Suggested Strategies in Water Treatment by Using Situ Pressure in Reverse Osmosis

Nowadays desalination is one of the main resources to obtain water in many areas. The most advanced method for water filtration is reverse osmosis. In this system, water is injected into semi-membranes by using power pumping, and its salt water is taken away from the solution. This paper has tried to offer guidelines to use the pressure created?in situ?of harvesting water, instead of utilizing power pump, which produces the necessary pressure for the reverse osmosis. These guidelines have been divided into 2 main groups: Using the created natural pressure, and the other way is using the pressures that are caused by the constructions built for harvesting water.

Improved Dual-image Quality with Reversible Data Hiding Using Translocation and Switching Strategy

Dual-image reversible data hiding(RDH)is a technique for hiding important messages.This technology can be used to safely deliver secret mes-sages to the recipient through dual images in an open network without being easily noticed.The recipient of the image must receive the two stego-images before the secret message can be completely retrieved.Imperceptibility is one of the main advantages of data hiding technology;to increase the imperceptibility,the quality requirements of the stego-images are relatively important.A dual ste-ganographic image RDH method,called a DS-CF scheme that can achieve a bet-ter steganographic image quality using the center folding(CF)strategy.In this paper,we developed a translocation and switching strategy(TaS)to shorten the distances between the stego-pixel coordinates and the cover pixel coordinates after information being hidden.Compared with the DS-CF scheme,our proposed DS-TaS scheme can effectively improve the quality of the steganographic images at the same level of embedding capability.The experimental results show that the PSNR of our DS-TaS scheme at k=1 was 55.66 dB,which is an increase of 1.5 dB,and is 51.43 dB for k=2,46.66 dB for k=3,and 40.91 dB for k=4.In addition,the PSNR values of the stego images was increased by 1.5,0.29,0.29,and 0.19 dB,respectively.This shows that our proposed dual-image RDH method can optimize the visual quality of the stego-images and is better than many other dual-image RDH techniques.

Research on the Development Strategy of Community Education Based on Reverse Socialization

The exploration of China's community education development practice is well-aligned progressing,community education for improving the quality of the whole people and promoting social development is of great significance.Reverse socialization plays an important role in alleviating intergenerational estrangement,providing new development direction and promoting balanced development of community education.To Promote the development of community education under the effect of reverse socialization,"the younger generations should influence the elder"so as to improve the cognition of community education;Optimize the policy,"new insights transforming the old concepts"so as to act well in community education publicity;At the mean time,the construction of characteristic community education work should be done as well.

Maximizing Supermarket Profits:Data-Driven Strategies for Pricing,Sales,and Forecasting

The actual circumstances of daily life are crucial for the purchasing and pricing strategies of supermarkets.Developing strategies based on these circumstances can assist businesses in ensuring profits and fostering win-win cooperation.This paper explores methods to maximize profit through purchasing and sales strategies.Initially,the relevant data for various categories of vegetables is integrated.Through histograms,their sales patterns are directly understood,highlighting the most popular vegetables.Upon analyzing each vegetable category,it becomes evident that their sales data do not conform to normal distributions.Therefore,Spearman correlation coefficients are calculated,revealing strong correlations between certain categories,such as aquatic roots and edible fungi.A line chart depicting the top ten selling vegetables indicates a noticeable periodicity.Traditional fitting methods struggle to adequately model the sales of each vegetable category and their relationship with cost-plus pricing.To address this,additional factors such as holidays,weeks,and months are incorporated using techniques like random forest regression.This approach yields cost-plus pricing dependence curves that better capture the relationship,while effectively managing noise.Regarding sales volume prediction,the original data displays significant volatility,necessitating the handling of outliers using the threshold method.For missing data,linear interpolation is employed to mitigate the impact of continuous missing values on prediction accuracy.Subsequently,Adam-optimized long short-term memory(LSTM)networks are utilized to forecast incoming quantities for the next seven days.By extrapolating from normal sales volume,market capacity is estimated,allowing for additional sales through discount strategies.This framework has the potential to increase original income by 1.1 times.

Treatment strategies for colorectal carcinoma with synchronous liver metastases: Which way to go?

AIM: To offer an up-to-date review of all availabletreatment strategies for patients with synchronous colorectal liver metastases(CLM).METHODS: A comprehensive literature search was performed to identify articles related to the management of patients with synchronous CLM. A search of the electronic databases PubMed, MEDLINE, and Google Scholar was conducted in September 2014.The following search terms were used: synchronous colorectal liver metastases, surgery, stage Ⅳ colorectal cancer, liver-first approach, and up-front hepatectomy.These terms were employed in various combinations to maximize the search. Only articles written in English were included. Particular attention was devoted to studies and review articles that were published within the last six years(2009-2014). Additional searches of the cited references from primary articles were performed to further improve the review. The full texts of all relevant articles were accessed by two independent reviewers.RESULTS: Poor long-term outcomes of patients with synchronous CLM managed by a traditional treatment strategy have led to questions about the timing and sequence of possible therapeutic interventions. Thus,alternative paradigms called reverse strategies have been proposed. Presently, there are four treatment strategies available:(1) primary first approach(or traditional approach) comprises resection of the primary colorectal tumor followed by chemotherapy;subsequent liver resection is performed 3-6 mo after colorectal resection(provided that CLM are still resectable);(2) simultaneous resection of the primary colorectal tumor and CLM during a single operation presents intriguing options for a highly select group of patients, which can be associated with significant postoperative morbidity;(3) liver-first(or chemotherapy-first) approach comprises preoperative chemotherapy(3-6 cycles) followed by liver resection,adjuvant chemotherapy, and resection of the primary colorectal tumor(it is best suited for patients withasymptomatic primary tumors and initially unresectable or marginally resectable CLM); and(4) up-front hepatectomy(or "true" liver-first approach) includes liver resection followed by adjuvant chemotherapy,colorectal resection, and adjuvant chemotherapy(strategy can be offered to patients with asymptomatic primary tumors and initially resectable CLM).CONCLUSION: None of the aforementioned strategies appears inferior. It is necessary to establish individual treatment plans in multidisciplinary team meetings through careful appraisal of all strategies.

Stable attenuation-compensated reverse time migration and its application to land seismic data

Intrinsic attenuation of the earth causes energy loss and phase distortion in seismic wave propagation.To obtain high-resolution imaging results,these negative effects must be considered during reverse time migration(RTM).We can easily implement attenuation-compensated RTM using the constant Q viscoacoustic wave equation with decoupled amplitude attenuation and phase dispersion terms.However,the nonphysical amplitude-compensation process will inevitably amplify the high-frequency noise in the wavefield in an exponential form,causing the numerical simulation to become unstable.This is due to the fact that the amplitude of the compensation grows exponentially with frequency.In order to achieve stable attenuation-compensated RTM,we modify the analytic expression of the attenuation compensation extrapolation operator and make it only compensate for amplitude loss within the effective frequency band.Based on this modified analytic formula,we then derive an explicit time-space domain attenuation compensation extrapolation operator.Finally,the implementation procedure of stable attenuation-compensated RTM is presented.In addition to being simple to implement,the newly proposed attenuation-compensated extrapolation operator is superior to the conventional low-pass filter in suppressing random noise,which will further improve the imaging resolution.We use two synthetic and one land seismic datasets to verify the stability and effectiveness of the proposed attenuationcompensated RTM in improving imaging resolution in viscous media.

Study on Control Strategy of Sulfur Dioxide Concentration in the Urban Area of Shijiazhuang

Two Gaussian air quality dispersion models, the industrial source complex short-term model (ISCST3) with and without modification have been used to simulate the pollutant concentration distribution in urban areas based on the meteorological data and the emissions distribution of sulfur dioxide. The verified data show that the modified model is more accurate in the urban area of Shijiazhuang. Using the modified model predictions, the control strategies of sulfur dioxide in the urban area have been studied, and the result show that the second long-term (to 2010) strategy can mitigate air pollution significantly and maintain pollution levels within permissible limits.

改进贝叶斯网络在变压器故障诊断中的应用

针对变压器故障诊断精度低的问题,文中提出一种基于改进黏菌优化算法(Improved Slime Mould Algorithm,ISMA)优化贝叶斯网络(Bayesian Network,BN)的变压器故障诊断方法。通过爬山算法对定向最大支撑树搜索得到贝叶斯网络初始结构即初始种群,在改进黏菌优化算法中引入反向学习策略,增加种群多样性。添加正弦-余弦算法(Sine Cosine Algorithm,SCA),更新解的位置以避免种群陷入局部最优。根据改良的无编码比值法选取变压器故障状态的特征,利用改进黏菌优化算法优化贝叶斯网络结构,提高基于贝叶斯网络的变压器故障诊断的准确率,并利用不同种类的测试函数验证了改进黏菌优化算法具有收敛速度快、收敛精度高的优良性能。仿真结果表明,ISMA-BN诊断模型的训练集和测试集准确率分别为98.2%和97.14%,具有一定的研究价值。

面向密度分布不均数据的加权逆近邻密度峰值聚类算法

针对密度分布不均数据,密度峰值聚类算法易忽略类簇间样本的疏密差异,导致误选类簇中心;分配策略易将稀疏区域的样本误分到密集区域,导致聚类效果不佳的问题,本文提出一种面向密度分布不均数据的加权逆近邻密度峰值聚类算法。该算法首先在局部密度公式中引入基于sigmoid函数的权重系数,增加稀疏区域样本的权重,结合逆近邻思想,重新定义了样本的局部密度,有效提升类簇中心的识别率;其次,引入改进的样本相似度策略,利用样本间的逆近邻及共享逆近邻信息,使得同一类簇样本间具有较高的相似度,可有效改善稀疏区域样本分配错误的问题。在密度分布不均、复杂形态和UCI数据集上的对比实验表明,本文算法的聚类效果优于IDPC-FA、FNDPC、FKNN-DPC、DPC和DPCSA算法。

基于改进海鸥优化算法的BP神经网络及其应用

针对传统反向传播(BP)神经网络在预测时随机产生的初始权值、阈值影响准确性的问题,提出一种改进的海鸥优化算法(ISOA)对BP神经网络的初始阈值、权值进行寻优。首先,为提高海鸥优化算法(SOA)的收敛精度和跳出局部最优的能力,使用Sine混沌映射初始化种群,引入非线性参数A,在海鸥攻击时引入乘除策略进行扰动,同时在攻击阶段后引入反向学习策略。然后,使用ISOA优化BP神经网络初始权值、阈值,解决对初值敏感和易陷入局部最优的问题。最后,在冻结裂隙砂岩动态冲击试验中进行峰值应力预测,结果表明:对比原始BP、粒子群优化(PSO)-BP和SOA-BP,ISOA优化后的BP神经网络对峰值应力预测精度更高。

基于RFIO的等效反向旋转随钻陀螺仪误差参数识别

为提高随钻姿态测量精度,提出一种基于旋转霜冰优化器(RFIO)的等效反向旋转随钻陀螺仪误差参数识别方法。首先对等效反向旋转惯导进行误差分析,以随钻姿态误差构造等效反向旋转误差目标函数,以钻停时角速度、加速度和磁场强度关系构造等效反向旋转误差约束条件,通过RFIO算法辨识误差参数。其次,根据历史目标函数、莱维飞行及RFIO迭代次数设计一种动态上下界策略,提高了目标函数的识别速度;根据当前目标函数值与最优值之间距离设计一种旋转游走策略,提高了误差参数识别精度。实验结果表明,工具面角和井斜角误差控制在±0.4°左右,方位角误差控制在±0.8°左右。与未等效反向旋转相比,工具面角精度提升约60%,井斜角精度提升约66.7%,方位角精度提升约70.4%。

针对顶点策略优化的三维模型可逆数据隐藏算法

随着3D打印技术和云计算的普及发展,三维模型的数据安全和隐私安全日益受到重视,研究模型无损恢复和存储海量数据的隐藏方法具有重要意义。基于此,以三维网格模型为载体,提出一种顶点策略优化的可逆数据隐藏方法。首先,对网格模型顶点进行划分,在全邻域中生成新的重心集合,根据改进的预测策略预测所有顶点集合的MSB以确定最大嵌入位;其次,在接受端应用顶点处拉普拉斯算子得到光滑恢复模型;最后,采用平均曲率可视化分析。为证明该算法的有效性,选取不同大小的网格模型与其他传统算法的嵌入率、SNR以及恢复模型进行对比。结果表明,该算法不仅提高了嵌入率,还在保留恢复模型局部细节特征的同时有效去除噪声,提高了模型恢复质量和视觉效果,进一步达到数据隐私安全的目的。

跨境电商退货逆向物流选择和策略

跨境电商是数字贸易的重要组成部分,也是一种新质生产力。在跨境电商迅猛发展的同时,退货逆向物流问题也日益严重。如何实现退货逆向物流降本增效,实现绿色逆向物流发展是跨境电商可持续发展的关键。本文通过对比分析法,将传统国际贸易成本分解方法融入跨境电商逆向物流研究,比较传统海外仓逆向物流退货模式和退运进境中心仓逆向物流模式,分析其流程和优缺点,并从预防场景角度、成本角度、效率角度、社会责任可持续发展角度阐述逆向物流创新解决策略。研究发现企业选择进境保税区中心仓的逆向物流方案,能实现最大化物品价值,提升商品资源利用率,减少海外废弃物品,强化企业社会责任,并践行了绿色可持续发展理念。通过提升平台、政府、企业数字化供应链赋能水平、借力AI精准预测分析、加强平台认证机制、构建逆向物流标准方案、创新简化通关模式、引导绿色发展理念以及完善跨境电商质量认证体系等多项措施,最终实现跨境电商逆向物流的高质量发展机制。

基于改进SCHO算法的局部遮荫光伏MPPT研究

针对局部遮荫工况下光伏阵列最大功率点跟踪(MPPT)存在的易陷入局部功率峰值点、跟踪时间长、跟踪精度低等问题,提出一种基于改进双曲正余弦优化(SCHO)的控制算法.其采用立方混沌映射初始化,提高初始候选解集的遍历性,并利用贝塔分布的概率特性修正SCHO算法的切换标准,提高算法与候选解集寻优进程的适配性.同时,对SCHO算法中的全局最优解、个体当前解引入平衡权重更新策略,且采用透镜成像反向学习策略对寻优后期的候选解进行扰动,采用比例收缩法对寻优空间进行动态钳位,提高算法的全局勘探及局部开发能力.Matlab仿真结果表明,相比其他控制算法,本文提出的改进SCHO算法能缩短MPPT时间、提高MPPT精度,故具有更优的MPPT性能,可为进一步提升光伏发电效率提供参考.

基于改进鲸鱼算法优化PID的转速控制

当汽轮机转速发生偏差时,需要操作员手动整定PID参数使转速达到稳定,但控制效果不佳且调节时间长,针对这一问题提出了一种改进的鲸鱼优化算法优化PID参数控制转速,通过引入反向学习策略和非线性收敛因子,在一台3MW的汽轮机的组态仿真中进行实验对比。结果表明:与鲸鱼算法、粒子群算法相比,改进鲸鱼算法寻优效果更好,进而有效地达到对汽轮机转速的控制。

基于改进鼠群优化算法的起重机主梁轻量化设计

为提高元启发式算法求解桥式起重机主梁优化问题的寻优精度与效率,文中提出一种改进的鼠群优化算法(IRSO)。该算法采用Hénon混沌随机反向学习初始化种群,提高算法的初始寻优性能;在追逐行为中,引入随机反向学习和高斯变异混合策略对鼠群进行逐维学习,增强算法的全局搜索能力;在搏斗行为中,采用翻筋斗搏斗搜索策略更新鼠群位置,增强算法的局部搜索能力;在算法中引入自适应余弦控制因子,实现算法控制参数之间的动态平衡,提高算法的整体寻优能力。仿真结果表明:与其他算法相比,IRSO算法寻优能力更优、收敛精度更高、稳定性和鲁棒性更强;同时,IRSO算法可高效地解决桥式起重机主梁轻量化设计问题,减重效果可达20.72%,具有较好的工程实际应用能力。

基于“可逆干预”理念的乡村闲置教育建筑更新策略研究

随着20世纪80年代我国计划生育的实行以及城镇化进程的加快,众多乡村教育机构被“撤点并校”,教育建筑亦被闲置废弃,造成了乡村物质空间的浪费与乡土文化的衰退。虽然近年来这些闲置的教育建筑逐渐被挖掘利用,但大多采用单向更新利用模式,使得部分空间功能固化且缺乏弹性,不利于闲置建筑的可持续利用。文章旨在结合当下“县域辐射”与“人口回流”的社会发展趋势,重新审视乡村闲置教育建筑的更新模式,并基于“可逆干预”理念,从时间、建构、代际三个维度提出了乡村闲置教育建筑功能空间的更新策略,以契合新时代乡村振兴多元功能需求,为同类闲置建筑空间的更新活化提供新的思路。

基于差分进化算法的瞬变电磁一维反演

实际采集的瞬变电磁数据包含电磁感应和激发极化效应,如何准确提取电阻率和极化率信息是电性源瞬变电磁数据处理的关键。首先,基于Cole⁃Cole复电阻率模型实现有限长电性源瞬变电磁法一维正演,在此基础上提出一种基于差分进化算法的电性源瞬变电磁一维反演方法。然后,在传统差分进化算法的基础上引入反向学习策略及控制参数自适应调节,加快反演的收敛速度,同时在目标函数中引入约束条件,构成最小构造反演,降低反演的多解性。最后,基于典型的三层地电模型和复杂多层模型进行理论模型测试,反演结果可有效恢复模型的电阻率和极化率。利用实测资料进行反演,反演得到的电阻率与OCCAM反演电阻率基本一致。在此电阻率约束的基础上,进一步反演得到极化率信息。反演结果准确地提取了实测数据中的电阻率信息,得到了地下介质的极化率分布,证明了算法的准确性和适用性。

基于改进麻雀搜索算法的PID参数整定系统设计

PID参数整定是PID控制中的一个关键步骤,常规的PID控制在一定程度上已被淘汰。为改善PID控制的效果和精度,文中将黄金正弦策略与精英反向学习策略相结合,提出一种改进的麻雀搜索算法。用23个标准函数对所提出的新方法进行了验证,实验结果证明了新方法的有效性。利用改进的麻雀搜索算法对PID控制参数进行了优化,并对该方法进行了仿真分析。结果表明,采用该方法进行PID参数整定时,其具有更好的稳定性、更高的精度和更好的性能。

基于改进帝王蝶算法的最大似然DOA估计

针对传统最大似然波达方向(maximum likelihood direction of arrival,ML-DOA)估计存在计算量大、估计精度差等问题,本文提出一种采用改进帝王蝶优化算法(improved monarch butterfly optimization algorithm,IMBO)的ML-DOA估计方法。IMBO算法通过精英反向学习策略对初始帝王蝶种群进行优化,得到适应度值较优的初始帝王蝶个体,进而能够改善帝王蝶种群的多样性;引入差分进化算法启发的变异操作以及自适应策略对帝王蝶个体的寻优方式进行改进,扩大了算法的搜索空间;引入了高斯-柯西变异算子,自适应调整变异步长,避免算法陷入局部最优。将IMBO应用于ML-DOA,实验表明,与传统的DOA估计算法相比,在不同信源数目、信噪比以及种群数量下,本文提出的算法收敛性能更好,均方根误差更低,运算量更小。