花后弱光对软质小麦籽粒产量与淀粉品质的影响

为探明花后弱光胁迫对软质小麦籽粒产量、淀粉粒度分布及黏度参数的影响,以软质小麦品种荃麦725和扬麦15为材料,在小麦开花后设置3个弱光处理即S1(遮光10%)、S2(遮光20%)、S3(遮光30%)处理,以自然光照条件为对照,分析花后不同程度弱光条件下软质小麦籽粒产量、淀粉粒度分布与黏度参数的差异。结果表明,与对照相比,花后弱光降低了小麦粒重、穗粒数和产量,提高了籽粒蛋白质含量,但使籽粒淀粉含量、籽粒蛋白质和淀粉积累量显著减少。花后弱光降低了软质小麦B型淀粉粒(粒径≤10μm)体积、表面积百分比,增加了A型淀粉粒(粒径>10μm)体积、表面积百分比,对A、B型淀粉粒数目百分比无显著影响。在B型淀粉粒中,弱光对粒径0.1~2.8μm的淀粉粒体积百分比的影响较粒径2.8~10μm的淀粉粒大。在A型淀粉粒中,弱光对粒径>22μm的淀粉粒体积百分比的影响大于粒径10~22μm的淀粉粒。弱光导致小麦籽粒淀粉峰值黏度、低谷黏度、最终黏度、稀懈值、回升值等参数显著下降。花后弱光虽然可增加软质小麦籽粒蛋白质含量,但显著降低籽粒淀粉含量以及蛋白质和淀粉积累量,不利于粒重与产量形成;弱光对B型淀粉粒的影响大于A型淀粉粒,会降低B型淀粉粒体积和表面积占比,但可增加A型淀粉粒体积和表面积占比,降低淀粉峰值黏度等参数。

Comparative Efficacy of Common Broad Leaf Herbicides against an Invasive Weed: <i>Parthenium</i><i>hysterophorus</i>L.

Parthenium hysterophorus L. (parthenium weed) is an annual weed that grows rapidly in disturbed land. It is considered as one of the most hazardous weeds in Pakistan as it poses serious health problems to livestock as well as severe allergenic reactions in humans. It has invaded the Punjab and Khyber Pakhtunkhwa provinces and also been spreading in other parts of the country where it poses a risk for the grazing lands, roadsides, forests, wet lands, waste lands and of all types of cropped and non-cropped areas in Pakistan. The present studies were carried out to determine the impact of four locally available broad leaf herbicides viz;Stomp 455 CS (pendimethalin), Buctril Super 60 EC (bromoxynil + MCPA), Vantage 48 SL (glyphosate) and Logran Extra 750 WG (triasulfuron + terbutryn) (@ recommended and ½ of recommended dose) against P. hysterophorus grown in pots at research field of CABI CWA, Rawalpindi. All herbicides were applied at three growth stages (rosette, bolted and flowering). The observations for the mortality of P. hysterophorus were made 2 and 4 weeks after spray. The glyphosate was the most effective and reported 100% mortality of P. hysterophorus plants at flowering stage followed by bromoxynil + MCPA (89%), pendimethalin (80%) and triasulfuron + terbutryn (61%) at recommended dose after 4 weeks of spray. All tested herbicides achieved a mortality between 38% - 86% at rosette while 54% - 96% mortality at bolted stage after 4 weeks. Initially, 2 weeks after spray at flowering stage glyphosate caused 53% wilting followed by 49% (bromoxynil + MCPA), 33% (pendimethalin) and 9% (triasulfuron + terbutryn) at their recommended doses. The results indicated that P. hysterophorus is the most susceptible to glyphosate and bromoxynil + MCPA, both these herbicides are very promising for the wilting and management of parthenium weed.

Earthquake Statistics and Earthquake Research Studies in Pakistan

This paper is about short review of earthquake statistics and efforts for earthquake mitigation, hazard and risk assessment studies in Pakistan. Pakistan and adjoining region lying between longitude 60°E to 78°E and latitude 20°N to 45°N are selected for the study as this region has a history of many large earthquakes because of its location in the region of intersection of three plates namely Indian, Eurasian and Arabian Sea plate. This paper is based on the study of both seismological history of the region which includes recent and historical seismicity based on earthquake catalogue as well as geological knowledge supplemented with available fault system information. In this study, Pakistan and adjoining regions are divided into 14 seismogenic zones. Seismicity of each zone is studied considering also the major cities in the respective zone and type of infrastructure which is mainly responsible for earthquake disaster rather than earthquake itself.

Isolation of Cr(Ⅵ) reducing bacteria from industrial effuents and their potential use in bioremediation of chromium containing wastewater

The present study was aimed to assess the ability of Bacillus sp.JDM-2-1 and Staphylococcus capitis to reduce hexavalent chromium into its trivalent form.Bacillus sp.JDM-2-1 could tolerate Cr（Ⅵ）（4800 μg/mL） and S.capitis could tolerate Cr（Ⅵ）（2800 μg/mL）.Both organisms were able to resist Cd^2＋（50 μg/mL）,Cu^2＋（200 μg/mL）,Pb^2＋（800 μg/mL）,Hg^2＋（50 μg/mL） and Ni2＋（4000 μg/mL）.S.capitis resisted Zn^2＋ at 700 μg/mL while Bacillus sp.JDM-2-1 only showed resistance up to 50 μg/mL.Bacillus sp.JDM-2-1 and S.capitis showed optimum growth at pH 6 and 7,respectively,while both bacteria showed optimum growth at 37°C.Bacillus sp.JDM-2-1 and S.capitis could reduce 85% and 81% of hexavalent chromium from the medium after 96 h and were also capable of reducing hexavalent chromium 86% and 89%,respectively,from the industrial effuents after 144 h.Cell free extracts of Bacillus sp.JDM-2-1 and S.capitis showed reduction of 83% and 70% at concentration of 10 μg Cr（Ⅵ）/mL,respectively.The presence of an induced protein having molecular weight around 25 kDa in the presence of chromium points out a possible role of this protein in chromium reduction.The bacterial isolates can be exploited for the bioremediation of hexavalent chromium containing wastes,since they seem to have a potential to reduce the toxic hexavalent form to its nontoxic trivalent form.

新型冠状病毒肺炎相关心肌损伤研究进展

新型冠状病毒肺炎(COVID-19)病原体是一种被命名为SARS-CoV-2的新型冠状病毒,具有较高的传染性和致病性。SARS-CoV-2感染者除典型呼吸系统表现外,可出现心悸、胸闷等症状及心电图、心肌酶谱、心功能等改变。SARS-CoV-2直接感染心肌、介导ACE2下调、引起细胞因子风暴等因素可能参与心肌损伤。本文基于目前对SARS-CoV-2感染患者的病例资料和最新研究报道,对COVID-19相关心肌损伤可能机制、临床表现、诊断及治疗进展做一综述。

Splenic hamartoma: A case report and review of the literature

Splenic hamartoma is a rare benign malformation, composed of an anomalous mixture of normal splenic elements, often found incidentally while working up other complaints or at autopsy. A splenic mass was incidentally found while evaluating the effects of a traffic accident in a 63-year-old woman. Abdominal computed tomography revealed a well-defined splenic mass with rim enhancement. The patient underwent splenectomy. The resected spleen contained a well-defined mass lesion measuring 3.5 cm × 3.0 cm. Microscopic examination revealed disorganized slit-like vascular channels lined by plump endothelial cells without atypia. The cells lining the vascular channels were positive for CD8, CD31, CD34 and vimentin. Endothelial cells that are positive for CD8 are a key feature that differentiates hamartoma from other vascular lesions of the spleen. Although this tumor is very rare, it must be included in the differential diagnosis of splenic mass-forming lesions.

Appendiceal Crohn's disease clinically presenting as acute appendicitis

AIM: To determine the incidence of appendiceal Crohn's disease(CD) and to summarize the characteristic histologic features of appendiceal CD.METHODS: We reviewed the pathology files of 2179 appendectomy specimens from January 2007 to May2013. The computer-assisted retrieval search facility was utilized to collect specimens. We selected those cases that were diagnosed as CD or chronic granulomatous inflammation and defined the final diagnosis according to the histologic findings of CD, including transmural lymphocytic inflammation, non-caseating epithelioid granulomas, thickening of the appendiceal wall secondary to hypertrophy of muscularis mucosa,mucosal ulceration with crypt abscesses, mucosal fissures, and fistula formation. RESULTS: We found 12 cases(7 male and 5 female patients, with an average age of 29.8 years) of appendiceal CD. The incidence of appendiceal CD was 0.55%.The chief complaints were right lower quadrant pain,abdominal pain, lower abdominal pain, and diarrhea.The duration of symptom varied from 2 d to 5 mo.The histologic review revealed appendiceal wall thickening in 11 cases(92%), transmural inflammation in all cases(100%), lymphoid aggregates in all cases(100%), epithelioid granulomas in all cases(100%), mucosal ulceration in 11 cases(92%), crypt abscesses in 5 cases(42%), perforation in 2 cases(17%), muscular hypertrophy in 1 case(8%), neural hyperplasia in 5 cases(42%), and perpendicular serosal fibrosis in 8 cases(67%).CONCLUSION: A typical and protracted clinical course, unusual gross features of the appendix and the characteristic histologic features are a clue in the diagnosis of appendiceal CD.

PEALD-deposited crystalline GaN films on Si(100) substrates with sharp interfaces

Polycrystalline gallium nitride(GaN) thin films were deposited on Si(100) substrates via plasma-enhanced atomic layer deposition(PEALD) under optimal deposition parameters. In this work, we focus on the research of the GaN/Si(100)interfacial properties. The x-ray reflectivity measurements show the clearly-resolved fringes for all the as-grown GaN films, which reveals a perfectly smooth interface between the GaN film and Si(100), and this feature of sharp interface is further confirmed by high resolution transmission electron microscopy(HRTEM). However, an amorphous interfacial layer(~ 2 nm) can be observed from the HRTEM images, and is determined to be mixture of Ga_xO_y and GaN by xray photoelectron spectroscopy. To investigate the effect of this interlayer on the GaN growth, an AlN buffer layer was employed for GaN deposition. No interlayer is observed between GaN and AlN, and GaN shows better crystallization and lower oxygen impurity during the initial growth stage than the GaN with an interlayer.

Conservation status evaluation of Berberis species across the Karakoram Mountain Ranges, Pakistan using IUCN Red List Categories and Criteria

Berberis species medicinal plants in Pakistan are endangered, high-value with important eco-cultural, commercial and livelihood roles in mountain communities. To assess the geographical distribution of Berberis species across the Karakoram Mountain Ranges in Pakistan, we used IUCN Red List Categories and Criteria （2001） to calculate the extent of occurrence （EOO, 〈100 km^2） and the area of occupancy （AOO, 〈10 km^2） of Berberis pseudumbellata subsp, pseudumbellata and B. pseudumbellata subsp. gilgitica. Overgrazing and habitat loss were key population- limiting factors. The two subspecies had contrasting responses to temperature, elevation, precipitation and insect susceptibility. B. pseudumbellata subsp, gilgitica is endemic to Gilgit-Baltistan and grows in single-cropping zone （ar- eas 〉 200 m a.s.1.）. Status evaluation revealed that both subspecies meet the criteria set for critically endangered species. Prolonged disregard of its declining population trend might lead to its extinction; therefore, integrated con- servation efforts are necessary.

Convolutional Neural Network Model for Fire Detection in Real-Time Environment

Disasters such as conflagration,toxic smoke,harmful gas or chemical leakage,and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent.The calamities are causing massive fiscal and human life casualties.However,Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco.The authors have proposed an early fire detection system uses machine and/or deep learning algorithms.The article presents an Intelligent Industrial Monitoring System(IIMS)and introduces an Industrial Smart Social Agent(ISSA)in the Industrial SIoT(ISIoT)paradigm.The proffered ISSA empowers smart surveillance objects to communicate autonomously with other devices.Every Industrial IoT(IIoT)entity gets authorization from the ISSA to interact and work together to improve surveillance in any industrial context.The ISSA uses machine and deep learning algorithms for fire-related incident detection in the industrial environment.The authors have modeled a Convolutional Neural Network(CNN)and compared it with the four existing models named,FireNet,Deep FireNet,Deep FireNet V2,and Efficient Net for identifying the fire.To train our model,we used fire images and smoke sensor datasets.The image dataset contains fire,smoke,and no fire images.For evaluation,the proposed and existing models have been tested on the same.According to the comparative analysis,our CNN model outperforms other state-of-the-art models significantly.

Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.

Mixed Convection Heat Transfer in Micropolar Nanofluid over a Vertical Slender Cylinder

Analysis is carried for the problem of boundary layer steady flow and heat transfer of a micropolar fluid containing nanoparticles over a vertical cylinder.The governing partial differential equations of linear momentum,angular momentum,heat transfer and nano concentration are reduced to nonlinear coupled ordinary differential equations by applying the boundary layer approximations and a suitable similarity transformation.These nonlinear coupled ordinary differential equations,subject to the appropriate boundary conditions,are then solved by using the homotopy analysis method.The effects of the physical parameters on the flow,heat transfer and nanoparticle concentration characteristics of the model are presented through graphs and the salient features are discussed.

Economic outlook of rice crops in Pakistan:a time series analysis (1970-2015)

Background:The basic objective of this study was to examine the relationship between the rice output,its production area,water availability,and agricultural gross domestic product(GDP)of Pakistan.Annual time series data for 1970–2015 were collected from the National Food Security and Research,Economic Survey of Pakistan,and Pakistan Bureau of Statistics(various publications).Methods:Rice crop data were analyzed using the ordinary least square method and the augmented Dickey–Fuller test.Were interpreted using the Johansen cointegration test.Results:Our study revealed the existence of a long-standing relationship between rice output,its production area,and water availability with the agricultural GDP of Pakistan.Regression results indicate that rice output and cultivated area have a significant and positive relationship with agricultural GDP,while water availability has a negative relationship.Conclusions:The study suggests that the government of Pakistan should design new policies and funding schemes for developing and improving water availability.

Credit margin of investment in the agricultural sector and credit fungibility: the case of smallholders of district Shikarpur, Sindh, Pakistan

Background:This study examines the access to credit,credit investment,and credit fungibility for small-holder farmers and medium-and large-scale farmers in the agricultural sector of the Shikarpur District of Sindh,Pakistan.Methods:A standardized questionnaire was used to collect data from 87 farmers in the Shikarpur District.We investigated the availability of credit and the use of credit fungibility by farmers with small-,medium-,and large-scale holdings by applying a credit fungibility ratio and an ANOVA technique.The factors that influence the farmers’access to agricultural credit were analyzed using a probit regression model.Results:The results revealed that farmers in both study groups used some amount of their agricultural credit for non-agricultural activities.Further,the results of the probit regression analysis showed that formal education,farming experience,household size,and farm size had a positive and significant influence on the farmers’access to agricultural credit.Conclusion:Based on these findings,our study suggests that a strong monitoring of farmers is needed in the study area.

Segregation behavior of magnetic ions in continuous flowing solution under gradient magnetic field

The research of magnetic separation starts from magnetic solid particles to nanoparticles, and in the research progress,particles become smaller gradually with the development of application of magnetic separation technology. Nevertheless,little experimental study of magnetic separation of molecules and ions under continuous flowing conditions has been reported. In this work, we designed a magnetic device and a ＂layered＂ flow channel to study the magnetic separation at the ionic level in continuous flowing solution. A segregation model was built to discuss the segregation behavior as well as the factors that may affect the separation. The magnetic force was proved to be the driving force which plays an indispensable role leading to the segregation and separation. The flow velocity has an effect on the segregation behavior of magnetic ions,which determines the separation result. On the other hand, the optimum flow velocity which makes maximum separation is related to the initial concentration of solution.

Kinetics and mechanistic investigation of epoxide/CO2 cycloaddition by a synergistic catalytic effect of pyrrolidinopyridinium iodide and zinc halides

Formation of styrene carbonate (SC) by the cycloaddition of CO2 to styrene oxide (SO) catalysed by pyrrolidinopyridinium iodide (PPI) in combination with zinc halides (ZnCl2, ZnBr2 and ZnI2) was investigated. Complete conversion of the SO to SC was achieved in 3 h with 100% selectivity using 1/0.5 molar (PPI/ZnI2) catalyst ratio under mild reaction conditions i.e., 100℃ and 10 bar CO2 pressure. The synergistic effect of ZnI2 and PPI resulted in more than 7-fold increase in reaction rate than using PPI alone. The cycloaddition reaction demonstrated the first-order dependence with respect to the epoxide, CO2 and catalyst concentrations. Moreover, the kinetic and thermodynamic activation parameters of SC formation were determined using the Arrhenius and Eyring equations. The positive values of △H(42.8 kJ mol^-1) and △G(102.3 kJ mol^-1) revealed endergonic and chemically controlled nature of the reaction, whereas the large negative values of △S(-159.4 J mol^-1 K^-1) indicate a highly ordered activated complex at the transition state. The activation energy for SC formation catalyzed by PPI alone was found to be 73.2 kJ mol^-1 over a temperature range of 100-140℃, which was reduced to 46.1 kJ mol^-1 when using PPI in combination with ZnI2 as a binary catalyst. Based on the kinetic study, a synergistic acid-based reaction mechanism was proposed.

Exploiting Data Science for Measuring the Performance of Technology Stocks

The rise or fall of the stock markets directly affects investors’interest and loyalty.Therefore,it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses.In our proposed study,six supervised machine learning(ML)strategies and deep learning(DL)models with long short-term memory(LSTM)of data science was deployed for thorough analysis and measurement of the performance of the technology stocks.Under discussion are Apple Inc.(AAPL),Microsoft Corporation(MSFT),Broadcom Inc.,Taiwan Semiconductor Manufacturing Company Limited(TSM),NVIDIA Corporation(NVDA),and Avigilon Corporation(AVGO).The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022(seventeen years)with 4280 samples.As already noted,multiple studies have been performed to resolve this problem using linear regression,support vectormachines,deep long short-termmemory(LSTM),and many other models.In this research,the Hidden Markov Model(HMM)outperformed other employed machine learning ensembles,tree-based models,the ARIMA(Auto Regressive IntegratedMoving Average)model,and long short-term memory with a robust mean accuracy score of 99.98.Other statistical analyses and measurements for machine learning ensemble algorithms,the Long Short-TermModel,and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data.Thus,the proposed research found the best model to be HMM,and LSTM was the second-best model that performed well in all aspects.A developedmodel will be highly recommended and helpful for early measurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments.

COVID-19 Outbreak Prediction by Using Machine Learning Algorithms

COVID-19 is a contagious disease and its several variants put under stress in all walks of life and economy as well.Early diagnosis of the virus is a crucial task to prevent the spread of the virus as it is a threat to life in the whole world.However,with the advancement of technology,the Internet of Things(IoT)and social IoT(SIoT),the versatile data produced by smart devices helped a lot in overcoming this lethal disease.Data mining is a technique that could be used for extracting useful information from massive data.In this study,we used five supervised ML strategies for creating a model to analyze and forecast the existence of COVID-19 using the Kaggle dataset“COVID-19 Symptoms and Presence.”RapidMiner Studio ML software was used to apply the Decision Tree(DT),Random Forest(RF),K-Nearest Neighbors(K-NNs)and Naive Bayes(NB),Integrated Decision Tree(ID3)algorithms.To develop the model,the performance of each model was tested using 10-fold cross-validation and compared to major accuracy measures,Cohan’s kappa statistics,properly or mistakenly categorized cases and root means square error.The results demonstrate that DT outperforms other methods,with an accuracy of 98.42%and a root mean square error of 0.11.In the future,a devisedmodel will be highly recommendable and supportive for early prediction/diagnosis of disease by providing different data sets.

A Query-Based Greedy Approach for Authentic Influencer Discovery in SIoT

The authors propose an informed search greedy approach that efficiently identifies the influencer nodes in the social Internet of Things with the ability to provide legitimate information.Primarily,the proposed approach minimizes the network size and eliminates undesirable connections.For that,the proposed approach ranks each of the nodes and prioritizes them to identify an authentic influencer.Therefore,the proposed approach discards the nodes having a rank(α)lesser than 0.5 to reduce the network complexity.αis the variable value represents the rank of each node that varies between 0 to 1.Node with the higher value ofαgets the higher priority and vice versa.The threshold valueα=0.5 defined by the authors with respect to their network pruning requirements that can be vary with respect to other research problems.Finally,the algorithm in the proposed approach traverses the trimmed network to identify the authentic node to obtain the desired information.The performance of the proposed method is evaluated in terms of time complexity and accuracy by executing the algorithm on both the original and pruned networks.Experimental results show that the approach identifies authentic influencers on a resultant network in significantly less time than in the original network.Moreover,the accuracy of the proposed approach in identifying the influencer node is significantly higher than that of the original network.Furthermore,the comparison of the proposed approach with the existing approaches demonstrates its efficiency in terms of time consumption and network traversal through the minimum number of hops.

Potential Inhibitory Effect of Vitamins Against COVID-19

Coronavirus disease 2019(COVID-19)is a current pandemic that has affected more than 195 countries worldwide.In this severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)pandemic,when treatment strategies are not yet clear and vaccines are not available,vitamins are an excellent choice to protect against this viral infection.The rationale behind this study was to examine the inhibitory effect of vitamins B,C,and D against the main protease of SARSCoV-2 and angiotensin-converting enzyme 2(ACE2),which have critical rolesin the immune system.Molecular docking,performed by using MOE-Dock of the Chemical Computing Group,was used to understand the mechanism.The vitamins all docked within the active sites of the Mpro(PDB ID:6LU7)and ACE2 receptor proteins(PDB ID:6VW1).Vitamins B and C delivered maximum energy scores against both targets,while vitamin D displayed a binding energy score of−7.9532 kcal/mol for M^(pro) and−7.9297 for ACE2.The efficiency of all three vitamins is higher than the binding energy score of chloroquine(−6.889 kcal/mol),which is now under clinical trials.The use of vitamins is beneficial,being immune system restorative,and they also act as anti-COVID agents.Although the potential beneficial effects of vitamin B and C are revealed through docking studies,further clinical trials are required for the validation of these results.