Sensors最新文献

{"title":"Separating Amplitude Response from Apparent Cyclic Drift in Self-Sensing Cementitious Composites.","authors":"Shaban Shahzad, Waqas Rafiq, Mohammed A Al-Osta, Asad Hanif, Salman Siddique","doi":"10.3390/s26196353","DOIUrl":"10.3390/s26196353","url":null,"abstract":"<p><p>The loading sensitivity of a self-sensing cementitious composite depends on the stress amplitude at which it is measured. That dependence is established, but its consequence for the protocol used to obtain the sensitivity has not been examined. Conventional protocols apply a few cycles at ascending amplitude levels, so the between-level component of cycle number is fixed by amplitude. The present study treats this as an identification problem: only the within-level repeated information can inform a cycle trend, and the data cannot identify a general cycle-history effect independently of amplitude. Three multi-walled carbon nanotube concrete cylinders of identical composition, one for each conditioning history, were investigated using a four-probe alternating-current method at 1 kHz under a three-level stepped-amplitude protocol in two loading variants, then loaded monotonically to failure. Across the six cyclic records, the correlation between cycle number and peak stress is r = 0.87 to 0.90, and 60% to 92% of the variance in loading sensitivity lies between amplitude levels rather than within them. With amplitude held fixed, within-level slopes are small and inconsistent in sign; however, three cycles per level cannot establish that a cycle effect is absent. Sensitivity magnitude falls between the middle and highest amplitude blocks in all six records, at 2.3 to 11.2 standard errors of the difference, three of which exceed their comparison-specific Welch critical value. At the lower amplitude step, the largest resolved difference is 0.673%/MPa in the continuous record of C1, whereas the partially saturated C2 records show no resolvable difference. Because amplitude and block order advance together under this protocol, these are differences among amplitude blocks and are not attributed to amplitude in isolation. The moisture-related pattern is an association across three individual specimens, not a replicated moisture effect. Monotonic loading provides a within-specimen extrapolation demonstration under the selected response family, not independent predictive validation. An experimental design with fixed-amplitude replication and a nominal precision calculation is specified.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aero-YOLO26: A Lightweight Detector for Small-Object Detection in UAV-Acquired Images with Collaborative Feature Optimization.","authors":"Zixiang Feng, Jiaying Li, Jialei Xu, Rui Quan","doi":"10.3390/s26196346","DOIUrl":"10.3390/s26196346","url":null,"abstract":"<p><p>Low-altitude remote sensing utilizing unmanned aerial vehicles (UAVs) has evolved into a core pillar of the space-air-ground integrated perception architecture. However, research on detection tasks in UAV aerial imagery grapples with three principal hurdles: feature annihilation of small targets, insufficient robustness under intricate environmental variations, and constrained computational capacity of onboard edge devices. To tackle these issues, a lightweight aerial small-target detection model denoted as Aero-YOLO26 was proposed for aerial image object detection. It introduced a C2f_GhostECA lightweight feature extraction module and a SlimSPPF streamlined multi-scale pooling module, and optimized the global receptive field of the Efficient Channel Attention (ECA) mechanism. Furthermore, it reconstructed the backbone and Path Aggregation Network (PAN)-Feature Pyramid Network (FPN) fusion network, enabling lightweight co-optimization of extraction, pooling, and fusion. Experiments on the VisDrone2019 dataset show that Aero-YOLO26n reduces the parameter count by 22.6% compared with YOLO26n, while improving precision, recall, mAP@50, mAP@50-95, and F1 score by 14.9%, 15.2%, 11.4%, 11.7%, and 14.8%, respectively. On the self-built UAV-MCT dataset, it attains 85.61% precision, 83.78% recall, mAP@50 of 90.82%, and mAP@50-95 of 59.60%, with average precision (AP) for cars and pedestrians reaching 99.4% and 96.6%. The proposed lightweight computer-vision object detection model using UAV-acquired imagery markedly bolsters the detection performance for small objects in UAV-acquired aerial imagery while achieving extreme model size compression, and its excellent performance demonstrates its potential for a wide range of application scenarios.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Bench Evaluation of a Sensor-Integrated Prosthetic-Leg Prototype.","authors":"Turar Seitkassymov, Yerkebulan Nurgizat, Nursultan Zhetenbayev, Aidos Sultan, Gani Sergazin, Kassymbek Ozhikenov","doi":"10.3390/s26196341","DOIUrl":"10.3390/s26196341","url":null,"abstract":"<p><p>This study presents the design and preliminary bench evaluation of a sensor-integrated prosthetic-leg prototype. The platform combines a multi-material PAHT-CF/TPU 95A foot, articulated knee and ankle mechanisms, two surface electromyography channels, an MPU6050 inertial measurement unit, an Arduino Uno controller, and MG995 servomotors. EMG threshold crossings select discrete knee-servomotor commands, while an accelerometer-derived inclination estimate selects between two ankle-servomotor commands. The evaluation comprised exploratory finite-element analyses, separate static compression tests, and qualitative demonstrations of the sensor-to-actuator pathways. The foot model predicted a maximum displacement of 25.96 mm under 400 N, whereas a separate compression test recorded approximately 7 mm at 1000 N; these values do not constitute quantitative model validation because the loading, constraints, and displacement measures differed. The shank model produced a maximum equivalent stress of 23.89 MPa, and a separate shank-knee assembly test reached approximately 1.85 kN. The foot-ankle connection produced a stress estimate of 203.6 MPa against an assumed strength value of 60 MPa, identifying a critical design limitation. Functional observations demonstrated sensor-triggered command execution, but joint tracking accuracy, response time, actuator output under load, and control repeatability were not quantified. The prototype therefore provides an initial laboratory platform for investigating mechanical and sensing integration; structural redesign and quantitative bench testing are required before any load-bearing or clinical use can be considered.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Semi-Supervised Adversarial Graph Prediction Model for Industrial Assets.","authors":"Weijie Kang, Yuze Wei, Junhao Wang, Chaoyin Ma, Bo Liu, Bing He, Guodong Duan","doi":"10.3390/s26196356","DOIUrl":"10.3390/s26196356","url":null,"abstract":"<p><p>In the field of Prognostics and Health Management (PHM), deep learning methods have shown excellent remaining useful life (RUL) prediction ability because of their powerful data fitting ability. Such methods require a large amount of failure history to train, but for industrial assets, their life-cycle data are often insufficient, and effective repairs or replacements are frequently carried out before the failure occurs. On the one hand, these suspension history data cannot be directly used by traditional deep learning methods, resulting in the certain loss of degradation information. On the other hand, the internal structure of industrial assets and the types of features are complex and diverse. Traditional methods use Euclidean space to represent relevant data, ignoring the correlation between features. Therefore, we develop a semi-supervised adversarial graph prediction (SAGP) framework that combines adversarial learning, graph neural networks, and semi-supervised training, using both failure and suspension histories for RUL prediction. Based on graph neural networks, SAGP can effectively obtain the degradation information in the failure and suspension history by representing the degradation data in the form of graphs and setting multi-objective functions. Through semi-supervised learning, SAGP looks for failure and suspension histories similar to the current state and generates simulated data through adversarial training to improve the robustness of the model and finally achieve RUL prediction. Tool, engine, and bearing datasets are used to verify the accuracy and reliability of the SAGP model.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SBOA-Inspired Particle Swarm Optimization of XGBoost for False Data Injection Detection in P2P Energy Trading.","authors":"Wenxuan Xu, Koichi Kobayashi, Yuh Yamashita","doi":"10.3390/s26196342","DOIUrl":"10.3390/s26196342","url":null,"abstract":"<p><p>Peer-to-peer (P2P) energy trading depends on the integrity of smart-meter measurements and coordinator messages. This paper presents an XGBoost detector for false data injection attacks and a hybrid hyperparameter-search mechanism combining particle swarm optimization (PSO) with three hunting-inspired operators from the secretary bird optimization algorithm (SBOA). A common candidate-evaluation budget enables comparison of fixed XGBoost, PSO-XGBoost and SBOA-PSO-XGBoost. The evaluation covers four Austin dataset settings, two attack scenarios and nine classifiers, using accuracy, precision, recall, F1 and computational cost. The equal-scenario mean F1 scores are 90.06%, 94.47% and 96.94% for the three XGBoost variants, respectively. The hybrid adds 2.47 percentage points over PSO with a 5.16% increase in total computation time, while its batch-prediction cost remains close to that of PSO-XGBoost. These comparisons characterize the detection-cost trade-off and the role of hybrid search under a shared evaluation protocol.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in Deep Learning Models for Ground Deformation Prediction Using InSAR Approach: A Systematic Review with Special Emphasis on Subsidence as a Geo-Environmental Hazard.","authors":"Nitish Kumar, Mohammad Soyeb Alam","doi":"10.3390/s26196358","DOIUrl":"10.3390/s26196358","url":null,"abstract":"<p><p>Ground deformation, especially subsidence, is a geo-environmental hazard that needs to be assessed and predicted in advance to mitigate its effects efficiently and in a timely manner. Interferometric Synthetic Aperture Radar (InSAR) provides high-resolution, all-weather ground deformation monitoring, while advances in Deep Learning (DL) have significantly improved its analytical capabilities. Despite growing interest in integrating DL with InSAR, existing systematic reviews have focused mainly on urban infrastructure, leaving mining-induced subsidence comparatively underexplored. This review addresses this gap by applying the PRISMA framework to systematically evaluate 162 peer-reviewed studies, including 21 core articles on DL-based mining subsidence and 58 supporting studies from related deformation applications. This paper examines DL architectures, including CNNs, LSTMs, GRUs, ConvLSTMs, Transformers, and Graph Neural Networks, and assesses their roles in data decomposition, spatiotemporal feature extraction, hyperparameter optimization, and multi-source data fusion. Comparative analysis of 71 models shows that attention-enhanced CNN-LSTM and ConvLSTM architectures consistently outperform single-model approaches. Key challenges include limited mining datasets, poor cross-site transferability, lack of standardized benchmarks, and insufficient geomechanical integration. Finally, we propose a seven-point research roadmap to support reliable, explainable, transferable, and operational early-warning systems for mining-induced subsidence monitoring.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fear-EEG-AI: A Conceptual and Operational Framework for Designing EEG-AI Systems for the Inference of Fear-Compatible Neurophysiological States.","authors":"Bladimir Serna, Ricardo Salazar, Gustavo A Alonso-Silverio, Rosario Baltazar, Elías Ventura-Molina, Antonio Alarcón-Paredes","doi":"10.3390/s26196349","DOIUrl":"10.3390/s26196349","url":null,"abstract":"<p><p>Electroencephalography (EEG) and artificial intelligence (AI) are central tools in affective computing, yet fear-related inference remains constrained by a gap between experimental performance and deployable evidence: high accuracy on controlled datasets guarantees neither neurobiological specificity, nor robustness across users, nor traceable inference. EEG does not measure fear directly; it captures cortical dynamics compatible with threat processing, arousal or vigilance. This paper introduces the Fear-EEG-AIFramework, a technology-independent architecture for designing, validating and deploying EEG-AI systems targeting fear-compatible neurophysiological states. Building on a prior systematic review, it organizes development into three hierarchical levels-neurobiological foundation, neuroelectric correlates and computational modeling-governed by six cross-cutting principles, and makes them evaluable through eight observable dimensions, the Fear-EEG-AI Alignment Index (FAI) and five validation evidence levels. Applied to the eight eligible studies of that corpus by two raters who had not drafted the instrument, with adjudication by a third, the rubric reached 67.2% exact agreement (quadratic-weighted κ=0.741; ICC(2,1) =0.744). Under 10,000 admissible weightings the equal-weight ordering survived in 10.1% of draws, and one of 56 ordered pairs met a stability criterion fixed before the pairwise results were tabulated. The index is therefore reported as a descriptor of methodological documentation and not as a ranking of implementations.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability-Guided Dynamic Fusion for Few-Shot Writer-Specific Stroke-Width Estimation in Offline Handwriting Biometric Sensing.","authors":"Jihan Liu","doi":"10.3390/s26196355","DOIUrl":"10.3390/s26196355","url":null,"abstract":"<p><p>Offline handwriting functions as a practical biometric sensing modality for identity verification and controlled stress testing of recognition pipelines, yet recovering writer-specific stroke appearance from only a few enrollment samples remains difficult. In trajectory-based rendering used to probe such sensing systems, stroke width is often fixed or only weakly adapted, so the sensed thickness cue fails to reflect individual writing habits. The key obstacle is an operational reliability imbalance: a learned width estimator and handcrafted geometric priors disagree in trustworthiness across writers and even along a single stroke, which makes a constant fusion rule brittle for few-shot personalization. We propose a confidence-guided dynamic fusion framework whose primary method is a deterministic confidence-to-β map (not a learned gate): point-wise pseudo-label confidence from reference images sets per-point blending weights between learned and rule-based widths, with \"reliability\" denoting this operational blending prior. On 73 HWDB writers under a strict writer-disjoint protocol with 240 adaptation runs, dynamic fusion reduces writer-level variance and cross-seed instability relative to fixed fusion while retaining competitive point-wise accuracy. A security-oriented sensing analysis further contrasts detector responses under generic versus writer-adapted rendering, showing that the framework can serve as a controllable probe of verification sensitivity. The method plugs into existing trajectory generators and supports reproducible evaluation of personalization fidelity and biometric sensing robustness.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667405/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Channel-Specific Stimulus Sensitivity, Reliability, and Complementarity of Multimodal Wearable Sensing During Short-Form Influencer Video Exposure.","authors":"Carolina Del-Valle-Soto, Violeta Corona, Regina Gómez Padilla, Fátima Sofía Walls Fernández, Juan-Carlos López-Pimentel, Carlos Mex-Perera, Paolo Visconti, José Varela-Aldás","doi":"10.3390/s26196354","DOIUrl":"10.3390/s26196354","url":null,"abstract":"<p><p>Multimodal wearable sensing is increasingly used to quantify audience engagement with short-form video, yet it remains unclear which sensing channels actually carry stimulus-driven information at the exposure durations typical of this format. We recorded electrodermal activity, photoplethysmographic pulse, triaxial acceleration, video-based eye tracking, and automated facial expression analysis from 50 young adults while they viewed 20 short-form influencer clips (5.6-19.3 s) presented in individually randomized order, yielding 1000 participant × clip trials and 12 primary indices spanning four modalities. Using crossed random-effects variance partitioning, split-half reliability with Spearman-Brown projection, permutation-based multivariate testing, participant-wise decoding, and inter-subject correlation, we show that stimulus-driven variance is strongly channel-specific: it reaches 13.7% for fixation rate and 10.2% for facial engagement but only 0.11% for pulse rate and 0.23% for RMSSD. Clip-level estimates were highly reliable for facial and oculomotor indices (r=0.82-0.94) and poor for autonomic indices (r=0.34-0.58), which would require 134-370 participants to reach ρ=0.80. Between-modality dependence was almost six times weaker than within-modality dependence, and five components were retained by parallel analysis. Oculomotor and facial features jointly identified individual clips at 16.8% balanced accuracy against a 5% chance level, whereas electrodermal and pulse features, taken separately, did not exceed chance. Electrodermal responses were nonetheless stimulus-locked in 18 of 20 clips. A per-second re-analysis of nine channels showed that the ordering partly reverses for within-clip dynamics: tonic electrodermal and pupil time courses were reliable across viewers (ρ50=0.88 and 0.93), facial time courses were not (ρ50≤0.69), and pulse rate was unreliable under both treatments. We derive explicit design guidance for sensor selection and sample size in short-exposure multimodal engagement studies.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct Acoustic Versus AUV-Assisted Hybrid Reporting for Subsea Pipeline Monitoring: A Matched End-to-End Evaluation.","authors":"Aissata Mamadou Diallo, Najat Rafalia, Jaafar Abouchabaka","doi":"10.3390/s26196337","DOIUrl":"10.3390/s26196337","url":null,"abstract":"<p><p>Subsea pipeline monitoring must deliver urgent alarms quickly while moving much larger inspection records over constrained underwater links. This creates a deployment choice: send all data through a prioritized acoustic network, or collect routine records with an autonomous underwater vehicle (AUV) undertaking a finite collection mission. We compare these complete reporting architectures under identical traffic, geometry, deadlines, available retry choices, and surface infrastructure. A predefined parameter search, independent confirmation and deployment controls test complete measurement recovery under finite storage, communication and vehicle resources. In the reference deployment, direct priority recovered <b>100%</b> of deadline-eligible raw measurement windows on time, compared with <b>0.84%</b> for the selected hybrid deployment. Lower submerged-equipment energy in the hybrid case coincided with extensive retained data and greater total mission energy. The result applies to the tested mission and controllers. It identifies collection opportunities, final transfer and mission availability as joint constraints that must be satisfied before local energy reductions can support a deployment decision.</p>","PeriodicalId":21698,"journal":{"name":"Sensors","volume":"26 19","pages":""},"PeriodicalIF":4.0,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13667693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}