Journal of Industrial Information Integration最新文献

{"title":"Designing a knowledge-enhanced framework to support supply chain information management","authors":"Peng Su,&nbsp;Dejiu Chen","doi":"10.1016/j.jii.2025.100874","DOIUrl":"10.1016/j.jii.2025.100874","url":null,"abstract":"<div><div>With globalization and outsourcing trends, modern industrial companies often rely on an extensive network of suppliers to construct sophisticated products. To maintain effective production planning and scheduling, integrating and managing the extensive information from supply chain has become increasingly critical. In particular, industrial companies, particularly those aiming to achieve Industry 4.0, enable to collect and analyze data related to their supply chains. Due to the vast amount of collected data, there is a continuous challenge in integrating and analyzing dependencies within the supplier network. While the development of Artificial Intelligence (AI) offers a promising solution for extracting and analyzing features from data, the inherently opaque and training-intensive nature of AI-enabled methods still present obstacles to effectively and efficiently analyzing information. To cope with this issue, this paper presents a knowledge-enhanced framework to support supply chain information integration and analysis by combining Knowledge Base (KB) and Graph Neural Networks (GNN). Specifically, constructing a KB enables the integration of extensive collected data with domain knowledge to generate structured and relational information. These knowledge-enhanced data support the training of GNN to encode information about supply chains. The resulting embeddings enable multiple inference tasks for analyzing graph-based data, supporting supply chain management. The case studies cover the usage of encoded embeddings for node classification, link prediction, and scenario classification. The proposed GNN outperforms baseline methods, demonstrating a promising solution for analyzing graph-based data in the context of supply chain management.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100874"},"PeriodicalIF":10.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved LVRT control in grid tied hybrid renewable energy system with optimized high gain converter","authors":"G. Ezhilarasi ,&nbsp;R. Senthil Kumar","doi":"10.1016/j.jii.2025.100878","DOIUrl":"10.1016/j.jii.2025.100878","url":null,"abstract":"<div><div>This research introduces advanced Low Voltage Ride through (LVRT) control strategies designed to enhance the resilience and stability of grid-connected wind and solar power generation systems. This LVRT control method is intended to efficiently infuse reactive power into grid according to grid code rules, using a Voltage Source Inverter (VSI). The proposed approach employs a LVRT method that determines the injection quantities of reactive and active currents. The strategy's adaptability is contingent upon the dropping ratio grid voltage, ensuring a dynamic response to varying grid conditions. For solar power production systems, the Photovoltaic (PV) voltage is augmented through the implementation of a Modified High Gain Boost Converter. This converter is equipped with a Chaotic Pigeon Optimized Proportional-Integral (CPO-PI) controller, providing an innovative solution for enhancing voltage levels in PV systems. The chaotic pigeon optimization aids in fine-tuning the PI controller, optimizing its performance for varying operational conditions. In the case of wind power generation systems, the control of Doubly-Fed Induction Generator (DFIG) based Wind Energy Conversion Systems (WECS) is achieved through a conventional PI controller. The entire work is simulated using Matlab Simulink platform and the attained outcomes prove that the developed work has highest conversion efficiency of 97.1 %. The integration of these strategies ensures an improved response to grid disturbances and voltage fluctuations, ultimately enhancing the overall performance and reliability of grid-connected renewable energy systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100878"},"PeriodicalIF":10.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence use in collaborative network processes","authors":"B. Andres ,&nbsp;P. Urze ,&nbsp;E. Araujo ,&nbsp;L.M. Camarinha-Matos","doi":"10.1016/j.jii.2025.100883","DOIUrl":"10.1016/j.jii.2025.100883","url":null,"abstract":"<div><div>This paper reviews the literature to analyse the use of artificial intelligence (AI) in collaborative processes among supply chain (SC) partners, thereby forming a collaborative network (CN). Given the growth of AI and its limited exploration in many business strategies, especially when collaboration among SC partners’ is established, this paper focuses on defining the lines of research and application of AI in CN processes, by presenting insights into how AI can improve the resilience and the antifragility. It examines the integration of AI in CN processes from the following perspectives: (i) the collaborative processes addressed among the CN partners, (ii) the decision-making level of the collaborative processes performed, (iii) the SC partners involved in the collaboration; (iv) the technologies combined with AI to support CN processes; (v) the programming languages implemented to develop AI algorithms; (vi) the SC sectors in which AI is mainly implemented to perform collaborative processes; and (vii) the potential of implementing AI in CN processes, in an increasingly turbulent and disruptive business world. The study focuses on SC in various sectors, including food, transport, logistics, manufacturing, healthcare or electronics, among others. In addition, the review provides a comprehensive understanding of the interplay between collaborative processes and AI-driven advances, identifying the technologies that can merge with AI to support CN processes. The results have enabled the development of a conceptual framework for AI use collaborative processes and outline the benefits, risks and challenges associated with the use of AI in CN, while proposing future research directions in this area.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100883"},"PeriodicalIF":10.4,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proposing a time-frequency analysis method using nonlinear wave modulation for machine learning-based detection of bolt looseness in non-gaussian noise environment","authors":"Naserodin Sepehry ,&nbsp;Hamidreza Amindavar ,&nbsp;Erfan Qanbari Qalehsari ,&nbsp;Seyed Mehdi Zahrai","doi":"10.1016/j.jii.2025.100877","DOIUrl":"10.1016/j.jii.2025.100877","url":null,"abstract":"<div><div>Detecting bolt loosening in bolted structures under non-Gaussian noisy environments is challenging with existing time-frequency analysis methods. This study proposes a novel fractional lower-order fractional synchrosqueezing-extracting transform (FLOFSSET) to effectively process linear chirp signals in such environments. Additionally, a data fusion approach based on the Dezert-Smarandache Theory is introduced to enhance machine learning model performance. A nonlinear wave modulation (NWM) technique combined with chirp signal excitation (chirp-NWM) is also implemented to improve early damage detection in bolted structures. The FLOFSSET method ensures high accuracy in analyzing close and far instantaneous frequencies, with superior energy concentration compared to conventional methods. Furthermore, decision-level data fusion is employed using machine learning models, including, Newton method for sparse support vector machines, sparse artificial neural network, and adaptive neuro-fuzzy Inference System. This fusion utilizes features extracted by the FLOFSSET method and a multi-sensor fusion method. The FLOFSSET method outperformed conventional approaches in analyzing close and far instantaneous frequencies, achieving higher energy concentration. For bolt-loosening detection, integrating FLOFSSET features with data fusion improved the average accuracy of machine learning models from 85 % to 99.5 %. In contrast, using conventional features reduced the accuracy of the combined models to 92.5 %. In scenarios with close instantaneous frequencies, only the FLOFSSET method accurately identified bolt loosening, with the fused model achieving 99.1 % accuracy. Conventional time-frequency analysis features were unable to accurately differentiate these frequencies, making them ineffective and resulting in misclassifications. The proposed FLOFSSET method, combined with Dezert-Smarandache Theory-based data fusion, significantly enhances the performance of machine learning models in detecting bolt loosening under non-Gaussian noise. FLOFSSET features provide higher discrimination capability and accuracy compared to conventional time-frequency methods, making it a robust tool for early damage detection in bolted structures.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100877"},"PeriodicalIF":10.4,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deriving optimal atomic layer deposition process conditions using machine learning","authors":"Jangwon Seo ,&nbsp;Hyo-Seok Hwang ,&nbsp;Sunyoung Park ,&nbsp;Seungmin Lee ,&nbsp;Dae Sin Kim ,&nbsp;Sun-Taek Lim ,&nbsp;Junhee Seok","doi":"10.1016/j.jii.2025.100879","DOIUrl":"10.1016/j.jii.2025.100879","url":null,"abstract":"<div><div>The increasing complexity and high aspect ratios of next-generation semiconductor structures have intensified pattern loading effects in atomic layer deposition (ALD) processes. These effects result in non-uniform thin-film deposition rates and thickness variations. Deriving optimal process conditions to ensure consistent thin-film deposition is essential for maintaining substrate uniformity and enhancing device performance. Consequently, computational fluid dynamics (CFD) simulations have established themselves as effective tools for deriving process conditions. However, their high computational resource demands and inefficiency in adapting to changing conditions highlight inherent limitations in their application. To address these challenges, this study proposes the atomic layer deposition-gaussian process regression (ALD-GPR) model, integrating multi-layer perceptron (MLP) and gaussian process regression (GPR). The ALD-GPR model accurately predicts partial pressure, a key indicator of thin-film uniformity, achieving an RMSE of 0.0074 and approximately 18 times faster computation speed than CFD simulators, demonstrating its potential as an efficient alternative. Additionally, variance-based and difference-based metrics were developed to quantitatively evaluate uniformity and derive optimal conditions for achieving uniform thin films. These metrics provide a practical framework for assessing and enhancing process uniformity in ALD operations. The proposed ALD-GPR model and metrics optimize ALD processes and significantly improve computational efficiency and accuracy, providing an approach applicable to semiconductor manufacturing and other industries.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100879"},"PeriodicalIF":10.4,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lightweight multiparty privacy set intersection protocol for internet of medical things","authors":"Zhuang Shan ,&nbsp;Leyou Zhang ,&nbsp;Qing Wu ,&nbsp;Fatemeh Rezaeibagha","doi":"10.1016/j.jii.2025.100863","DOIUrl":"10.1016/j.jii.2025.100863","url":null,"abstract":"<div><div>The development of privacy-preserving data exchange protocols through Privacy Set Intersection (PSI) protocols has emerged as a critical enabler for secure information exchange in the Internet of Medical Things (IoMT), particularly for applications requiring coordinated data analysis across distributed healthcare systems. Current PSI implementations face two fundamental limitations: a lack of efficient multi-user extension capabilities and vulnerability to quantum computing threats, which significantly limit their use in modern smart healthcare platforms. In this paper, we present a new construction based on the symmetric key pseudorandom function over lattice to overcome these challenges. First, a pseudorandom generator over LWE problems is proposed to construct the pseudorandom functions (PRFs) and oblivious key–value storage (OKVS). The proposed PRF achieves 1-almost key-homomorphic. Based on the proposed PRFs and OKVS, an efficient multi-party PSI protocol is introduced. In this framework, lattice-based cryptography is implemented for PRFs operation and OKVS encoding, ensuring semantic security against quantum adversaries and collusion attacks even when untrusted cloud servers process sensitive patient data. Integration of virtual set elements with probabilistic validity checks, enabling efficient detection of data tampering while preserving protocol efficiency. The results of simulation experiments and security analysis show that the proposals achieve user privacy, collusion resistance, verification of computational results, and low computational cost.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"47 ","pages":"Article 100863"},"PeriodicalIF":10.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-sensor data fusion across dimensions: A novel approach to synopsis generation using sensory data","authors":"Palash Yuvraj Ingle,&nbsp;Young-Gab Kim","doi":"10.1016/j.jii.2025.100876","DOIUrl":"10.1016/j.jii.2025.100876","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) and autonomous ground vehicles are increasingly outfitted with advanced sensors such as LiDAR, cameras, and GPS, enabling real-time object detection, tracking, localization, and navigation. These platforms generate high-volume sensory data, such as video streams and point clouds, that require efficient processing to support timely and informed decision-making. Although video synopsis techniques are widely used for visual data summarization, they encounter significant challenges in multi-sensor environments due to disparities in sensor modalities. To address these limitations, we propose a novel sensory data synopsis framework designed for both UAV and autonomous vehicle applications. The proposed system integrates a dual-task learning model with a real-time sensor fusion module to jointly perform abnormal object segmentation and depth estimation by combining LiDAR and camera data. The framework comprises a sensory fusion algorithm, a 3D-to-2D projection mechanism, and a Metropolis-Hastings-based trajectory optimization strategy to refine object tubes and construct concise, temporally-shifted synopses. This design selectively preserves and repositions salient information across space and time, enhancing synopsis clarity while reducing computational overhead. Experimental evaluations conducted on standard datasets (i.e., KITTI, Cityscapes, and DVS) demonstrate that our framework achieves a favorable balance between segmentation accuracy and inference speed. In comparison with existing studies, it yields superior performance in terms of frame reduction, recall, and F1 score. The results highlight the robustness, real-time capability, and broad applicability of the proposed approach to intelligent surveillance, smart infrastructure, and autonomous mobility systems.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100876"},"PeriodicalIF":10.4,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of direct energy deposition systems with an optimized process planning algorithm","authors":"Tao Zhao ,&nbsp;Zhaoyang Yan ,&nbsp;Haihua Liu ,&nbsp;Bin Zhang ,&nbsp;Rui Pan ,&nbsp;Jun Xiao ,&nbsp;Fan Jiang ,&nbsp;Shujun Chen","doi":"10.1016/j.jii.2025.100875","DOIUrl":"10.1016/j.jii.2025.100875","url":null,"abstract":"<div><div>Directed Energy Deposition (DED) has gained significant interest from the industrial sectors due to its ability to fabricate medium-to-large scale parts with high productivity and low capital investment. Within DED technologies, DED-Arc stands out as a promising method for practical industrial applications. However, the process planning presents challenges for developing an automated system suitable for industrial use. When preparing large, complex, and multi-feature parts, inappropriate trajectory and process strategies can often result in issues such as poor molding accuracy, lengthy manufacturing cycles, and significant material waste. Overall, there is currently no integrated process planning software for DED that can effectively improve manufacturing efficiency. Knowledge from many disciplines, such as computer science, material engineering, mechanical engineering, and industrial system engineering, is advantageous to develop such a process planning system. The system integrates optimal process parameters with a global path planning methodology, enabling the unified manufacturing of complex components through the synchronization of process, trajectory, parameters, and structure via integrated printing equipment. This paper proposes a composite path that integrates the advantages of the contour offset path and zigzag path, and optimizes the overlap distance through experiments and overlap models to achieve gap-free printing with multiple passes in a single deposition layer. By establishing a geometric model of deposition forming, the influence of multi-layer, multi-pass edge collapse (material shortage regions) on the geometric fidelity of parts is analyzed, and a constraint-compensation path planning method is proposed. Compared with the traditional method, the material utilization rate of the proposed method increases from 80 % to 95.6 %. This significant increase in material utilization rate indicates that the constraint-compensation forming control method is an effective way to reduce material waste and improve the overall efficiency and quality of the additive manufacturing process. This is critical in the development of high-quality, complex parts that require precise and accurate deposition of material layers.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100875"},"PeriodicalIF":10.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144137286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating smart production and multi-objective reverse logistics for the optimum consumer-centric complex retail strategy towards a smart factory’s solution","authors":"Biswajit Sarkar ,&nbsp;Sandipa Bhattacharya ,&nbsp;Mitali Sarkar","doi":"10.1016/j.jii.2025.100856","DOIUrl":"10.1016/j.jii.2025.100856","url":null,"abstract":"<div><div>In response to complex retail challenges, the elasticity of production and logistic efficacy can minimize expenses, which always meets consumer satisfaction. The approach evolves by implementing a retail logistics strategy to integrate advanced production information under diverse trading modes. The research promotes a high level of consumer service, and on the other hand, the remanufacturing establishes a significant retail insight by holistically defining the prime pillar in the success of reverse e-commerce platforms. In reality, the advent of strategy actualizes quantifying success components in retail logistics practices with an adequate understanding of consumer preferences. The approach intensifies consumer expectations to trigger retailing efforts and ensures effective interaction, which is a prominent challenge to retailers. In this context, consumer-centric retail strategy is reshaped under the robust paradigm to facilitate the impact of the retail economy in the logistics framework. The study proposes a comprehensive mathematical framework with multiple objectives following restrictions to implement the marketing operations under two distinct retail scenarios. The findings demonstrate consumer loyalty and outline the logistics experiences with declining total costs and delivery time by looking at cooperative interaction methodology. An excellent measure reveals a consumer service goal of reducing the 34% expenses and 25% delivery time of the product by expanding a 20% green advantage into logistics retailing. The empirical analysis revamps the conceptual underpinnings of the reverse logistics business and extends a novel opportunity in marketing. More specifically, the trend has led to anticipating the subject’s intention under the logistics operational services.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100856"},"PeriodicalIF":10.4,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mixed reality lab for assembly and disassembly of industrial products","authors":"Simone Cantarelli,&nbsp;Daniela Francia,&nbsp;Gian Maria Santi,&nbsp;Alfredo Liverani,&nbsp;Matteo Fiori","doi":"10.1016/j.jii.2025.100873","DOIUrl":"10.1016/j.jii.2025.100873","url":null,"abstract":"<div><div>The advent of virtual simulation and Augmented Reality has had a profound impact on industrial processes, enhancing operational efficiency and optimising production workflows. This paper presents a comprehensive study on the development and implementation of a Mixed Reality Lab designed for the assembly and disassembly of industrial products. The primary objective is to create an advanced and intuitive Virtual Reality (VR) environment that facilitates industrial operators’ learning and reduces training time while minimising errors.</div><div>The project encompasses the scanning and 3D modelling of industrial components using various technologies, including photogrammetry, structured light, laser scanning, and LiDAR. These techniques are evaluated for their precision and effectiveness in generating accurate 3D models. The VR environment is developed using Unity, incorporating features that support interactive and immersive user experiences.</div><div>Detailed methodologies for the creation and refinement of point clouds, mesh generation, and virtual environment setup are provided. The assembly and disassembly processes are tested using a brake assembly as a case study, thereby demonstrating the system’s ability to manage complex industrial tasks. The results indicate significant improvements in training efficiency and operational precision, with potential applications across various industrial sectors.</div><div>This study concludes by summarising the findings, discussing the implications for industrial training and operations, and suggesting directions for future research in the field of Mixed Reality applications in industry.</div></div>","PeriodicalId":55975,"journal":{"name":"Journal of Industrial Information Integration","volume":"46 ","pages":"Article 100873"},"PeriodicalIF":10.4,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}