Miracle-dz/UniFAR-FTUs huggingface dataset:
Linear and Non-Linear Vegetation Trend Analysis throughout Iran Using Two Decades of MODIS NDVI Imagery

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran’s greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices.
Optical Fiber Probe Microcantilever Sensor Based on Fabry–Perot Interferometer

Optical fiber Fabry–Perot sensors have long been the focus of researchers in sensing applications because of their unique advantages, including highly effective, simple light path, low cost, compact size, and easy fabrication. Microcantilever-based devices have been extensively explored in chemical and biological fields while the interrogation methods are still a challenge. The optical fiber probe microcantilever sensor is constructed with a microcantilever beam on an optical fiber, which opens the door for highly sensitive, as well as convenient readout. In this review, we summarize a wide variety of optical fiber probe microcantilever sensors based on Fabry–Perot interferometer. The operation principle of the optical fiber probe microcantilever sensor is introduced. The fabrication methods, materials, and sensing applications of an optical fiber probe microcantilever sensor with different structures are discussed in detail. The performances of different kinds of fiber probe microcantilever sensors are compared. We also prospect the possible development direction of optical fiber microcantilever sensors.
Estimation of Beat-by-Beat Blood Pressure and Heart Rate From ECG and PPG Using a Fine-Tuned Deep CNN Model

Given that current cuffless blood pressure (BP) measurement technologies feature acceptable overall accuracy, this paper proposed a sufficiently accurate cuffless BP estimation method based on photoplethysmography (PPG) and electrocardiography (ECG) signals. This study used single-channel PPG and ECG signals to estimate heart rate (HR), diastolic BP (DBP), and systolic BP (SBP). A modified long-term recurrent convolutional network comprising a multi-scale convolution network and a long short-term memory (LSTM) network was used to develop a deep learning model for accurately estimating BP and HR. The PPG and ECG signal data of 1551 patients were obtained from the Data Sets-UCI Machine Learning Repository of the University of California, Irvine. The study dataset comprised ECG, PPG, and arterial BP (ABP) signals from the PhysioNet MIMIC II dataset. The original signals were processed by removing noise and artifacts. The aforementioned dataset contains 12,000 records in a hierarchical data format, with each record containing three signals, namely 125-Hz ECG signals from channel II (ECG lead II), 125-Hz PPG signals from the fingertip, and 125-Hz invasive ABP signals. To validate the stability and performance of the developed model, ten-fold cross-validation was conducted. The mean absolute error (MAE) (standard deviation (SD)) values of the developed model for predicting SBP, DBP, and HR were 2.24 mmHg (3.59 mmHg), 1.40 mmHg (2.56 mmHg), and 0.84 bpm (2.23 bpm), respectively. In addition, the estimated SBP and DBP values satisfied the standards of the British Hypertension Society and the Association for the Advancement of Medical Instrumentation. Compared with the methods proposed in other studies, the deep learning model developed in this study required a lower number of layers to provide accurate SBP, DBP, and HR estimations. The results of this study confirmed the effectiveness of the proposed deep learning architecture.
A self-determination theoretical approach into survival strategies of on-campus and off-campus students from low-income families

An enormous number of students from rural-based universities are drawn from provinces which are primarily regarded as rural provinces in South Africa, due to underdevelopment which is rhetorically blamed on the legacy of apartheid. Moreover, a vast number of these students are enrolled at universities as first generation and largely come from low-income families, characterised by elements of poverty. These students often experience literacy problems, mostly because of the type of high school they attended. Be that as it may, these students need to survive, hence, the need for this type of exploration, which sought to understand survival strategies of students from low -income families in a South African rural-based university. This study or article is solely rooted on Self-determination theory as the theoretical point of departure. Nevertheless, it also incorporates elements of Bandura’s Social cognitive theory as a supplementary theory. Following a qualitative research design, the population of the study comprised students from low-income families. Purposive and snowball sampling procedures were used to select students’ questionnaires with open-ended questions, whilst content analysis was used as a data analysis tool. The findings of the article exhibit several complexities and difficulties that students from low-income families endure such as lack of funds, materials and being exposed to crime. Students make use of financial aid schemes, peer support groups and university’s financial aid to survive. The article has implications for higher education institutions on how they can adopt a variety financial aid practices that could be tailored towards assisting on-campus and off-campus students from low-income families.
A Convolutional Neural-Network-Based Training Model to Estimate Actual Distance of Persons in Continuous Images

Distance and depth detection plays a crucial role in intelligent robotics. It enables drones to understand their working environment to avoid collisions and accidents immediately and is very important in various AI applications. Image-based distance detection usually relies on the correctness of geometric information. However, the geometric features will be lost when the object is rotated or the camera lens image is distorted. This study proposes a training model based on a convolutional neural network, which uses a single-lens camera to estimate humans’ distance in continuous images. We can partially restore depth information loss using built-in camera parameters that do not require additional correction. The normalized skeleton feature unit vector has the same characteristics as time series data and can be classified very well using a 1D convolutional neural network. According to our results, the accuracy for the occluded leg image is over 90% at 2 to 3 m, 80% to 90% at 4 m, and 70% at 5 to 6 m.
Structuring Circular Objectives and Design Strategies for the Circular Economy: A Multi-Hierarchical Theoretical Framework

Most frameworks for dealing with the complexity of designing for the circular economy have limitations in terms of correlating different domains of knowledge, correlating highly complex design strategies, and facilitating the process of design strategies’ discovery and development. This paper discusses how managers and designers can create products that can be circulated for several life cycles by considering five different circular objectives (i.e., maintenance/longevity, reuse, refurbishment, remanufacture and/or recycling). To achieve one or more of these objectives, multiple design strategies can be used at various phases of each product life cycle and throughout the product’s lifetime. A literature review is used in this article to evaluate how circular objectives and design strategies are classified in terms of relevance, product life cycle phases, and product life cycles. The three classifications are merged to create a novel conceptual framework, which is then tested through the use of four circular case studies to map out life cycles, circular objectives, and design strategies. The framework may help managers and designers better understand how their businesses and products interact along the supply chain, allowing them to establish more effective product lifetime plans.
Comparing Mycobacterium tuberculosis RNA Accuracy in Various Respiratory Specimens for the Rapid Diagnosis of Pulmonary Tuberculosis

Objective To evaluate the rapid diagnostic accuracy of Mycobacterium tuberculosis RNA (TB-RNA) for pulmonary tuberculosis (PTB) in a large patient sample and to evaluate the difference in TB-RNA diagnostic accuracy in various respiratory specimens. Methods Patient medical records were retrospectively reviewed to determine the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) of the acid-fast bacillus (AFB) smear and TB-RNA to evaluate their diagnostic accuracy against final clinical diagnosis. Results Of the 2336 patients ultimately included, 1123 provided 1 sputum specimen each and 1213 provided 1 bronchoalveolar lavage fluid (BALF) specimen each. The overall sensitivity, specificity, PPV, NPV, and AUC of the AFB smear were 36.2%, 86.4%, 90.6%, 27.3%, and 0.61, respectively. The overall sensitivity, specificity, PPV, NPV, and AUC of TB-RNA for the rapid detection of PTB were 57.4%, 99.4%, 99.7%, 39.3%, and 0.78, respectively. When sputum and BALF specimens were used for AFB smear testing, the sensitivity, specificity, PPV, NPV, and AUC of the AFB smear were 44.5%, 81.5%, 87.5%, 33.5%, and 0.63; and 29.2%, 92.7%, 94.8%, 22.5%, and 0.61, respectively. The sensitivity, specificity, PPV, NPV, and AUC of TB-RNA for the rapid detection of PTB using sputum were 49.6%, 99.3%, 99.5%, 40.4%, and 0.74, respectively; whereas those of TB-RNA determined using BALF were 63.9%, 99.5%, 99.8%, 38.0%, and 0.82, respectively. Conclusion The diagnostic accuracy of TB-RNA for PTB was moderate and considerably better than that of the AFB smear. The diagnostic accuracy of TB-RNA for various respiratory specimens differed; the diagnostic accuracy of TB-RNA for BALF specimens was substantially better than that for sputum samples, and BALF specimens were more suitable for TB-RNA.
Genetic confirmation of a hybrid between two highly divergent cardinalid species: A rose‐breasted grosbeak (Pheucticus ludovicianus) and a scarlet tanager (Piranga olivacea)

Abstract Using low‐coverage whole‐genome sequencing, analysis of vocalizations, and inferences from natural history, we document a first‐generation hybrid between a rose‐breasted grosbeak (Pheucticus ludovicianus) and a scarlet tanager (Piranga olivacea). These two species occur sympatrically throughout much of eastern North America, although were not previously known to interbreed. Following the field identification of a putative hybrid, we use genetic and bioacoustic data to show that a rose‐breasted grosbeak was the maternal parent and a scarlet tanager was the paternal parent of the hybrid, whose song was similar to the latter species. These two species diverged >10 million years ago, and thus it is surprising to find a hybrid formed under natural conditions in the wild. Notably, the hybrid has an exceptionally heterozygous genome, with a conservative estimate of a heterozygous base every 100 bp. The observation that this hybrid of such highly divergent parental taxa has survived until adulthood serves as another example of the capacity for hybrid birds to survive with an exceptionally divergent genomic composition.
Fabrication of titanium dioxide/reduced graphene oxide nanoﬁbers for photodegradation of tartrazine dye in aqueous solution

Titanium dioxide/reduced graphene oxide composite nanofibers were fabricated by electrospin - ning technique. The nanofibers have been prepared by optimizing the ratio of reduced graphene oxide (rGO) to titanium dioxide (TiO 2 ). The morphological, structural and optical properties have been studied by scanning electron microscopy, X-ray diﬀraction, Raman spectroscopy, and UV−Vis spectrophotometry. The surface area has been measured using Brunauer–Emmett–Teller method. The photocatalytic efficiency has been detected by the degradation of tartrazine dye under visible light. The results present that the degradation efficiency reaches 92% for the nanocomposite under visible light. However, the degradation efficiency was 10% for TiO 2 only. Moreover, the prepared nanofibers have acceptable stability for several times use. The high efficiency of prepared nano - fibers is due to the presence of rGO which improves the photocatalytic activity of nanofibers to degradation of harmful pollutant in water.
Analysis of decomposing wood wastes in the Lagos Lagoon using terminal-restriction fragment length polymorphism and illumina sequencing platform

Indiscriminate disposal of wood shavings and sawdust into the Lagos lagoon usually constitute environmental hazard and varying degree of threats to marine biotic communities. In this study we applied terminal-restriction fragment length polymorphism (T-RFLP) and illumina sequencing of 16S ribosomal RNA gene, to describe the microbial ecology of decomposing wood wastes in the Lagos lagoon, Nigeria. The terminal restriction fragments (TRFs) generated corresponds to over 100 bacterial genera and numerous uncultured bacterial clones. Phylogenetic analysis of the TRFs of 16S rRNA gene sequences and cultured bacterial sequences established genetic relatedness and divergent relationships. T-RFLP in combination with illumina sequencing platform identified bacterial species (Acinetobacter sp., Clostridium sp., Planctomyces sp., Escherichia coli, Pantoea agglomerans, Dyella japonica, Ochrobactrum sp., Prevotella sp., Runella slithyformis, Enterobacter aerogenes, Gordonia amicalis, Klebsiella sp., Thalassospira sp. and Serratia sp.) not previously accounted for in culture-dependent analysis. Bacterial strains such as Ruminococcus flavefaciens, Fibrobacter succinogenes, Simiduia agarivorans, Microbulbifer sp. and Cellulomonas fimi further identified in illumina sequences were found to be involved in the biodegradation of lignin and lignin derivatives. Hence, understanding of the diverse autochthonous microbiota of decomposing wood wastes in the Lagos lagoon is essential in the development of effective biotechnology programme, to tackling the menace of sawdust pollution.
The Cycle of Carbon Dust in the ISM

Carbonaceous matter is an important and ubiquitous component of interstellar dust. However, observations of shocked regions of the interstellar medium (ISM) seem to show that this dust may be rather more easily eroded in shock waves than current models predict. We have therefore recently reevaluated the life-cycle of carbonaceous materials in the interstellar medium through a study of the erosion of hydrogenated amorphous carbon (a-C:H) grains and PAH molecules in supernova-generated shocks. Here we adopt a-C:H as a more realistic form for the carbon dust, rather than the usually-assumed ‘graphite’. We find that with a-C:H grains we can get a better agreement with observations. These results imply that the cycling of carbon into and out of dust must be much faster than previously thought, and that the re-formation of hydrocarbon dust in the ISM must be efficient. The evolution of a-C:H grains in the ISM, principally through progressive ‘aromatisation’ and eventual photoor collisional-fragmentation (in photon-dominated regions and shocks), may be a primary source of the aromatic emission band carriers. We discuss this within the context of the nature of a-C:H carbon dust and its likely evolution in the ISM.
Accurate Reliability Boundary Evaluation of Approximate Arithmetic Circuit

Approximate arithmetic circuit (AAC) has emerged as a promising high-performance and energy-efficient circuit paradigm, which can be used in many applications with inherent error tolerance. To guarantee the usability of AACs and the availability of resilient applications, it is necessary to analyze the reliability of AACs. Most current literature focus on the error characteristics of AACs and few methods can be applied to estimate the reliability of AACs. These methods mostly have exponential time complexities and evaluate the average reliability assuming the input combinations are equally likely. In reality, the primary input (PI) signals can be given with any probability from 0 to 1. In this article, we assume that the PIs have random signal probabilities and propose approaches to reliability boundary estimation for AACs. First, we propose a new efficient and accurate method to evaluate the reliability of AACs. The method mainly calculates the AAC reliability for an input vector set, and furthermore, during the calculation, the correlation problem is considered to increase accuracy. Then, based upon the proposed AAC reliability evaluation method, we present the approaches to finding the reliability boundary. Randomly given signal probabilities of every PI, two heuristic search algorithms are utilized to find the lowest reliability. A comparison of the results on three series of AACs and the circuits in the EvoApprox8b library confirms that the proposed reliability evaluation method is more accurate and efficient than the previous method. Further experiments verify the plausibility of the calculated reliability boundary of AACs.
Tolerability of Atovaquone—Proguanil Application in Common Buzzard Nestlings

Simple Summary Many wild animals, and particularly birds, are commonly infected and can suffer health consequences by blood parasites related to Plasmodium, the causative agents of malaria in humans. Atovaquone–proguanil (Malarone®, GlaxoSmithKline) is one of the most popular drugs for the treatment of malaria infections in humans and is commonly used for the treatment of birds in captivity. Our aim was to test the potential effects of Malarone® within one week of treatment on the growth rate, body condition, and blood chemistry of common buzzard nestlings, a widely distributed Eurasian bird of prey. We found no evidence of detrimental effects of a single dose in common buzzard nestlings with an average dosage of 11 mg/kg, compared with the 7 mg/kg recommended daily dosage in humans. Although Malarone® is commonly used in wildlife rehabilitation centres, and our results do not indicate acute toxicity, further studies are needed to determine the half-life and potential long-term effects of Malarone® treatment in birds. Abstract Differences in drug tolerability among vertebrate groups and species can create substantial challenges for wildlife and ex situ conservation programmes. Knowledge of tolerance in the use of new drugs is, therefore, important to avoid severe toxicity in species, which are both commonly admitted in veterinary clinics and are of conservation concern. Antimalarial drugs have been developed for use in human medicine, but treatment with different agents has also long been used in avian medicine, as haemosporidian infections play a major role in many avian species. This study investigates the effects of the application of atovaquone–proguanil (Malarone®, GlaxoSmithKline) in common buzzards (Buteo buteo). The potential effects of treatment on body condition, growth rate, and chemical blood parameters of nestlings were assessed. All individuals survived the treatment, and no effects on body condition, growth rate, and chemical blood parameters were observed. Our results suggest the tolerability of Malarone® in common buzzards at a single dose of on average 11 mg/kg body weight. For its safe use, we recommend further studies to determine pharmacokinetics in different avian species as well as to assess the effects of repeated treatment.
A platform penalty for news? How social media context can alter information credibility online

ABSTRACT Growing concern about dubious information online threatens the credibility of legitimate news. We examine two possible mechanisms for this effect on social media. First, people might view all news on social media as less credible. Second, questionable information elsewhere in a news feed might discredit legitimate news coverage. Findings from a preregistered experiment confirm that people see information on Facebook as less credible than identical information on news websites, though the effect is small, suggesting that observational data overstates this platform penalty. Prior exposure to low (versus high) credibility information on Facebook also reduces engagement with a target article, but not its perceived credibility. However, exploratory analyses show that the effects of prior exposure to low credibility information vary depending on the plausibility of the target article, decreasing the credibility of a less plausible article (a spillover effect) but increasing the credibility of a more plausible one (a contrast effect).
The design and use of a digital tool to support the development of preschool children’s logical reasoning

Abstract This article describes the conceptual development of a learning app, “Representations”, developed for preschool children (4–5 years of age). In this article we analyze how the app supported the development of logical reasoning via a variety of decoding, encoding, conditionals and debugging activities, which were supported in the game play by the children’s own user generated content (UGC). Research such as this is important as logical reasoning in young children correlates positively with later mathematics achievement. The learning app is used within a broader program, which also included a range of off-device learning. Data indicates the success of our design and implementation in terms of children’s engagement with the app and in terms of their early learning of important logical reasoning concepts.