Crime SciencePub Date : 2026-01-01Epub Date: 2026-01-28DOI: 10.1186/s40163-025-00266-6
Jolyon Miles-Wilson, Celestin Okoroji
{"title":"<i>policedatR</i>: a comprehensive R package for stop and search data in England and Wales.","authors":"Jolyon Miles-Wilson, Celestin Okoroji","doi":"10.1186/s40163-025-00266-6","DOIUrl":"10.1186/s40163-025-00266-6","url":null,"abstract":"<p><p>Research on Stop and Search in England and Wales is constrained by substantial barriers to data access, inconsistent geographic coverage, and technical complexity. This paper presents <i>policedatR</i>, an R package that addresses these challenges by providing streamlined access to comprehensive stop and search data from the data.police.uk Application Programming Interface (API). <i>policedatR</i> automates data acquisition across multiple geographic scales, enriches datasets with population estimates and geographic identifiers, and includes functions for analysing the data, including calculating ethnic disproportionality. We describe the architecture and main functionalities of <i>policedatR</i> and demonstrate its capabilities and utility with analyses of temporal trends, geographic variation and ethnic disparities at national (e.g. countrywide, Police Force Area) and local (e.g. sub-local authority) levels. We also provide an example of how data acquired using the package can be harmonised with other datasets (in this case the English Indices of Deprivation) to explore broader questions on stop and search and society. By transforming thousands of individual API calls into a straightforward analytical workflow, <i>policedatR</i> facilitates rigorous empirical research and <i>supports</i> democratic accountability in policing.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s40163-025-00266-6.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"15 1","pages":"11"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13099712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783966","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}
Crime SciencePub Date : 2026-01-01Epub Date: 2026-03-14DOI: 10.1186/s40163-026-00276-y
David Buil-Gil, Ken Pease
{"title":"Unstructured spare time and crime: toward an integrative model.","authors":"David Buil-Gil, Ken Pease","doi":"10.1186/s40163-026-00276-y","DOIUrl":"https://doi.org/10.1186/s40163-026-00276-y","url":null,"abstract":"<p><p>Criminological theorizing over the past half century has shown little convergence or integration. Three strands of criminological theory can be identified: dispositional approaches (emphasizing self-control, social learning, biological, and morality theories), ecological theories (emphasizing the crime consequences of dysfunctional communities), and opportunity theories (focusing primarily on places and artifacts that enable or facilitate crime). The discipline's progress has not resulted in a convergence of theoretical propositions. This article offers a potential route toward reconciling these approaches, provisionally termed the Unstructured Spare Time model of crime. It begins with an overview of relevant criminological theories and highlights enduring tensions between individual- and opportunity-based approaches. It then reviews previous integrative efforts, noting their contributions and limitations. The Unstructured Spare Time model is introduced as a conceptual bridge among these traditions. The model posits that unstructured spare time, at the level of individuals, geographic areas, and time periods, is shaped by personal factors, broader social changes, and the spatial organization of cities and towns. This unstructured time, in turn, influences both individual readiness for crime and the availability of crime opportunities. The model advances a dynamic view of how time-use patterns mediate the relationship between personal traits, community conditions, structural factors, and exposure to and engagement in crime. Its central contribution lies in focusing explanation and, by extension, prevention and intervention strategies on a single, observable factor: unstructured spare time. The article summarizes empirical support from recent studies and concludes by outlining directions for future research and refinement of the model.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"15 1","pages":"10"},"PeriodicalIF":2.6,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13099811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147783983","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}
Crime SciencePub Date : 2025-01-01Epub Date: 2025-10-02DOI: 10.1186/s40163-025-00256-8
Mariam Elgabry, Darren Nesbeth, Paul Ekblom, Shane Johnson
{"title":"BAKE: a novel framework for iterative security design for identifying criminally-exploitable vulnerabilities in biotechnology products.","authors":"Mariam Elgabry, Darren Nesbeth, Paul Ekblom, Shane Johnson","doi":"10.1186/s40163-025-00256-8","DOIUrl":"10.1186/s40163-025-00256-8","url":null,"abstract":"<p><p>Emerging \"in-body\" monitoring, such as via ingestible devices, promises the future of personalised health, yet discussions of crime and security implications remain of low priority. Here, we develop and deploy the scenario building of the Delphi process and the prototyping of the hackathon through a hybrid hackathon Delphi framework that we have labelled \"BAKE\". The aim of BAKE is to capture insight from experts regarding the risks posed by these devices; and to produce evidence for the utility of the model as a mechanism to identify at an early stage of design/development, criminally-exploitable vulnerabilities in biotechnology (bio-electronic devices), especially medical products/services. Findings from four expert groups include the identification of four crime forms (e.g., corporate exploitation, data breaches). Five secure by design principles (e.g., end-to-end encryption) and four governance mechanisms (e.g., independent body) were recognised. Four stakeholders were identified (e.g., technical, advocates for equitable treatment). Results indicate that the inclusion of non-traditional experts and early career researchers within the hackathon model can allow the identification of highly challenging threats within the cyber-physical device system. We demonstrated that hosting a hackathon with an embedded Delphi process can instigate secure by design thinking earlier in the product development life cycle of any emerging technology.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"14 1","pages":"16"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145233589","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}
Crime SciencePub Date : 2025-01-01Epub Date: 2025-10-02DOI: 10.1186/s40163-025-00258-6
Amal Ali, Jasmine Oware, Jonathan Jackson, Ben Bradford
{"title":"The compounding effect: how neighbourhood dynamics shape police deployment and use of force.","authors":"Amal Ali, Jasmine Oware, Jonathan Jackson, Ben Bradford","doi":"10.1186/s40163-025-00258-6","DOIUrl":"10.1186/s40163-025-00258-6","url":null,"abstract":"<p><strong>Background: </strong>Calls for service are a major driver of police activity, yet their role in shaping the neighbourhood distribution of police use of force remains under-explored. Understanding where and why force is used requires examining how these calls cluster spatially-and how police interpret and respond to them.</p><p><strong>Methods: </strong>Using administrative data from an English police force (2018-2021), we analyse how neighbourhood characteristics-including mental health prevalence, racial composition, socioeconomic deprivation, residential instability, and crime rates-predict patterns of police deployment and use of force. We link call-for-service records with force incident data to trace the process from (a) call initiation to (b) priority grading, (c) TASER-equipped officer deployment, and (d) eventual use of force.</p><p><strong>Results: </strong>Calls for service are concentrated in disadvantaged neighbourhoods with elevated mental health need. These areas are also more likely to experience police use of force (including TASER). Yet public demand is refracted through institutional filters-such as call grading and officer deployment decisions-that concentrate how and where force is ultimately applied.</p><p><strong>Conclusions: </strong>Police use of force does not result from isolated actions, but from a sequence of decisions that compound the existing spatial clustering of public calls for service. Structural disadvantage, mental health distress and operational decision-making interact to concentrate force in already over-burdened communities. Addressing disproportionate use of force requires reform not only of police practice, but also of the upstream social conditions that generate repeated crisis response.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1186/s40163-025-00258-6.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"14 1","pages":"15"},"PeriodicalIF":2.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145233640","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":"Exploring the usefulness of the INLA model in predicting levels of crime in the City of Johannesburg, South Africa","authors":"Toshka Coleman, Paul Mokilane, Mapitsi Rangata, Jenny Holloway, Nicolene Botha, Renee Koen, Nontembeko Dudeni-Tlhone","doi":"10.1186/s40163-024-00219-5","DOIUrl":"https://doi.org/10.1186/s40163-024-00219-5","url":null,"abstract":"<p>Crime prediction serves as a valuable tool for deriving insightful information that can inform policy decisions at both operational and strategic tiers. This information can be used to identify high-crime areas, and optimise resource allocation and personnel management for crime prevention. Traditionally, techniques such as the Poisson model and regression analysis have been widely used for crime prediction. However, recent statistical advancements have introduced Integrated Nested Laplace Approximations (INLA) as a promising alternative for spatial and temporal data analysis. This study focuses on crime prediction using the INLA model. Specifically, the first-order autoregressive model under the INLA modelling framework is employed on longitudinal data for crime predictions in different regions of the City of Johannesburg, South Africa. The model parameters and hyperparameters considering space and time are estimated through the INLA model. In this work, the suitability and performance of the INLA model for crime prediction is assessed, which effectively captures spatial and temporal patterns. This study contributes to research by first introducing a novel approach for South African crime prediction. Secondly, it develops a model using no demographic information other than clustering attributes as an exogenous variable. Thirdly, it quantifies prediction uncertainty. Finally, it addresses data scarcity through demonstrating how INLA can provide reliable crime predictions, where conventional methods are limited. Based on our findings, the INLA model ranked areas by crime levels, obtaining a 29.3% Mean Absolute Percentage Error (MAPE) and 0.8 <span>(R^2)</span> value for crime predictions. These findings and contributions presents the potential of INLA in advancing evidence-based decision-making for crime prevention.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"69 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crime SciencePub Date : 2024-09-03DOI: 10.1186/s40163-024-00223-9
Mangai Natarajan
{"title":"Rapid assessment of human–elephant conflict: a crime science approach","authors":"Mangai Natarajan","doi":"10.1186/s40163-024-00223-9","DOIUrl":"https://doi.org/10.1186/s40163-024-00223-9","url":null,"abstract":"<p>An interdisciplinary approach has the potential not only to help solve conservation-centric problems but also to enrich and improve evidence-based scientific research. Crime science, an offshoot of criminology, provides a comprehensive, solution-oriented approach that transcends disciplinary boundaries and bridges science and practice for developing effective conservation interventions to real-life problems such as Human Elephant Conflict (HEC). This paper focuses on HEC as a conservation concern, but the resultant behaviors toward elephants, people, and their property are criminology’s concern. Using the Action Research paradigm, a rapid assessment of human–elephant conflict (HEC) in India was undertaken to identify contextual solutions. This study utilized problem-oriented field research methods that enabled the gathering of data on elephant habitat-landscape, villagers’ lifestyle (habitat) in the fringe areas, their current approaches in dealing with the conflict, the challenges forest officials face to mitigate HEC, and the assistance provided by district administrators to protect villagers and their corps and HEC-related deaths. The qualitative inquiry, including observation of village-forest fringe areas, focus group discussions with villagers, and interviews with forest officers and rangers, and district administrators/collectors who are handlers, guardians, and managers of the conflict space, provided rich data in identifying situational practical measures and underscored the role of crime science in providing a conceptual framework to gather evidence in addressing HEC in forest areas. The findings of the research suggest that human–animal convergence space is the source (or location) of conflict and criminology-driven situational crime prevention measures, including increasing effort, risks, reducing rewards and provocations, and removing excuses might mitigate the conflict, requiring coordinated efforts by villagers, forest and district administrators, and local law enforcers.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"191 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crime SciencePub Date : 2024-08-22DOI: 10.1186/s40163-024-00220-y
N. Trajtenberg, S. Fossati, C. Diaz, A. E. Nivette, R. Aguilar, A. Ahven, L. Andrade, S. Amram, B. Ariel, M. J. Arosemena Burbano, R. Astolfi, D. Baier, H.-M. Bark, J. E. H. Beijers, M. Bergman, D. Borges, G. Breeztke, I. Cano, I. A. Concha Eastman, S. Curtis-Ham, R. Davenport, C. Droppelman, D. Fleitas, M. Gerell, K.-H. Jang, J. Kääriäinen, T. Lappi-Seppälä, W.-S. Lim, R. Loureiro Revilla, L. Mazerolle, C. Mendoza, G. Meško, N. Pereda, M. F. Peres, R. Poblete-Cazenave, E. Rojido, S. Rose, O. Sanchez de Ribera, R. Svensson, T. van der Lippe, J. A. M. Veldkamp, C. J. Vilalta Perdomo, R. Zahnow, M. P. Eisner
{"title":"The heterogeneous effects of COVID-19 lockdowns on crime across the world","authors":"N. Trajtenberg, S. Fossati, C. Diaz, A. E. Nivette, R. Aguilar, A. Ahven, L. Andrade, S. Amram, B. Ariel, M. J. Arosemena Burbano, R. Astolfi, D. Baier, H.-M. Bark, J. E. H. Beijers, M. Bergman, D. Borges, G. Breeztke, I. Cano, I. A. Concha Eastman, S. Curtis-Ham, R. Davenport, C. Droppelman, D. Fleitas, M. Gerell, K.-H. Jang, J. Kääriäinen, T. Lappi-Seppälä, W.-S. Lim, R. Loureiro Revilla, L. Mazerolle, C. Mendoza, G. Meško, N. Pereda, M. F. Peres, R. Poblete-Cazenave, E. Rojido, S. Rose, O. Sanchez de Ribera, R. Svensson, T. van der Lippe, J. A. M. Veldkamp, C. J. Vilalta Perdomo, R. Zahnow, M. P. Eisner","doi":"10.1186/s40163-024-00220-y","DOIUrl":"https://doi.org/10.1186/s40163-024-00220-y","url":null,"abstract":"<p>There is a vast literature evaluating the empirical association between stay-at-home policies and crime during the COVID-19 pandemic. However, these academic efforts have primarily focused on the effects within specific cities or regions rather than adopting a cross-national comparative approach. Moreover, this body of literature not only generally lacks causal estimates but also has overlooked possible heterogeneities across different levels of stringency in mobility restrictions. This paper exploits the spatial and temporal variation of government responses to the pandemic in 45 cities across five continents to identify the causal impact of strict lockdown policies on the number of offenses reported to local police. We find that cities that implemented strict lockdowns experienced larger declines in some crime types (robbery, burglary, vehicle theft) but not others (assault, theft, homicide). This decline in crime rates attributed to more stringent policy responses represents only a small proportion of the effects documented in the literature.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"43 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Understanding the role of mobility in the recorded levels of violent crimes during COVID-19 pandemic: a case study of Tamil Nadu, India","authors":"Kandaswamy Paramasivan, Saish Jaiswal, Rahul Subburaj, Nandan Sudarsanam","doi":"10.1186/s40163-024-00222-w","DOIUrl":"https://doi.org/10.1186/s40163-024-00222-w","url":null,"abstract":"This research investigates the potential link between mobility and violent crimes in Tamil Nadu, India, using an empirical study centred on the COVID-19 pandemic waves (2020–2022). The goal is to understand how these events influenced crime, employing a counterfactual approach. The study employs the XGBoost algorithm to forecast counterfactual events across different timeframes with varying levels of mobility. The mobility data sources include historical bus and passenger records spanning a decade, along with Google Community Mobility Reports added during the pandemic phases. The foundation for crime analysis is built upon the univariate time series of violent crimes reported as First Information Reports from 2010 to 2022. Results indicate a significant correlation between mobility and violent crimes when mobility drops below a specific threshold. However, no such correlation is observed when mobility is above this threshold during the non-pandemic periods. The COVID-19 pandemic had a major impact on people’s and vehicular mobility, especially during the complete lockdown periods of the first two waves, and also affected crime rates. The decrease in recorded incidents could also be attributed to fewer criminal opportunities. Additionally, this could be due to unfavourable situational factors, such as victims’ limited access to appropriate health and law enforcement agencies to report crimes. Furthermore, frontline services were busy with pandemic-related commitments, which could have contributed to a lack of crime registration even when crimes were committed.","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"99 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crime SciencePub Date : 2024-07-26DOI: 10.1186/s40163-024-00217-7
Vania Ceccato, Patryk Mentel, Ned Levine, Manne Gerell
{"title":"Shootings across the rural–urban continuum","authors":"Vania Ceccato, Patryk Mentel, Ned Levine, Manne Gerell","doi":"10.1186/s40163-024-00217-7","DOIUrl":"https://doi.org/10.1186/s40163-024-00217-7","url":null,"abstract":"<p>In this article, we investigate situations involving firearm violence in Sweden. The spatiotemporal distribution of records is assessed in different contexts across the country and linked to land use, demographic, and socio-economic characteristics by area and by street segment. The findings emphasize the prevalence of evening shootings, particularly in economically disadvantaged areas where young people congregate in public places often in residential areas, in parks, in playgrounds, and in transit areas. Although two-thirds of shootings took place in larger urban municipalities, our study sheds light on the non-uniform distribution of gun violence along the rural–urban continuum. We conclude by offering suggestions for future research and practical interventions to address this pressing issue that negatively affects people and communities.</p>","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"27 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crime SciencePub Date : 2024-07-23DOI: 10.1186/s40163-024-00218-6
Apriel D. Jolliffe Simpson, Chaitanya Joshi, Devon L. L. Polaschek
{"title":"Modeling behavioral patterns of family violence aggressors","authors":"Apriel D. Jolliffe Simpson, Chaitanya Joshi, Devon L. L. Polaschek","doi":"10.1186/s40163-024-00218-6","DOIUrl":"https://doi.org/10.1186/s40163-024-00218-6","url":null,"abstract":"The presumption that family violence will repeat and escalate is embedded in practices including risk assessment and case management. However, there is limited evidence that further episodes are inevitable, or that subsequent episodes will increase in severity. Therefore, we need to better understand temporal patterns in aggressor behavior to inform how risk is conceptualized in practice. For a sample of 2115 family violence aggressors who came to police attention in Integrated Safety Response catchment areas in Aotearoa New Zealand, we collected information New Zealand Police routinely recorded about reported harm between 2018 and 2020. We used a hidden Markov model to estimate the latent (i.e., unmeasurable) states behind the information reported to police, and modeled aggressors’ movement between those states over time. We identified three latent states. The first contained low or no reported harm, the second contained low probabilities of reported harm, and the third involved a high probability of reported verbal abuse and a moderate probability of reported physical violence. We identified four pathways through the latent states over the two-year follow-up period, which we called No reported harm, High reported harm, Low reported harm, and De-escalation. The findings add to the body of research indicating that family violence aggressors do not inevitably repeat or escalate their harmful behavior, and that a small subset of cases account for a large proportion of reported harm. This study demonstrates how information that police routinely collect can be used to estimate aggressors’ latent behavioral states and model pathways communicating the probability that they will continue to come to police attention for family violence, contributing to improved risk assessment and practice.","PeriodicalId":37844,"journal":{"name":"Crime Science","volume":"63 1","pages":""},"PeriodicalIF":6.1,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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