Miracle-dz/UniFAR-FTUs huggingface dataset:
Transitioning to Sponge City: A Key to Water Resource Management during Floods in Assam

Flooding in the recent times is one of the society’s most significant threats from climate change and dramatic weather events. As such, integrating sponge city construction and green infrastructure have become the new paradigm for a sustainable urban storm water management policy. Sponge city initiative can be implemented in Assam too. After more than seven decades of yearly perennial floods, we know by now that the mighty Brahmaputra cannot be tamed. Nor Assam can fight climate change alone. Sponge city strategy would help Assam to work in unison with nature and sustainably manage rainwater and floodwater which could be a huge advantage not only to the environment but also to the local population as well. This paper aims to suggest certain green infrastructures and sponge city strategies which have been implemented by China and western nations in managing floods. Adopting such strategies may not be able to remove all issues completely but it might definitely work out to reduce the disastrous effect of floods. It will also help to manage water resources efficiently in the State.
D-NAT: Data-Driven Non-Ideality Aware Training Framework for Fabricated Computing-In-Memory Macros

To enable energy-efficient computation for deep neural networks (DNNs) at edge, computing-in-memory (CIM) is proposed to reduce the energy costs during intense off-chip memory access. However, CIM is prone to multiply-accumulate (MAC) errors due to non-idealities of memory crossbars and peripheral circuits, which severely degrade the accuracy of DNNs. In this work, we propose a Data-Driven Non-ideality Aware Training (D-NAT) framework to compensate for the accuracy degradation. The proposed D-NAT framework has the following contributions: 1) We measured a fabricated SRAM-based CIM macro to obtain a data-driven MAC error model (D-MAC-EM). Based on the derived D-MAC-EM, we analyze the impact of the non-idealities on DNN’s accuracy. 2) To make DNNs robust to the non-idealities of CIM macros, we incorporate the measured D-MAC-EM into DNN’s training procedure. Moreover, we propose a statistical training mechanism to better estimate the gradients of the discrete D-MAC-EM. 3) We investigate trade-offs between quantization range and quantization errors. To mitigate the quantization errors in activations, we introduce extended PACT (E-PACT) that adaptively learns the upper and lower bounds of input activations for each layer. Simulation results show that our proposed D-NAT improves the accuracy of ResNet20, VGG8, ResNet34, and VGG16 by 78.98%, 71.8%, 72.04%, and 57.85%, respectively, which reaches the ideal upper bound of the quantized model. Lastly, the D-NAT framework is validated on an FPGA platform with the fabricated SRAM-based CIM macro chip. Based on the measurement results, D-NAT successfully recovers the accuracy under non-idealities of a real SRAM-based CIM macro.
Effect of Elevated Temperature and Excess Light on Photosynthetic Efficiency, Pigments, and Proteins in the Field-Grown Sunflower during Afternoon

This study examined the photosynthetic responses of two sunflower hybrids to elevated temperatures and excess light intensity in the flowering stage by measuring the chlorophyll a fluorescence (ChlF) under morning and afternoon field conditions to determine the photosynthetic pigment contents and the relative accumulation of photosynthetic proteins. The morning environmental conditions were considered optimal, while the afternoon was characterised by elevated temperatures and excess light intensity. The minimum fluorescence intensity (F0), the electron-flux-reducing end electron acceptors at the photosystem I acceptor side per reaction centre (RE0/RC), and the D1 protein had significant, high, and positive correlations with the environmental conditions, which indicates that they were the most useful in the sunflower-stress-response research. In hybrid 7, the elevated temperatures and the excess light intensity resulted in the inactivation of the oxygen-evolving complex, which was indicated by the positive L, K, and J steps, the increase in the maximum quantum yield of PSII (TR0/ABS), the decrease in the electron transport further than the primary acceptor QA (ET0/(TR0-ET0)), the reduction in the performance index (PIABS), and the higher relative accumulation of the light-harvesting complex of the photosystem (LHCII). Hybrid 4 had smaller changes in the fluorescence curves in phases O–J and J–I, and especially in steps L, K, J, and I, and a higher PIABS, which indicates a more efficient excitation energy under the unfavourable conditions. As the tested parameters were sensitive enough to determine the significant differences between the sunflower hybrids in their photosynthetic responses to the elevated temperatures and excess light intensity in the flowering stage, they can be considered useful selection criteria. The development of more adaptable sunflower hybrids encourages sustainable sunflower production under stressful growing conditions.
On GPS Fault-Tolerance for City-Bus Tracking System Using Wireless Sensor Networks

In modern crowded cities, public transportation is one of primary ways for people to go to work, shopping, etc. Therefore, it is necessary to provide an application which estimates buses’ real-time current location, supported by Google map application which displays bus location and expected arrival time. In this work, we propose a city-bus tracking system which contains two localization methods: First, using GPS method. Second, a fault-tolerance method using Wireless Sensor Networks (WSNs), in case of GPS signal failure, e.g.; due to weather conditions or physical obstacles. In this method, wireless sensor boards are deployed in selected locations along buses routes in city, and each bus is equipped by a wireless sensor board and GPS. When GPS signal is bad, the second method is considered, where bus-sensor node sends a “Hello” packet, while bus moving, to nodes deployed over bus route. Based on the Received Signal Strength Indicator (RSSI) and number of route nodes which received the “Hello” packet, the proposed localization method is used to estimate bus location.
Palladium Phthalocyanines Varying in Substituents Position for Photodynamic Inactivation of Flavobacterium hydatis as Sensitive and Resistant Species

Antimicrobial photodynamic therapy (aPDT) has been considered as a promising methodology to fight the multidrug resistance of pathogenic bacteria. The procedure involves a photoactive compound (photosensitizer), the red or near infrared spectrum for its activation, and an oxygen environment. In general, reactive oxygen species are toxic to biomolecules which feature a mechanism of photodynamic action. The present study evaluates two clinical isolates of Gram-negative Flavobacterium hydatis (F. hydatis): a multidrug resistant (R) and a sensitive (S) strain. Both occur in farmed fish, leading to the big production losses because of the inefficacy of antibiotics. Palladium phthalocyanines (PdPcs) with methylpyridiloxy groups linked peripherally (pPdPc) or non-peripherally (nPdPc) were studied with full photodynamic inactivation for 5.0 µM nPdPc toward both F. hydatis, R and S strains (6 log), but with a half of this value (3 log) for 5.0 µM pPdPc and only for F. hydatis, S. In addition to the newly synthesized PdPcs as a “positive control” was applied a well-known highly effective zinc phthalocyanine (ZnPcMe). ZnPcMe showed optimal photocytotoxicity for inactivation of both F. hydatis R and S. The present study is encouraging for a further development of aPDT with phthalocyanines as an alternative method to antibiotic medication to keep under control the harmful pathogens in aquacultures’ farms.
Side Face Contour Extraction Algorithm for Driving Fatigue Detection

Fatigue-driving detection, which employs pattern recognition measure to discover driver’s fatigue status, is considered as a key technique to improve road safety. However, the widely used frontal face recognition systems have some problems, such as low recognition accuracy, lack of real-time response, and high algorithm complexity. As a very efficient supplement, in this paper, a side face contour extraction algorithm is proposed, which is composed of side face basic extraction, image correction and side face contour extraction. Experiment results show that proposed algorithm could easily extract nose and chin contour line from side face image effectively, which helps in detecting nodding posture caused by fatigue and lays a good foundation for fatigue state’s real-time tracking application.
Video behavior recognition algorithm based on two-stream heterogeneous convolutional neural network

In video behavior recognition, making full use of the spatio-temporal information contained in video frame is the critical point to further improve the recognition accuracy. In our study, we propose a video behavior recognition algorithm based on two-stream heterogeneous network, so that we can further extract the spatio-temporal feature information in the video frame, and finally take full advantage of the spatio-temporal features in the video sequence. In view of the characteristics of RGB and optical flow images, this paper uses DenseNet121 and Inception-V4 to structure a two-stream network, to fully extract the spatio-temporal feature information in the video. In this paper, UCF101 dataset is used to evaluate the algorithm, and the recognition accuracy is 91.7%, which verified the reliability of the proposed algorithm.
Analysis of the unsteady flow of centrifugal agricultural auto water pumps with variable curvatures.

Existing research on agricultural auto water pumps mainly concentrate on structure optimization; nonetheless, a few works examine the characteristics of the inner flow field. The current study conducted an unsteady numerical simulation calculation of agricultural auto water pumps using an impeller model with a 0.5 mm blade tip clearance (σ) and ANSYS CFX software. The unsteady flow results were analyzed, and results indicated that the unsteady characteristics of the lift undergo significant periodic changes due to the dynamic and static interference of the cut water and the impeller. This change in frequency is in accordance with the passing frequency of the blade. The pressure fluctuation varies at different monitoring points on the same volute section. The pressure fluctuation decreases first and then increases from the bottom volute to the back volute. Additional dither components were also detected at the monitoring points on the bottom volute. The pressure fluctuation is maximized at the circumferential monitoring points proximal to the water cut. The distance between the circumferential monitoring points and the water cut increases with the circumference angle, whereas the fluctuation amplitude decreases. The results of fluid dynamics provide useful references to determine the reduction in the vibration and noise of agricultural auto water pumps.
A Novel Three-Section Self-Pulsating DFB Laser with Hybrid Grating

A 1. 55μm InGaAsP-InP three-section DFB laser with hybrid grating is fabricated and self-pulsations (SP) with frequencies around 20GHz are observed. The mechanism of SP generation in this device is researched. Furthermore, the important role of the phase tuning section on the SP is investigated.
Learning Biologically Interpretable Latent Representations from Gene Expression Data

Gene expression data are typically high dimensional with low sample size. This leads to several statistical and analytical challenges that one needs to overcome in order to analyze and infer the underlying biological mechanisms of such data. To this end, several dimensionality reduction techniques have been proposed. Dimensionality reduction techniques learn a lower dimensional space (latent space), of newly constructed features and represent the data as a sum of those (latent representations). The projection of the data to the latent feature space compresses the data, retains the significant information and reduces noise. Typical dimensionality reduction techniques, such as Principal Component Analysis, derive latent representations that are uninterpretable biologically. In order to regain a degree of interpretability, other methods return sparse latent representations. Particularly, the new features are constructed as linear combinations of only a few of the molecular quantities. However, sparse latent representations are still hard to interpret biologically as they do not directly correspond to the known biological pathways or other known genesets. In this thesis, we present a novel algorithm for feature construction and dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features are constrained to directly correspond to known molecular pathways and can be interpreted as pathway activity scores. PASL is evaluated both on simulated and real data. We show that PASL retains the predictive information for disease classification on new, unseen datasets. We also show that differential activation analysis provides complementary information to standard geneset enrichment analysis. Μαθαίνοντας βιολογικά ερμηνεύσιμες κρυφές αναπαραστάσεις από δεδομένα γονιδιακών εκφράσεων
Anxiety and Depression in Metabolic-Dysfunction-Associated Fatty Liver Disease and Cardiovascular Risk

(1) Background: The relationship between anxiety and depression in metabolic-dysfunction-associated fatty liver disease (MAFLD) and cardiovascular (CV) risk remains uncertain. Therefore, we aimed to assess whether anxiety and depression are associated with increased CV risk in MAFLD. (2) Methods: We conducted a cross-sectional observational study involving 77 subjects (39 MAFLD patients, 38 controls), between January and September 2020. Hepatic steatosis was assessed using a combination of hepatic ultrasonography and SteatoTestTM. CV parameters were evaluated using echocardiography and Doppler ultrasound. Self-reported questionnaires pertaining to symptoms of anxiety and depression were used. Anxiety was evaluated using Lehrer Woolfolk Anxiety Symptom Questionnaire (LWASQ), further divided into somatic, behavioral, and cognitive factors, as well as a global score, and depression using Beck Depression Inventory (BDI). (3) Results: MAFLD patients presented significantly higher BDI scores (p-value 0.009) and LWASQ global scores (p-value 0.045) than controls. LWASQ somatic factor was significantly associated with global longitudinal strain (GLS) in linear analysis (−0.0404, p-value = 0.002), while it lost significance following multivariate analysis (−0.0166, p-value = 0.124). Although group (MAFLD vs. controls) predicted BDI, LWASQ global score, and LWASQ somatic factor in linear regression, they lost significance in multivariate analysis. Moreover, the relationship between interventricular septal wall thickness (IVSWT) and BDI, LWASQ global score, and LWASQ somatic factor was significant in linear analysis, but statistical significance disappeared after multivariate analysis. (4) Conclusions: Although MAFLD patients presented increased anxiety and depression risk in univariate analysis, this association lost significance in multivariate analysis. A significant association between GLS levels and LWASQ somatic factor, in addition to IVSWT in anxiety and depression in univariate analysis, was observed, but was lost after multivariate analysis.
DESIGN AND SIMULATION OF STAND ALONE INTEGRATED RENEWABLE ENERGY SYSTEM FOR REMOTE AREAS

The availability of energy plays an important role in the development of a country.Electricity availability is essential for economical growth and improvementin quality of life of rural and semi-urban population. In order to achieve this, rural electrification should gain importance. Rural electrification requiresgrid extensionofthe existing power systemto remote areas which is difficult and costly. Impact of burning fossil fuels on environment andrapid depletion increases the supply demand gap. Development of Integrated Renewable Energy System for isolated load is a promisingway outfor rural electrification. Integrated renewable energy systems generate power making use of renewable energy sources that are available regionally which can electrify rural households. In this paper, an attempt has been made to develop an Integrated renewable energy system for power generation using solar and wind resources.Integration of solar and wind systems has been done for supplying isolated load and the model has been implemented in MATLAB/SIMULINK and the results are summarized. KeywordsRenewable Sources, Solar System, Wind System, Integrated Renewable Energy System, Power. --------------------------------------------------------------------***----------------------------------------------------------------------
Characterising farmers' adoption factors of cleanliness levels of vegetable farming systems

‘Clean and safe’ agricultural products are an important issue among consumers, farmers and governments. Many developing countries develop their produce at various points along the ‘clean’ continuum based on four different production practices related to the use of synthetic chemicals. Organic farming is applied to technologies which do not use chemicals or synthetic fertilisers during production or processing. Safe-use and pesticide-free practices lie between organic and conventional practices, and are possible steps when converting conventional farms to organic farms. The four farming systems are also viewed as a series of ‘clean and safe’ farming systems, with conventional vegetable (CV) being the least ‘clean and safe’, safe-use vegetable (SUV) being more ‘clean and safe’, pesticide free vegetable (PFV) more so again, and organic vegetable (OV) being the most ‘clean and safe’ system. The main purpose of this paper is to investigate factors affecting the adoption of 'clean and safe' farming systems in northern Thailand. To examine the patterns of adoption based on this continuum notion regarding cleanliness level, we used an ordered logistic regression. Farm-level data on vegetable production were collected from random samples of farms using these technologies in northern Thailand. The results of the analysis of farming system adoption show that the important significant factors are membership of farmers’ groups, proportion of female family members working on the farm, location, NGO financial sources, and ownership of freehold land. Thus, in developing more ‘clean and safe’ farming systems and practices the above factors need to be considered.
New Prediction of the Number of Charging Electric Vehicles Using Transformation Matrix and Monte-Carlo Method

An Electric Vehicle (EV) is operated with the electric energy of a battery in place of conventional fossil fuels. Thus, a suitable charging infrastructure must be provided to expand the use of electric vehicles. Because the battery of an EV must be charged to operate the EV, expanding the number of EVs will have a significant influence on the power supply and demand. Therefore, to maintain the balance of power supply and demand, it is important to be able to predict the numbers of charging EVs and monitor the events that occur in the distribution system. In this paper, we predict the hourly charging rate of electric vehicles using transformation matrix, which can describe all behaviors such as resting, charging, and driving of the EVs. Simulation with transformation matrix in a specific region provides statistical results using the Monte-Carlo Method.
Research and Application of 3D Clothing Design Based on Deep Learning

In order to fully explore the multi-scale features of 3D clothing design, at the same time, so as to realize the extraction of the clothing itself and the overall characteristics of the clothing, a research and application of 3D clothing design based on deep learning is proposed. Deep learning neural network model is applied to recognize 3D human shape features, the characteristic line is obtained by the threshold judgment, feature points are obtained based on the identified feature lines, the body circumference curve is fitted, and the measurement of human body size is realized. Simulation results show that for the training of convolutional neural networks in the research of feature recognition algorithms the final feature recognition accuracy is made to reach 92.56%. The training network based on the front projection has a good recognition effect for the neck, armpit, and waist, while the side projection has a good recognition effect for the back neck, shoulder, chest, waist, and buttocks. In general, the characteristic lines of the key parts of the human body have been effectively identified. The author uses convolutional neural networks in deep learning, carrying out 3D scanning human body feature recognition, a large number of experiments have verified the effectiveness and feasibility of the algorithm.
Genetic variability of the S gene of chronic hepatitis B virus in Baghdad

A total of 240 patients with chronic hepatitis B virus were included in the study, including 124 men and 116 females ranging in age from 6 to 78 years, PCR was used to amplify the Hepatitis B viral gene segment. There were only 26 positive samples for PCR amplification of a gene HBV S with strong positive bands submitted for sequencing. The NCBI BLASTn engine showed about 98% to 99% sequences similarities between the sequenced samples and the intended reference target sequences. By comparing the observed nucleic acid sequences of these investigated samples with the retrieved nucleic acid sequences (GenBank acc. MK840532.1), the details of its sequences were highlighted, and the total length of the amplified amplicons was also determined, the alignment results of the 548 bp samples revealed the presence of ten nucleic acid variations represented by ten nucleic acid substitutions in the analyzed samples in comparison with the most similar referring reference nucleic acid sequences (GenBank acc. no. MK840532.1), Our results indicated the presence of ten nucleic acid variants observed in the investigated samples, namely 163C>T, 184T>C, 210T>C, 217C>A, 274A>G, 310T>C, 336G>A, 346G>A, 372A>T, and 405A>C.
Estimating the surface runoff from natural environment data

Purpose The purpose of this paper is to estimate quick and low-cost processes for surface runoff potential on the basis of natural environmental attributes. Design/methodology/approach An approach based on the natural environmental attributes and on the Cook’s method was used for maximal peak flows of surface runoff, as well as for assigning weights to the considered attributes. Used attributes are as follow: steepness, bedrock (lithology), soil (texture, genesis, thickness, and permeability coefficient), drainage density, and favorable features to surface storage. Findings Using natural environmental attributes from previous available studies, adapted from different scales, the authors obtain a low-cost potential surface runoff chart, which can be useful for planning, impact and hazard analysis, and decision purposes in an area without large financial resources, like small communities in developing countries. Despite the common scarcity of data in these communities, often regional basic studies of soil and bedrock are available, making this kind of analysis possible. Originality/value The highlights are quick and low-cost procedures in characterizing the natural environment for planning activities, providing the basis for further detailing, which focus on solving local problems. This approach to runoff estimation allows for the definition of the criteria, considering the potential geodynamic processes. Thus, this kind of study may be very useful for land use planning in developing countries.