform well in previous studies involving controllable dampers, is considered. Because the semi‐active damper is a controllable energy‐dissipation device that cannot add mechanical energy to the structural system, the proposed hybrid control strategy is fail‐safe in that the bounded‐input, bounded‐output stability of the controlled structure is guaranteed. Numerical simulation results show that the performance of the proposed hybrid control strategy is quite effective in protecting seismically excited cable‐stayed bridges. Copyright © 2004 John Wiley & Sons, Ltd.
IMPROVING FOOD POLICIES FOR A CLIMATE INSECURE WORLD: EVIDENCE FROM ETHIOPIA

Climate change and weather shocks have multi-faceted impacts on food systems with important implications for economic policy. Combining a longitudinal household survey with high-resolution climate data, we demonstrate that both climate and weather shocks increase food insecurity; cash assistance and participation in Ethiopia’s Productive Safety Net Programme have reduced food insecurity; but food assistance has been ineffective. Importantly, households with savings, and those that stored their harvest to sell at higher prices rather than for home use, suffered less from food insecurity, yet both strategies are harder for the poorest and most food insecure households to adopt. Our paper provides micro-founded evidence needed to design policies that both improve agricultural yields in the context of a changing climate and target households’ abilities to cope with shocks that put upwards pressure on food prices.
PRODUCTIVITY IN UK HEALTHCARE DURING AND AFTER THE COVID-19 PANDEMIC

Measured health output in the UK declined sharply during the Covid-19 pandemic, despite the evident large increase in some National Health Service (NHS) activities such as critical care, and the new test and trace and vaccination programmes. We explain the measurement methods applied to public services that produced the published decline, in the context of the inherent difficulties of defining and measuring health output and productivity; and describe the changes in practice that had to take place in NHS hospitals. We also discuss, on the basis of these changes including increased use of technology, the likely outlook for healthcare productivity. We conclude that within NHS England capacity, constraints have contributed to substantial falls in non-Covid-19 healthcare output and argue that increased capacity in the social infrastructure of the health service is essential to enable higher productivity in an uncertain environment.
sRNAs enriched in outer membrane vesicles of pathogenic Flavobacterium psychrophilum interact with immune genes of rainbow trout

Outer membrane vesicles (OMVs) released by gram-negative bacteria during host-pathogen interactions harbor cargos, such as DNA, RNA, toxins, and virulence factors. We hypothesized that sRNAs carried within OMVs of Flavobacterium psychrophilum interact with host immune genes and affect their expression. OMVs were isolated from F. psychrophilum and visualized using transmission electron microscopy (TEM). RNA-Seq datasets generated from whole-cell F. psychrophilum and their OMVs indicated enrichment of specific sRNAs in the OMVs compared to the parent cell. Fluorescent in situ hybridization (FISH) and confocal microscopy confirmed the expression of a randomly chosen sRNA. Integrated RNA-Seq analyses of host transcriptome and bacterial sRNAs on day 5 post-infection of F. psychrophilum-resistant and -susceptible rainbow trout genetic lines revealed 516 protein-coding, 595 lncRNA, and 116 bacterial sRNA differentially expressed (DE) transcripts. Integrated and network analyses of these DE transcripts revealed immune genes targeted by bacterial sRNAs. On the top of these genes, an isoform encoding anaphase-promoting complex subunit 13 (ANAPC13_1) was highly upregulated and exhibited interaction and reciprocal expression with 21 DE sRNAs enriched in OMVs and/or located in pathogenicity islands (PAIs). In vitro treatment of the rainbow trout epithelial cell line RTgill-W1 with OMVs showed signs of cell autolysis accompanied by dynamic changes in expression of host genes when profiled 24h following treatment. The OMV-enriched sRNAs, soFE013584 and soFE002123, showed high interactions with the protection of telomeres 1 gene (POT1); essential for chromosome stability and cellular viability. Modulation of the host gene expression following OMV-treatment, which favors elements from the phagocytic, endocytic, and antigen presentation pathways in addition to HSP70, HSP90, and cochaperone proteins, provided evidence for a potential role of OMVs in boosting the host immune response. In conclusion, our work identified novel microbial targets and inherent characteristics of OMVs that could open up new avenues of treatment and prevention of fish infections.
Immersion-cooled heat sinks for electronics: insight from high-speed photography

The development of effective heat sinks for the primary heat-dissipating component of a typical portable electronics device is an ongoing challenge. Thermal management using air-cooling is limited by the inherently poor thermal properties of the coolant. Other alternatives, including liquid immersion cooling, phase-change materials, and heat pipes, may merit consideration if the basic mechanisms can be reliably predicted. This study sheds light on the nucleation characteristics of an etched cavity-enhanced surface for use in an immersion-cooled heat sink. The target application is a high-density multi-chip module with several heat dissipating sources. High-speed photography was used to record parameters such as bubble interactions, bubble size, departure frequency and active site density while varying the cavity spacing and heat flux. The cavities, which are approximately 40 /spl mu/m, are arranged in a square cluster 12.7 mm on each side. It was determined that the contribution of latent heat as a heat dissipation mechanism is only minor (less than 16%). In addition, it is proposed that the latent heat dissipation percentage may be used as a thermal performance indicator. Interactions between neighboring heat sources were also studied. These interactions decreased the bubble departure frequency and thereby affected the latent heat contribution.
A Review of E-Logistics Model From Consumer Satisfaction and Information Technology Perspective

The worldwide logistics company is beginning to shift from the traditional logistics business model into the E-Logistics business model that benefits digitalization and Information Communication Technology. The implementation of e-logistics can be cost-effective in planning, booking, management, billing, route planning, and track and trace area. For the successful implementation of e-logistics, a success factor of E-logistics needs to be studied to reach efficient and effective E-Logistics implementation. The purpose of this study is to identify the driving factor in e-logistics from a consumer and information technology perspective and propose an e-logistics conceptual model from the consumer and information technology perspective. This study uses a Systematic Literature Review and identifies driving factors of e-logistics: Low Distribution Charges, Low Transit Time, Effective Information Flow, Higher Business Safety, Faster Order Fulfillment, E-Payment, E-Traceability, E-Procurement, E-Collaboration, Website Design, Consumer Satisfaction, and Information Technology. This study also proposed the conceptual model.
The Experimental Study on the Influence of Human Body to Measurement of High Voltage Power Frequency Electric Field

Based on the basic theory of electromagnetic field, the Electric Field Distortion (EFD) in power frequency electric field caused by induced current of human body has been analyzed. The enhancement factor of the electric field distortion is introduced to reduce the influences caused by human body in the measurement of high voltage electric fields. The Ansoft Maxwell is used to simulate and calculate the electric field distribution under the influence of the human body to have the value of enhancement factor. In addition, the enhancement factor has been corrected by experiment with the electromagnetic field analyzer EFA300. With the enhancement factor introduced in this paper, the measurement error can be reduced.
How to improve the on-site MOE assessment of old timber beams combining NDT and visual strength grading

For the conservation and restoration of old timber structures, the knowledge of the mechanical properties of each element is fundamental. For this reason, various nondestructive techniques were developed and investigated since the 1990s. Some of them provide very good results, but the solutions and the proposed models were applied only in few circumstances as a consequence of the on-site restrictive working conditions: no possibility to remove the elements, limitation to ends and faces accessibility and unknown density. The on-site inspection, including the visual strength grading, has been identified as the first step for the timber assessment. In this research, 13 old timber members of Silver Fir (Abies alba Mill.) were visually graded and tested with different nondestructive techniques for the density and modulus of elasticity (MOE) estimation: flexural and longitudinal vibrational tests, stress wave transmission time and Pilodyn penetration depth. The timber elements were also tested in a four-point bending test to determinate the local and global MOE. Finally, a reliable method, applicable to the limiting on-site conditions, was proposed and the results were showed.
On the Workload of Vigilance: Comparison of the NASA-TLX and the MRQ

The utility of the Multiple Resources Questionnaire (MRQ) as a workload index in vigilance was assessed by comparing the sensitivity of the scale to that of the NASA Task Load Index (NASA-TLX) in response to challenges provided by variations in task-type and operating environment. Both instruments showed that global workload was high and that it was greater in the context of Successive (absolute judgment) as compared to Simultaneous (comparative judgment) type tasks. In addition, the MRQ indicated that workload was greater when observers operated in a multi-task as compared to a single-task environment, a dimension to which the NASA-TLX was not sensitive. The MRQ added to diagnosticity in the workload picture by identifying information-processing resources utilized across experimental conditions and sets of resources that were condition specific, resources not encompassed by the NASA-TLX. The results of this study indicate that the MRQ can be of effective value in measuring the workload imposed by vigilance tasks
An Efficient Implementation of Online Model Predictive Control With Field Weakening Operation in Surface Mounted PMSM

Model-predictive-controller (MPC), one of the optimal control policies, has gained more attention in servo drive and other industrial applications in recent years due to evident control performance benefits compared to more classical control methods. However, an MPC algorithm solves a constrained optimization problem at each step that brings a substantial computational burden over classical control policies. This study focuses on improving the computational efficiency of an online MPC algorithm and then demonstrates its practical feasibility on the field weakening operation in high-speed PMSM control applications where the sampling frequency is in the order of $\mu s$ . We implement the existing dual active set solver by replacing two standard methods in the matrix update step to reduce the overall computational cost of the algorithm. We also rearrange the linear approximation for the constraints on voltage and current by taking the tradeoff between accuracy and speed into account. We finally verify the efficiency and effectiveness of the proposed structure via processor-in-the-loop simulations and physical platform experiments.
The Impact of Perceived Usefulness, Lifestyle, and Trust on Attitudes and Intentions to Use M-Wallet

The increase in smartphone users has changed people's lifestyles to adopt m-wallet. This condition is the reason for the growth of Fintech firms to meet the needs of users in transactions. The research proposes determining the impact of perceived usefulness, lifestyle, and trust on attitudes and intentions to use m-wallets. This research uses a verification method, and the unit of analysis is 336 m-wallet users in Bandung, Indonesia. The research data was analyzed with Structural Equation Modeling (SEM) based on covariance. As a result, perceived usefulness, lifestyle, and trust affect attitudes and intentions to adopt m-wallets. Trust has proved to mediate the correlation between perceived usefulness and intent to adopt m-wallets. Providers should improve apps with flexibility and provide many benefits, present accurate information, satisfy promises, implement protection systems, and recover financial troubles made by failed technology.
Impacts of aerosols on available solar energy at Mbour, Senegal

This study aims to evaluate the available solar potential (direct normal on the overall horizontal plane) and to estimate the observed impact of aerosols on solar radiation at Mbour, Senegal (16.958 °W; 14. 394 °N) using observations from solar instruments and AERONET during 2006. The results show the presence of a good available solar potential. At intra-seasonal timescale, the total and direct normal energy are stronger in May with a mean value of 7 kWh/m2/day and February with an average value of 5.50 kWh/m2/day. The lower available total and direct normal solar energies are, respectively, found in August (5.31 kWh/m2/day) and July (2.90 kWh/m2/day). The observations of AERONET show that aerosol optical depth values are higher in June (0.7) and lower in February (0.16). These results are consistent with the observed trends of total and direct normal energy during those months. Case studies of the influence of aerosols on available solar energy show a mean decrease of 10% and 28%, respectively, for the .
Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation

With the emergence of online social networks, social-based items recommendation has become a popular research direction. Recently, Graph Convolutional Networks have shown promising results by modeling the information diffusion process in graphs. It provides a unified framework for graph embedding that can leverage both the social graph structure and node features information. In this paper, we improve the embedding output of the graph-based convolution layer by adding a number of transformer layers. The transformer layers with attention architecture help discover frequent patterns in the embedding space which increase the predictive power of the model in the downstream tasks. Our approach is tested on two social-based items recommendation datasets, Ciao and Epinions and our model outperforms other graph-based recommendation baselines.
Syntactic Knowledge-Infused Transformer and BERT Models

Attention-based deep learning models have demonstrated significant improvement over traditional algorithms in several NLP tasks. The Transformer, for instance, is an illustrative example that generates abstract representations of tokens that are input to an encoder based on their relationships to all tokens in a sequence. While recent studies have shown that such models are capable of learning syntactic features purely by seeing examples, we hypothesize that explicitly feeding this information to deep learning models can significantly enhance their performance in many cases. Leveraging syntactic information like part of speech (POS) may be particularly beneficial in limited-training-data settings for complex models such as the Transformer. In this paper, we verify this hypothesis by infusing syntactic knowledge into the Transformer. We find that this syntax-infused Transformer achieves an improvement of 0.7 BLEU when trained on the full WMT ’14 English to German translation dataset and a maximum improvement of 1.99 BLEU points when trained on a fraction of the dataset. In addition, we find that the incorporation of syntax into BERT fine-tuning outperforms BERT BASE on all downstream tasks from the GLUE benchmark, including an improvement of 0.8% on CoLA.
Solving Unconverged Learning of Pairs Trading Strategies with Representation Labeling Mechanism

A pairs trading strategy (PTS) constructs a market-neutral portfolio whose value typically moves back and forth around a mean price level; investors short (long) the portfolio when its value reaches the upside (downside) opening threshold and close the position when the value reverts to the mean to earn the price difference. Recent machine learning models select the open and stop-loss thresholds either heuristically or chosen from a limited set, which significantly limits the investment performance. We address this by creating a wider set of open/stop-loss threshold recommendations that generally cover all possible scenarios; but regressionor classification-based deep learning methods for recommending thresholds fail to converge. Thus, we design a representative labeling mechanism that selects representative open and stop-loss thresholds from all possible optimal thresholds according to the selection frequencies of the thresholds and the k-means algorithm. Experiments suggest that training the multi-scale residual network with stock pairs relabeled by representative thresholds yields better investment performance than other methods in the literature.
Antelope: A Framework for Dynamic Selection of Congestion Control Algorithms

Most congestion control mechanisms are designed for specific network environments. Hence, there is no known algorithm that achieves uniformly good performance in all scenarios for all flows. Rather than devising such a one-size-fits-all algorithm, we propose a system to dynamically switch between the most suitable congestion control mechanisms for specific flows in specific environments. This raises a number of challenges, which we address through the design and implementation of Antelope, a system that can dynamically reconfigure to use the most suitable congestion control mechanism for an individual flow. We build a machine learning approach to learn which algorithm works best for individual conditions and implement kernel-level support for dynamically adjusting congestion control algorithms. We have implemented Antelope in Linux, and evaluated it in both emulated and production networks. We show that in WAN, DCN, and cellular networks, Antelope achieves an average 16% improvement in throughput compared with BBR; compared with Cubic, Antelope achieves an average 19% improvement in throughput and 10% reduction in delay.
DOVE: Diagnosis-driven SLO Violation Detection

Service-level objectives (SLOs), as network performance requirements for delay and packet loss typically, should be guaranteed for increasing high-performance applications, e.g., telesurgery and cloud gaming. However, SLO violations are common and destructive in today’s network operation. Detection and diagnosis, meaning monitoring performance to discover anomalies and analyzing causality of SLO violations respectively, are crucial for fast recovery. Unfortunately, existing diagnosis approaches require exhaustive causal information to function. Meanwhile, existing detection tools incur large overhead or are only able to provide limited information for diagnosis. This paper presents DOVE, a diagnosis-driven SLO detection system with high accuracy and low overhead. The key idea is to identify and report the information needed by diagnosis along with SLO violation alerts from the data plane selectively and efficiently. Network segmentation is introduced to balance scalability and accuracy. Novel algorithms to measure packet loss and percentile delay are implemented completely on the data plane without the involvement of the control plane for fine-grained SLO detection. We implement and deploy DOVE on Tofino and P4 software switch (BMv2) and show the effectiveness of DOVE with a use case. The reported SLO violation alerts and diagnosis-needing information are compared with ground truth and show high accuracy (>97%). Our evaluation also shows that DOVE introduces up to two orders of magnitude less traffic overhead than NetSight. In addition, memory utilization and required processing ability are low to be deployable in real network topologies.
Estimation of damping in suspension bridges

In this paper, sources of damping data and the accuracy of measurement methods are examined. Methods of energy dissipation in components of the structure are evaluated to help explain the observations and to provide a basis for rational prediction of values. Data derived from full scale tests carried out on 28 bridges are presented in tabular form. (A)
Conflict-Aware Safe Reinforcement Learning: A Meta-Cognitive Learning Framework

In this paper, a data-driven conflict-aware safe reinforcement learning (CAS-RL) algorithm is presented for control of autonomous systems. Existing safe RL results with predefined performance functions and safe sets can only provide safety and performance guarantees for a single environment or circumstance. By contrast, the presented CAS-RL algorithm provides safety and performance guarantees across a variety of circumstances that the system might encounter. This is achieved by utilizing a bilevel learning control architecture: A higher meta-cognitive layer leverages a data-driven receding-horizon attentional controller (RHAC) to adapt relative attention to different system's safety and performance requirements, and, a lower-layer RL controller designs control actuation signals for the system. The presented RHAC makes its meta decisions based on the reaction curve of the lower-layer RL controller using a meta-model or knowledge. More specifically, it leverages a prediction meta-model (PMM) which spans the space of all future meta trajectories using a given finite number of past meta trajectories. RHAC will adapt the system's aspiration towards performance metrics (e.g., performance weights) as well as safety boundaries to resolve conflicts that arise as mission scenarios develop. This will guarantee safety and feasibility (i.e., performance boundness) of the lower-layer RL-based control solution. It is shown that the interplay between the RHAC and the lower-layer RL controller is a bilevel optimization problem for which the leader (RHAC) operates at a lower rate than the follower (RL-based controller) and its solution guarantees feasibility and safety of the control solution. The effectiveness of the proposed framework is verified through a simulation example.