Myotis bat STING attenuates aging-related inflammation in female mice

Bats,notable as the only flying mammals,serve as natural reservoir hosts for various highly pathogenic viruses in humans(e.g.,SARS-CoV and Ebola virus).Furthermore,bats exhibit an unparalleled longevity among mammals relative to their size,particularly the Myotis bats,which can live up to 40 years.However,the mechanisms underlying these distinctive traits remain incompletely understood.In our prior research,we demonstrated that bats exhibit dampened STING-interferon activation,potentially conferring upon them the capacity to mitigate virus-or aging-induced inflammation.To substantiate this hypothesis,we established the first in vivo bat-mouse model for aging studies by integrating Myotis davidii bat STING(MdSTING)into the mouse genome.We monitored the genotypes of these mice and performed a longitudinal comparative transcriptomic analysis on MdSTING and wild-type mice over a 3-year aging process.Blood transcriptomic analysis indicated a reduction in aging-related inflammation in female MdSTING mice,as evidenced by significantly lower levels of pro-inflammatory cytokines and chemokines,immunopathology,and neutrophil recruitment in aged female MdSTING mice compared to aged wild-type mice in vivo.These results indicated that MdSTING knock-in attenuates the aging-related inflammatory response and may also improve the healthspan in mice in a sex-dependent manner.Although the underlying mechanism awaits further study,this research has critical implications for bat longevity research,potentially contributing to our comprehension of healthy aging in humans.

Chromosome-level genome and population genomics of the intermediate horseshoe bat(Rhinolophus affinis)reveal the molecular basis of virus tolerance in Rhinolophus and echolocation call frequency variation

Horseshoe bats(genus Rhinolophus,family Rhinolophidae)represent an important group within chiropteran phylogeny due to their distinctive traits,including constant high-frequency echolocation,rapid karyotype evolution,and unique immune system.Advances in evolutionary biology,supported by high-quality reference genomes and comprehensive whole-genome data,have significantly enhanced our understanding of species origins,speciation mechanisms,adaptive evolutionary processes,and phenotypic diversity.However,genomic research and understanding of the evolutionary patterns of Rhinolophus are severely constrained by limited data,with only a single published genome of R.ferrumequinum currently available.In this study,we constructed a high-quality chromosome-level reference genome for the intermediate horseshoe bat(R.affinis).Comparative genomic analyses revealed potential genetic characteristics associated with virus tolerance in Rhinolophidae.Notably,we observed expansions in several immune-related gene families and identified various genes functionally associated with the SARS-CoV-2 signaling pathway,DNA repair,and apoptosis,which displayed signs of rapid evolution.In addition,we observed an expansion of the major histocompatibility complex class II(MHC-II)region and a higher copy number of the HLA-DQB2 gene in horseshoe bats compared to other chiropteran species.Based on whole-genome resequencing and population genomic analyses,we identified multiple candidate loci(e.g.,GLI3)associated with variations in echolocation call frequency across R.affinis subspecies.This research not only expands our understanding of the genetic characteristics of the Rhinolophus genus but also establishes a valuable foundation for future research.

Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network assaults.To protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT networks.This study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data complexity.Following rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning models.Augmentation using the BAT algorithm enhances dataset variability.Three deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data aspects.Their predictions form a meta-feature set for a subsequent meta-learner,which combines model strengths.Conventional classifiers validate meta-learner features for broad algorithm suitability.This comprehensive method demonstrates high accuracy and robustness in IoMT intrusion detection.Evaluations were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen categories.Experimental results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

Inflow and outflow permeability tests in a very soft clay under low stresses

In situ inflow and outflow permeability tests with the BAT probe at SarapuíII soft clay test site are presented.A description of the BAT permeability test is provided,discussing its advantages and shortcomings,especially in the case of very soft clays under low stresses.Pore pressures were monitored during probe installation and were found to be slightly lower than piezocone u2 pore pressures,consistent with the position of the filter.The role of filter tip saturation was investigated after the usual saturation procedure provided an unsatisfactory pore pressure response during probe installation.Results show that the vacuum saturation procedure provides adequate response during installation and increases the reliability of the coefficient of permeability determination in early measurements.Both inflow and outflow tests yielded similar results,indicating that careful execution of the test can lead to good test repeatability regardless of the loading condition.Various sequences of alternated inflow and outflow tests have yielded similar results,indicating that soil reconsolidation and filter clogging were negligible in the tests performed.Data are presented concerning the relationship between index parameters and the in situ coefficient of permeability for SarapuíII clay,which plot outside the range of existing databases.

Urban growth scenario projection using heuristic cellular automata in arid areas considering the drought impact

Arid areas with low precipitation and sparse vegetation typically yield compact urban pattern,and drought directly impacts urban site selection,growth processes,and future scenarios.Spatial simulation and projection based on cellular automata(CA)models is important to achieve sustainable urban development in arid areas.We developed a new CA model using bat algorithm(BA)named bat algorithm-probability-of-occurrence-cellular automata(BA-POO-CA)model by considering drought constraint to accurately delineate urban growth patterns and project future scenarios of Urumqi City and its surrounding areas,located in Xinjiang Uygur Autonomous Region,China.We calibrated the BA-POO-CA model for the drought-prone study area with 2000 and 2010 data and validated the model with 2010 and 2020 data,and finally projected its urban scenarios in 2030.The results showed that BA-POO-CA model yielded overall accuracy of 97.70%and figure-of-merits(FOMs)of 35.50%in 2010,and 97.70%and 26.70%in 2020,respectively.The inclusion of drought intensity factor improved the performance of BA-POO-CA model in terms of FOMs,with increases of 5.50%in 2010 and 7.90%in 2020 than the model excluding drought intensity factor.This suggested that the urban growth of Urumqi City was affected by drought,and therefore taking drought intensity factor into account would contribute to simulation accuracy.The BA-POO-CA model including drought intensity factor was used to project two possible scenarios(i.e.,business-as-usual(BAU)scenario and ecological scenario)in 2030.In the BAU scenario,the urban growth dominated mainly in urban fringe areas,especially in the northern part of Toutunhe District,Xinshi District,and Midong District.Using exceptional and extreme drought areas as a spatial constraint,the urban growth was mainly concentrated in the"main urban areas-Changji-Hutubi"corridor urban pattern in the ecological scenario.The results of this research can help to adjust urban planning and development policies.Our model is readily applicable to simulating urban growth and future scenarios in global arid areas such as Northwest China and Africa.

Descriptive and Comparative Histology of the Urinary System of the Hematophagous Bats Desmodus rotundus and Diphylla ecaudata

Desmodus rotundus and Diphylla ecaudata, both of which are mammals of the order Chiroptera, Desmodontidae family, their diet consisting exclusively of blood. D. rotundus is the main vector and transmitter of the rabies virus, which affects human beings as well as several livestock species so the study of this bat species is of high importance within the fields of animal agriculture and public health. The present study describes and compares the histologic characteristics of the urinary system of two hematophagous bat species. A total of 5 bats from each species were captured in the municipalities of Progreso de Obregón, Hidalgo (D. rotundus), and Huayacocotla, Veracruz (D. ecaudata). Organs belonging to the urinary system were extracted: kidneys, ureters, urinary bladder, and urethra;samples were fixed using 10% formalin and processed by the paraffin embedding technique, obtaining sections of 5 µm thickness, which in turn were stained using hematoxylin-eosin (H-E) and Gomori trichrome (GT) stains. From the obtained histologic preparations, a descriptive and comparative analysis of the structural organography of the urinary system of both species was made, and no noteworthy histological differences between samples were noted. The present research is intended to provide a framework for future studies of these species’ currently understudied microscopic anatomy.

What Is the Impact of Ploughing on Eucalyptus Plantations in Bambou Mingali (Plateaux Batéké, Republic of Congo)?

Soil ploughing is an important stage in the preparation of planting, causing disturbance to the physical, chemical and biological properties of the soil. Soil ploughing can affect the availability of nutrients and water resources, and its effect can be short, medium or long-term. Soil ploughing accelerates surface heating and air circulation and encourages mineralisation by transforming organic matter into mineral salts, making nutrients soluble and accessible to plants. The aim of this study is to determine how soil ploughing affects the distribution of nutrients in the soil profile. The study focuses on nitrogen, carbon, phosphorus, calcium and magnesium, which are major elements of soil fertility on the Batéké plateaux in Congo. The results indicate that ploughing significantly modifies the distribution at depth des elements nutritifs: there is more accumulation at the surface than at depth (ei: nitrogen 1.34 t/ha ± 0.035 at 10 cm compared with 1.034 t/ha ± 0.098 at 50 cm) with a higher concentration of carbon (13.89 t/ha ± 0.87) followed by nitrogen (1.34 t/ha ± 0.035).

Sedimentary Dynamics of the Sands of the Cover Formation in the Batéké Plateaus (Republic of Congo): Paleoenvironmental Implications

The depositional environment of the sands of the cover formation is discussed. This study aims to determine the paleoenvironments of deposition of the sands of the cover formation in the Batéké Plateaus by studying sedimentary dynamics based on the description of lithological facies in the field and granulometric analyses in the laboratory. In the field, six (6) lithostratigraphic logs were surveyed and 42 sand samples were taken for laboratory analysis. In the laboratory, the samples underwent granulometric, sieving and sedimentometry analyses, after washing with running water using a 63 µm sieve. These analyses made it possible to determine the granulometric classes of the samples. The sieving results allowed to determine the granulometric parameters (mean, standard deviation, mode, median, skewness, flattening or kurtosis) using the method of moments with the software “Gradistat V.8”, granulometric parameters with which the granulometric facies, the mode of transport and the deposition environment were determined using the diagrams. Morphoscopy made it possible to determine the form and aspect of the surface of the quartz grains constituting these sands. Granulometric analyses show that these silty-clay or clayey-silty sands are fine sands and rarely medium sands, moderately to well sorted and rarely well sorted. The dominant granulometric facies is hyperbolic (sigmoid), with parabolic facies being rare. The primary mode of transport of these sands is saltation, which dominates rolling. The dispersion of points in the diagrams shows that these sands originate from two depositional environments: aeolian and fluvial. Morphoscopic analysis reveals the presence of clean rounded matt grains (RM), dirty rounded matt grains (RS), shiny blunt grains (EL) and shiny rounded grains (RL). The rounded matt grains exhibit several impact marks. The presence of dirty rounded grains with a ferruginous cement on their surface indicates that these sands have been reworked. These sands have undergone two types of transport, first by wind (aeolian environment) and then by water (fluvial environment).

火电厂运行优化调度算法分析

提出了一种改进的Bat算法,用于寻求火电厂经济调度的最优解。由于火电厂运行同时考虑了热电联产机组的发热量和发电量,因此对于优化工具来说是一个复杂而又巨大的挑战。应用改进Bat方法来提高传统Bat算法的高质量求解搜索能力和搜索速度。这种方法是建立在三个修改的Bat基础上。首先是自适应频率调整,其次是更新速度的最佳范围,第三是良好解的保留条件,目的是改善传统Bat的搜索性能。通过对7、24和48单元系统和IEEE 14总线系统的测试,并与传统Bat方法和其他现有方法的结果进行比较,评价了该方法的有效性。结果表明,该方法是求解火电厂运行优化调度问题的一种有效的元启发式算法。

FINGDING A WAY OUT Cities carry out distinct strategies in batting with the exacerbating traffic jams

Alottery was broadcast live both on TV and over the Internet in Beijing on January 26. The "talk-of-the-town" lottery attracted at least 187,000 pairs of attentive eyes of people who were not yearning for

基于改进Gmapping算法的果园二维环境地图精准构建

果树树冠的季节性变化及果树成长和衰老造成的果树特征变化会影响已构建的果园三维环境地图的匹配,故本文提出一种基于改进Gmapping算法的果园二维环境地图精准构建算法。首先该算法对Gmapping算法的前端里程计和后端优化部分分别进行改进,以提高果园二维环境地图的构建精度。对于前端里程计部分采用改进的R-GPF地面分割方法提高其初始定位精度,对于后端优化部分采用BAT启发式自适应重采样方法提高其最终定位精度。然后进行梨园环境对比试验。通过改进R-GPF方法与原始R-GPF方法的对比,改进R-GPF方法的激光雷达里程计输出频率可达到15.58 Hz,最大横向偏差小于25 cm,横向偏差均值为12.7 cm,标准差为13.4 cm,其各方面性能都优于原R-GPF方法的激光雷达里程计。通过新算法与基于原R-GPF的Gmapping算法对比,新算法所得的梨树列间距离偏差始终保持在20 cm范围内,行间距离偏差均值为10.3 cm,标准差为6.3 cm,比基于原R-GPF的Gmapping算法分别减小50%、43.41%和32.26%;同时,梨树行间距离偏差相对于里程计横向偏差的减小侧面反映出后端BAT启发式自适应重采样方法的有效性。本文提出的算法能够提高果园二维地图构建精度,可以满足后续重定位、导航等作业的精度要求。

BAT评分联合CTA点征对幕上自发性脑出血患者早期血肿扩大的预测价值研究

目的研究BAT评分联合CTA点征对幕上自发性脑出血患者早期血肿扩大的预测价值。方法回顾性分析2021年9月—2022年4月连续于首都医科大学附属北京天坛医院急诊就诊且发病至一站式CT检查时间在6 h内的幕上自发性脑出血患者的资料,根据发病后24~48 h复查头颅CT与基线CT的血肿体积对比,分为血肿扩大组和非血肿扩大组。比较两组患者的基本临床资料、CTA点征及非增强CT(non-contrast computed tomography sign,NCCT)特点(低密度征、混合征等)影像资料、BAT评分(blend sign,any hypodensity,timing of NCCT score,BAT)。采用单因素分析和多因素logistic回归分析血肿扩大的危险因素,并绘制ROC曲线分析BAT评分、CTA点征及两者结合对自发性脑出血患者早期血肿扩大的预测价值。结果共纳入97例患者,血肿扩大组28例,非血肿扩大组69例。血肿扩大组患者CTA点征,NCCT低密度征、混合征的发生率均高于非血肿扩大组,BAT评分中位数和BAT评分≥3分的患者比例均高于非血肿扩大组,发病至基线影像时间短于非血肿扩大组,基线出血体积和基线随机血糖水平高于非血肿扩大组,上述差异均有统计学意义。多因素logistic回归分析结果显示,CTA点征阳性(OR 31.828,95%CI 5.350~189.337,P<0.01)、BAT评分≥3分(OR 71.976,95%CI 5.391~960.899,P<0.01)、基线出血体积(OR 1.029,95%CI 1.003~1.055,P=0.03)和基线随机血糖(OR 1.355,95%CI 1.070~1.714,P=0.01)可独立预测血肿扩大。ROC曲线分析显示,基线出血体积预测血肿扩大的AUC为0.762,BAT评分≥3分预测血肿扩大的AUC为0.716,CTA点征阳性预测血肿扩大的AUC为0.756,BAT评分≥3分联合CTA点征阳性预测血肿扩大的AUC为0.833。De Long检验提示两者联合预测效力优于单独预测效力(P<0.05)。结论幕上自发性脑出血患者发病6 h内,BAT评分≥3分及CTA点征阳性都可有效预测血肿扩大,两者联合预测的效力更佳。

EPAS1 prevents telomeric damage-induced senescence by enhancing transcription of TRF1,TRF2,and RAD50

Telomeres are nucleoprotein structures located at the end of each chromosome,which function in terminal protection and genomic stability.Telomeric damage is closely related to replicative senescence in vitro and physical aging in vivo.As relatively long-lived mammals based on body size,bats display unique telomeric patterns,including the upregulation of genes involved in alternative lengthening of telomeres(ALT),DNA repair,and DNA replication.At present,however,the relevant molecular mechanisms remain unclear.In this study,we performed cross-species comparison and identified EPAS1,a well-defined oxygen response gene,as a key telomeric protector in bat fibroblasts.Bat fibroblasts showed high expression of EPAS1,which enhanced the transcription of shelterin components TRF1 and TRF2,as well as DNA repair factor RAD50,conferring bat fibroblasts with resistance to senescence during long-term consecutive expansion.Based on a human single-cell transcriptome atlas,we found that EPAS1 was predominantly expressed in the human pulmonary endothelial cell subpopulation.Using in vitro-cultured human pulmonary endothelial cells,we confirmed the functional and mechanistic conservation of EPAS1 in telomeric protection between bats and humans.In addition,the EPAS1 agonist M1001 was shown to be a protective compound against bleomycin-induced pulmonary telomeric damage and senescence.In conclusion,we identified a potential mechanism for regulating telomere stability in human pulmonary diseases associated with aging,drawing insights from the longevity of bats.

RBEBT: A ResNet-Based BA-ELM for Brain Tumor Classification

Brain tumor refers to the formation of abnormal cells in the brain.It can be divided into benign and malignant.The main diagnostic methods for brain tumors are plain X-ray film,Magnetic resonance imaging(MRI),and so on.However,these artificial diagnosis methods are easily affected by external factors.Scholars have made such impressive progress in brain tumors classification by using convolutional neural network(CNN).However,there are still some problems:(i)There are many parameters in CNN,which require much calculation.(ii)The brain tumor data sets are relatively small,which may lead to the overfitting problem in CNN.In this paper,our team proposes a novel model(RBEBT)for the automatic classification of brain tumors.We use fine-tuned ResNet18 to extract the features of brain tumor images.The RBEBT is different from the traditional CNN models in that the randomized neural network(RNN)is selected as the classifier.Meanwhile,our team selects the bat algorithm(BA)to opti7mize the parameters of RNN.We use fivefold cross-validation to verify the superiority of the RBEBT.The accuracy(ACC),specificity(SPE),precision(PRE),sensitivity(SEN),and F1-score(F1)are 99.00%,95.00%,99.00%,100.00%,and 100.00%.The classification performance of the RBEBT is greater than 95%,which can prove that the RBEBT is an effective model to classify brain tumors.

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

The pupil recognition method is helpful in many real-time systems,including ophthalmology testing devices,wheelchair assistance,and so on.The pupil detection system is a very difficult process in a wide range of datasets due to problems caused by varying pupil size,occlusion of eyelids,and eyelashes.Deep Convolutional Neural Networks(DCNN)are being used in pupil recognition systems and have shown promising results in terms of accuracy.To improve accuracy and cope with larger datasets,this research work proposes BOC(BAT Optimized CNN)-IrisNet,which consists of optimizing input weights and hidden layers of DCNN using the evolutionary BAT algorithm to efficiently find the human eye pupil region.The proposed method is based on very deep architecture and many tricks from recently developed popular CNNs.Experiment results show that the BOC-IrisNet proposal can efficiently model iris microstructures and provides a stable discriminating iris representation that is lightweight,easy to implement,and of cutting-edge accuracy.Finally,the region-based black box method for determining pupil center coordinates was introduced.The proposed architecture was tested using various IRIS databases,including the CASIA(Chinese academy of the scientific research institute of automation)Iris V4 dataset,which has 99.5%sensitivity and 99.75%accuracy,and the IIT(Indian Institute of Technology)Delhi dataset,which has 99.35%specificity and MMU(Multimedia University)99.45%accuracy,which is higher than the existing architectures.

甲状腺激素与脂肪组织的进展性研究

甲状腺激素(TH)紊乱与全身能量代谢的深刻变化有关。TH的主要靶点是产热棕色脂肪组织(BAT),它可以通过表达在棕色脂肪细胞中的甲状腺激素受体(TRs)直接刺激,也可以通过表达在下丘脑神经元中的甲状腺激素受体(TRs)间接刺激。有证据表明,甲状腺激素也可诱导白色脂肪组织褐变。在这篇综述中,我们将讨论和总结近年来TH介导棕色脂肪组织活性和白色脂肪组织褐变的潜在机制。

英国核电通用设计审查特色与实践

英国核安全和环境监管体系中存在很多极具英国特色的要求(UK context),包括Safety Case、ALARP和BAT等概念。这些具有英国特色的要求需要核电厂的设计方(Designer)、执照申请方(Licensee)或运行单位(Operator)在英国核电项目的设计、建造、运行和退役全生命周期中对其有深入理解并加以应用。本文通过作者五年来从事华龙一号英国通用设计审查(GDA)的实践,着重介绍了三个具有英国特色的概念并提出了自己的认识。

Technique for Multi-Pass Turning Optimization Based on Gaussian Quantum-Behaved Bat Algorithm

The multi-pass turning operation is one of the most commonly used machining methods in manufacturing field.The main objective of this operation is to minimize the unit production cost.This paper proposes a Gaussian quantum-behaved bat algorithm(GQBA)to solve the problem of multi-pass turning operation.The proposed algorithm mainly includes the following two improvements.The first improvement is to incorporate the current optimal positions of quantum bats and the global best position into the stochastic attractor to facilitate population diversification.The second improvement is to use a Gaussian distribution instead of the uniform distribution to update the positions of the quantum-behaved bats,thus performing a more accurate search and avoiding premature convergence.The performance of the presented GQBA is demonstrated through numerical benchmark functions and amulti-pass turning operation problem.Thirteen classical benchmark functions are utilized in the comparison experiments,and the experimental results for accuracy and convergence speed demonstrate that,in most cases,the GQBA can provide a better search capability than other algorithms.Furthermore,GQBA is applied to an optimization problem formulti-pass turning,which is designed tominimize the production cost while considering many practical machining constraints in the machining process.The experimental results indicate that the GQBA outperforms other comparison algorithms in terms of cost reduction,which proves the effectiveness of the GQBA.

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

The major environmental hazard in this pandemic is the unhygienic dis-posal of medical waste.Medical wastage is not properly managed it will become a hazard to the environment and humans.Managing medical wastage is a major issue in the city,municipalities in the aspects of the environment,and logistics.An efficient supply chain with edge computing technology is used in managing medical waste.The supply chain operations include processing of waste collec-tion,transportation,and disposal of waste.Many research works have been applied to improve the management of wastage.The main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and transparency.To over-come these issues,in this paper we implement an efficient Naive Bayes classifier algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical waste.To minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is used.The accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.