Interdigital MnO_(2)/PEDOT Alternating Stacked Microelectrodes for High-Performance On-Chip Microsupercapacitor and Humidity Sensing

For microelectronic devices,the on-chip microsupercapacitors with facile construction and high performance,are attracting researchers'prior consideration due to their high compatibility with modern microsystems.Herein,we proposed interchanging interdigital Au-/MnO_(2)/polyethylene dioxythiophene stacked microsupercapacitor based on a microfabrication process followed by successive electrochemical deposition.The stacked configuration of two pseudocapacitive active microelectrodes meritoriously leads to an enhanced contact area between MnO_(2)and the conductive and electroactive layer of polyethylene dioxythiophene,hence providing excellent electron transport and diffusion pathways of electrolyte ions,resulting in increased pseudocapacitance of MnO_(2)and polyethylene dioxythiophene.The stacked quasi-solid-state microsupercapacitors delivered the maximum specific capacitance of 43 mF cm^(-2)(211.9 F cm^(-3)),an energy density of 3.8μWh cm^(-2)(at a voltage window of 0.8 V)and 5.1μWh cm^(-2)(at a voltage window of 1.0 V)with excellent rate capability(96.6%at 2 mA cm^(-2))and cycling performance of 85.3%retention of initial capacitance after 10000 consecutive cycles at a current density of 5 mA cm^(-2),higher than those of ever reported polyethylene dioxythiophene and MnO_(2)-based planar microsupercapacitors.Benefiting from the favorable morphology,bilayer microsupercapacitor is utilized as a flexible humidity sensor with a response/relaxation time superior to those of some commercially available integrated microsensors.This strategy will be of significance in developing high-performance on-chip integrated microsupercapacitors/microsensors at low cost and environment-friendly routes.

Room Temperature Synthesis of Vertically Aligned Amorphous Ultrathin NiCo-LDH Nanosheets Bifunctional Flexible Supercapacitor Electrodes

Developing a simple scalable method to fabricate electrodes with high capacity and wide voltage range is desired for the real use of electrochemical supercapacitors.Herein,we synthesized amorphous NiCo-LDH nanosheets vertically aligned on activated carbon cloth substrate,which was in situ transformed from Co-metal-organic framework materials nano-columns by a simple ion exchange process at room temperature.Due to the amorphous and vertically aligned ultrathin structure of NiCo-LDH,the NiCo-LDH/activated carbon cloth composites present high areal capacities of 3770 and 1480 mF cm^(-2)as cathode and anode at 2 mA cm^(-2),and 79.5%and 80%capacity have been preserved at 50 mA cm^(-2).In the meantime,they all showed excellent cycling performance with negligible change after>10000 cycles.By fabricating them into an asymmetric supercapacitor,the device achieves high energy densities(5.61 mWh cm^(-2)and 0.352 mW cm^(-3)).This work provides an innovative strategy for simplifying the design of supercapacitors as well as providing a new understanding of improving the rate capabilities/cycling stability of NiCo-LDH materials.

TRAPP复合物与自噬关系的研究进展

转运蛋白颗粒(TRAPP)是一种高度保守的多亚基蛋白复合物,参与囊泡运输、细胞自噬等过程.细胞自噬是真核生物中高度保守的降解和循环通路,在维持细胞稳态和应对环境压力中起着重要作用.近年来,TRAPP复合物与自噬关系的研究较为广泛,研究表明多个TRAPP复合物亚基参与自噬过程,且TRAPP复合物不同亚基的突变或缺失也与多种人类疾病有关.对TRAPP复合物参与调控细胞自噬机制的深入研究有助于了解一些人类疾病的发生和发展.

Pushing the Electrochemical Performance Limits of Polypyrrole Toward Stable Microelectronic Devices

Conducting polymers have achieved remarkable attentions owing to their exclusive characteristics,for instance,electrical conductivity,high ionic conductivity,visual transparency,and mechanical tractability.Surface and nanostructure engineering of conjugated conducting polymers offers an exceptional pathway to facilitate their implementation in a variety of scientific claims,comprising energy storage and production devices,flexible and wearable optoelectronic devices.A two-step tactic to assemble high-performance polypyrrole(PPy)-based microsupercapacitor(MSC)is utilized by transforming the current collectors to suppress structural pulverization and increase the adhesion of PPy,and then electrochemical co-deposition of PPy-CNT nanostructures on rGO@Au current collectors is performed.The resulting fine patterned MSC conveyed a high areal capacitance of 65.9 mF cm^(−2)(at a current density of 0.1 mA cm^(−2)),an exceptional cycling performance of retaining 79%capacitance after 10,000 charge/discharge cycles at 5 mA cm^(−2).Benefiting from the intermediate graphene,current collector free PPy-CNT@rGO flexible MSC is produced by a facile transfer method on a flexible substrate,which delivered an areal capacitance of 70.25 mF cm^(−2) at 0.1 mA cm^(−2) and retained 46%of the initial capacitance at a current density of 1.0 mA cm^(−2).The flexible MSC is utilized as a skin compatible capacitive micro-strain sensor with excellent electromechanochemical characteristics.

自噬相关蛋白激酶的研究进展

自噬是细胞在应激过程中为了维持细胞稳态进行自我降解的过程,适度的细胞自噬有利于细胞生存,但是自噬水平过高或过低都会导致细胞内稳态失衡,甚至细胞死亡,且自噬水平变化与癌症等多种疾病相关.激酶在自噬调控通路中必不可少,近年来对于自噬的研究越来越深入,关于调节自噬进程的蛋白激酶研究也更加全面,有些蛋白激酶直接作为自噬核心蛋白发挥作用,比如unc-51样自噬激活蛋白激酶1(ULK1)/自噬相关蛋白1(Atg1)和Ⅲ类磷脂酰肌醇3-激酶(PI3K),还有些蛋白通过磷酸化自噬核心蛋白对自噬进行调控,比如雷帕霉素靶点蛋白(TOR)、AMP激活蛋白激酶(AMPK)、蛋白激酶C(PKC)、丝裂原活化蛋白激酶(MAPK).以细胞自噬为基础,对蛋白激酶,TOR,ULK1/Atg1,MAPK,PI3K,AMPK和PKC的研究现状进行了综述.

稻壳和麦麸对鲜玉米秸秆青贮品质的影响

为更好地利用稻壳和麦麸,本试验将2者与新鲜玉米(Zea mays L.)秸秆按不同比例混合青贮,发酵60天后测定其青贮品质和营养成分,以期筛选出混贮的最佳比例。结果表明,与鲜玉米秸秆单独青贮(对照)相比,麦麸和鲜玉米秸秆混合青贮的干物质、可溶性碳水化合物和粗蛋白含量均显著提高(P<0.05),但丁酸含量超过1%,发酵品质较差;与对照相比,稻壳与鲜玉米秸秆混合青贮的粗蛋白、可溶性碳水化合物含量和氨态氮/总氮的值均显著下降(P<0.05);稻壳、麦麸与鲜玉米秸秆混合青贮的发酵品质与对照相当,但干物质含量显著提高(P<0.05),且M1F1(5%麦麸+10%稻壳+85%鲜玉米秸秆)处理的Kaiser法评分最高。因此,建议将麦麸、稻壳和鲜玉米秸秆按5∶10∶85的鲜重比混合青贮。

LDH-based nanomaterials for photocatalytic applications:A comprehensive review on the role of bi/trivalent cations, anions,morphology, defect engineering, memory effect, and heterojunction formation

Using sunlight to drive chemical reactions via photocatalysis is paramount for a sustainable future.Among several photocatalysts,employing layered double hydrides(LDH) for photocatalytic application is most straightforward and desirable owing to their distinctive two-dimensional(2D) lamellar structure and optical attributes.This article reviews the advancements in bimetallic/trimetallic LDHs and various strategies to achieve high efficiency toward an outstanding performing photocatalyst.Firstly,the tuning of LDH components that control the electro nic and structural properties is explained.The tu ning obtained through the adoption,combination,and incorporation of different cations and anions is also explained.The progress of modification methods,such as the adoption of different morphologies,delamination,and defect engineering towards enhanced photocatalytic activities,is discussed in the mainstream.The band engineering,structural characteristics,and redox tuning are further deliberated to maximize solar energy harvesting for different photocatalytic applications.Finally,the progress obtained in forming hierarchical heterostructures through hybridization with other semiconductors or conducting materials is systematically disclosed to get maximum photocatalytic performance.Moreover,the structural changes during the in-situ synthesis of LDH and the stability of LDH-based photocatalysts are deliberated.The review also summarizes the improvements in LDH properties obtained through modification tactics and discusses the prospects for future energy and environmental applications.

Recent advances in titanium carbide MXene-based nanotextures with influential effect of synthesis parameters for solar CO_(2)reduction and H_(2)production:A critical review

Photocatalytic solar to energy conversion is considered an attractive approach for overcoming energy crises and environmental concerns.Recently,titanium carbide(Ti_(3)C_(2))MXenes have been recognized as promising cocatalysts based on their metallic conductivity,excessive active reaction sites,and enlarged surface area.The current review focuses on the properties and applications of Ti_(3)C_(2)MXenes useful in the field of photocatalysis.More specifically,surface modification of Ti_(3)C_(2)MXenes by varying synthesis parameters to get pure materials and also composites with the role of functional groups towards solar energy conversion applications is highlighted in this review.The effect of etching and oxidizing pathways to get an efficient cocatalyst has been discussed in detail.Considering the significant effect of parameters,optimum synthesis conditions such as etchant type,concentration,time and type of intercalant in both the Ti_(3)C_(2)synthesis approaches for improved photoactivity are discussed.Additionally,the surface modification of Ti_(3)C_(2)through oxidation for TiO2growth on its surface is deliberated with a detailed discussion on etchant type,concentration,etching time,and environmental factors.The optimum oxidation condition,including temperature,time,and environment for thermal treatment of Ti_(3)C_(2),were also included.Lastly,the review summarizes the conclusion and future perspectives for solar energy conversion applications.

Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.

Classification of Gastric Lesions Using Gabor Block Local Binary Patterns

The identification of cancer tissues in Gastroenterology imaging poses novel challenges to the computer vision community in designing generic decision support systems.This generic nature demands the image descriptors to be invariant to illumination gradients,scaling,homogeneous illumination,and rotation.In this article,we devise a novel feature extraction methodology,which explores the effectiveness of Gabor filters coupled with Block Local Binary Patterns in designing such descriptors.We effectively exploit the illumination invariance properties of Block Local Binary Patterns and the inherent capability of convolutional neural networks to construct novel rotation,scale and illumination invariant features.The invariance characteristics of the proposed Gabor Block Local Binary Patterns(GBLBP)are demonstrated using a publicly available texture dataset.We use the proposed feature extraction methodology to extract texture features from Chromoendoscopy(CH)images for the classification of cancer lesions.The proposed feature set is later used in conjuncture with convolutional neural networks to classify the CH images.The proposed convolutional neural network is a shallow network comprising of fewer parameters in contrast to other state-of-the-art networks exhibiting millions of parameters required for effective training.The obtained results reveal that the proposed GBLBP performs favorably to several other state-of-the-art methods including both hand crafted and convolutional neural networks-based features.

Parasitic Effects of Solitary Endoparasitoid, Aenasius bambawalei Hayat (Hymenoptera: Encyrtidae) on Cotton Mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae)

Parasitization has an enormous impact on host physiology, development and reproduction. The effect of parasitism by endoparasitoid, Aenasius bambawalei Hayat (Hymenoptera: Encyrtidae) on survival and reproduction of mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) on different host stages was studied. Our data reveals that host mealybugs parasitized by the wasp at the 2nd instar stage were died during the 3rd instar stage of their life. However, those parasitized at the 3rd instar stage could reach the adult stage and were able to produce their progeny. After 6 days of parasitization, all parasitized hosts of the 2nd, 3rd and adult stages were died except the 1st instar. Results showed that parasitized host mealybugs had significantly lower reproductive potential than the unparasitized ones. Maximum parasitoid emergence was recorded in the 3rd instar host stage. This basic research regarding survival and reproduction of the parasitized host mealybugs would be very helpful in devising sustainable biological control strategies for cotton mealybug.

Fusion of Infrared and Visible Images Using Fuzzy Based Siamese Convolutional Network

Traditional techniques based on image fusion are arduous in integrating complementary or heterogeneous infrared(IR)/visible(VS)images.Dissimilarities in various kind of features in these images are vital to preserve in the single fused image.Hence,simultaneous preservation of both the aspects at the same time is a challenging task.However,most of the existing methods utilize the manual extraction of features;and manual complicated designing of fusion rules resulted in a blurry artifact in the fused image.Therefore,this study has proposed a hybrid algorithm for the integration of multi-features among two heterogeneous images.Firstly,fuzzification of two IR/VS images has been done by feeding it to the fuzzy sets to remove the uncertainty present in the background and object of interest of the image.Secondly,images have been learned by two parallel branches of the siamese convolutional neural network(CNN)to extract prominent features from the images as well as high-frequency information to produce focus maps containing source image information.Finally,the obtained focused maps which contained the detailed integrated information are directly mapped with the source image via pixelwise strategy to result in fused image.Different parameters have been used to evaluate the performance of the proposed image fusion by achieving 1.008 for mutual information(MI),0.841 for entropy(EG),0.655 for edge information(EI),0.652 for human perception(HP),and 0.980 for image structural similarity(ISS).Experimental results have shown that the proposed technique has attained the best qualitative and quantitative results using 78 publically available images in comparison to the existing discrete cosine transform(DCT),anisotropic diffusion&karhunen-loeve(ADKL),guided filter(GF),random walk(RW),principal component analysis(PCA),and convolutional neural network(CNN)methods.

Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities

Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things(IoT).The IoT is the backbone of smart city applications such as smart grids and green energy management.In smart cities,the IoT devices are used for linking power,price,energy,and demand information for smart homes and home energy management(HEM)in the smart grids.In complex smart gridconnected systems,power scheduling and secure dispatch of information are the main research challenge.These challenges can be resolved through various machine learning techniques and data analytics.In this paper,we have proposed a particle swarm optimization based machine learning algorithm known as a collaborative execute-before-after dependency-based requirement,for the smart grid.The proposed collaborative execute-before-after dependencybased requirement algorithm works in two phases,analysis and assessment of the requirements of end-users and power distribution companies.In the rst phases,a xed load is adjusted over a period of 24 h,and in the second phase,a randomly produced population load for 90 days is evaluated using particle swarm optimization.The simulation results demonstrate that the proposed algorithm performed better in terms of percentage cost reduction,peak to average ratio,and power variance mean ratio than particle swarm optimization and inclined block rate.

Quantum Particle Swarm Optimization Based Convolutional Neural Network for Handwritten Script Recognition

Even though several advances have been made in recent years,handwritten script recognition is still a challenging task in the pattern recognition domain.This field has gained much interest lately due to its diverse application potentials.Nowadays,different methods are available for automatic script recognition.Among most of the reported script recognition techniques,deep neural networks have achieved impressive results and outperformed the classical machine learning algorithms.However,the process of designing such networks right from scratch intuitively appears to incur a significant amount of trial and error,which renders them unfeasible.This approach often requires manual intervention with domain expertise which consumes substantial time and computational resources.To alleviate this shortcoming,this paper proposes a new neural architecture search approach based on meta-heuristic quantum particle swarm optimization(QPSO),which is capable of automatically evolving the meaningful convolutional neural network(CNN)topologies.The computational experiments have been conducted on eight different datasets belonging to three popular Indic scripts,namely Bangla,Devanagari,and Dogri,consisting of handwritten characters and digits.Empirically,the results imply that the proposed QPSO-CNN algorithm outperforms the classical and state-of-the-art methods with faster prediction and higher accuracy.

Boosting the electrochemical performance and reliability of conducting polyme microelectrode via intermediate graphene for on-chip asymmetric micro-supercapacitor

High-performance anode is hurdle for on-chip planar microsupercapacitor(MSC).Polypyrrole(PPy)is a highly attractive pseudocapacitive material,but its low cycling stability,and low adhesion with current collector hinder its practicability.Herein we propose one-prong generic strategy to boost the cycling stability of PPy.For our strategy,the electrochemical deposition of multilayered reduced graphene oxide(rGO)on micropatterned Au is utilized,and the resultant rGO@Au pattern is then used for growing highly porous PPy nanostructures by facile electrochemical polymerization.The fabricated PPy anode on rGO@Au has quasi rectangular cyclic voltammetry curves up to-0.7 V and exceptional cycling stability,retaining82%of capacitance after 10,000 charge/discharge cycles in 2 M KCl electrolyte.The outstanding reliability of PPy on rGO@Au is due to the flexibility of rGO,accommodating structural pulverization and providing a promising background for the nucleation of highly porous nanostructure.Further,an all-polymer based asymmetric aqueous MSC(AMSC)is constructed with PPy anode and PEDOT cathode,which exhibited excellent electrochemical performance compared with conventional symmetric MSCs based on conducting polymers.The constructed AMSC delivered a maximum areal capacitance of 15.9 m F cm^-2(99.3 F cm^-3),high specific energy and power densities of 4.3μWh cm^-2(27.03 mWh cm^-3)and 0.36 W cm^-2(0.68 W cm^-3)at 1.4 V,respectively.The enhanced electrochemical performances can be illustrated by nucleation mechanism,in which surface topology of r GO generates a promising background for nucleation and electrochemical growth of nanoporous pseudocapacitive conducting polymers with superior interfacial contact and improved surface area.

Influence of Integrated Nutrients on Growth, Yield and Quality of Maize (Zea mays L.)

A field experiment was conducted to evaluate the integrated nutrients effect on growth, yield and quality of maize (Zea mays L.) during spring, 2009, at the Agronomic Research Area, University of Agriculture, Faisalabad. The ex-periment was laid out in Randomized Complete Block Design (RCBD) having three replications with following treat-ments: T1 (control), T2(recommended NPK @ 200-120-125 kg ha-1), T3 [single spray of multinutrient (a solution mixture of micronutrients i.e;Zn = 2%, Fe = 1%, B = 1%, Mn = 1%, Cu = 0.2% and macronutrients N = 1%, K2O = 2%, S = 2%) @ 1.25Lha-1], T4 (recommended NPK @ 200-120-125 kg ha-1 + single spray of multinutrient @ 1.25L ha-1), T5 (recom-mended NPK @ 200-120-125 kg ha-1 + two spray of multinutrient @1.25Lha-1) and T6 (recommended NPK @ 200-120-125 kg ha-1+ three spray of multinutrient @ 1.25Lha-1).The recommended dose of NPK in addition with single spray of Multi-nutrients substationally improved all growth parameters, ear characteristics and also enhanced macronutrients use efficiency up to 11.5% which induced significant increase in grain yield as compared to control and also in the treatment where recommended dose of NPK was applied alone. The quality parameter of maize (oil contents) significantly improved by foliar application of multinutrients solution but recommended dose of fertilizer in addition to single spray of Multi-nutrients was economical.

Subsurface geological model of sedimentary and metasedimentary wedge from Mansehra to Battal based on gravity data,Hazara area,Pakistan

This study is aimed to delineate the subsurface structural elements using geophysical techniques in the Haraza area of Pakistan.We investigated the Oghi and Battal thrust faults,sedimentary and metasedimentary wedge,and the absolute crustal thickness based on terrestrial gravity data.Unlike seismic survey relying on wave propagation,magnetic survey is based on both attraction and repulsion,and electrical and electromagnetics on induction.The attractive gravity field produces relatively simpler patterns of anomalies,like a series of highs and lows over regions with undulating basements and buried structures.A qualitative interpretation of gravity data reveals a good deal of information.During the collision of Indian and Eurasian Plates,compressional structures were developed in the Lesser Himalayas or northwest of the Hazara Kashmir Syntaxis.The study mainly focuses on the western limb of the Hazara Kashmir Syntaxis.The regional and local Bouguer anomalies were incorporated to delineate the regional structural units.The gravity model is computed through geophysical technique along with profile A-A'from Mansehra to the Battal area that demarcates the blind Oghi Thrust and emergent Battal Thrust.Tanol Formation of Precambrian age demarcates the Oghi Thrust near Kotli Pine while the Battal Thrust is demarcated within the Mansehra Granite of Cambrian to Ordovician age near Battal.Along with the Battal Thrust,fault gouge and breccias have been observed during the field studies.The total thickness of the sedimentary/metasedimentary wedge in the Mansehra and Battal areas was estimated to be 13.6 km and 14.2 km.In comparison,the total thickness of crust in the Mansehra and Battal areas was 51.6 km and 52.2 km,respectively.

Properties of Certain Subclasses of Analytic Functions Involving q-Poisson Distribution

By using the basic(or q)-Calculus many subclasses of analytic and univalent functions have been generalized and studied from different viewpoints and perspectives.In this paper,we aim to define certain new subclasses of an analytic function.We then give necessary and sufficient conditions for each of the defined function classes.We also study necessary and sufficient conditions for a function whose coefficients are probabilities of q-Poisson distribution.To validate our results,some known consequences are also given in the form of Remarks and Corollaries.

A Comprehensive Combination of Apparent and Shear Viscoelastic Data during Polymer Flooding for EOR Evaluations

We present a comprehensive workflow to obtain the best insights into the viscoelastic behavior of polymers. Viscoelasticity is depicted in most cases by the current commercially available polymers used for EOR applications. The phenomenon is debated to be one of the reasons for additional oil recovery during polymer flooding applications. It is somehow accepted that polymer increases volumetric sweep efficiency owing to improved mobility ratio. Recently researches have explained that flooding polymers in porous media with elastic characteristics could recover additional oil, due to the improved microscale oil displacement (pore-scale). This study focuses on the analysis of polymer viscoelasticity based on single-phase core, sand-pack and capillary tube (CT) experiments coupled with their detailed rheological characterization, in order to evaluate polymer behavior in porous media. A combination of hydrolyzed polyacrylamides (HPAM) polymers as well as a bio polymer is presented throughout this evaluation. The evaluation of the data is addressed on the basis of pressure drop across the pores, separating the shear associated pressure by the extensional thickening associated pressure. Apart from that, viscoelastic dependence of the converging-diverging geometry has been experimented. Based on the observed behavior through porous media, HPAM polymers are compared with bio polymers. Moreover, the behavior of solutions with induced mechanical degradation (pre-sheared) is compared with non-sheared solutions. Similarly, concentrations with different polymer solutions are evaluated. The results obtained in this work allow for additional understanding of polymer solutions behavior in flooding applications. Furthermore?The results support?the definition of optimized workflows to assess their behavior under flow through porous media. Finally this evaluation helps to describe the parameter that defines polymer viscoelastic properties.

Tropospheric Scintillation Estimation Using 10 Years Meteorological Data

This paper presents estimation of tropospheric scintillation based on ITU-R model from previous 10 years meteorological data.Scintillation As(p) is critical in designing of microwave links for achieving optimal performance.There are many phenomenons that cause degradation to signal during transmission through the earth's atmosphere one of them is scintillation.Scintillation estimation is based on measurements of surface temperature T and relative humidity RH.Input parameter for ITU-R scintillation estimation model is the monthly average of wet part of refractivity Nwet.MATLAB software tool is employed to show the results for tropospheric scintillation in various seasons for time percentage p and frequencies ranging from 4GHz to 20 GHz.