Miracle-dz/UniFAR-FTUs huggingface dataset:
Moments, not minutes: The nature-wellbeing relationship

A wealth of literature has evidenced the important role that the greater-than-human natural environment plays in our mental health and wellbeing (reviews by Bratman et al., 2019; Capaldi et al., 2014, 2015; Pritchard et al., 2019). Spending time in nature, engaging with nature directly and indirectly, and a strong sense of nature connectedness (a psychological/emotional connection with nature) have each been shown to positively impact wellbeing. Few studies, however, have examined the importance that various nature-related factors have on our wellbeing when examined in concert with each other, with none including factors of nature connection and engagement. In the current study, using a national United Kingdom sample of 2,096 adults, we provide new insights into this gap in the literature. Our primary focus was on examining, when considered simultaneously, the patterns and relative predictive importance to hedonic wellbeing (i.e., happiness), eudaimonic wellbeing (i.e., worthwhile life), illbeing (i.e., depression and anxiety), and general physical health of five nature-related factors: (1) nature connectedness, (2) time in nature, (3) engagement with nature through simple everyday activities, (4) indirect engagement with nature, and (5) knowledge and study of nature. A consistent pattern of results emerged across multiple analytical approaches (i.e., correlations, linear regression, dominance analyses, commonality analysis), wherein time in nature was not the main (or significant) predictive nature-related factor for wellbeing. Rather, nature connectedness and engaging with nature through simple activities (e.g., smelling flowers) consistently emerged as being the significant and prominent factors in predicting and explaining variance in mental health and wellbeing. Implications for practical application and policy/programme planning are discussed.
CoDE: Collocation for Demonstration Encoding

Roboticists frequently turn to Imitation learning (IL) for data efﬁcient policy learning. Many IL methods, canonicalized by the seminal work on Dataset Aggregation (DAgger), combat distributional shift issues with older Behavior Cloning (BC) methods by introducing oracle experts. Unfortunately, access to oracle experts is often unrealistic in practice; data frequently comes from manual ofﬂine methods such as lead-through or teleoperation. We present a data-efﬁcient imitation learning technique called Collocation for Demonstration Encoding (CoDE) that operates on only a ﬁxed set of trajectory demonstrations by modeling learning as empirical risk minimization. We circumvent problematic back-propagation through time problems by introducing an auxiliary trajectory network taking inspiration from collocation techniques in optimal con-trol. Our method generalizes well and is much more data efﬁcient than standard BC methods. We present experiments on a 7-degree-of-freedom (DoF) robotic manipulator learning behavior shaping policies for efﬁcient tabletop operation.
Soil carbon dynamics in a humid tropical zone in SE Nigeria: Environmental influences and 
conservation prioritization

Tropical landscapes are generally known to store much carbon in its varied ecosystems, but information required to prioritize and conserve areas with much soil carbon concentration is scanty. This study provides insight on the patterns of carbon storage and its dynamics in an extensive humid zone in SE Nigeria. Soil carbon data was downloaded from soil grids of 250 m carbon stocks for the first 30 cm of the soil profile and compared with the land use/cover, geology and digital elevation model of the region. Carbon estimates varied over the four epochs used and ranged from 29-177, 29-172, 12-172 and 4-177 tons/ha in 1992, 2001, 2010 and 2018, respectively. Even though land cover variation was quite distinct in the zone (between the northern zone with much cover and the southern zone with less), it was not seen as the major factor that influenced soil carbon in the zone. Much of the explanations on carbon concentration and variability were mainly from the geology of the zone which is categorized into six major classes. But three out of the categories were seen to dominate the carbon store: Ajali formation, Mamu formation and Nsukka formation. This exogenous factor (geology), which is not uniform, was seen as the major variable to consider in deciding areas that will be prioritized for carbon conservation in the zone. Proper land use strategies and policy frameworks that would help to maintain proper baselines and further enhance adequate carbon conservation were equally recommended. Key words: Carbon management, conservation, land use change, modified ecosystems, prioritization, tropical.
TOPSIS Method Based on the Correlation Coefficient of Interval-Valued Intuitionistic Fuzzy Soft Sets and Aggregation Operators with Their Application in Decision-Making

The correlation coefficient between the two parameters plays a significant part in statistics. Furthermore, the exactness of the assessment of correlation depends upon information from the set of discourses. The data collected for various statistical studies are full of ambiguities. The idea of interval-valued intuitionistic fuzzy soft sets is an extension of intuitionistic fuzzy soft sets that is used to express insufficient evaluation, uncertainty, and anxiety in decision-making. Intuitionistic fuzzy soft sets consider two different types of information, such as membership degree and nonmembership degree. In this paper, the concepts and properties of the correlation coefficient and the weighted correlation coefficient of interval-valued intuitionistic fuzzy soft sets are proposed. A prioritization technique for order preference by similarity to the ideal solution based on interval-valued intuitionistic fuzzy soft sets of correlation coefficients and the weighted correlation coefficient is introduced. We also proposed interval-valued intuitionistic fuzzy soft weighted average and interval-valued intuitionistic fuzzy soft weighted geometric operators and developed decision-making techniques based on the proposed operators. By using the developed techniques, a method for solving decision-making problems is proposed. To ensure the applicability of the proposed methods, an illustrative example is given. Finally, we present a comparison of some existing methods with our proposed techniques.
TGANs with Machine Learning Models in Automobile Insurance Fraud Detection and
Comparative Study with Other Data Imbalance Techniques

A data-driven Fraud detection model for insurance business can be seen as a two-phase method. Phase I is data-preprocessing of a given dataset, in which, handling class imbalance is a major challenge. Phase II is that of classification using Machine Learning models. It is important to comprehend if there is any influence of the technique used in Phase I on the efficiency of the model used for Phase II. A natural query that intrigues one is whether there is a golden combination of a technique in Phase I and a specific model in Phase II for assured best performance of a Fraud Detection Model.In this work, we study a few techniques for handling data imbalance issue namely, SMOTE, MWMOTE, ADASYN and TGAN in combination with various classifier models like Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), LightGBM, XGBoost and Gradient Boosting Machines (GBM). The study is conducted on a dataset for motor vehicle insurance fraud detection.We present a comparison of various combinations of data imbalance technique and classifier models. It is observed that the combination of TGAN in Phase I and GBM in Phase II gives the best performance. This combination performs best in terms of important metrics such as false positive rate, precision and specificity. We obtained the lowest false positive rate of 0.0011 and precision of 0.9988 which minimizes the most critical risk for the insurance company of falsely classifying a non-fraud claim as a fraud. Finally, the specificity of 0.9989 indicates that the model was also very good at predicting the non-fraudulent claim.
The synergy of complex event processing and tiny machine learning in industrial IoT

Focusing on comprehensive networking, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations. Various intelligent sensors play a central role, as they generate a vast amount of real-time data that can provide insights into manufacturing. Complex event processing (CEP) and machine learning (ML) have been developed actively in the last years in IIoT to identify patterns in heterogeneous data streams and fuse raw data into tangible facts. In a traditional compute-centric paradigm, the raw field data are continuously sent to the cloud and processed centrally. As IIoT devices become increasingly pervasive, concerns are raised since transmitting such an amount of data is energy-intensive, vulnerable to be intercepted, and subjected to high latency. Decentralized on-device ML and CEP provide a solution where data is processed primarily on edge devices. Thus communications can be minimized. However, this is no mean feat because most IIoT edge devices are resource-constrained with low power consumption. This paper proposes a framework that exploits ML and CEP's synergy at the edge in distributed sensor networks. By leveraging tiny ML and μCEP, we now shift the computation from the cloud to the resource-constrained IIoT devices and allow users to adapt on-device ML models and CEP reasoning rules flexibly on the fly. Lastly, we demonstrate the proposed solution and show its effectiveness and feasibility using an industrial use case of machine safety monitoring.
Comparison of Psychiatric Disorders Between Levetiracetam Combined With Lacosamide and Perampanel

Background The number of patients with epilepsy receiving perampanel or lacosamide as an add-on treatment following levetiracetam treatment has increased. Although levetiracetam causes psychiatric disorders, it is unclear whether they occur with the combined use of these antiepileptic drugs.Objective To determine the frequency of psychiatric disorders in patients using lacosamide or perampanel as an add-on therapy to levetiracetam. Setting A single-center retrospective cohort study. Methods Patients who received levetiracetam, lacosamide, and perampanel between April 1, 2014 and April 30, 2019 in Hachioji Medical Center were selected. They were classified into the levetiracetam+lacosamide or levetiracetam+perampanel group. Medical records from the start of combination therapy contained patient background and the incidence of psychiatric disorders. Main outcome measure Onset of psychiatric disorders. Results Forty-four patients used levetiracetam+lacosamide and 50 used levetiracetam+perampanel. The incidence of psychiatric disorders was significantly lower (p = 0.000047) with levetiracetam+lacosamide (6.8%) than with levetiracetam+perampanel (46%). The time to the onset of psychiatric disorders was within 1 month of dose initiation or increase in one case (33.3%) with levetiracetam+lacosamide and 16 cases (76.2%) with levetiracetam+perampanel. The median time to onset was 56 and 6.5 days with levetiracetam+lacosamide and levetiracetam+perampanel, respectively. There was no significant difference in antiepileptic drug dosages owing to the presence or absence of psychiatric disorders. Conclusion As the frequency of psychiatric disorders was higher with levetiracetam+perampanel therapy, levetiracetam+lacosamide may be preferable. These disorders tended to develop within 1 month of therapy and were not dose-dependent. Antiepileptic drugs should be cautiously prescribed to avoid psychiatric disorders.
FACTORS INFLUENCING CONSUMERS IN USING SHOPEE FOR ONLINE PURCHASE INTENTION IN EAST COAST MALAYSIA

Shopee online has become increasingly popular among consumers for purchasing purposes as compared to brick-and-mortar retail due to the benefits and enjoyment that come with it. However, Shopee has facing intense competitive challenges with other competitors in Malaysia. In addition, it is also difficult for Shopee to compete with major leader of online shopping portal although this platform offers the same function and usage as its contender. Hence, it is important to understand consumer perceptions on the benefit and intention of using Shopee. This study examines consumer perceived usefulness, perceived ease of use, perceived price, perceived convenience and perceived trust adapted from the Technology Acceptance Model (TAM). 208 respondents who have experience online purchasing in East Cost of Malaysia were invited to participate in this study using online survey. Data were analyzed using descriptive, correlation and multiple regression analyses. The findings show that perceived convenience and perceived trust have strong influence towards consumer intention to use Shopee as platform to do online purchase. It is suggested that this study will help practitioners to understand consumer online shopping perception and intention in order to induce visitation and usage of Shopee.
Annexin A1 (ANXA1): A Systematic Review of Its Role in Inflammation

Inflammation is a body response towards any injury or tissue damage. It involves the accumulation of neutrophils and release of inflammatory mediators. ANXA1, a 37 kDa glucocorticoid inducible protein plays an important role in resolving inflammation. The unique N-terminal and its mimetic peptide exert strong anti-inflammatory actions. This study was conducted to review the roles of ANXA1 in inflammation and identify any other reported roles. Electronic search was done whereby a total of 3797 articles were located from three databases, namely Ovid MEDLINE, Science Direct, and PubMed. Articles on ANXA1 and inflammation were selected based on inclusive criteria and review papers were excluded. Bias analysis was performed based on bias risk tool and 27 articles were included in the study. It was found that ANXA1 was able to resolve inflammation in many inflammatory diseases. Upon treatment with glucocorticoid, ANXA1 is induced and its significant expressions in tissues are important in resolving inflammation. However, this effect can be reversed by administering an anti-annexin antibody. This protein also acts on members of all formyl peptide receptors (FPR) and activates them to initiate reaction. It acts mainly by causing the death of neutrophils through apoptosis. In addition, ANXA1 is identified as a marker in cancer cells which determines the survival rates. In conclusion, ANXA1 is a key modulator in resolving inflammation in many diseases and is actively being induced upon glucocorticoid treatment.
AI – New Avenue for Drug Discovery and Optimization

The artificial intelligence (AI) used in drug treatment have to do with matching patients to their predicting drug-target or drug-drug interactions, optimal drug or combination of drugs, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the suitable drug for a patient typically requires the patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The forecast of drug relations often relies on similarity metrics, pretentious that drugs with similar structures or targeted and similar behaviour or may interfere with each other. Deciding the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamics data.
Influence of feedlot manure on reactive phosphorus in rainfall runoff during the transition from continuous to legacy phases

Abstract: Little research has compared land application of stockpiled (SM) or composted (CM) beef feedlot manure with straw (ST) or wood-chip (WD) bedding on loss of reactive phosphorus (RP) in runoff. We conducted a 6 yr (2013–2018) rainfall simulation-runoff study and utilized surface (0–5 cm) soil collected from a long-term (since 1998) field experiment on a clay loam soil in southern Alberta, Canada. The treatments consisted of SM or CM with ST or WD bedding applied at 13, 39, and 77 Mg·ha−1 (dry weight), as well as an unamended control and mineral fertilizer treatment. Surface soil was collected from all treatments after 15–17 (C15, C16, C17; 2013–2015) continual annual applications, and then 1–3 yr (L1–L3, 2016–2018) into the legacy phase after manure applications were first discontinued in 2015. The soil was packed into runoff trays, and flow-weighted mean concentrations (FWMCs) and mass loads of RP5 (<5 μm filter) in runoff water were determined during rainfall simulations. Our findings generally supported our null hypothesis of similar RP5 losses for manure type (CM = SM) and bedding (ST = WD) for most years. Successively higher application rates increased RP5 loss by 32%–121%. Termination of long-term applications dramatically reduced FWMCs by 58%–77% and mass loss by 56%–65% from the C17 to L3 years. This suggests an accumulation of soil P during continuous phase and depletion during legacy phase; therefore, lower application rates or termination of applications may reduce RP5 loss in runoff.
Patterns of network coopetition in a merged tourism destination

ABSTRACT This study investigates patterns of coopetition in a merged tourism destination in North Denmark, where three towns are in focus. This is done using a mixed-method synergistic social network analysis (SSNA) to measure tourism actor network characteristics and qualitatively analyse informal network coopetition within and between towns. The findings show that coopetition takes place on two levels, and the patterns are defined as passive, reactive, and proactive. The actors in the three towns show different motivations to engage in coopetitive relationships, co-location being an important factor. Recommendations to support coopetition in new merging destinations are provided.
Failure Mode and Effects Analysis of CIGS Thin Film PV Modules Using Thermography Analysis and IV Measurements

This article reports on failure modes and effects analysis of CIGS thin-film modules in photovoltaic power plants. Common faults in a small photovoltaic power station consisting of 85 Copper Indium Gallium Selenide (CIGS) PV modules are analyzed under outdoor conditions using infrared thermography inspection and current-voltage measurements. The statistical features extraction of thermal images is conducted for fault detections and diagnosis in correlation with IV measurement in order to understand the relation of modules power loss with its thermal signatures. The capability of the statistical feature extraction to identify modules with more than one fault has been improved by a careful analysis of their histograms and I-V measurements. A general classification matrix of CIGS for fault detection and diagnosis using statistical features and IV measurements is established. The matrix is very useful for the operation and the maintenance planning of PV plants.
Cross Fusion Net: A Fast Semantic Segmentation Network for Small-Scale Semantic Information Capturing in Aerial Scenes

Capturing accurate multiscale semantic information from the images is of great importance for high-quality semantic segmentation. Over the past years, a large number of methods attempt to improve the multiscale information capturing ability of the networks via various means. However, these methods always suffer unsatisfactory efficiency (e.g., speed or accuracy) on the images that include a large number of small-scale objects, for example, aerial images. In this article, we propose a new network named cross fusion net (CF-Net) for fast and effective extraction of the multiscale semantic information, especially for small-scale semantic information. In particular, the proposed CF-Net can capture more accurate small-scale semantic information from two aspects. On the one hand, we develop a channel attention refinement block to select the informative features. On the other hand, we propose a cross fusion block to enlarge the receptive field of the low-level feature maps. As a result, the network can encode more accurate semantic information from the small-scale objects, and the segmentation accuracy of the small-scale objects is improved accordingly. We have compared the proposed CF-Net with several state-of-the-art semantic segmentation methods on two popular aerial image segmentation data sets. Experimental results reveal that the average $F_{1}$ score gain brought by our CF-Net is about 0.43% and the $F_{1}$ score gain of the small-scale objects (e.g., cars) is about 2.61%. In addition, our CF-Net has the fastest inference speed, which proves its superiority in the aerial scenes. Our code will be released at: https://github.com/pcl111/CF-Net.
Vascular Epiphyte Assemblages on Isolated Trees along an Elevational Gradient in Southwest Panama

Ongoing destruction of tropical forests makes isolated pasture trees potentially important for the persistence of original forest dwellers such as many vascular epiphytes. We studied epiphyte assemblages on 100 isolated trees at ten pasture sites in southwest Panama along an elevational gradient ranging from 140 to 1240 m a.s. l. We analysed epiphyte species composition (richness, similarity) and registered climate and host trait variables of potential influence on their occurrence. We found a total of 5876 epiphyte individuals belonging to 148 species. Epiphyte abundance, species richness and diversity all varied about 4-fold among the 10 sites, with a high similarity of epiphyte assemblages among sites. Two sites at 870 and 1050 m a.s. l. did not fit into the overall elevational trend of increased abundance, species richness and diversity. However, all three measures were significantly correlated with humidity as the independent variable. This highlights that a gradient in humidity, and not elevation as such, is responsible for the typical elevational changes in epiphyte assemblages, so that special local conditions may lead to deviations from expected patterns. Our documentation of current elevational diversity patterns also provides a baseline for the study of long-term changes in epiphyte assemblages in anthropogenically modified landscapes.
A Fuzzy Clustering Based Anomaly Node Detection Method for Publish/Subscribe Distributed Systems

Timely and accurate understanding of the node running status can effectively control the propagation of faults in publish/subscribe distributed systems, which is of great significance to ensure the reliable operation of applications. A method of anomaly node detection based on fuzzy k-means clustering algorithm is proposed. Compared with the traditional K-means algorithm, this algorithm introduces fuzzy membership matrix to realize fuzzy clustering, and uses the idea of local reachability density to improve the selection method of cluster centers. Experimental results show that this method can effectively detect anomaly node in publish / subscribe distributed systems with higher accuracy, recall and F-measure than traditional K-means algorithm. The precision of publish anomaly detection and network anomaly detection is improved by 10.53% and 38.6% respectively.