Energy dissipation mechanism and ballistic characteristic optimization in foam sandwich panels against spherical projectile impact

This study systematically examines the energy dissipation mechanisms and ballistic characteristics of foam sandwich panels(FSP)under high-velocity impact using the explicit non-linear finite element method.Based on the geometric topology of the FSP system,three FSP configurations with the same areal density are derived,namely multi-layer,gradient core and asymmetric face sheet,and three key structural parameters are identified:core thickness(t_(c)),face sheet thickness(t_(f))and overlap face/core number(n_(o)).The ballistic performance of the FSP system is comprehensively evaluated in terms of the ballistic limit velocity(BLV),deformation modes,energy dissipation mechanism,and specific penetration energy(SPE).The results show that the FSP system exhibits a significant configuration dependence,whose ballistic performance ranking is:asymmetric face sheet>gradient core>multi-layer.The mass distribution of the top and bottom face sheets plays a critical role in the ballistic resistance of the FSP system.Both BLV and SPE increase with tf,while the raising tcor noleads to an increase in BLV but a decrease in SPE.Further,a face-core synchronous enhancement mechanism is discovered by the energy dissipation analysis,based on which the ballistic optimization procedure is also conducted and a design chart is established.This study shed light on the anti-penetration mechanism of the FSP system and might provide a theoretical basis for its engineering application.

Health diagnosis of ultrahigh arch dam performance using heterogeneous spatial panel vector model

Currently,more than ten ultrahigh arch dams have been constructed or are being constructed in China.Safety control is essential to long-term operation of these dams.This study employed the flexibility coefficient and plastic complementary energy norm to assess the structural safety of arch dams.A comprehensive analysis was conducted,focusing on differences among conventional methods in characterizing the structural behavior of the Xiaowan arch dam in China.Subsequently,the spatiotemporal characteristics of the measured performance of the Xiaowan dam were explored,including periodicity,convergence,and time-effect characteristics.These findings revealed the governing mechanism of main factors.Furthermore,a heterogeneous spatial panel vector model was developed,considering both common factors and specific factors affecting the safety and performance of arch dams.This model aims to comprehensively illustrate spatial heterogeneity between the entire structure and local regions,introducing a specific effect quantity to characterize local deformation differences.Ultimately,the proposed model was applied to the Xiaowan arch dam,accurately quantifying the spatiotemporal heterogeneity of dam performance.Additionally,the spatiotemporal distri-bution characteristics of environmental load effects on different parts of the dam were reasonably interpreted.Validation of the model prediction enhances its credibility,leading to the formulation of health diagnosis criteria for future long-term operation of the Xiaowan dam.The findings not only enhance the predictive ability and timely control of ultrahigh arch dams'performance but also provide a crucial basis for assessing the effectiveness of engineering treatment measures.

How R&D investment promotes green technology innovation in the context of digitalization?-An empirical analysis based on provincial panel data

Green technology innovation is an important driving force and source to promote my country’s high-quality development,and it is the core path to achieve sustainable development.This paper uses my country’s provincial panel data from 2016 to 2019 to study the impact mechanism of R&D investment on green technology innovation,and introduces the level of digitization,using the panel threshold model to discuss its role in the impact mechanism of R&D investment on green technology innovation.The study found that when the level of digitalization in a region is low,increasing R&D investment does not necessarily improve the ability of green technology innovation;when the level of digitalization is relatively high,R&D investment has a positive role in promoting green technology innovation.Therefore,it is necessary to improve policies to encourage enterprises to increase investment in research and development;at the same time,it is necessary to promote the coordinated development of digital foundation,digital investment,digital literacy,digital economy and digital application,and promote the deep integration of digitalization and green technology innovation.

A Modified Principal Component Analysis Method for Honeycomb Sandwich Panel Debonding Recognition Based on Distributed Optical Fiber Sensing Signals

The safety and integrity requirements of aerospace composite structures necessitate real-time health monitoring throughout their service life.To this end,distributed optical fiber sensors utilizing back Rayleigh scattering have been extensively deployed in structural health monitoring due to their advantages,such as lightweight and ease of embedding.However,identifying the precise location of damage from the optical fiber signals remains a critical challenge.In this paper,a novel approach which namely Modified Sliding Window Principal Component Analysis(MSWPCA)was proposed to facilitate automatic damage identification and localization via distributed optical fiber sensors.The proposed method is able to extract signal characteristics interfered by measurement noise to improve the accuracy of damage detection.Specifically,we applied the MSWPCA method to monitor and analyze the debonding propagation process in honeycomb sandwich panel structures.Our findings demonstrate that the training model exhibits high precision in detecting the location and size of honeycomb debonding,thereby facilitating reliable and efficient online assessment of the structural health state.

Composite Panels from the Combination of Rice Husk and Wood Chips with a Natural Resin Based on Tannins Reinforced with Sugar Cane Molasses Intended for Building Insulation: Physico-Mechanical and Thermal Properties

The objective of this work is to develop new biosourced insulating composites from rice husks and wood chips that can be used in the building sector. It appears from the properties of the precursors that rice chips and husks are materials which can have good thermal conductivity and therefore the combination of these precursors could make it possible to obtain panels with good insulating properties. With regard to environmental and climatic constraints, the composite panels formulated at various rates were tested and the physico-mechanical and thermal properties showed that it was essential to add a crosslinker in order to increase certain solicitation. an incorporation rate of 12% to 30% made it possible to obtain panels with low thermal conductivity, a low surface water absorption capacity and which gives the composite good thermal insulation and will find many applications in the construction and real estate sector. Finally, new solutions to improve the fire reaction of the insulation panels are tested which allows to identify suitable solutions for the developed composites. In view of the flame tests, the panels obtained are good and can effectively combat fire safety in public buildings.

Advancements in Photovoltaic Panel Fault Detection Techniques

This paper examines the progression and advancements in fault detection techniques for photovoltaic (PV) panels, a target for optimizing the efficiency and longevity of solar energy systems. As the adoption of PV technology grows, the need for effective fault detection strategies becomes increasingly paramount to maximize energy output and minimize operational downtimes of solar power systems. These approaches include the use of machine learning and deep learning methodologies to be able to detect the identified faults in PV technology. Here, we delve into how machine learning models, specifically kernel-based extreme learning machines and support vector machines, trained on current-voltage characteristic (I-V curve) data, provide information on fault identification. We explore deep learning approaches by taking models like EfficientNet-B0, which looks at infrared images of solar panels to detect subtle defects not visible to the human eye. We highlight the utilization of advanced image processing techniques and algorithms to exploit aerial imagery data, from Unmanned Aerial Vehicles (UAVs), for inspecting large solar installations. Some other techniques like DeepLabV3 , Feature Pyramid Networks (FPN), and U-Net will be detailed as such tools enable effective segmentation and anomaly detection in aerial panel images. Finally, we discuss implications of these technologies on labor costs, fault detection precision, and sustainability of PV installations.

Comprehensive Examination of Solar Panel Design: A Focus on Thermal Dynamics

In the 21st century, the deployment of ground-based Solar Photovoltaic (PV) Modules has seen exponential growth, driven by increasing demands for green, clean, and renewable energy sources. However, their usage is constrained by certain limitations. Notably, the efficiency of solar PV modules on the ground peaks at a maximum of 25%, and there are concerns regarding their long-term reliability, with an expected lifespan of approximately 25 years without failures. This study focuses on analyzing the thermal efficiency of PV Modules. We have investigated the temperature profile of PV Modules under varying environmental conditions, such as air velocity and ambient temperature, utilizing Computational Fluid Dynamics (CFD). This analysis is crucial as the efficiency of PV Modules is significantly impacted by changes in the temperature differential relative to the environment. Furthermore, the study highlights the effect of airflow over solar panels on their temperature. It is found that a decrease in the temperature of the PV Module increases Open Circuit Voltage, underlining the importance of thermal management in optimizing solar panel performance.

血清外泌体microRNAs单一及组合panel对结直肠癌的诊断价值

目的 检测血清外泌体miR-15b、miR-16、miR-21和miR-31在结直肠癌患者中的表达水平,并评估其对结直肠癌的诊断价值。方法 选取2018年3月至2022年5月本院收治的123例直肠癌患者(结直肠癌组)及117例大肠腺瘤患者作为研究对象(结直肠癌组),并纳入150例健康对照者作为对照(健康对照组)。提取血清外泌体中miR-15b、miR-16、miR-21和miR-31,qRT-PCR比较4种miRNA在各组中表达水平差异。受试者工作特征(ROC)曲线评价4种miRNA单一及组合panel对结直肠癌的诊断价值。结果 结直肠癌组血清miR-15b、miR-16、miR-21和miR-31水平高于健康对照组(P <0.05),且miR-15b、miR-21和miR-31水平高于结直肠腺瘤组(P <0.05)。Ⅲ-Ⅳ期、伴淋巴结浸润、低分化程度结直肠癌组患者血清miR-15b、miR-21和miR-31水平分别高于Ⅰ-Ⅱ期、不伴淋巴结浸润、中-高分化程度患者,miR-15b、miR-21和miR-31水平与临床TNM分期、伴淋巴结浸润呈正相关,与分化程度呈负相关。4种miRNA中诊断价值最高的指标是miR-15b,其敏感度和特异度分别为81.33%和91.80%(P <0.05);且miR-15b、miR-21和miR-31的组合panel具有更好的诊断价值,其敏感度和特异度分别为95.06%和94.44%(P <0.05)。结论 miR-15b、miR-16、miR-21和miR-31在结直肠癌患者外周血血清中异常表达,miR-15b、miR-21和miR-31水平与结直肠癌的发病及进展密切相关,且miR-15b、miR-21和miR-31组合panel具有作为结直肠癌的新型诊断模型的潜力。

Automated Classification of Snow-Covered Solar Panel Surfaces Based on Deep Learning Approaches

Recently,the demand for renewable energy has increased due to its environmental and economic needs.Solar panels are the mainstay for dealing with solar energy and converting it into another form of usable energy.Solar panels work under suitable climatic conditions that allow the light photons to access the solar cells,as any blocking of sunlight on these cells causes a halt in the panels work and restricts the carry of these photons.Thus,the panels are unable to work under these conditions.A layer of snow forms on the solar panels due to snowfall in areas with low temperatures.Therefore,it causes an insulating layer on solar panels and the inability to produce electrical energy.The detection of snow-covered solar panels is crucial,as it allows us the opportunity to remove snow using some heating techniques more efficiently and restore the photovoltaics system to proper operation.This paper presents five deep learning models,■-16,■-19,ESNET-18,ESNET-50,and ESNET-101,which are used for the recognition and classification of solar panel images.In this paper,two different cases were applied;the first case is performed on the original dataset without trying any kind of preprocessing,and the second case is extreme climate conditions and simulated by generating motion noise.Furthermore,the dataset was replicated using the upsampling technique in order to handle the unbalancing issue.The conducted dataset is divided into three different categories,namely;all_snow,no_snow,and partial snow.The fivemodels are trained,validated,and tested on this dataset under the same conditions 60%training,20%validation,and testing 20%for both cases.The accuracy of the models has been compared and verified to distinguish and classify the processed dataset.The accuracy results in the first case showthat the comparedmodels■-16,■-19,ESNET-18,and ESNET-50 give 0.9592,while ESNET-101 gives 0.9694.In the second case,the models outperformed their counterparts in the first case by evaluating performance,where the accuracy results reached 1.00,0.9545,0.9888,1.00.and 1.00 for■-16,■-19,ESNET-18 and ESNET-50,respectively.Consequently,we conclude that the second case models outperformed their peers.

Sensitivity analysis of spacecraft in micrometeoroids and orbital debris environment based on panel method

To reduce the risk of mission failure caused by the MM/OD impact of the spacecraft,it is necessary to optimize the design of the spacecraft.Spacecraft survivability assessment is the key technology in the optimal design of spacecraft.Spacecraft survivability assessment includes spacecraft impact sensitivity analysis and spacecraft component vulnerability analysis under MM/OD environment.The impact sensitivity refers to the probability of a spacecraft encountering an MM/OD impact while in orbit.Vulnerability refers to the probability that each component of a spacecraft may fail or malfunction when impacted by space debris.Yet this paper mainly analyzes the impact sensitivity and proposes a spacecraft sensitivity assessment method under the MM/OD environment based on a panel method.Under this panel method,a spacecraft geometric model is discretized into small panels,and whether they are impacted by MM/OD or not is determined through the analysis of the shielding or shadowing relationships between panels.The number of impacts on each panel is obtained through calculation,and accordingly the probability of each spacecraft component encountering MM/OD impact can be acquired,thus generating the impact sensibility.This paper extracts data from the NASA’s ORDEM2000,the ESA’s MASTER8 as well as the SDEEM2015(Space Debris Environmental Engineering Model developed by HIT),and uses the PCHIP(Piecewise Cubic Hermite Interpolating Polynomial)method to interpolate and fit the size-flux relationship of space debris.Compared with linear interpolation and cubic spline interpolation,the fitting results through the method are relatively more accurate.The feasibility of this method is also demonstrated through two actual examples shown in this paper,whose results are close to those from ESABASE,although there are some minor errors mainly due to different debris data input.Through the cross-check by three risk assessment software-BUMPER,MDPANTO and MODAOST-under standard operating conditions,the feasibility of this method is again verified.

Regulation of the growth of sprouting roots of black locust seedlings using root barrier panels

How can we regulate an invasive alien species of high commercial value?Black locust(Robinia pseudoacacia L.)has a unique capacity for seed dispersal and high germination.Field surveys indicate that black locust increases its growing area with sprouting roots and the elongation of horizontal roots at a soil depth of 10 cm.Therefore,a method to regulate the development of horizontal roots could be eff ective in slowing the invasiveness of black locust.In this study,root barrier panels were tested to inhibit the growth of horizontal roots.Since it is labor intensive to observe the growth of roots in the fi eld,it was investigated in a nursery setting.The decrease in secondary fl ush,an increase in yellowed leafl ets,and the height in the seedlings were measured.Installing root barrier panels to a depth of 30 cm eff ectively inhibit the growth of horizontal roots of young black locust.

Developing strategy for rural transformation to alleviate poverty in Pakistan:Stylized facts from panel analysis

Sustainable income growth and poverty reduction remain critical challenges at the forefront of research in Pakistan,particularly in rural areas.To overcome these challenges,the role of rural transformation(RT)has emerged and gained importance in recent years.The present study is based on district-level data and covers the period from 1981 to 2019.The study attempts to quantify the role of rural transformation in boosting rural per capita income and alleviating rural poverty in the country.The study also aims to explore the impact of stages of rural transformation on rural per capita income and rural poverty alleviation.The empirical findings reveal that rural transformation(RT_(1)and RT_(2))is essential in enhancing rural per capita income and alleviating rural poverty.The role of the share of high-value crops(RT_(1))is more pronounced than the share of non-farm employment(RT_(2))in boosting rural per capita income and poverty alleviation.The trend of larger contribution of RT_(1)to enhance rural per capita income also continued at 2nd stage of rural transformation.In the case of poverty reduction,at 3rd stage of rural transformation,the role of RT_(2)is dominant.Our results indicate that districts at higher stages of rural transformation(both RT_(1)and RT_(2))tend to correlate positively with increased rural per capita income and reduced poverty rates,suggesting that progress in rural transformation is associated with improved economic conditions.However,it is important to note that this correlation does not necessarily imply a direct causal relationship between rural transformation and these economic outcomes;other factors may have influenced this relationship.In addition,the welfare impacts are more noticeable among the districts where a simultaneous shift from grain crops to cash crops and from farm employment to non-farm employment is observed.The study provides baseline information to learn experiences from fast-growing districts and to replicate the strategies in other districts,which boosts the RT process that may increase rural per capita income and enhance poverty reduction efforts.

Ghost-Retina Net:Fast Shadow Detection Method for Photovoltaic Panels Based on Improved Retina Net

Based on the artificial intelligence algorithm of RetinaNet,we propose the Ghost-RetinaNet in this paper,a fast shadow detection method for photovoltaic panels,to solve the problems of extreme target density,large overlap,high cost and poor real-time performance in photovoltaic panel shadow detection.Firstly,the Ghost CSP module based on Cross Stage Partial(CSP)is adopted in feature extraction network to improve the accuracy and detection speed.Based on extracted features,recursive feature fusion structure ismentioned to enhance the feature information of all objects.We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression,respectively.Finally,in order to achieve fast detection,the Ghost strategy is chosen to lighten the size of the algorithm.The results of the experiment show that the average detection accuracy(mAP)of the algorithm can reach up to 97.17%,the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second(FPS),which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment.The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.

Perforation studies of concrete panel under high velocity projectile impact based on an improved dynamic constitutive model

The finite-depth concrete panels have been widely applied in the protective structures,and its impact resistance and dynamic fracture failures,especially the scabbing/perforation limits,under high velocity projectile impact,are mainly concerned by protective engineers,which are numerically studied based on an improved dynamic concrete model in this study.Firstly,based on the framework of the KCC(Karagozian&Case concrete)model,a dynamic concrete model is proposed which considers an independent tensile damage model and a continued transition between dynamic tensile and compressive properties.Secondly,the strength surface,equation of state and damage parameters of the proposed model are comprehensively calibrated by a triaxial compressive test with high confinement pressure,the rationality of which is further verified based on the single element tests,e.g.,uniaxial and triaxial compression as well as uniaxial,biaxial and triaxial tension.Thirdly,a series of projectile high velocity impact tests on thin and thick concrete panels are simulated,which indicates that the projectile residual velocity and dynamic fracture failures are reproduced satisfactorily,while the KCC model underestimates both the spalling and scabbing dimensions severely.Finally,based on the validated concrete model and finite element analyses approach,the validations of the existing five empirical formulae are evaluated,in terms of the depth of penetration(DOP)and scabbing/perforation limits of concrete panel.Both the Army corps of engineers(ACE)and modified National Defense Research Committee(NDRC)formulae are recommended in the design of the protective structure to avoid scabbing failure.

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

Photovoltaic(PV)boards are a perfect way to create eco-friendly power from daylight.The defects in the PV panels are caused by various conditions;such defective PV panels need continuous monitoring.The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants.In general,conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation.The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process.To increase the accuracy and to reduce the processing time,a new Convolutional Neural Network(CNN)architecture is required.Hence,in the present work,a new Real-time Multi Variant Deep learning Model(RMVDM)architecture is proposed,and it extracts the image features and classifies the defects in PV panels quickly with high accuracy.The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images.The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person.The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset.The results show that 98%of the accuracy and recall values in the fault detection and classification process.

Breeding design in wheat by combining the QTL information in a GWAS panel with a general genetic map and computer simulation

A large amount of genome-wide association study(GWAS)panels together with quantitative-trait locus(QTL)information associated with breeding-targeted traits have been described in wheat(Triticum aestivum L.).However,the application of mapping results from a GWAS panel to conventional wheat breeding remains a challenge.In this study,we first report a general genetic map which was constructed from 44 published linkage maps.It permits the estimation of genetic distances between any two genetic loci with physical map positions,thereby unifying the linkage relationships between QTL,genes,and genomic markers from multiple genetic populations.Second,we describe QTL mapping in a wheat GWAS panel of 688 accessions,identifying 77 QTL associated with 12 yield and grain-quality traits.Because these QTL have known physical map positions,they could be mapped onto the general map.Finally,we present a design approach to wheat breeding by using known QTL information and computer simulation.Potential crosses between parents in the GWAS panel may be evaluated by the relative frequency of the target genotype,trait correlations in simulated progeny populations,and genetic gain of selected progenies.It is possible to simultaneously improve yield and grain quality by suitable parental selection,progeny population size,and progeny selection scheme.Applying the design approach will allow identifying the most promising crosses and selection schemes in advance of the field experiment,increasing predictability and efficiency in wheat breeding.

Indian Solar Panel Initiatives in Reducing Carbon Dioxide Emissions

Environmental degradation and the emission of greenhouse gases particularly carbon dioxide have expanded problems to human wellness and to the atmosphere. The second-most populated country in the globe, India, is among the primary users of conventional resources, which leads to global warming. The growth rate is anticipated to raise more before 2050, which will cause the brisk industrial expansion and rising energy demand to both increases. In order to reduce carbon emissions and meet energy requirements, many countries use alternate usage of renewable energy particularly solar energy. In this review we aim to study solar panel schemes initiated by India, mainly focusing on National Solar Mission. This study also reviews the present solar installed capacity, solar panel scheme 2022, and initiatives and outcomes of solar panels in residences and offices. This study reviewed that by using solar panel resources, the (MNRE) Ministry of New and Renewable Energy hopes to help the Indian Government reach its purpose of 100 GW solar installed capacity by end of 2022. Despite having an amazing 40 GW of solar power installed capacity till December 2021, India is still far from reaching its own goal of 100 GW by March 2023 as per NSM. In essence, this means that India will need to change a few of its ongoing plans further.

Assessment of the stiffened panel performance in the OTEC seawater tank design:Parametric study and sensitivity analysis

Ocean thermal energy conversion(OTEC)is a process of generating electricity by exploiting the temperature difference between warm surface seawater and cold deep seawater.Due to the high static and dynamic pressures that are caused by seawater circulation,the stiffened panel that constitutes a seawater tank may undergo a reduction in ultimate strength.The current paper investigates the design of stiffening systems for OTEC seawater tanks by examining the effects of stiffening parameters such as stiffener sizes and span-over-bay ratio for the applied combined loadings of lateral and transverse pressure by fluid motion and axial compression due to global bending moment.The ultimate strength calculation was conducted by using the non-linear finite element method via the commercial software known as ABAQUS.The stress and deformation distribution due to pressure loads was computed in the first step and then brought to the second step,in which the axial compression was applied.The effects of pressure on the ultimate strength of the stiffener were investigated for representative stiffened panels,and the significance of the stiffener parameters was assessed by using the sensitivity analysis method.As a result,the ultimate strength was reduced by approximately 1.5%for the span-over-bay ratio of 3 and by 7%for the span-over-bay ratio of 6.

Naturally Nitrogen-Doped Biochar Made from End-of-Life Wood Panels for SO_(2) Gas Depollution

Reconstituted wood panels have several advantages in terms of ease of manufacturing,but their shorter life span results in a huge amount of reconstituted wood panels being discarded in sorting centers yearly.Currently,the most common approach for dealing with this waste is incineration.In this study,reconstituted wood panels were converted into activated biochar through a two-step thermochemical process:(i)biochar production using pilot scale fast pyrolysis at 250 kg/h and 450℃;and(ii)a physical activation at three temperatures(750℃,850℃ and 950℃)using an in-house activation furnace(1 kg/h).Results showed that the first stage removed about 66% of the nitrogen from the wood panels in the form of NO,NH3,and trimethylamine,which were detected in small amounts compared to emitted CO_(2).Compared to other types of thermochemical conversion methods(e.g.,slow pyrolysis),isocyanic acid and hydrogen cyanide were not detected in this study.The second stage produced activated biochar with a specific surface area of up to 865 m^(2)/g at 950℃.The volatile gases generated during activation were predominantly composed of toluene and benzene.This two-step process resulted in nitrogen-rich carbon in the form of pyrrolic and pyridinic nitrogen.Activated biochars were then evaluated for their SO_(2) retention performance and showed an excellent adsorption capacity of up to 2140 mg/g compared to 65 mg/g for a commercial activated carbon(889 m^(2)/g).End-of-life reconstituted wood panels and SO_(2) gas are problematic issues in Canada where the economy largely revolves around forestry and mining industries.

Superiority of Bayesian Imputation to Mice in Logit Panel Data Models

Non-responses leading to missing data are common in most studies and causes inefficient and biased statistical inferences if ignored. When faced with missing data, many studies choose to employ complete case analysis approach to estimate the parameters of the model. This however compromises on the susceptibility of the estimates to reduced bias and minimum variance as expected. Several classical and model based techniques of imputing the missing values have been mentioned in literature. Bayesian approach to missingness is deemed superior amongst the other techniques through its natural self-lending to missing data settings where the missing values are treated as unobserved random variables that have a distribution which depends on the observed data. This paper digs up the superiority of Bayesian imputation to Multiple Imputation with Chained Equations (MICE) when estimating logistic panel data models with single fixed effects. The study validates the superiority of conditional maximum likelihood estimates for nonlinear binary choice logit panel model in the presence of missing observations. A Monte Carlo simulation was designed to determine the magnitude of bias and root mean square errors (RMSE) arising from MICE and Full Bayesian imputation. The simulation results show that the conditional maximum likelihood (ML) logit estimator presented in this paper is less biased and more efficient when Bayesian imputation is performed to curb non-responses.