Miracle-dz/UniFAR-FTUs huggingface dataset:
Managing for cultural harvest of a valued introduced species, the Pacific rat (Rattus exulans) in Aotearoa New Zealand

Indigenous peoples’ relationships with biodiversity are often poorly recognised in conservation decision-making, but are critical to Indigenous identity and lifeways. These relationships extend to introduced species that are rarely protected under legislation. Kiore (Rattus exulans, Pacific rat) is a species introduced to Aotearoa New Zealand (hereafter Aotearoa) by Māori (the Indigenous people of Aotearoa) as a food source and bio-indicator of ecosystem state. Once common, kiore are now restricted in numbers and range, and widely considered an unwanted organism by conservation managers and some Māori. However, tribal group Ngātiwai wish to safeguard cultural access to remaining kiore on Mauitaha Island. Therefore, the goals of our study were to assess body condition and the reproductive and disease status of kiore on Mauitaha. Of 16 kiore caught, body condition based on body length to mass ratio was similar to that recorded on other islands in Aotearoa. Subcutaneous fat levels were moderate, but lower in individuals with disease inflammation. The results suggest satisfactory population health, but regular monitoring to identify temporal trends in kiore abundance and condition is important for cultural harvesting and long-term population survival. Planning for harvesting by future generations requires transforming conservation biology through Indigenous perspectives, through further assessment of methods, management and agency, examining how Indigenous knowledge and conventional science can be used to balance ecological and cultural trade-offs. Further consideration of ecological habitat and risk is also required for kiore, because the reserve is a single small island, and national conservation priorities focus on native species protection in ecosystems that exclude humans.
Teacher Professional Development Needs in Using Digital Technology for Quality of Education

The research aims to obtain the professional development model needs of teachers in mastering digital technology to improve the quality of education. Technology development in education has forced all parties to integrate various educational activities according to current conditions. The research method used mixed research involving qualitative and quantitative data analysis. Data were collected through a survey which was completed with observation and interviews. Analysis of qualitative data is in the form of narrative through data reduction, data presentation, and concluding stages. In contrast, quantitative data is the result of digital technology needs presented in percentages. The analysis results show that digital technology is still not fully used in teaching and learning activities. This also impacts teachers' understanding of the technology application model in schools and students' achievement.
Total Phenolic Compounds, Carotenoids and In Vitro Antioxidant Activity of Three Traditional Indigenous Medicinal Plants of Saskatchewan, Canada

Medicinal plants are an important component in Indigenous cultures. Aralia nudicaulis L. , Rubus idaeus L. , and Rosa arkansana Porter were analyzed for total phenolic compounds, carotenoids and antioxidant activity by DPPH (2,2-diphenyl-1-picrylhydrazyl), FRAP (ferric-reducing antioxidant power), and ABTS (2,2-azino-bis-(3-ethylbenzothiazoline-6-sulphonic acid). The samples were harvested in Saskatchewan, Canada, with the help of an Indigenous Traditional Knowledge Keeper and the analyses were performed by spectrophotometry. The results showed that total phenolic compounds amount ranged from 0.08 to 0.88 mg GAE/mg d.w. and the total carotenoid contents ranged from 0.03 to 1.26 mg/g d.w. The in vitro antioxidant activity ranged from 0.02 to 0.71 μmol TE/mg d.w. by DPPH, from 0.05 to 2.53 μmol TE/mg d.w. by FRAP, and from 0.04 to 1.06 μmol TE/mg d.w. by ABTS. Rosa arkansana leaves stood out with higher amounts of total phenolic compounds (TPC) (0.88 ± 0.02 mg GAE/g d.w.), carotenoids (TC) (1.26 ± 0.03 mg/g d.w.) and antioxidant activity (TAA) by DPPH (0.71 ± 0.01 μmol TE/mg d.w.), ABTS (1.06 ± 0.01 μmol TE/mg d.w.) and FRAP (2.32 ± 0.01 μmol TE/mg d.w.), with the same amount of Rubus idaeus belowground (2.53 ± 0.01 μmol TE/mg d.w.) in last technique (2.32 ± 0.01 μmol TE/mg d.w.). The first principal component describes 83.88% of the total variance and all the variables have high influence on this component (factor loadings: T = 0.976, TC = 0.735, TAA by DPPH = 0.955, FRAP = 0.894 and ABTS = 0.996), demonstrating that these samples do not have large dissimilarity. The second principal component represents 13.64% of the total variance, and the TC is the dominant variable on the second principal component (0.658). Aralia nudicaulis, Rubus idaeus, and Rosa arkansana had interesting amounts of total phenolic compounds, total carotenoids and in vitro antioxidant activity. Rosa arkansana leaves and Rubus idaeus have the highest amount of total phenolic compounds and antioxidant activity in this study. Rosa arkansana leaves are also a good source of carotenoids, and so, they have great potential health benefits and use in industry as a source of bioactive compounds with high antioxidant activity. This study enriches the literature on medicinal plants used by Indigenous people of Saskatchewan and surrounding Canada. More studies are necessary to identify its applications, security and to assess which compounds generate the benefits reported by Traditional Knowledge Keepers.
AOL4PS: A Large-scale Data Set for Personalized Search

Personalized search is a promising way to improve the quality of Websearch, and it has attracted much attention from both academic and industrial communities. Much of the current related research is based on commercial search engine data, which can not be released publicly for such reasons as privacy protection and information security. This leads to a serious lack of accessible public data sets in this field. The few publicly available data sets have not become widely used in academia because of the complexity of the processing process required to study personalized search methods. The lack of data sets together with the difficulties of data processing has brought obstacles to fair comparison and evaluation of personalized search models. In this paper, we constructed a large-scale data set AOL4PS to evaluate personalized search methods, collected and processed from AOL query logs. We present the complete and detailed data processing and construction process. Specifically, to address the challenges of processing time and storage space demands brought by massive data volumes, we optimized the process of data set construction and proposed an improved BM25 algorithm. Experiments are performed on AOL4PS with some classic and state-of-the-art personalized search methods, and the experiment results demonstrate that AOL4PS can measure the effect of personalized search models.
Evaluating reliability of tree-patterns in extreme-K categorical samples problems

Exploratory Data Analysis (EDA) approaches are adopted to address the difficult extreme-K categorical sample problem. Due to observed data's categorical nature, all comparisons among populations are performed by comparing their distributions in the form of a histogram with symbolic bins. A distance measure is designed to evaluate the discrepancy between two symbol-based histograms to facilitate Hierarchical Clustering (HC) algorithms. The resultant binary HC-tree then serves as the basis for our EDA task of discovering tree-patterns of interest. Since each population-leaf's location within a binary HC-tree's geometry is expressed through a binary code sequence, a binary code segment characterizes all commonly shared tree-patterns for all members. We then generate a large ensemble of mimicries of the observed dataset based on multinomial distributions and construct a large ensemble of binary HC-trees. Upon each identified tree-pattern which we determined based on the observed dataset, we evaluate its reliability and uncertainty through two histograms.
Game Attack–Defense Graph Approach for Modeling and Analysis of Cyberattacks and Defenses in Local Metering System

We propose a game attack–defense graph (GADG) approach that integrates the attack–defense graph and the game theory to model and analyze cyberattacks and defenses in the local metering system (LMS). Different from previous studies concentrating on static analyses of cybervulnerabilities, the GADG method considers correlations among these vulnerabilities. Besides, to avoid the uncertainty brought by unilateral analysis, we introduce the game theory for the interaction analysis between the attacker and the defender. The mixed-strategy Nash equilibrium that they finally reach can serve as the input for the inference of system states. We also propose two eliminating algorithms to reduce the complexity of solving mixed-strategy Nash equilibrium on large-scale LMS. In the case study, our GADG model has been conducted on a real LMS, and the results prove its efficiency. This research aims to assist the power company in optimizing the allocation of limited defensive resources, especially specific in attacking steps. Note to Practitioners—This article is motivated by the practical demand of protecting the cybersecurity of the local metering system (LMS), which is a critical subsystem of the smart grid and has been widely deployed to collect and transmit time-stamped information. Different from typical research works on local metering cybersecurity, which focuses on the analysis and identification of cybervulnerabilities, in this study, real experiments considering the interactive scenarios between the attacker and the defender are conducted on the LMS. Based on this, we developed the attack graph to clearly illustrate various cyberattack paths and their corresponding effects. We then applied the game theory model on the attack graph to obtain the optimal attack and defense strategy under each attack scenario. Based on this, the defender, i.e., power company, can optimally allocate limited defense resources among various attacking steps according to our model. The proposed model and method are demonstrated on one exemplar LMS used by our industrial partner China Southern Grid (CSG). Given the generalizability of the proposed model and method, they can be applied to other similar cyber–physical systems.
Human Appropriation of Net Primary Production: From a Planet to a Pixel

Human appropriation of net primary production (HANPP) is a substantial improvement upon 20th century attempts at developing an ecological footprint indicator because of its measurability in relation to net primary production, its close relationship to other key footprint measures, such as carbon and water, and its spatial specificity. This paper explores HANPP across four geographical scales: through literature review, the planet; through reanalysis of existing data, variations among the world’s countries; and through novel analyses, U.S. counties and the 30 m pixel scale for one U.S. county. Results show that HANPP informs different sustainability narratives at different scales. At the planetary scale, HANPP is a critical planetary limit that improves upon areal land use indicators. At the country macroscale, HANPP indicates the degree to which meeting the needs of the domestic population for provisioning ecosystem services (food, feed, biofiber, biofuel) presses against the domestic ecological endowment of net primary production. At the county mesoscale, HANPP reveals the dependency of metropolitan areas upon regional specialized rural forestry and agroecosystems to which they are teleconnected through trade and transport infrastructures. At the pixel microscale, HANPP provides the basis for deriving spatial patterns of remaining net primary production upon which biodiversity and regulatory and cultural ecosystem services are dependent. HANPP is thus a sustainability indicator that can fulfill similar needs as carbon, water and other footprints.
In Vitro Mass Micropropagation of Mammillaria Vetula ssp. Gracilis var. Arizonica Snowcap

A mass micropropagation method was developed for Mammillaria vetula ssp. gracilis cv. Arizonica Snowcap, a cactus species with high ornamental value. A culture media based in MS medium (Murashige and Skoog, 1962) supplemented with 0.1 mg l− 1 NAA + 0.3 mg l− 1 KIN + 5.0 mg l− 1 GA3 + 30 g l− 1 sucrose (MSM-1), was developed specifically for the establishment and multiplication of Mammillaria vetula cv. Snowcap offshoots, let us to produce a proliferation rate of 34.9 ± 5.9 new offshoots per explant. The non-rooted offshoots were rooted in MSM-2, consisting in MS salts supplemented with 0.1 mg l− 1 NAA + 0.3 mg l− 1 KIN plus 5 mg l− 1 ancymidol and a lower dose (10 g l− 1) of sucrose, with a rooting rate of 100%. The rate of acclimatization of the rooted offshoots was 100%. The full process of micropropagation from aseptic establishment in vitro to the end of plant acclimation takes approximately between 5 and 6 months and the plant production can be maintained continuously during all year.
Development and characterization of the CCD detector for the Colorado Ultraviolet Transit Experiment (CUTE) cubesat

The Colorado Ultraviolet Transit Experiment (CUTE) is a 6U cubesat housing a Cassegrain telescope and a nea rultraviolet (NUV) spectrograph designed to study the composition and mass-loss rates of exoplanet atmospheres. This instrument covers a bandpass of 250 - 330 nm with a peak effective area of ~28 cm2 and a resolving power of ~2000. The instrument focal plane consists of a back illuminated CCD driven by clocking and readout electronics developed at the Laboratory for Atmospheric and Space Physics (LASP). Special consideration is given to achieving low noise readout due to typical observation signal levels and time constraints of exoplanet transit observations. Additionally, the electronics driving the CCD are space constrained and designed to fit in a cubesat 1/2U volume. Prior to installation in the flight instrument the detector system parameters are optimized and characterized at LASP in a custom ultraviolet detector test chamber. Engineering and technical details including system gain, quantum efficiency, and read noise are discussed. We present the development, optimization, and characterization results of the CCD and associated readout electronics developed for the CUTE instrument.
Synergistically improved flame retardancy of the cotton fabric finished by silica-coupling agent-zinc borate hybrid sol

The sol-gel process has been applied to cotton fabrics to cover the fibers with a silica-based film, which can improve their thermal oxidation and combustion behaviors. Silica sol, silane coupling agent 3-glycidoxypropyltrimethoxysilane (KH560) and flame retardant zinc borate (ZB) are used together to prepare SiO2-KH560-ZB hybrid sol, which is then finished on the surface of cotton fabric through impregnation and baking. Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), limiting oxygen index analysis (LOI) and microscale combustion calorimetry (MCC) are used to characterize functional groups, thermal stability and flammability properties of finished fabrics. Surface morphology of sol modified cotton fabrics are analyzed by scanning electron microscopy (SEM) and energy dispersive X-Ray spectroscopy (EDX). Results show the sols are successfully converted into gel coatings onto cotton fabrics, and continuous smoldering phenomenon of resulted fabrics disappears. ΔLOI/Δm of SiO2-KH560-ZB coated cotton fabric reaches the highest of 0.37%/g, char residue rate is as high as 28.43% and the peak heat release rate (PHRR) is reduced by 26.9% (83.7 W/g). KH560 has a significant coupling effect on combining components in the sol system and increases compatibility between sols and cotton fabrics. ZB (cooling, separation, dilution and suppression), silica sol (physical barrier) and KH560 show excellent synergistic effects in enhancing overall flame retardancy.
Comparing Bayesian Model Averaging and Reliability Ensemble Averaging in Post-Processing Runoff Projections under Climate Change

This study investigated the strength and limitations of two widely used multi-model averaging frameworks—Bayesian model averaging (BMA) and reliability ensemble averaging (REA), in post-processing runoff projections derived from coupled hydrological models and climate downscaling models. The performance and weight distributions of five model ensembles were thoroughly compared, including simple equal-weight averaging, BMA, and REAs optimizing mean (REA-mean), maximum (REA-max), and minimum (REA-min) monthly runoff. The results suggest that REA and BMA both can synthesize individual models’ diverse skills with comparable reliability, despite of their different averaging strategies and assumptions. While BMA weighs candidate models by their predictive skills in the baseline period, REA also forces the model ensembles to approximate a convergent projection towards the long-term future. The type of incorporation of the uncertain future climate in REA weighting criteria, as well as the differences in parameter estimation (i.e., the expectation maximization (EM) algorithm in BMA and the Markov Chain Monte Carlo sampling method in REA), tend to cause larger uncertainty ranges in the weight distributions of REA ensembles. Moreover, our results show that different averaging objectives could cause much larger discrepancy than that induced by different weighting criteria or parameter estimation algorithms. Among the three REA ensembles, REA-max most resembled BMA because the EM algorithm of BMA converges to the minimum aggregated error, and thus emphasize the simulation of high flows. REA-min achieved better performance in terms of inter-annual temporal pattern, yet at the cost of compromising accuracy in capturing mean behaviors. Caution should be taken to strike a balance among runoff features of interest.
Examining Cloud Macrophysical Changes over the Pacific for 2007–2017 Using CALIPSO, CloudSat, and MODIS Observations

Cloud macrophysical changes over the Pacific from 2007 to 2017 are examined by combining CALIOP and CloudSat (CALCS) active-sensor measurements, and these are compared with MODIS passive-sensor observations. Both CALCS and MODIS capture well-known features of cloud changes over the Pacific associated with meteorological conditions during El Niño-Southern Oscillation (ENSO) events. For example, mid (cloud tops at 3–10 km) and high (cloud tops at 10–18 km) cloud amounts increase with relative humidity (RH) anomalies. However, a better correlation is obtained between CALCS cloud volume and RH anomalies, confirming more accurate CALCS cloud boundaries than MODIS. Both CALCS and MODIS show that low cloud (cloud tops at 0–3 km) amounts increase with EIS and decrease with SST over the eastern Pacific, consistent with earlier studies. It is also further shown that the low cloud amounts do not increase with positive EIS anomalies if SST anomalies are positive. While similar features are found between CALCS and MODIS low cloud anomalies, differences also exist. First, compared to CALCS, MODIS shows stronger anti-correlation between low and mid/high cloud anomalies over the central and western Pacific, which is largely due to the limitation in detecting overlapping clouds from passive MODIS measurements. Second, compared to CALCS, MODIS shows smaller impacts of mid and high clouds on the low troposphere (< 3 km). The differences are due to the underestimation of MODIS cloud layer thicknesses of mid and high clouds.
On the Sensitivity of the Simulated Diurnal Cycle of Precipitation to 3-Hourly Radiosonde Assimilation: A Case Study over the Western Maritime Continent

The diurnal cycle is the most prominent mode of rainfall variability in the tropics, governed mainly by the strong solar heating and land–sea interactions that trigger convection. Over the western Maritime Continent, complex orographic and coastal effects can also play an important role. Weather and climate models often struggle to represent these physical processes, resulting in substantial model biases in simulations over the region. For numerical weather prediction, these biases manifest themselves in the initial conditions, leading to phase and amplitude errors in the diurnal cycle of precipitation. Using a tropical convective-scale data assimilation system, we assimilate 3-hourly radiosonde data from the pilot field campaign of the Years of Maritime Continent, in addition to existing available observations, to diagnose the model biases and assess the relative impacts of the additional wind, temperature, and moisture information on the simulated diurnal cycle of precipitation over the western coast of Sumatra. We show how assimilating such high-frequency in situ observations can improve the simulated diurnal cycle, verified against satellite-derived precipitation, radar-derived precipitation, and rain gauge data. The improvements are due to a better representation of the sea breeze and increased available moisture in the lowest 4 km prior to peak convection. Assimilating wind information alone was sufficient to improve the simulations. We also highlight how during the assimilation, certain multivariate background error constraints and moisture addition in an ad hoc manner can negatively impact the simulations. Other approaches should be explored to better exploit information from such high-frequency observations over this region.
A Numerical Investigation on the Combined Effects of MoSe2 Interface Layer and Graded Bandgap Absorber in CIGS Thin Film Solar Cells

The influence of Molybdenum diselenide (MoSe2) as an interfacial layer between Cu(In,Ga)Se2 (CIGS) absorber layer and Molybdenum (Mo) back contact in a conventional CIGS thin-film solar cell was investigated numerically using SCAPS-1D (a Solar Cell Capacitance Simulator). Using graded bandgap profile of the absorber layer that consist of both back grading (BG) and front grading (FG), which is defined as double grading (DG), attribution to the variation in Ga content was studied. The key focus of this study is to explore the combinatorial effects of MoSe2 contact layer and Ga grading of the absorber to suppress carrier losses due to back contact recombination and resistance that usually occur in case of standard Mo thin films. Thickness, bandgap energy, electron affinity and carrier concentration of the MoSe2 layer were all varied to determine the best configuration for incorporating into the CIGS solar cell structure. A bandgap grading profile that offers optimum functionality in the proposed configuration with additional MoSe2 layer has also been investigated. From the overall results, CIGS solar cells with thin MoSe2 layer and high acceptor doping concentration have been found to outperform the devices without MoSe2 layer, with an increase in efficiency from 20.19% to 23.30%. The introduction of bandgap grading in the front and back interfaces of the absorber layer further improves both open-circuit voltage (VOC) and short-circuit current density (JSC), most likely due to the additional quasi-electric field beneficial for carrier collection and reduced back surface and bulk recombination. A maximum power conversion efficiency (PCE) of 28.06%, fill factor (FF) of 81.89%, JSC of 39.45 mA/cm2, and VOC of 0.868 V were achieved by optimizing the properties of MoSe2 layer and bandgap grading configuration of the absorber layer. This study provides an insight into the different possibilities for designing higher efficiency CIGS solar cell structure through the manipulation of naturally formed MoSe2 layer and absorber bandgap engineering that can be experimentally replicated.