Miracle-dz/UniFAR-FTUs huggingface dataset:
Line-transect versus point-transect sampling: the effects of survey area and survey effort on method efficiency for Geoffroy’s spider monkeys

Abstract Context. Line-transect sampling is considered to be a more efficient survey method than point-transect sampling to estimate population densities and abundance of many animal species. Aims. In the present study, we tested whether this claim holds true when surveying arboreal fast-moving primate species occurring at low densities, and whether the potential difference in efficiency can be explained by the difference in the size of the survey area between the methods. We further examined the impact of survey effort for point-transect sampling. Methods. We conducted line- and point-transect sampling for Geoffroy’s spider monkeys (Ateles geoffroyi) in the same locations and compared the numbers of detected individual monkeys and the probability of their occurrence per survey between the two methods. We further compared the data from point-transect sampling gathered within three different waiting periods. Key results. We found a higher probability to detect monkeys and a higher number of monkeys during line-transect sampling than during point-transect sampling, but more spider monkeys were detected at point transects when controlling for the size of the survey area. More monkey detections were made during the first 10 min than during the second and third 10-min periods of point-transect surveys. Conclusions. We showed that line-transect sampling is more efficient than point-transect sampling when surveying Geoffroy’s spider monkeys in a flat landscape of tropical forest with homogenous visibility. We discuss factors influencing survey results and recommend 20 min as the maximum waiting time at point transects when surveying arboreal mammals. Implications. Our study has provided a quantitative approach to compare efficiency across survey methods for fast-moving arboreal animals that occur at low densities, and supports the use of point-transect sampling in sites where line-transect sampling is not feasible, such as in human-modified landscapes.
Investigating differences in population recovery rates of two sympatrically nesting sea turtle species

Estimating life‐history traits and understanding their variation underpins the management of long‐lived, migratory animals, while knowledge of recovery dynamics can inform the management of conservation‐dependent species. Using a combination of nest counts and individual‐based life‐history data collected since 1993, we explore the drivers underlying contrasting population recovery rates of sympatrically nesting loggerhead (Caretta caretta) and green (Chelonia mydas) turtles in North Cyprus. We found that nest counts of loggerhead and green turtles from 28 beaches across the island increased by 46% and 162%, respectively over the past 27 years. A Bayesian state‐space model revealed that, at our individual‐based monitoring site, nesting of green turtles increased annually at four times the rate of that of loggerhead turtles. Furthermore, we found that loggerhead turtles nesting at the individual‐based monitoring site had stable reproductive parameters and average adult survival for the species and are the smallest breeding adults globally. Based on results from multiple matrix model scenarios, we propose that higher mortality rates of individuals in all age classes (likely driven by differences in life history and interaction with fisheries), rather than low reproductive output, are impeding the recovery of this species. While the increase in green turtles is encouraging, the Mediterranean population is estimated to have around 3,400 adults and is restricted to the Eastern Basin. The recovery of loggerhead turtles is likely to be compromised until mortality rates in the region are adequately quantified and mitigated. As survival of immature individuals is a powerful driver for sea turtle population numbers, additional efforts should target management at pelagic and neritic foraging areas. Understanding threats faced by immature life stages is crucial to accurately parameterise population models and to target conservation actions for long‐lived marine vertebrates.
A galaxy of apps: Mobile app reliance and the indirect influence on political participation through political discussion and trust

The power of app-driven mobile phones was first unleashed in 2011 when they were used to mobilize protesters and gain support for political movements in the United States and abroad. Mobile devices have since become the bedrock of political activism. To examine the influence of app reliance on offline and online political participation, this study builds on the Orientation-Stimulus-Reasoning-Orientation-Response (O-S-R-O-R) model by (a) applying the model to mobile apps, (b) testing whether trust in, and reliance on political discussion are mediators between reliance on apps and political participation, and (c) using trust in both offline and online discussion as measures of cognitive elaboration. This study’s path model suggests that app reliance is related to online political discussion, which, in turn, is related to online political participation, but not offline participation. Although both offline and online discussion are linked to offline and online trust in political discussion, trust in political discussion does not influence either offline or online political participation.
Effects of prediction error on episodic memory retrieval: evidence from sentence reading and word recognition

ABSTRACT Prediction facilitates word processing in the moment, but the longer-term consequences of prediction remain unclear. We investigated whether prediction error during language encoding enhances memory for words later on. German-speaking participants read sentences in which the gender marking of the pre-nominal article was consistent or inconsistent with the predictable noun. During subsequent word recognition, we probed participants’ recognition memory for predictable and unpredictable nouns. Our results indicate that individuals who demonstrated early prediction error during sentence reading, showed enhanced recognition memory for nouns overall. Results from an exploratory step-wise regression showed that prenominal prediction error and general reading speed were the best proxies for recognition memory. Hence, prediction error may facilitate recognition by furnishing memory traces built during initial reading of the sentences. Results are discussed in the light of hypotheses positing that predictable words show a memory disadvantage because they are processed less thoroughly.
Adaptive colour restoration and detail retention for image enhancement

Computer vision-based crowd understanding and analysis technology has been widely used in public safety due to the rapid growth of population and the frequent occurrence of various accidents. Improving imaging quality is the key to improve the performance of crowd analysis, density estimation, target recognition, segmentation, and detection in computer vision tasks. Due to the complex imaging environment such as fog and low illumination, some images taken in outdoor environment often have the problems of colour distortion, lack of details, and the poor imaging quality, which affect the subsequent visual tasks. To improve the imaging quality and visual effect, an adaptive colour restoration and detail retention-based method is proposed for image enhancement. First, to overcome the problem of colour distortion caused by low illumination and fog, a multi-channel fusion based adaptive image colour restoration method is proposed. To make the enhancement result more consistent with human observation, the detail retention-based method is applied to enhance the details. Experimental results demonstrate that the authors’ results are effective and outperform the compared methods both in visual and objective evaluations.
The coming age of adversarial social bot detection

Social bots are automated accounts often involved in unethical or illegal activities. Academia has shown how these accounts evolve over time, becoming increasingly smart at hiding their true nature by disguising themselves as genuine accounts. If they evade, bots hunters adapt their solutions to find them: the cat and mouse game. Inspired by adversarial machine learning and computer security, we propose an adversarial and proactive approach to social bot detection, and we call scholars to arms, to shed light on this open and intriguing field of study.
Valve Detection for Autonomous Water Pipeline Inspection Platform

Water distribution and transmission lines are indispensable to urban infrastructure. The water pipelines are subject to both structural and functional deterioration due to various reasons including aging, negligence, and high demand for water supply. Hence, to ensure a safe and reliable water supply, the water utilities need to perform routine pipe condition assessment. The condition assessment is usually carried out by visual inspection with the machine vision system carried by a robotic platform. The inspection platforms will capture the internal condition of the water pipelines in a video stream. However, the robotic platform frequently experiences difficulties while traversing through the valves installed along the pipeline. This inhibits and disrupts the inspection process of the water pipelines. Therefore, this article proposes a deep learning-based automatic valve detection framework to facilitate the robot’s navigation and ensure continuous inspection without any interruptions. The valve detection model is developed by combining MobileNet-160 and Feature Pyramid Network and is named as MFPN. The developed framework also employs a generative adversarial network to solve the sparse dataset issues and improve the generality of the framework. The comparative study and ablation analyses demonstrate that the proposed framework can achieve a higher mAP value of 89.11% in comparison with the state of the art. Hence, this light weight and efficient solution can be deployed to the robotic platform for real-time valve detection and enable autonomous navigation of the robotic platform for condition assessment of water pipelines.
A nonintrusive reduced order method for the NVH assessment and automotive structural dynamics

The goal of this work is to develop a computational method able to optimize the design process of a car structure and provide a tool which can support designers during the decision-making phase. The design of a car body-in-white (BIW) structure is the process which goes from the initial idea to the final approved model. During this phase, which represents the most time-consuming part of the whole development process, designers have to deal with very complex parametric problems where material and geometric characteristics of the car components are the unknown. Any change in these parameters might significantly affect the global behaviour of the car. A target which is very sensitive to small variations of the parameters is the noise and vibration response of the vehicle (NVH test), which strictly depends on the global static and dynamic stiffness. In order to find the optimal solution, a lot of configurations exploring all the possible parametric combinations need to be tested. Standard numerical methods are computationally very expensive when applied to this kind of multidimensional problems. An alternative is represented by reduced order models (ROM), which are based on the idea that the essential behaviour of complex systems can be accurately described by simplified low-order models. In this work, the encapsulated proper generalized decomposition (Encapsulated-PGD) toolbox, based on the PGD Least-Squares approximation [3] is proposed. As a main advantage, this ROM technique requires only one offline computation. The latter provides a separable solution which depends explicitly on an a-priori unknown number of parametric and mechanic modes or snapshots. Then, during an online stage, the solution can be particularized in realtime for any set of the parameters. In a previous work [4], a coupling of the PGD method with the Inertia Relief technique was implemented in order to perform the parametric static analysis of an unconstrained structures. A novel algebraic approach allowed to incorporate both material and complex geometric parameters and to perform shape optimization. Here, the method is extended to the case of a parametric generalized eigenvalue problem, in order to identify how a variation of user-defined parameters affects the dynamic response of the structure in terms of dominant eigenmodes and related natural frequencies. Moreover, thanks to the nonintrusive format of the toolbox, an interaction with commercial software is possible, which makes it particularly interesting for real industrial applications.
Factors Affecting Grafting Density in Surface‐Initiated ATRP: A Simulation Study

In surface-initiated atom transfer radical polymerization, knowledge of grafting density is of significant interest because it is one of the determining properties of grafted polymer. It is well known that not all of the immobilized initiators can grow into polymer chains. However, little is known about why this happens and what affects the grafting efficiency. The lack of information is partly due to the difficulty in experimental determination of grafting density on flat substrates. To circumvent the problem, Monte Carlo simulation with bond fluctuation model is used in this study to investigate the effects of various reaction conditions on the grafting density. The simulation results show lower grafting density when less deactivator is present. In systems with lower deactivator concentration, the number of monomer added per activation cycle is higher. Coupling this with close proximity of immobilized initiators results in chains initiated at earlier time to shield their neighboring initiator moieties from adding mono­mers, thus lowering the grafting density in such a system. These simulation results also provide an explanation to the seemingly conflicting trend reported in the literatures.
Background Representation Learning With Structural Constraint for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is a popular topic in remote sensing image intelligent interpretation. To detect anomaly, many methods for background representation have been proposed. However, the prior information of background and anomaly is not fully explored in these methods. To tackle this issue, we combine low-rank dictionary learning (LRDL) with total variation (TV) constraint for hyperspectral anomaly detection. To be specific, the LRDL is introduced for background representation to explore the low-rank priori of background. Considering the smooth structural characteristic of background in spatial, we introduce the TV constraint on coefficients matrix for better background representation learning. Then the residual part is used to discriminate anomaly. The experiments on three real data sets demonstrate the effectiveness of the proposed method compared with state-of-the-art algorithms in hyperspectral anomaly detection.
Photovoice for enhanced healthy blue space research: an example of use from urban India

ABSTRACT Four general research strands are known to study the salutogenic aspects of urban surface waters (urban blue spaces): the experiential, the experimental, the preference-based, and the quantitative spatial approach. Although this scientific toolkit offers various ways to assess the health benefits that people can derive from blue space experiences, the considerable knowledge gaps on blue health require enhancing the existing methodological approaches. With the overall aim to promote blue health within cities and to better guide and convince urban planning and policy, photovoice is proposed as an innovative methodology to enhance application-oriented healthy blue space research. This paper builds upon the experiences made in applying photovoice in a blue health study targeting senior citizens in urban India. Despite methodological difficulties (e.g. participation level), which need further clarification, it is highlighted that photovoice is appropriate to record environmental perceptions and blue health experiences and to help uncover the underlying mechanisms in the blue space–health relationship (e.g. health-enabling landscape elements) that can better explain the linkages between urban waters, health and wellbeing.
DPPH free radical scavenging activity of two extracts from agelanthus dodoneifolius (Loranthaceae) leaves

Agelanthus dodoneifolius (Loranthaceae) commonly named African mistletoe is traditionally used in Burkina Faso as a decoction for the treatment of asthma, stomach-ache and cardiovascular diseases. The current study was designed to assess the DPPH free radical scavenging effect and the flavonoid and phenolic contents of the decoction and maceration extracts from Agelanthus dodoneifolius leaves. Moreover, the phytochemical analysis was also carried out. The decoction and maceration were prepared in water. Phytochemical analysis was realized according to the standard protocols. The scavenging activity was determined using the radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. The flavonoid and phenolic contents were assessed using the Neu and Folin-Ciocalteu reagents, respectively. The phytochemical investigation showed the presence of flavonoid, tannins, steroids, triterpenes, carotenoids, anthocyanins and sugars in Agelanthus dodoneifolius dried and powdered leaves. Both decoction and maceration exhibited a significant dose-dependent radical scavenging activity with EC50 values of 8.20 ± 0.25 and 13.20 ± 0.37 μg/mL, respectively. This significant radical scavenging effect may be attributed to their richness in flavonoid and phenolic compounds. This study enhances the ability of Agelanthus dodoneifolius leaves as excellent natural source for antiradical scavenging. Therefore, the results may be taken account for the development of an herbal medicine to treat diseases related to an oxidative damage.
Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids

Several efforts need to be performed in transportation and energy production to mitigate the current environmental issues that are related to fossil fuel use. The implementation of DC microgrids and the use of electric vehicles seem to be an adequate solution. However, various technical challenges have to be addressed, like grid stability issues. Thus, this case report assesses the impact of an electric vehicle load in a DC microgrid, subject to nonlinear control theory. The EV battery pack is modeled and simulated. Subsequently, it is included as a load in an available model of nonlinear control of DC microgrids. The results demonstrate high stability with this new load and the feasibility of its implementation.
The reflective fostering programme: evaluating the intervention co-delivered by social work professionals and foster carers

Purpose There is little evidence regarding how to best support the emotional wellbeing of children in foster care. This research paper presents the evaluation of an adaptation of the Reflective Fostering Programme, a group-based program to support foster carers. This study aimed to explore whether a version of the Programme, co-delivered by a social work professional and an experienced foster carer, was acceptable and relevant to foster carers and to gather data on programme effectiveness. Design 38 foster carers attended the Programme and took part in this study. Data was collected regarding carerand child-focused outcomes at pre-intervention, post-intervention and 4month follow-up. Focus interviews were also conducted to further assess acceptability and relevance for foster carers. Findings Analysis of quantitative outcome showed statistically significant improvements in all outcomes considered including foster carers stress and carer-defined problems, as well as carer-reported measures of child difficulties. Focus group interviews with foster carers suggested that the Programme as co-delivered by a foster carer and a social worker was felt to be relevant and helpful to foster carers. Originality
Resveratrol loaded PLGA:D-α-tocopheryl polyethylene glycol 1000 succinate blend nanoparticles for brain cancer therapy

trans-Resveratrol (RSV) is a natural molecule proved for cancer preventive and therapeutic activities without any potential side effects. In recent years, RSV is also proved for its cytotoxic potential against glioma. However, practical application of RSV in glioma chemotherapy is limited because of its low systemic circulation time and short biological half-life resulting from rapid metabolism and accelerated elimination from the blood pool. Therefore, the main objective of this study was to enhance systemic circulation time and short biological half-life of RSV using poly(D,L-lactide-co-glycolide)–D-α-tocopheryl polyethylene glycol 1000 succinate blend nanoparticles (RSV-PLGA-BNPs). RSV-PLGA-BNPs were successfully formulated and optimized by a single-emulsion solvent-evaporation technique. RSV-PLGA-BNPs were evaluated by various state of the art techniques for extensive nanoparticulate characterization. Cytotoxicity against C6 cells, cellular internalization and haemocompatibility were studied for proving anticancer potential and safety of RSV-PLGA-BNPs. Pharmacokinetic and tissue distribution studies were carried out in Charles Foster rats after intravenous (i.v) administration. RSV-PLGA-BNPs showed significantly higher cytotoxicity and excellent cell internalization in C6 glioma cells. Haemocompatibility studies suggested that RSV-PLGA-BNPs are safe for i.v administration. Pharmacokinetic studies showed prolonged systemic circulation of RSV-PLGA-BNPs up to 36 h with approximately 18.11 times higher plasma half-life than RSV solution. Tissue distribution studies showed higher brain accumulation of RSV-PLGA-BNPs than RSV. Therefore, RSV-PLGA-BNPs can be applied as a potential tool for enhancing systemic circulation and plasma half-life with superior anticancer efficacy against glioma.
The Field Guide to 3D Printing in Microscopy

The maker movement has reached the optics labs, empowering researchers to actively create and modify microscope designs and imaging accessories. 3D printing has especially had a disruptive impact on the field, as it entails an accessible new approach in fabrication technologies, namely additive manufacturing, making prototyping in the lab available at low cost. Examples of this trend are taking advantage of the easy availability of 3D printing technology. For example, inexpensive microscopes for education have been designed, such as the FlyPi (1). Also, the highly complex robotic microscope OpenFlexure (2) represents a clear desire for the democratisation of this technology. 3D printing facilitates new and powerful approaches to science and promotes collaboration between researchers, as 3D designs are easily shared. This holds the unique possibility to extend the open-access concept from knowledge to technology, allowing researchers from everywhere to use and extend model structures. Here we present a review of additive manufacturing applications in microscopy, guiding the user through this new and exciting technology and providing a starting point to anyone willing to employ this versatile and powerful new tool.
New record genus and a new species of Allodiatrype from China based on morphological and molecular characters

The genus Allodiatrype (Diatrypaceae, Xylariales) is reported for the first time collected in Taiyang River National Forest Park, Yunnan Province. An undescribed Allodiatrype species is described and illustrated as a new species based on a combination of morphological characteristics and sequences data of ITS, SSU and TUB2. The new species is characterized by its Y-shaped or cruciate-shaped ostiolar surface, yellowish-green to the green inner layer, and hyaline, ellipsoidal to cylindrical or elongate-allantoid ascospores. The differences between the new species and the related fungi are discussed.