Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Support Interactions and Reactivity Trends of Single Metal Atom Catalysts over TiO2

Supported single metal atoms embody active, efficient catalysts. Much effort in the literature has focused on late transition metals, but understanding the reactivity and stability trends across all metals is key to developing new metal catalysts. Metal–support interactions are especially important since they determine stability. We used density functional theory (DFT) to model binding of all 29 transition metal atoms and 8 post-transition metal atoms to TiO2, a prototypical support. The binding energies ranged from very strong (up to −7.6 eV) for early transition metals to weak (>−1 eV) for late transition metals. Adsorbed early transition metals were strongly cationic while adsorbed later transition metals were slightly cationic. Stability calculations (aggregation, diffusion, and substitution) indicated that several metal atoms were stable on the surface. Statistical models demonstrate that metal–oxygen bond dissociation energies correlate well with the binding of the metal atoms to the surface, while .
Aircraft observations and sub‐km modelling of the lake–land breeze circulation over Lake Victoria

The lake–land breeze circulation over Lake Victoria was observed in unprecedented detail with a research aircraft during the HyVic pilot flight campaign in January 2019. An evening and morning flight observed the lake and land breezes respectively under mostly dry conditions. The circulation was observed at various heights along a transect across the lake and onshore in Tanzania. Profiles of the lower troposphere were recorded by dropsondes over the lake and land. Convection‐permitting MetUM simulations with different horizontal grid‐spacings (including sub‐km) were run for the flight periods. During the evening flight, the aircraft crossed the lake breeze front over land at 1627 LT, approximately 50 km to the east of the lake shore, recording a 6 g · kg −1 decrease in specific humidity and reversal in wind direction over ∼ 5 km. During the morning flight, a shallow land breeze was observed across the eastern shore at 0545 LT. At least one region of increased and deeper moisture (previously seen in simulations but never observed) was sampled over the lake surface between 0527 and 0855 LT. This bulge of moisture was likely formed from the lifting of near‐surface moist air above the lake by low‐level convergence. The observations and model simulations suggest that low‐level convergence occurred at the leading edge of the land breeze, which had detached from the main land breeze and independently propagated westward across the lake with wave‐like characteristics. The MetUM simulations were able to reasonably reproduce the lake breeze front, bulge feature, and its propagation, which is a major achievement given the sparse observational data for model initialisation in this region. However, some timing, resolution and boundary‐layer depth biases require further investigation. Overall, this pilot campaign provided an unprecedented snapshot of the Lake Victoria lake–land breeze circulation and motivates a more comprehensive field campaign in the future.
The Relationship Between Apache Ii Score And Nurses’ Occupational Stress In Icu

Occupational stress remains one of the major concerns among nurses working in ICU, which affects the individuals as well as the quality of patient care. This paper focuses on investigating the correlation between the ICU patient severity and the nurses’ occupational stress. The paper also identifies a number of occupational stress factors. A total of 81 ICU nurses from the teaching hospitals in Eastern Taiwan have been selected to take part in this study through purposive sampling. Cross-sectional study was adopted for this study and interviews were done through a structured questionnaire. The data was collected using the APACHE II scale and the Nursing Stress scale. The result of the correlation analysis between disease severity and occupational stress was found to be insignificant (p > .05). The regression model analysis shows that patient severity plays a moderation effect between nurses’ characteristics and occupational stress. The results of this study could serve as reference to policymakers and nursing administrators to create a supportive environment. Policy changes to improve the nurses’ working conditions were also proposed in this paper.
Path Planning Optimization for Driverless Vehicle in Parallel Parking Integrating Radial Basis Function Neural Network

To optimize performances such as continuous curvature, safety, and satisfying curvature constraints of the initial planning path for driverless vehicles in parallel parking, a novel method is proposed to train control points of the Bézier curve using the radial basis function neural network method. Firstly, the composition and working process of an autonomous parking system are analyzed. An experiment concerning parking space detection is conducted using an Arduino intelligent minicar with ultrasonic sensor. Based on the analysis of the parallel parking process of experienced drivers and the idea of simulating a human driver, the initial path is planned via an arc-line-arc three segment composite curve and fitted by a quintic Bézier curve to make up for the discontinuity of curvature. Then, the radial basis function neural network is established, and slopes of points of the initial path are used as input to train and obtain horizontal ordinates of four control points in the middle of the Bézier curve. Finally, simulation experiments are carried out by MATLAB, whereby parallel parking of driverless vehicle is simulated, and the effects of the proposed method are verified. Results show the trained and optimized Bézier curve as a planning path meets the requirements of continuous curvature, safety, and curvature constraints, thus improving the abilities for parallel parking in small parking spaces.
Precursory atmospheric circulations with Rossby wave trains leading to Eurasian extreme cold events

This work examines precursory atmospheric circulations with various wave trains contributing to extreme cold weather over central Eurasia in boreal winter from 1979 to 2019. By conducting extended empirical orthogonal function (EEOF) on the preceding propagation circulation fields 2 weeks before the onset of extreme cold event (ECE) cases, three types of ECEs with different disturbance origins are classified and analysed. Type 1 denotes the positive phase of EEOF mode 1, shows as negative phase of Arctic Oscillation‐like pattern. The outbreak of this type of ECE is affected by a wave train originating from Baffin Bay, where an anomalous anticylonic system persisted under a background of weakened westerlies over middle‐high latitudes. Type 2 is picked from positive phase of EEOF mode 2, manifests as a developing blocking system that forms over Scandinavia and shifts to the Barents area. It is found that the blocking system is mainly strengthened by the downstream dispersion process of wave packets that are generated at the northern exit area of the North Pacific westerly jet, where exist anomalous cyclonic zonal wind shear and precipitation. Type 3 is selected from the negative phase of EEOF mode 2, which has a similar origin to type 2 but with the North Pacific jet exit more southward. Then, the generated wave packets propagate to Europe along the northerly jet stream over the North Atlantic, which acts as a waveguide and extends the wave train to downstream. In a word, these three types of precursory atmospheric wave train patterns that bring extreme cold anomalies to Eurasia possess diverse disturbing sources and downstream development mechanisms, and the essential role of the westerly jet is further highlighted. The results, which are investigated based on a quasi‐biweekly time scale, may deepen our understanding of the atmospheric genesis of extreme weather and improve extended‐range weather forecast.
A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service.
Design of ZnO-Drug Nanocarriers against the Main Protease of SARS-CoV-2 (COVID-19): An In Silico Assay

The treatment of coronavirus diseases (COVID-19) is a principal aim worldwide that is required restore public health in the population. To this end, we have been studied several kinds of de novo and repurposed drugs to investigate their ability to inhibit the replication of the virus which causes the current pandemic—the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, finding a vehicle that promotes the controlled dosage is vital for avoiding secondary effects. For this reason, the present work exposes a nanostructured carrier based on ZnO, which is coupled to three repurposed drugs (Chloroquine, Dipyridamole, and Lopinavir) to understand the chemical interaction of the formed composite. The designed composites are modeled and optimized using the DFT formalism. In obtaining exergonic adsorption energies, we found values between 0.582 to 2.084 eV, depending on the used drug. At the same time, the HOMO orbitals demonstrate the electronic overlap between the ZnO-Np and the Lopinavir, which is the molecule with the higher adsorption energy. Finally, we carried out a docking assay to investigate the interaction of free drugs and composites with the main protease of the SARS-CoV-2, finding that the coupling energy of the composites (at around to 0.03 eV) was higher, compared with the free drugs. As such, our results suggest a controlled dosage of the drug on the SARS-CoV-2 target.
Impact of Protein-Polymer Interactions in the Antimicrobial Activity of Lysozyme/Poly(3,4-ethylenedioxythiophene) Biocapacitors

Biocapacitors constructed by combining lysozyme (LYZ) and poly(3,4-ethylenedioxythiophene) (PEDOT) retained the bactericidal activity of the protein when this was encapsulated within the polymeric matrix but lost the antimicrobial behaviour when the LYZ was adsorbed onto the polymer. In this work we use atomistic Molecular Dynamics simulations to examine the influence of protein···polymer interactions in the bactericidal activity of LYZcontaining biocapacitors. Results show that the anisotropic forces exerted by oxidized PEDOT chains on the adsorbed protein induce small structural changes that locally affect at the active centre, breaking the intra-residue interactions associated with the antibacterial mechanism. Conversely, isotropic polymer···protein interactions in biocapacitors with encapsulated LYZ do not affect the stability of the active centre. These observations suggest that the strong repulsive or attractive forces between p-doped polymer chains and the positively or negatively charged LYZ residues, respectively, are the only ones responsible for the protein activity.
A fully distributed energy‐aware multi‐level clustering and routing for WSN‐based IoT

One of the major problems in wireless sensor networks (WSNs) is that resource‐constrained sensor nodes consume their limited batteries quickly due to long‐distance data communications. The communication distance of the nodes can be decreased using clustering architectures and multi‐hop data transmissions; hence, the lifetime of the network can be increased. In this study, two‐level intra‐cluster and multi‐level inter‐cluster communication are proposed. The coverage area of the second‐level clusters is dynamically determined according to the distance of cluster heads to the BS. Self‐organized nodes in the network designate the clustering ranges, the clusters, and the cluster heads without a central control mechanism using a fully distributed approach. Moreover, with the help of static clustering, the control messages generated by the system are decreased. The proposed approach, FDEAM, is compared with recent approaches in terms of the network lifetime, network energy consumption, cluster head alteration frequency, dead parent statistics, and data collection. The results show that FDEAM outperforms state‐of‐the‐art approaches for all performance metrics.
Accuracy Assessment of ICESat-2 Ground Elevation and Canopy Height Estimates in Mangroves

Rapid and accurate ecological surveys of mangroves are of great significance for coastal protection and global carbon balance assessments. Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) data provide an opportunity to conduct large-scale surveys of mangroves. The purpose of this study was to assess the expressiveness of ICESat-2 data for ground and canopy height retrievals in mangroves. First, the ICESat-2 data were processed to obtain the ground and canopy heights of mangrove areas. Second, the accuracies of the ground and canopy heights retrieved from the ICESat-2 data were verified by airborne light detection and ranging (LiDAR) data. Finally, we analyzed the influence of various factors on the ground and canopy height estimation accuracies. The results showed that the average errors of ICESat-2 for the ground and canopy heights were 0.28 and −0.21 m and that the root mean squared errors (RMSEs) were 0.96 and 2.50 m. The accuracies of the ICESat-2 ground and canopy height estimates differed significantly when day/night and strong/weak beams were used. The strong beams at night provided the most accurate estimations of canopy height (RMSE% = 24.4%) and are thus the most suitable choice for studying mangrove areas. In addition, the results indicated that slope is the variable that has the greatest influence on the accuracy of the ground elevation estimates of the four factors above, while the accuracy of canopy height estimates is significantly affected by the canopy height itself. Overall, our study found that ICESat-2 data are suitable for ecological investigations of mangroves.
Learning Analytics for Primary and Secondary Schools

Over the past decade, the increasing use of learning analytics opened the possibility of making data-driven decisions for improving student learning. Driven by the strong university adoption of learning analytics, most early learning analytics research focused on issues specific to tertiary education. With the broader adoption of educational technologies in primary and secondary education and the emergence of new classroom-focused technologies, there has been a growing awareness of the potentials of learning analytics for supporting students and diagnosing their learning progress in pre-university contexts. This special section focused on investigating, developing, and evaluating state-of-the-art learning analytics approaches within primary and secondary school settings. In this editorial, we summarize the papers of the special section and discuss the challenges and opportunities for learning analytics within the school context. We conclude with the discussion around the opportunities for future work and the implications of this special section for the field of learning analytics.
Redox Modulators Determine Luminol Luminescence Generated by Porphyrin-Coordinated Iron and May Repress “Suicide Inactivation”

Iron porphyrin catalysts of the luminol reaction (horseradish peroxidase, hemoglobin, cytochrome c, and hemin) interact with diverse reducing compounds. Here, it is demonstrated how the chemiluminescence yield is modulated by such interactions. The compounds accepted as substrates protect the catalyst against the “suicide inactivation” caused by high peroxide concentrations. The reducing agents not accepted by the catalyst inhibit light production either by generating a futile redox cycle of the luminophore or by irreversibly inactivating the catalytic center. In the case of a futile cycle, light emission resumes as soon as the reducing agents in the reaction are consumed, whereas with an irreversible inactivation, light emission does not recover. The characteristics of luminescence enhancement and quenching depending on interfering agents are also reported here. They reveal details about the relative redox potentials of the involved compounds. It is discussed how this should be considered when the luminol reaction is used for quantitative analyses and when unpurified samples with a broad compound matrix are to be assayed.
Physico-empirical methods for estimating soil water characteristic curve under different particle size

Soil water characteristic curve (SWCC) is an important property of soil, but directly measuring methods are costly and time-consuming. Many attempts have been made to predict the water characteristic curve indirectly from soil particle size distribution (PSD) and basic physical properties. In this study, the Arya-Paris (A-P) model and Non-similar midia concept (NSMC) model were applied to predict the SWCC indirectly with six soil samples in arid regions. The objective of this study was to compare the two physico-empirical methods and assess the applicability of them under different particle size of soil. The results showed that the A-P model was superior to NSMC model on prediction precision, and more suited to predict silty sand and silt, those soils with medium texture. While using Arya-Paris method, the scaling parameter, α (parameter used to scale pore lengths based on spherical particles to natural soil pore lengths), was estimated by non-linear fitting, linear fitting and as a constant. It was found that the A-P model with non-linear α that used improved logistic growth equation was the best method to estimate SWCC, which had a maximum coefficient of determination (R2) and minimum root mean square error (RMSE).
Phenology of greenhouse thrips (Heliothrips haemorrhoidalis) on kiwifruit vines, shelter trees and alternative host plants

The greenhouse thrips, Heliothrips haemorrhoidalis is a quarantine pest of kiwifruit. There is a need for reliable information on its phenology in kiwifruit orchards to inform the development of new management options. Numbers of larval, pupal and adult greenhouse thrips were counted on leaves of the two main kiwifruit cultivars Actinidia chinensis var. deliciosa (‘Hayward’) at two sites at different times and A. chinensis var. chinensis ‘Zesy002’ at one site. Greenhouse thrips were also counted on leaves of shelter tree species Cryptomeria japonica, and other plants present on kiwifruit orchards, blackberry (Rubus fruticosus s.l.), barberry (Berberis glaucocarpa), and wineberry (Aristotelia serrata) across a number of sites at different times. There was a strong seasonal pattern to the phenology of greenhouse thrips and it was relatively synchronous for all the host plants surveyed. In general, number of greenhouse thrips on foliage increased from January to peak in April or May before declining in late autumn or winter and remaining low until the following January or February. The phenology of greenhouse thrips followed the same seasonal pattern for a variety of host plants found on kiwifruit orchards at sites in the Bay of Plenty across two two-year time periods. Therefore, host species does not appear to be a factor affecting the phenology of thrips. Other, non-host factors such as microclimate may be important drivers of phenology but they require further study. The consistency of the seasonal pattern of relative abundance means that there is a well-defined window to target for thrips management.
Design of Inverted Nano-Cone Arrayed SERS Substrate for Rapid Detection of Pathogens

Rapid detection of bacteria is a very critical and important part of infectious disease treatment. Sepsis kills more than 25 percent of its victims, resulting in as many as half of all deaths in hospitals before identifying the pathogen for patients to get the right treatment. Raman spectroscopy is a promising candidate in pathogen diagnosis given its fast and label-free nature, only if the concentration of the pathogen is high enough to provide reasonable sensitivity. This work reports a new design of surface-enhanced Raman spectroscopy (SERS) substrate which will provide high enough sensitivity and fast and close contact of the target structure to the optical hot spots for immunomagnetic capturing-based bacteria-concentrating technique. The substrate uses inverted nanocone structure arrays made of transparent PDMS (Polydimethylsiloxane) to funnel the light from the bottom to the top of the cones where plasmonic gold nanorods are located. A high reflective and low loss layer is deposited on the outer surface of the cone. Given the geometry of cones, photons are multi-reflected by the outer layer and thus the number density of photons at hotspots increases by an order of magnitude, which could be high enough to detect immunomagnetically densified bacteria.