Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution mechanism of dominant genera and species of bacteria under different river velocity

Flow velocity as the basic hydrodynamic condition significantly affects bacterial proliferation and biofilm formation, yet how different flow velocity influences the attachment and distribution of dominant bacteria in different ecological types is poorly understood. In this work, We collected samples from the bottom, side walls and water samples at the flow rates of 0.794m/s and 0.538m/s, respectively. Through high-throughput sequencing identification and analysis technology, it was found that the composition abundance of Acinetobacter and Psychrobacter, as dominant bacteria, could reach 70%-80% in the floating state, and the attached bacteria at low flow rates were about 20% higher than those at high flow rates. The results showed that the high flow rate inhibited the attachment behavior of dominant bacteria and changed the species composition structure in the overlapping area of multiple samples, which provided a new direction for the prevention and control of microbial corrosion and pollution in waters.
Protective role of Cecropin AD against LPS-induced intestinal mucosal injury in chickens

Introduction Cecropin AD (CAD), a renowned antimicrobial peptide, has shown promising potential in treating various bacterial infections. This study investigates the protective effects of CAD against lipopolysaccharide (LPS)-induced intestinal adversities in chickens. Methods Sixty SPF-grade chicks were divided into groups and exposed to different dosages of CAD, followed by LPS administration. The study assessed the impact of CAD on intestinal mucosal injury markers, oxidative stress, and inflammation. Results LPS significantly increased Diamine oxidase (DAO) and D-lactate (D-LA) levels, both indicators of intestinal mucosal injury. CAD treatment substantially attenuated these elevations, particularly at higher dosages. Additionally, CAD markedly reduced oxidative stress in intestinal tissues, as shown by normalized antioxidant levels and decreased reactive oxygen species. Histological analysis supported these findings, showing better-preserved villi structures in CAD-treated groups. Furthermore, CAD significantly reduced IL-6 and IL-8 expression post-LPS stimulation and effectively regulated the NLRP3 inflammasome pathway, decreasing associated factors like NLRP3, Caspase-1, IL-1b, and IL-18. Discussion The study demonstrates CAD's therapeutic potential in alleviating LPS-induced intestinal injuries. The protective effects are primarily attributed to its anti-inflammatory and antioxidative actions and modulation of the NLRP3 inflammasome pathway.
Total Knee Arthroplasty in Patients with Ipsilateral Hip Fusion: Technical Notes and Literature Review

Numerous studies report the success and outcomes of the total knee arthroplasty (TKA); however, few papers present patients with knee osteoarthritis and ipsilateral hip fusion. One controversy when treating patients requiring a TKA with prior ipsilateral hip fusion is whether to first perform a total hip arthroplasty (THA) of the fused hip, followed by the ipsilateral TKA, or to proceed with the TKA without replacing the hip; studies suggest that the position of the fused hip is a key factor when making this therapeutical decision. In addition, performing a TKA in patients with an ipsilateral fused hip may require modifications to the surgical technique generated by the lack of joint mobility in the hip. We identified 12 studies encompassing 30 patients with hip fusion and ipsilateral TKA in current orthopedic literature, but only six offered insights on patient positioning on the operating table during surgery. This study aims to review the current literature on patients with knee osteoarthritis and prior ipsilateral hip fusion and to present some technical considerations when performing a TKA on a 75-year-old patient with hip ankylosis who underwent a total ipsilateral knee arthroplasty in our clinic.
Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels

The maritime domain is the most utilised environment for bulk transportation, making maritime safety and security an important concern. A major aspect of maritime safety and security is maritime situational awareness. To achieve effective maritime situational awareness, recently many efforts have been made in automatic anomalous maritime vessel movement behaviour detection based on movement data provided by the Automatic Identification System (AIS). In this paper we present a review of state-of-the-art automatic anomalous maritime vessel behaviour detection techniques based on AIS movement data. First, we categorise some approaches proposed in the period 2011 to 2016 to automatically detect anomalous maritime vessel behaviour into distinct categories including statistical, machine learning and data mining, and provide an overview of them. Then we discuss some issues related to the proposed approaches and identify the trend in automatic detection of anomalous maritime vessel behaviour.
Jordanian Nurse Leaders’ Empowering Behaviors and Contributing Factors as Perceived by Staff Nurses

Background: Leader empowering behaviors have a positive effect on quality nursing care, nurses’ job satisfaction and organizational outcomes. Leaders empower nurses through motivating staff, enhancing the meaningfulness of work, facilitating nurses’ efforts to learn, grow and use their skills to participate in decision-making. They also exhibit confidence in employees’ abilities and performance, support them with information and resources, and increase their work autonomy. This study aimed to evaluate Jordanian nurse leaders’ empowering behaviors and shed light on the contributing factors as perceived by staff nurses. Methods: Descriptive, cross-sectional, correlational designs were used. A convenience sample of 414 registered nurses from 12 hospitals in Jordan was recruited to complete the leader empowering behaviors scale developed by Hui. Results: Jordanian staff nurses reflected moderate levels of leader empowering behaviors. Seven factors were found to affect Jordanian nurse leaders’ empowering behaviors as perceived by staff nurses: shift work, staff participation in continuing education programs, leadership style, hospital type, nurses’ age and experience, and daily census rate of the department. A multiple linear regression model debriefed three variables (continuing education programs, nurses’ experience, and shift work). These variables explained approximately 55% of the variance in leader empowering behaviors. Conclusion: This study deepens the understanding that nurses’ capabilities and experience are the most important factors in nurse leader empowering behaviors as perceived by Jordanian nurses. For better empowerment, nurse managers should encourage staff nurse participation in different continuing education programs to advance their skills and abilities, and enhance professional development.
Exploit Both Patterns and Structural Parameters by Multimodal Learning to Predict and Design Metasurfaces

A multimodal (MM) deep learning (DL) model is proposed that seamlessly combines the fully connected (FC) and convolutional layers to enhance the prediction accuracy and the design efficiency of metasurfaces. The convolutional layers in the model compensate for the inability of FC models to extract features from topology patterns in the metasurface. Specific regions instead of the whole metasurface are binarized by the equivalent-circuit (EC)-model-based region selection method to avoid heavy computational cost for the convolutional layers, which makes the proposed model applicable to multilayer metasurfaces with complex patterns. The proposed model is validated numerically by predicting and designing a complex frequency-selective rasorber (FSR). Numeric results demonstrate that the proposed model achieves 30.1% more prediction accuracy and 61% more design efficiency when compared with FC models to design the rasorber to satisfy specific performance requirements.
Cascaded Iterative Training Model and Parallel Multi-Classifiers for Visual Object Tracking

Because of the superiority in distinguishing the target from the background by learning mechanism, discriminative classifier attracts a great deal of attention in target detection and tracking systems. Generally, a discriminative classifier is learned from the initial image patch. However, the other algorithms, using only one image frame inhibit the performance of the classifier. A novel training strategy of cascade iteration, which is based on the multiple image frames, is used to initialize the discriminative classifier for the purpose of weakening the initialization limitation and improving the algorithms’ adaptability and robustness. Furthermore, to cope with the changes in natural images, online learning is usually adopted to enhance the performance of classifiers, but it easily leads to tracking failures, especially in the case of full occlusion. In order to attenuate the influence of full occlusion, a multi-classifiers parallel tracking algorithm is proposed by designing the multi-classifiers and inducing extra constraints. The experiments are performed on the visual tracking benchmark, and the proposed algorithm is more robust while the target is fully occluded. The simulation results show that the proposed algorithm exhibits better performance compared to the state-of-the-art trackers, especially with KCF, TLD, and STRUCK.
Dual Class Token Vision Transformer for Direction of Arrival Estimation in Low SNR

In this letter, we propose a deep learning-based method for the direction of arrival (DOA) estimation in the low signal-to-noise ratio (SNR) scenario. Specifically, the DOA estimation is modeled as a multi-label classification task, and a novel dual class token Vision Transformer (DCT-ViT) is designed to fit it. Different from the classical ViT architecture with a single class token, the DCT-ViT includes two class tokens which are located at the beginning and end of the latent vector sequence, respectively. This architecture enables enhanced information mining and feature extraction from the array signal data in order to improve the accuracy of DOA estimation. Furthermore, a single DCT-ViT model can accommodate different source numbers by leveraging a training dataset with different numbers of sources. Simulation results illustrate that our proposed method outperforms existing methods in the low SNR scenario, including classical model-based and other deep learning-based methods.
Microzooplankton Grazing Exerts a Strong Top‐Down Control on Unicellular Cyanobacterial Diazotrophs

Unicellular cyanobacterial diazotrophs (UCDs) are important nitrogen (N) fixers in marine ecosystems. However, the top‐down control on UCDs is poorly understood, especially for microzooplankton grazing that is often considered to be the dominant loss factor for primary production in oligotrophic oceans. In this study, we investigated the growth and microzooplankton grazing rates of the major UCD taxa (UCYN‐A1, UCYN‐A2/3/4, UCYN‐B, and UCYN‐C) in the South China Sea and the Luzon Strait using a combination of dilution experiments and quantitative PCR. All UCD taxa were actively grazed by microzooplankton, with higher grazing rates occurring in deeper layers of the photic zone. Microzooplankton grazing rates of UCDs were significantly higher than those of phytoplankton as whole, indicating that UCDs might be preferentially grazed by microzooplankton assemblages. The growth and microzooplankton grazing rates of UCDs were almost balanced, suggesting that a substantial amount of the UCD‐derived N is transferred through the food web via microzooplankton in these regions. Our study highlights the importance of microzooplankton grazing in the fate of UCD‐derived N and indicates that UCDs contribute significantly to the plankton food web in oligotrophic oceans where new N is mainly originated from dinitrogen fixation.
Therapeutic and Toxicological Aspects of Some Metal Nanoparticles on The Central Nervous System: A Review

RAPIDLY developing technology, nanotechnology significantly impacts several therapeutic application domains. Nanotechnology, developed in 1974, focuses on creating materials with a size between 1 and 100 nanometres. It has applications in research, agriculture, and infection treatment delivery tests. Numerous issues pertaining to the welfare and productivity of animals can be resolved by it. Despite their distinctive benefits and uses in both the home and commercial areas, the usage of materials with diameters in nanometres have inspired concerns about worker, consumer, and environmental safety. Nanoparticles have the potential to harm humans and animals through interactions through a variety of processes due to their little dimensions and other distinctive properties. In this review article we have reported and mentioned the toxicity of different selected types of nanoparticles especially metallic nanoparticles such as gold, titanium dioxide, silver, iron oxide and manganese oxide nanoparticles and in addition, we focused on their mechanisms of action of toxicity on central nervous system especially on brain.
Scenario optimization for the tokamak ramp-down phase in RAPTOR. Part B: safe termination of DEMO plasmas

An optimized plasma current ramp-down strategy is critical for safe and fast termination of plasma discharges in a tokamak demonstration fusion reactor (DEMO), both in planned and emergency scenarios, avoiding plasma disruptions and excessive heat loads to the first wall. Plasma stability limits and machine-specific technical requirements constrain the stable envelope through which the plasma must be navigated. Large amounts of auxiliary heating are required throughout the ramp-down phase, to avoid a radiative collapse in the presence of intrinsic tungsten and seeded xenon impurities, as quantitatively estimated in this work. As the plasma current is reduced, the current density becomes increasingly peaked, reflected by a growing value of the internal inductance ℓi3 , resulting in reduced controllability of the vertical position of the plasma. The feasibility of different plasma current ramp-down rates is tested by applying an automated optimization framework embedding the RAPTOR core transport solver. Optimal time traces for plasma current Ip(t) and plasma elongation κ(t) are proposed, to satisfy an Ip -dependent upper limit on the plasma internal inductance, as obtained from vertical stability studies using the CREATE-NL code, as well as a constraint on the time evolution of q 95, to avoid an ideal MHD mode. A negative current density near the plasma edge is observed in our simulations, even for the most conservative Ip ramp-down rate, indicating significant transient dynamics due to a large resistive time.
Square pegs in round holes: the mental health needs of young adults and how well these are met by services - an explorative study

Purpose – This research paper aims to examine the mental health needs of young adults and to discuss how well these are met from the vantage‐point of the patients. Young adulthood is identified as an epidemiological risk for developing mental health problems and so the care provided during these developmental years is investigated to assess efficacy and experience.Design/methodology/approach – After interviewing 35 young adults, it was found most people are aware of the genesis to their problems and have strong views about the level of support and types of treatments offered.Findings – Like other writings, this research finds that many of the needs presented by young adults are unique to this transitional phase in life and administratively determined age structures on which current mental health care is configured do not adequately meet these needs.Research limitations/implications – The small sample size (35) and the geographical region covered means caution should be exercised in drawing any generalisat.
A model for determining thresholds for initiation of shallow landslides under near-saturated conditions in the East Coast region, New Zealand

Tephra-covered pastoral hillslopes in the East Coast region of the North Island, New Zealand, are prone to shallow landslides during rainstorms. Past records of climatic and soil hydrological conditions suggest that many of these shallow landslides could have been triggered under near-saturated conditions. The prime cause is most likely to be soil-strength reduction as moisture content changes from unsaturated to saturated after the wetting front reaches the critical shear plane. Approximate thresholds for triggering shallow landslides may be predicted if the time that the wetting front reaches the critical shear plane is estimated for a given set of climatic variables. This paper presents the development of an easy-to-use model to obtain different combinations of rainfall thresholds, slope angles and soil hydraulic properties that would initiate shallow landslides on pastoral hill country in the East Coast region. We assume that the landslide could be triggered when the soil becomes saturated as the wetting front approaches the critical shear plane. The model incorporates a slope-stability model to locate the critical shear plane and a rainfall-infiltration model to estimate the time for the wetting front to reach this critical plane. The application of this model is demonstrated using easily measured field and laboratory soil hydraulic parameters for a hillslope in the East Coast region.
Observing Temporally Varying Synoptic‐Scale Total Alkalinity and Dissolved Inorganic Carbon in the Arctic Ocean

The long‐term absorption by the oceans of atmospheric carbon dioxide is leading to the slow decline of ocean pH, a process termed ocean acidification (OA). The Arctic is a challenging region to gather enough data to examine the changes in carbonate chemistry over sufficient scales. However, algorithms that calculate carbonate chemistry parameters from more frequently measured parameters, such as temperature and salinity, can be used to fill in data gaps. Here, these published algorithms were evaluated against in situ measurements using different data input types (data from satellites or in situ re‐analysis climatologies) across the Arctic Ocean. With the lowest uncertainties in the Atlantic influenced Seas (AiS), where re‐analysis inputs achieved total alkalinity estimates with Root Mean Squared Deviation (RMSD) of 21 μmol kg−1 and a bias of 2 μmol kg−1 (n = 162) and dissolved inorganic carbon RMSD of 24 μmol kg−1 and bias of −14 μmol kg−1 (n = 262). AiS results using satellite observation inputs show similar bias but larger RMSD, although due to the shorter time span of available satellite observations, more contemporary in situ data would provide further assessment and improvement. Synoptic‐scale observations of surface water carbonate conditions in the Arctic are now possible to monitor OA, but targeted in situ data collection is needed to enable the full exploitation of satellite observation‐based approaches.
Why cognitive absorption is not enough: The role of knowledge absorption capacity and technological opportunity for individual learning

The purpose of this paper is to address the basic question why does high absorptive training programmes are not always beneficial for individual level learning? Thus, we seek to understand when and how cognitive absorption is beneficial for individual learning. The proposed model was tested using data obtained in a field study (N = 371) and in an experiment (N = 119). For field study, data was obtained at two points in time from three data sources (co-workers, subordinates, supervisors) working at a private commercial bank operating in Pakistan. For laboratory experiment, data was collected from the business students of a private sector university in Pakistan. The obtained data for both studies were analysed for random coefficient models with Mplus. Based on the motivation-ability-opportunity theory of behaviour, we proposed a model. It was found that cognitive absorption and highest individual learning was contingent upon the individual level knowledge absorption capacity. It was further found that training programmes with high cognitive absorption are likely to produce high levels of individual learning when the participants also have both high level of knowledge absorption capacity and technological opportunity. With this research, we inform practitioners that in these learner-focused trainings, personal characteristics of the participants and technology play vital role in determining effectiveness for high level of individual learning. The research findings will help practitioners understand what they need to add in training programmes for high level individual learning experience. Doing so will bring best value in form of higher learning to the cost of trainings.
Advancing shipping NOx pollution estimation through a satellite-based approach

Abstract Estimating shipping nitrogen oxides (NOx) emissions and their associated ambient NO2 impacts is a complex and time-consuming task. In this study, a satellite-based ship pollution estimation model (SAT-SHIP) is developed to estimate regional shipping NOx emissions and their contribution to ambient NO2 concentrations in China. Unlike the traditional bottom–up approach, SAT-SHIP employs satellite observations with varying wind patterns to improve the top–down emission inversion methods for individual sectors amidst irregular emission plume signals. Through SAT-SHIP, shipping NOx emissions for 17 ports in China are estimated. The results show that SAT-SHIP performed comparably with the bottom–up approach, with an R2 value of 0.8. Additionally, SAT-SHIP reveals that the shipping sector in port areas contributes ∼21 and 11% to NO2 concentrations in the Yangtze River Delta and Pearl River Delta areas of China, respectively, which is consistent with the results from chemical transportation model simulations. This approach has practical implications for policymakers seeking to identify pollution sources and develop effective strategies to mitigate air pollution.
Development of Single-Segment Substitution Lines and Fine-Mapping of qSPP4 for Spikelets Per Panicle and qGW9 for Grain Width Based on Rice Dual-Segment Substitution Line Z783

Single segment substitution line (SSSL) libraries are an ideal platform for breeding by design. To develop SSSLs-Xihui18 covering the whole genome, a novel rice chromosome segment substitution line (CSSL), Z783, carrying two substitution segments (average length of 6.55 Mb) on Chr.4 and Chr.9 was identified, which was a gap in the library previously. Z783 was developed from the progeny of recipient “Xihui18” (an indica restorer line) and donor “Huhan3” (a japonica cultivar) by advanced backcross combined molecular marker-assisted selection (MAS). It displayed multiple panicles and less spikelets and wide grains. Then, a F2 population derived from Xihui18/Z783 was used to map quantitative trait loci (QTLs) for yield-related traits by the mixed linear model method. Nine QTLs were detected (p < 0.05). Furthermore, three SSSLs were constructed by MAS, and all 9 QTLs could be validated, and 15 novel QTLs could be detected by these SSSLs by a one-way ANOVA analysis. The genetic analysis showed that qSSP4 for less spikelets and qGW9 for wide grain all displayed dominant gene action in their SSSLs. Finally, qSSP4 and qGW9 were fine-mapped to intervals of 2.75 Mb and 1.84 Mb, on Chromosomes 4 and 9, respectively. The results lay a solid foundation for their map cloning and molecular breeding by design.