Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking

Recent advancements in the Vehicular Ad-hoc Network(VANET)have tremendously addressed road-related challenges.Specifically,Named Data Networking(NDN)in VANET has emerged as a vital technology due to its outstanding features.However,the NDN communication framework fails to address two important issues.The current NDN employs a pull-based content retrieval network,which is inefficient in disseminating crucial content in Vehicular Named Data Networking(VNDN).Additionally,VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous vehicles.Although various solutions have been proposed for detecting vehicles’behavior,they inadequately addressed the challenges specific to VNDN.To deal with these two issues,we propose a novel push-based crucial content dissemination scheme that extends the scope of VNDN from pullbased content retrieval to a push-based content forwarding mechanism.In addition,we exploitMachine Learning(ML)techniques within VNDN to detect the behavior of vehicles and classify them as attackers or legitimate.We trained and tested our system on the publicly accessible dataset Vehicular Reference Misbehavior(VeReMi).We employed fiveML classification algorithms and constructed the bestmodel for illusion attack detection.Our results indicate that RandomForest(RF)achieved excellent accuracy in detecting all illusion attack types in VeReMi,with an accuracy rate of 100%for type 1 and type 2,96%for type 4 and type 16,and 95%for type 8.Thus,RF can effectively evaluate the behavior of vehicles and identify attacker vehicles with high accuracy.The ultimate goal of our research is to improve content exchange and secureVNDNfromattackers.Thus,ourML-based attack detection and preventionmechanismensures trustworthy content dissemination and prevents attacker vehicles from sharing misleading information in VNDN.

Oxygen-doped Carbon Nitride Nanocages with Efficient Photon-to-Electron Conversion for Selective Oxidation of Xylose/Xylan to Yield Xylonic Acid

Highly efficient photon-to-electron conversion is crucial for achieving photocatalytic conversion.In this study,oxygen-doped carbon nitride nanocages(O@CNNCs)were engineered via dual strategies of morphology-controlled heteroatom doping,which was successfully used in the photocatalytic selective oxidation of xylose/xylan to xylonic acid.The nanocage-shaped O@CNNCs had a larger surface area,which was 4.02 times of carbon nitride(CN).Furthermore,with the assistance of morphology regulation and O-doping,O@CNNCs exhibit highly efficient photon-to-electron conversion,enhanced visible-light utilization,high photocurrent,low resistance,and fast separation/migration of electron-hole pairs.Correspondingly,the photocatalytic oxidation of xylose to xylonic acid using O@CNNCs was successfully achieved under mild reaction conditions with a yield of 83.4%.O@CNNCs have excellent recyclability,in which the yield of xylonic acid in the 5th cycle was 98.2%of its initial use.The O@CNNC photocatalytic system was also suitable for macromolecular xylan,and a xylonic acid yield of 77.34 mg was obtained when 100 mg xylan was used.The oxidation-active species captured experiments indicated that holes were crucial for the selective oxidation of xylose to xylonic acid.Overall,this study provides a new strategy for the preparation of photocatalysts with excellent photon-to-electron conversion and selective oxidation of biomass-derived feedstocks to xylonic acid.

GmCHS8和GmIFS2基因共同决定大豆中异黄酮的积累

大豆异黄酮受多基因控制,采用传统育种方法提高大豆异黄酮含量比较困难。我们之前的研究表明,CHS8基因在异黄酮生物合成过程中发挥着重要作用,但CHS8基因过量表达并不能显著提高异黄酮含量。本研究利用Microarray技术,检测了高异黄酮品种RCATAngra(RCAT)和低异黄酮品种Harovinton(HVNT)的18362基因在种子发育过程中表达水平的变化以及大豆种子中异黄酮累积的趋势,利用RT-PCR证实CHS8和IFS2分别是CHSs和IFSs基因家族中的主要基因;证实CHS8是类苯基丙醇主路径中的主基因,并发现异黄酮支路中的IFS2基因在RCAT和HVNT品种中表达差异亦达到显著水平。进一步利用发根农杆菌转化系统,在大豆上分别过量表达CHS8、IFS2和CHS8+IFS2,前两者异黄酮含量较对照分别提高65.9%和34.4%,但增幅未达显著水平;而后者则提高了82.3%,增幅达极显著水平(P<0.0001),因而证实大豆中异黄酮的积累由CHS8和IFS2基因共同决定。

Assessment of Clinical Learning Environment, Supervision (CLES) among Nursing Students, Hyderabad, Sindh, Pakistan

Background: In nursing education for better teaching and essential professional skills, the clinical practice plays a substantial role. Practice at clinical settings permits students to convert theoretic knowledge into the knowledge of the skills mandatory for the care of the patient. Clinical learning environment (CLE) is an important part in education of nursing and has a sizable influence on the students’ learning. Objective: The purpose of this study is to examine perception and satisfaction of nursing students with their CLEs in Hyderabad, Pakistan. Methods: This cross-sectional study was conducted at three nursing institutes of Hyderabad from December 2018 to January 2019 among 342 nursing students. Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T) assessment tool was used as the instrument to identify the students’ perception about the learning environment in clinical setting. Results: The mean age of the participants was 25.6 ± 4.93 with majority of them male (70.7%). Three domains, pedagogical atmosphere, supervisory relationship and nurse teacher role in clinical practice showed good reliability of more than 70%. Highest domains vise mean score was obtained for nursing premises on the ward (3.315) whereas lowest for nurse teacher role in clinical practice (NT) (3.062). Analysis of variance revealed that three domains supervisory relationship, leadership style of the ward manager and premises of nursing showed significant mean score difference among supervisor title. Conclusion: It was found that students valued positive supervision, ward manager leadership style premises of nursing on the ward as positive CLE. Learning environment varies between gender, clinical settings and supervision. Medicine ward appeared to deliver the finest learning situations for the nursing program.

Formation of crystalline particles from phase change emulsion： Influence of different parameters

Solidification or crystallization of phase change emulsion in the form of fine emulsion drops in a direct contact coolant at temperatures below their freezing point was studied. This work is mainly focused on the size and shape of the generated partides from phase change emulsified fats. Size of the particles is the major or key factor being considered during their formation, however, other factors that govern the particle size and shape were also observed. The operating parameters of the process were optimized in order to obtain particles of smaller size ranges in the window of current operating conditions. The crystallization of complex emulsion maffices is very difficult to control in the bulk at desired requirement. Hence, the emulsion drop to particle formation has advan- tage in comparison with the bulk solidification or crystallization. The main objective of this work is to achieve spherical emulsion particles in a direct contact cooling system. Parameters like： stat）ility, characterization, viscos- ity, and the effect of different energy inputs were examined. Moreover, the effects of the capillary size, interracial tension, temperature of the emulsion on the particle size were also monitrored.

Effect of loading rate and time delay on the tangent modulus method(TMM)in coal and coal measured rocks

Non-destructive techniques of in-situ stress measurement from oriented cored rocks have great potential to be developed as a cost cost-effective and reliable alternative to the conventional overcoring and hydraulic fracturing methods.The tangent modulus method(TMM)is one such technique that can be applied to oriented cored rocks to measure in-situ stresses.Like the deformation rate analysis(DRA),the rock specimen is subjected to two cycles of uniaxial compression and the stress-tangent modulus curve for the two cycles is obtained from the stress-strain curve.A bending point in the tangent modulus curve of the first cycle is observed,separating it from the tangent modulus curve of the second cycle.The point of separation between the two curves is assumed to be the previously applied maximum stress.A number of experiments were conducted on coal and coal measured rocks(sandstone and limestone)to understand the effect of loading conditions and the time delay.The specimens were preloaded,and cyclic compressions were applied under three different modes of loading,four different strain rates,and time delays of up to one week.The bending point in the stress-tangent modulus curves occurred approximately at the applied pre-stress levels under all three loading modes,and no effect of loading rate was observed on the bending points in TMM.However,a clear effect of time delay was observed on the TMM,contradicting the DRA results.This could be due to the sensitivity of TMM and the range of its applicability,all of which need further investigation for the in-situ stress measurement.

Clean production of lactic acid by selective carbon-carbon bond cleavage of biomass feedstock over a novel carbon-bismuth oxychloride photocatalyst

The use of functional materials such as carbon-bismuth oxyhalides in integrated photorefineries for the clean production of fine chemicals requires restructuring.A facile biomass-assisted solvothermal fabrication of carbon/bismuth oxychloride nanocomposites(C/BiOCl)was achieved at various temperatures.Compared with BiOCl and C/BiOCl-120,C/BiOCl-180 exhibited higher crystallinity,wider visible light absorption,and a faster migration/separation rate of photoinduced carriers.For the selective C–C bond cleavage of biomass-based feedstocks photocatalyzed by C/BiOCl-180,the xylose conversion and lactic acid yield were 100%and 92.5%,respectively.C/BiOCl-180 efficiently converted different biomass-based monosaccharides to lactic acid,and the efficiency of pentoses was higher than that of hexoses.Moreover,lactic acid synthesis was favored by all active radicals including superoxide ion(·O_(2)^(−)),holes(h^(+)),hydroxyl radical(·OH),and singlet oxygen(^(1)O_(2)),with·O_(2)^(−)playing a key role.The fabricated photocatalyst was stable,economical,and recyclable.The use of biomass-derived monosaccharides for the clean production of lactic acid via the C/BiOCl-180 photocatalyst has opened new research horizons for the investigation and application of C–C bond cleavage in biomass-based feedstocks.

Elucidating the dynamics of solvent engineering for perovskite solar cells

Researchers working in the field of photovoltaic are exploring novel materials for the efficient solar energy conversion.The prime objective of the discovery of every novel photovoltaic material is to achieve more energy yield with easy fabrication process and less production cost features.Perovskite solar cells (PSCs)delivering the highest efficiency in the passing years with different stoichiometry and fabrication modification have made this technology a potent candidate for future energy conversion materials.Till now,many studies have shown that the quality of active layer morphology,to a great extent,determines the performance of PSCs.The current and potential techniques of solvent engineering for good active layer morphology are mainly debated using primary solvent,co-solvent (Lewis acid-base adduct approach)and solvent additives.In this review,the dynamics of numerously reported solvents on the morphological characteristics of PSCs active layer are discussed in detail.The intention is to get a clear understanding of solvent engineering induced modifications on active layer morphology in PSC devices via different crystallization routes.At last,an attempt is made to draw a framework based on different solvent coordination properties to make it easy for screening the potent solvent contender for desired PSCs precursor for a better and feasible device.

Geotechnical forensic investigation of a slope failure on silty clay soil--A case study

Qila Bala Hisar is one of the noteworthy places of Peshawar,Khyber Pakhtunkhwa.The fort was constructed on a flled ground during the 18th century and it was renovated several times by the occupants ever since.Recently,due to an earthquake of magnitude 7.3,the upper part of the south-western wall of the fort collapsed.The collapse of the wall was attributed to the failure of the retained slope.This research was undertaken to characterize the slope material,study causal factors of failure and evaluate remedial strategy.The investigation involved conventional field and laboratory testing and geophysical investigation using electrical resistivity technique to evaluate the nature of stratum.Also,X-ray Diffraction and Scanning Electron Microscopy was used to study the slope material at a molecular level to evaluate the existence of swelling potential.The analysis has shown that excessive seepage of water caused by the poor maintenance of runoff and sewage drains is the causal factor triggered by the seismic event.A remedial strategy involving soil nails,micro piles and improvement of the surface drainage is recommended.

Kigelia africana Fruit:Constituents,Bioactivity, and Reflection on Composition Disparities

Objective: Kigelia africana, a tropical tree, which has long been used in African traditional medicine. The objective of the current study has been identifying the constituents of K. africana and verifying its utilities in traditional medicine. Materials and Methods: The methanol extract of K. africana fruits was subjected to chromatographic fractionation utilizing different techniques. The methanol extract together with the isolated compounds were tested for their bioactivities in a series of cell-based assays. Results: The current work led to isolation and characterization of nine constituents including iridoid glycosides, phenylpropanoid derivatives, and a eucommiol derivative. The hexanes extract caused inhibition of the opportunistic yeast; Cryptococcus neoformans Pinh. The chloroform extract exhibited substantial antileishmanial activity of Leishmania donovani. Verminoside(1) showed weak inhibition of the CB1, CB2, and Kappa opioid receptors. Compound 4 exhibited weak inhibition of the Kappa and Mu opioid receptors. The hexanes and the chloroform extracts of K. africana exhibited inhibitory activity against the pathogenic parasite Trypanosoma brucei. The ethyl acetate extract showed the same activity. Conclusions: This is the first report on the isolation of coniferyl 4-0-(3-D-glucopyranoside(7), a eucommiol derivative(crescentin IV)(6), and 6-feruloylcatalpol(4) from the genus Kigelia. It is also the first report on the separation of ajugol(2), catalpol(3), and specioside(5) from the fruits of K. africana. Revision of the^1 H and ^(13)C-NMR spectra of 6-feruloylcatalop(4) and 6-p-hydroxycinnamoylcatalpol(5, specioside) is described. Further, the results of the in vitro assays corroborate the traditional utility of this plant in medicine.