Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation and Identification of a Metabolic Intermediate in the Selective Dechloroethylation of One of the Four Stereoisomers of 4-Methylcyclophosphamide1

The in vitro cytotoxicity in bioassay or cell culture assays of (-)-c/s-4-methylcyclophosphamide when me tabolized by microsomes from the livers of male Wistar rats was significantly less than that of metabolized (+)c/s-, (+)-frans-, and (-)-rrans-4-methylcyclophosphamide. However, metabolism of the individual Stereoisomers by liver microsomes from female BALB/c mice yielded metabolite mixtures of approximately equal cytotc 'Â¡city. The extent of metabolism by rat liver microsomes f the four Stereoisomers was similar when assessed by sub strate disappearance, but the yield of alkylating metabo lites (expressed as phosphoramide mustard equivalents) was comparable in the case of three of the isomers but substantially less for the (-)-c/s-isomer. A new metabolite was isolated following the incubation of (-)-cis-4-methylcyclophosphamide with rat liver micro somes. It was identified by its breakdown products and its electron impact and chemical ionization mass spectra, especially by the presence in the latter of a pseudomolecular ion. Its structure was 2-[(2-chloroethyl)(2-chloro-1hydroxyethyl)amino]tetrahydro-4-methyl-2H-1,3,2-oxazaphosphorine 2-oxide, an intermediate in dechloroethylation. Species variation (mouse, rat, and rabbit) in the forma tion of this metabolite of low cytotoxicity (-1% of that of 4hydroxy-4-methylcyclophosphamide) was noted and ac corded well with the observed species differences in cytotoxicity assays.
The Nuclear Factor NF-kB Pathway in Inflammation

The nuclear factor NF-kB pathway has long been considered a prototypical proinflammatory signaling pathway, largely based on the role of NF-kB in the expression of proinflammatory genes including cytokines, chemokines, and adhesion molecules. In this article, we describe how genetic evidence in mice has revealed complex roles for the NF-kB in inflammation that suggest both proand anti-inflammatory roles for this pathway. NF-kB has long been considered the “holy grail” as a target for new anti-inflammatory drugs; however, these recent studies suggest this pathway may prove a difficult target in the treatment of chronic disease. In this article, we discuss the role of NF-kB in inflammation in light of these recent studies.
Unlashing the next wave of business models in the internet of things era: A systematic literature review and new perspectives for a research agenda

Pervasive digitization of products and services open additional avenues for the next wave of business model opportunities. Most of firms are aware of the monetization potentials that the Internet of Things (IoT) has to offer, however, they still struggle to create a compelling value propositions. Despite the attention of both research and practice onto business models and the IoT, only few concepts and research endeavors regarding their intersections exist. This paper tends to unleash the specificity of the business models within the IoT technologies, and motivate new, ecosystem, perspective for upcoming research. Following a rigorous methodology for a comprehensive and systematic literature review, we develop five literature clusters related to the Internet of Things-driven business model research, evaluate and analyze the papers within clusters, and finally identify the gaps and propose directions for future research.
Towards Greener, Smarter and Healthier Office Building Management via Cognitive Digital Twin

In this thesis, we propose an AI-driven digital twin solution to empower and drive “greener, smarter and healthier” buildings in the tropical environment (e.g., Singapore). Our solution aims to leverage the existing off-the-shelf hardware (e.g., sensors, wearables, etc.) and synergistically combine an emerging digital twin paradigm with the AIoT technologies, for digitalizing and optimizing the HVAC control strategy via a deep learning-based approach. We plan to optimize the HVAC system of buildings from the following three perspectives, energy efficiency, occupant comfort and behaviour study. The first challenge is the implementation of the HVAC control policy because the changes of the thermal states in a building environment are influenced by various factors. The relationships among these influencing factors are hard to model and are always different in different building environments. To address this challenge, we propose a deep reinforcement learning based framework, DeepComfort, for thermal comfort control in buildings. We validate the performance of DeepComfort in simulation environment and the experimental results show that our approaches can improve the performance of thermal comfort prediction by 14.5% and reduce the energy consumption of HVAC by 4.31% while improving the occupants’ thermal comfort by 13.6%. The second challenge we meet with is the modeling of human behaviour. We want to derive a novel human behaviour influence mechanism to motivate occupants’ behavioural changes to form sustainable energy-saving habits. Hence, we conduct national surveys, human behavioural experiments, and AI-based deep analytics to build the online personalized behaviour influence models for energy-saving behaviour studies. The national survey helps us better understand people’s orientations and perceptions of energy-saving behaviour, and we get insights in both social and psychological ways from the survey. We upgrade the HVAC system of the experiment lab, and design the new experiment v procedure for data collection and building influence model. We also design the problem formulation of our behaviour influence model. However, due to the explosion of COVID19, our original plan was delayed because large gatherings are strictly not allowed. As a result, the experiment starts in December 2020 and it is not finished yet. In the mean time, we extend our research of smart building in the area of health. We propose a novel method for respiratory symptoms detection by using the built-in sensors of smart device, and we verify the method getting a quite accurate result on a small scale dataset we collected ourselves in office environment.
A TinyML Approach to Human Activity Recognition

Human Activity Recognition has been a favorite topic for the scholars not only because of its wide scale acceptance in the industry but areas which may help in medical and in our normal household works as well. Since to make this technology available to the last person standing in the queue it is important that models compiled and trained in this field are not just high performing but optimized as such with incurs the least overhead. And thus bringing TinyML into the picture which has specialty in the field of optimizing the model w.r. t. the size of the model, energy consumption, network bandwidth usage etc. Thus this work includes using optimizing techniques such as pruning and quantization on the pre-proposed models and analyze the changes it causes in such models w.r. t accuracy and size. Our work is able infer that by using both Pruning and Quantization techniques on a human activity recognition model we can compress a model up to 10 time without hampering severe diversion to the accuracy of the model. We have taken three models and UCI-HAR dataset and compare the outcomes of the experiment.
Crowd behavior anomaly detection using correlation of optical flow magnitude

Nowadays, crowd monitoring is a contentious issue. Because of the increasing population and diversity of human activities, crowd scenarios in the real world are becoming more common, demanding the need for an automotive anomaly detection system. Crowd behavior is influenced by the thoughts and attitudes of others around them. An unexpected event can turn a peaceful crowd into a riot. A mechanism based on optical flow must be implemented to compensate for all of these factors. The amount of motion present in two successive frames is estimated using optical flow. It includes information on velocity in the x & y plane, along with magnitude and line of action. By means of “anomalous event” in this paper is quick and sudden dispersal of the crowd. For detecting an event the magnitude of two successive frames should be taken into account followed by estimating a correlation. We expect a high correlation, slight motion, and low rate of change in velocities at non-anomalous events, but as soon as an anomalous event occurs, the correlation begins to decrease with a significant change in velocity and large motion vectors. The methodology was tested on a dataset from the University of Minnesota that included 11 movies from three different circumstances. Almost all anomalous occurrences in videos were successfully detected using this method.
New Polarimetric Data for the Galilean Satellites: Europa Observations and Modeling

This paper is dedicated to a long-standing problem of the shape of the negative branch of polarization (NBP) for Jupiter's moon Europa, determination of which is crucial for the characterization of the icy regolith on this satellite and similar objects, as well as for further progress in understanding light scattering by particulate surfaces. To establish the shape of Europa's NBP, in 2018–2021 we accomplished high-precision disk-integrated polarimetry of Europa in the UBVR I bands using the identical two-channel photoelectric polarimeters mounted on the 2.6 m Shajn reflector of the Crimean Astrophysical Observatory and the 2 m telescope of the Peak Terskol Observatory. We found that the polarization dependence on the phase angle in each filter is an asymmetric curve with a sharp polarization minimum Pmin≈−0.3% at phase angle αmin≤0. °4 , after which the polarization degree gradually increases to positive values, passing the inversion angle at α inv ≈ 6° − 7°. Within the error limits, the parameters P min, αmin , and α inv of the NPB are independent of the wavelength in the visible spectrum. The polarization curve clearly demonstrates the so-called polarization opposition effect (POE). Our analysis of the previous and new polarimetric observations of Europa allows us to conclude that the POE is caused by coherent backscattering of sunlight on microscopic icy grains covering Europa’s surface. Computer modeling with the numerical radiative transfer coherent backscattering method demonstrates that the best fit to the polarimetric observations and geometric albedo of Europa is provided by a regolith layer of elementary single-scattering albedo ∼0.985 and extinction mean free path length 2π l/λ eff ≈ 150, λ eff representing the effective wavelength in the UBVR I spectral bands.
A Multiwavelength Study of the Sgr B Region: Contiguous Cloud–Cloud Collisions Triggering Widespread Star Formation Events?

The Sgr B region, including Sgr B1 and Sgr B2, is one of the most active star-forming regions in the Galaxy. Hasegawa et al. originally proposed that Sgr B2 was formed by a cloud–cloud collision (CCC) between two clouds with velocities of ∼45 km s−1 and ∼75 km s−1. However, some recent observational studies conflict with this scenario. We have reanalyzed this region, by using recent, fully sampled, dense-gas data and by employing a recently developed CCC identification methodology, with which we have successfully identified more than 50 CCCs and compared them at various wavelengths. We found two velocity components that are widely spread across this region and that show clear signatures of a CCC, each with a mass of ∼106 M ⊙. Based on these observational results, we suggest an alternative scenario, in which contiguous collisions between two velocity features with a relative velocity of ∼20 km s−1 created both Sgr B1 and Sgr B2. The physical parameters, such as the column density and the relative velocity of the colliding clouds, satisfy a relation that has been found to apply to the most massive Galactic CCCs, meaning that the triggering of high-mass star formation in the Galaxy and starbursts in external galaxies can be understood as being due to the same physical CCC process.
The comparison of adjoint-based grid adaptation and feature-based grid adaptation method

Grid adaption is a popular method to enhance the resolution of flow field and the precision of numerical simulation, which automatically optimizes the grid distribution instead of manual complicated work. There exist usually two grid adaptation methods, the feature based grid adaption and adjoint based grid adaption, the former focuses on shocks, vortexes and other features of flow field, and the latter focuses on lift, drag and other aerodynamic characteristics. The comparison of adjoint based grid adaption and feature based grid adaption method is investigated in this paper. Numerical simulations show that both feature adaption and adjoint adaption could improve the resolution of flow field and the precision of numerical simulation such as lift and drag. As for the flow features, the feature adaptation could capture the obvious shock waves and vortexes in the flow field, the adjoint adaptation, however, only captures the flow features that are contributory to the accuracy of aerodynamic characteristics. As for the aerodynamic characteristics, some shock waves and vortexes have little influences to the forces, so the feature adaptation is not efficient as adjoint adaptation, which could greatly improve the accuracy of aerodynamic characteristics.
RETRACTED: Design and Development of Person Re-Identification system based spatial features

Primary intention of this research is to design and develop an effectual approach for re-identifying the persons. The proposed approach involves four stages named human identification, object tracking, feature extraction, and matching. The four stages will be implemented by matching the probe videos and gallery videos. Initially, the videos will be subjected to the human identification phase. First stage will be the identification of human depending on the spatial features. It is important to consider that person re-identification will be carried out using the video surveillance data which can be probe data or gallery data. For human identification, the spatial features will be extracted and subjected to gait based Bayesian network for identifying the humans. Then, the object tracking phase will be carried out for tracking the objects from the videos. Here, the Neighborhood Search Algorithm will be used for tracking the objects. Then, the tracked objects will be given to the feature extraction process in which the features like mean, variance, speed, and motion deviations will be extracted. Finally, in matching phase, the matching will be performed between the probe videos and gallery videos and the person re-identification will be done using the extracted features based on the Bayesian probability-based matching. Finally, the matched outputs will be utilized for re-identifying the persons. The implementation of the proposed approach will be done using the Grid and Caviar Data sets. The performance of the proposed technique will be evaluated using three metrics, such as accuracy, sensitivity and specificity and will be compared with that of existing methods.
Overload Response Analysis of UAV in Severe Weather

Turbulence will occur when the aircraft passes through the gust area, which will affect the handling quality and ride quality of the aircraft, increase the bending moment of the wing root and shorten the fatigue life of the structure. At present, in engineering calculation, when the gust load of UAV is considering, the UAV is usually regarded as a rigid body according to the general specification, and the overload response is obtained by solving the dynamic equations of rigid body. The flexibility of UAV is not considered, and the more realistic harsh wind field environment is not considered, so there is a certain deviation between the results and the actual values. Therefore, the gust load was studied considering the aircraft elasticity and the more realistic harsh wind field environment, and compares the calculation results with that considering the standard wind field, and makes a full assessment of the gust overload of UAV. It provides the basis for the design of aircraft structure strength and flight safety, and has important engineering significance.
Numerical Simulation of Robustness to Large Displacement of Optical Flow Based on Deep Learning

Traditional optical flow algorithm based on prior is sensitive to large displacement, in order to solve the problems, one optical flow algorithm based on deep learning is introduced to measure the global velocity of fluorescent oil film. The test results of numerical simulation show that the Average Endpoint Error (AEE) of HS, LK and Flownet2 are 2.4604 pixels, 2.3898 pixels and 0.9916 pixels in terms of warping 0.1s respectively, and the error of FlowNet2 is always the lowest under the conditions of large displacement. Therefore, the algorithm is more robust to large displacement than the others that is currently being used, which has certain exploration significance and engineering reference value.
Human joints estimation system of rescue robot with occlusion

The human body is often in a partially visible state during rescue at the disaster site. At the same time, when the human body is grasped, the camera located on the actuator at the end of the robotic arm cannot fully bring the human body into the field of view. The existing human pose estimation algorithms do not perform well in this situation, and a joint detection method that can estimate the complete human pose through partial human body information and can perform data refresh in real time is required. We use the occlusion part classification module to extract the weight vector, re-weight it and the joint features extracted from AlphaPose, and add human body geometric constraints when generating 3D human body joints to obtain more accurate 3D human body joint positions. Finally, the robot is successfully used to complete the human grasping experiment, which verifies the effectiveness of the algorithm.
A meta-analytical review of intervention experiments to reduce food waste

To reduce food waste, many behavioural intervention experiments have been conducted worldwide, but their effectiveness remains unclear. To assess their impacts, we present a meta-analysis based on 58 studies, selected after screening 1143 papers, which were conducted between 2011 and 2021 covering 26 533 participants. We confirm that behavioural interventions have a moderate effect ( z = 0.22) on food waste reduction, with education programs having the most significant impact and informational feedback having the least. We also show that interventions in elementary and middle school settings marginally improve the overall effect size (P < 0.1), and controlled experiments exhibit a higher effect size compared to pre-post experiments in education interventions (P < 0.05). Finally, we present a roadmap to guide future research in the next decade to further improve our understanding on the effects of behavioural interventions to reduce food waste.
Deep learning for circular dichroism of nanohole arrays

Chiral metasurfaces with nanohole structures have a strong circular dichroism (CD) response and are easy to prepare. Therefore, they are widely used in many fields, such as biological monitoring and analytical chemistry. In this work, a deep learning (DL) framework based on the convolutional neural network (CNN) is proposed to predict the CD response of chiral metasurfaces. A dataset containing many data values is used to predict CD values, which are found to be highly consistent with those obtained from COMSOL Multiphysics simulation. Results show that the proposed CNN-based DL model is about a thousand of times faster than conventional finite element methods. It can accurately map chiral metasurfaces and predict their optical response with negligible loss functions. The insights gained from this research may be helpful in the study of complex optical chirality and the design of highly sensitive sensing systems in DL networks.
SEROLOGICAL AND BIOLOGICAL DETECTION OF TOMATO MOSAIC VIRUS IN SOIL AND SEEDS OF TOMATO PLANTS IN DUHOK PROVINCE *

Tomato mosaic virus (ToMV) is one of the most important viruses on tomato; contaminated soil may be the main source and reservoir of transmission. Diseased plants were collected from Kamona and Xanike villages, Duhok province. DAS-ELISA technique its detection in the soil and contaminated seeds, and clarify the virus transmission from contaminated seeds to seedlings. Using indicator plants (Biological detection) was to identify virus from contaminated soil. The result showed that six samples out of 14 soil samples gave a positive reaction with 42.8% is transmission ratio from soil. Symptoms on indicator plants such as: mosaic, malformation, vein clearing on C. qinoa ; the stunted plants with localized necrotic lesions and malformation on pepper plants. DAS-ELISA from tomato seeds displayed in 11 samples out of 18 samples revealed strong reactions and seeds transmission proportion were 61%. Seedlings were germinated from 60 infected tomato seeds under plastic house condition but only 4 transplants exhibited ToMV symptoms of mosaic, and the percentage of transmission was 7.8%.
Evaluating satellite estimates of particulate backscatter in the Evaluating satellite estimates of particulate backscatter in the global open ocean using autonomous profiling floats global open ocean using autonomous profiling floats

Coral reefs are the most diverse habitats in the marine realm. Their productivity, structural complexity, and biodiversity critically depend on ecosystem services provided by corals that are threatened because of climate change effects—in particular, ocean warming and acidifica-tion. The coral holobiont is composed of the coral animal host, endosymbiotic dinoflagellates,
Assessment and Comparison of Commonly Used Eutrophication Indexes Yaygın olarak Kullanılan Ötrofikasyon Indekslerinin Değerlendirilmesi ve Karşılaştırılması

Eutrophication is a phenomenon that causes the degradation of water quality which results in negative impacts on living and non-living environment of any water body. Therefore, monitoring, evaluation and classification of water quality are the utmost important issue for policy makers and regional institutions/organizations in order to assess the quality and sustainable use of water bodies and to take appropriate measures. Regional conventions such as the Helsinki Convention, Oslo-Paris Convention, Barcelona Convention – Programme for the Assessment and Control of Marine Pollution in the Mediterranean, Strategic Action Programme and Integrated Coastal Zone Management as well as legislative instruments such as the Water Framework Directive, Marine Strategy Framework Directive, Urban Wastewater Treatment Directive and Nitrates Directive in Europe; the Clean Water Act, Water Quality Act, National Environmental Policy and Coastal Zone Management Act issued by the United States Environmental Protection Agency addresses the importance of monitoring for eutrophication. The eutrophication indexes have been developed in line with regional requirements using specific data sets of state variables and parameters, representative of a location. In this study, we reviewed commonly used eutrophication indexes within the areas applied, parameters, methods and classification scales and presented the trophic status equivalences between the indexes. This study aimed to provide a comprehensive examination of eutrophication indexes to researchers in trophic status assessment of any water body.