Miracle-dz/UniFAR-FTUs huggingface dataset:
Enabling Rapid Large-Scale Seismic Bridge Vulnerability Assessment Through Artificial Intelligence

Departments of transportation (DOTs) throughout the United States maintain vast bridge databases that house information such as bridge services, dimensions, materials, inspection reports, and photographs. These databases are expensive to maintain and have evolved quite gradually over the years. They are meant to be substantial enough, at a bare minimum, to support typical asset management activities and to prioritize maintenance tasks. There is great potential to make use of them to support other decisions. However, these databases often lack certain detailed information related to substructure elements, which is necessary for seismic vulnerability assessment, for example, and would be time-consuming to gather for thousands of bridges in a given region or state. In this study, a technique was demonstrated and validated that reduces the time needed to collect this information, by leveraging artificial intelligence to automate the identification of substructure types using images. We defined categories appropriate for vulnerability assessment task, classifiers were trained to identify visual content, and their performance evaluated. In this paper we illustrate a method to determine whether to use artificial intelligence, human visual confirmation, or a combination of the two, to identify bridge substructure types based on accuracy, cost, and risk tolerance. The technical approach was validated using images from Indiana. This leveraging of artificial intelligence for automated identification of critical bridge characteristics from readily available images could empower asset owners, such as DOTs, to assess their inventory more frequently and with confidence.
Design of Chinese Opera Cultural Platform Based on Digital Twins and Research on International Cultural Communication Strategies

As an important part of a country’s diplomatic strategy, international cultural communication is increasingly valued by countries. But the traditional way of communication has been unable to keep up with the pace of the times. Digital twin technology can realize virtual reality, which can be applied to the design and dissemination of opera cultural platforms. The influence of a country’s culture not only depends on whether its content has unique charm but also depends on whether it has advanced means of communication and strong communication ability. The purpose of this paper is to study how to design an opera cultural platform based on digital twins. This paper proposes a three-dimensional transformation algorithm based on digital twin, which can play a good role in the design of the opera culture platform. The experimental results in this paper are shown in Table 1. In 2016, the economic benefits brought by international cultural communication to China were 35.889 billion yuan. It increased by 3.735 billion yuan year-on-year, with a growth rate of 10.4%. By 2019, the economic benefits brought by international cultural communication to China were 50.730 billion yuan, with an increase of 49.66% over the previous year and a growth rate of 9.7%. It can be seen that, through continuous external communication, this aspect shows the cultural strength of the country and obtains an excellent international reputation and social benefits. On the other hand, it reaped considerable economic benefits. Therefore, it is very necessary to study the strategies of international cultural communication.
Noninvasive Management of Fractured Indwelling Tunneled Pleural Catheter Valve

Tunneled indwelling pleural catheters (IPCs) are frequently used to palliate symptomatic dyspnea due to recurrent pleural effusions. The drainage valve of IPCs is an important component of the catheter as fracture of the valve leads to malfunctioning of the IPCs. Replacement of the catheter includes risks such as pain, infection, pneumothorax, and procedure cost. We report two cases of malfunctioning tunneled IPC drainage valves repaired by our noninvasive method and discuss the need for a repair kit and a standardized approach to this repair in case of nonavailability of repair kits.
EdgePhase: A Deep Learning Model for Multi‐Station Seismic Phase Picking

In this study, we build a multi‐station phase‐picking model named EdgePhase by integrating an Edge Convolutional module with a state‐of‐the‐art single‐station phase‐picking model, EQTransformer. The Edge Convolutional module, a variant of Graph Neural Network, exchanges information relevant to seismic phases between neighboring stations. In EdgePhase, seismograms are first encoded into the latent representations, then converted into enhanced representations by the Edge Convolutional module, and finally decoded into the P‐ and S‐phase probabilities. Compared to the standard EQTransformer, EdgePhase increases the precision (fraction of phase identifications that are real) and recall (fraction of phase arrivals that are identified) rate by 5% on our training and test data sets of Southern California earthquakes. To evaluate its performance in regions of different tectonic settings, we applied EdgePhase to detect the early aftershocks following the 2020 M7.0 Samos, Greece earthquake. Compared to a local earthquake catalog, EdgePhase produced 190% additional detections with an event distribution more conformative to a planar fault interface, suggesting higher fidelity in event locations. This case study indicates that EdgePhase provides a strong regional generalization capability in real‐world applications.
“It’s a dent, not a break”: an exploration of how care co-ordinators understand and navigate boundaries in early intervention in psychosis services

ABSTRACT Background Early Intervention in Psychosis Services (EIP) offer a unique service model for people experiencing a first episode of psychosis. They are intensive case-management services which adopt an assertive outreach approach, employing flexible boundaries to meet clients in the community, and support them towards recovery and holistic goals. Current boundary theory is therefore not easily applied to this clinical context. This study aimed to explore how care-co-ordinators in EIP develop their understanding and practise around navigating boundaries. Methods Participants were 13 EIP care co-ordinators. Semi-structured interviews with participants were conducted and analysed using grounded theory. Results A concentric model emerged, defining different layers of influence impacting care co-ordinators’ navigation of boundaries. This included higher governing levels (e.g. professional codes of conduct), factors and challenges specific to EIP culture, client characteristics and individual care co-ordinator decision-making processes. A flexible, case-by-case approach was adopted in response to challenges. Discussion Findings suggest clinical implications for how care co-ordinators can jointly construct appropriate boundaries with clients and families, and discuss dilemmas using the team resource. This model can be used as a tool in clinical practice to aid clinicians’ thinking and reflection around boundaries within EIP.
Atherogenic Index of Plasma in Non-Alcoholic Fatty Liver Disease: Systematic Review and Meta-Analysis

(1) Background: Approximately a billion people worldwide are affected by NAFLD, which places a high clinical burden and financial cost on society. Liver biopsy is the gold standard for diagnosing NAFLD, but its invasivity limits the early diagnosis of NAFLD. Hence, it is important to look for alternate techniques in detecting and diagnosing NAFLD. NAFLD is associated with atherosclerosis. The purpose of this study was to assess the effectiveness of the atherogenic index of plasma (AIP) as a non-invasive modality for predicting NAFLD. (2) Methods: A search using electronic databases PubMed, EMBASE, and Scopus was carried out to find observational studies, looking at research that had been published up until the date of 11 May 2022. The included studies’ quality, risk of bias, and internal validity were evaluated using the QUADAS-2 quality assessment tool. The key summary outcomes were the mean difference (MD) and area under the curve (AUC). (3) Results: A total of eight studies (81,178 participants) were included in our review, while 17% of the included participants had NAFLD. A sex distribution of 57.8% men and 42.2% women was observed. The AIP between NAFLD and the controls was not significant (MD 0.212 [95% CI 0.231–0.655]). A significant MD in AIP between the males and females with NAFLD was observed (MD 0.246 [95% CI 0.098–0.395]). The AIP predicted NAFLD with an AUC of 0.764 as well as in males (AUC 0.761) and females (AUC 0.733). (4) Conclusions: There was a substantial MD in the AIP between both sexes, but there was no significant difference in the AIP values between patients with NAFLD and the controls. The AIP is a reliable biomarker for the diagnosis of NAFLD since its ability to predict the development of NAFLD was comparable to that of the other biomarkers.
An algorithm for the network design problem based on the maximum entropy method

Network design problem (NDP) is a well known NP-hard problem which involves topology selection (subset of possible links), routing determination (paths for the offered traffic) and possibly capacity assignment. The goal is to minimize the cost, which can be a combination of the link costs and delay penalties, under possible additional constraints. Network design and analysis almost always involve underdetermined systems, especially when routing policy has to be determined. The maximum entropy method (MEM) is a relatively new technique for solving underdetermined systems which has been successfully applied in many different areas. It is intuitively clear that an optimal network should not have overloaded or underutilized links. The maximum entropy constraint favors uniform distribution and gives a starting topology and routing with smoothly distributed traffic that is expected to be close to the optimal solution. We adjusted the network design problem, primarily the routing feasibility, to the maximum entropy method requirements. Computationally feasible algorithm is developed which implements the standard maximum entropy method, includes adjustments for problems that do not involve probabilities initially, calculates a function that substitutes large sparse matrix, includes heuristic that speeds up calculations by avoiding to invert Jacobian matrix at each iteration, determines variables that define constraints for the routing feasibility, includes additional constraints that direct uniformity of the solution in the desirable direction, cancels opposing traffic and excludes underutilized links. Mentioned additional constraints are "soft", which is a unique feature of this algorithm, in the sense that they do not have to be satisfied; the solution will be pulled in the direction of satisfying them as much as possible. Some theoretical results are also established that direct initial approximation. Proposed algorithm computes a reasonable solution that is robust with respect to often required dynamic changes of the cost function. The maximum entropy solution can be a good starting point for further optimization considering that the cost function with delay penalties involves queuing theory that is usually computationally expensive.
Efficient Pre-Coding Techniques for Wavelet-Based Image Compression∗

The principle of transform coding is a successfully established concept in image compression. In this paper we introduce a coding method using a fast wavelet transform and an uniform quantizer combined with a new framework of pre-coding techniques which are based on the concepts of partitioning, aggregation and conditional coding (PACC). Following these concepts, the data object emerging from the quantizer will be first partitioned into different subsources. Parts of correlations within and between different subsources will then be captured by aggregating homogeneous elements into data structures like run-length codes or zerotrees. By using models based on conditional probabilities we are able to recover correlations between the structures constructed before as well as cross-correlations between different subsources which will be utilized in a final arithmetic coding stage. Experimental results show that our proposed coding methods have a rate-distortion (RD) performance comparable to or even better than the best zerotree-based still image coders in the published literature with the advantage of a less demanding computational complexity. In addition, we propose and evaluate a wavelet-based video coding algorithm which outperforms the very efficient MPEG-4 Video Verification Model (VM 5.1) in both subjective and objective quality.
Traffic signal optimization for partially observable traffic system and low penetration rate of connected vehicles

Observability and controllability are two critical requirements for a partially observable transportation system. This paper proposes a stepwise signal optimization framework with connected vehicle (CV) data as input to solve both challenges. First, a Bayesian deduction method based on low‐penetration CV data is established to estimate the traffic volume. Thereafter, an offline signal optimization model is constructed to simultaneously optimize the flexible lane settings and signal timings, which are set as the prior information for the third step. In the third step, an online deep recurrent Q‐learning (DRQN) signal optimization model dynamically adjusts signal settings based on real‐time traffic information. Numerical experiments demonstrate that the model outperforms the actuated control, the online DQRN model without offline filter, and the back‐pressure model by 9%–66% and 7%–29% in two networks. This study innovatively combines traffic state estimation and traffic signal control as an integrated process. It contributes to an improved understanding of traffic control in a CV environment and lays a solid foundation for future traffic control strategies.
The versatility of tensor fascia lata allografts for soft tissue reconstruction

The purpose of this retrospective study was to introduce our successful use of tensor fascia lata allograft to reconstruct various soft tissue defects. Since May 2021, we have applied tensor fascia lata allografts in eight cases. A frozen type of fascia of 0.6 mm thickness was used in all cases, and allografts were covered by vascularized soft tissue. We used tensor fascia lata allograft in eight cases to cover the infected wounds, donor site closure, and pedicle protections. These were abdominal wall and back reconstructions following rectus muscle and latissimus dorsi muscle harvest, coverage of infected spine wound after posterior fusion, pressure ulcer reconstruction, and pedicle protection of free and pedicle flaps. The follow‐up periods were from one to 14 months. None of the cases showed wound problems after initial reconstruction using tensor fascia lata allografts. Tensor fascia lata allograft could be an excellent cost‐effective surgical option comparable to autologous tissue grafts. Level of evidence: IV.
Chunking in Visual Working Memory: Are Visual Features of Real-World Objects Stored in Chunks?

Are visual features of real-world objects stored as bound units? Previous research has shown that simple visual features (e.g., colored squares or geometric shapes) can be effectively bound together when forming predictable pairs in memory tasks. Through a “memory compression” process, observers can take advantage of these features to compress them into a chunk. However, a recent study found that visual features in real-world objects are stored independently. In the present study, we explored this issue by using drawings of fruits as memory stimuli, presenting four pictures of fruit in separate test trials in which we required observers to remember eight total features (i.e., four colors and four shapes). In the congruent trials, the color of the fruit matched its natural appearance (e.g., a red apple), while in incongruent trials, the color of the fruit mismatched its natural appearance (e.g., a red banana). We paired the shape of the fruits randomly with a color (without replacement). According to chunking theory, if visual features of real-world objects are stored in a chunk, the highest memory capacity should be accompanied by the longest response time in congruent trials due to an extra decoding process required from the chunk. We did find that participants had the highest memory capacity in the congruent condition, but their response times in the congruent condition were significantly faster than in the incongruent condition. Thus, observers did not undergo a decoding process in the congruent condition, and we concluded that visual features in real-world objects are not stored in a chunk.
Identifying trajectories of fatigue in patients with primary mitochondrial disease due to the m.3243A > G variant

Severe fatigue is a common complaint in patients with primary mitochondrial disease. However, less is known about the course of fatigue over time. This longitudinal observational cohort study of patients with the mitochondrial DNA 3243 A>G variant explored trajectories of fatigue over 2 years, and characteristics of patients within these fatigue trajectories. Fifty‐three adult patients treated at the Radboud University Medical Center Nijmegen were included. The majority of the patients reported consistent, severe fatigue (41%), followed by patients with a mixed pattern of severe and mild fatigue (36%). Then, 23% of patients reported stable mild fatigue levels. Patients with a stable high fatigue trajectory were characterized by higher disease manifestations scores, more clinically relevant mental health symptoms, and lower psychosocial functioning and quality of life compared to patients reporting stable low fatigue levels. Fatigue at baseline and disease manifestation scores predicted fatigue severity at the 2‐year assessment (57% explained variance). This study demonstrates that severe fatigue is a common and stable complaint in the majority of patients. Clinicians should be aware of severe fatigue in patients with moderate to severe disease manifestation scores on the Newcastle Mitochondrial Disease Scale, the high prevalence of clinically relevant mental health symptoms and overall impact on quality of life in these patients. Screening of fatigue and psychosocial variables will guide suitable individualized treatment to improve the quality of life.
Andrographis Echioides Molecular Characterization and Phylogenetic Study Using DNA Barcoding Technique

With the advancement in various molecular diagnostic tools, DNA Barcoding has emerged as a gold standard molecular diagnostic tool across the globe. Since ancient times, medicinal plants have been widely used in Indian Ayurvedic medicine for treating a variety of ailments. Plants of the genus Andrographis have been extensively used for treating different types of ailments. In this study, rarely studied medicinal plant species were isolated, sequenced at the genetic level and studied for their evolutionary characteristics using phylogenetic analysis. In the present study, the identity of A. echioides was confirmed by targeting different barcoding genes such as ribulose-bisphosphate carboxylase, internal transcribed spacer, RNA polymerase-beta subunit, maturase K, and photosystem II protein D1 genes using a phylogenetic approach. After successful isolation and amplification of genomic DNA, specific primers were utilised for sequencing of each barcoding gene, followed by nucleotide BLAST analysis to determine the sequence percent identity of each gene with that from other plant species. The best homologs were then utilised for conducting phylogenetic analysis which confirmed the identity of the plant as Andrographis echioides.
Scientific accompaniment: a new model for integrating program development, evidence and evaluation

Purpose Calls for the development and dissemination of evidence-based programs to support children and families have been increasing for decades, but progress has been slow. This paper aims to argue that a singular focus on evaluation has limited the ways in which science and research is incorporated into program development, and advocate instead for the use of a new concept, “scientific accompaniment,” to expand and guide program development and testing. Design/methodology/approach A heuristic is provided to guide research–practice teams in assessing the program’s developmental stage and level of evidence. Findings In an idealized pathway, scientific accompaniment begins early in program development, with ongoing input from both practitioners and researchers, resulting in programs that are both effective and scalable. The heuristic also provides guidance for how to “catch up” on evidence when program development and science utilization are out of sync. Originality/value While implementation models provide ideas on improving the use of evidence-based practices, social service programs suffer from a significant lack of research and evaluation. Evaluation resources are typically not used by social service program developers and collaboration with researchers happens late in program development, if at all. There are few resources or models that encourage and guide the use of science and evaluation across program development.
Analysis of wide-IF-band 65 nm-CMOS mixer for 77-110 GHz radio-astronomical receiver design

This manuscript presents the design of a W-band receiver in which an radio frequency-low noise amplifier (RF-LNA), a wideband mixer, intermediate frequency (IF) amplification, a local oscillator frequency (LO) tripler and a driving amplifier are all integrated into one single chip of 1050 × 820 μm 2 . To effectively extend the mixer's IF bandwidth while retaining its conversion gain, impacts of the mixing transistor's drain bias and output loading impedance are explored using a dual-modulation conversion-matrix method, which allows both the LO-induced transconductance modulation and channel-conductance modulation to be considered simultaneously. It is shown that, by merging the input capacitance of the IF amplifier into a high-impedance artificial transmission line, an actively biased mixer can have constant conversion gain over broad bandwidth. A 77–110 GHz 65 nm-complementary metal-oxide-semiconductor (CMOS) receiver with 33 GHz IF bandwidth is then designed and measured. Its conversion gain and noise figure are 10 and 20 dB, respectively, and the input-referred P1 dB is −15 dBm; the overall power consumption is 330 mW under 1.3 V drain bias.
Investigating the high‐shear mixed catalytic esterification of lauric acid with methanol using central composite design

Lauric acid (LA) is the precursor of methyl laurate (ML), which is identified as a valuable large cetane number methyl ester. In this work, LA is converted via high‐shear mixing (HSM)‐assisted homogeneous acid esterification with methanol at varying reaction conditions. A central composite design (CCD) is used to investigate formally the simultaneous influence of methanol:LA ratio (2‐13 mol/mol), temperature (30°C‐60°C), mixing speed (500‐2000 rpm), catalyst mass (0.25‐4.0 g) and time (1‐12 min) on LA conversion. By combining CCD statistical results and response surface methodology, it is found that catalyst mass, methanol:LA ratio and temperature are the most influencing factors on LA conversion, while only the quadratic effects of catalyst mass and methanol:LA and three of the 10 possible two‐way linear interactions, viz., methanol:LA ratio‐catalyst mass, temperature‐time and temperature‐catalyst mass, are statistically significant. Obtained numerically, the maximum LA conversion (92%) is achieved at 60°C, 13 methanol:LA ratio, 4.0 g of catalyst, 2000 rpm and 12 min. Interestingly, assisting the said esterification by HSM results in a little mixing speed‐sensitive process with low mixing speed and reduced time operation being sufficient to conduct the reaction efficiently.