Miracle-dz/UniFAR-FTUs huggingface dataset:
The MAPM (Mapping Air Pollution eMissions) method for
inferring particulate matter emissions maps at city-scale from in situ
concentration measurements: description and demonstration of
capability

Abstract. MAPM (Mapping Air Pollution eMissions) is a two-year project whose goal is to develop a method to infer particulate matter (PM) emissions maps from in situ PM concentration measurements. Central to the functionality of MAPM is an inverse model. The input of the inverse model includes a spatially-distributed prior emissions estimate and PM measurement time series from instruments distributed across the desired domain. Here we describe the construction of this inverse model, the mathematics underlying the retrieval of the resultant posterior PM emissions maps, the way in which uncertainties are traced through the MAPM processing chain, and plans for future development of the processing chain. To demonstrate the capability of the inverse model developed for MAPM, we use the PM2.5 measurements obtained during a dedicated winter field campaign in Christchurch, New Zealand, in 2019 to infer PM2.5 emissions maps on city scale. The results indicate a systematic overestimation in the prior emissions for Christchurch of at least 40–60 %, which is consistent with some of the underlying assumptions used in the composition of the bottom-up emissions map used as the prior, highlighting the uncertainties in bottom-up approaches for estimating PM2.5 emissions maps. The paper also presents the results of two sets of observing system simulation experiments (OSSEs) that explore how measurement uncertainties affect the computation of the derived emissions maps, and the extent to which using emissions maps from one day as the prior for the next day improves the ability of the inversion system to characterize the emissions sources. We find in the first case that a smaller number of high-accuracy instruments performs significantly better than a higher number of low-accuracy instruments. In the second case, the results are ultimately inconclusive, showing the need for further investigations that are beyond the scope of this study.
A Combined Sudoku and Synthetic Colour Image Techniques for Cryptographic Key Generation

The exchange of sensitive digital information has increased significantly in the last decade due to the internet’s growth and availability. More robust encryption techniques were introduced to secure the data-in-transit and data-at-rest. The strength of these encryption algorithms depends on the keys. Keys are generated using Pseudorandom Number Generators (PRNG). The key generation algorithm defines these PRNGs. Although there are many robust key generation algorithms, there is a demand for new key generation algorithms to break hackers’ experience and knowledge. This paper proposed a new key generation algorithm using value generated from sudoku matrices and synthetic color image. 8 × 8 sudoku matrices are generated randomly for each session. These sudoku matrices are solved using backtracking and recursive programming. The solved sudoku matrices are used to extract bits from pixels of the synthetic color image. The extracted bits are then combined to form a key. Further, randomness in the key is tested using the NIST (National Institute of Standards and Technology).
Mechanism of Apatite Formation Induced by Silanol Groups

The essential requirement for an artificial material to bond to bone is formation of bonelike apatite on its surface in body environment. The formation of the apatite has been proposed to be induced by silanol groups on the surface of the material. In the present study, the mechanism of apatite formation induced by the silanol groups was investigated in a biomimetic process. A collodion film supported by a titanium grid which faced to a CaO-SiO2-based glass was soaked for various periods in an acellular simulated body fluid with ion concentrations nearly equal to those of the human blood plasma. The film was observed by a transmission electron microscope equipped with energy dispersive X-ray spectrometer. Silicate ions rich in silanol groups, which were released from the glass, were attached onto the collodion film. They first combined only with calcium ions in the fluid to form a calcium silicate. Later, the calcium ions in the calcium silicate combined with phosphate ions in the fluid to form an amorphous calcium phosphate with a low Ca/P atomic ratio. The amorphous calcium phosphate, increasing its Ca/P atomic ratio, eventually transformed into a crystalline apatite. This indicates that the silanol groups induce the apatite formation not directly but through the formation of calcium silicate and amorphous calcium phosphate with a low Ca/P atomic ratio. The formation of calcium silicate at the initial stage might be attributed to interaction between negatively charged silanol groups and positively charged calcium ions.
A fast level set image segmentation driven by a new region descriptor

In order to deal with the intensities inhomogeneities and to overcome the effect of different types of noise in the image segmentation process, we have formulated a new level set function to implement a fast and robust active contour model. The proposed model was formulated by combining the SBGFRLS model and Legendre polynomials. With the aim of ensuring the segmentation accuracy and for dealing in the best way with the presence of noises and inhomogenous distribution of intensity, we deﬁne a local region descriptor for image intensities based on the Legendre polynomial. Instead of using the average intensity of the region, we regularised our level set function using a Gaussian ﬁltering process. Experimental results on challenging images demonstrate the efﬁciency, robustness and the low cost and computational time of our model against the well-known active contour models.
Research on photovoltaic ultra short-term power prediction algorithm based on attention and LSTM

Based on the actual monitoring historical data of photovoltaic power station, combined with the actual engineering demand of photovoltaic microgrid on the user side, the lightweight algorithm of ultra short-term photovoltaic power prediction is studied, which is conducive to improving the operation efficiency and economy of power system. In this paper, the ultra short-term power prediction of photovoltaic power station is carried out by combining the LSTM algorithm with attention mechanism. Firstly, Pearson correlation coefficient method is used to reduce the dimension of the data set. The data with low correlation between weather variables and power to be predicted and historical power are eliminated, and the algorithm model structure is simplified. Then, the attention mechanism is combined with LSTM network to improve the effectiveness of the prediction model for long time series input. The proposed model is trained and compared with the data of a photovoltaic power station. The results show that the model achieves good experimental results in different weather conditions, and can effectively improve the prediction accuracy.
Association Between Household Air Pollution and Infant and Child Mortality in Myanmar: Evidence From The First Demographic and Health Survey

Background: Household air pollution (HAP) from solid fuel use (SFU) for cooking has been considered a public health threat, particularly for women and children in low and middle-income countries (LMICs), with limited evidence. This study was undertaken to investigate the effects of HAP on neonatal, infant, and under-five child mortality in Myanmar. Methods: This cross-sectional study employed data from the Myanmar Demographic and Health Survey (MDHS), the first nationally representative survey conducted in 2016. Data were collected from MDHS based on stratified two-stage cluster sampling design applied in urban and rural areas. The sample consists of 3249 under-five children in the household with a 98% response rate. Exposure measures were HAP (coal and biomass) and level of exposure to HAP (no exposure, moderate and high exposure). The main outcomes were neonatal, infant, and under-five child mortality reported by mothers presented in rates and risk ratios with 95% confidence intervals, accounting for survey weight and cluster variation. Results: The prevalence of SFU was 79.0%. The neonatal, infant and under-five child mortality rates were 26, 45, and 49 per 1,000 live births, respectively. The risks of infant (aRR 2.02; 95% CI: 1.01-4.05) and under-five mortality (aRR 2.16; 95% CI: 1.07-4.36) mortality were higher among children from households with SFU compared to children from households using clean fuel. When applying an augmented measure of exposure to HAP by incorporating SFU and the kitchen's location, the likelihoods of infant and under-five mortality were even higher among moderate and highly exposed children than unexposed children with similar trends. Neonatal mortality was not associated with either HAP exposure or levels of exposure to HAP.Conclusion: Infants and under-five children are at higher risk of mortality from exposure to HAP. Increasing access to cookstoves and clean fuels is imperative to reduce the risk of infant and under-five child mortality in LMICs, including Myanmar.
Mycotoxines : Effets sur la Santé Humaine

Les mycotoxines sont des substances toxiques secretees par des champignons microscopiques ou moisissures. Elles sont connues surtout en raison des intoxications qu’elles provoquent chez les hommes suite a la consommation d’aliments contamines et des problemes economiques qu’elles entrainent pour l’industrie agroalimentaire. Certaines mycotoxines sont cancerogenes ou sont suspectees de l’etre. En milieu professionnel, les salaries peuvent se trouver exposes aux mycotoxines par voie respiratoire et/ou voie cutanee. L’objectif de cet article est de faire le point sur les origines, les proprietes, les substrats, la toxicite et les mecanismes d’action des principales mycotoxines. Les risques inherents aux mycotoxines sur la sante humaine seront abordes.
Object detection on dental x-ray images using deep learning method

Radiological examination has an important role in determining the diagnosis of dental problems and making decisions about the right type of treatment according to the case indications. Dental x-ray is a medical procedure for taking pictures of the inside of the mouth using radiation fluid, where the results are used diagnostically to help the dentist see the entire structure of the jaw bone and teeth, and dental problems that cannot be seen directly. Dental radiographic interpretation, which is generally performed by dentists, is a time-consuming and error-prone process due to high variations in tooth structure, low experience levels, and fatigue factors experienced by dentists. The workload of a dentist and the occurrence of misdiagnosis can be reduced by the existence of a system that can automatically interpret the x-ray results. To overcome these problems, a model will be developed to be able to detect objects in the dental panoramic x-ray images using Mask R-CNN, one of the methods in Deep Learning. Deep Learning is an artificial intelligence function that modelled the workings of human brain in processing data and creating patterns for use in decision making. With the detection of objects in panoramic x-ray image automatically, it is expected to save time, improve the quality of dental care, and also the quality of diagnosis made by dentists.
The antimicrobial activity of two phenolic acids against foodborne Escherichia coli and Listeria monocytogenes and their effectiveness in a meat system

Ready-to-eat meats are susceptible to pathogenic contamination during their production, distribution, and sale. This study evaluated the antimicrobial effects of two phenolic acids (caffeic and ferulic acids) against foodborne pathogens in cold-cut meat at low-temperature conditions. The individual and combined antibacterial activities of caffeic and ferulic acids against Escherichia coli O157:H7 ATCC 43888 and Listeria monocytogenes ATCC 7644 were determined by diffusion disk assay in broth media and cold-cut meat. Broth media and meat samples already inoculated with E. coli and L. monocytogenes were treated with caffeic acid, ferulic acid, and their combination at the concentrations of 150 ppm and 200 ppm and stored at 4°C. Microbial growths were monitored at 0, 24, 48, and 72 h. Caffeic acid at 200 ppm exhibited a zone of inhibition of 12.33 mm on E. coli, and ferulic acid revealed a zone of inhibition of 11.00 mm on L. monocytogenes. The combination of caffeic ferulic acid at a concentration of 200 ppm was most effective against E. coli, demonstrating a synergistic effect over 72 h at 4°C in both broth media and meat. For meat samples, the combination of caffeic acid and ferulic acid exhibited a log reduction of 3.63 CFU/g at 150 ppm and 2.51 CFU/g at 200 ppm against E. coli O157:H7 at the end of cold storage. Caffeic acid alone exhibited an overall log reduction of 2.48 CFU/g at 150 ppm and 2.75 CFU/g at 200 ppm against L. mono-cytogenes. These results indicate the ability of caffeic and ferulic acids, individually and in combination, to reduce pathogenic contamination and improve safety of cold-cut meats.
Resistivity-induced coupling between voltage distribution and vibrations in dielectric elastomers

Dielectric elastomers are soft actuators made of a thin layer of elastomer sandwiched between compliant electrodes. Because of the resistivity of the electrodes, the voltage is not uniform on the actuator at high frequencies. We present experimental evidence that the voltage spatial distribution is coupled to the membrane vibrations, as well as a model based on Maxwell’s equations in a moving frame to explain the coupling. The model is validated experimentally, and is used to explain the physics of the observed coupling. As a result, information on the membrane deformation can be inferred from voltage measurements, which opens self-sensing possibilities at high frequencies.
Dual‐band‐independent tunable multiple‐input–multiple‐output antenna for 4G/5G new radio access network applications

Slot ‐ based dual ‐ band, low ‐ profile frequency reconfigurable (FR) multiple ‐ input–multiple ‐ output (MIMO) antenna design is presented here. Each element is composed of concentric annular slots that are loaded reactively using varactor diodes. Accurate placement of diodes and optimal biasing circuit configuration result in complete control on each of the tuned bands. The unique feature of the proposed work is the independent as well as concurrent tuning of each band over a wide frequency range from 1.7 to 3.8 GHz. Moreover, the competitive advantage of independent tuning and its narrow ‐ band (NB) operation may result in better power management for RF front ‐ end devices. The proposed antenna is fabricated using an RO ‐ 4350 substrate with board dimensions 60 � 120 � 0.76 mm 3 . The proposed dual ‐ band compact antenna design is suitable to be utilized in the current fourth generation (4G) wireless standards as well as for upcoming fifth generation (5G) new radio access networks (RANs) with sub 6 ‐
The structural characteristics and electrical of MoS2 and MoS2/graphene: a first-principles study

MoS2/graphene, as a promising composite candidate of lithium anode, is widely used because of high capacity and high capability of rate. The hetero interface of MoS2/graphene plays a critical role on its electrochemical performance. In this paper, first principles investigations are conducted to study the details of the DOS, band structure, electron density of MoS2 / graphene hetero junction by first principles calculations, which prove that this system is promising to be used in lithium batteries, gas sensors and other domains, for example, like super capacitors and catalysis.
Bioactivities of Colocasia affinis Schott and Profiling of its Bioactive Polyphenols by HPLC-DAD

Traditional medicines including Ayurveda, Unani have been used as therapeutics in the Indian subcontinent since 5000 BC. Because of the abundance of secondary metabolites, medicinal plants are becoming priceless natural resources for better drug development. Till date almost no ethnopharmacological evidences were available on Colocasia genus except Colocasia esculenta. As a part of our continuing assessment of evidence based use of traditional medicine, we have identified bioactive polyphenols in the ethanolic leaves extract of Colocasia affinis Schott using HPLC-DAD method and evaluated its anti-allergy, anti-inflammatory and anti-diabetic potentials for the first time. Chromatographic investigation showed the presence of p-coumaric acid, trans-ferulic acid, rosmarinic acid, myricetin and kaempferol in the leaves extract. Pharmacological profile exploration demonstrated its anti-inflammatory activity at a dose of 250- and 500-mg/kg in xylene-induced ear edema in mice. The extract significantly reduced the allergy like symptoms such as sneezing, scratching, rhinorrhea, redness and swelling in Tolune diisocyanate (TDI) sensitized allergy model mice at 250- and 500-mg/kg doses. Besides, the extract also exhibited reduction in the level of neutrophils, lymphocytes and eosinophils in a dose dependent manner in the experimental animal. The extract also confirmed reduction of blood glucose levels in the experimental mice at a dose of 250- and 500-mg/kg. Therefore, it is evident that C. affinis demonstrated possesses medicinal values through its anti-inflammatory, anti-allergic and anti-hyperglycaemic potentials. Bangladesh Pharmaceutical Journal 24(1): 1-10, 2021
Deforestation reshapes land-surface energy-flux partitioning

Land-use and land-cover change significantly modify local land-surface characteristics and water/energy exchanges, which can lead to atmospheric circulation and regional climate changes. In particular, deforestation accounts for a large portion of global land-use changes, which transforms forests into other land cover types, such as croplands and grazing lands. Many previous efforts have focused on observing and modeling land–atmosphere–water/energy fluxes to investigate land–atmosphere coupling induced by deforestation. However, interpreting land–atmosphere–water/energy-flux responses to deforestation is often complicated by the concurrent impacts from shifts in land-surface properties versus background atmospheric forcings. In this study, we used 29 paired FLUXNET sites, to improve understanding of how deforested land surfaces drive changes in surface-energy-flux partitioning. Each paired sites included an intact forested and non-forested site that had similar background climate. We employed transfer entropy, a method based on information theory, to diagnose directional controls between coupling variables, and identify nonlinear cause–effect relationships. Transfer entropy is a powerful tool to detective causal relationships in nonlinear and asynchronous systems. The paired eddy covariance flux measurements showed consistent and strong information flows from vegetation activity (gross primary productivity (GPP)) and physical climate (e.g. shortwave radiation, air temperature) to evaporative fraction (EF) over both non-forested and forested land surfaces. More importantly, the information transfers from radiation, precipitation, and GPP to EF were significantly reduced at non-forested sites, compared to forested sites. We then applied these observationally constrained metrics as benchmarks to evaluate the Energy Exascale Earth System Model (E3SM) land model (ELM). ELM predicted vegetation controls on EF relatively well, but underpredicted climate factors on EF, indicating model deficiencies in describing the relationships between atmospheric state and surface fluxes. Moreover, changes in controls on surface energy flux partitioning due to deforestation were not detected in the model. We highlight the need for benchmarking model simulated surface-energy fluxes and the corresponding causal relationships against those of observations, to improve our understanding of model predictability on how deforestation reshapes land surface energy fluxes.
Convolutional neural network for smoke and fire semantic segmentation

In recent decades, global warming has contributed to an increase in the number and inten-sity of wildﬁres destroying millions hectares of forest areas and causing many casualties each year. Firemen must therefore have the most effective means to prevent any wildﬁre from breaking out and to ﬁght the blaze before being unable to contain and extinguish it. This article will present a new network architecture based on Convolutional Neural Network to detect and locate smoke and ﬁre. This network generates ﬁre and smoke masks in an RGB image by segmentation. The purpose of this work is to help ﬁremen in assessing the extent of ﬁre or monitor an incipient ﬁre in real time with a camera embedded in a vehicle. To train this network, a database with the corresponding images and masks has been created. Such a database will allow to compare the performances of different networks. A comparison of this network with the best segmentation networks such as U-Net and Yuan networks has highlighted its efﬁciency in terms of location accuracy, reduction of false positive classiﬁcations such as clouds or haze. This architecture is also efﬁcient in real time.
On Moving Target Techniques for Network Defense Security

The traditional technologies, tools and procedures of any network cannot be protected from attackers due to the unchanged services and configurations of the networks. To get rid of the asymmetrical feature, Moving Target Defense technique constantly changes the platform conformation which reduces success ratio of the cyberattack. Users are faced with realness with the increase of continual, progressive, and smart attacks. However, the defenders often follow the attackers in taking suitable action to frustrate expected attackers. The moving target defense idea appeared as a preemptive protect mechanism aimed at preventing attacks. This paper conducts a comprehensive study to cover the following aspects of moving target defense, characteristics of target attacks and its limitation, classifications of defense types, major methodologies, promising defense solutions, assessment methods and applications of defense. Finally, we conclude the study and the future concern proposals. The purpose of the study is to give general directions of research regarding critical features of defense techniques to scholars seeking to improve proactive and adaptive moving target defense mechanisms.
Study on The Effect of Humic Acid on The Migration Distance of Wetting Front During Water Infiltration

Humic acid is widely used at this stage, and studying the influence of humic acid on the transportation distance of wet front in the process of soil water infiltration is helpful to agricultural production. Orthogonal test table L16 (45) was used to design the test plan. Three test factors, humic acid, soil bulk density, and initial soil moisture content, were selected, and four levels were selected for each factor for experimental research. The test results show that all three factors have an impact on the distance of the wet front migration, but humic acid has the largest and most obvious impact.