Miracle-dz/UniFAR-FTUs huggingface dataset:
A geospatial machine learning prediction of arsenic distribution in the groundwater of Murshidabad district, Bengal, India: spatio-temporal pattern and human health risk

Arsenic (As) contamination of groundwater in parts of South and Southeast Asia is a public health disaster. Millions of people living in these regions could be chronically exposed to drinking water with As concentrations above the World Health Organization’s provisional guideline of 10 µg/L. Recent field investigations have shown that the distribution of groundwater As in many shallow aquifers in India and Bangladesh is evolving rapidly due to massive irrigation pumping. This study compares a decade-old dataset of As concentration measurements in groundwater with a dataset of recent measurements using geospatial machine learning techniques. We observed that the probability of As concentrations >10 µg/L was much greater in the regions between two major rivers than in the regions close to the Ganges River on the eastern border of the study area, where As concentrations >10 µg/L had been measured prior to 2005. The greater likelihood that As is present away from the river channel and is found instead in the interfluvial regions could be attributed to the transport and flushing of aquifer As due to intense groundwater pumping for agriculture. We estimated that about 2.8 million people could be chronically exposed to As concentrations >10 μ g/L. This high population-level exposure to elevated As concentrations could be reduced through targeted well-testing campaigns, promoting well-switching, provisions for safe water access, and developing plans for raising public awareness. Policymakers could use the ternary hazard map to target high-risk localities for priority house connections of piped water supply schemes to help reduce human suffering. The results the proportion µg/L 68% 58%, positive steps declining population-level to elevated As concentrations. Our analysis further showed that the mean As concentrations in most blocks situated in the eastern part of
Energy-efficient association mechanism designed for uplink CDMA-based HCNs

ABSTRACT To reduce the energy consumption and prolong the battery lifetimes of mobile terminals, the design of energy-efficient association scheme is necessary and meaningful. To this end, we design a type of association mechanism (scheme) to maximize the whole energy efficiency (WEE) for uplink code-division-multiple-access (CDMA)-based heterogeneous cellular networks (HCNs), and formulate it as a WEE maximization problem with users’ SINR (signal-to-interference-plus-noise ratio) constraints. Considering that the finally formulated problem is in a fractional mixed-integer form and hard to tackle, we introduce an energy efficiency (EE) parameter to convert it into a parametric subtractive form. At last, we design two two-layer iterative algorithms for the formulated problem, whose primary difference lies in the treatment of SINR constraints. In general, the outer loop utilizes a bisection method to search the optimal EE parameter, and the inner loop alternately optimizes the association index and transmit power. At last, we give the corresponding convergence analyses for some subalgorithms, and also give some detailed complexity analyses for the whole algorithm. In the simulation, we introduce an existing energy-efficient association mechanism for comparison, and finally investigate the impacts of different network parameters on the association performance.
Parametric Design Studies of Mass-Related Global Warming Potential and Construction Costs of FRP-Reinforced Concrete Infrastructure

Fibre-reinforced polymers (FRPs) are a promising corrosion-resistant alternative to steel reinforcement. FRPs are, however, generally costly and have a high energy demand during production. The question arises whether the high performance of FRPs and possible savings in concrete mass can counterbalance initial costs and environmental impact. In this paper, a parametric design study that considers a broad range of concrete infrastructure, namely a rail platform barrier, a retaining wall and a bridge, is conducted to assess the mass-related global warming potential and material costs. Design equations are parametrised to derive optimum reinforced concrete cross-sectional designs that fulfil the stated requirements for the serviceability limit state and ultimate limit state. Conventional steel reinforcement, glass and carbon FRP reinforcement options are evaluated. It is observed that the cross-sectional design has a significant influence on the environmental impact and cost, with local extrema for both categories determinable when the respective values become a minimum. When comparing the cradle-to-gate impact of the different materials, the fibre-reinforced polymer-reinforced structures are found to provide roughly equivalent or, in some cases, slightly more sustainable solutions than steel-reinforced structures in terms of the global warming potential, but the material costs are higher. In general, the size of the structure determines the cost competitiveness and sustainability of the FRP-reinforced concrete options with the rail platform barrier application showing the greatest potential.
Visual Analytics: Design Study for Exploratory Analytics on Peer Profiles, Activity and Learning Performance for MOOC Forum Activity Assessment

The massively open online course (MOOC) has become an increasingly popular alternative platform for education due to its open concept and free features. Due to its features that allow enrolment on a massive scale and participation across the globe, it presented new analytic challenges. The vast amount and variety of data generated pose challenges for the learning analytics community to analyse especially concerning peer presence and peer learning. Forum activity data offers the opportunity to assess the relationship between forum activities and user backgrounds with the learner’s progression and retention rate. Furthermore, there are several challenges in implementing data visualization in real-world scenarios such as different task characterisation compared to the existing analytics, along with varied factors on the usability of visualization among the domain analysts. Despite many research on learning analytics, most of the approaches were data-driven and there were only a handful of studies that were focused on interactive visualization design to facilitate MOOC forum user activity assessment using real-world scenarios and educational theories-driven. Our design study aims to investigate and formulate a visual analytic design to facilitate enriched visual analysis towards assessing forum activity in Malaysian MOOC, particularly in pattern and relationship exploration on the user diverse background and activities with the learning performance. This paper presents our review on visual learning analytics and current MOOC practice in Malaysia, our design study methodology and proposed conceptual visual analytics design on visualizing forum activity data.
Data Quality of Travel Behavior Studies: Factors Influencing the Reporting Rate of Self-Reported and GPS-Recorded Trips in Persons with Disabilities

In persons with disabilities, the accuracy of self-report data collection methods in travel behavior studies may be influenced by disease-related dysfunctions. The present study determines in detail whether disease-related physical, cognitive or psychosocial impairments contribute to the reporting rate of subjective self-report travel diaries and objective GPS tracking devices, besides socio-demographic and trip-related characteristics. The reporting rate of both data collection methods was analyzed in 108 persons with Multiple Sclerosis with various disability severities during a seven-day data collection period. The results demonstrated that there was only limited influence of disease-related dysfunctions on the reporting rate of both data collection methods, as well as only limited significant differences between subgroups with various disability severity. Overall, the data quality of the diary was higher than the quality of the GPS data: 66% of the trips were reported in both data collection methods, while overall more than one fifth of all trips were forgotten and not registered by GPS, and 11% were forgotten and not reported in the diary. Self-report travel diaries seemed to be more suitable for persons with a higher disability severity, as these persons more often forgot to take their GPS device with them when making a trip because of several organizational issues related to their mobility limitations.
Quantitative relationships between river and channel-belt planform patterns

Channel planform patterns arise from internal dynamics of sediment transport and fluid flow in rivers and are affected by external controls such as valley confinement. Understanding whether these channel patterns are preserved in the rock record has critical implications for our ability to constrain past environmental conditions. Rivers are preserved as channel belts, which are one of the most ubiquitous and accessible parts of the sedimentary record, yet the relationship between river and channel-belt planform patterns remains unquantified. We analyzed planform patterns of rivers and channel belts from 30 systems globally. Channel patterns were classified using a graph theory-based metric, the Entropic Braided Index (eBI), which quantifies the number of river channels by considering the partitioning of water and sediment discharge. We find that, after normalizing by river size, channel-belt width and wavelength, amplitude, and curvature of the belt edges decrease with increasing river channel number (eBI). Active flow in single-channel rivers occupies as little as 1% of the channel belt, while in multichannel rivers it can occupy >50% of the channel belt. Moreover, we find that channel patterns lie along a continuum of channel numbers. Our findings have implications for studies on river and floodplain interaction, storage timescales of floodplain sediment, and paleoenvironmental reconstruction.
Full Wafer Process Control Through Object Detection Using Region-Based Convolutional Neural Networks

Full wafer measurement techniques are used in the semiconductor industry to acquire information at a large scale to control process variation or detect potential defects. This process usually results in the generation of full wafer images, containing various objects that need to be identified to evaluate their impact on the final product performance. Artificial intelligence is very powerful to automate this identification routine. In this paper, we present the application of Region-based Convolutional Neural Networks (RCNN) for enhanced process control from full wafer images gathered by two industrial metrology equipments.
Assessing the Impacts of Autonomous Vehicles on Road Congestion Using Microsimulation

The introduction of autonomous vehicles has been considered as a possible option for reducing traffic congestion in many transport studies. Many types of models, in particular car-following microsimulation models have been adopted in most studies. The impacts of autonomous vehicles (AVs) on congestion, however, have not yet been concluded. This could be because different researchers use different forms of car-following models to assess these impacts, or because the utilised modelling approaches and their parameters are different in different studies. In particular, two of the important parameters that are associated with car-following models are the used values for maximum acceleration and the average desired time gaps. While the values of these parameters can be adjusted and controlled by the ACC controllers in the AV, they can also be controlled by the users. Therefore, assigning unrealistic values to these parameters could well result in unrealistic conclusions. This paper investigated the impacts of the maximum acceleration and the average desired time gaps on congestion levels using the loss-time indicator. The analysis was carried out on the Hanshin expressway in Japan and was tested and assessed using the Helly (FACC) car-following microsimulation model. This includes estimating the values of the desired time gap from real traffic time-gap distributions. The Hanshin expressway is an urban toll highway of 273 km that extends from Osaka to Kobe, representing the Hanshin area in Japan. The Hanshin highway serves a huge traffic volume that consists of private and freight vehicles that operate within the Hanshin area. This area represents one of three major municipal areas in Japan including Tokyo and Nagoya. A total of 740,000 vehicles per day travel on the expressway. As a result, there is significant congestion on the Hanshin expressway. There have been various plans put in place to ease congestion ranging from building new roads to the implementation of traffic-demand-management measures. However, the predictions of the impacts of such measures do not provide any evidence that they would ease traffic congestion. Other possible measures that could be investigated for easing traffic congestion include technology-based solutions such as autonomous vehicles. The modelling results recommend that the results obtained from microsimulation models should be taken with care, and good attention should be paid to the parameters used and their values in the model. The values assigned to driving-behaviour parameters, the maximum values of acceleration, and the time-gap settings, for example, control the final outcomes of the models.
Analysis and optimization of a resistive‐feedback inverter LNA

This article presents a new transistor size rule for a resistive‐feedback inverter implemented as a low‐noise amplifier (LNA) for wideband applications. To enhance the noise figure (NF) and voltage gain (Av), the inverter LNA requires large transconductance (Gm), resulting in large current consumption (ID). The proposed large‐NMOS inverter‐LNA obtains a better power efficiency (Gm/ID) with a smaller transistor compared with the conventional large‐PMOS structure. Because there are trade‐off relationships between NF, Av, 3‐dB bandwidth, and power dissipation in the inverter‐LNA design, a figure‐of‐merit (FOM) including all of these parameters is maximized by varying the transistor size obtained from the graphical optimization. Thus, the proposed new size ratio rule achieves superior LNA performances compared to the conventional rule, as demonstrated by the theoretical analysis, simulation, optimization, and measurement. Measurements show a maximum power gain of 16.8 dB, a minimum NF of 1.84 dB, and a maximum third‐order input intercept point of −9.4 dBm over 0.05 to 1.4 GHz while the LNA consumes 9.6 mW from a 1.5 V supply. The proposed LNA is implemented using a Samsung 65‐nm RF CMOS process.
Case Report: First Report of Fatal Legionella pneumophila and Klebsiella pneumoniae Coinfection in a Kidney Transplant Recipient

A very rare case of pulmonary Klebsiella pneumoniae-Legionella pneumophila coinfection in a double kidney transplanted man affected by the chronic renal disease is described. Cases of Legionnaires' disease with an incubation period of 14 days have rarely been documented. Despite the long period of hospitalization, typing of clinical and environmental L. pneumophila strains demonstrated that the patient's home water distribution system was the source of infection, highlighting that Legionella house contamination can be a hidden risk, especially for immune-compromised people.
Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review

Background: Maintaining a healthy gait into old age is key to preserving the quality of life and reducing the risk of falling. Nonlinear dynamic analyses (NDAs) are a promising method of identifying characteristics of people who are at risk of falling based on their movement patterns. However, there is a range of NDA measures reported in the literature. The aim of this review was to summarise the variety, characteristics and range of the nonlinear dynamic measurements used to distinguish the gait kinematics of healthy older adults and older adults at risk of falling. Methods: Medline Ovid and Web of Science databases were searched. Forty-six papers were included for full-text review. Data extracted included participant and study design characteristics, fall risk assessment tools, analytical protocols and key results. Results: Among all nonlinear dynamic measures, Lyapunov Exponent (LyE) was most common, followed by entropy and then Fouquet Multipliers (FMs) measures. LyE and Multiscale Entropy (MSE) measures distinguished between older and younger adults and fall-prone versus non-fall-prone older adults. FMs were a less sensitive measure for studying changes in older adults’ gait. Methodology and data analysis procedures for estimating nonlinear dynamic measures differed greatly between studies and are a potential source of variability in cross-study comparisons and in generating reference values. Conclusion: Future studies should develop a standard procedure to apply and estimate LyE and entropy to quantify gait characteristics. This will enable the development of reference values in estimating the risk of falling.
Self-Driving Car Acceptance and the Role of Ethics

Mass availability of self-driving cars is ante portas and independent of their sophistication, unavoidable fatal accidents will occur where the car will have to take life and death decisions. However, there is a knowledge gap, since the impact, that the ethical frameworks (used in the car's decision-making process) have on the overall acceptance of self-driving cars, is not well investigated. This paper addresses the key question: In the scope of unavoidable accidents, what is the effect of different ethical frameworks governing self-driving car decision-making, on their acceptance? This quantitative positivist research investigates the link of selected ethical frameworks, i.e., utilitarianism, deontology, relativism, absolutism (monism), and pluralism, to the acceptance of self-driving cars. It is hypothesized that they have an impact on the acceptance of the self-driving cars, and a model linking them to it is proposed and assessed. All five selected ethical frameworks investigated are found to have an effect on self-driving car acceptance, which implies actions for several involved stakeholders as these may be a deciding factor for the success or failure of the self-driving car market introduction.
Mechanism of Viral Suppression among HIV Elite Controllers and Long-Term Nonprogressors in Nigeria and South Africa

A subgroup among people living with HIV (PLHIV) experience viral suppression, sometimes to an undetectable level in the blood and/or are able to maintain a healthy CD4+ T-cell count without the influence of antiretroviral (ARV) therapy. One out of three hundred PLHIV fall into this category, and a large sample of this group can be found in areas with a high prevalence of HIV infection such as Nigeria and South Africa. Understanding the mechanism underpinning the nonprogressive phenotype in this subgroup may provide insights into the control of the global HIV epidemic. This work provides mechanisms of the elite control and nonprogressive phenotype among PLHIV in Nigeria and South Africa and identifies research gaps that will contribute to a better understanding on HIV controllers among PLHIV.
Unmanned Aerial Vehicle Classification, Applications and Challenges: A Review

In past few years, unmanned aerial vehicles (UAV) or drones has been a hot topic encompassing technology, security issues, rules and regulations globally due to its remarkable advancements and uses in remote sensing and photogrammetry applications. This review paper highlights the evolution and development of UAV, classification and comparison of UAVs along with Hardware and software design challenges with diverse capabilities in civil and military applications. Further, safety and security issues with drones, existing regulations and guidelines to fly the drone, limitations and possible solutions have also been discussed.
An Overview of Carrier-based Modulation Methods for Z-Source Inverter

Abstract Single-stage energy converters, in particular, the Z-Source Inverter (ZSI) or impedance source inverter, has gained significant attention in the recent years. ZSI ensures flexible energy conversions (dc–dc, dc–ac, ac–ac and ac–dc) because of its unique ability to boost the output voltage in typical renewable energy systems. The impedance network integrated between the energy source and the load contributes to the unique functionality of the ZSI. As substantial research has been conducted on the ZSI, this article provides a review on the operation of ZSI. The article initially examines the various topologies commonly adopted for the application of the ZSI. Subsequently, details of the various modulation methods that are commonly used to obtain the voltage boosting using ZSI are documented. Additionally, the phenomenon of neutral point formation, which is an important impediment to the adoption of multilevel ZSIs and the limitation of the modulation methods, is explained.
Asymmetric Effectiveness of Monetary and Fiscal Policies: Evidence from Turkey

This study assesses the asymmetric effectiveness of Turkey's monetary policy and fiscal policy under the inflation targeting regime in the period of 2006-2020. We employed the non-linear autoregressive distributed lag (NARDL) method and Hatemi-J asymmetric causality test with the assistance of the St. Louis equation, which relates the growth in nominal income with the growth in money supply and public expenditures. The NARDL model revealed that an increase in money supply and gross domestic product (GDP) has a positive relationship. On the other hand, a decrease in money supply and government expenditures have no significant relationship with GDP. In addition, Hatemi-J asymmetric causality results showed an asymmetric causality between money supply and GDP. It demonstrates that the money supply in Turkey during the period 2006-2020 is endogenous.
Laser Sintering Approaches for Bone Tissue Engineering

The adoption of additive manufacturing (AM) techniques into the medical space has revolutionised tissue engineering. Depending upon the tissue type, specific AM approaches are capable of closely matching the physical and biological tissue attributes, to guide tissue regeneration. For hard tissue such as bone, powder bed fusion (PBF) techniques have significant potential, as they are capable of fabricating materials that can match the mechanical requirements necessary to maintain bone functionality and support regeneration. This review focuses on the PBF techniques that utilize laser sintering for creating scaffolds for bone tissue engineering (BTE) applications. Optimal scaffold requirements are explained, ranging from material biocompatibility and bioactivity, to generating specific architectures to recapitulate the porosity, interconnectivity, and mechanical properties of native human bone. The main objective of the review is to outline the most common materials processed using PBF in the context of BTE; initially outlining the most common polymers, including polyamide, polycaprolactone, polyethylene, and polyetheretherketone. Subsequent sections investigate the use of metals and ceramics in similar systems for BTE applications. The last section explores how composite materials can be used. Within each material section, the benefits and shortcomings are outlined, including their mechanical and biological performance, as well as associated printing parameters. The framework provided can be applied to the development of new, novel materials or laser-based approaches to ultimately generate bone tissue analogues or for guiding bone regeneration.