颈椎前路Hybrid手术和颈椎后路单开门椎管扩大成形术治疗多节段脊髓型颈椎病临床疗效分析

目的:分析颈椎前路Hybrid手术和颈椎后路单开门椎管扩大成形术(EODL)治疗多节段脊髓型颈椎病的疗效,探讨多节段脊髓型颈椎病患者手术方式的选择。方法:对2017年7月—2020年7月在首都医科大学附属北京中医医院手术治疗的70例多节段脊髓型颈椎病患者进行回顾性分析,根据手术方式不同,分为前路组35例和后路组35例,前路组患者行Hybrid手术[颈椎前路椎间盘切除融合术(ACDF)联合人工颈椎间盘置换术(ACDR)],后路组患者行EODL。记录2组患者住院时间、手术时间、术中出血量和术后引流量,通过日本骨科协会(JOA)评分、JOA改善率、颈椎残障功能指数(NDI)、疼痛视觉模拟评分(VAS)和术后满意度评分进行疗效评价,统计2组患者术后并发症发生情况。结果:与后路组比较,前路组患者术中出血量、术后引流量、住院时间和手术时间均明显减少(P<0.01),术前各项评分差异无统计学意义(P>0.05)。末次随访时,与后路组比较,前路组患者JOA评分和JOA改善率明显升高(P<0.01),NDI评分和VAS评分明显降低(P<0.01)。与术前比较,末次随访时2组患者JOA评分明显升高(P<0.01),NDI和VAS评分均明显降低(P<0.01)。按术后满意度评分评价,2组患者术后满意度均较高。2组患者术后并发症发生率比较差异无统计学意义(P>0.05)。结论:颈椎前路Hybrid手术和EODL在治疗多节段脊髓型颈椎病方面均取得了较为满意的疗效。Hybrid手术具有出血量少和手术时间短等优点,临床上应根据患者实际情况选择最适宜的术式。

基于GRAPES全球分析系统的Hybrid-3DVAR混合同化研究

本文基于我国自主研发的GRAPES全球3DVAR同化系统,利用NCEP全球集合预报产品和time-lagged方法,针对膨胀系数、集合样本数和集合权重系数,开展了每日4次、连续一周的GRAPES全球Hybrid-3DVAR混合同化研究。结果表明:所有试验中,集合样本取60个、集合权重取0.5时,得到的混合同化分析和预报误差最小;在该混合同化系统中,在高层也考虑静态背景误差协方差和集合背景误差协方差的耦合,可避免混合同化方案分析场误差在150 hPa及以上过大,并超过3DVAR分析场误差的情况。

基于Hybrid-LCA模型的装配式夹芯保温墙板碳评价

为了量化不同构造的夹芯保温墙板的减排效果,选取6种典型保温构造,基于Hybrid-LCA法建立外墙全生命周期的碳评价模型。量化分析表明:夹芯保温体系的减排效果明显优于外保温体系。以建筑垃圾为保温芯材的夹芯保温墙板减排效果明显优于其他保温墙体。复合夹芯保温墙板减排效果较好,但保温性能下降。普通混凝土夹芯保温墙板物化阶段减排效果优于预制清水混凝土和轻骨料混凝土夹芯保温墙板,同时墙体厚度小的优势增加了住房使用面积,但增加了运营阶段碳排放;预制清水混凝土与轻骨料混凝土夹芯保温墙板物化阶段减排效果差异不大,但由于其保温性能的提升,可降低建筑物运营能耗。

复杂建设环境下基于Hybrid A^(*)算法的铁路平面线形绿色优化设计

随着“双碳经济下绿色铁路”理念的兴起,将“绿色生态”融入到铁路平面线路优化已成为近年来的研究热点。本文以铁路建设成本与生态破坏成本的协同优化为目标,引入并改进了一种自动驾驶导航算法(Hybrid A^(*)算法),以适应复杂的铁路设计问题,同时考虑最小曲线半径、最大曲线半径、最短曲线长度、最短夹直线长度、缓和曲线长度等铁路线形约束。研究结果表明:(1)改进后算法以离散网格方式整合外部环境因素,实现渐进式全局探索,获取接近全局最优的铁路线路设计结果;(2)该方法在复杂外部环境约束下,无需预设水平交点位置和数量,可自动生成符合线路-环境耦合约束的优化平面线路方案。

基于Hybrid A^(*)算法的变压器声级巡检系统研究与设计

随着国内变电站数目逐步增加,采用固定式声级监测终端对变压器声级检测的方式已经满足不了日常检测的需求。为解决固定式声级监测终端方式成本高、维护复杂、设备利用率低等问题,该文率先提出了一种变压器声级巡检系统,并设计了最优声级巡检路径。通过场地定位传感器生成变压器场地栅格图信息,采用Hybrid A^(*)算法将场地栅格图信息生成符合国标所要求的最优声级巡检路径检测点;针对所开发的变压器声级巡检装置,采用生成的巡检路径对变压器进行声级测定作业,对测定的声级数据进行分析处理。测试结果表明,该文开发的系统与设计算法的变压器声级巡检时间、检测效率以及数据的采集准确性都优于固定式声级监测终端方式,系统完成变压器声级巡检全过程的成功率可达95%。

Total Fat, Fatty Acid Composition, Tocopherol and Tocotrienol Profiles in Fermented Beans of Ten Controlled Pollinated Cocoa (Theobroma cacao L.) Hybrids from Cameroon

This study aimed to discriminate ten Cameroonian cocoa hybrids according to their total fat, fatty acid composition, tocopherol and tocotrienol profiles. Six cocoa clones from the gene banks of the Cameroon Cocoa Development Corporation were used to create hybrids. The determination of fatty acid composition was carried out by using a gas chromatography (GC) apparatus coupled by a flame ion detector (FID). Tocopherol and tocotrienol analysis was performed by upper high-performance liquid chromatography (UHPLC). Information on the impact of the genotype on the cocoa fat composition was provided. The major fatty acids (FA) in fermented samples are stearic (34.57%), palmitic (26.13%), oleic (34.13%) and linoleic (3.16%) acids. (35.05% to 35.6%). SCA12 × ICS40, SCA12 × SNK13, SNK13 × T79/501 have the least hard cocoa butters. Tocopherols analysis showed a predominance of γ-tocopherols (94.64 ± 1.51 to 292.16 ± 3.17 µg∙g−1), whereas only a small amount of β and δ-tocopherol (from 0.46 to 2.78 µg∙g−1 and 0.12 to 5.82 respectively) was observed. No γ-tocotrienol was found in fermented samples. A differentiation in terms of total fat and tocopherol content was observed amongst hybrids with the same mother-clone, suggesting an impact of pollen on these compounds.

不同耦合类型的三维Hybrid神经元同步动力学

神经元网络的信息传递与多个神经元间的耦合同步密切相关.大量研究表明,神经元耦合系统的同步化问题是研究大脑处理信息的关键.本文基于三维混合神经元模型研究了神经元的复杂放电,该模型由Wilson模型的快子系统和H-R模型的慢子系统组成.该模型能够重现大脑皮层神经元一系列包括规则峰放电、快速峰放电和簇放电等神经动力学行为.本文基于三维混合神经元模型,探讨了三维混合神经元在电突触耦合、化学突触耦合和磁通耦合条件下的同步放电行为,模拟膜电位序列图、相位差图等探讨恒同及非恒同下耦合强度对神经元同步放电的影响.本研究将为人们进一步了解神经系统疾病的发病机制提供指导和帮助,并为神经科学领域提供可能的研究思路.

Hybrid IPOM: A Novel Technique for the Management of Incisional Hernia

Background and Aim: The incidence of incisional hernias has been reported to be around 15%. In the present scenario, a wide array of surgical procedures are available for their better management. In this study, we intend to share our experience with one novel technique, “Hybrid IPOM (Intraperitoneal onlay meshplasty)” as a management option for a selected cohort of patients. Methods: This prospective study was undertaken during January 2019 to July 2023 at King Abdullah medical city, Makkah. A total of 51 cases were selected for Hybrid IPOM repair as per inclusion criteria;medium sized (4 - 10 cm) hernia defects;uncomplicated hernias;age more than 18 years. The follow-up period of the patients varied from 6 months to 4 years. The operation commenced with open hernia dissection, mesh deployment into abdomen, defect closure and then conversion to laparoscopy for the posterior mesh placement. Results: A total of 51 cases were repaired successfully with this technique. 48 out of 51 cases were incisional hernias secondary to some primary procedure done either for hernias itself or some other intra-abdominal pathology. The three cases were primary hernias falling in medium to large category with unaesthetic overlying skin. The age range was 19 to 72 years. The mean (range) operative time was 135 (90 - 240) min, and the average blood loss was 70 ml. The mean (range) hospital stay was 3 (2 - 11) days. All patients returned to routine work within 2 - 3 weeks of surgery. The median follow-up was 15 (6 - 48) months. Of the 51 cases, 3 patients developed seroma (managed conservatively), 1 patient developed a large hematoma (needed evacuation), and 1 patient developed superficial wound infection (managed with antibiotics). Two patients had recurrences;one patient had previously failed multiple repairs, and the other developed a postoperative hematoma. None of our patients had an iatrogenic bowel injury. Conclusion: Hybrid IPOM technique is a safe, feasible and easily reproducible technique. It may prove easier especially for beginners in laparoscopy, as it achieves faster and easy adhesiolysis thereby reducing operative time and easier establishment of the pneumoperitoneum. Besides, it gives the chance to excise ugly scars and improve the cosmesis.

Implementation of Fuzzy Logic Control into an Equivalent Minimization Strategy for Adaptive Energy Management of A Parallel Hybrid Electric Vehicle

As government agencies continue to tighten emissions regulations due to the continued increase in greenhouse gas production, automotive industries are seeking to produce increasingly efficient vehicle technology. Hybrid electric vehicles (HEVs) have been introduced to mitigate problems while improving fuel economy. HEVs have led to the demand of creating more advanced controls software to consider multiple components for propulsive power in a vehicle. A large section in the software development process is the implementation of an optimal energy management strategy meant to improve the overall fuel efficiency of the vehicle. Optimal strategies can be implemented when driving conditions are known a prior. The Equivalent Consumption Minimization Strategy (ECMS) is an optimal control strategy that uses an equivalence factor to equate electrical to mechanical power when performing torque split determination between the internal combustion engine and electric motor for propulsive and regenerative torque. This equivalence factor is determined from offline vehicle simulations using a sensitivity analysis to provide optimal fuel economy results while maintaining predetermined high voltage battery state of charge (SOC) constraints. When the control hierarchy is modified or different driving styles are applied, the analysis must be redone to update the equivalence factor. The goal of this work is to implement a fuzzy logic controller that dynamically updates the equivalence factor to improve fuel economy, maintain a strict charge sustaining window of operation for the high voltage battery, and reduce computational time required during algorithm development. The adaptive algorithm is validated against global optimum fuel economy and charge sustaining results from a sensitivity analysis performed for multiple drive cycles. Results show a maximum fuel economy improvement of 9.82% when using a mild driving style and a 95% success rate when maintaining an ending SOC within 5% of the desired SOC regardless of starting SOC.

Software Defect Prediction Using Hybrid Machine Learning Techniques: A Comparative Study

When a customer uses the software, then it is possible to occur defects that can be removed in the updated versions of the software. Hence, in the present work, a robust examination of cross-project software defect prediction is elaborated through an innovative hybrid machine learning framework. The proposed technique combines an advanced deep neural network architecture with ensemble models such as Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The study evaluates the performance by considering multiple software projects like CM1, JM1, KC1, and PC1 using datasets from the PROMISE Software Engineering Repository. The three hybrid models that are compared are Hybrid Model-1 (SVM, RandomForest, XGBoost, Neural Network), Hybrid Model-2 (GradientBoosting, DecisionTree, LogisticRegression, Neural Network), and Hybrid Model-3 (KNeighbors, GaussianNB, Support Vector Classification (SVC), Neural Network), and the Hybrid Model 3 surpasses the others in terms of recall, F1-score, accuracy, ROC AUC, and precision. The presented work offers valuable insights into the effectiveness of hybrid techniques for cross-project defect prediction, providing a comparative perspective on early defect identification and mitigation strategies. .

Polyurethane Hybrid-Based Wood Adhesive: Review

Based on commercially available polyvinyl alcohol (PVA) stabilised polyvinyl acetate (PVAc), emulsion adhesives are neither heat nor moisture-resistant and show weak strength at high relative humidity and high temperatures. Pre- or post-crosslinking is another method used to manufacture a conventional vinyl-based homopolymers or copolymers system with improved water resistance. Vinyl neodecanoate (VeoVa), N-methylolacrylamide (NMA), Methacrylamide, methacrylic acid (MAA), and other self-crosslinking comonomers are typically inserted to produce highly water-resistant vinyl based homopolymers or copolymers. Additionally, organic crosslinkers like glyoxal, glutaraldehyde, citric acid, tartaric acid, and the like, as well as inorganic crosslinkers like acidic metal salts like aluminium chloride, aluminium nitrate, boric acid, and the like, can be used to prepare the highly water-resistant vinyl based homopolymers or copolymers. It is also possible to combine the self-crosslinking comonomers with the organic crosslinkers. Recently, a different hybrid chemistry has been developed that improves lap shear strength, has outstanding water resistance, good durability, and doesn’t require any additional crosslinker agents. Two distinct polymers were combined to develop hybrid polymers. They usually involve mixing an organic polymer with a polymer. There are many capping agents that are used for polyurethanes to produce acrylics that are capped with polyurethane and used as an oligomer in PVAc wood glue. Here, in this paper, we reviewed the different hybrid chemistry based on polyurethane chemistry for wood bonding applications.

Hybrid Effects on MHD Mixed Convective Boundary Layer Flow through a Sloped Plate in Existence of Nanofluid-Saturated Porous Medium

This study examines the effects of heat, mass, and boundary layer assumptions-based nanoparticle characteristics on the hybrid effects of using MHD in conjunction with mixed convective flow through a sloped vertical pore plate in the existence of medium of porous. Physical characteristics such as thermo-diffusion, injection-suction, and viscous dissipation are taken into consideration, in addition to an equally distributed magnetic force utilized as well in the completely opposite path of the flow. By means of several non-dimensional transformations, the momentum, energy, concentration, and nanoparticle volume fraction equations under investigation are converted in terms of nonlinear boundary layer equations and computationally resolved by utilizing the sixth-order Runge-Kutta strategy in combination together with the iteration of Nachtsheim-Swigert shooting procedure. By contrasting the findings produced for a few particular examples with those found in the published literature, the correctness of the numerical result is verified, and a rather good agreement is found. Utilizing various ranges of pertinent factors, computing findings are determined not only regarding velocity, temperature, and concentration as well as nanoparticle fraction of volume but also concerning with local skin-friction coefficient, local Nusselt and general Sherwood numbers associated with nanoparticle Sherwood number. The findings of the study demonstrate that increasing the fluid suction parameter decreases the velocity and temperature of the flow field in conjunction with concentration and has a variable impact on the nanoparticle fraction of volume, despite an increasing behavior in the local skin friction coefficient and local Nusselt as well as general Sherwood numbers and an increasing behavior in the local nanoparticle Sherwood number. Furthermore, enhancing a Schmidt number leads to a reduction in the local nanoparticle Sherwood number and a rise in the nanoparticle proportion of volume. Along with concentration, it also reduces temperature and velocity. However, it also raises the local Sherwood and Nusselt numbers and reduces the local skin friction coefficient.

Numerical Assessment of the Thermal Efficiency of a Concentrated Photovoltaic/Thermal (CPV/T) Hybrid System Using Air as Heat Transfer Fluid

In this paper, we propose a thermal model of a hybrid photovoltaic/thermal concentration system. Starting from the thermal balance of the model, the equation is solved and simulated with a MATLAB code, considering air as the cooling fluid. This enabled us to evaluate some of the parameters influencing the electrical and thermal performance of this device. The results showed that the temperature, thermal efficiency and electrical efficiency delivered depend on the air mass flow rate. The electrical and thermal efficiencies for different values of air mass flow are encouraging, and demonstrate the benefits of cooling photovoltaic cells. The results show that thermal efficiency decreases air flow rate greater than 0.7 kg/s, whatever the value of the light concentration used. The thermal efficiency of the solar cell increases as the light concentration increases, whatever the air flow rate used. For a concentration equal to 30 sun, the thermal efficiency is 0.16 with an air flow rate equal to 0.005 kg/s;the thermal efficiency increases to 0.19 with an air flow rate equal to 0.1 kg/s at the same concentration. An interesting and useful finding was that the proposed numerical model allows the determination of the electrical as well as thermal efficiency of the hybrid CPV/T with air flow as cooling fluid.

Nested Levels of Hybrid Cryptographical Technique for Secure Information Exchange

Data security is a very important part of data transmission over insecure channels connected through high-speed networks. Due to COVID-19, the use of data transmission over insecure channels has increased in an exponential manner. Hybrid cryptography provides a better solution than a single type of cryptographical technique. In this paper, nested levels of hybrid cryptographical techniques are investigated with the help of Deoxyribonucleic Acid (DNA) and Paillier cryptographical techniques. In the first level, information will be encrypted by DNA and at the second level, the ciphertext of DNA will be encrypted by Paillier cryptography. At the decryption time, firstly Paillier cryptography will be processed, and then DAN cryptography will be processed to get the original text. The proposed algorithm follows the concept of Last Encryption First Decryption (LEFD) at the time of decryption. The computed results are depicted in terms of tables and graphs.

Advancing Type II Diabetes Predictions with a Hybrid LSTM-XGBoost Approach

In this paper, we explore the ability of a hybrid model integrating Long Short-Term Memory (LSTM) networks and eXtreme Gradient Boosting (XGBoost) to enhance the prediction accuracy of Type II Diabetes Mellitus, which is caused by a combination of genetic, behavioral, and environmental factors. Utilizing comprehensive datasets from the Women in Data Science (WiDS) Datathon for the years 2020 and 2021, which provide a wide range of patient information required for reliable prediction. The research employs a novel approach by combining LSTM’s ability to analyze sequential data with XGBoost’s strength in handling structured datasets. To prepare this data for analysis, the methodology includes preparing it and implementing the hybrid model. The LSTM model, which excels at processing sequential data, detects temporal patterns and trends in patient history, while XGBoost, known for its classification effectiveness, converts these patterns into predictive insights. Our results demonstrate that the LSTM-XGBoost model can operate effectively with a prediction accuracy achieving 0.99. This study not only shows the usefulness of the hybrid LSTM-XGBoost model in predicting diabetes but it also provides the path for future research. This progress in machine learning applications represents a significant step forward in healthcare, with the potential to alter the treatment of chronic diseases such as diabetes and lead to better patient outcomes.

Polyvinyl Acetate and Vinyl Acetate-Ethylene Hybrid Adhesive: Synthesis, Characterization, and Properties

The goal is to develop a hybrid IPN network of polyvinyl acetate (PVAc) and ethylene-vinyl acetate (VAE). In this research work, the vinyl acetate (VAc)/ VAE hybrid emulsion and polyvinyl acetate emulsion (PVAc) were effectively synthesized. Emulsions with various characteristics have been developed by adjusting the weight ratios between the vinyl acetate monomer and the VAE component. The impacts on the mechanical, thermal, and physical properties of the films were investigated using tests for pencil hardness, tensile shear strength, pH, contact angle measurement, differential scanning calorimetry (DSC), and viscosity. When 5.0 weight percent VAE was added, the tensile shear strength in dry conditions decreased by 18.75% after a 24-hour bonding period, the heat resistance decreased by 26.29% (as per WATT 91) and the tensile shear strength decreased by approximately 36.52% in wet conditions (per EN 204). The pristine sample’s results were also confirmed by the contact angle test. The interpenetrating network (IPN) formation in hybrid PVAc emulsion as primary bonds does not directly attach to PVAc and VAE chains. The addition of VAE reduced the mechanical properties (at dry conditions) and heat resistance as per WATT 91. Contact angle analysis demonstrated that PVAc adhesives containing VAE had increased water resistance when compared to conventional PVA stabilised PVAc homopolymer-based adhesives. When compared to virgin PVAc Homo, the water resistance of the PVAc emulsion polymerization was enhanced by the addition of VAE.

Contribution to the Full 3D Finite Element Modelling of a Hybrid Stepping Motor with and without Current in the Coils

The paper presents our contribution to the full 3D finite element modelling of a hybrid stepping motor using COMSOL Multiphysics software. This type of four-phase motor has a permanent magnet interposed between the two identical and coaxial half stators. The calculation of the field with or without current in the windings (respectively with or without permanent magnet) is done using a mixed formulation with strong coupling. In addition, the local high saturation of the ferromagnetic material and the radial and axial components of the magnetic flux are taken into account. The results obtained make it possible to clearly observe, as a function of the intensity of the bus current or the remanent induction, the saturation zones, the lines, the orientations and the magnetic flux densities. 3D finite element modelling provide more accurate numerical data on the magnetic field through multiphysics analysis. This analysis considers the actual operating conditions and leads to the design of an optimized machine structure, with or without current in the windings and/or permanent magnet.

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

High-Order Solitons and Hybrid Behavior of (3 + 1)-Dimensional Potential Yu-Toda-Sasa-Fukuyama Equation with Variable Coefficients

In this paper, some exact solutions of the (3 + 1)-dimensional variable-coefficient Yu-Toda-Sasa-Fukuyama equation are investigated. By using Hirota’s direct method and symbolic computation, we obtained N-soliton solution. By using the long wave limit method, the N-order rational solution can be obtained from N-order soliton solution. Then, through the paired complexification of parameters, the lump solution is obtained from N-order rational solution. Meanwhile, we obtained a hybrid solution between 1-lump solution and N-soliton (N=1,2) by using the long wave limit method and parameter complex. Furthermore, four different sets of three-dimensional graphs of solitons, lump solutions and hybrid solutions are drawn by selecting four different sets of coefficient functions which include one set of constant coefficient function and three sets of variable coefficient functions.

Advancing Early Detection of Colorectal Adenomatous Polyps via Genetic Data Analysis: A Hybrid Machine Learning Approach

In this study, a hybrid machine learning (HML)-based approach, incorporating Genetic data analysis (GDA), is proposed to accurately identify the presence of adenomatous colorectal polyps (ACRP) which is a crucial early detector of colorectal cancer (CRC). The present study develops a classification ensemble model based on tuned hyperparameters. Surpassing accuracy percentages of early detection approaches used in previous studies, the current method exhibits exceptional performance in identifying ACRP and diagnosing CRC, overcoming limitations of CRC traditional methods that are based on error-prone manual examination. Particularly, the method demonstrates the following CRP identification accuracy data: 97.7 ± 1.1, precision: 94.3 ± 5, recall: 96.0 ± 3, F1-score: 95.7 ± 4, specificity: 97.3 ± 1.2, average AUC: 0.97.3 ± 0.02, and average p-value: 0.0425 ± 0.07. The findings underscore the potential of this method for early detection of ACRP as well as clinical use in the development of CRC treatment planning strategies. The advantages of this approach are highly expected to contribute to the prevention and reduction of CRC mortality.