Miracle-dz/UniFAR-FTUs huggingface dataset:
Sorption of polycyclic aromatic hydrocarbons to mineral surfaces

Minerals contribute crucially to the retention of polycyclic aromatic hydrocarbons (PAHs) in subsurface environments. To investigate the sorption behaviour to mineral surfaces batch sorption experiments were conducted using three PAHs (phenanthrene, pyrene, benzo(a)pyrene) and three mineral sorbents that were representative of subsurface materials (quartz, goethite‐coated quartz, quartz‐montmorillonite mixture). Sorption kinetics showed an instantaneous, considerable PAH sorption to all minerals, except for phenanthrene sorption to quartz at small aqueous‐phase concentrations. Apparent sorption equilibrium was achieved after 4 hours of contact time. The sorption characteristics were fitted to six isotherm models by applying Monte Carlo simulation and nonlinear regression. Best‐fit models were obtained by a model discrimination approach. Phenanthrene and pyrene sorption were best described by the Freundlich isotherm model, with the exception of phenanthrene sorption to quartz (linear isotherm). Good fit results for quartz were also obtained for the combined linear‐Freundlich isotherm. Benzo(a)pyrene sorption to all minerals followed linear high‐affinity isotherms. In the case of phenanthrene and pyrene, the Monte Carlo simulations resulted in mean values with small standard deviations for the isotherm parameters, indicating a negligible influence of the experimental uncertainties on the accuracy of the fitted parameters. For phenanthrene, (i) linear isotherms to quartz and goethite‐coated quartz and (ii) a nonlinear concave‐shaped isotherm to quartz–montmorillonite, assuming a pore‐filling process to micropores formed by clay aggregates, were confirmed. For pyrene, nonlinear convex‐shaped isotherms to the mineral surfaces were assessed. A specific sorption affinity of pyrene to the goethite surface indicated a non‐covalent cation‐π interaction. Small sorption affinities to quartz–montmorillonite support an unfavourable partitioning into the adjacent water.
Prevalence and correlates of suicidal ideation among the general population in China during the COVID-19 pandemic

Abstract Background The coronavirus disease 2019 (COVID-19) pandemic is a major threat to the public. However, the comprehensive profile of suicidal ideation among the general population has not been systematically investigated in a large sample in the age of COVID-19. Methods A national online cross-sectional survey was conducted between February 28, 2020 and March 11, 2020 in a representative sample of Chinese adults aged 18 years and older. Suicidal ideation was assessed using item 9 of the Patient Health Questionnaire-9. The prevalence of suicidal ideation and its risk factors was evaluated. Results A total of 56,679 participants (27,149 males and 29,530 females) were included. The overall prevalence of suicidal ideation was 16.4%, including 10.9% seldom, 4.1% often, and 1.4% always suicidal ideation. The prevalence of suicidal ideation was higher in males (19.1%) and individuals aged 18–24 years (24.7%) than in females (14.0%) and those aged 45 years and older (11.9%). Suicidal ideation was more prevalent in individuals with suspected or confirmed infection (63.0%), frontline workers (19.2%), and people with pre-existing mental disorders (41.6%). Experience of quarantine, unemployed, and increased psychological stress during the pandemic were associated with an increased risk of suicidal ideation and its severity. However, paying more attention to and gaining a better understanding of COVID-19-related knowledge, especially information about psychological interventions, could reduce the risk. Conclusions The estimated prevalence of suicidal ideation among the general population in China during COVID-19 was significant. The findings will be important for improving suicide prevention strategies during COVID-19.
Voltage effects on the volumetric flow rate in cone-jet mode electrospraying

Electrospray data collected demonstrate the detailed dependence of volumetric flow rate through an electrospray system upon the applied voltage. The sensitivity of nominal flow rate to applied voltage was found to be higher for lower nominal flow rates. For a volumetric flow rate ∼4nL∕s a 25% change in flow rate per kilovolt was recorded over a cone-jet mode stability range spanning ∼1.5kV. This volumetric flow rate voltage sensitivity holds particular significance for potential colloid electrospray propulsion systems, which operate at or near minimum flow rate conditions. Analysis is presented to show that the change in flow rate due to change in voltage cannot be ascribed to evaporation from the meniscus as has been suggested by others.
Influence of Salinity and Fertilization Level on the Nutrient Distribution in Tomato Plants under a Polyethylene Greenhouse in the Mediterranean Area

The experiment was conducted in a polyethylene greenhouse with the ‘Pitenza’ variety of tomato crop on a sand‐mulched sandy loam soil using trickle irrigation. The experimental design was two‐factorial with four randomized blocks. The treatments applied were the result of a combination of two irrigation water qualities (C2S1 and C3S2) and three fertilization levels: F1, half of the recommended rate; F2, the recommended rate [i.e., 7.1 mmol L−1 of nitrate (NO3 −)–nitrogen (N), 0.5 mmol L−1 of phosphoric acid (H2PO4 −)–phosphorus (P) and 3.1 mmol L−1 of potassium (K+)], and F3, double the recommended rate. Total content of nutrients [N, P, K, calcium (Ca), magnesium (Mg)] and sodium (Na) were determined for different plant parts (pinch rest, harvested fruit, developing fruit, leaf, and stem) for the whole season. Also, the relative sink strengths between fruit and vegetative plant parts were calculated. The main mineral constituent of the tomato plant was K, surpassing the N content, and the minor constituent was P. With the same level of fertilization, the influence of salinity was significant at F1 (half of the recommended rate) and F2 (the recommended rate) but not at the highest level. Plant nutrient distribution showed the same behavior for N, P, and K, the fruit being the nutrient sink. On the other hand, the same tendency was observed for Ca and Mg, stored mainly in the leaf. Nevertheless, Na presented low levels in young tissues, whereas the levels were higher and similar in fruit, leaf, and stem.
ASYMPTOTIC SIZE OF KLEIBERGEN’S LM AND CONDITIONAL LR TESTS FOR MOMENT CONDITION MODELS

An influential paper by Kleibergen (2005, Econometrica 73, 1103–1123) introduces Lagrange multiplier (LM) and conditional likelihood ratio-like (CLR) tests for nonlinear moment condition models. These procedures aim to have good size performance even when the parameters are unidentified or poorly identified. However, the asymptotic size and similarity (in a uniform sense) of these procedures have not been determined in the literature. This paper does so. This paper shows that the LM test has correct asymptotic size and is asymptotically similar for a suitably chosen parameter space of null distributions. It shows that the CLR tests also have these properties when the dimension p of the unknown parameter θ equals 1. When p ≥ 2, however, the asymptotic size properties are found to depend on how the conditioning statistic, upon which the CLR tests depend, is weighted. Two weighting methods have been suggested in the literature. The paper shows that the CLR tests are guaranteed to have correct asymptotic size when p ≥ 2 when the weighting is based on an estimator of the variance of the sample moments, i.e., moment-variance weighting, combined with the Robin and Smith (2000, Econometric Theory 16, 151–175) rank statistic. The paper also determines a formula for the asymptotic size of the CLR test when the weighting is based on an estimator of the variance of the sample Jacobian. However, the results of the paper do not guarantee correct asymptotic size when p ≥ 2 with the Jacobian-variance weighting, combined with the Robin and Smith (2000, Econometric Theory 16, 151–175) rank statistic, because two key sample quantities are not necessarily asymptotically independent under some identification scenarios. Analogous results for confidence sets are provided. Even for the special case of a linear instrumental variable regression model with two or more right-hand side endogenous variables, the results of the paper are new to the literature.
Prediction of the Gas Holdup in Industrial-Scale Bubble Columns and Slurry Bubble Column Reactors Using Back-Propagation Neural Networks

The total gas holdup and the holdup of large gas bubbles were predicted in bubble column reactors (BCRs) and slurry bubble column rectors (SBCRs) using two Back-Propagation Neural Networks (BPNNs). Over 3880 and 1425 data points for gas holdup and Large gas bubble holdup respectively, covering wide ranges of gas-liquid-solid physical properties, operating variables, reactor geometry, and gas sparger type/size, were employed to develop, train and validate the two neural networks. The developed BPNN for gas holdup has a topology of [14,9-7,1] and was able to predict the trained and untrained data with an average absolute relative error (AARE), standard deviation, and regression coefficient (R2) of 16, 19 and 90%, respectively. The developed BPNN for large gas bubble holdup has a topology of [14,8,1] and was capable of predicting the trained and untrained data with AARE, standard deviation, and R2 of 10, 14 and 93%, respectively. The BPNNs were then used to predict the effects of pressure, superficial gas velocity, temperature and catalyst loading on the total syngas holdup for Low-Temperature Fischer-Tropsch (LTFT) synthesis carried out in a 5 m ID SBCR. The predicted total syngas holdup appeared to increase with increasing reactor pressure, superficial gas velocity and the number of orifices in the gas sparger. The predicted syngas holdup, however, was found to decrease with increasing catalyst loading and reactor temperature. Also, under similar LTFT operating conditions (P = 3 MPa, T = 513 K, CW = 30 and 50 wt%), the total syngas holdup values predicted for H2/CO ratio of 2:1 and cobalt-based catalyst are consistently lower than those obtained for H2/CO ratio of 1:1 and iron oxide catalyst in the superficial gas velocity range from 0.005 to 0.4 m/s. These predictions are in perfect agreement with reported literature trends, which underscore the reliability and validity of the developed BPNNs in predicting the total syngas holdup and the holdup of large gas bubbles in large-scale bubble columns and SBCRs operating under industrial conditions.
Per-Hop Reversed Packet Auctions for Cooperative Routing in Mobile Wireless Networks

Many applications could benefit from multi-hop communications through users’ mobile devices. A key issue is how to incentivize users to cooperate in both routing and relay of messages by sharing their device’s precious resources. Previous works on the subject have either tackled cooperation in the relay of messages alone or in both routing and relay functionalities. In the latter case, path selection is usually carried out at the destination node, which renders significant delays because the selected path needs to be conveyed all the way back to the source node before any data packet can be transmitted. This is certainly unsuitable in mobile scenarios. This paper presents the performance of the “Tightness” strategy, which allows the routing and relaying of messages “on-the-go”, via per-hop reversed packet auctions. At each hop, the sender asks for bids from potential relays according to a “budget” attached to the data packet, through which the auction winner gets paid and can pay for others in subsequent auctions. The auction winner is chosen not only based on bid value, but also on the estimated relay’s likelihood to deliver the packet to destination. Likewise, each potential relay makes a bid considering its own chances to deliver the packet to destination. A fine is also announced in every auction, that must be paid by all relays if the packet is not delivered to destination within a “deadline” expressed in number of hops. The performance of the Tightness strategy is evaluated for both static and mobile scenarios and compared to two baseline strategies according to different performance metrics.
Empirical Formulas of SIRW and Its Application in Half-Mode SIRW Hybrid Ring Coupler Design

Two brief empirical formulas of substrate integrated ridged waveguide(SIRW) are derived to calculate the equivalent parameters between SIRW and ridged waveguide. Using these formulas, a wideband hybrid ring coupler based on half-mode substrate integrated ridged waveguide(HMSIRW) is designed conveniently. The simulated and measured results show that the bandwidth increased by 7% compared to half-mode substrate integrated waveguide hybrid ring coupler.
Soil Emissions of Nitric Oxide and Nitrous Oxide from Injected Anhydrous Ammonium and Urea

This study characterizes soil emissions of NO and N{sub 2}O from banded applications of anhydrous ammonium (AA) and urea over the period from 6 May 1994 to 12 September 1994 from a losses soil in western Tennessee. The N application rate for both sources was 168 kg ha{sup {minus}1}. Fertilizer type strongly influenced emissions of N{sub 2}O (F = 231; P = 0.0001) and NO (F = 69; P = 0.0001). During the 129 d measurement period, the AA treatment lost 12.33 Kg of N{sub 2}O-N or 7.33% of the applied N. The N{sub 2}O-N loss from the urea treatment was about one-half that from AA; 6.34 kg ha{sup {minus}1} or 3.77% of the applied N. Loss of NO-N from both treatment was small compared with N{sub 2}O-N loss. The urea treatment lost 0.27 kg ha{sup {minus}1} as NO-N and the AA treatment lost 0.2 kg ha{sup {minus}1} during the study period. While the measured loss rate of N{sub 2}O-N from AA is similar to previous literature estimates, our values for urea are 20 to 40 times greater than the current literature reports of N{sub 2}O-N loss of 0.1 to 0.2% of the urea applied. Higher N{sub 2}O losses frommore » urea in this study may be related to the fact that urea was banded below the soil surface, whereas urea has been broadcast on the soil surface in other N{sub 2}O emissions studies. 31 refs., 5 figs., 2 tabs. « less
Competitive Analysis of the Top Gradient Boosting Machine Learning Algorithms

In this paper, we compare four state-of-the-art gradient boosting algorithms viz. XGBoost, CatBoost, LightGBM and SnapBoost. All these algorithms are a form of Gradient Boosting Decision Trees(GBDTs). They find wide usage across competitive machine learning contests like Kaggle, due to their flexibility and considerably faster training times. Since a typical vanilla GBDT is usually implemented as a black box model, our research makes an attempt to help improve the explainability of GBDTs. We performed an exhaustive 360 degree comparative analysis of each of the four algorithms by training and testing them on diverse datasets leveraging IBM's PowerAIR AC922 CPU. The analysis was performed using two approaches; One was by training the baseline algorithms on the datasets, and the other was by performing systematic hyperparameter optimization (HPO) using the HyperOpt framework. Although the HPO process is resource-intensive, the Power System architecture facilitated lower training times without compromising the algorithm's accuracy for each of the datasets. We present the accuracy scores and training times across the four datasets for both the aforementioned approaches. The results imply that despite interesting trends observed across all the datasets, there is no clear winner that excels equally in every aspect of performance
Weapon Detection in Real-Time CCTV Videos Using Deep Learning

Security and safety is a big concern for today’s modern world. For a country to be economically strong, it must ensure a safe and secure environment for investors and tourists. Having said that, Closed Circuit Television (CCTV) cameras are being used for surveillance and to monitor activities i.e. robberies but these cameras still require human supervision and intervention. We need a system that can automatically detect these illegal activities. Despite state-of-the-art deep learning algorithms, fast processing hardware, and advanced CCTV cameras, weapon detection in real-time is still a serious challenge. Observing angle differences, occlusions by the carrier of the firearm and persons around it further enhances the difficulty of the challenge. This work focuses on providing a secure place using CCTV footage as a source to detect harmful weapons by applying the state of the art open-source deep learning algorithms. We have implemented binary classification assuming pistol class as the reference class and relevant confusion objects inclusion concept is introduced to reduce false positives and false negatives. No standard dataset was available for real-time scenario so we made our own dataset by making weapon photos from our own camera, manually collected images from internet, extracted data from YouTube CCTV videos, through GitHub repositories, data by university of Granada and Internet Movies Firearms Database (IMFDB) imfdb.org. Two approaches are used i.e. sliding window/classification and region proposal/object detection. Some of the algorithms used are VGG16, Inception-V3, Inception-ResnetV2, SSDMobileNetV1, Faster-RCNN Inception-ResnetV2 (FRIRv2), YOLOv3, and YOLOv4. Precision and recall count the most rather than accuracy when object detection is performed so these entire algorithms were tested in terms of them. Yolov4 stands out best amongst all other algorithms and gave a F1-score of 91% along with a mean average precision of 91.73% higher than previously achieved.
A Comparison Study on the Alignment of Prosodic and Semantic Units and Its Effects on F0 Shifting in L1 and L2 English Spontaneous Speech

Linguistic work on speech planning has established that advanced planning involves both the complexity of the subsequent utterance (i.e., utterance length) and the semantic completeness of the upcoming speech (e.g., whether the whole utterance reaches the termination of semantic units). Yet relatively little attention has been paid to the preplanning capacity of second-language (L2) learners. This study investigates whether learners are capable of pitch-related preplanning based on semantic units in discourse (DUs) in L2 spontaneous speech production. We analyzed relationships between f0 metrics and the semantic completeness of prosodic units (PUs) in English spontaneous speech by native speakers and EFL learners of Mandarin and Cantonese. The results indicate that PU-DU left alignment introduces an initial f0 up-shifting, and PU-DU right alignment produces a final f0 down-shifting in both L1 and L2 speech, suggesting speakers’ sensitivity to the initiation and termination of DUs. Critically, only in L1 speech the initial f0 height correlates with PU-DU right alignment, the final f0 is connected to the left alignment, and the f0 slope is strongly related to PU-DU left and right alignment conditions. The absence of correlation in L2 speech might reflect learners’ limited capacity of preplanning a whole DU in spontaneous speech production.
Analogue of Electromagnetically Induced Transparency Based on Bright–Bright Mode Coupling Between Spoof Electric Localized Surface Plasmon and Electric Dipole

We report a novel type of electromagnetically induced transparency (EIT) analogue based on metamaterials (MMs) with hybrid structures, in which spiral and cut-wire structures serve as bright-mode resonators with the spoof electric localized surface plasmons and electric dipole resonance. With the simulations and experiments, the EIT analogue generated by the frequency detuning and hybridization between the resonators is verified. The simulated surface charge density and electric and magnetic field distributions, together with the theoretical analysis based on the Lorentz oscillator model, reveal the principle of the EIT analogue. Moreover, the transmission window of the MM demonstrates linear dependence on the refractive index’s change of surrounding media, showing the potential as a tool for environmental detection.
Novel Footstep Features Using Dominant Frequencies for Personal Recognition

There are two main contributions in this article. One is that an extraction means of dominant frequencies is proposed for the first time. The footstep events (FEs) from diverse subjects are analyzed comparatively in frequency domain. Besides the extraction of dominant frequencies containing rich feature information is successfully accomplished after numerous experiments. The other is novel footstep features. Seven simple but effective features are developed and assessed based on dominant frequencies. A SVM is utilized as classifier, exactly identifying which person a FE belongs to. 92.41% precision, 91.3% recall and 91.85% ${F}_{1}$ on average are obtained in personal recognition experiment. Moreover, our seven features show a best performance in the comparison about our features and some features studied previously, even under various SNR. It is worth mentioning that our original signals are collected in noisy environment to approach real application scenarios, without any polishing such as filtering, amplifying. Good classification results could be acquired with poor signal, which is enough to demonstrate that our features are robust and preferable. Our human recognition scheme only involves a microphone to collect footstep sounds and easy classification method, with the characteristics of small calculation and low experimental cost.
Electricity Consumption of Remote Villages in Sarawak Powered by Off-grid Solar System

Sarawak in Malaysian Borneo is still largely covered by dense rainforest over rugged terrain, which brings great challenges to providing 24/7 reliable electricity to communities that are too remote to be connected to the grid. Nonetheless, the Sarawak government is committed to achieving its target of 99% electrification by this year and towards full electrification by the year 2025 through various methods including the off-grid Sarawak Alternative Rural Electrification Scheme (SARES). Through SARES, the most remote rural villages are supplied with centralised solar power systems or micro-hydro solutions. This paper aims to analyse the electricity consumption pattern for 12 selected villages which are powered by off-grid solar systems under the SARES project. The considered parameters include load factor, system's utilisation level, and battery charging behaviour. In addition, the electricity usage behaviour for festive and non-festive days was also compared. It is expected that the findings from this analysis can support the current SARES solar system's design, and better inform the future design for similar user groups.
Distribution of Heavy Metals (Cr, Cu, Zn, Pb, Cd, As) in Roadside Sediments, Seoul Metropolitan City, Korea

Urban roadside sediments were collected for five months from eleven sites in Seoul Metropolitan City and were examined for spatial and temporal variation of heavy metal composition (Cr, Cu, Zn, Pb, Cd and As). Samples for analysis were treated with 0.1 N HCl for partial extraction and with mixed strong acid (HF + HNO3 + HClO4) for total extraction. Using the normalization of the acid extractable metal concentrations with aluminum (element/Al), two statistical approaches, i.e., two-way ANOVA test and R-mode factor analysis (FA), were performed to examine the major sources of variability in the heavy metal composition. The FA results show that Pb and Cr are strongly dependent on site-specific traffic density, whereas Cu, Cd and Zn vary with site characterization (especially the percentage of metal industry) as well as the temporal amount of precipitation. Arsenic, however, does not show any distinct change. As the roadside sediments are heavily polluted with heavy metals (especially Zn and Cu), the management and disposal of the sediments have to be guided by appropriate environmental measures.