Miracle-dz/UniFAR-FTUs huggingface dataset:
Inter‐laboratory differences in the apparent quantum yield for the photochemical production of dissolved inorganic carbon in inland waters and implications for photochemical rate modeling

Solar radiation initiates photochemical oxidation of dissolved organic carbon (DOC) to dissolved inorganic carbon (DIC) in inland waters, contributing to their carbon dioxide emissions to the atmosphere. Models can determine photochemical DIC production over large spatiotemporal scales and assess its role in aquatic C cycling. The apparent quantum yield (AQY) spectrum for photochemical DIC production, defined as mol DIC produced per mol chromophoric dissolved organic matter‐absorbed photons, is a critical model parameter. In previous studies, the principle for the determination of AQY spectra is the same but methodological specifics differ, and the extent to which these differences influence AQY spectra and simulated aquatic DIC photoproduction is unclear. Here, four laboratories determined AQY spectra from water samples of eight inland waters that are situated in Alaska, Finland, and Sweden and span a nearly 10‐fold range in DOM absorption coefficients. All AQY values fell within the range previously reported for inland waters. The inter‐laboratory coefficient of variation (CV) for wavelength‐integrated AQY spectra (300–450 nm) averaged 38% ± 3% SE, and the inter‐water CV averaged 63% ± 1%. The inter‐laboratory CV for simulated photochemical DIC production (conducted for the five Swedish lakes) averaged 49% ± 12%, and the inter‐water CV averaged 77% ± 10%. This uncertainty is not surprising given the complexities and methodological choices involved in determining DIC AQY spectra and needs to be considered when applying photochemical rate modeling. Thus, we also highlight current methodological limitations and suggest future improvements for DIC AQY determination to reduce inter‐laboratory uncertainty.
Link User Identities Across Social Networks Based on Contact Graph and User Social Behavior

With the rapid development of Social Networking Services (SNSs), linking online user IDs is becoming increasingly important to internet service providers. Existing methods can achieve matching adjacent IDs between different services, where adjacent IDs mean the IDs that send message loggings at the same physical location. However, nonadjacent IDs also need to be matched in reality, which is a key challenge. In this paper, a new method based on users social behaviors and contact graph is put forward to realize linking of IDs across domains. This method can be used for matching both adjacent IDs and nonadjacent IDs. Specifically, all the IDs are mapped to contact graph. And we utilize a set matching algorithm based on the contact graph to find out the set of candidate IDs and generate confidence score by means of this algorithm to select the most appropriate matching. Our experimental results show that our algorithm is capable of identifying not only the set of adjacent IDs that belong to one same user but also the set of nonadjacent IDs that belong to one same user.
LesionAir : a low-cost tool for automated skin cancer diagnosis and mapping

Skin cancer is the most common form of cancer in the United States; one out of every five Americans develops skin cancer at some point in their lifetime. Diagnosing cancerous lesions early is critical as it significantly increases the chance of survival. However, current techniques for diagnosing skin cancer lack specificity and sensitivity, resulting in many unnecessary biopsies and missed diagnoses. Although some researchers have increased diagnostic efficacy by quantitatively diagnosing skin cancer in an automated fashion, these methods require extremely bulky, expensive, and complicated equipment. This thesis presents the design and testing of LesionAir, a small, low-cost skin cancer diagnostic tool that measures the full-field compliance of the skin which is well known to correlate strongly with skin cancer by applying a vacuum force to the tissue and measuring precise deflection using structured light 3D reconstruction. Image processing algorithms determine additional morphological information about the potentially cancerous lesion. A pilot study of ten patients with suspect lesions validated LesionAir's effectiveness. After biopsy and analysis, a dermatopathologist confirmed the diagnosis of skin cancer in tissue that LesionAir identified as noticeably stiffer, and the regions of this stiffened tissue aligned nearly perfectly with the bounds established by the histological tests, which showed the method determines the precise coordinates that must be excised to safely remove all cancerous tissue. This technology can thus enable patients, primary care physicians, and dermatologists to rapidly identify and diagnose skin cancer with diagnostic quality not seen before from any equipment in this class. Thesis Supervisor: Alexander H. Slocum Title: Pappalardo Professor of Mechanical Engineering
Examining the determinants of electricity consumption in the nine South African provinces: A panel data application

South Africa has been experiencing a series of power cuts since 2008 due to the state‐owned electricity distributor's (Eskom) insufficient power generation capacity. The ongoing energy crisis has negatively impacted the growth and development of the South African economy. Therefore, it is imperative to have an insight into what explains electricity demand in an attempt to address issues facing the energy sector and electricity access at the provincial level. The study's main objective is to examine the determinants of electricity demand in the nine South African provinces from 1995 to 2019 to highlight regional intervention and policy implementation potential. The empirical examination was carried out through two‐panel estimation techniques, namely Fixed Effects and the Pooled Mean Group, to account for time‐invariant unobservable individual aspects that can be associated with the observed explanatory variables and to provide both short‐ and long‐run coefficients thus allowing the intercept, the slope coefficients and the regression for electricity demand to be province‐specific in the short‐run. The results demonstrate the importance of examining the demand for electricity at the provincial level and providing critical province‐specific developments that can potentially influence economic growth.
Study of microstrip line‐fed flower‐shaped patch antenna providing enhanced bandwidth and radiation efficiency

This article presents the simulation and experimental studies of a broadband flower‐shaped patch antenna with semi‐elliptical partial ground plane fed through tapered microstrip line. The conventional circular patch is reshaped to obtain the proposed broadband flower‐shaped patch, which has distinct smaller segments and larger perimeter without changing the (largest) patch radius. The effect of different configurations of feed line and ground plane on the impedance bandwidth of the antenna is studied through simulation using Ansys' High Frequency Structure Simulator (HFSS) software. The simulated and measured bandwidths of the proposed antenna with optimum configurations of feed line and ground plane for input VSWR ≤ 2 are found to be 142% (2.3–13.6 GHz) and 135.88% (2.1–11.0 GHz), respectively. The radiation patterns of the antenna are bidirectional and omni‐directional in E‐ and H‐plane respectively. The proposed antenna has high radiation efficiency of more than 80% and reasonable gain over whole operating frequency band. Further, the input and radiation characteristics of the proposed antenna are compared with the respective characteristics of identical antenna excited using coplanar waveguide (CPW) feed. The bandwidth improvement of proposed antenna compared to reference one is explained using leaky wave concept. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:2041–2046, 2016
Anti-inflammatory activity of Sabicea brevipes Wernharm (Rubiaceae)

Over the years, medicinal plants have been employed in the treatment of inflammation and related ailments. This study evaluated the anti-inflammatory potential of the aerial parts of S. brevipes. The extracts and fractions were further evaluated for anti-inflammatory activity in carrageenan-induced rat model at varying doses (200 and 400 mg/kg doses, orally) for 5 h of treatment. The result of the phytochemical screening showed the presence of alkaloids, terpenoids, glycosides, flavonoids and tannins in the aerial parts of the plant. The in vivo anti-inflammatory study exhibited inhibition of 42% and 44%, 47% and 36%, 33% and 31%, and 43% and 42% for methanol extract n-hexane fraction, ethyl acetate fraction, and methanol fraction, at 200 and 400 mg/kg doses, respectively. The positive control (diclofenac sodium) showed an inhibition value of 51% at 5 mg/kg dose. Finally, it is concluded that S. brevipes possesses anti-inflammatory potential which validates the enthnomedicinal claim of the plant.
AIM2 Promotes Gastric Cancer Cell Proliferation via the MAPK Signaling Pathway

Background Gastric cancer (GC) is a highly prevalent tumor type. The dysregulated expression of melanoma deficiency factor 2 (AIM2) has been observed in a range of tumor types. Herein, we explore the role of AIM2 in the regulation of GC progression. Methods Gastric cancer cells BGC-823 and MGC-803 in logarithmic growth phase were divided into blank group (control), Control group (NC) and SH-AIM2 group, respectively. Control group and SH-AIM2 group were transfected with AIM2 NC and SH-AIM2, respectively. Nude mice were divided into blank group (control) and SH-AIM2 group, and the treatment methods were the same as above. Differential AIM2 expression in GC tissues was assessed via bioinformatics analyses, after which western blotting was used for analyzing the AIM2 levels in tumor and paracancerous tissues from five stomach cancer patients. In addition, qPCR and protein imprinting were used to assess AIM2 expression levels in GC cells, and AIM2 knockdown was conducted in MGC-803 and BGC-823cells, after which colony formation and EdU incorporation assay were utilized to assess cell proliferation. The oncogenic role of AIM2 was then assessed in mice and validated through immunohistochemical analyses. Results GC tissues and cell lines exhibited marked AIM2 overexpression. AIM2 knockdown significantly impaired GC cell proliferation and migration, as confirmed through in vitro assays. In vivo experiments showed that both the increment ability and invasion and migration ability of AIM2 knockdown group were significantly lower than that of control and NC the change of AIM2 protein level would affect the change of MAPK pathway related protein level. Conclusions AIM2 knockdown markedly suppresses the proliferation, migration, as well as invasion of GC cells via the inhibition of MAPK signaling, thereby slowing tumor progression. Overall, these results suggest that further analyses of AIM2 may offer clinically valuable insights that can aid in the treatment of human GC.
Review on the application of physiological and biomechanical measurement methods in driving fatigue detection

Previous studies have identified driving fatigue as the main cause of road traffic accidents, therefore, the aim of this literature review is to explore the characteristics of driving fatigue both physically and mentally as well as to explore the technology available to measure the process of fatigue physiologically. We performed e-searching in the field of fatigue detection methods through keywords tracking. The instruments studied have their own strength and weakness, and some are intrusive while the others are non-intrusive. The accuracy and stability of measurements are also varied between those instruments. In order to create more reliable fatigue detection methods, it is necessary to involve more instruments with an inter-disciplinary approach. Our intention is to make this study as a stepping stone for a more comprehensive in-vehicle real-time man-machine interaction study. Such study will not only be useful to prevent traffic accidents but also to bridge man and machine communication in the vehicle control along with developing newer technology in the field of vehicle automation.
A thematic exploration of three countries’ government communication during the COVID-19 crisis and corresponding media coverage

ABSTRACT This study adds to the current literature on crisis communication by exploring differences in COVID-19 governmental crisis communication and variances in the media coverage of that communication through thematic quantitative content analysis across three countries: New Zealand, The United States and The United Kingdom. Specifically, this research seeks to find the extent to which media ideology plays a role in reporting health crises. Results demonstrated that universally, press conferences were based on scientific advice and relied upon symbols that contributed to the clarity of health communication about COVID-19. Conversely, media coverage stressed economic challenges overall, but conservative newspapers focused more on health and the economy, whereas liberal newspapers leaned more towards politics. These findings demonstrate that even in major health crises, the ideologies of newspapers can play an important role in the framing of information.
A Nonlinear Matrix Decomposition for Mining the Zeros of Sparse Data

We describe a simple iterative solution to a widely recurring problem in multivariate data analysis: given a sparse nonnegative matrix X , how to estimate a low-rank matrix Θ such that X ≈ f ( Θ ), where f is an elementwise nonlinearity? We develop a latent variable model for this problem and consider those sparsifying nonlinearities, popular in neural networks, that map all negative values to zero. The model seeks to explain the variability of sparse high-dimensional data in terms of a smaller number of degrees of freedom. We show that exact inference in this model is tractable and derive an expectation-maximization (EM) algorithm to estimate the low-rank matrix Θ . Notably, we do not parameterize Θ as a product of smaller matrices to be alternately optimized; instead, we estimate Θ directly via the singular value decomposition of matrices that are repeatedly inferred (at each iteration of the EM algorithm) from the model’s posterior distribution. We use the model to analyze large sparse matrices that arise from data sets of binary, grayscale, and color images. In all of these cases, we find that the model discovers much lower-rank decompositions than purely linear approaches.
Multimodal interaction for enhancing team coordination on the battlefield

Team coordination is vital to the success of team missions. On the battlefield and in other hazardous environments, mission outcomes are often very unpredictable because of unforeseen circumstances and complications encountered that adversely affect team coordination. In addition, the battlefield is constantly evolving as new technology, such as context-aware systems and unmanned drones, becomes available to assist teams in coordinating team efforts. As a result, we must re-evaluate the dynamics of teams that operate in high-stress, hazardous environments in order to learn how to use technology to enhance team coordination within this new context. In dangerous environments where multi-tasking is critical for the safety and success of the team operation, it is important to know what forms of interaction are most conducive to team tasks. We have explored interaction methods, including various types of user input and data feedback mediums that can assist teams in performing unified tasks on the battlefield. We’ve conducted an ethnographic analysis of Soldiers and researched technologies such as sketch recognition, physiological data classification, augmented reality, and haptics to come up with a set of core principles to be used when designing technological tools for these teams. This dissertation provides support for these principles and addresses outstanding problems of team connectivity, mobility, cognitive load, team awareness, and hands-free interaction in mobile military applications. This research has resulted in the development of a multimodal solution that enhances team coordination by allowing users to synchronize their tasks while keeping an overall awareness of team status and their environment. The set of solutions we’ve developed utilizes optimal interaction techniques implemented and evaluated in related projects; the ultimate goal of this research is to learn how to use technology to provide total situational awareness and team connectivity on the battlefield. This information can be used to aid the research and development of technological solutions for teams that operate in hazardous environments as more advanced resources become available.
Mode excitation in the microstrip slot-coupled three-layer hemispherical dielectric resonator antenna

In this study, a microstrip slot-coupled multilayer dielectric resonator antenna (DRA) structure is rigorously analysed from the modal perspective, taking into account all layers. The choice of layer permittivities of the multilayer structure to reduce the Q-factor and obtain optimum bandwidth performance of the antenna is outlined. Next, the mechanism of bandwidth enhancement for the centred and offset slots is examined taking into account the excited modes in the structure. It is found that while the centred slot couples to one DRA mode, an offset slot can couple to multiple DRA modes together with the slot mode. Thereafter, the excitation efficiency of modes in the multilayer DRA structure is highlighted to thoroughly characterise the modal contribution in the coupling behaviour for the centred and offset-slots.
Improving Early Prognosis of Dementia Using Machine Learning Methods

Early and precise prognosis of dementia is a critical medical challenge. The design of an optimal computational model that addresses this issue, and at the same time explains the underlying mechanisms that lead to output decisions, is an ongoing challenge. In this study, we focus on assessing the risk of an individual converting to Dementia in the short (next year) and long (one to five years) term, given only a few early-stage observations. Our goal is to develop a machine learning model that could assist the prediction of dementia from regular clinical data. The results show that combining various machine learning techniques together can successfully define ways to identify the risks of developing dementia over the following five years with accuracies considerably above average rates. These findings suggest that accurately developed models can be considered as a promising tool to improve early dementia prognosis.
Epidemic Prediction using Machine Learning and Deep Learning Models on COVID-19 Data

ABSTRACT A catastrophic epidemic of Severe Acute Respiratory Syndrome-Coronavirus, commonly recognised as COVID-19, introduced a worldwide vulnerability to human community. All nations around the world are making enormous effort to tackle the outbreak towards this deadly virus through various aspects such as technology, economy, relevant data, protective gear, lives-risk medications and all other instruments. The artificial intelligence-based researchers apply knowledge, experience and skill set on national level data to create computational and statistical models for investigating such a pandemic condition. In order to make a contribution to this worldwide human community, this paper recommends using machine-learning and deep-learning models to understand its daily accelerating actions together with predicting the future reachability of COVID-19 across nations by using the real-time information from the Johns Hopkins dashboard. In this work, a novel Exponential Smoothing Long-Short-Term Memory Networks Model (ESLSTM) learning model is proposed to predict the virus spread in the near future. The results are evaluated using RMSE and R-Squared values.
Option Pricing by Neural Stochastic Differential Equations: A Simulation-Optimization Approach

Classical option pricing models rely on prior assumptions made on the dynamics of the underlying assets. While empirical evidence showed that these models may partially explain the option prices, their performance may be poor when the actual situation deviates from the assumptions. Neural network models are capable of learning the underlying relationship through the data. However, to avoid over-fitting, these models require massive amount of data, which are not available for option pricing problems. We propose a new model by integrating neural networks to a classical option pricing model, thus increasing the model flexibility while requiring a reasonable amount of data. We show that the training of the model, also known as the calibration, may be formulated into a simulation optimization problem, and it may be solved in a way that is compatible to the training of neural networks. Preliminary numerical results show that our approach appears to work well.
Regulation of StTCP15 gene expression and tuber dormancy characteristics of potato by gibberellic acid, abscisic acid, and low temperature

Potato ( Solanum tuberosum L.) cv. Eshu 10 was used to investigate the effects of exogenous gibberellic acid (GA 3 ), abscisic acid (ABA), and low-temperature stress on changes of hormone content, expression patterns of StTCP15 gene, and tuber dormancy characteristics. Under GA 3 treatment and low-temperature stress, tuber dormancy was broken in about one week sooner compared with the control group, but ABA treatment did not significantly promote the breaking of tuber dormancy. The results of hormone determination using liquid chromatography-mass spectrometry (LC-MS/ MS) showed that the content of ABA in tubers treated with GA 3 or low-temperature stress was lower than in the control group, and it was higher than in the control group under ABA treatment. The GA 3 content of tubers was higher than in the control group under GA 3 treatment and lower under low-temperature stress. During dormancy, the ABA content continued to increase and GA 3 content fluctuated, ABA content rapidly decreased and GA 3 content rapidly increased when the dormancy was breaking, and both ABA content and GA 3 content increased during germination. The results from the assay of real-time quantitative PCR showed that the expression of the StTCP15 gene was continuously increased during the dormant period in all groups, and the expression of the StTCP15 gene was the highest at the time of dormancy release. The expression of the StTCP15 gene was increased about 15 times on the 7 th d under low-temperature stress and was restored at room temperature. Thus, the StTCP15 gene can respond to GA 3 , ABA, and low-temperature stress and may be involved in the release of potato tuber dormancy.