Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Information Fusion for Weather Systems and Clouds Identification From Satellite Images

Seeing the cloud and then understanding the weather is one of the important means for people to forecast weather. There has been a certain progress in the use of deep learning technology for weather forecasting, especially in the automatic understanding of disaster weather from satellite image, which can be seen as the image classification problem. Publicly available satellite image benchmark database tries to link weather directly with satellite images. However, single image modal is far from enough to correctly identify weather systems and clouds. Thus, we integrate images with meteorological elements, in which five kinds of meteorological elements, such as season, month, date stamp, and geographic longitude, and latitude, are labeled. To effectively use such various modalities for clouds and weather systems identification through satellite image classification tasks, we propose a new satellite image classification framework: multimodal auxiliary network (MANET). MANET consists of three parts: image feature extraction module based on convolutional neural network, meteorological information feature extraction module based on perceptron, and layer-level multimodal fusion. MANET successfully integrates the multimodal information, including meteorological elements and satellite images. The experimental results show that MANET can achieve better weather systems and clouds and land cover classification results based on satellite images.
MCR Scaffolds Get Hotter with 18F-Labeling

Imaging techniques, such as positron emission tomography (PET), represent great progress in the clinical development of drugs and diagnostics. However, the efficient and timely synthesis of appropriately labeled compounds is a largely unsolved problem. Numerous small drug-like molecules with high structural diversity can be synthesized via convergent multicomponent reactions (MCRs). The combination of PET labeling with MCR synthesis of biologically active compounds can greatly simplify radioanalytical and imaging-based analysis. In a proof-of-concept study, we optimized robust on-site radiolabeling conditions that were subsequently applied to several structurally different drug-like MCR scaffolds (e.g., arenes, β-lactam, tetrazole, and oxazole). These labeled scaffolds were synthesized via pinacol-derived aryl boronic esters (arylBPin) by copper-mediated oxidative 18F-fluorination with radiochemical conversions (RCCs) from 15% to 76%.
Time-Dependent Parabolic Finite Difference Formulation for Harmonic Sound Propagation in a Two-Dimensional Duct with Flow

An explicit finite difference real time iteration scheme is developed to study harmonic sound propagation in aircraft engine nacelles. To reduce storage requirements for future large 3D problems, the time dependent potential form of the acoustic wave equation is used. To insure that the finite difference scheme is both explicit and stable for a harmonic monochromatic sound field, a parabolic (in time) approximation is introduced to reduce the order of the governing equation. The analysis begins with a harmonic sound source radiating into a quiescent duct. This fully explicit iteration method then calculates stepwise in time to obtain the 'steady state' harmonic solutions of the acoustic field. For stability, applications of conventional impedance boundary conditions requires coupling to explicit hyperbolic difference equations at the boundary. The introduction of the time parameter eliminates the large matrix storage requirements normally associated with frequency domain solutions, and time marching attains the steady-state quickly enough to make the method favorable when compared to frequency domain methods. For validation, this transient-frequency domain method is applied to sound propagation in a 2D hard wall duct with plug flow.
d-Amino Acids and Classical Neurotransmitters in Healthy and Type 2 Diabetes-Affected Human Pancreatic Islets of Langerhans

The pancreatic islets of Langerhans are clusters of cells that function as endocrine units synthesizing and releasing insulin and a range of additional peptide hormones. The structural and chemical characteristics of islets change during type 2 diabetes development. Although a range of metabolites including neurotransmitters has been reported in rodent islets, the involvement of these cell-to-cell signaling molecules within human pancreatic islets in the pathophysiology of type 2 diabetes is not well known, despite studies suggesting that these molecules impact intra- and inter-islet signaling pathways. We characterize the enigmatic cell-to-cell signaling molecules, d-serine (d-Ser) and d-aspartate (d-Asp), along with multiple classical neurotransmitters and related molecules, in healthy versus type 2 diabetes-affected human islets using capillary electrophoresis separations. Significantly reduced d-Ser percentage and gamma-aminobutyric acid (GABA) levels were found in type 2 diabetes-affected islets compared to healthy islets. In addition, the negative correlations of many of the signaling molecules, such as d-Ser percentage (r = −0.35), d-Asp (r = −0.32), serotonin (r = −0.42), and GABA (r = −0.39) levels, with hemoglobin A1c (HbA1c) levels and thus with the progression of type 2 diabetes further demonstrate the disruption in intra- or inter-islet signaling pathways and suggest that these cell-to-cell signaling molecules may be potential therapeutic targets.
PIV Measurements and Mechanisms of Adjacent Synthetic Jets Interactions

Interactions of adjacent synthetic jet actuators with varying relative amplitude and the relative phase of driving voltage are measured using a particle image velocimetry (PIV). Varying relative amplitude or relative phase of driving voltage of the adjacent actuators vectors the direction of the ensuing merged jet of the adjacent synthetic jets. The vectoring mechanism of the adjacent vortex pairs, attract-impact causing deflection (AICD), is provided to explain why the merged jet is generally vectored to the side of the phase-leading synthetic jet or the synthetic jet with higher driving voltage.
A Machine Learning Adaptive Beamforming Framework for 5G Millimeter Wave Massive MIMO Multicellular Networks

The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fifth-generation millimeter-wave multicellular networks, where massive multiple-input multiple-output configurations are employed in all active base stations of the considered orientations. In this context, beamforming is performed with the help of a predefined set of configurations that can deal with various traffic scenarios by properly generating highly directional beams on demand. In parallel, a machine learning (ML) beamforming approach based on the k-nearest neighbors ( ${k}$ -NN) approximation has been considered as well, which is trained in order to generate the appropriate beamforming configurations according to the spatial distribution of throughput demand. Performance is evaluated statistically, via a developed system level simulator that executes Monte Carlo simulations in parallel. Results indicate that the achievable spectral efficiency (SE) and energy efficiency (EE) values are aligned with other state of the art approaches, with reduced hardware and algorithmic complexity, since per user beamforming calculations are omitted. In particular, considering a two-tier cellular orientation, then in the non-ML approach EE and SE can reach up to 5 Mbits/J and 36 bps/Hz, respectively. Both metrics attain the aforementioned values when the ML-assisted beamforming framework is considered. However, beamforming complexity is further reduced, since the ML approach provides a direct mapping among the considered throughput demand and appropriate beamforming configuration.
On the packet error rate of convolutional codes

The authors survey various recent approaches to achieving good packet error rate (PER) performance and compare with the sphere packing bound (SPB). As a reference to what can be expected for short codes, the Hamming (8,4) and Golay (24,12) codes with optimum decoding are 0.5 dB from the SPB at a PER of 10/sup -4/. For somewhat larger packet lengths, up to about 100 bits, block codes such as BCH codes with suboptimal soft decision decoding provide PER performance within 0.45 dB of the SPB.
A Survey on Formal Verification Techniques for Safety-Critical Systems-on-Chip

The high degree of miniaturization in the electronics industry has been, for several years, a driver to push embedded systems to different fields and applications. One example is safety-critical systems, where the compactness in the form factor helps to reduce the costs and allows for the implementation of new techniques. The automotive industry is a great example of a safety-critical area with a great rise in the adoption of microelectronics. With it came the creation of the ISO 26262 standard with the goal of guaranteeing a high level of dependability in the designs. Other areas in the safety-critical applications domain have similar standards. However, these standards are mostly guidelines to make sure that designs reach the desired dependability level without explicit instructions. In the end, the success of the design to fulfill the standard is the result of a thorough verification process. Naturally, the goal of any verification team dealing with such important designs is complete coverage as well as standards conformity, but as these are complex hardware, complete functional verification is a difficult task. From the several techniques that exist to verify hardware, where each has its pros and cons, we studied six well-established in academia and in industry. We can divide them into two categories: simulation, which needs extremely large amounts of time, and formal verification, which needs unrealistic amounts of resources. Therefore, we conclude that a hybrid approach offers the best balance between simulation (time) and formal verification (resources).
Uniform and Simplified Small-Signal Model for Inductive Power Transfer Systems

This article explores the unification and simplification of the small-signal model for representative inductive power transfer systems. Various famous compensations are included, such as the S–S, LCC–S, and LCC–LCC. Through system decomposition, the uniform model concept is applied to the inverter, rectifier, and resonant tank. Meanwhile, the model complexity and similarity are discussed for various resonant tanks, and proper simplification is used to reduce the model order. The uniform equivalent circuit model is finally proposed for the target systems, based on which the explicit transfer function can be represented by a general polynomial equation. The model order of the S–S, LCC–S, and LCC–LCC systems are five, two, and five, respectively. The experiment shows the model is accurate up to one-fifth of the switching frequency for all the target systems.
Effects of Climate Change Knowledge on Adolescents’ Attitudes and Willingness to Participate in Carbon Neutrality Education

The achievement of carbon neutrality has become increasingly important. Therefore, it the use of education to increase public understanding of carbon neutrality and facilitate low-carbon behaviors is urgent. Climate change knowledge is an effective measure to promote people’s interest and enthusiasm for specific educational projects. The present study analyzed the effects of climate change knowledge on adolescents’ attitudes and their willingness to participate in carbon neutrality education and validated the mediating effect of environmental responsibility. The findings showed that climate change knowledge improves adolescents’ attitudes toward carbon neutrality education and that environmental responsibility plays a mediating role in this. The findings provide insightful implications for carbon neutrality related policymaking and education promotion.
State Economic Impacts of Industrial Park Development: Evidence from Virginia’s Tobacco Region Megasite Program

Industrial park development is commonly advocated by state and local economic developers to aid in business attraction efforts. However, there is limited evidence of its effectiveness in promoting economic growth, particularly in rural and lagging regions in comparison to other economic development tools. This paper examines the literature on the relationship between business site availability and economic development and the rationale for public sector industrial land development assistance. Results from a survey of local economic developers in Virginia are used to characterize industrial park absorption and occupant attributes. Using this information, ex-ante state economic and tax revenue impact analyses are performed for a state-funded industrial park development program serving a rural region of Virginia, the Tobacco Region Megasite Program. The paper examines conditions that affect the economic and tax revenue impact potential of industrial parks and highlights remaining information gaps.
Optimized electrocoagulation technology using response surface methodology to control H2 production and treatment effect of fracturing flowback fluid treated by electrocoagulation

Fracturing flowback fluid is the most important contaminant associated with oil and gas explo - ration and production, which requires low-cost and sustainable technologies for treatment. This study proposed an electrocoagulation (EC) method that is time saving, highly efficient, and easy to apply to deal with this problem. A Box–Behnken design coupled with the response surface methodology (RSM) was used to construct a model of the EC process to intensively control hydro - gen (H 2 ) production and to reduce potential safety risks while achieving high treatment efficiency. The model describes the change in the responses of decolorization rate and H 2 production in accordance with electrolysis current, plate spacing, electrolysis time, and S / V . Results show that the turbidity removal rate, decolorization rate, and H 2 production are predicted to be 87.03%, 89.14%, and 138.12 mL under the maximum H 2 production process. On the contrary, the turbidity removal rate, decolorization rate, and H 2 production are predicted to be 91.23%, 95.7%, and 7.86 mL, respec - tively, under minimum H 2 production conditions obtained through the model. The relative error between all the predicted and measured values is less than 2.5%. Overall, the RSM is feasible to optimize and predict the process conditions, treatment effect, and H 2 production of EC-treated fracturing flowback fluid. This optimization and prediction study can provide a reference for the efficient and safe application of EC to improve the treatment effect and control the H 2 production in the wastewater treatment process.
Training Autoencoders with Noisy Training Sets for Detecting Low-rate Attacks on the Network

A Network-based Intrusion Detection System (NIDS) monitors network traffic and analyses it to look for any sign of malicious behaviour. A NIDS may be using of these two methods to look for malicious activities, signature-based or anomaly-based. A Signature-based NIDS relies on a database of rulesets to determine whether a packet or a flow is malicious. Therefore, it suffers when the database is not updated regularly or when a zero-day attack appears. An Anomaly-based NIDS works by learning the behaviour of normal traffic and looking for anomalous activities. The anomalous activities are then deemed malicious. In doing so, this kind of NIDS does not have to rely on an updated database. It can identify deviation from the normal behaviour by training itself with some training data obtained from the organisation network traffic. The issue is cleaning the network traffic data from a real-world capture is time-consuming. Thus, in this paper, we proposed an anomaly detection method that was trained with network traffic that contains malicious activities. We were looking for evidence of whether using Autoencoders is robust to noisy data in the training set. Our experiments show that the detection method can achieve an F2-score of 0.87 for FTP traffic, 0.83 for HTTP traffic, and 0.98 for SMTP traffic. These results were obtained from models that had been trained with a training set which contains 0.3% of malicious traffic.
The Determination of Cannabinoids in Urine Samples Using Microextraction by Packed Sorbent and Gas Chromatography-Mass Spectrometry

Cannabis is the most consumed illicit drug worldwide, and its legal status is a source of concern. This study proposes a rapid procedure for the simultaneous quantification of Δ9-tetrahydrocannabinol (THC), 11-hydroxy-Δ9-tetrahydrocannabinol (11-OH-THC), 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH), cannabidiol (CBD), and cannabinol (CBN) in urine samples. Microextraction by packed sorbent (MEPS) was used to pre-concentrate the analytes, which were detected by gas chromatography–mass spectrometry. The procedure was previously optimized, and the final conditions were: conditioning with 50 µL methanol and 50 µL of water, sample load with two draw–eject cycles, and washing with 310 µL of 0.1% formic acid in water with 5% isopropanol; the elution was made with 35 µL of 0.1% ammonium hydroxide in methanol. This fast extraction procedure allowed quantification in the ranges of 1–400 ng/mL for THC and CBD, 5–400 ng/mL for CBN and 11-OH-THC, and 10–400 ng/mL for THC-COOH with coefficients of determination higher than 0.99. The limits of quantification and detection were between 1 and 10 ng/mL using 0.25 mL of sample. The extraction efficiencies varied between 26 and 85%. This analytical method is the first allowing the for determination of cannabinoids in urine samples using MEPS, a fast, simple, and low-cost alternative to conventional techniques.
The Anticancer Efficacy of Thiourea-Mediated Reduced Graphene Oxide Nanosheets against Human Colon Cancer Cells (HT-29)

The current research focuses on the fabrication of water-soluble, reduced graphene oxide (rGO) employing thiourea (T) using a simple cost-effective method, and subsequently examining its anticancer characteristics. The cytotoxicity caused by graphene oxide (GO) and T-rGO is investigated in detail. Biological results reveal a concentration-dependent toxicity of GO and T-rGO in human colon cancer cells HT-29. A decrease in cell viability alongside DNA fragmentation is observed. Flow cytometry analysis confirms the cytotoxic effects. The novelty in this work is the use of raw graphite powder, and oxidants such as KMNO4, NaNO3, and 98 percent H2SO4 to produce graphene oxide by a modified Hummers method. This study demonstrates a simple and affordable procedure for utilising thiourea to fabricate a water-soluble reduced graphene oxide, which will be useful in a variety of biomedical applications.
Low power Dadda multiplier using approximate almost full adder and Majority logic based adder compressors

An Approximate computing is widely used to have energy-efficient system design in Very Large-Scale Integration (VLSI). This approach is best suited for signal processing and multimedia applications where low power consumption is the main concern. Faster and significant results can be obtained from an approximate computing at the cost of reduced accuracy. In this work, we proposed a very novel design approaches based on various monolithic 4:2 compressors. Proposed approach is applied to have reduced stages in the partial product multiplication. Proposed Monolithic compressor had outperformed over various 4:2 compressors. Our proposed method is based on majority logic based with the use of Dadda multiplication. A new-partial product reduction format is implemented by this multiplier, which reduces the maximum output delay. This method of approach significantly reduces the utilization of number of MOSFETs compared to other multiplier such as Wallace Tree Multipliers. Simulation results are compared with conventional Dadda multiplier and ML based 4:2 compressor. Proposed approximate computing based almost full adder based majority logic based Dadda multiplier achieves reduction of 60.93% in area utilization 72.48% reduction in dynamic power reduction while processing time is also reduced by 72.98%. Dadda multiplication outperforms the other compressors.
Heterogeneity of the NIH3T3 Fibroblast Cell Line

The embryonic mouse fibroblast cell line NIH3T3 is widely used in life science research, including the study of cell cycle control and primary cilia. Fibroblasts are the most important cell type in connective tissue, as they produce components of the extracellular matrix and determine tissue architecture. However, they are very heterogeneous and consist of subtypes specific to their organ of residence, among others. The NIH3T3 cell line was derived from whole mouse embryos that developed to pre-birth and is therefore most likely composed of different fibroblast subtypes. Furthermore, prolonged proliferation may have influenced their cellular composition. A heterogeneous cell population is unsuitable for any sophisticated research project. We found that the proportion of ciliated cells in the total NIH3T3 cell population was highly variable and asked whether this was a consequence of cellular heterogeneity and what molecular signatures were associated with it. We have established sub-cell lines by clonal expansion of single cells and characterized them morphologically and molecularly. Eventually, a myofibroblast-like and a fibroblast-like cell line were generated that differ in ciliation and proliferation. These homogeneous cell lines are valuable for a more detailed study of their molecular signatures, not least to uncover further the molecular pathways that contribute to the formation of the primary cilium.
Interpreting Universal Adversarial Example Attacks on Image Classification Models

Mitigating adversarial deep learning attacks remains challenging, partly because of the ease and low cost in carrying out such attacks. Therefore, in this article, we focus on the understanding of universal adversarial example attack on image classification models. Specifically, we seek to understand the difference(s) between adversarial examples in two adversarial datasets (DAmageNet and PGD dataset) and clean examples in ImageNet learned by the classification model, and whether we can use such findings to resist adversarial example attacks. We also seek to determine if we can retrain a discriminator to discriminate whether the input image is an adversarial example, using adversarial training. We then design a number of experiments (e.g., class activation map (CAM) analysis, feature map analysis, feature maps/filters changing, adversarial training, and binary classification model) to help us determine whether the universal adversarial dataset can be successfully used to attack the classification model. This, in turn, contributes to a better understanding of adversarial defenses over pretrained classification model from an interpretation perspective. To the best of our knowledge, this work is one of the earliest works to systematically investigate the interpretation of universal adversarial example attack on image classification models, both visually and quantitatively.
The Ever-Evolving Nature of Health Literacy in Organizations: A Commentary on the 2021 JPHMP Article “Updating Health Literacy for Healthy People 2030”

H ealthy People is a data-driven policy framework that public health managers and practitioners use to guide their work (eg, collaborations, logic models). In 2021, JPHMP published the article by Santana et al, titled “Updating Health Literacy for Healthy People 2030: Defining Its Importance for a New Decade in Public Health. ” Their article informs public health managers and practitioners that, henceforth with Healthy People 2030, 2 definitions of health literacy—that is, individual and organizational—will be used to appraise how well health promotion efforts align with the framework’s objectives. Santana et al relay that organizational health literacy is defined as “the degree to which organizations equitably enable individuals to find, understand, and use information and services