Miracle-dz/UniFAR-FTUs huggingface dataset:
Toxicity Assessment of Colloidal Nanofertilizers Using Zebrafish Embryo Model through Acute Toxicity Assay

Chemical fertilizers are used in large quantities to boost the plant's development. Approximately 90 % of the fertilizer used is lost due to runoff and other processes, resulting in surface and groundwater contamination downstream. Nanofertilizers are believed to be more ecologically friendly and effective when used in small quantities. The use of nanomaterials in agriculture is not always successful. Nanoparticles may readily be discharged into water or the air, where they are ingested by living creatures, causing toxicity in humans, animals, and aquatic life. The aquatic environment has been contaminated with fertilizer runoff, which has been found to have fatal and sublethal impacts on aquatic species. In this work, the harmful effects of NPK-nanofertilizers were determined using the zebrafish embryo toxicity test (ZFET). To summarize, all nanofertilizers were dissolved in deionized water and diluted into concentration ranges in embryo medium. The toxicity of the fertilizer sample was next assessed on the early development of zebrafish embryos from 24 hours post-exposure (hpe) to 120 hpe. The survival rate, LC50, hatching rate, heart rate, and teratogenicity were all assessed. Toxicity of nanofertilizers T1, T2, and T3 to zebrafish embryos was moderate, with LC50 values of 45.7, 38.56, and 19.52 mM, respectively. While no teratogenic defect was seen in embryos treated with the respective samples from 0 hpe to 120 hpe, there was no teratogenic defect observed in the embryos treated with the respective samples from 0 hpe to 120 hpe. The larval heartbeat and hatching rate are unaffected by the nanofertilizer samples. As a result, the current study lays the groundwork for understanding the developmental toxicity of nanofertilizers in zebrafish embryos. Because little is known about the harmful effects of nanofertilizers on aquatic vertebrate species, this knowledge is essential for future research evaluating aquatic risk from nanofertilizers.
Performance of Composite Slabs Using Profiled Steel Decking Reinforced with Cold-Formed C-Channel Steel

There are several different types, shapes and geometries of profiled steel decking that are available in the market for composite slabs construction. The application of the system in construction industry usually depends on the design tables provided by the manufacturer of steel decking. Engineers and builders use the design tables according to the required spans and thicknesses in order to facilitate the design of composite slabs for their construction. However, architectural designs might be an obstacle toward applying the system directly because of the limitation toward spans and thicknesses that can be provided by the design tables. In this research, reinforced profiled steel deckings using cold formed C-channel were used to construct full-scale composite slabs with a purpose to increase the span provided by manufacturer design tables and the capacity of the system for in-situ construction. The full-scale composite slabs system was simply-supported and tested in four-point bending with single control shear spans. The structural behaviour of the reinforced slabs system was investigated. The test comprises 12 specimens which include control (CD slabs) and reinforced decks (RD slabs) samples. The composite slab flexural behaviour was monitored using the LVDT instruments toward bending deformation at the critical points and toward slip displacement between the metal decks and the concrete. The load was applied until total damage was observed for the composite slabs system. The performances of the tested RD slabs were comparable with the corresponding CD slabs which indicated by the samples load carrying capacity, end-slippage, and concrete-steel composite action.
2D EXPERIMENTAL MODELLING OF HYDRODYNAMIC EFFECTS OF SUBMERGED BREAKWATERS

Laboratory 2D experiments have been carried out in 3 m wide wave flume to investigate wave transmission, wave induced-setup and current over submerged breakwater/reef structures. This paper is aimed at advancing the understanding of overtopping flow across submerged breakwater with different crest widths under different submergences. The experiments have been carried out in a central 1m wide channel so as to minimize/prevent return flow over the structure. Analysis of data shows that flux is most influenced by breakwater crest width rather than other variables. The models provided by Symonds et al. (1995) and Gourlay and Colleter (2005) are investigated for calculating current passing over the breakwater/reef due to wave breaking. With adjustment to the calibration factors and modification of models predictive capabilities are improved.
The Association of Pre-existing Diagnoses of Alzheimer’s Disease and Parkinson’s Disease and Coronavirus Disease 2019 Infection, Severity and Mortality: Results From the Korean National Health Insurance Database

Objectives Despite the numerous studies on coronavirus disease 2019 (COVID-19), data regarding the impact of pre-existing diagnoses of Alzheimer’s disease (AD) and Parkinson’s disease (PD) on the susceptibility to and outcome of COVID-19 are limited. We aimed to determine whether patients with AD/PD had a higher likelihood of contracting COVID-19 and experiencing worse outcomes. Methods Data from patients with confirmed diagnoses of COVID-19 (n = 8,070) from January to June 2020 and control participants (n = 121,050) who were randomly selected to match the patients on the basis of age and sex were extracted from the Korean National Health Insurance Database. Pre-existing diagnoses of AD and PD were identified based on medical claim codes. The associations of pre-existing AD or PD with contracting COVID-19, developing severe COVID-19 and dying due to COVID-19 were examined using a logistic regression model. The participants’ age, sex, income, comorbidity score, and history of hypertension/diabetes were assessed as covariates. Results COVID-19 cases were more likely to have a pre-existing AD diagnosis (adjusted odds ratio [aOR] = 2.11, 95% confidence interval [CI] = 1.79–2.50, P-value < 0.001) than controls. COVID-19 cases were more likely to have a pre-existing PD diagnosis than controls, although this estimate did not quite reach statistical significance (aOR = 1.41, 95% CI = 1.00–2.00, P-value = 0.054). Pre-existing AD was related to severe disease and mortality from COVID-19 (aOR = 2.21, 95% CI = 1.64–2.98; aOR = 2.21, 95% CI = 1.00–2.00). Pre-existing PD was not associated with mortality (aOR = 1.54, 95% CI = 0.75–3.16) but was associated with severe disease (aOR = 2.89, 95% CI = 1.56–5.35). Conclusion We found that COVID-19 infection was significantly associated with a pre-existing diagnosis of AD but not with a pre-existing diagnosis of PD. Patients with pre-existing AD had higher odds of developing severe COVID-19 and dying. Pre-existing PD was only associated with a higher odds of developing severe COVID-19.
Investigating the Problematic Use of Sexually Explicit Internet Content: A Survey Study among German Internet Users

ABSTRACT Studies showed that some individuals use sexually explicit Internet content in an excessive, potentially pathological manner, which can have serious negative consequences. However, existing studies often suffer from measurement and sampling issues. In the current study, we adapted a widely acknowledged DSM-5-based scale to assess the prevalence of the problematic use of sexually explicit Internet content (PUSIC) in a random-quota sample of German Internet users (N = 1,019, including 468 users of sexually explicit content). We detected a prevalence rate of 3.4% problematic users among participants that use sexually explicit Internet content. PUSIC was positively related to increased depression, anxiety, and loneliness.
Are financial inclusion and bank stability friends or enemies? Evidence from MENA banks

ABSTRACT This study investigated the effect of financial inclusion on bank stability in the Middle East and North Africa (MENA) region. To achieve this goal, we used a sample of MENA banks for the 2004–2017 period, and we performed system-generalized method of moments (SGMM) as an empirical approach. Overall, the empirical findings indicate that greater financial inclusion significantly increases bank stability. As bank specifics, we found that bank stability is more sensitive to an increase in the non-performing loans (NPLs) ratio and bank size. However, a sufficient level of liquidity significantly increases bank stability. Finally, the results show that bank stability could benefit from a stable macroeconomic environment.
Towards efficient vision transformer inference: a first study of transformers on mobile devices

Convolution neural networks (CNNs) have long been dominating the model choice in on-device intelligent mobile applications. Recently, we are witnessing the fast development of vision transformers, which are notable for the use of the self-attention mechanism, have demonstrated the superiority in accuracy over CNNs. However, vision transformers are with expensive computation costs, and their inference efficiency on resource-constrained mobile devices are still unclear by now. This brings a lot of uncertainty for on-device intelligence to benefit from the vision transformers. In this work, we carry out the first empirical study to investigate the possibility of efficiently deploying vision transformers on mobile devices. Our twofold study (i) profiles the representative vision transformers to understand the inference performance on commercial mobile devices and the behind reasons; and (ii) study multi-dimensional DNN acceleration approaches to achieve minimal latency. Results show that it is too expensive for vision transformer inference on mobile devices. Its inference is 1.58x-41x slower than CNNs. By removing the redundant Attention heads and FFN layers, DeiT-Tiny saves 23.2\% latency with negligible 0.75\% accuracy loss. Our study provides 7 insightful findings for future efficient vision transformer optimization and design.
Performance and strut efficiency factor of concrete deep beams reinforced with GFRP bars

Abstract : Deep reinforced concrete beams are commonly used as transfer girders or bridge bents, at which its safety is often crucial for the stability of the whole structure. Such elements are exposed to the aggressive environment in northern climates causing steel-corrosion problems due to the excessive use of de-icing salts. Fiber-reinforced polymers (FRP) emerged as non-corroded reinforcing materials to overcome such problems in RC elements. The present study aims to address the applicability of concrete deep beams totally reinforced with FRP bars. Ten full-scale deep beams with dimensions of 1200 × 300 × 5000 mm were constructed and tested to failure under two-point loading. Test variables were shear-span depth ratio (equal to 1.47, 1.13, and 0.83) and different configurations of web reinforcement (including vertical and/or horizontal web reinforcement). Failure of all specimens was preceded by crushing in the concrete diagonal strut, which is the typical failure of deep beams. The test results indicated that, all web reinforcement configurations employed in the tested specimens yielded insignificant effects on the ultimate strength. However, strength of specimens containing horizontal-only web reinforcement were unexpectedly lower than that of specimens without web reinforcement. The web reinforcement’s main contribution was significant crack-width control. The tested specimens exhibited reasonable deflection levels compared to the available steel-reinforced deep beams in the literature. The development of arch action was confirmed through the nearly uniform strain distribution along the length of the longitudinal reinforcement in all specimens. Additionally, the basic assumption of the strut-and-tie model (STM) was adequately used to predict the strain distribution along the longitudinal reinforcement, confirming the applicability of the STM for FRP-reinforced deep beams. Hence, a STM based model was proposed to predict the strength of FRP-reinforced deep beams using the experimental data, in addition to the available experimentally tested FRP-reinforced deep beams in the literature. Assessment of the available STMs in code provisions was conducted identifying the important parameters affecting the strut efficiency factor. The tendency of each parameter (concrete compressive strength, shear span-depth ratio, and strain in longitudinal reinforcement) was individually evaluated against the efficiency factor. Strain energy based calculations were performed to identify the appropriate truss model for detailing FRP-reinforced deep beams, hence, only four specimens with vertical web reinforcement exhibited the formation of two-panel truss model. The proposed model was capable to predict the ultimate capacity of the tested deep beams. The model was also verified against a compilation of a data-base of 172 steel-reinforced deep beams resulting in acceptable level of adequacy. The ultimate capacity and performance of the tested deep beams were also adequately predicted employing a 2D finite element program (VecTor2),…
An Empirical Model for 1-D Molecular Communication Diffusive Channels Under Reflecting and Absorbing Boundary Conditions

In this paper, we derive an empirical model that matches, with a 0.99 correlation level, a one dimensional molecular communication diffusive channel impulse response with an absorbing and a reflecting boundary conditions. Under the same conditions, we also derive the fraction of absorbed molecules function. In addition to that, we use bit error rate analysis to show the significance of a reflecting boundary on the inter symbol interference, and demonstrate how a reflecting transmitter enhances the performance of the channel by increasing the molecule absorption rate for a given symbol period.
Economic and Stable Operation of Meshed MTDC/AC Grid as Affected by the Spatiotemporal Complementarities of Geographically Dispersed Wind Generation

Complementarities of geographically dispersed wind resources have been well recognized and evaluated based on the statistics of wind data. This paper investigates the effect of spatiotemporal complementarities of grid-connected offshore wind farms on operational cost and stability of meshed DC/AC grids with VSC-based MTDC networks. The investigation is carried out for a real power system in Jiangsu Province, China, and has used the real hourly wind data provided by the China Meteorological Administration (CMA) in terms of the unit commitment (UC) as well as the probabilistic small-signal angular stability. Several distributed offshore wind farms are interconnected via a VSC-based MTDC network to send power to the southern area in Jiangsu Province. Computational results of the UC and system probabilistic stability demonstrate that significant saving of operational cost and stability improvement can be brought about from the complementarities of geographically distributed offshore wind farms.
Case study for Data Center Distributed Monitoring

In just a few years, following a merger and new regional assignments, the same company has seen an exponential increase in hardware and software under management. Although a monitoring system was obviously already in place, a re-evaluation of it was necessary. In addition to the increase in the amount of objects to be monitored, their type has also changed: by managing healthcare infrastructures, which are critical by their nature, constant, reliable and fast monitoring has become essential. Starting from the first decision to keep the monitoring tool already in operation, its distributed architecture was then revised, optimizing it in the monitoring management subdivisions and providing for multi-level redundancies, also with a view to the continuous growth of the objects to be monitored.
Performance Analysis of the IEEE 802.11p EDCA for Vehicular Networks in Imperfect Channels

The IEEE 802.11p protocol was designed to support effective inter-vehicle communications for Intelligent Transportation Systems (ITS). The Medium Access Control (MAC) sub-layer in IEEE 802.11p employs Enhanced Distributed Channel Access (EDCA) mechanism in order to support differentiated Quality-of-Service (QoS) by introducing four Access Categories (ACs). Performance evaluation of 802.11p EDCA has attracted many research interests. However, most of the existing works assume perfect wireless channels that are error-free. In this paper, we propose a new analytical model based on a 3-D Markov chain and Queuing analysis to evaluate the IEEE 802.11p EDCA under imperfect channels with both saturated and unsaturated traffic. All influential factors of the 802.11p EDCA are considered, including the backoff counter freezing, AIFS defers, the internal collision, and finite MAC buffer sizes. The effectiveness and accuracy of the developed model have been validated through extensive ns-3 simulation experiments.
Collaborative Beamforming for Agents with Localization Errors

We consider a group of agents that estimate their locations in an environment through sensor measurements and aim to transmit a message signal to a client via collaborative beamforming. Assuming that the localization error of each agent follows a Gaussian distribution, we study the problem of forming a reliable communication link between the agents and the client that achieves a desired signal-to-noise ratio (SNR) at the client with minimum variability. In particular, we develop a greedy subset selection algorithm that chooses only a subset of the agents to transmit the signal so that the variance of the received SNR is minimized while the expected SNR exceeds a desired threshold. We show the optimality of the proposed algorithm when the agents’ localization errors satisfy certain sufficient conditions that are characterized in terms of the carrier frequency.
High-Dimensional Robust Mean Estimation via Outlier-Sparsity Minimization

We study the robust mean estimation problem in high dimensions, where less than half of the datapoints can be arbitrarily corrupted. Motivated by compressive sensing, we formulate the robust mean estimation problem as the minimization of the ℓ0-‘norm’ of an outlier indicator vector, under a second moment constraint on the datapoints. We further relax the ℓ0-‘norm’ to the ℓp-norm (0 < p ≤ 1) in the objective and prove that the global minima for each of these objectives are order-optimal for the robust mean estimation problem. Then we propose a computationally tractable iterative ℓp-minimization and hard thresholding algorithm based on the proposed optimization problems. Empirical studies demonstrate that the proposed algorithm outperforms state-of-the-art robust mean estimation methods.
Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels

This letter presents an incremental voxel-based lidar-inertial odometry (LIO) method for fast-tracking spinning and solid-state lidar scans. To achieve the high tracking speed, we neither use complicated tree-based structures to divide the spatial point cloud nor the strict k nearest neighbor (k-NN) queries to compute the point matching. Instead, we use the incremental voxels (iVox) as our point cloud spatial data structure, which is modified from the traditional voxels and supports incremental insertion and parallel approximated k-NN queries. We propose the linear iVox and PHC (Pseudo Hilbert Curve) iVox as two alternative underlying structures in our algorithm. The experiments show that the speed of iVox reaches 1000–2000 Hz per scan in solid-state lidars and over 200 Hz for 32 lines spinning lidars only with a modern CPU while still preserving the same level of accuracy.
Optimal Channel-aware Bayesian Estimation with 1-bit Quantization

We address the optimal quantizer design problem for distributed Bayesian parameter estimation where each sensor quantizes its local observation into one bit and transmit it through non-ideal channels to the Fusion Center. We first develop an asymptotic performance limit (PL) as a performance bound for any quantizer design with a known prior. Aided by this PL, we derive the optimal quantizer and near optimal quantizer with set of observation models that achieves the PL, thus solve a set of optimal quantizer design problem for distributed estimation.
Monostatic FMCW Radar Architecture for Multifunction Full-Duplex Radios

In this paper, an experimental multifunction transceiver capable of simultaneous radar sensing and two-way data transmission is presented. To achieve this, the setup transmits a frequency-modulated continuous-wave (FMCW) waveform with data modulated into it with frequency-shift keying (FSK). A similar FSK-FMCW waveform is sent by a secondary transmitter as the signal-of-interest to measure data reception symbol-error rate. The transmitted FSK-FMCW waveform is used in the down-mixing, to allow for straightforward self-interference attenuation with a highpass filter. Downmixing with a sweeping waveform causes the other received signals to move in spectrum according to the sweeping pattern. For data reception, the effect needs to be compensated for with digital signal processing. The radar and data reception performance is verified through measurements.
Spectral Methods for Testing of Digital Circuits

Except where reference is made to the work of others, the work described in this dissertation is my own or was done in collaboration with my advisory committee. This dissertation does not include proprietary or classified information. Permission is granted to Auburn University to make copies of this dissertation at its discretion, upon the request of individuals or institutions and at their expense. The author reserves all publication rights. Auburn University and was actively involved in the development of an interactive database and matrix population modeling/simulation software package " AvesModeler ". He obtained his Master of Science degree in Fall 2004. He joined the doctoral program at Auburn Uni-the area of verification of DDR memory protocols. In the summer of 2008, he interned with NXP Semiconductors, The Netherlands, and worked on static timing analysis and silicon timing data. Due to increasing design complexities of digital circuits in recent years, a growing problem in Very Large Scale Integrated (VLSI) digital circuit testing is the exponential rise in the test generation complexity and an increasing need for high quality test vectors. For Built-In Self-Test (BIST) of digital circuit, the in-built pattern generator shows increased area overhead, as larger number and more specific patterns need to be generated. In this thesis we address these issues of digital circuit testing. We propose a novel test generation algorithm for sequential circuits using spectral methods. We generate test vectors for faults defined at Register-Transfer Level (RTL) and analyze them for spectral properties. New test vectors are generated using these properties to detect all faults of the circuit. Our proposed algorithm shows equal or improved test coverage and reduced test generation time as compared to a commercial sequential test generation tool, FlexTest, for various benchmark circuits. For an experimental processor PARWAN, FlexTest achieved a test coverage of 93.40% requiring 1403 test vectors in 26430 CPU seconds. The proposed spectral method achieved a coverage of 98.23% requiring 2327 vectors in 2442 CPU seconds. We also propose a Design-For-Testability (DFT) method at RTL which enables improved test coverage and reduced test generation time. v We define N-model tests that target faults belonging to N specified fault models of choice. We propose a method for minimizing these tests using Integer Linear Programming (ILP) without reducing the individual fault model coverage. Stuck-at, transition, and pseudo stuck-at IDDQ faults are used as illustrations. The proposed method shows a noticeable reduction in test …