Miracle-dz/UniFAR-FTUs huggingface dataset:
Narratives of Herbal Medicine Utilisation in the United Kingdom: Scoping Literature Review

Using thematic analysis of existing literature, this scoping review aims to explore the narratives of people using herbal medicine (HM) in the United Kingdom. Understanding who is using HM and why will enable better ways of facilitating the use of HM, as well as assist in designing future research. Ethnic groups were found to be primary users of HM in the United Kingdom. A sense of heritage continues to be important for these participants as it allows tradition and culture to stay alive within communities, as well as the ritualistic purposes of these plants. For women, another key demographic, concepts surrounding the naturalness of HMs are associated with the idea of femineity and self-healing. A reoccurring theme in the literature focusing on both ethnic groups and women’s perceptions is the judgement from healthcare practitioners/professionals (HCPs) when addressing the use of HM. However, studies that investigated the perceptions of HCPs on HM confirmed that they often were supportive of using HM where standard treatments had been unsuccessful, and if a patient had anecdotal evidence of a herb having been effective. Delving deeper into public narratives of HM usage will allow conventional healthcare systems to effectively integrate alternative approaches, as well as ensuring that future research into the benefits of HMs is relevant to how people use them.
Assisted restoration interventions drive functional recovery of tropical wet forest tree communities

Choosing appropriate forest restoration interventions is challenging. Natural regeneration can rapidly facilitate forest recovery in many situations. However, barriers such as dispersal limitation and competition with non-native species can require assisted restoration approaches to facilitate plant community recovery. We used a study that has directly compared the outcomes of tropical wet forest restoration interventions across 11 replicate sites in southern Costa Rica. Within this framework, we examined the functional recovery trajectories of recruiting tree sapling communities across a gradient of restoration interventions including low (natural regeneration), intermediate (applied nucleation), and high (plantation) initial resource-investment, which we compared to remnant reference forest. We collated leaf and stem functional traits for tree species that comprised the bulk of recruiting saplings, then determined how community-weighted trait means and functional diversity metrics changed over a decade across treatments. Results show that assisted restoration approaches (applied nucleation, plantation) sped the development of more functionally diverse tree communities, more than tripling the functional richness (FRic) of recruiting communities when compared to natural regeneration. However, functional dispersion (i.e., the trait range of dominant species) was equivalent across interventions, and between 28 and 44% lower than remnant forest, indicating that increases in FRic under assisted restoration were driven by species recruiting in low abundances (<10 individuals across treatments). Recruits in assisted restoration treatments also had 10–15% tougher, less-palatable leaves, and leaves were even tougher in reference forest, which could be driven by increasing herbivory pressure along the gradient of interventions. Results show that tracking simple metrics such as species richness can mask a more mechanistic understanding of ecosystem recovery that is elucidated by taking a functional trait-driven approach toward evaluating outcomes. For example, our work identified a paucity of dense-wooded species recruiting across restoration interventions, wood density was 11–13% lower in restoration treatments than reference forests, underscoring such species as prime targets for enrichment planting. Overall, findings suggest that assisted restoration can catalyze the functional recovery of naturally recruiting tree communities in landscapes that are slow to recover naturally and highlight the importance of evaluating how different components of functional diversity shift over time to fully understand restoration outcomes.
Elucidation of the di-c-glycosylation steps during biosynthesis of the antitumor antibiotic, kidamycin

Kidamycins belong to the pluramycin family of antitumor antibiotics that contain di-C-glycosylated angucycline. Owing to its interesting biological activity, several synthetic derivatives of kidamycins are currently being developed. However, the synthesis of these complex structural compounds with unusual C-glycosylated residues is difficult. In the kidamycin-producing Streptomyces sp. W2061 strain, the genes encoding the biosynthetic enzymes responsible for the structural features of kidamycin were identified. Two glycosyltransferase-coding genes, kid7 and kid21, were found in the kidamycin biosynthetic gene cluster (BGC). Gene inactivation studies revealed that the subsequent glycosylation steps occurred in a sequential manner, in which Kid7 first attached N,N-dimethylvancosamine to the C10 position of angucycline aglycone, following which Kid21 transferred an anglosamine moiety to C8 of the C10-glycosylated angucycline. Therefore, this is the first report to reveal the sequential biosynthetic steps of the unique C-glycosylated amino-deoxyhexoses of kidamycin. Additionally, we confirmed that all three methyltransferases (Kid4, Kid9, and Kid24) present in this BGC were involved in the biosynthesis of these amino-deoxyhexoses, N,N-dimethylvancosamine and anglosamine. Aglycone compounds and the mono-C-glycosylated compound obtained in this process will be used as substrates for the development of synthetic derivatives in the future.
Fine-scale foraging segregation in a green turtle (Chelonia mydas) feeding ground in the Bijagós archipelago, Guinea Bissau

Green turtles (Chelonia mydas) are highly dependent on neritic foraging areas throughout much of their life. Still, knowledge of recruitment dynamics, foraging habits, and habitat use in these areas is limited. Here, we evaluated how the distribution and food preferences of green sea turtles from different life stages varied within a foraging aggregation. We focused on two islands in Guinea-Bissau, Unhocomo and Unhocomozinho, using water captures and survey dives to record habitat use and characteristics, and stable isotopes to infer diet. Additionally, we used stable isotopes to infer their diet. Two habitat types were sampled: deeper (2.26 ± 0.4 m) rocky sites fringed by mangrove with macroalgae, and sandy shallows (1.37 ± 0.12 m) surrounded by rocky reefs with macroalgae and seagrass. The two benthic communities were similar isotopically and in terms of species composition, except for the presence or absence of seagrass, which had unique signatures. We captured 89 turtles ranging from 35 cm to 97 cm in curved carapace length (i.e., juvenile to adult stages). Size distribution was habitat-dependent, with most smaller turtles present in sandy shallows and larger turtles favoring slightly deeper rocky sites. Turtle isotopic signatures differed between the habitat of capture, regardless of size, revealing a marked dichotomy in foraging preference. All turtles fed primarily on macroalgae, mostly rhodophytes. However, individuals captured in sandy habitats had evident seagrass skewed isotopic signatures. Larger turtles may be unable to use the more diverse shallower foraging sites due to increased vulnerability to predation. Despite the proximity of the sampled foraging sites (2.7 km apart), the two foraging subgroups seem to maintain consistently different feeding habits. Our study highlights how heterogeneous green turtle foraging habits can be within populations, even at small geographic scales.
Genotyping-by-sequencing of Canada’s apple biodiversity collection

There are over 10,000 named apple (Malus X. domestica Borkh) cultivars (Way et al., 1991), but most apple production relies on a small number of elite cultivars. These elite cultivars are also the primary source of breeding material used when generating new cultivars (Migicovsky et al., 2021a). Apple production and improvement could greatly benefit from incorporating more diverse cultivars for purposes including disease resistance (Khan and Korban, 2022) and unusual fruit attributes (Migicovsky and Myles, 2017). Before leveraging such potential benefits, it would first be necessary to comprehensively assess phenomic and genomic diversity across diverse apples. Apples, like many other woody perennials, are obligately outcrossing and highly heterozygous, meaning that in order to retain genetically identical individuals over time, clonal propagation is used (Miller and Gross, 2011). As a result, living germplasm collections are critical for the ex situ conservation of these woody perennials. However, these collections may also serve other purposes including as genetic mapping populations for crop improvement (Migicovsky et al., 2019). Canada’s Apple Biodiversity Collection (ABC) is one of the most diverse collections of apples in the world, which was designed to enable genetic mapping. The ABC is located at the Agriculture and Agri-Food Canada (AAFC) Kentville Research and Development Centre in Nova Scotia, Canada. A comprehensive description of the ABC was recently published (Watts et al., 2021). Briefly, the collection consists primarily of apple accessions from the United States Department of Agriculture (USDA) Plant Genetic Resources Unit apple germplasm collection in Geneva, New York, USDA, but also includes additional accessions from Canada. Although there are currently only 1,119 accessions (planted in duplicated) in the ABC, initial grafting efforts included additional accessions in order to ensure backups were available. As a result, the dataset presented here includes some accessions that are not planted in the ABC. However, because sequencing data were available for these accessions, we have retained them. The accessions primarily belong to the cultivated apple, M. domestica, but also include Malus sieversii (Ledeb.) M. Roem. , the primary progenitor species of M. domestica. In addition to phenotypic descriptions of the ABC, sequencing the accessions in the collection provides a valuable resource not only for researchers working on the collection, OPEN ACCESS
Exosomes in pathogenesis, diagnosis, and treatment of pulmonary fibrosis

Pulmonary fibrosis (PF) is a group of interstitial lung diseases that seriously endanger human life and health. Despite the current advances in research on the pathogenesis and treatment of PF, the overall quality of survival and survival rates of PF patients remain low, prompting the search for more effective therapeutic approaches. Exosomes are nanoscale vesicles with diameters ranging from approximately 30–150 nm, capable of transporting a variety of molecules in the body and mediating intercellular communication. There is an increasing number of studies focusing on the role of exosomes in PF. This review demonstrates the significance of exosomes in the pathogenesis, diagnosis, and treatment of PF. Exosomes are able to influence inflammatory, immune, and extracellular matrix deposition processes in PF and regulate the corresponding cytokines. Some exosomes detected in sputum, blood, and bronchoalveolar lavage fluid may be used as potential diagnostic and prognostic biomarkers for PF. Exosomes derived from several cells, such as mesenchymal stem cells, have demonstrated potential as PF therapeutic agents. Drug delivery systems using exosomes may also provide new insights into PF therapy.
Bayesian Uncertainty Modelling for Cloud Workload Prediction

Providers of cloud computing systems need to manage resources carefully to meet the desired Quality of Service and reduce waste due to overallocation. An accurate prediction of future demand is crucial to allocate resources to service requests without excessive delays. Current state-of-the-art methods such as Long Short-Term Memory-based models make only point forecasts of demand without considering the uncertainty in their predictions. Forecasting a distribution would provide a more comprehensive picture and inform resource scheduler decisions. We investigate Bayesian Neural Networks and deep learning models to predict workload distribution and evaluate them on the time series forecasting of CPU and memory workload of 8 clusters on the Google Cloud data centre. Experiments show that the proposed models provide accurate demand prediction and better estimations of resource usage bounds, reducing overprediction and total predicted resources, while avoiding underprediction. These approaches have good runtime performance making them applicable for practitioners.
Improving 5G NR Uplink Channel Estimation with Artificial Neural Networks: A Practical Study on NR PUSCH Receiver

In this paper, an artificial neural network-based approach is proposed to improve uplink channel estimation for 5G New Radio (NR) Physical Uplink Shared Channel (PUSCH). Specifically, the Artificial Neural Network (ANN) is used to in-terpolate the least squares (LS) estimates obtained from PUSCH demodulation reference signal (DMRS) over the entire resource grid. The conventional and ANN-based methods are compared with respect to their channel estimation accuracy and corresponding decoding performance for different 3GPP NR-compliant configurations and under different channel conditions. A special focus is given to high-speed cases in which channel frequency response (CFR) changes rapidly. It is observed that ANN-based method can yield more accurate channel estimation with less DMRS signaling, leading to better decoding performance and enhanced uplink data capacity. It is also noted that with proper training ANN-based interpolator can develop noise and inter-layer interference mitigation capability.
Automatic Identification System Signals propagation in troposphere ducting conditions

This work draws the attention on the influence of tropospheric ducting on the propagation of Automatic Identification System (AIS) frequencies. The propagation of AIS signals in the cases of elevated range-dependent and surface-based range-dependent ducts formed by elevated trapping layers is studied. The importance of the accuracy of the refractivity profiles' approximation is briefly discussed. The AIS signals propagation is modeled by the parabolic wave equation method for which the refractivity profiles serve as environmental input. The propagation simulations results show that the AIS frequencies can be more affected by elevated trapping layers leading to the formation of elevated ducts than by large surface-based ducts also formed by elevated layers. The results once again outline that the reconstruction of refractivity profiles must be as accurate as possible and that for long maritime paths multiple profiles may be needed to obtain reliable AIS data.
Evaluation of performance degradation of high temperature proton exchange membrane fuel cells using a simple start-stop testing protocol

The objective of this paper is to determine the effect of accelerated start-stop cycling protocol on a high temperature polymer electrolyte membrane fuel cell performance under operating temperature of 160 $^{\circ}\mathbf{C}$ and current density of 0.4 A cm-2. The degradation rates during 200 start-stop cycles are analysed and discussed. The results are supported by polarization curves and electrochemical impedance spectroscopy. The overall degradation over 200 start-stop cycles is -49 mV and the average degradation rate per one cycle is -0.25 mV.
Optimization of Service Scheduling in Computing Force Network

To improve the users satisfaction requesting services in Computing Force Network(CFN), the issues and challenges of existing service scheduling mechanisms are examined firstly in this paper. Then in view of users' specific preference and expectation for services, a service optimization scheduling algorithm based on Bkd-tree is proposed, taking service response time, service scheduling cost, availability, and successability into consideration. The method covers a search algorithm and a reconstructing strategy. To improve the efficiency of selecting the service instances that satisfy user expectation, a pruning strategy is used to narrow search space. To support dynamic updates of the service instances, a reconstructing strategy is proposed. The experimental results show that the improved service scheduling algorithm can schedule service instances with the best overall performance while maintaining higher efficiency.
Congestion Detection and Link Control via Feedback in RDMA Transmission

Researchers and practitioners are exploiting Remote Direct Memory Access (RDMA) technology to improve the efficiency of distributed machine learning and meet the demands of data-center applications. RDMA requires lossless network link to fully unleash its power. RDMA Over Converged Ethernet (RoCE) v2 focuses on congestion control, but fails to achieve efficient packet loss recovery; Improved RoCE NIC (IRN) addresses this issue based on RoCEv2, but does not use the Priority-based Flow Control (PFC) to maintain the advantage of RoCEv2 in detecting congestion. This paper proposes a method of congestion detection and link control via feedback in RDMA transmission, namely Feedback Data Flow Control (FDFC), that does not rely on PFC. FDFC detects and controls the link condition in real time to achieve the goals of precise detection, congestion control, and efficient packet loss recovering.
Feature Query Networks: Neural Surface Description for Camera Pose Refinement

Accurate 6-DoF camera pose estimation in known environments can be a very challenging task, especially when the query image was captured at viewpoints strongly differing from the set of reference camera poses. While structure-based methods have proved to deliver accurate camera pose estimates, they rely on pre-computed 3D descriptors coming from reference images often misaligned with query images. This discrepancy can subsequently harm downstream camera pose estimation tasks. In this paper we introduce the Feature Query Network (FQN), a ray-based descriptor regressor that can be used to query descriptors at known 3D locations under novel viewpoints. We show that the FQN is able to model viewpoint-dependency of high-dimensional keypoint descriptors and bring significant relative improvements to structure-based visual localization baselines.
On Differences Between Transmission Line-based Wave Digital Models of Gysel Power Dividers

The paper proposes different designs of the wave digital model (WDM) of three-port microwave device. By adopting transmission line (TL)-based wave digital approach to design the model, the wave-flow equivalents to TLs and circuit elements, as well as interconnection networks are required. As a case study, two different Gysel power dividers are modeled and their differently generated TL-based WDMs are compared regarding magnitude and phase responses.
Diversity-Based Media Search

In this paper, we outline a method for searching a set of information based on both the individual diversity of the constituent elements of the set as well as its overall ensemble diversity. Using the example of searching user accounts on Instagram, we are able to perform searches based on the representative diversity (across race, age, gender, body type, skin tone, and disability) of the posts as well as the overall diversity of the search results.
Classification of Wafer Backside Images Via Fasterrcnn-Based Neural Network

Automatic classification of defect patterns in wafer backside image stands as a challenging problem in semiconductor manufacturing industry, especially different wafer backside defects start to impact the front side pattern defined and cause yield loss because of the design rule shrinkage. Deep neural network has recently shown decent progress in image classification, including wafer bin maps, but has rarely been applied in wafer backside image identification as far as we are aware. A Faster-RCNN based neural network model has been proposed for defect pattern detection and classification in wafer backside images. As a prepositive strategy, customized data augmentation has been proposed to diminish the expense of explicit manual supervision. Meanwhile, Class Activation Map (CAM) of each image has been generated to guide the training process. The results of this practical study showed the validity and practical viability of the model even at little expense of manual-labeled images.
Paramagnetic Susceptibility of CoCl2·6H2O and its Deuterated Salts

The paramagnetic susceptibility of a single crystal of CoCl 2 ·6H 2 O was measured by the usual a-c bridge method. Transition to the antiferromagnetic state was observed at about 3.3°K. The easy axis is along the crystalline c axis. The g values determined in the paramagnetic region, below 20°K are: g c = g b =4.9 and g c ⊥ =2.9. Some considerations about the anisotropic susceptibilities in the paramagnetic state are given from the standpoint of crystalline field theory. The exchange interaction in this salt is discussed considering the interaction path between the magnetic ions which involves the linkages of chlorine atoms or water molecules. The measurements on the deuterated salts are intended to make clear the effect of the hydrogen bond on the magnetic exchange interaction.
Influence of Parasitic Capacitance and Resistance on Performance of 6T-SRAM for Advanced CMOS Circuits Design

In this paper, the impacts of parasitic capacitance and resistance on the performance of 6T-SRAM are investigated comprehensively by a design-technology co-optimization (DTCO) method and TCAD simulation. It is found that the impact of parasitic capacitance on 6T-SRAM read and write operations is all adverse. However, the influence of parasitic resistance on 6T-SRAM is double-sided and can be used to improve the performance of 6T-SRAM. An optimization strategy of layout and route is proposed for SRAM with high speed, high noise stability or high read and write capabilities. These results provide a useful guidance for the 6T-SRAM layout design toward advanced node CMOS circuits.
Outage-Constrained Transceiver Power Loading: A Deep Learning Approach to Robust Massive MIMO Downlink

In this research, we consider massive multiple-input multiple-output (MIMO) communication systems, where the base station (BS) equipped with a uniform planar array (UPA) serves several multi-antennas users based on imperfect channel state information (CSI). By utilizing the statistical properties of channel estimation error, our purpose is to design beamformers to minimize the total transmitted power, subject to certain quality of service (QoS) outage probability. Inspired by the Chebyshev Inequality, we relax the probabilistic QoS constraints by constructing a function on the channel estimation error, whose mean value is a significant multiple of its standard deviation, where the multiple is determined by the error distribution assumption and required outage probability. Then we develop an iterative algorithm correspondingly, which is able to properly allocate the transmitted power. The standard deviation plays a crucial role in power allocation, yet is time-consuming to be solved by the iterative manner. For this reason, we construct a deep learning (DL)-based framework to obtain the standard deviation directly. Experimental results prove the proposed robust transceiver can improve the outage probability remarkably, while the DL-based framework can distinctly reduce the computational complexity.