Analysing police diversion for simple possession as a policy idea.

In this paper we propose an ontology model to describe the domain of searching over multiple data sources (SND). The ultimate goal of the model is to bridge the heterogeneity and to solve the distribution of the multiple networked data sources. Based on the model a query reconstruction algorithm is proposed to translate the initial query into different local queries suitable for individual data sources. And the result processing module defines grammar rules to extract the useful data returned by the individual data source, consolidates the retrieved data into one unified result, and presents the results in the web browser. In the ontology model two constraint rules are defined to evaluate the performance of SND and experiment results show that the federated retrieval offers a reliable and efficient reproduction with the retrieved results from the independent data sources. Moreover, the figures indicate the time comparisons among multiple data sources and give the contribution of each data source by the returned searching results based on the same query set.

Forecasting stock price movements with multiple data sources: Evidence from stock market in China

Dietary intake is influenced by multiple systems, as highlighted in the Social- Ecological Model, including community influences like community programs. In this context, parks and recreation administrators may have a role in the types of snacks and beverages provided during youth sports. The current study focused on understanding park administrators’ experiences relative to the youth sports environment, including their responsibility and influence on the food environment. This was an exploratory qualitative case study conducted in Utah. Semi-structured interviews with parks and recreation administrators were completed via phone by a research assistant. A qualitative case study analysis was conducted by two researchers. In addition to the interviews, the websites of all the park and recreation sites were searched and phone calls were made to check physical locations for nutrition fliers/information. Three themes emerged through qualitative case study analysis. The first theme was the administrators’ role in the youth parks and recreation activities. The second theme was the administrators’ awareness of the food environment within youth sports. The final theme was the administrators’ role in influencing more nutritious snacks at these youth sporting activities. The results from this case study suggest that the parks and recreation administrators within Utah valued the importance of nutritional snacks and beverages within youth sporting activities and were supportive of the food environment improving. Several of the parks and recreation administrators in this study agreed that their further involvement (i.e., guidelines on snacks and beverages) in the youth sports food environment could improve the environment and better effect youth who are participating, thus enhancing opportunities to improve overall health and well-being. The results from this study show that administrators could bring awareness to youth sports nutrition and support guidelines for the types of snacks and beverages brought to youth sporting activities. Administrators could work with dietitians to develop information that would be appropriate to distribute to youth sports participants and parents. Providing information about what kinds of snacks to bring has the possibility to improve the conditions of the youth sports food environment. Additionally, consideration for policy changes in youth sports and recreation center facilities could be explored.

It is challenging to identify meaningful gene networks because biological interactions are often condition-specific and confounded with external factors. It is necessary to integrate multiple sources of genomic data to facilitate network inference. For example, one can jointly model expression datasets measured from multiple tissues with molecular marker data in so-called genetical genomic studies. In this paper, we propose a joint conditional Gaussian graphical model (JCGGM) that aims for modeling biological processes based on multiple sources of data. This approach is able to integrate multiple sources of information by adopting conditional models combined with joint sparsity regularization. We apply our approach to a real dataset measuring gene expression in four tissues (kidney, liver, heart, and fat) from recombinant inbred rats. Our approach reveals that the liver tissue has the highest level of tissue-specific gene regulations among genes involved in insulin responsive facilitative sugar transporter mediated glucose transport pathway, followed by heart and fat tissues, and this finding can only be attained from our JCGGM approach.

Former mining areas are often the source of severe environmental and social problems, including environmental degradation. In the context of sustainable development, putting mined areas to productive use can reduce pressure on the natural environment and positively contribute to the local economy and community well-being. Transforming mined areas into more positive uses requires a holistic and sustainable approach to address these challenges. Co-production emphasizes the importance of involving various stakeholders in decision-making and solution implementation, including government, local communities, industry, and non-governmental organizations. Such collaboration is essential to ensure inclusive and sustainable engagement. Co-production can ensure that decisions taken consider the interests of all stakeholders and maximize long-term benefits. This article will review and investigate the application of co-production principles in managing former mining areas to achieve sustainable goals. Using a qualitative case study analysis and literature review, we identified the practices and application of co-production principles in managing a former mining area, “Tebing Breksi,” in Sambirejo Village, Sleman Regency, Indonesia. Our findings show that partnering in action co-production can be vital to realizing positive and sustainable transformation in reusing ex-mining areas. With the solid involvement of various stakeholders and the implementation of targeted strategies, transforming the former mining area “Tebing Breksi” into a sustainable destination can be easily achieved and has proven to have significant economic and environmental impacts for the local and surrounding communities.

Climate change mitigation is a global imperative, necessitating multifaceted efforts from various sectors. This paper digs into civil society's critical role in mitigating climate change, focusing on the ways that civil society organizations empower communities, advocate for policy reforms, and implement grassroots projects. The goal of this study is to thoroughly examine civil society's various contributions to climate change mitigation and provide conclusions that can guide more effective collaboration strategies. The study's fundamental research topic is the need to understand the exact mechanisms by which civil society plays a crucial role in climate change mitigation. This includes their involvement in community empowerment, public awareness campaigns, legislative advocacy, and the of sustained programs. The study intends to offer light on the particular characteristics of civil society in stimulating climate action by exploring these aspects. This study adopts a mixed‐methods approach in its methodology in Ernakulam district, Kerala. It entails doing a qualitative analysis of case studies to examine the numerous ways in which civil society organizations work to mitigate climate change. Quantitative data are also collected to analyze the impact of their activities on policy reforms, community participation, and climate resilience. Civil society encourages communities to take ownership of climate action through education, capacity‐building, and resource allocation, supporting sustainable practices at the grassroots level. The study further demonstrates that civil society plays a significant part in holding the government to account regarding their climate guarantees. Their activities, such as renewable energy projects and sustainable agriculture practices, generate community‐led solutions that help to accomplish global climate targets while additionally enhancing local resilience. Furthermore, civil society promotes collaboration among the commercial sector, governments, and communities, leading to a more inclusive approach to climate change mitigation. These insights may assist policymakers, stakeholders, and civil society organizations develop more effective climate change mitigation strategies, thereby ensuring a sustainable and resilient future.

Selection of genes that are differentially expressed and critical to a particular biological process has been a major challenge in post-array analysis. Recent development in bioinformatics has made various data sources available such as mRNA and miRNA expression profiles, biological pathway and gene annotation, etc. Efficient and effective integration of multiple data sources helps enrich our knowledge about the involved samples and genes for selecting genes bearing significant biological relevance. In this work, we studied a novel problem of multi-source gene selection: given multiple heterogeneous data sources (or data sets), select genes from expression profiles by integrating information from various data sources. We investigated how to effectively employ information contained in multiple data sources to extract an intrinsic global geometric pattern and use it in covariance analysis for gene selection. We designed and conducted experiments to systematically compare the proposed approach with representative methods in terms of statistical and biological significance, and showed the efficacy and potential of the proposed approach with promising findings.

Some research on the population in a European country, referred to as the target population, may not provide a sufficient sample size and require pooling multiple national data sources. The researcher can access multiple national data sources one at a time but cannot access them simultaneously at an individual patient level, because of regulation requirements on data sharing. We propose a strategy that enables the combination of information from multiple data sources when IPD are not simultaneously accessible to researchers. This strategy starts with a protocol for extracting information consistently from all data sources. It uses the matching adjusted indirect comparison (MAIC) approach to derive the weights to align the means of the identified covariates of each data source to those of a target study population. The final analysis will be a meta-analysis on the weight adjusted estimates of treatment effect from individual data sources.

An increasing number and variety of platforms are now capable of collecting remote sensing data over a particular scene. For many applications, the information available from any individual sensor may be incomplete, inconsistent or imprecise. However, other sources may provide complementary and/or additional data. Thus, for an application such as image feature extraction or classification, it may be that fusing the mulitple data sources can lead to more consistent and reliable results. Unfortunately, with the increased complexity of the fused data, the search space of feature-extraction or classification algorithms also greatly increases. With a single data source, the determination of a suitable algorithm may be a significant challenge for an image analyst. With the fused data, the search for suitable algorithms can go far beyond the capabilities of a human in a realistic time frame, and becomes the realm of machine learning, where the computational power of modern computers can be harnessed to the task at hand. We describe experiments in which we investigate the ability of a suite of automated feature extraction tools developed at Los Alamos National Laboratory to make use of multiple data sources for various feature extraction tasks. We compare and contrast this software's capabilities on 1) individual data sets from different data sources 2) fused data sets from multiple data sources and 3) fusion of results from multiple individual data sources.

This article analyzes the role of ideas, domestic actors, and international influences in migration policy change (MPCh) in Argentina, Brazil, and Chile. Building on 67 in-depth interviews with key actors in migration governance, public declarations of government representatives, and relevant legislation, we argue that the increased power of “securitist” actors within national bureaucracies shaped MPCh in all three countries. Between 2015 and 2019, these actors promoted a set of programmatic ideas and policy proposals that linked migration to security issues and distinguished between “good” and “bad” immigrants, emulating Global North countries. This set of ideas resulted in policy change at the country level, but at the same time, national-level policy change coexisted with continuity at the regional level. This article contributes to the literature on migration governance, first, by extending the geographical focus of migration policy studies, which frequently focus on party politics, coalitions, and public opinion, beyond the Global North. Second, we further current explanations of MPCh and policy contradictions by differentiating between continuity and change in programmatic ideas, policy proposals, and public philosophies. Third, we advance the regional migration policy literature by distinguishing between different groups of actors within national bureaucracies and enhancing understanding of these actors’ roles at both the national and international levels. Across its sections, this article shows that policy ideas—where and from whom they come—matter. By unpacking the different types of ideas that influence policy shifts and the actors who promote them, we can better understand apparent contradictions in migration policy.

BackgroundPan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior.ResultsWe found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not.ConclusionsWe address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.

PurposeThe purpose of this paper is to examine relations between police and vulnerable social groups in Serbia as a post‐conflict society in transition.Design/methodology/approachThe research is based on a qualitative methodology that generated data from 13 round tables and focus groups.FindingsAlthough the attitude of the police is gradually changing, representatives of minority groups perceive that the police still hold preconceived stereotypes regarding some societal groups. In order for the police to gain the confidence of these minority groups, they must proactively seek to engage these groups more meaningfully. Institutions in Serbia have still not developed an appropriate strategy for dealing with minority group victims of violence. In addition to legal problems, there are those issues that are embedded within wider cultural stereotypes. Excessive centralization and the hierarchical structure of the police have tended to exacerbate relations between the police and minority communities. Members of these ethnic minorities therefore tend to view the perceived inappropriateness of responses to inter‐ethnic incidents as being due to a number of causes.Research limitations/implicationsOwing to the qualitative approach, the results are not easily generalizable, but they indicate the most important problems of Serbian policing regarding marginal social groups.Practical implicationsThis research may be a useful source of information for the Ministry of Interior in attempts to improve relations with minority groups.Originality/valueThis paper reveals problems in relations between the police and vulnerable social groups and draws on the views of both minorities and the police. In Serbia, there is no similar research into this issue.

With the rapid development of database technologies, multiple data sources may be available for a given learning task (e.g. collaborative filtering). However, the data sources may contain different types of features. For example, users' profiles can be used to build recommendation systems. In addition, a model can also use users' historical behaviors and social networks to infer users' interests on related products. We argue that it is desirable to collectively use any available multiple heterogeneous data sources in order to build effective learning models. We call this framework heterogeneous learning. In our proposed setting, data sources can include (i) nonoverlapping features, (ii) nonoverlapping instances, and (iii) multiple networks (i.e. graphs) that connect instances. In this paper, we propose a general optimization framework for heterogeneous learning, and devise a corresponding learning model from gradient boosting. The idea is to minimize the empirical loss with two constraints: (1) there should be consensus among the predictions of overlapping instances (if any) from different data sources; (2) connected instances in graph datasets may have similar predictions. The objective function is solved by stochastic gradient boosting trees. Furthermore, a weighting strategy is designed to emphasize informative data sources, and deemphasize the noisy ones. We formally prove that the proposed strategy leads to a tighter error bound. This approach consistently outperforms a standard concatenation of data sources on movie rating prediction, number recognition, and terrorist attack detection tasks. Furthermore, the approach is evaluated on AT&T's distributed database with over 500 000 instances, 91 different data sources, and over 45 000 000 joined features. We observe that the proposed model can improve out-of-sample error rate substantially.

We aimed to develop ideas on continuity of cancer care. In-depth qualitative interviews were conducted with 28 people. Seven had cancer. Each person with cancer nominated a close person and a primary and secondary health care professional. We examined from four perspectives: experiences of the initial diagnosis; subsequent treatment; views on continuity of care; information given about the illness; psychological/physical impact of cancer and communication with professionals, family and friends. Perceived continuity of care was influenced by the actions of patients', involvement of close contacts and engagement in shared decision making. Additionally communication between primary and secondary care, the role of various health professionals and hospital administrative systems strongly influenced continuity of care. Informational, management and relational continuity have been previously described. Our data uncovered the effect of patients' actions and the involvement of close friends and families on continuity of cancer care. People with cancer should be enabled to influence continuity of their care. Full recognition of the role of health professionals, different approaches to sharing information with patients and tightening of hospital administrative systems should also be considered.

With advances in data collection technologies, multiple data sources are assuming increasing prominence in many applications. Clustering from multiple data sources has emerged as a topic of critical significance in the data mining and machine learning community. Different data sources provide different levels of necessarily detailed knowledge. Thus, combining multiple data sources is pivotal to facilitate the clustering process. However, in reality, the data usually exhibits heterogeneity and incompleteness. The key challenge is how to effectively integrate information from multiple heterogeneous sources in the presence of missing data. Conventional methods mainly focus on clustering heterogeneous data with full information in all sources or at least one source without missing values. In this paper, we propose a more general framework T-MIC (Tensor based Multi-source Incomplete data Clustering) to integrate multiple incomplete data sources. Specifically, we first use the kernel matrices to form an initial tensor across all the multiple sources. Then we formulate a joint tensor factorization process with the sparsity constraint and use it to iteratively push the initial tensor towards a quality-driven exploration of the latent factors by taking into account missing data uncertainty. Finally, these factors serve as features to clustering. Extensive experiments on both synthetic and real datasets demonstrate that our proposed approach can effectively boost clustering performance, even with large amounts of missing data.