Miracle-dz/UniFAR-FTUs huggingface dataset:
A resurrection study reveals limited evolution of phenology in response to recent climate change across the geographic range of the scarlet monkeyflower

Abstract Premise of the study As global climate change alters drought regimes, rapid evolution of traits that facilitate adaptation to drought can rescue populations in decline. The evolution of phenological advancement can allow plant populations to escape drought, but evolutionary responses in phenology can vary across a species' range due to differences in drought intensity and standing genetic variation. Methods Mimulus cardinalis, a perennial herb spanning a broad climatic gradient, recently experienced a period of record drought. Here, we used a resurrection study comparing flowering time and stem height at first flower of pre‐drought ancestors and post‐drought descendants from northern‐edge, central, and southern‐edge populations in a common environment to examine the evolution of drought escape across the latitudinal range. Key results Contrary to the hypothesis of the evolution of advanced phenology in response to recent drought, flowering time did not advance between ancestors and descendants in any population, though storage condition and maternal effects could have impacted these results. Stem height was positively correlated with flowering time, such that plants that flowered earlier were shorter at first flower. This correlation could constrain the evolution of earlier flowering time if selection favors flowering early at a large size. Conclusions These findings suggest that rapid evolution of phenology will not rescue these populations from recent climate change. Future work is needed to examine the potential for the evolution of alternative drought strategies and phenotypic plasticity to buffer M. cardinalis populations from changing climate.
A sensorless control technique for low cost AC/DC converters

This paper proposes a sensorless technique capable to reconstruct the input currents of a three-phase active rectifier using only a DC-link voltage sensor and a PLL circuit in the line side. It has been developed in order to design a low cost three-phase active rectifier and it is based on the novel PMM (power matching modulation) technique and on the control of the capacitor energy. The proposed system guarantees close-to-unity power factor, excellent output voltage regulation, limited current harmonic content, high efficiency and low cost.
The Effect of Covid-19 Pandemic on the Adoption of Internet Banking in Indonesia: Islamic Bank and Conventional Bank

This study aims to examine the effect of perceived usefulness (PU), perceived ease of use (PEU), trust (TR), subjective norm (SN), and attitude (AT) on customer's Intention to Adopt Internet Banking (IAIB) at Islamic banks and conventional banks before and during the Covid-19 pandemic in Indonesia The research model is based on the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) This study involves 213 respondents for Islamic banks and 410 respondents for conventional banks from 25 provinces in Indonesia Data was analyzed using partial least square (PLS) regression with the Structural Equation Model (SEM) method The result of data analysis confirms several hypotheses taken from the literature The results before the Covid-19 pandemic showed that AT and SN influence IAIB in Islamic banks Whereas in conventional banks, AT, PU, SN, and TR influence IAIB While during the Covid-19 pandemic, it shows that the AT, PU, IB, SN, and customer TR influence IAIB in Islamic banks and conventional banks From the analysis, it was found that the PEU variable did not have a significant effect on the intention of customers of Islamic banks and conventional banks to use Internet banking
A validation study of the Korean version of the thriving at work measurement

Purpose The purpose of this study is to validate the Korean version of the thriving at work measurement by Porath et al. (2012). Design/methodology/approach After translating the thriving at work measurement into Korean, the researchers assessed the validity and reliability of the measurement in a Korean working context using two different samples. In Study 1, the study validated the translated measurement using Rasch’s (1960) model, exploratory factor analysis and a reliability test with a sample of 322 employees. In Study 2, the study conducted a confirmatory factor analysis, a reliability test and a convergent and discriminant validity test using a sample of 187 employees. Findings Based on the analyses, this paper concluded that thriving at work has a two-factor construct and eight-item thriving at work measurement was better than the original 10-item measurement. The eight-item measurement demonstrated good discriminant and convergent validity. Originality/value This study validated the thriving at work measurement in a Korean context using Rasch’s (1960) model from the item response theory perspective.
Resistive network model of the weft-knitted strain sensor with the plating stitch-Part 2: Resistive network model during the elongation along course direction

Based on the static resistive network investigated in Part 1 of this series, the resistive network model of the weft-knitted strain sensor with the plating stitch is explored under the elongation along course direction, and it changes with the conductive loops’ configuration and contact situation. Since the voltage is applied at both ends of the course, under a specific stretching state, the resistive network model can be reduced to a resistance network connected in series in the course direction and parallel in the wale direction, which determines that the sensor’s equivalent resistance increases with the growth of the conductive wale number, and decreases with the raise of the conductive course number. Through experiment and model calculation, it can be obtained that in the initial stage of stretching, the contact resistances’ changes are the main factors affecting the mechanical–electrical performance of the sensor. Then as the sensor is further stretched, the length-related resistances of the conductive yarn segments begin to affect the sensor’s properties due to yarns’ slippage and self-elongation. In addition, the weft jacquard plating technology makes the strain of the sensor reach about 32% before yarns’ slippage and self-elongation, which expands the sensor’s measurable strain range, and avoids irreversible deformation of the sensor after repeated use in this range. It can be verified that the sensor’s gauge factor can be improved by reducing the conductive course number and increasing the conductive wale number. It should be noted that the ground yarn will reduce the gauge factor of the sensor during stretching, so it is necessary to choose a ground yarn with a smaller fineness than the conductive face yarn and good elasticity in practical.
Are African Stock Markets Inefficient or Adaptive? Empirical Literature

This chapter reviews empirical studies on weak form of efficiency with the aim of establishing whether the African market is inefficient or adaptive. The reviewed studies are categorised based on their methodological approaches to compare the power of linear and non-linear models in testing for weak-form efficiency. The studies on calendar anomalies, an indication of weak-form inefficiency, are reviewed to assess whether these anomalies are adaptive as portrayed by the relatively recent theory of adaptive market hypothesis (AMH). The scope of reviewed studies is also extended to developed and emerging markets to gain a broad comparison of the findings. This review revealed that non-linear dependence has been revealed in stock returns suggesting that non-linear models are best fit to test for the stock market efficiency. Reviewed studies produced contradictory findings with some supporting and others rejecting weak-form efficiency. Thus, most studies support the AMH, which suggests that market efficiencies and anomalies are time changing. This chapter concludes that most of the existing studies on AMH have been carried out in markets other than Africa, and hence, further empirical studies on the evolving and changing nature of efficiency in African stock markets are recommended.
ARE STUDENTS ENGAGING IN ONLINE CLASSROOMS

Online learning plays a crucial role in our education system, especially in this time of COVID-19 pandemic, in which online learning is widely and fully implemented in the education system across the world. It is not easy to ensure consistent engagement from students with this current norm in education. Are the students engaging with their peers, their instructor or even with the content of the course? Referring to Moore’s interaction framework, this study is aimed to examine further engagement methods used in online classrooms. A set of survey consists of 24 items were distributed and completed by fifty-five students from a public university in Malaysia. The results revealed the functions of the learner-to-learner, learner-to-instructor, and learner-to-content engagements in the online classroom, in which learner-to-instructor engagement plays the most significant role in maintaining students’ engagement. The findings of the study could serve as one of the references by educators and even students to improve strategies and performances in online learning. Article visualizations:
Osteoarthritis detection by applying quadtree analysis to human joint knee X-ray imagery

ABSTRACT Knee Osteoarthritis (OA) is considered as one of the most popular diseases for elder people with the age of 60 and upper. In addition, there are over 10 million people in Thailand were affected by Knee OA. Age and weight are the two main risk factors to make people have knee OA. When OA has appeared to the patient, it is totally difficult to recovery back as the normal. Thus, knee OA early detection is the most important factor to prevent knee OA. The typical way to detect and analyze OA is the X-ray imaging application. This research study is directed to the early detection of OA by applying image processing (specifically the shape analysis) applied with classification techniques to knee X-ray imagery. The basic idea of the work is to find a region of interest, use shape decomposition technique and build a classifier that can later classify between OA or non-OA imageries. Firstly, the quadtree decomposition is applied to X-ray images for analyzing the regions of interest (ROI) of the knee X-ray image. There are three data sets include (i) whole knee (Dataset 1), (ii) knee joint space ROI (Dataset 2), and (iii) the application Otsu's method to knee joint space ROI (Dataset 3). Secondly, feature selection is adopted to this work in order to reduce the feature space in terms of the number of values and dimensions. Lastly, the classifier generator is applied to generate the desired classifieds which can be used to classify knee images between non-OA (normal case) and OA. The challenge of the study is how to know which ROI and the threshold value in shape decomposition are suitable to use for the classification process. The data were obtained 128 male and female participants were used for the evaluation while OA imagery was presented as 66 images. The results obtained show that the Dataset 3 sub-image is the most appropriate region of interest to consider come along with the threshold value of 10. The best classification performance in term of an Area Under the ROC Curve (AUC) value of 0.917 was recorded.
Privately protected areas in increasingly turbulent social contexts: strategic roles, extent, and governance

Abstract This paper considers increasing global reliance on privately protected areas (PPAs) and associated nature-based tourism (NBT). A targeted literature review was guided by three research questions, which sought to understand how neoliberal concepts and dynamics manifest in private forms of conservation; how private forms of conservation have manifested in Chile; and, how Chileans’ attitudes towards their government’s neoliberal policies might affect their expectations of PPAs. The search strategy resulted in 284 resources. Three dimensions of neoliberal approaches to PPAs emerged from the data each highlighting specific vulnerabilities: a loss of the social embeddedness of nature; an imposition of global, capital dynamics; and conflicting discourses and assumptions. Results suggest that, in order to improve long-term support and integration of PPAs and NBT, greater attention needs to be given to social well-being outcomes (including equity and justice concerns), building of social capital, and the preservation of local identities and histories. Additionally, regional and PPA-specific land-use planning needs to incorporate greater public engagement, cross-jurisdictional coordination, and transparent and inclusive decision-making.
Are Sterols Useful for the Identification of Sources of Faecal Contamination in Shellfish? A Case Study

This work aimed to identify the major source(s) of faecal pollution impacting Salcott Creek oyster fisheries in the UK through the examination of the sterol profiles. The concentration of the major sewage biomarker, coprostanol, in water overlying the oysters varied between 0.01 µg L−1 and 1.20 µg L−1. The coprostanol/epicoprostanol ratio ranged from 1.32 (September) to 33.25 (February), suggesting that human sewage represents the key input of faecal material into the estuary. However, a correlation between the sterol profile of water above the oysters with that of water that enters from Tiptree Sewage Treatment Works (r = 0.82), and a sample from a site (Quinces Corner) observed to have a high population of Brent geese (r = 0.82), suggests that both sources contribute to the faecal pollution affecting the oysters. In identifying these key faecal inputs, sterol profiling has allowed targeted management practices to be employed to ensure that oyster quality is optimised.
Deep learning assisted well log inversion for fracture identification

Manual fracture identification methods based on cores and image logging pseudo‐pictures are limited by the expense and the amount of data. In this paper, we propose an integrated workflow, which takes the fracture identification as an end‐to‐end project, to combine the boundary detection and the deep learning classification to recognize fractured zones with accurate locations and reasonable thickness. We first apply the discrete wavelet transform algorithm and a boundary detection method named changing point detection to enhance the fracture sensibility of acoustic logs and segment the whole logging interval into non‐overlapping subsections by estimating boundaries. The deep neural network based auto‐encoders and the convolutional neural network classifier are then implemented to extract the hidden information from logs and categorize the subsections as the fractured or non‐fractured zones. To validate the feasibility of this workflow, we apply it to the logging data from a real well. Compare with the benchmarks provided by the support vector machine , random forest and Adaboost model, the one‐dimensional well profile predicted by the proposed changing point detection‐deep learning classifier is more consistent with the manual identification result.
Integrating climate service co-production into spatial planning in Jakarta

ABSTRACT Co-production in climate change has arisen in response to improving climate knowledge. Climate service co-production provides climate knowledge for decision-making, benefiting vulnerable communities with useful and usable information such as flooding risk areas. A climate service co-production project, including mapping technologies, has been conducted in Jakarta, Indonesia. However, it has not been integrated into mainstream spatial planning. This paper investigates an enabling environment for the integration of climate service co-production and spatial planning using a collaborative governance framework. We conducted semi-structured interviews, photovoice, and focus group discussions. The results highlight factors to enable integration; (1) an institutional reform to put climate service co-production on the agenda, (2) top-down priorities aligned with local-level needs, (3) local government will and leadership to execute the tasks and actions towards climate resilience, (4) trust of the local government with the community, (5) integrated geospatial data to have an effective climate knowledge to spatial planning.
Morphological characterization of local durian of Banten Province, Indonesia

Durian (Duriozibethinus) is an exotic fruit with relatively advanced varietal improvement prospects. Indonesia is one of the countries with high durian diversity, one of which is located in Banten Province. This study aims to determine the diversity and relationships of several local durians in Pandeglang and Lebak Regencies of Banten Province based on the morphological characters of leaves, flowers, and fruit. The durian samples were obtained by purposive sampling and interview methods. The morphological characterization was carried out according to the guidelines of the “Bioversity International” including the description of stems, leaves, flowers, fruits, and seeds. After the characterization, scoring, standardization, and similarity index (IS) were calculated between samples. Cluster analysis was carried out using Cluster Observation with Ntsys 2.0 software. The analysis was conducted on 50 characters in 20 local durian accessions. The results showed that the clustering analysis was divided into three clusters with a similarity coefficient of 0.64. Group I consisted of SangkanWangi 2. Group II consisted of Hauk, Ketan Jaya, Grinsing, Sikucing, Kadu Katung, Jangkung, Sibening, Siradio, Siceer, Sipedang, Sikempong, Sicayut, and Sijabrig. Meanwhile, Group III consisted of Siemas, Baranjang, Kaduhejo, Kadukuning, Sangkan Wangi 1, and Sibening.
Use of Correlated Data for Nonparametric Prediction of a Spatial Target Variable

The kriging methodology can be applied to predict the value of a spatial variable at an unsampled location, from the available spatial data. Furthermore, additional information from secondary variables, correlated with the target one, can be included in the resulting predictor by using the cokriging techniques. The latter procedures require a previous specification of the multivariate dependence structure, difficult to characterize in practice in an appropriate way. To simplify this task, the current work introduces a nonparametric kernel approach for prediction, which satisfies good properties, such as asymptotic unbiasedness or the convergence to zero of the mean squared prediction error. The selection of the bandwidth parameters involved is also addressed, as well as the estimation of the remaining unknown terms in the kernel predictor. The performance of the new methodology is illustrated through numerical studies with simulated data, carried out in different scenarios. In addition, the proposed nonparametric approach is applied to predict the concentrations of a pollutant that represents a risk to human health, the cadmium, in the floodplain of the Meuse river (Netherlands), by incorporating the lead level as an auxiliary variable.
Analysis Of Respiratory Signal For Anxiety Disorder Identification

Anxiety disorder identification (ADI) is becoming increasingly important in addressing mental health across the population. Traditionally, ECG has been found to be effective as a means to estimate stress and anxiety, and the effect of respiratory signal has been considered as undesirable. In this work, we study how respiratory signal can be effectively used for ADI. The data for this study was collected from normal population, subjects with anxiety disorders and regular meditators, at the Department of Neurology and the Department of Psychiatry, Amrita Institute of Medical Sciences (AIMS), Kerala, over a period of 1.5 years. We used respiratory rate variability (RRV) features as input to support vector machine (SVM) classifier for our baseline ADI system. We noticed that the baseline ADI gave very low classification performance, 63.88%, 83.43% and 69.23% absolute respectively, for sensitivity, specificity and accuracy. We observed large within class person specific variations (PSV) in the RRV features among the controls and the effect of these variations in the RRV features is a nuisance factor affecting the performance of the ADI adversely. To minimize the effect of PSV, we explored several techniques such as covariance normalization (CVN) and Fisher vector encoding (FVE), and on combining CVN and FVE (RFE-CVN-FVE-SVM-ADI), we obtained a sensitivity of 91.66% absolute, specificity of 95.23% absolute and accuracy of 92.30% absolute, which is an improvement of 27.78% absolute sensitivity, 11.80% absolute specificity and 23.07% absolute accuracy, over the baseline ADI. The optimum sets of features were selected using recursive feature elimination (RFE) algorithm. Respiratory signal can be effectively used in ADI. The study scientifically establishes the role of meditation and yoga in reducing anxiety and stress disorders, thus helping in the overall wellness of patients with psychiatric disorders, in their speedy recovery.
The Antioxidant Content and Activity of Various Plant Organs of Kitolod (Isotoma longiflora)

Antioxidants play a crucial role in human health owing to their ability to neutralize oxidative reactions. Kitolod ( Isotomalongiflora ) is one of the medicinal plants that can be used as a source of antioxidant. The aim of study wasto compare the content of antioxidant compounds (flavonoids, phenolics, chlorophylls, carotenoids, and ascorbic acid) in various organs of the kitolod plant. The plant organs studied includingroots, stems, leaves, flowers, and fruits. The extraction of each plant organ wasconducted by maceration technique using ethanol as a solvent at the room temperature for 48 hours. The analysis of total flavonoids, phenolics, and ascorbic acid was performed by colorimetry method using AlCl 3 , Folin, and sulfosalicyclic acid reagent respectively, while chlorophylls and carotenoids was determined by direct colorimetry method with methanol as a solvent. The antioxidant activity of various plant organs of kitolod was analyzed using the DPPH method. The highest total flavonoid, phenolic, chlorophyll, and carotenoid compounds werefound in leaves, respectively by 10.48, 1.46, 7.25, and 56.98 ppm. The highest ascorbic acid content and antioxidant activity were obtained fromfruits.The research findings provide new and important information aboutthe contents and antioxidant activity of the secondary metabolites (flavonoid, phenolic, chlorophyll, carotenoid, and ascorbic acid) in each organ of kitolod plant. The information from the results of this study can be used to increase the medicinal value ofkitolod plants.