Nutech functional score: A novel scoring system to assess spinal cord injury patients

AIM To develop a new scoring system, nutech functional scores(NFS) for assessing the patients with spinal cord injury(SCI).METHODS The conventional scale, American Spinal Injury Association's(ASIA) impairment scale is a measure which precisely describes the severity of the SCI.However, it has various limitations which lead to incomplete assessment of SCI patients.We have developed a 63 point scoring system, i.e., NFS for patients suffering with SCI.A list of symptoms either common or rare that were found to be associated with SCI was recorded for each patient.On the basis of these lists, we have developed NFS.RESULTS These lists served as a base to prepare NFS, a 63 point positional(each symptom is sub-graded and get points based on position) and directional(moves in direction BAD → GOOD) scoring system.For non-progressive diseases, 1, 2, 3, 4, 5 denote worst, bad, moderate, good and best(normal), respectively.NFS for SCI has been divided into different groups based on the affected part of the body being assessed, i.e., motor assessment(shoulders, elbow, wrist, fingers-grasp, fingers-release, hip, knee, ankle and toe), sensory assessment, autonomic assessment, bed sore assessment and general assessment.As probability based studies required a range of(-1, 1) or at least the range of(0, 1) to be useful for real world analysis, the grades were converted to respective numeric values.CONCLUSION NFS can be considered as a unique tool to assess the improvement in patients with SCI as it overcomes the limitations of ASIA impairment scale.

A Scoring System to Assess Patients with Diabetes: Nutech Functional Score

Background: Diabetes mellitus (DM) is a metabolic disorder characterized by hyperglycemia. The symptoms of hyperglycemia include polyuria, polydypsia, polyphagia, blurred vision and weight loss. Various diagnostic tests are used for the diagnosis of DM in patients, but the findings of these tests cannot be assumed to be completely valid. This study aimed at developing a novel scoring system to assess the patients suffering from DM. Method: We assessed the patients based on various diagnostic tests available for DM and prepared a single list of these tests. The tests were categorized and graded based on the World Health Organization (WHO) criteria. Further, we coverted the grades into numeric values for easy use. Results: NFS for diabetes is an 11-point scoring system that assesses the patient’s condition before and after therapy. To facilitate the conduct of probability based studies, we have converted the scores into numeric values in the range of (0, 1). Each symptom is graded as (1, 2, 3, 4, 5) that runs in BAD → GOOD direction. Conclusion: NFS is a beneficial scoring system that can be used worldwide to assess the patients with DM.

Therapeutic potential of human embryonic stem cells in type 2 diabetes mellitus

AIM:To evaluate the safety and efficacy of human embryonic stem cells(h ESCs)for the management of type 2 diabetes mellitus(T2DM).METHODS:Patients with a previous history of diabetes and its associated complications were enrolled and injected with hE SC lines as per the defined protocol.The patients were assessed using Nutech functional score(NFS),a numeric scoring scale to evaluate the patients for 11 diagnostic parameters.Patients were evaluated at baseline and at the end of treatment period 1(T1).All the parameters were graded on the NFS scale from 1to 5.Highest possible grade(HPG)of 5 was considered as the grade of best improvement.RESULTS:Overall,94.8%of the patients showed improvement by at least one grade of NFS at the end of T1.For all the 11 parameters evaluated,54%of patients achieved HPG after treatment.The four essential parameters(improvement in glycated hemoglobin(HbA 1c)and insulin level,and fall in number of other oral hypoglycemic drugs with and without insulin)are presented in detail.For Hb A1c,72.6%of patients at the end of T1 met the World Health Organization cut off value,i.e.,6.5%of HbA 1c.For insulin level,65.9%of patients at the end of T1 were able to achieve HPG.After treatment,the improvement was seen in 16.3%of patients who required no more than two medications along with insulin.Similarly,21.5%of patients were improved as their dosage regimen for using oral drugs was reduced to 1-2 from 5.CONCLUSION:hE SC therapy is beneficial in patients with diabetes and helps in reducing their dependence on insulin and other medicines.

A Functional Scoring System to Assess the Level of Impairment in Patients with Low Vision Impairment

Background: Low vision is referred to as visual impairment (VI) if it is not cured through surgery, drugs, spectacles or contact lenses. It interferes with day-to-day living activities and is associated with the major eye blinding diseases. Numerous tests are used to carry out diagnosis of VI, but their outcomes are unreliable. Objective: To develop a functional scoring system, for accessing the ailment of patients with VI based on observations and clinical symptoms. Methods and Findings: We prepared the list of all possible symptoms that were associated with low vision. Based on this list, we established a scoring system, Nutech Functional Score (NFS), which is a 33-point positional and directional scoring system that evaluates the patient with VI. The scores have been converted into numeric values for conducting probability based studies. All the symptoms are graded between 1 to 5 that runs in BAD → GOOD direction. Conclusion: NFS is a distinctive tool that can be used globally to evaluate the patients with low vision.

3D Printed Integrated Gradient-Conductive MXene/CNT/Polyimide Aerogel Frames for Electromagnetic Interference Shielding with Ultra-Low Reflection

Construction of advanced electromagnetic interference(EMI)shielding materials with miniaturized,programmable structure and low reflection are promising but challenging.Herein,an integrated transition-metal carbides/carbon nanotube/polyimide(gradient-conductive MXene/CNT/PI,GCMCP)aerogel frame with hierarchical porous structure and gradient-conductivity has been constructed to achieve EMI shielding with ultra-low reflection.The gradient-conductive structures are obtained by continuous 3D printing of MXene/CNT/poly(amic acid)inks with different CNT contents,where the slightly conductive top layer serves as EM absorption layer and the highly conductive bottom layer as reflection layer.In addition,the hierarchical porous structure could extend the EM dissipation path and dissipate EM by multiple reflections.Consequently,the GCMCP aerogel frames exhibit an excellent average EMI shielding efficiency(68.2 dB)and low reflection(R=0.23).Furthermore,the GCMCP aerogel frames with miniaturized and programmable structures can be used as EMI shielding gaskets and effectively block wireless power transmission,which shows a prosperous application prospect in defense industry and aerospace.

Stability analysis and modeling for the three-dimensional Darcy-Forchheimer stagnation point nanofluid flow towards a moving surface

In this research,the three-dimensional(3D)steady and incompressible laminar Homann stagnation point nanofluid flow over a porous moving surface is addressed.The disturbance in the porous medium has been characterized by the Darcy-Forchheimer relation.The slip for viscous fluid is considered.The energy equation is organized in view of radiative heat flux which plays an important role in the heat transfer rate.The governing flow expressions are first altered into first-order ordinary ones and then solved numerically by the shooting method.Dual solutions are obtained for the velocity,skin friction coefficient,temperature,and Nusselt number subject to sundry flow parameters,magnetic parameter,Darcy-Forchheimer number,thermal radiation parameter,suction parameter,and dimensionless slip parameter.In this research,the main consideration is given to the engineering interest like skin friction coefficient(velocity gradient or surface drag force)and Nusselt number(temperature gradient or heat transfer rate)and discussed numerically through tables.In conclusion,it is noticed from the stability results that the upper branch solution(UBS)is more reliable and physically stable than the lower branch solution(LBS).

关于环境大气NMHC在线监测仪器比对验收方法的实验构想

由于很多监测机构或工业园区,购买了环境大气NMHC在线监测仪器,但使用HJ604的方法对仪器进行比对验收,比对结果存在极大的误差,导致对仪器的验收不通过。本文参考了HJ-759与TO-15的方法,对其进行改进后,提出一仪器比对验收的实验构想:利用相同的校准气体与检测器,三级冷冻预浓缩仪做前处理进样,苏玛罐采样,模拟在线仪器的检测,对所有化合物的色谱峰进行加和后再做定量,在实验室进行比对分析。该方法提供更低的检测下限,同时兼顾到对低碳化合物的检测,能更加准确的检测、还原环境大气或厂界大气的真实浓度,为在线环境大气NMHC监测仪器的比对验收工作提供参考。

Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy supply.Plant pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling price.In the agriculture industry,the identification of fungal diseases plays a vital role.However,it requires immense labor,greater planning time,and extensive knowledge of plant pathogens.Computerized approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several times.Therefore,the proposed study presents a new framework for the recognition of fruits and vegetable diseases.This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)model.The localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model.The localized images passed as input to classify the different types of plant diseases.The classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 fully-connected.The proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.

3D Semantic Deep Learning Networks for Leukemia Detection

White blood cells(WBCs)are a vital part of the immune system that protect the body from different types of bacteria and viruses.Abnormal cell growth destroys the body’s immune system,and computerized methods play a vital role in detecting abnormalities at the initial stage.In this research,a deep learning technique is proposed for the detection of leukemia.The proposed methodology consists of three phases.Phase I uses an open neural network exchange(ONNX)and YOLOv2 to localize WBCs.The localized images are passed to Phase II,in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model.The segmented images are used in Phase III,in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs.The proposed methodology is validated on three publically available benchmark datasets,namely ALL-IDB1,ALL-IDB2,and LISC,in terms of different metrics,such as precision,accuracy,sensitivity,and dice scores.The results of the proposed method are comparable to those of recent existing methodologies,thus proving its effectiveness.

Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large distance.Therefore,a method is proposed for the classification and recognition of different types of human gait.The proposed approach is consisting of two phases.In phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human gait.In this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal information.In phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted scores.The proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark datasets.The proposed method achieved better/improved prediction scores as compared to the recent existing works.

Morphogenesis of human embryonic stem cells into mature neurons under in vitro culture conditions

AIM To describe the morphogenesis of different neuronal cells from the human embryonic stem cell(h ESC) line,SCT-N,under in vitro culture conditions.METHODS The directed neuronal cell line was produced from a single,spare,pre-implantation stage fertilized ovum that was obtained during a natural in vitro fertilization process. The h ESCs were cultured and maintained as per our proprietary in-house technology in a Good Manufacturing Practice,Good Laboratory Practice and Good Tissue Practice compliant laboratory. The cell line was derived and incubated in aerobic conditions. The cells were examined daily under a phase contrast microscope for their growth and differentiation. RESULTS Different neural progenitor cells(NPCs) and differentiating neurons were observed under the culture conditions. Multipotent NPCs differentiated into all three types of nervous system cells,i.e.,neurons,oligodendrocytes and astrocytes. Small projections resembling neurites or dendrites,and protrusion coming out of the cells,were observed. Differentiating cells were observed at day 18 to 20. The differentiating neurons,neuronal bodies,axons,and neuronal tissue were observed on day 21 and day 30 of the culture. On day 25 and day 30,prominent neurons,axons and neuronal tissue were observed under phase contrast microscopy. 4',6-diamidino-2-phenylindole staining also indicated the pattern of differentiating neurons,axonal structure and neuronal tissue. CONCLUSION This study describes the generation of different neuronal cells from an h ESC line derived from biopsy of blastomeres at the two-cell cleavage stage from a discarded embryo.

Use of Human Embryonic Stem Cells in the Treatment of Diabetes Mellitus: A Case Series

Introduction: Diabetes mellitus (DM), a metabolic disorder, is known to be highly prevalent in people aged 40 - 60 years in developing countries whereas in developed countries, it mostly affects people above the age of 60 years. It is of two types: DM type I, an autoimmune disorder that mostly onsets after an infection and DM type II that is commonly associated with obesity. Several treatments are available for the treatment of DM, but none has successfully cured diabetes. Nowadays, stem cell therapy is being investigated for use in the treatment of DM and has shown positive results. Case Report: Our study presented results of three diabetic patients who were treated with human embryonic stem cell (hESC) therapy. Following the therapy, blood glucose levels were reduced. An improvement was observed in eye sight, stamina, gait pattern endurance, mental focus ability and muscle strength. There was a reduction in secondary side effects of high blood sugar such as affectation of cardiac, kidneys, polyneuropathy, vision etc. No adverse events and teratoma formation were observed after the treatment. Conclusion: It was concluded that hESCs showed good therapeutic potential in the treatment of patients with diabetes.

大气中苯系物快速监测技术的研究

采用低温预浓缩采样技术,气象色谱法,研究苯系物的快速检测方法与出峰特点。同时采用美国PAMS推荐的57种光化学前驱物,来模拟环境大气或厂界大气,研究在不同测试条件下,PAMS标气的色谱峰分离情况。由实验结果的对比可以得知,苯系物在色谱柱的分离时间至少要在30分钟以上,才能实现苯系物与其他常见挥发性有机物的有效分离和准确定量。所有标明在15分钟内能实现苯系物的准确定性和定量,都是值得商榷的。

Malaria Parasite Detection Using a Quantum-Convolutional Network

Malaria is a severe illness triggered by parasites that spreads via mosquito bites.In underdeveloped nations,malaria is one of the top causes of mortality,and it is mainly diagnosed through microscopy.Computer-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected groups.Therefore,in this study,we present a new idea based on the ensemble quantum-classical framework for malaria classification.The methods comprise three core steps:localization,segmentation,and classification.In the first core step,an improved FRCNN model is proposed for the localization of the infected malaria cells.Then,the RGB localized images were converted into YCbCr channels to normalize the image intensity values.Subsequently,the actual lesion region was segmented using a histogram-based color thresholding approach.The segmented images were employed for classification in two different ways.In the first method,a CNN model is developed by the selection of optimum layers after extensive experimentation,and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of themicroscopicmalaria images.Second,a quantum-convolutionalmodel is employed for informative feature extraction from microscopicmalaria images,and the extracted feature vectors are supplied to the softmax layer for classification.Finally,classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to CNN.The proposed models attain a precision rate greater than 90%,thereby proving that these models performed better than the existing models.

Modeling and numerical analysis of nanoliquid (titanium oxide, graphene oxide) flow viscous fluid with second order velocity slip and entropy generation

The prime objective of the present communication is to examine the entropy-optimized second order velocity slip Darcy–Forchheimer hybrid nanofluid flow of viscous material between two rotating disks.Electrical conducting flow is considered and saturated through Darcy–Forchheimer relation.Both the disks are rotating with different angular frequencies and stretches with different rates.Here graphene oxide and titanium dioxide are considered for hybrid nanoparticles and water as a continuous phase liquid.Joule heating,heat generation/absorption and viscous dissipation effects are incorporated in the mathematical modeling of energy expression.Furthermore,binary chemical reaction with activation energy is considered.The total entropy rate is calculated in the presence of heat transfer irreversibility,fluid friction irreversibility,Joule heating irreversibility,porosity irreversibility and chemical reaction irreversibility through thermodynamics second law.The nonlinear governing equations are first converted into ordinary differential equations through implementation of appropriate similarity transformations and then numerical solutions are calculated through Built-in-Shooting method.Characteristics of sundry flow variables on the entropy generation rate,velocity,concentration,Bejan number,temperature are discussed graphically for both graphene oxide and titanium dioxide hybrid nanoparticles.The engineering interest like skin friction coefficient and Nusselt number are computed numerically and presented through tables.It is noticed from the obtained results that entropy generation rate and Bejan number have similar effects versus diffusion parameter.Also entropy generation rate is more against the higher Brinkman number.

CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

Worldwide cotton is the most profitable cash crop.Each year the production of this crop suffers because of several diseases.At an early stage,computerized methods are used for disease detection that may reduce the loss in the production of cotton.Although several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex background.Due to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease classification.Therefore in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×1000.After that,the extracted features are serially concatenated having a feature vector lengthN×2000.Themost prominent features are selected usingEmperor PenguinOptimizer(EPO)method.The method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle cotton-leaf-infection-II.The EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,respectively.The classification is performed using 5,7,and 10 folds cross-validation.The Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

An Efficient Connectivity Restoration Technique(ECRT)for Wireless Sensor Network

Node failure in Wireless Sensor Networks(WSNs)is a fundamental problem because WSNs operate in hostile environments.The failure of nodes leads to network partitioning that may compromise the basic operation of the sensor network.To deal with such situations,a rapid recovery mechanism is required for restoring inter-node connectivity.Due to the immense importance and need for a recovery mechanism,several different approaches are proposed in the literature.However,the proposed approaches have shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity restoration.Moreover,most of these approaches rely on the excessive mobility of nodes for restoration connectivity that affects both coverage and energy consumption.This paper proposes a novel technique called ECRT(Efficient Connectivity Restoration Technique).This technique is capable of restoring connectivity due to single and multiple node failures.ECRT achieves energy efficiency by transmitting a minimal number of control packets.It is also coverage-aware as it relocates minimal nodes while trying to restore connectivity.With the help of extensive simulations,it is proven that ECRT is effective in connectivity restoration for single and multiple node failures.Results also show that ECRT exchanges a much smaller number of packets than other techniques.Moreover,it also yields the least reduction in field coverage,proving its versatility for connectivity restoration.

Investigation of the sodium storage mechanism of iron fluoride hydrate cathodes using X-ray absorption spectroscopy and mossbauer spectroscopy

Elucidation of a reaction mechanism is the most critical aspect for designing electrodes for highperformance secondary batteries.Herein,we investigate the sodium insertion/extraction into an iron fluoride hydrate(FeF_(3)·0.5H_(2)O)electrode for sodium-ion batteries(SIBs).The electrode material is prepared by employing an ionic liquid 1-butyl-3-methylimidazolium-tetrafluoroborate,which serves as a reaction medium and precursor for F^(-)ions.The crystal structure of FeF_(3)·0.5H_(2)O is observed as pyrochlore type with large open 3-D tunnels and a unit cell volume of 1129A^(3).The morphology of FeF_(3)·0.5H_(2)O is spherical shape with a mesoporous structure.The microstructure analysis reveals primary particle size of around 10 nm.The FeF_(3)·0.5H_(2)O cathode exhibits stable discharge capacities of 158,210,and 284 mA h g^(-1) in three different potential ranges of 1.5-4.5,1.2-4.5,and 1.0-4.5 V,respectively at 0.05 C rate.The specific capacities remained stable in over 50 cycles in all three potential ranges,while the rate capability was best in the potential range of 1.5-4.5 V.The electrochemical sodium storage mechanism is studied using X-ray absorption spectroscopy,indicating higher conversion at a more discharged state.Ex-situ M?ssbauer spectroscopy strengthens the results for reversible reduction/oxidation of Fe.These results will be favorable to establish high-performance cathode materials with selective voltage window for SIBs.

Optimal‑Control‑Based Eco‑Driving Solution for Connected Battery Electric Vehicle on a Signalized Route

Speed advisory systems have been used for connected vehicles to optimize energy consumption.However,for their practical utilization,presence of preceding vehicles and signals must be taken into account.Moreover,for Battery Electric Vehicles(BEVs),factors that deteriorate battery’s life cycle and discharging time must also be considered.This paper proposes an eco-driving control for connected BEV with traffic signals and other safety constraints.Traffic signals are considered as interior point constraints,while inter-vehicle distance with preceding vehicles,vehicle speed and battery charging/discharging limits,are considered as state safety constraints.Backward-forward simulator based Speed Guidance Model is applied to follow the optimized velocity under powertrain safety limitations.Effectiveness of the proposed methodology is tested on a 5.3-km route in Islamabad,Pakistan.Real traffic data using Simulation of Urban Mobility under different driving scenarios is considered.Using the proposed method,around 21% energy can be saved compared to the preceding vehicles that followed their random velocities under the same traffic and route conditions.This means the EV controlled by the proposed method can have longer driving range.Furthermore,the host BEV has crossed signals during their green time without collision with preceding vehicles.Low charging rates and terminal Depth of Discharge indicate less number of charging cycles,thus proving the usefulness of the proposed solution as battery’s lifesaving strategy.

Graphene transfer methods: A review

Graphene is a material with unique properties that can be exploited in electronics, catalysis, energy, and bio-related fields. Although, for maximal utilization of this material, high-quality graphene is required at both the growth process and after transfer of the graphene film to the application-compatible substrate. Chemical vapor deposition (CVD) is an important method for growing high-quality graphene on non-technological substrates (as, metal substrates, e.g., copper foil). Thus, there are also considerable efforts toward the efficient and non-damaging transfer of quality of graphene on to technologically relevant materials and systems. In this review article, a range of graphene current transfer techniques are reviewed from the standpoint of their impact on contamination control and structural integrity preservation of the as-produced graphene. In addition, their scalability, cost- and time-effectiveness are discussed. We summarize with a perspective on the transfer challenges, alternative options and future developments toward graphene technology.