Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting ecosystem shift in a Salt Lake by using remote sensing indicators and spatial statistics methods (case study: Lake Urmia basin)

The consequences of unsustainable human activities on the environment are often delayed, when it is too late to compensate. New approaches are based on the use of “spatial statistics” of leading indicators to measure the “critical slowing down” in a degraded ecosystem, when it is reaching to a tipping point. This research predicts the tipping points in the ecosystem of Lake Urmia Basin (LUB) based on spatial statistics. By Remote Sensing (RS) indicators, their effectiveness in assessing the state of the ecosystem was evaluated in a 16-years period (2002-2017). Seven spectral indicators (NDVI, NDWIv,NDWIw,NDSI,SRDI, NMDI and MVWR) were extracted from ten MODIS images. Ability of the indicators to identify critical point in time-series was investigated by five spatial statistic methods (Moran’s-I, Getis-Ord-Gi, Geary’s-C, variance, and skewness). The results showed that Moran’s-I is more successful in predicting the ecosystem tipping point(s) in comparison with other methods. In addition, the ability to predict ecosystem trends by the autocorrelation of MVWR is higher than other indicators. According to results, the tipping points of LUB occurred in the years of 2008 to 2010 and 2015. For further studies, it is recommended to use radar indicators for identifying tipping points of the similar vulnerable ecosystems.
A Combined Pyro- and Hydrometallurgical Approach to Recycle Pyrolyzed Lithium-Ion Battery Black Mass Part 1: Production of Lithium Concentrates in an Electric Arc Furnace

Due to the increasing demand for battery raw materials such as cobalt, nickel, manganese, and lithium, the extraction of these metals not only from primary, but also from secondary sources like spent lithium-ion batteries (LIBs) is becoming increasingly important. One possible approach for an optimized recovery of valuable metals from spent LIBs is a combined pyroand hydrometallurgical process. According to the pyrometallurgical process route, in this paper, a suitable slag design for the generation of slag enriched by lithium and mixed cobalt, nickel, and copper alloy as intermediate products in a laboratory electric arc furnace was investigated. Smelting experiments were carried out using pyrolyzed pelletized black mass, copper(II) oxide, and different quartz additions as a flux to investigate the influence on lithium-slagging. With the proposed smelting operation, lithium could be enriched with a maximum yield of 82.4% in the slag, whereas the yield for cobalt, nickel, and copper in the metal alloy was 81.6%, 93.3%, and 90.7% respectively. The slag obtained from the melting process is investigated by chemical and mineralogical characterization techniques. Hydrometallurgical treatment to recover lithium is carried out with the slag and presented in part 2.
Microbial and Sensory Analysis of Soy and Cow Milk-Based Yogurt as a Probiotic Matrix for Lactobacillus rhamnosus GR-1

Plant-based milk alternatives represent a growing sector of the functional food industry due to consumer demand for more nutritious and sustainable options. Soymilk is abundant in fibre, phytosterols, and isoflavones. In contrast, cow milk has a high cholesterol and caloric content, superior organoleptic characteristics, and a well-established probiotic delivery matrix. Supplementing cow milk with soymilk to produce probiotic yogurt may enhance the nutritional value, sensory profile, and probiotic delivery capacity of the final product. In order to investigate the probiotic potential and sensory appeal of this blend, four yogurt mixtures were prepared by incorporating 0% (T1), 25% (T2), 50% (T3), or 75% (T4) soymilk in cow milk. The viability of Lactobacillus rhamnosus GR-1 and pH were evaluated during fermentation (6 h) and refrigerated storage (30 days). Additionally, consumer acceptability was determined through a sensory evaluation. L. rhamnosus GR-1 reached viable counts of 108 colony forming units (CFU)/mL in all treatments. Sensory panellists provided higher hedonic scores to T1 for appearance and texture compared to T2–T4, but flavour and overall acceptability ratings amongst T1–T4 were comparable. These results serve as an indication for the successful fortification of cow and soymilk yogurt mixtures with L. rhamnosus GR-1.
A Classifier-Assisted Level-Based Learning Swarm Optimizer for Expensive Optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have become one popular method to solve complex and computationally expensive optimization problems. However, most existing SAEAs suffer from performance degradation with the dimensionality increasing. To solve this issue, this article proposes a classifier-assisted level-based learning swarm optimizer on the basis of the level-based learning swarm optimizer (LLSO) and the gradient boosting classifier (GBC) to improve the robustness and scalability of SAEAs. Particularly, the level-based learning strategy in LLSO has a tight correspondence with the classification characteristic by setting the number of levels in LLSO to be the same as the number of classes in GBC. Together, the classification results feedback the distribution of promising candidates to accelerate the evolution of the optimizer, while the evolved population helps to improve the accuracy of the classifier. To select informative and valuable candidates for real evaluations, we devise an <inline-formula> <tex-math notation="LaTeX">${L}1$ </tex-math></inline-formula>-exploitation strategy to extensively exploit promising areas. Then, the candidate selection is conducted between the predicted <inline-formula> <tex-math notation="LaTeX">${L}1$ </tex-math></inline-formula> offspring and the already real-evaluated <inline-formula> <tex-math notation="LaTeX">${L}1$ </tex-math></inline-formula> individuals based on their Euclidean distances. Extensive experiments on commonly used benchmark functions demonstrate that the proposed optimizer can achieve competitive or better performance with a very small training dataset compared with three state-of-the-art SAEAs.
An Internet of Thing based Agribot (IOT- Agribot) for Precision Agriculture and Farm Monitoring

: Developing nations like India have a huge potential for agricultural business and better cultivation. Because of the large size of cultivation land, improper water supply systems and lack of technology-based agricultural practices, there is a huge gap among expected and actual quantity and quality of agricultural products. Hence there is a need for significant revival in agribusiness using emerging technologies. The article proposes an intelligent water framework device called Agribot designed for the agricultural industry to minimize the water wastage and a better supply of cultivating materials using the Internet of Things (IoT). Our proposed IOT-Agribot will energize the water framework, improve the cost-effective water usage and reduce the labor force to achieve precision agriculture. The proposed IOT-Agribot has performed well for variable weather conditions, soli type, moisture content and crops.
Performance of mobile users with text-only and text-and-icon menus in seated and walking situations

ABSTRACT We investigated mobile users’ performance on text-only and text-and-icon menu interfaces of short and medium lengths. We conducted two experiments, with 18 participants each, on four mobile menus in two common usage situations, seated and walking. In Experiment 1, seated participants conducted known-item searches of word menus on short and medium text-only menus and short and medium text-and-icon menus. In Experiment 2, we replicated the first experiment in a walking context of use. We used selection time and error as performance measures in both experiments, plus walking speed in the second. Results showed that, when seated, participants recorded faster times in selecting targets with text-and-icon medium menu than text-only medium menu. Participants, when walking, had significantly faster selection times with text-and-icon short menu than text-only short menu, and with text-and-icon medium menu than text-only medium menu. Text-and-icon medium menu also resulted in fewer incorrect selections by users than text-only medium menu in the walking situation. Further, when comparing menus of the same length in the walking situation, text-and-icon menu had a more efficient learning curve than text-only menu. Walking speed was better with text-and-icon medium menu than text-only medium menu. We discuss results and highlight theoretical and practical implications.
Measurement Patterns: User-Oriented Strategies for Dealing with Measurements and Dimensions in Making Processes

The majority of errors in making processes can be tracked back to errors in dimensional specifications. While technical aspects of measurement, such as precision and speed have been extensively studied in metrology, the user aspects of measurement received significantly less attention. While little research exists that specifically addresses the user aspects of handling dimensions, various systems have been built that embed new interactive modalities, processes, and techniques which significantly impact how users deal with dimensions or conduct measurements. However, these features are mostly hidden in larger system contributions. To uncover and articulate these techniques, we conducted a holistic literature survey on measurement practices in crafting techniques and systems for rapid prototyping. Based on this survey, we contribute 10 measurement patterns, which describe reusable elements and solutions for common difficulties when dealing with dimensions throughout workflows for making physical artifacts.
Infectious Diseases, Market Uncertainty and Oil Market Volatility

We examine the predictive power of a daily newspaper-based index of uncertainty associated with infectious diseases (EMVID) for oil-market volatility. Using the heterogeneous autoregressive realized volatility (HAR-RV) model, we document a positive effect of the EMVID index on the realized volatility of crude oil prices at the highest level of statistical significance, within-sample. Importantly, we show that incorporating EMVID into a forecasting setting significantly improves the forecast accuracy of oil realized volatility at short-, medium-, and long-run horizons. Our findings comprise important implications for investors and risk managers during the unprecedented episode of high uncertainty resulting from the COVID-19 pandemic.
"We Cried on Each Other’s Shoulders": How LGBTQ+ Individuals Experience Social Support in Social Virtual Reality

Although social support can be a vital component of gender and sexual identity formation, many LGBTQ+ individuals often lack offline social networks for such support. Traditional online technologies also reveal several challenges in providing LGBTQ+ individuals with effective social support. Therefore, social VR, as a unique online space for immersive and embodied experiences, is becoming popular within LGBTQ+ communities for supportive online interactions. Drawing on 29 LGBTQ+ social VR users’ experiences, we investigate the types of social support LGBTQ+ users have experienced through social VR and how they leverage unique social VR features to experience such support. We provide one of the first empirical evidence of how social VR innovates traditional online support mechanisms to empower LGBTQ+ individuals but leads to new safety and equality concerns. We also propose important principles for rethinking social VR design to provide all users, rather than just the privileged few, with supportive experiences.
Determination of the activity and kinetic parameters of polyphenol oxidase enzyme in crude extracts of some Jordanian plants

The activity and kinetic parameters of the enzyme polyphenol oxidase (PPO) in crude extracts of the fruits of Paradise apple (Pyrus malus L.), cucumber (Cucumis sativus L.), squash (Cucurbita pepo L.), pomegranate rind and seeds (Punica granatum L.), and the leaf of cactus (Opuntia ficus-indica (L.) were investigated. The highest browning intensity was found in P. apple, followed by pomegranate seeds and cucumber fruits. The optimum activity of the enzyme was shown at pH 7.0 for P. apple and cucurbita, but it was found at pH 6.0 for the rind and seeds of the pomegranate. The PPO of cactus leaf and cucumber fruits showed two peaks at pH 4.0 and 7.0. On the other hand, the optimal temperature found for PPO activity in these plants was around 40°C. A strong correlation with value (R) = 0.9485 between the browning intensity and PPO activity in all studied samples was obtained.
Pre-service Teachers’ Beliefs about Language Learning Inventory (BALLI) on Bilingualism: Getting Insights to Developing Knowledge

The goal of pre-service teacher (PST) programs is to provide students with pragmatic working experience and pedagogy that they need for becoming eligible teachers. In a bilingual classroom, however, some perspectives about second language learning (SLL) held by PSTs are too arbitrary. To assist PSTs in developing concepts of second language acquisition as related to educational settings and to cultivate PSTs’ burgeoning educational beliefs, this study was conducted using the Beliefs About Language Learning Inventory (BALLI). Seventeen PSTs who were enrolled in a university located along the southern U.S. border provided data for the present study. Three of the 27 belief statements included in the BALLI survey were critically analyzed. This critical analysis explored why certain PSTs’ beliefs about second language learning diverged from social reality at schools. Based on the analysis and results from our findings, suggestions about how to improve the bilingual education for PSTs are provided.
A Low-Cost Grip Pen Sensing Tool to Detect Handwriting Disorders

Handwriting is a complex motor activity. Neurodevelopmental disorders, such as dysgraphia can lead to severe handwriting difficulties. These dysfunctions could be predicted by measuring the pen’s motion, the pressure of the tip and the grip force while handwriting. Various hardware platforms have been proposed to sense pen movement. However, the grip force sensing is more challenging and often requires high-end sensing devices. In this paper, we propose a low-cost pen grip sensing tool to detect pressures and positions of fingers on the pen. It consists of a flexible, mutual-capacitive sensor rolled around the pen and connected to an inexpensive microcontroller. We provide initial tests of our grip sensing tool on multiple single-touch conditions. The results show a homogeneity over the flexible sensor and a linearity of the response with several curvatures. Such novel device open new opportunities for neurodevelopemental disorders research and new possibilities for clinical purposes.
Theoretical study of excitation energy transfer and nonlinear spectroscopy of photosynthetic light‐harvesting complexes using the nonperturbative reduced dynamics method

The highly efficient excitation energy transfer (EET) processes in photosynthetic light‐harvesting complexes have attracted much recent research interests. Experimentally, spectroscopic studies have provided important information on the energetics and EET dynamics. Theoretically, due to the large number of degrees of freedom and the complex interaction between the pigments and the protein environment, it is impossible to simulate the whole system quantum mechanically. Effective Hamiltonian models are often used, in which the most important degrees of freedom are treated explicitly and all the other degrees of freedom are treated as a thermal bath. However, even with such simplifications, solving the real‐time quantum dynamics could still be a difficult task. A particular challenging case in simulating the EET dynamics and related spectroscopic phenomena lies in the so‐called intermediate coupling regime, where the intermolecular electronic couplings and the electronic–vibrational couplings are of similar strength. In this article, we review theoretical studies of linear and nonlinear spectroscopic signals of photosynthetic light‐harvesting complexes, using the nonperturbative hierarchical equations of motion (HEOM) approach. Simulations were performed for the EET dynamics, various types of linear spectra, two‐dimensional electronic spectra, and pump–probe spectra. Benchmark tests of several approximate methods related to the HEOM approach were also discussed. The results show that the nonperturbative HEOM approach is an effective method in simulating the EET dynamics and spectroscopic signals of photosynthetic light‐harvesting complexes. Important insights into EET pathways, quantum effects including quantum delocalization, and quantum coherence in photosynthetic light‐harvesting complexes were also obtained through such simulations.
Assessing the use of cross-orbit Sentinel-1 images in land cover classification

ABSTRACT Land cover is the easiest detectable indicator of human intervention on land. Urban, peri-urban and agriculture areas present a complex combination of land cover, which makes classification challenging. Getting more detailed information is the aim of any classification method. In this study, improving land cover type classification using cross-orbit Sentinel-1 images is evaluated. To avoid uncertainties, three sites in different weather conditions and locations but approximately the same land cover is selected. For each study area, three datasets including polarimetric features extracted from (1) ascending orbit image, (2) descending orbit image, and (3) combined ascending and descending orbit images are produced and used for classification. Land cover classification is performed following a supervised Support Vector Machine (SVM) exploiting all three datasets. Consequently, the radar cross-orbit integrated dataset produced the most accurate land cover map. Classifications show overall accuracies (OA) of 84%, 85%, and 75%, and Kappa coefficients (K) of 0.67, 0.75, and 0.55 for Skane, Tehran, and Sherbrooke regions, respectively. Fortunately, the accuracy results are at least 4% better than single-orbit classification. In other words, the proposed mapping approach has proved that using information from both the ascending and descending dual-polarized images could achieve a more accurate classification map than using a single-orbit image individually.
“Picture the Audience...”: Exploring Private AR Face Filters for Online Public Speaking

Faced with public speaking anxiety, one common piece of advice is to picture the audience in a new light, using your mind’s eye. With Augmented Reality (AR) face filters, it becomes possible to literally change how one sees oneself or others. In this paper, we explore privately applied AR filters during online public speaking. Private means that these effects are only visible to the speaker. To investigate this possibly controversial concept, we conducted an online survey with 100 respondents to gather a diverse set of initial impressions, possible boundaries, and guidelines. Following this, we built a prototype of a private AR web-based video-calling application, and pilot-tested it with 16 participants to gain more in-depth insights. Based on our results, we outline key user perspectives and opportunities for the private application of AR face filters during online public speaking and discuss them in the context of previous literature on this topic.
New Zealand’s Residential Price Dynamics: Do Capability to Consume and Government Policies Matter?

The objectives of this paper are to provide empirical evidences on whether i) government policies represented by mortgage rate and loan-to-value ratio are associated with the dynamics of the New Zealand Residential Price Index, ii) macroeconomic factors such as house price to income ratio and inflation rate that proxy capability to consume, as well as population growth rate are the driving forces of housing market dynamics of the country. After testing a battery of statistical assumptions, this study adopts the Autoregressive Distributed Lag (ARDL) cointegration method to examine data from Quarter 1, 2009 to Quarter 2, 2019 for the short and long-run relationships among the variables. Findings show that mortgage rate, loan-to-value ratio, and inflation rate have negative long-run relationship with Residential Price Index. On the other hand, population growth rate and house price to income ratio are shown to have positive long-run impact on housing price. Results obtained from Error Correction Model reveal that whenever there is a short-run shock in the residential price dynamics, the Residential Price Index will take about three quarters to fully restore back to its long-run equilibrium. Additionally, mortgage rate, population growth rate, loan-to-value ratio and house price to income ratio are found to significantly Granger-cause New Zealand's residential price dynamics during the study period.
A temperature of 20°C in the UK winter: a sign of the future?

In late February 2019, the UK experienced a spell of exceptional warmth for the time of year. For the first time, temperatures in excess of 20°C were recorded in the UK in a winter month in London, parts of west and north Wales and Manchester. Temperature records tend to be broken by narrow margins, but on 26 February the previous winter record was exceeded by 1.5 degC. Was this event a sign A temperature of 20°C in the UK winter: a sign of the future?
Which Factors Predict the Chat Experience of a Natural Language Generation Dialogue Service?

In this paper, we proposed a conceptual model to predict the chat experience in a natural language generation dialog system. We evaluated the model with 120 participants with Partial Least Squares Structural Equation Modeling (PLS-SEM) and obtained an R-square (R2) with 0.541. The model considers various factors, including the prompts used for generation; coherence, sentiment, and similarity in the conversation; and users’ perceived dialog agents’ favorability. We then further explore the effectiveness of the subset of our proposed model. The results showed that users’ favorability and coherence, sentiment, and similarity in the dialogue are positive predictors of users’ chat experience. Moreover, we found users may prefer dialog agents with characteristics of Extroversion, Openness, Conscientiousness, Agreeableness, and Non-Neuroticism. Through our research, an adaptive dialog system might use collected data to infer factors in our model, predict the chat experience for users through these factors, and optimize it by adjusting prompts.
Navigating the Curricular Maze: Examining the Complexities of Articulated Pathways for Transfer Students in Engineering

ABSTRACT States and institutions employ articulation agreements to streamline curricular pathways. We investigate the efficacy of that streamlining by considering how course sequences, enacted through pre- and co-requisites, relate to graduation rates for transfer students at different time intervals. Applying a curricular complexity framework that quantifies the complexities of curriculum pathways, we compared the curricular complexities of transfer and first-time-in-college (FTIC) pathways in engineering and correlate those complexity scores with graduation rates at different time intervals. The institutions examined in this study include a mid-Atlantic research university and four of its largest feeder community colleges geographically distributed across the state. We found that, in aggregate, transfer student pathways are less complex than FTIC pathways, although complexity metrics vary across engineering disciplines and sending institutions. Although curricular complexity correlates with graduation rates for FTIC students at different time intervals, the same relationship does not hold for transfer students. The curricular complexity metric is useful for understanding the complexity FTIC students encounter in engineering and correlates with their graduation rates at different time intervals. However, the tool falls short of capturing some nuances of curricular complexity for transfer students. We suggest ways to enhance the metric to depict complexities in curriculum for transfer students.