Engineering reports : open access最新文献

{"title":"A Comprehensive Review of Deep Learning Techniques for Anomaly Detection in IoT Networks: Methods, Challenges, and Datasets","authors":"Roya Morshedi,&nbsp;S. Mojtaba Matinkhah","doi":"10.1002/eng2.70415","DOIUrl":"https://doi.org/10.1002/eng2.70415","url":null,"abstract":"<p>With the rapid growth of the Internet of Things (IoT) and the widespread deployment of smart connected devices, ensuring the security of these networks has become a critical challenge. Anomaly detection is considered one of the most effective techniques for identifying abnormal behaviors and cyber-attacks in IoT networks. In recent years, deep learning techniques have gained significant attention in this domain due to their powerful capabilities in automatic feature extraction and modeling complex patterns. This review article provides a comprehensive overview of deep learning methods applied to anomaly detection in IoT networks. Various deep architectures including CNNs, LSTMs, autoencoders, GANs, and hybrid models are analyzed and compared. In addition, commonly used datasets such as CICIDS2017, BoT-IoT, NSL-KDD, and TON_IoT are introduced and evaluated in terms of their quality and suitability for deep learning-based models. Key challenges including the lack of real-world data, high resource consumption, vulnerability to adversarial attacks, and lack of interpretability are also discussed. Finally, potential future research directions are suggested to enhance the performance and real-world applicability of deep learning-based anomaly detection systems in IoT environments.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70415","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Study on the Properties of Controlled Low-Strength Materials Prepared by Pipe Jacking Slag","authors":"Jianchen Song,&nbsp;Xuebo Song,&nbsp;Feilun Luo,&nbsp;Liang Xiong","doi":"10.1002/eng2.70374","DOIUrl":"https://doi.org/10.1002/eng2.70374","url":null,"abstract":"<p>As a byproduct of urban pipeline network construction, pipe jacking waste soil exhibits characteristics of massive volume, regional specificity, and complex composition. The presence of surface-active components impedes drainage consolidation, thereby restricting its resource utilization. This study developed self-compacting Controlled Low Strength Materials (CLSM) using waste soil from utility tunnel pipe jacking operations, investigating the effects of water-solid ratio, lime-soil ratio, and fly ash-to-cement ratio (F/C) on flowability, water secretion rate, and compressive strength. Experimental results demonstrate that: (1) Water–solid ratio predominantly governs CLSM flowability; (2) Compressive strength decreases with increasing F/C but enhances with higher lime-soil ratios; (3) Optimized mixtures achieved flowability (100–200 mm), water secretion rate (&lt; 3%), and compressive strength (0.35–0.7 MPa) meeting trench backfill specifications. The developed CLSM exhibits self-compacting properties and high flowability, satisfying both operational performance and mechanical requirements for engineering applications. This research provides technical parameters for sustainable recycling of pipe jacking waste in urban underground engineering projects.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70374","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Integrated Experimental and Machine Learning Approach for Machinability Assessment and Tool Life Prediction in Drilling of 14NiCr10 Alloy Using AlTiN-Coated Carbide Tools","authors":"Adik M. Takale,&nbsp;Uday A. Dabade,&nbsp;Manjunath G. Avalappa,&nbsp;Uttam U. Deshpande,&nbsp;Mukesh Kumar","doi":"10.1002/eng2.70397","DOIUrl":"https://doi.org/10.1002/eng2.70397","url":null,"abstract":"<p>To maximize tool life and process efficiency in high-performance drilling applications, it is necessary to examine the machinability of 14NiCr10 alloy using AlTiN-coated carbide tools with varying cobalt compositions. We propose a method to evaluate tool wear, cutting force, temperature, and the number of holes drilled before resharpening during experimental trials in dry cutting settings. In comparison to normal tools, carbide tools with a higher cobalt content demonstrated better wear resistance, lower thermal load, and a 51% increase in tool life during experiments. To validate the measured mechanical and thermal loads, we carried out simulation tests using Finite Element Analysis (FEA) on temperature distribution, torque, and stress behavior loads. For normal and increased cobalt content tool versions, the values stayed within safe operating limits. We trained the sensor-acquired force and temperature data using the Decision Tree Regressor to create a machine learning-based predictive model, further improving process reliability. With more than 90% of tool life estimates falling within an acceptable error range of ±10%, the model exhibited excellent predictive accuracy. Our method provides a comprehensive hybrid framework for machining high-strength alloys by utilizing a combination of simulation, machine learning, experimental analysis, and enhancing tool performance. Thus, it facilitates predictive maintenance and supports the development of smart manufacturing processes.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70397","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving Changes: Analyzing the Factors Influencing Lean Manufacturing Adoption in Tanzania Through Structural Equation Modeling (SEM)","authors":"Juma M. Matindana,&nbsp;Francis D. Sinkamba,&nbsp;Mussa I. Mgwatu","doi":"10.1002/eng2.70414","DOIUrl":"https://doi.org/10.1002/eng2.70414","url":null,"abstract":"<p>With growing competition among manufacturing industries in Tanzania, there is a need to adopt lean manufacturing (LM). The adoption of LM in Tanzania and other developing countries is low. This study identifies drivers for LM implementation in the country. Survey and purposive sampling were used to collect responses from 243 manufacturing industries in Tanzania. Partial least squares—structural equation modeling (PLS-SEM) and relative importance index (RII) were used to determine and rank the drivers for LM. PLS-SEM involved the development of a measurement and structural model for drivers of LM adoption using Smart PLS 4. Model fit indices on the effects of drivers on the adoption of LM, such as the normed fit index (NFI), were ≥ 0.7, demonstrating the model was good. External and policy drivers positively impact the adoption of LM in Tanzania. The drivers are to increase capacity to fulfill demands, establish standard operating procedures, balance workload on different workstations, reduce lead time, and improve process control. Identifying the drivers enhances competition among local industries, which, in turn, improves the sector's contribution to the country's gross domestic product. Furthermore, it assists policymakers in setting appropriate policies and strategies for promoting industrial growth in Tanzania.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145146563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Modulation Recognition Based on a Lightweight Transformer Network Under Strong Interference Conditions","authors":"Dingrui Liu,&nbsp;Pengli Liu,&nbsp;Jialin Chen,&nbsp;Ke Xiong,&nbsp;Bo Jing,&nbsp;Dongsheng Liao","doi":"10.1002/eng2.70405","DOIUrl":"https://doi.org/10.1002/eng2.70405","url":null,"abstract":"<p>In complex environments with strong electromagnetic interference, which are characterized by high noise levels and low signal-to-noise ratios (SNRs), deep learning improves the efficiency and accuracy of automatic modulation recognition (AMR) in electronic reconnaissance operations. The deep-learning architecture Transformer, a prominent neural network model, captures global feature dependencies in parallel through a multi-head attention mechanism. This improves both the receptive field and the flexibility of the network. However, Transformer fails to effectively model local, subtle features, and its high computational complexity creates challenges in mobile deployment. To address these limitations under conditions of heavy interference, this paper proposes a mobile convolution self-attention network (MCSAN), which utilizes multiple inverted residual blocks to extract local signal features, reducing the spatial dimensions while increasing the channel dimensions of the feature map. Additionally, a novel global window self-attention (GWSA) block is inserted after different inverted residual blocks to extract global signal features. GWSA reduces computational complexity and achieves higher accuracy than conventional multi-head attention mechanisms. In this paper, we evaluate MCSAN under conditions of severe interference using the RML2016.10a dataset at SNRs as low as −20 dB. Additionally, we analyze the model's architecture, hyperparameters, and confusion matrices. Finally, we compare this model to existing deep learning-based AMR models. Our experimental results demonstrate that MCSAN effectively improves recognition accuracy while requiring considerably fewer computational resources and parameters than current Transformer-based AMR approaches. Notably, MCSAN achieves a recognition accuracy of 53.21% even at an SNR of −20 dB.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Some Vegetable Oil-Based Additives for Petroleum Industry: A Review of Sunflower, Rapeseed and Used Cooking Oil-Derived Surfactants","authors":"Roland Nagy,&nbsp;Gábor Zoltán Nagy,&nbsp;Ditta Adrienn Gerbovits","doi":"10.1002/eng2.70345","DOIUrl":"https://doi.org/10.1002/eng2.70345","url":null,"abstract":"<p>The increasing environmental concerns associated with conventional petroleum-based additives—such as high toxicity, poor biodegradability, and long-term ecological persistence—have driven the development of more sustainable alternatives. This review focuses on the synthesis and industrial relevance of bio-based green gemini surfactants and sulphurized vegetable oil-derived extreme pressure (EP) additives, especially those prepared from sunflower oil, rapeseed oil, and used cooking oil. These compounds offer advantages including lower environmental impact, high thermal stability, and effective lubrication or interfacial properties in petroleum-related applications. The paper summarizes recent advances in the field, outlines key mechanisms, and explores their potential in enhanced oil recovery, metalworking, and lubrication. By compiling and evaluating current literature, the work contributes to identifying environmentally friendly and industrially viable bio-additive candidates.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Mosquito-Nets: A Natural Approach to Controlling Malaria Using Larvicidal Plant Extracts and Internet of Things","authors":"Juliet Onyinye Nwigwe,&nbsp;Kennedy Chinedu Okafor,&nbsp;Ogonna Christiana Ani,&nbsp;Titus Ifeanyi Chinebu,&nbsp;Okafor Ijeoma Peace,&nbsp;Omowunmi Mary Longe,&nbsp;Kelvin Anoh","doi":"10.1002/eng2.70407","DOIUrl":"https://doi.org/10.1002/eng2.70407","url":null,"abstract":"<p>Malaria mosquitoes, Anopheles, are well-known for carrying and spreading the malaria pathogens, known as Plasmodium. The public health challenge it brings has remained a global health challenge, of which the most robust control measures include mosquito-treated nets and electronic mosquito killer lamps. Due to health and cost problems, for example, in developing countries, these methods are not suitable for controlling mosquitoes and their plasmodiumic pathogens. In this study, we propose the use of two natural plant (e.g., <i>Petiveria alliacea</i> and <i>Hyptis suavolens</i> leaf) extracts that are cheap, ubiquitous, and effective for the control of mosquitoes, especially in temperate regions such as sub-Saharan Africa. On top of that, the study uses memory, non-locality, and fractal properties of fractal-fractional derivatives from compartmental modeling to capture susceptibility of infected persons, wider coverage, and heterogeneous breeding of mosquitoes, respectively, to evaluate the effectiveness of the two leaf extracts as natural larvicides against <i>Anopheles</i> mosquitoes. To measure the effectiveness of the two plant extracts in controlling malaria, this study develops a basic reproduction number model of Anopheles mosquitoes and evaluates the endemic points of the model. Comparing the results of larvicidal control with those of mosquito-treated nets, the proposed larvicidal control achieved 94.86% efficacy when applied alone and 96.83% efficacy when combined with mosquito nets, each outperforming mosquito nets (83.33%). These findings position compartmental fractal fractional-order modeling as an innovative tool for bioinformatic disease vector control. The study also presents a smart mosquito-net model where data collected from the host nodes on the performance of larvicides in mosquito and malaria control are transmitted via the Internet of Things infrastructure to the edge and cloud servers for computation, processing, artificial intelligence analytics, and policy-making.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Manufacturing Efficiency in Developing Countries: A Value Stream Mapping Case Study of a Tanzanian Rice Mill","authors":"Juma Mohamed Matindana,&nbsp;Francis Daudi Sinkamba","doi":"10.1002/eng2.70401","DOIUrl":"https://doi.org/10.1002/eng2.70401","url":null,"abstract":"<p>As competition among industries increases in terms of quality, price, and flexibility, there is a need for industries to apply new advanced manufacturing philosophies, such as Lean Manufacturing (LM), for operational excellence in today's dynamic market. Manufacturing industries in Tanzania are now thinking of applying LM tools such as value stream mapping (VSM) to detect and eliminate waste in their production processes for improvements in their operations and the contribution of the manufacturing sector to the Gross Domestic Product (GDP) of the country, which stands at 8% as of date. This study applied VSM for one rice milling industry as a case study of food industries to identify nonvalue-added and value-added activities. The study comprised three phases, which were data collection from the industry, analysis of data, and mapping of actual and future state maps using the EdrawMax software version 10.5.0. Future state maps indicated that there would be significant improvements in the reduction of lead time by 44.3%, cycle time increase by 5%, increase in employee performance indicator from 88.3% to 91.7%, increase in quantitative production indicator from 82.8% to 90.5%, and increment of income generated after the elimination of identified activities which do not add value in their production operations. The study is beneficial for manufacturing owners and practitioners as it highlights how organizations can improve operational efficiency in terms of time reduction and an increase in income using VSM. The study recommends that the owners and practitioners of Tanzanian manufacturing industries and other developing countries should conduct VSM for each production process of their operations to improve the efficiency of their organizations and the contribution of the sector to the economy of the country.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70401","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Latent Feature-Based Type 2 Diabetes Prediction Using a Hybrid Stacked Sparse Autoencoder and Machine Learning Models","authors":"Abdussamad,&nbsp;Hanita Daud,&nbsp;Rajalingam Sokkalingam,&nbsp;Muhammad Zubair,&nbsp;Iliyas Karim Khan,&nbsp;Zafar Mahmood","doi":"10.1002/eng2.70358","DOIUrl":"https://doi.org/10.1002/eng2.70358","url":null,"abstract":"<p>Early and precise prediction of Type 2 diabetes is vital for effective intervention. However, extracting meaningful insights from high-dimensional datasets with sparse values remains challenging. Sparsity and redundant features often hinder traditional machine learning algorithms' ability to identify informative patterns. While conventional Stacked Sparse Autoencoders (SSAE) can capture key features in dense data, they typically struggle with high-dimensional sparse data, reducing classification accuracy. To address this limitation, the study proposes a Hybrid Stacked Sparse Autoencoder (HSSAE) algorithm designed for robust feature extraction and classification in sparse data environments. The architecture incorporates L1 and L2 regularization within a binary cross-entropy loss and employs dropout and batch normalization to improve generalization and training stability. The HSSAE algorithm's performance was tested with a sigmoid classifier and various machine learning techniques. When combined with a sigmoid layer, the model achieved 89% accuracy and an <i>F</i>1 score of 0.89. It also outperformed baseline models when integrated with traditional classifiers; notably, the HSSAE + K-Nearest Neighbor (KNN) achieved an <i>F</i>1 score of 0.91, a recall of 0.98, 90% accuracy, and the lowest hamming loss of 0.10. Comparative evaluations included baseline classifiers like Logistic Regression (LR), KNNs, Naïve Bayes (NB), AdaBoost, and XGBoost, applied directly to the preprocessed dataset. An ablation study tested these classifiers on features extracted via the SSAE. In both cases, the HSSAE algorithm showed superior performance across all metrics. These findings demonstrate the HSSAE algorithm's effectiveness in extracting discriminative features from sparse, high-dimensional data, emphasizing its potential for clinical decision support systems requiring high accuracy and reliability.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70358","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Assessment of Mechanical and Electrochemical Properties of Additively Manufactured IN 718 Alloy Using Taguchi and Super Ranking Approaches","authors":"Pooja. G. Thorat,&nbsp;Avinash Lakshmikanthan,&nbsp;Mohan Nagaraj,&nbsp;Manjunath Patel Gowdru Chandrashekarappa,&nbsp;Oguzhan Der,&nbsp;Chithirai Pon Selvan,&nbsp;Raghupatruni Venkata Satya Prasad","doi":"10.1002/eng2.70400","DOIUrl":"https://doi.org/10.1002/eng2.70400","url":null,"abstract":"<p>The increased adoption of IN 718 alloy in marine and aerospace applications faces critical challenges due to aggressive chloride-induced degradation, making understanding its corrosion resistance imperative. Evaluating its mechanical performance (micro-hardness: MH and ultimate tensile strength: UTS) is equally essential and represents a critical area of study. The mechanical performance of IN 718 alloy is reliant on four influencing variables (laser power, scan speed, laser beam spot size, and layer thickness) of the selective laser melting (SLM) technique. The Taguchi L₉ matrix is designed to study and analyze the parameters and optimize the responses. Laser power showed a dominant impact on the mechanical performance of printed parts. Taguchi determined that optimal conditions were found to be different for both UTS and MH. The super ranking method determined that optimized SLM conditions resulted in MH and UTS values of 344.8 HV and 1051.2 MPa, as experimentally determined. Microstructural characterization was performed on IN 718 alloy powder, and fracture morphology was conducted at different parametric conditions. The corrosion behavior of optimized SLM-processed IN 718 alloy was evaluated in 0.1 M H₂SO₄ with varying NaCl concentrations (0.1–0.7 M) using potentiodynamic polarization and electrochemical impedance at room temperature. The addition of 0.7 M NaCl to 0.1 M H₂SO₄ provided the highest inhibition activity for IN 718 alloy, indicating that printed optimized parts can enhance its corrosion resistance in acidic environments.</p>","PeriodicalId":72922,"journal":{"name":"Engineering reports : open access","volume":"7 9","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/eng2.70400","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145111227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}