Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential of Using Land Snails (Eobania vermiculata and Monacha obstructa) for Monitoring the Essential and Non-Essential Heavy Metal in Ismailia City, Egypt

ABSTRACT To assess the role of land snails as the monitor for heavy metal pollution and to determine the species-specific accumulation of metals in Eobania vermiculata and Monacha obstructa. A field survey of heavy metals was carried out in two different areas in Ismailia city, Egypt. The study was conducted to evaluate the essential and non-essential heavy metals in the tissue of land snails and compared them with their levels in shell and soil to find out which can accumulate more biotic or abiotic? The heavy metals concentrations of essential and non-essential heavy metals of soft tissues of two snail species and sediment collected from Ring Road, Ismailia, in Egypt had the highest levels of heavy metals as compared to another site in most seasons except in spring season levels of essential (Cu, Mn, Zn, Co, Mo, Ca and Mg) and non-essential metals (Cd, Pb, Al, and B) were higher in El-Balah Road than Ring Road. Therefore, El Balah Road was less polluted as compared with Ring Road which was more crowded with cars. The ranges of essential metals in samples of the soft tissue of E. vermiculata along with the two sites, Ring Road and El Balah Road, were higher for Ca and Mg than other essential metals. However, Al as a non-essential metal in soft tissue samples of E. vermiculata for the two sites was the most common metal. Moreover, the levels of the same metals were elevated in M. obstructa along with the two sites. The results indicated that E. vermiculata and M. obstructa soft tissues do not reflect the environmental bioavailability of heavy metals as the shells do. Therefore, the shells of E. vermiculata and M. obstructa could be considered as a more useful bioindicator for Co, Cu, Mn, Zn, Mo, Ca, Mg, and Pb.
Construction and demolition waste research: a bibliometric analysis

ABSTRACT Although construction and demolition (C&D) waste has drawn increasing attention from scholars, there is a lack of study to summarize the latest development of C&D waste research. By using a bibliometric analysis method, this study carries out a holistic review of C&D waste articles published from 1994 to 2017. It shows that the number of C&D waste articles has risen eight-times within the period. This study also demonstrates the social networks among the authors, countries, and organizations. Based on the keywords cluster analysis, the C&D waste research can be divided into five clusters, including (1) environmental concerns of C&D waste; (2) recyclability of C&D waste; (3) performance and behaviour tests of recycled products; (4) C&D waste management; and (5) C&D waste with industrial ecology. Meanwhile, the research status and future directions are discussed as well. The results would be valuable for understanding the streams and trends in C&D waste research.
Health Risk Assessment of Hazardous Metals in Seafood from Ka-Bangha River, Khana, Rivers State, Nigeria

1. World Bank Africa Centre of Excellence in Public Health and Toxicological Research (ACEPUTOR), University of Port Harcourt, Nigeria 2. Centre for Marine Pollution Monitoring & Seafood Safety (CEMPOS), University of Port Harcourt, Nigeria 3. Department of Biochemistry, Toxicology Unit, University of Port Harcourt, East-West Road, P.M. B 5323 Port Harcourt, Rivers State, Nigeria. 4. Department of Biochemistry, Faculty of Natural Sciences, Chukwuemeka Odumegwu Ojukwu University, Anambra State, Nigeria. * Corresponding Author: kc.patrick-iwuanyanwu@uniport.edu. ng
The Friday Effect: How Communal Religious Practice Heightens Exclusionary Attitudes

Abstract Does attending communal religious services heighten the tendency to express exclusionary attitudes? Drawing on responses from thousands of Muslims, we identify how the ritual Friday Prayer systematically influences congregants' political and social attitudes. To isolate the independent role of this religious behavior, we exploit day-of-the-week variation in survey enumeration, which we assume to be plausibly uncorrelated with likely confounders, including self-reported religiosity. In our primary analysis, six variables charting various modes of intolerance each indicate that frequent attenders interviewed on Fridays (that is, proximate to the weekly communal prayer) were significantly more likely to express sectarian and antisecular attitudes than their counterparts. To test the potential mechanism behind this tendency, we rely on a controlled comparison between Egyptian and Algerian subgroups, as well as an original survey experiment in Lebanon. Evidence from both analyses is consistent with arguments that elite political messaging embedded in religious rituals spurs much of the observed variation.
Minimal Total Deviation in TCAM Load Balancing

Traffic splitting is a required functionality in networks, for example for load balancing over multiple paths or among different servers. The capacities of the servers determine the partition by which traffic should be split. A recent approach implements traffic splitting within the ternary content addressable memory (TCAM), which is often available in switches. It is important to reduce the amount of memory allocated for this task since TCAMs are power consuming and are also required for other tasks such as classification and routing. Previous work showed how to compute the smallest prefix-matching TCAM necessary to implement a given partition exactly. In this paper we solve the more practical case, where at most n prefix-matching TCAM rules are available, restricting the ability to implement exactly the desired partition. We consider the L1 distance between partitions, which is of interest when overloaded requests are simply dropped, and we want to minimize the total loss. We prove that the Niagara algorithm [1] can be used to find the closest partition in L1 to the desired partition, that can be realized with n TCAM rules. Moreover, we prove it for arbitrary partitions, with (possibly) non-integer parts.
"Al24.2Si3.25Cu24.2Ni24.2Ti24.2 high-entropy intermetallic alloy: Effect of nitrogen environment sintered microstructure and mechanical properties "

In this study, a lightweight Al 24 . 2 Si 3 . 25 Cu 24 . 2 Ni 24 . 2 Ti 24 . 2 high-entropy alloy was developed by sonication blending of the pure elements followed by the high-temperature sintering in the nitrogen gas environment. Microstructural characterization of the sintered alloy showed a fair dispersion of the elemental particles in the matrix during the blending process and the absorption of nitrogen, which led to the formation of multiple intermetallic compounds, including the nitrides, during the sintering process. Mechanical characterization of the sintered alloy revealed signiﬁcantly high microhardness values with relatively lower compressive strength in Al 24 . 2 Si 3 . 25 Cu 24 . 2 Ni 24 . 2 Ti 24 . 2 alloy.
3D Inundation Mapping: A Comparison Between Deep Learning Image Classification and Geomorphic Flood Index Approaches

Inundation mapping is a critical task for damage assessment, emergency management, and prioritizing relief efforts during a flooding event. Remote sensing has been an effective tool for interpreting and analyzing water bodies and detecting floods over the past decades. In recent years, deep learning algorithms such as convolutional neural networks (CNNs) have demonstrated promising performance for remote sensing image classification for many applications, including inundation mapping. Unlike conventional algorithms, deep learning can learn features automatically from large datasets. This research aims to compare and investigate the performance of two state-of-the-art methods for 3D inundation mapping: a deep learning-based image analysis and a Geomorphic Flood Index (GFI). The first method, deep learning image analysis involves three steps: 1) image classification to delineate flood boundaries, 2) integrate the flood boundaries and topography data to create a three-dimensional (3D) water surface, and 3) compare the 3D water surface with pre-flood topography to estimate floodwater depth. The second method, i.e., GFI, involves three phases: 1) calculate a river basin morphological information, such as river height (hr) and elevation difference (H), 2) calibrate and measure GFI to delineate flood boundaries, and 3) calculate the coefficient parameter (α), and correct the value of hr to estimate inundation depth. The methods were implemented to generate 3D inundation maps over Princeville, North Carolina, United States during hurricane Matthew in 2016. The deep learning method demonstrated better performance with a root mean square error (RMSE) of 0.26 m for water depth. It also achieved about 98% in delineating the flood boundaries using UAV imagery. This approach is efficient in extracting and creating a 3D flood extent map at a different scale to support emergency response and recovery activities during a flood event.
Design of a Computer Stereo Vision Based Road Marking System

Existing methods to put white and yellow marking on the road are based on manual measurement of the road dimensions and manual marking of guide lines. It is a time consuming, cumbersome and labour intensive process. This was the motivation for this work to develop a system which can automatically apply road markings according to the width of the road. In this paper, a stereo-vision based road marking system is presented. Computer stereo-vision has been used to measure the width of the road which helps us decide the number of lanes on the road. To generate the disparity map, a semi-global matching algorithm has been used, incorporating the advantages of both local and global matching algorithms. Efficient algorithms have been used for camera calibration and to estimate the road width. For deciding the number of lanes, the standards laid down by Indian Road Congress (IRC) have been followed.
Ultra-Wide Band Double-Slot Podal and Antipodal Vivaldi Antennas Feed by Compact Out-Of-Phase Power Divider Slot for Fluid Properties Determination

In this paper, double slot podal and antipodal ultra-wideband (UWB) microstrip antennas for a fluid property measurement system are proposed. Among different feeding techniques, out of phase uni-planner power divider approach is used. The performance verification of the proposed antenna is explained, along with a performance comparison of the antenna bandwidth, feeding, and the realized gain. The suggested podal antenna has an impedance bandwidth from 2.4 to 15.4 GHz, with a maximum gain of 11.3 dBi in the 12 GHz region while the antipodal antenna has a 2.8 GHz to 16 GHz impedance bandwidth, with a maximum gain of 10.4 dBi in the 10 GHz region. Within the intended band, the radiation pattern had an excellent directivity characteristic. The implementation of the proposed antenna is calibrated by measuring the propagated signals response via various liquid specimens using UWB radar, which might be applied for fluid sensing and prediction purposes. The proposed antenna was connected to an NVA-R661 module of Xethru Inc. for measuring the sample delay and peak-to-peak amplitude of the received signals passing through specimens. The measured parameters at a different radar frequency range of transmission are applied by drawing the fluid viscous analogy based on Poiseuille’s law hypothesis, showing clear differentiation between the test specimens.
How Does Leader Narcissism Influence Firm Internationalization?

The impact of leader characteristics on international activities has been identified in the literature, but leader narcissism, as a psychological feature, has long been neglected, especially in the field of international business. Based on an empirical study of Chinese construction firms over the period between 2006 and 2016, this paper discusses the positive relationships between leaders’ narcissism and firms’ pace of internationalization, as well as the tendency to choose developing countries as destinations. The data also raise strong support for the argument that firm internal factor-foreign listing and external factor-institutional environment (anticorruption) weaken the effects of leader narcissism on international patterns. These research results indicate the effects of leaders’ personality on international management under different internal and external environments and contribute to upper echelons theory. Meanwhile, it is emphasized that leaders’ personal decision-making plays an important role in the strategy and performance of construction firms.
Visual-SLAM Classical Framework and Key Techniques: A Review

With the significant increase in demand for artificial intelligence, environmental map reconstruction has become a research hotspot for obstacle avoidance navigation, unmanned operations, and virtual reality. The quality of the map plays a vital role in positioning, path planning, and obstacle avoidance. This review starts with the development of SLAM (Simultaneous Localization and Mapping) and proceeds to a review of V-SLAM (Visual-SLAM) from its proposal to the present, with a summary of its historical milestones. In this context, the five parts of the classic V-SLAM framework—visual sensor, visual odometer, backend optimization, loop detection, and mapping—are explained separately. Meanwhile, the details of the latest methods are shown; VI-SLAM (Visual inertial SLAM) is reviewed and extended. The four critical techniques of V-SLAM and its technical difficulties are summarized as feature detection and matching, selection of keyframes, uncertainty technology, and expression of maps. Finally, the development direction and needs of the V-SLAM field are proposed.
Differentiating Fissure‐Fed Lava Flow Types and Facies Using RADAR and LiDAR: An Example From the 2014–2015 Holuhraun Lava Flow‐Field

Distinguishing between lava types and facies using remote sensing data is important for interpreting the emplacement history of lava flow‐fields on Earth and other planetary bodies. Lava facies typically include a mixture of lava types and record the collective emplacement history of material preserved at a particular location. We seek to determine if lava facies in the 2014–2015 Holuhraun lava flow‐field are discernible using radar roughness analysis. Furthermore, we also seek to distinguish between lava types using high resolution Light Detection and Ranging (LiDAR) data. We extracted circular polarization ratios (CPR) from the Uninhabited Aerial Vehicle Synthetic Aperture Radar and cross‐polarization (VH/VV) data from the Sentinel‐1 satellite to analyze the surface roughness of three previously mapped lava facies: rubbly, spiny, and undifferentiated rubbly–spiny. Using the Kruskal‐Wallis test, we reveal that all but one pair of the facies are statistically separable. However, the populations overlap by 88%–89% for CPR and 64%–67% for VH/VV. Therefore, owing to large sample populations (n > 2 × 105), slight differences in radar data may be used to probabilistically infer the presence of a particular facies, but not directly map them. We also calculated the root‐mean‐square slope and Hurst exponents of five different lava types using LiDAR topography (5 cm/pixel). Our results show minute differences between most of the lava types, with the exception of the rubbly pāhoehoe, which is discernible at 1σ. In brief, the presence of “transitional” lava types (e.g., rubbly pāhoehoe) within fissure‐fed lava flow‐fields complicates remote sensing‐based mapping.
Representation of natural reflectance spectra by auto-associative neural network

The representation of spectral reflectance data by linear systems has been extensively studied [1-3] and it has been shown that colour information can be accurately represented by relatively few, although certainly more than three, parameters. In contrast Usui, Nakauchi and Nakano [4] have suggested that a non-linear system such as an auto-associative neural network can allow surface spectral reflectance data to be encoded by, and subsequently recovered from, just three parameters. We have repeated the analysis by Usui et al. using a set of 1269 Munsell reflectance spectra and have considered the representation of colour signals resulting from the spectral energy distributions of the Munsell surfaces viewed under D65 illumination. A five-layer wine-glass-shaped auto-associative neural network was used to encode and subsequently decode both reflectance spectra and colour signals. The middle layer of the neural network contained between 2 and 6 units so that the network was constrained to find efficient representations. Colour difference errors of reconstruction reduced with increasing number of hidden units as expected on the basis of theoretical considerations. Errors were smaller for networks trained with reflectance data rather than with colour signals derived from D65 illumination. Colour signals for surfaces viewed under D65 illumination are less constrained than the spectral reflectances of surfaces themselves. Spectral properties of light sources must be taken into account in computations of sampling rates required for recovery of colour signals and the subsequent recovery of surface reflectance spectra.
Jet Engine Modeling Using T-MATS with Experimental Verification

Modeling of compressors and other complex thermodynamic structures is an important process in the design of these systems. Finding models that not only accurately describe the system, but also are convenient to create and modify is still considered a challenge. Many models have been created using the Moore-Greitzer model. This model has been used in many works throughout the years with some of those being detailed here. A model of a small experimental compressor rig was developed in Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). To verify the accuracy and capabilities of this toolbox, a comparison between actual experimental test data and data generated through simulation using the model generated. These two outcomes are compared and it is found that they have a good correlation with each other. The error is small between the experimental data and the simulation data which indicates utility of such simulation models for use in further research.
Game Theoretic Analysis of Age of Information for Slotted ALOHA Access With Capture

A recent line of analysis discusses the age of information as a better performance metric than throughput or delay to evaluate the performance of medium access techniques, especially when applied to remote sensing applications and more in general the Internet of Things. Fully analytical investigations based on game theory have shown how selfish players can behave efficiently in random access systems if they are driven by AoIbased objectives. In this paper, we expand this kind of reasoning to the case of a slotted ALOHA system with capture effect. We present a fully analytical derivation of some notable cases, based on the existing literature, and we highlight the impact of some parameters, specifically the cost coefficient and the capture threshold, towards achieving an efficient allocation that represents an equilibrium point for the network management. It is shown that, when the capture effect is relatively strong, which is easier when the number of terminals is limited, the Nash equilibrium of the system achieves near-optimal performance.
Open Multi-Access Network Platform with Dynamic Task Offloading and Intelligent Resource Monitoring

We constructed an open multi-access network platform using open source hardware and software. The open multi-access network platform is characterized by flexible utilization of network functions, integral management and control of wired and wireless access networks, zero-touch provisioning, intelligent resource monitoring, and dynamic task offloading. We also propose application-driven dynamic task offloading that utilizes intelligent resource monitoring to ensure effective task processing in edge and cloud servers. For this purpose, we developed a mobile application and server applications for the open multi-access network platform. To investigate the feasibility and availability of our developed platform, we experimentally and analytically evaluated the effectiveness of application-driven dynamic task offloading and intelligent resource monitoring. The experimental results demonstrate that application-driven dynamic task offloading could reduce real-time task response time and traffic over metro and core networks.
Subcutaneous BoNT/A Injection for Intractable Pain and Disability in Complex Regional Pain Syndrome: A Case Report

We treated a 51-year-old woman with refractory Complex Regional Pain Syndrome type I (CRPS-I) involving her left hand and forearm with subcutaneous injections of BoNT/A. The injections were performed every 3 months, with a total of six treatments. Each treatment was able to effectively improve pain and motor impairment; however, the duration of the effect was limited to only a few months. BoNT/A could improve patients’ quality of life with CRPS; however, extensive clinical studies are needed to determine its role in clinical practice.
A combinatorial proof of Aldous–Broder theorem for general Markov chains

Aldous–Broder algorithm is a famous algorithm used to sample a uniform spanning tree of any finite connected graph G$$ G $$ , but it is more general: given an irreducible and reversible Markov chain M$$ M $$ on G$$ G $$ started at r$$ r $$ , the tree rooted at r$$ r $$ formed by the first entrance steps in each node (different from the root) has a probability proportional to ∏e=(e−,e+)∈Edges(t,r)Me−,e+$$ {\prod}_{e=\left({e}^{-},{e}^{+}\right)\in \mathsf{Edges}\left(t,r\right)}{M}_{e^{-},{e}^{+}} $$ , where the edges are directed toward r$$ r $$ . In this article we give proofs of Aldous–Broder theorem in the general case, where the kernel M$$ M $$ is irreducible but not assumed to be reversible (this generalized version appeared recently in Hu, Lyons, and Tang [5]). We provide two new proofs: an adaptation of the classical argument, which is purely probabilistic, and a new proof based on combinatorial arguments. On the way we introduce a new combinatorial object that we call the golf sequences.