Miracle-dz/UniFAR-FTUs huggingface dataset:
Macroinvertebrates’ response to different land use in lowland streams from Uruguay: use of artificial substrates for biomonitoring

ABSTRACT The use of macroinvertebrates as indicators of water quality is an effective and low-cost tool, which is widely implemented in biomonitoring programmes. Certain taxa are characteristic of impaired watercourses (e.g. Oligochaeta, Chironomidae and Amphipoda), while others are characteristic of good-quality watercourses (e.g. Ephemeroptera, Plecoptera and Trichoptera; EPT). In this work, we evaluated the response of the macroinvertebrate assemblages to different land uses. For this purpose, artificial substrate colonization experiments were conducted in streams including urban (U, n = 3), intensive agriculture and dairy production (AD, n = 4) and extensive cattle ranching (CR, n = 4) land uses. Because in Uruguay pristine ecosystems are practically non-existent and streams of low order streams associated with extensive cattle production represent the lowest deterioration water quality condition, CR sites were used as control reference streams. Physicochemical water parameters were measured. For macroinvertebrate sampling, 10 artificial substrates were installed in each stream. A total of 110 artificial substrates were analysed. Each sample/site consisted of a plastic mesh bag of 1.2 cm opening, which were filled with 160 cm3 (including 73.3 ± 5.8 cm3 interstitial water) of stones sieved between 1.3 and 1.5 cm. During the summer of 2018 (February), devices were left in streams for 15 days for communities to colonize them. U and AD land use represented the stressors with the highest impact on the attributes and composition of the macroinvertebrate communities. Abundance of EPT was higher in CR, meanwhile Caenogastropoda dominated in AD, and Amphipoda in U sites. Macroinvertebrate groups to order level classification were able to effectively discriminate between different land uses. The use of artificial substrate also demonstrated to be efficient method for monitoring macroinvertebrate community. We did not find a correlation between the physicochemical water parameters and the macroinvertebrate community. In this context, we propose a rapid and cost-effective biomonitoring approach, capable of estimating the degree of impact of different land uses.
Pseudo-Graph Neural Networks On Ordinary Differential Equations

In this paper, we extend the idea of continuous-depth models to pseudo graphs and present pseudo graph ordinary differential equations (PGODE), which are inspired by the neural ordinary differential equation (NODE) for data in the Euclidean domain. All existing graph networks have discrete depth. A pseudo graph neural network (PGNN) is used to parameterize the derivative of hidden node states, and the output states are the solution to this ordinary differential equation (ODE). A memory-efficient framework with precise gradient estimates is then proposed for free-form ODEs. We also introduce the framework of continuous–depth pseudo graph neural networks (PGNNs) on ODE by blending discrete structures and differential equations
A Systematic Literature Review on the Characteristics and Effectiveness of Web Application Vulnerability Scanners

Web applications have been a significant target for successful security breaches in the last few years. They are currently secured, as a primary method, by searching for their vulnerabilities with specialized tools referred to as Web Application Vulnerability Scanners (WVSs). Although, these dynamic approaches of testing have some advantages, there is still a scarcity of studies that explore their features and detection capabilities in a systematic way. This article reports findings from a Systematic Literature Review (SLR) to look into the characteristics and effectiveness of the most frequently used WVSs. A total of 90 research papers were carefully evaluated. Thirty (30) WVSs were collected and reported, with only 12 having at least one quantitative assessment of effectiveness. These 12 WVSs were evaluated by 15 original evaluation studies. We found that these evaluations tested mostly only two of the Open Web Application Security Project (OWASP) Top Ten vulnerability types: SQL injection (SQLi) (13/15) and Cross-Site Scripting (XSS) (8/15). Additionally, only one work evaluated six of the OWASP Top Ten vulnerability types and for only one scanner. We also found that the reported detection rates were highly dissimilar between these 15 evaluations. Based on these surprising results we suggest avenues for future directions.
Interplay between Alba and Cren7 Regulates Chromatin Compaction in Sulfolobus solfataricus

Chromatin compaction and regulation are essential processes for the normal function of all organisms, yet knowledge on how archaeal chromosomes are packed into higher-order structures inside the cell remains elusive. In this study, we investigated the role of archaeal architectural proteins Alba and Cren7 in chromatin folding and dynamics. Atomic force microscopy revealed that Sulfolobus solfataricus chromatin is composed of 28 nm fibers and 60 nm globular structures. In vitro reconstitution showed that Alba can mediate the formation of folded DNA structures in a concentration-dependent manner. Notably, it was demonstrated that Alba on its own can form higher-order structures with DNA. Meanwhile, Cren7 was observed to affect the formation of Alba-mediated higher-order chromatin structures. Overall, the results suggest an interplay between Alba and Cren7 in regulating chromatin compaction in archaea.
Cost-Effective Strong Consistency on Scalable Geo-Diverse Data Replicas

The Raft algorithm maintains strong consistency across data replicas in Cloud. This algorithm places nodes, i.e., leader and follower, to serve read/write requests spanning geo-diverse sites. As the workload increases, Raft shall provide proportional scale-out performance. However, traditional scale-out techniques are bottlenecked in Raft with an exponentially increased performance penalty when provisioned sites exhaust local resources. To provide scalability in Raft, this paper presents a cost-effective mechanism that enables elastic auto scaling in Raft, called BW-Raft. BW-Raft extends the original Raft with the following abstractions: (1) secretary nodes that take over expensive log synchronization operations from the leader, relaxing the performance constraint on locks. (2) observer nodes that handle reads only, improving throughput for typical data intensive services. These abstractions are stateless, allowing elastic scale-out on unreliable yet cheap spot instances. In theory, we prove that BW-Raft can preserve the strong consistency guarantee from Raft at scale-out, handling 50X more nodes, compared to the original Raft. We have prototyped the BW-Raft on key-value services and evaluated it with many state-of-the-arts on Amazon EC2 and Alibaba Cloud. Our results show that within the same budget, BW-Raft incurs 5-7X less resource footprint increment than Multi-Raft. Using spot instances, BW-Raft can reduces costs by 84.5%, compared to Multi-Raft. In the real world experiments, BW-Raft improves goodput of the 95th-percentile SLO by 9X, thus, serves an alternative for distributed service scaling out with strong consistency.
Harmonic Injection Based Distance Protection for Line With Converter-Interfaced Sources

Converter-interfaced renewable energy sour- ces are directly integrated into the power system using power electronics, so unique fault characteristics may lead to the maloperation of traditional distance relays. In this article, to deal with this issue, a harmonic injection- based distance protection method is proposed. The controllable harmonic current is injected without revising the controller structure, and the harmonic frequency and magnitude are determined according to the grid feature. Since the proportional integral cannot track the sine component without the static error, the actual frequency response will be deduced using the transfer function. On this basis, the harmonic impedance measured at the relay point can accurately reflect the fault distance due to the disappearance of the remote infeed, so the proposed method has a strong ability to withstand the fault resistance. Meanwhile, it only needs a few modifications to the phasor extraction method in original protection devices, so it is easy for engineering applications. Furthermore, typical fault ride through requirements can be still satisfied. Simulation results in PSCAD and real-time digital simulator verify the proposed method in different fault scenarios.
Criminality and Income Inequality in Indonesia

We investigate whether a nexus exists between income inequality and criminal activity in Indonesia. Additionally, we examine socioeconomic variables and potential links with criminal actions (i.e., crime rate, murder, rape, physical abuse, robbery, and fraud). We use the generalized method of moments (GMM) approach, employing data for 34 provinces in Indonesia over the period of 2010–2019. The results indicate that income inequality is associated with higher criminal activity. Overall, lower unemployment, larger investment (foreign and domestic), and higher human development (education and health) can help reduce crime in Indonesia. However, higher income can reduce physical abuse and crime rates, but theft and fraud increase with income growth. Rising unemployment increases rape, abuse, robbery, and fraud. Still, unemployment does not affect murder, suggesting that non-economic factors are dominant in explaining murder and violent crimes. Furthermore, income inequality can increase robbery and fraud, although it has no significant effects on murder, rape, and abuse. Government spending on social assistance and more efficient settlement of criminal acts can lower crime rates.
Can mechanism of injury improve trauma diversion? A retrospective cross-sectional study

Objective: The objective of this study was to determine the impact of adding selected mechanism of injury (MOI) to the existing trauma diversion criteria adopted in Hong Kong. Method: This is a cross-sectional study based on the trauma registry of New Territory West Cluster (NTWC) of Hospital Authority from January 2017 to December 2019. All adult patients aged 18 years or above were recruited if their injury occurred in the catchment area of Pok Oi Hospital (POH) or Tin Shui Wai Hospital (TSWH). Performance of the protocol before and after MOI criteria being added in terms of over-diversion and under-diversion rate was determined. Model discrimination was evaluated by plotting the receiver operating characteristic curve, and the area under the curve was compared before and after MOI criteria added. Net reclassification improvement and integrated discrimination improvement indices were evaluated. Result: A total of 502 patients were included for analysis. Before MOI criteria were added, the over-diversion rate and under-diversion rate were 31.1% and 54.7%, respectively. After MOI criteria were added, the over-diversion rate and under-diversion rate were 33.1% and 34.3%, respectively. The receiver operating characteristic curve of current primary trauma diversion (PTD) criteria had an area under the curve of 66.9% (95% confidence interval: 63%–71%). After adding MOI criteria, the new receiver operating characteristic curve yielded an area under the curve of 73.7% (95% confidence interval: 70%–78%), which is significantly better (p < 0.001). Net reclassification improvement and integrated discrimination improvement indices indicated that including MOI criteria would improve the model prediction. Conclusion: Adding mechanism of injury can improve trauma diversion protocol performance.
Novel vibration-based technique for detecting water pipeline leakage

Abstract Exacerbating the imbalance between demand for freshwater and available water resources is the sub-optimal performance of water distribution systems, which are plagued with leaks that cause significant losses of treated freshwater. This paper presents an approach for leak detection that involves continuous monitoring of the changes in the correlation between surface acceleration measured at discrete locations along the pipeline length. A metric called leak detection index is formulated based on cross-spectral density of measured pipe surface accelerations for detecting the onset and assessing the severity of leaks. The proposed non-invasive approach requires minimal human intervention and works under normal operating conditions of the pipeline system without causing any operational disturbances. The approach is demonstrated on a 76 mm diameter polyvinyl chloride pipeline test system considering varying leak severities. The preliminary results presented in this paper seem promising and lead to several interesting questions that will require further research.
A Fast and More Accurate Seed-and-Extension Density-Based Clustering Algorithm

Clustering algorithms have been widely studied in many scientific areas, such as data mining, knowledge discovery, bioinformatics and machine learning. A density-based clustering algorithm, called density peaks (DP), which was proposed by Rodriguez and Laio, outperforms almost all other approaches. Although the DP algorithm performs well in many cases, there is still room for improvement in the precision of its output clusters as well as the quality of the selected centers. In this study, we propose a more accurate clustering algorithm, seed-and-extension-based density peaks (SDP). SDP selects the centers that hold the features of their clusters while building a spanning forest, and meanwhile, constructs the output clusters in a seed-and-extension manner. Experiment results demonstrate the effectiveness of SDP, especially when dealing with clusters with relatively high densities. Precisely, we show that SDP is more accurate than the DP algorithm as well as other state-of-the-art clustering approaches concerning the quality of both output clusters and cluster centers while maintaining similar running time of the DP algorithm, particularly for a variety of time-series data. Moreover, SDP outperforms DP in the dynamic model in which data point insertion and deletion are allowed. From a practical perspective, the proposed SDP algorithm is obviously helpful to many application problems.
Biodiesel production using Calophyllum inophyllum (Tamanu) seed oil and its compatibility test in a CI engine

ABSTRACT Tamanu-based biodiesel can be used as a non-conventional fuel blended with conventional fuel in a compression ignition engine without any need for modification in engine design. The three main processes involved in production of biodiesel are removal of OH group by H2SO4 (acid) and KOH (base), and removal of soapy water. The molar ratios tried in this process were 4:1 to 10:1 and the yield was maximum at 6:1 (86.15%). In this present study, production of biodiesel from Tamanu oilseeds, its performance analysis and emission characteristics in a CI engine were extensively studied. Prepared Tamanu-based biodiesel was used in a four-stroke CI engine and performance analysis was done. The blend proportions used in the diesel engine were 10–40 wt. % biodiesel (B10, B20, B30 and B40). Analysis of emissions such as NOX, CO, and HC from a CI engine has shown that the biodiesel blends produced fewer emissions than that of commercial diesel. Results revealed that there is a substantial reduction of emissions and an increase in performance of the biodiesel blends tested. Moreover, the amounts of toxic pollutants emitted from the biodiesel blends are significantly less when compared to commercial diesel.
The Cosmic Muon Images Demonstrator within the REINFORCEProject

Cosmic Muon Images is a muon tomography citizen science framework developed within the EU-funded (GA-872859) REINFORCE project (Research Infrastructures FOR Citizens in Europe). REINFORCE aspires to decrease the knowledge gap between research and society. Citizen scientists will engage in the process of scientific discovery by analyzing detector data from four major physics domains. Muon Tomography, Gravitational Waves, Neutrino Astronomy and High Energy Physics join forces with people from all over the world to do science and discuss detection techniques, signal vs background discrimination, environmental effects on measurement, and broadcast the impact of fundamental research on everyday life and on societal progress. In this effort we want to reach the broader audience possible, and this means also making our data accessible to as many people as possible. SonoUno is a user centered software developed within REINFORCE that allows people with different sensory styles to explore scientific data, both visually and through sonorization. Cosmic Muon Images utilizes the Zooniverse website to bring muon tomography to the general public and the citizen science community. Muon telescope data are visualized with 3D and 1D plots with the goal being the identification of patterns through a series of lines and points on these plots. The results of this pattern identification will be used to train machine learning (ML) algorithms to discriminate between signal and background events. We will then evaluate the performance of these ML algorithms to more traditional track reconstruction and event selection algorithms that are already in use. The key factor being how they com-pare with respect to speed and pattern recognition accuracy. Furthermore, a large dataset catalogued by eye will act as a stepping-stone towards other studies like background identification and Monte Carlo simulation development. Citizen scientists will benefit through their to calibration and detection techniques for muon tele-scopes to make their scientific contributions more robust. This will also provide a more fulfilling experience by learning new staff which are for the most They are going to learn about the different applications of muon tomography in fields like volcanology, archaeology, civil engineering, hazard monitoring and others.
Nested structure is dependent on visitor sex in the flower‒visitor networks in Kyoto, Japan

Abstract The characteristics of flower‒visitor networks, comprised of multiple species interacting with each other, predict ecological and evolutionary processes. Intraspecific and interspecific variations in interaction patterns should affect network structures. Because female and male visitors usually differ in flower‐visiting patterns due to mating strategy, visitor sex should affect nestedness, in which specialist species interact with a subset of species that interact with generalist species. I hypothesized that a network of male visitors and flowering plants would be more nested than a female network because males are less picky about which flowers they visit. To examine the effect of visitor sex on nestedness, I used museum specimens of insects and built 11 flower–visitor species networks, each composed of female and male subnetworks, and compared the strength of nestedness and related network metrics between the subnetworks. I found that male subnetworks were significantly more nested than female ones, and species networks were less nested than male or female subnetworks. The result may be attributable to the by‐chance selection of flowers by males. Because a nested structure is predicted to promote community stability in mutualistic flower–visitor networks, the greater nestedness of male subnetworks may suggest a positive effect of male visitors on pollination community stability.
Decentralised H ∞ filtering of interconnected discrete-time systems

This paper studies the decentralised filtering problem for interconnected discrete-time systems. In the developed decentralised filter scheme, only local output information is available for each local filter. The dynamics of the original system and the dynamics of the proposed filter are expressed as an augmented system. The system matrices of the augmented system consist of matrix inversion terms, which are computationally expensive and even meet numerical stability problems. In order to avoid the matrix inversion computation, this paper derives novel computationally attractive sufficient conditions without inversion terms to guarantee the performance of the augmented system. Then, these conditions are transformed into linear matrix inequalities. The decentralised filter parameters can be obtained by solving linear matrix inequalities. Finally, two simulation examples are given to demonstrate the effectiveness and the advantages of the proposed approach.
Dual-Mode Data Forwarding Scheme Based on Interest Tags for Fog Computing-Based SIoVs

Social Internet of vehicles (SIoVs) is a combination of vehicular ad-hoc networks (VANETs) and mobile social networks (MSNs). Although social relationships between vehicle nodes are more stable than location changes of vehicle nodes in SIoVs, there always are the problems of data forwarding optimization and adaptability for dynamic networks. In this paper, we propose a dual-mode data forwarding scheme based on interest tags for fog computing-based SIoVs. In the first data forwarding mode, the vehicle nodes calculate and use the cooperation degrees to select the next cooperative forwarding nodes until the data is forwarded to the destination node. In the second data forwarding mode, the RSUs assist the data forwarding of vehicle nodes based on the RSU ranking mechanism of interests, where the fog servers calculate the RSU ranking table of all the interests and the related RSU selects the top- ${k}$ RSUs to forward the data according to the RSU ranking table. The experimental results show that our proposed scheme is more efficient and stable than other related schemes by the comparisons of delivery ratio, overhead ratio and average hop count.
Xanthine oxidase targeted model setup and its application for antihyperuricemic compounds prediction by in silico methods

To achieve potential alternatives for hyperuricemia therapeutics, a novel structure-docking energy relationship model was established for high-throughput screening inhibitors of xanthine oxidase (XO). Molecular docking was performed between XO and 311 natural compounds from 6 traditional Chinese herbs. Then, structure-docking energy relationship model was simulated between molecular docking energy and 63 molecular descriptors by multiple linear regressions (MLR), principal component regression (PCR), and artificial neural network (ANN), respectively. The results showed that the ANN model was the best model to predict the docking energy of XO with the coefficient of determination (R2) and mean squared error (MSE) at 0.8746 and 0.9414, respectively. The data of XO inhibitory activity were consistent with the prediction in vitro, which was also further confirmed by hyperuricemia cell model. The results suggested that the structure-docking energy relationship model provides a paradigm framework for the screening of XO inhibitors.
Early Sensing of Tomato Brown Rugose Fruit Virus in Tomato Plants via Electrical Measurements

Viruses of the Tobamovirus genus caused severe losses in many economically important crops, such as pepper, tomato, and cucurbits. Disease symptoms of the Tobamovirus tomato brown rugose fruit virus (ToBRFV) are visually detected about 10–14 days postinfection. During the “latent” asymptomatic period, infected plants constitute a primary source for virus spread to the entire crop via workers’ hands and tools. In the present letter, we have built the infrastructure of an electrical sensor for the early detection of ToBRFV in tomatoes. We have uncovered the changes in the electrical characteristics of ToBRFV-infected tomato plants, and we have noticed that ∼3 days postinfection, the plants demonstrated changes in the I-V curve and a significant reduction of their electrical conductivity. This observation emphasizes that the impact of the ToBRFV on the electrical properties of infected tomato plants appears much earlier than the visible symptoms. Thus, this letter paves the way toward a novel early detection approach of ToBRFV, which will reduce the primary infection source, minimize the secondary spread of the virus, improve yield quality, and minimize economic losses.