Miracle-dz/UniFAR-FTUs huggingface dataset:
Advancement in hemp ﬁ bre polymer composites: a comprehensive review

: Natural fibres as reinforcement for composite materials have witnessed a resurgence of interest in the past few years, largely due to ecological concerns, legislative directives and technological advancements. Hemp is one of the most popular natural fibres used as reinforcement in polymers owing to its superior mechanical properties. At present, hemp fibres have attracted the global interest of design engineers for developing composites having extensive applications in automobiles, electrical, construction and packaging industries. Although several literatures explore different aspects of hemp fibre reinforced composites, there is no proper literature that summarizes the surface treatment, processing techniques, mechanical performance and hybridization of hemp fibre composites. This review is envisioned to put forth a comprehensive summary of the research work published in the field of hemp fibre reinforced composites with special reference to the structure of hemp fibres, different methods for surface modification and processing techniques to fabricate the composites based on thermoplastic, ther-moset and biopolymers. The paper also focuses on the effects of surface treatment on the mechanical performance of the composites.
Influence of carbon sources on the physicochemical properties and yield of bacterial cellulose production for drug delivery

: Bacterial cellulose (BC) is a biopolymer of great interest for application in various industrial and medical areas, due to its peculiar characteristics of biocompatibility, tensile strength, high water retention capacity and crystallinity. BC can be produced using culture media with different nutrient sources, under static or agitated cultivation conditions. The objectives of this work were to produce BC membranes, using the bacterial strain Komagateibacter hansenii ATCC 23769, in static culture, using media containing different sources of carbon (glucose, fructose, glycerol and ethanol) and nitrogen (yeast extract and peptone) and evaluate comparatively dry mass yield, swelling capacity. The BC obtained processed and characterized by Fourier transform spectroscopy (FTIR) and field emission scanning electron microscopy (FEG-SEM) to evaluate the influence of cultivation media in relation to parameters fiber density, thickness and entanglement of the BC, characteristics of extreme importance for future biomedical applications. The BC produced were impregnated with antibiotic amoxicillin (AMX) and tested for the sustained release capacity of this drug. Membranes with significant swelling capacity were obtained. FTIR spectra demonstrated the maintenance of vibrational modes for the main functional groups of bacterial cellulose, therefore, the different compositions of carbon and nitrogen sources would not influence the chemical properties of the obtained BC membranes. The result of the SEM analyzes allowed the measurement of fiber thicknesses, analysis of the degrees of intertwining and porosity, demonstrating differences in the BC produced in the different cultivation media, in relation to these parameters. All BC membranes produced were capable of releasing, in a sustained manner, for 48 h, the antibiotic AMP and the best results for all tests were produced in M2, M3 M4 and FRU media. The results demonstrate the potential of the SBC to be able to contribute to the design of new drug delivery systems with biomedical applications.
Department of Land Economy Environment, Law & Economics

As an alternative carbon pricing tool to carbon taxes, carbon emission trading systems (ETS) combine market incentives with policy targets. Inevitably, ETS markets are linked to domestic and international political environment. Yet, there is a lack of research on the relationship between political uncertainty and activities in carbon emission trading markets. Using data from four carbon ETS markets in China between 2014 and 2019, our logistic regression and AR(1)-GARCH estimations suggest a negative relationship between political uncertainty and carbon emission trading volume, and a risk premium on carbon emission trading daily returns for political uncertainty in China. We also identified significant variations among the responses to political uncertainty in these markets. The locally focused empirical strategy distinguishes our research from existing studies where the analyses are at the national level. The empirical findings from the four largest carbon ETS markets in China are helpful for policymakers in China and beyond to understand how ETS markets work in China, where political uncertainty has always been playing an important role in economic activities.
Capture of Group A Streptococcus by Open-Microfluidic CandyCollect Device in Pediatric Patients

Objective Determine the feasibility of using CandyCollect, a lollipop-inspired open-microfluidic pathogen collection device, to capture Group A Streptococcus (GAS) and compare user preference for CandyCollect, conventional pharyngeal swabs, or mouth swabs in children with pharyngitis and their caregivers. Methods Children (aged 5-17 years) with pharyngitis who tested positive for GAS via rapid antigen detection test from a pharyngeal swab were included in the study at an ambulatory care clinic in Madison, WI. Caregiver-child dyads were enrolled in the study so that both could provide user feedback regarding the experience. Immediately after the clinical visit, children were asked to suck on two mouth swabs and two CandyCollect devices. GAS presence was confirmed and measured with qPCR. Results All child participants (30/30) were positive for GAS by qPCR on both the mouth swab and CandyCollect. Caregivers ranked CandyCollect as a good sampling method overall (27/30), and all caregivers (30/30) would recommend the CandyCollect for children 5 years and older. Twenty-three of 30 children ''really like'' the taste and 24/30 would like to use the CandyCollect if another test was needed. All caregivers (30/30) and most children (28/30) would be willing to use the CandyCollect device at home. Conclusion All participants (30/30) tested positive for GAS on all three collection methods (pharyngeal swab, mouth swab, and CandyCollect). While both caregivers and children like the CandyCollect device, some caregivers would prefer a shorter collection time. Future work includes extending the capabilities of CandyCollect to additional pathogens and shortening collection time while maintaining the attractive form of the device.
Diagnostic performance of serum CA-125 for overall and complicated acute appendicitis: a systematic review and meta-analysis.

Background: This study aimed to analyze the diagnostic performance of serum CA-125 in acute appendicitis (AA). Methods: This review was registered in PROSPERO (CRD42023450988). We included prospective or retrospective original clinical studies evaluating the diagnostic performance of serum CA-125 in AA. A search was conducted in PubMed, Web of Science, Scopus, and OVID. Search terms and keywords were: (appendicitis OR appendectomy) AND (CA-125 OR CA125). Two independent reviewers selected the articles and extracted relevant data. Methodological quality was assessed using the QUADAS2 index. A synthesis of the results, standardization of the metrics, and three random-effect meta-analyses were performed. Results: Five studies with data from 533 participants (including 219 patients with a confirmed diagnosis of AA and 107 controls) were included in this review. The random-effect meta-analysis of serum CA-125 (AA vs controls) included 5 articles (125 AA and 70 controls) and resulted in a non-significant mean difference [95% CI] of -6.80 [-20.51,6.92] U/mL (p=0.33). The meta-analysis by subgroups that included only male patients resulted in a significant mean difference [95% CI] of 3.48 [0.46,6.49] U/mL (p=0.02) Conclusions: Although serum CA-125 does not appear to be a good overall marker for the diagnosis of AA, our subgroup analyses show that this marker could be useful for diagnosing AA in males. It also appears to be a potentially useful tool for discriminating complicated and uncomplicated AA. However, the limited number of included studies precludes drawing generalizable conclusions. Future prospective studies focused on males are required to confirm these findings.
Targeted drug delivery into glial scar using CAQK peptide in a mouse model of multiple sclerosis

Abstract In multiple sclerosis, lesions are formed in various areas of the CNS, which are characterized by reactive gliosis, immune cell infiltration, extracellular matrix changes and demyelination. CAQK peptide (peptide sequence: cysteine–alanine–glutamine–lysine) was previously introduced as a targeting peptide for the injured site of the brain. In the present study, we aimed to develop a multifunctional system using nanoparticles coated by CAQK peptide, to target the demyelinated lesions in animal model of multiple sclerosis. We investigated the binding of fluorescein amidite–labelled CAQK and fluorescein amidite–labelled CGGK (as control) on mouse brain sections. Then, the porous silicon nanoparticles were synthesized and coupled with fluorescein amidite–labelled CAQK. Five days after lysolecithin-induced demyelination, male mice were intravenously injected with methylprednisolone-loaded porous silicon nanoparticles conjugated to CAQK or the same amount of free methylprednisolone. Our results showed that fluorescein amidite–labelled CAQK recognizes demyelinated lesions in brain sections of animal brains injected with lysolecithin. In addition, intravenous application of methylprednisolone-loaded nanoparticle porous silicon conjugated to CAQK at a single dose of 0.24 mg reduced the levels of microglial activation and astrocyte reactivation in the lesions of mouse corpus callosum after 24 and 48 h. No significant effect was observed following the injection of the same dose of free methylprednisolone. CAQK seems a potential targeting peptide for delivering drugs or other biologically active chemicals/reagents to the CNS of patients with multiple sclerosis. Low-dose methylprednisolone in this targeted drug delivery system showed significant beneficial effect.
Evolution and Development of Tendrils in Bignonieae (Lamiales, Bignoniaceae)1

Abstract The tribe Bignonieae includes all Neotropical lianescent Bignoniaceae. The leaves of Bignonieae are generally 2- or 3-foliolate, with terminal leaflets modified into a tendril. These tendrils have varied morphologies and are thought to have been involved in the diversification of Bignonieae. Little, however, is still known about the biology and evolution of tendrils. This study investigated the evolution and development of tendril types in Bignonieae in order to further understand how changes in leaf morphogenesis led to current patterns of variation in tendril morphology. For that, we investigated the ontogeny of 11 species representing a wide diversity of tendril types (i.e., simple, trifid, and multifid) and used a recently published phylogeny of Bignonieae as the basis to reconstruct patterns of evolution for tendril types. For those analyses, we used maximum likelihood (ML) and maximum parsimony (MP) approaches, with both ACCTRAN and DELTRAN optimization schemes implemented in the latter. Ancestral character state reconstructions of tendril type suggest that the ancestral condition for the whole tribe (Core Bignonieae plus Perianthomega Bureau ex Baill.) is a lack of tendrils, whereas parsimony reconstructions indicate an ambiguous ancestral condition. However, all reconstructions suggest that trifid tendrils represent the ancestral condition for the Core Bignonieae. Other tendril types evolved subsequently through a series of developmental changes. Furthermore, tendril ontogenetic studies provided key information for the resolution of major ambiguities in the ancestral state reconstructions of tendril type in Bignonieae. For instance, in Bignonia callistegioides Cham. and B. prieurei DC. (simple tendrils), no traces of remnant lateral branches from a trifid-tendrilled ancestor were found, corroborating the ACCTRAN hypothesis of a simple tendril condition for the ancestor of that lineage. In Tanaecium pyramidatum (Rich.) L. G. Lohmann (trifid tendrils), on the other hand, we detected a pattern and rate of tendril differentiation that initially followed the same pattern seen in taxa with simple tendrils (i.e., Cuspidaria DC. and Fridericia Mart.), with a developmental delay relative to other trifid-tendrilled species, favoring a simple-tendrilled ancestor hypothesis and supporting the ML and ACCTRAN optimizations. The interpretation of the ontogenetic data in light of a robust phylogenetic framework led to specific hypotheses about the evolutionary processes and respective changes in gene regulation that may have led to the current tendril morphologies found in Bignonieae. In particular, we suggest that tendril evolution involved heterochrony and hypothesize that changes in the expression of genes that are associated with compound leaf development may have led to the diversity of tendril morphology currently observed in Bignonieae.
Millimetric devices for nerve stimulation: a promising path towards miniaturization

Nerve stimulation is a rapidly developing field, demonstrating positive outcomes across several conditions. Despite potential benefits, current nerve stimulation devices are large, complicated, and are powered via implanted pulse generators. These factors necessitate invasive surgical implantation and limit potential applications. Reducing nerve stimulation devices to millimetric sizes would make these interventions less invasive and facilitate broader therapeutic applications. However, device miniaturization presents a serious engineering challenge. This review presents significant advancements from several groups that have overcome this challenge and developed millimetric-sized nerve stimulation devices. These are based on antennas, mini-coils, magneto-electric and opto-electronic materials, or receive ultrasound power. We highlight key design elements, findings from pilot studies, and present several considerations for future applications of these devices.
LiteVSR: Efficient Visual Speech Recognition by Learning from Speech Representations of Unlabeled Data

This paper proposes a novel, resource-efficient approach to Visual Speech Recognition (VSR) leveraging speech representations produced by any trained Automatic Speech Recognition (ASR) model. Moving away from the resource-intensive trends prevalent in recent literature, our method distills knowledge from a trained Conformer-based ASR model, achieving competitive performance on standard VSR benchmarks with significantly less resource utilization. Using unlabeled audio-visual data only, our baseline model achieves a word error rate (WER) of 47.4% and 54.7% on the LRS2 and LRS3 test benchmarks, respectively. After fine-tuning the model with limited labeled data, the word error rate reduces to 35% (LRS2) and 45.7% (LRS3). Our model can be trained on a single consumer-grade GPU within a few days and is capable of performing real-time end-to-end VSR on dated hardware, suggesting a path towards more accessible and resource-efficient VSR methodologies.
Smart Roads: Roadside Perception, Vehicle-Road Cooperation and Business Model

Smart roads have become an essential component of intelligent transportation systems (ITS). The roadside perception technology, a critical aspect of smart roads, utilizes various sensors, roadside units (RSUs), and edge computing devices to gather real-time traffic data for vehicle-road cooperation. However, the full potential of smart roads in improving the safety and efficiency of autonomous vehicles only can be realized through the mass deployment of roadside perception and communication devices. On the one hand, roadside devices require significant investment but can only achieve monitoring function currently, resulting in no profitability for investors. On the other hand, drivers lack trust in the safety of autonomous driving technology, making it difficult to promote large-scale commercial applications. To deal with the dilemma of mass deployment, we propose a novel smart-road vehicle-guiding architecture for vehicle-road cooperative autonomous driving, based on which we then propose the corresponding business model and analyze its benefits from both operator and driver perspectives. The numerical simulations validate that our proposed smart road solution can enhance driving safety and traffic efficiency. Moreover, we utilize the cost-benefit analysis (CBA) model to assess the economic advantages of the proposed business model which indicates that the smart highway that can provide vehicle-guided-driving services for autonomous vehicles yields more profit than the regular highway.
Embodied Adversarial Attack: A Dynamic Robust Physical Attack in Autonomous Driving

As physical adversarial attacks become extensively applied in unearthing the potential risk of security-critical scenarios, especially in autonomous driving, their vulnerability to environmental changes has also been brought to light. The non-robust nature of physical adversarial attack methods brings less-than-stable performance consequently. To enhance the robustness of physical adversarial attacks in the real world, instead of statically optimizing a robust adversarial example via an off-line training manner like the existing methods, this paper proposes a brand new robust adversarial attack framework: Embodied Adversarial Attack (EAA) from the perspective of dynamic adaptation, which aims to employ the paradigm of embodied intelligence: Perception-Decision-Control to dynamically adjust the optimal attack strategy according to the current situations in real time. For the perception module, given the challenge of needing simulation for the victim's viewpoint, EAA innovatively devises a Perspective Transformation Network to estimate the target's transformation from the attacker's perspective. For the decision and control module, EAA adopts the laser-a highly manipulable medium to implement physical attacks, and further trains an attack agent with reinforcement learning to make it capable of instantaneously determining the best attack strategy based on the perceived information. Finally, we apply our framework to the autonomous driving scenario. A variety of experiments verify the high effectiveness of our method under complex scenes.
FuXi-S2S: An accurate machine learning model for global subseasonal forecasts

Skillful subseasonal forecasts beyond 2 weeks are crucial for a wide range of applications across various sectors of society. Recently, state-of-the-art machine learning based weather forecasting models have made significant advancements, outperforming the high-resolution forecast (HRES) from the European Centre for Medium-Range Weather Forecasts (ECMWF). However, the full potential of machine learning models in subseasonal forecasts has yet to be fully explored. In this study, we introduce FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning based subseasonal forecasting model that provides global daily mean forecasts up to 42 days, covering 5 upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S integrates an enhanced FuXi base model with a perturbation module for flow-dependent perturbations in hidden features, and incorporates Perlin noise to perturb initial conditions. The model is developed using 72 years of daily statistics from ECMWF ERA5 reanalysis data. When compared to the ECMWF Subseasonal-to-Seasonal (S2S) reforecasts, the FuXi-S2S forecasts demonstrate superior deterministic and ensemble forecasts for total precipitation (TP), outgoing longwave radiation (OLR), and geopotential at 500 hPa (Z500). Although it shows slightly inferior performance in predicting 2-meter temperature (T2M), it has clear advantages over land area. Regarding the extreme forecasts, FuXi-S2S outperforms ECMWF S2S globally for TP. Furthermore, FuXi-S2S forecasts surpass the ECMWF S2S reforecasts in predicting the Madden Julian Oscillation (MJO), a key source of subseasonal predictability. They extend the skillful prediction of MJO from 30 days to 36 days.
Drones Guiding Drones: Cooperative Navigation of a Less-Equipped Micro Aerial Vehicle in Cluttered Environments

Reliable deployment of Unmanned Aerial Vehicles (UAVs) in cluttered unknown environments requires accurate sensors for Global Navigation Satellite System (GNSS)-denied localization and obstacle avoidance. Such a requirement limits the usage of cheap and micro-scale vehicles with constrained payload capacity if industrial-grade reliability and precision are required. This paper investigates the possibility of offloading the necessity to carry heavy sensors to another member of the UAV team while preserving the desired capability of the smaller robot intended for exploring narrow passages. A novel cooperative guidance framework offloading the sensing requirements from a minimalistic secondary UAV to a superior primary UAV is proposed. The primary UAV constructs a dense occupancy map of the environment and plans collision-free paths for both UAVs to ensure reaching the desired secondary UAV's goals even in areas not accessible by the bigger robot. The primary UAV guides the secondary UAV to follow the planned path while tracking the UAV using Light Detection and Ranging (LiDAR)-based relative localization. The proposed approach was verified in real-world experiments with a heterogeneous team of a 3D LiDAR-equipped primary UAV and a micro-scale camera-equipped secondary UAV moving autonomously through unknown cluttered GNSS-denied environments with the proposed framework running fully on board the UAVs.
LQG Information Design

A linear-quadratic-Gaussian (LQG) game is an incomplete information game with quadratic payoff functions and Gaussian payoff states. This study addresses an information design problem to identify an information structure that maximizes a quadratic objective function. Gaussian information structures are found to be optimal among all information structures. Furthermore, the optimal Gaussian information structure can be determined by semidefinite programming, which is a natural extension of linear programming. This paper provides sufficient conditions for the optimality and suboptimality of both no and full information disclosure. In addition, we characterize optimal information structures in symmetric LQG games and optimal public information structures in asymmetric LQG games, with each structure presented in a closed-form expression.
Children, Parents, and Misinformation on Social Media

Children encounter misinformation on social media in a similar capacity as their parents. Unlike their parents, children are an exceptionally vulnerable population because their cognitive abilities and emotional regulation are still maturing, rendering them more susceptible to misinformation and falsehoods online. Yet, little is known about children's experience with misinformation as well as what their parents think of the misinformation's effect on child development. To answer these questions, we combined a qualitative survey of parents (n=87) with semi-structured interviews of both parents and children (n=12). We found that children usually encounter deep fakes, memes with political context, or celebrity/influencer rumors on social media. Children revealed they"ask Siri"whether a social media video or post is true or not before they search on Google or ask their parents about it. Parents expressed discontent that their children are impressionable to misinformation, stating that the burden falls on them to help their children develop critical thinking skills for navigating falsehoods on social media. Here, the majority of parents felt that schools should also teach these skills as well as media literacy to their children. Misinformation, according to both parents and children affects the family relationships especially with grandparents with different political views than theirs.