Collection Framework Research Articles

On the biology of blackbellied angler, Lophius budegassa Spinola, 1807, in the north-western Mediterranean

In the Mediterranean Sea, the blackbellied angler, Lophius budegassa Spinola, 1807, represents a relevant fraction of the commercial by-catch of bottom trawling and small-scale fisheries that use passive gears (e.g., set nets, bottom longlines, etc.). Despite its importance, there are no comprehensive studies on the population dynamics of this species, and there is still scarce and scattered knowledge of its biology and ecology. Biomass, morphometric, and biological data of the blackbellied angler were collected from 1994 to 2021 by means of the Mediterranean International Bottom Trawl Survey (MEDITS) programme in the Ligurian and northern Tyrrhenian Seas, and in the EU Data Collection Framework during the period of 2006-2021. Furthermore, ageing was performed on specimens collected from 2013 to 2021 during the MEDITS programme. There were high values for the density indices and the spatial distribution of biomass in the central sector of the investigated area (the Tuscan Archipelago), mostly on the continental shelf. The length-weight relationship showed negative allometric growth when considering the sexes together, and no difference between the sexes. The size at first maturity (L50) was estimated at 39.4 cm total length (TL) in females and at 29.2 cm TL in males. The obtained Von Bertalanffy growth parameters were: in females, L∞ = 74.50 cm, k = 0.10 years-1, and t0 = -0.40 years; in males, L∞ = 61.30 cm, k = 0.14 years-1, and t0 = -0.38 years. There were differences in growth between males and females for L∞ (χ2 = 10.04; p < 0.05) and k (χ2 = 6.12; p < 0.05).

Catalyzing Organizational Change for Equity in Graduate Education: A Case Study of Adopting Collective Impact in a College of Engineering

Graduate education in engineering is an extremely challenging, complex entity that is difficult to change. The purpose of this exploratory research paper was to investigate the applicability of the Collective Impact framework, which has been used within community organizing contexts, to organize the change efforts of a center focused on advancing equitable graduate education within engineering. We sought to understand how the conditions of Collective Impact (i.e., common agenda, backbone organization, mutually reinforcing activities, shared measurement system, and continuous communication) could facilitate the organization of equity-focused change efforts across a college of engineering at a single institution. To achieve this, we took an action research approach. We found the Collective Impact framework to be a useful tool for organizing cross-sectional partnerships to facilitate equity-focused change in graduate education; we also found the five conditions of Collective Impact to be applicable to the higher education context, with some intentional considerations and modifications. Through coordinated efforts, the Collective Impact framework can support the goal of reorienting existing decentralized structures, resource flows, and decision processes to foster bottom-up and top-down change processes to advance equitable support for graduate students.

BitTrace

Bitcoin is a digital currency system built on the foundation of fairness. However, some malicious miners, driven by their own interests, employ unfair tactics such as selfish mining to compete, which disregard the legitimate miners' investments in computational power and energy consumption. In order to assess the efficiency of the Bitcoin system in real-time and promptly detect malicious miners in the network, this paper proposes a data collection framework called BitTrace, which addresses the issues of low efficiency, lack of timeliness, and data loss in traditional data collection frameworks. BitTrace enables real-time collection and analysis of the blockchain formation process, storing it as structured data. Furthermore, the paper discusses factors that influence the efficiency of data collection and proposes a topological control scheme based on the DPC algorithm to enhance the integrity and efficiency of data collection. Researchers can explore various research areas and applications, such as selfish mining detection and legitimate mining strategy research.

RETRACTED: Multifactor optimized mobile sink data collection framework for hybrid wireless sensor network‐long term evolution‐assisted Internet of Things network

SummaryThe wireless sensor network‐assisted Internet of Things convergence has diverse applications. In most applications, the sensors are battery‐powered, and it is necessary to use the energy judiciously to extend their functional duration effectively. Mobile sinks‐based data collection is used to extend the lifespan of these networks. However, providing a scalable and effective solution with consideration for multicriteria factors of quality of service and lifetime maximization is still a challenge. This work addresses this problem with a hybrid wireless sensor network with long‐term evolution‐assisted architecture. The issue of maximizing lifetime and providing multifactor quality of service is solved as a two‐stage optimization problem involving clustering and data collection path scheduling. Hybrid meta‐heuristics is used to solve the clustering optimization problem. Minimal Steiner tree‐based graph theory is applied to schedule the data collection path for sinks. Unlike existing works, the lifetime maximization without QoS degradation is addressed by hybridizing multiple approaches of multicriteria optimal clustering, optimal path scheduling, and network adaptive traffic class‐based data scheduling. This hybridization extends the network's lifespan and improves the QoS regarding packet transmission within the proposed solution. Through simulation analysis, the introduced approach yields a noteworthy increase of at least 6% and reduces packet delivery delay by 26% compared with existing methodologies.

Collusive spam detection from Chinese community question answering sites: A collective classification framework

With Community Question Answering (CQA) sites evolving into quite popular knowledge-sharing platforms on the Internet, they have also become ideal places for various spammers to spread fake or promotional information. Recently, with the rapid development of crowdsourcing systems, numerous malicious users have launched organized spam campaigns, conducting many spam accounts to carry out collusive spamming activities on CQA sites. In these campaigns, the spammers do not act independently but post deceptive questions and answers (Q&As) collaboratively, which makes the Q&As closely related to each other, but the spam clues of them are even less visible. Therefore, most existing spam detection works may fail to detect these carefully organized and posted collusive CQA spam. In this paper, taking Baidu Zhidao, a popular CQA platform in Chinese, as the study object, we propose a Collective Classification framework for community Question Answering spam detection (CCQA), which collectively identifies the collusive CQA spam using Q&A features and the correlations among Q&As. First, we define the Deceptive Pattern of Q&As, based on which the real Q&A groups are extracted. Then, we extract several highly discriminative Q&A features from both individual and group levels, and propose several types of correlations, which correlate the Q&As that are more likely to have the same labels. After uniformly modeling the Q&As, features, and correlations in the Attributed Heterogeneous Information Network (AHIN), a semi-supervised collective classification algorithm is proposed to detect the collusive Q&A spam. Experimental results on a real-life dataset demonstrate that CCQA can accurately detect the collusive CQA spam, and outperform a number of competitive baselines.

Footstone of Metaverse: A Timely and Secure Crowdsensing

Recently, the metaverse has been a hot research topic that fuses multiple cutting-edge techniques, including virtual reality (VR), augmented reality (AR), artificial intelligence (AI), Internet of Things (IoT), and 5th generation (5G) networks. Supported by these powerful cornerstones, people can establish a hyper spatiotemporal virtual world that seamlessly links to the real world and create personal activities such as art creation, concert display, and stock trading. Virtual-reality mapping demands enormous information transmission between two worlds without sensible latency since delayed data could severely impact user experiences. Besides, intrinsic flaws in fundamental technologies also aggravate the vulnerabilities in the metaverse due to real-time interaction. Hence, this paper first proposes a timely and secure data collection framework based on crowdsensing for metaverse modeling. In our proposed Data encryption, Transmission and Perception optimization (DTP) model, the metaverse servers can recruit workers to gather environmental parameters and user information through five layers architecture (i.e., perception layer, link layer, transmission layer, operation layer, and application layer). Each layer takes responsibility for data computation and encryption, which can stimulate data dissemination in a fast and reliable manner for the metaverse. Furthermore, we present a healthcare use case that introduces a bidirectional mapping of patient data between physical and virtual space. This use case illustrates the prospects of the DTP model. Finally, we conclude this paper and summarize several significant future research directions.

Insights into a collective impact partnership to promote consumption of healthy drinks in North East Melbourne: A qualitative case study.

To explore insights and perspectives of a collective impact (CI) partnership taking on a new project that aimed to reduce sugary drink consumption and promote water as the drink of choice across North East Melbourne. A qualitative case study was undertaken. Semi-structured interviews were conducted with key stakeholders in the partnership. Data were analysed using thematic analysis. Fourteen organisations participated in the North East Healthy Drinks Alliance. The data demonstrated that investing in developing a common agenda supported the establishment of a CI approach. The backbone organisation was found to have played a crucial role in coordinating the activities of the Alliance. This coordination was found to be particularly important in terms of ensuring that organisations were able to work on mutually reinforcing activities at their own pace. Program planning and reporting was managed through open continuous communication by the backbone organisation. The data collected pertains to the activities of the Alliance in its first 2 years, prior to the development of a shared measurement strategy, thus no data was collected on this aspect of the collective impact framework. Although some participants were found to have limited knowledge of CI, this did not seem to hinder their participation in the Alliance. Selecting a relevant and accessible focus area and investing in developing a common agenda supported the establishment of a CI approach. SO WHAT?: The CI framework offers a valuable tool for undertaking cross-sectoral, local partnerships for health.

Multi-factor optimized mobile sink data collection framework for hybrid WSN-LTE assisted IoT network

The convergence of wireless sensor network-assisted Internet of Things has diverse applications. In most applications, the sensors are battery-powered, and it is necessary to use the energy judiciously to extend their functional duration effectively. Mobile sinks-based data collection is used to extend the lifespan of these networks. But providing a scalable and effective solution with consideration for multi-criteria factors of quality of service and lifetime maximization is still a challenge. This work addresses this problem with a hybrid wireless sensor network-Long term evolution assisted architecture. The problem of maximizing lifetime and providing multi-factor quality of service is solved as a two-stage optimization problem involving clustering and data collection path scheduling. Hybrid meta-heuristics is used to solve the clustering optimization problem. Minimal Steiner tree-based graph theory is applied to schedule the data collection path for sinks. Unlike existing works, the lifetime maximization without QoS degradation is addressed by hybridizing multiple approaches of multi-criteria optimal clustering, optimal path scheduling, and network adaptive traffic class-based data scheduling. This hybridization helps to extend the lifetime and enhance the QoS regarding packet delivery within the proposed solution. Through simulation analysis, the introduced approach yields a noteworthy increase of at least 6% and reduces packet delivery delay by 26% compared to existing methodologies.

College preparation for a medical career in the United States.

A college degree is required to enter medical school in the United States. A remarkably high percentage of students entering college have pre-medical aspirations but relatively few end up as medical students. As an "applied science", education about medicine is usually thought to be beyond the purview of a liberal arts curriculum. Students therefore receive little education about a medical career, or information about the many alternative careers in health science. Instead, they take courses for Medical College Admission Test (MCAT) preparation and medical school application prerequisites in biology, chemistry, physics, and math. These classes give them little insight into a real medical career. The current report considers this mismatch between student needs in health science and available resources in colleges across the United States. A Collective Case Series framework was used to obtain qualitative data. Key informant interviews were requested from a convenience sample of representatives from 20 colleges, with six colleges providing extensive data. Three institutions collected data specifically on students who matriculated college interested in a career as a physician. At these schools, one-half to one-quarter of students who said they were interested in medicine at the beginning of college ended up not applying to medical school. At each of the six schools, we saw a wide range of generally sparse academic and professional advising involvement and a very limited number of classes that discussed concepts directly related to careers in health science. Looking at this data, we provide a novel conceptual model as a potential testable solution to the problem of an underexposed and unprepared student population interested in medicine. This includes a brief series of courses intended to inform students about what a career in medicine would fully entail to help foster core competencies of empathy, compassion and resilience.

A Framework for Data Collection and Management to Support the Comprehensive Development of African Agriculture

Agriculture is a key source of food and health for humanity. However, the hazardous way this activity is still cared for in some developing countries like the Republic of Benin, with poor quality farming data collection mechanisms, can guarantee neither quality and sustainability of production, nor comprehensiveness and efficiency of agricultural interventions. Such shortcomings ineluctably contribute to the limited success of the Comprehensive African Agriculture Development Program (CAADP) promoted by the African Union and its regional chapters, in most African countries. Based on these preliminary remarks, this paper suggests a systematic and automated monitoring, evaluation, learning, and adaptation (MEL) framework that will further be tested, consolidated, validated, and promoted, first in the Republic of Benin, and later in other African countries. This framework is made of local, regional, and national geographic information systems (GIS) which will further serve as databases for planning and interventions in the agricultural sector. The proposed GIS superposes lands’ details (sizes, soils’ characteristics, rainfall data, etc.), stakeholders’ details (demographics, crops, livestock, agricultural practices, etc.), and agricultural value chains’ details. Local, regional, and national agricultural value chains’ development platforms will bring stakeholders together, and serve as sources and co-users of the GIS data. Local, regional, and national MEL and GIS management skilled AEAS agents will facilitate systematic data collection and GIS database management. The data collection and treatment tools should allow the use of all kinds of data formats, including voice and image records, given the oral orientation of most agricultural stakeholders. This original idea emerged from observations during national and regional field experiences, and from the literature that revealed the efficiency of automated real-time and quality data for comprehensive and evidence-based decision-making in development interventions.

Application of Deep Learning and Intelligent Sensing Analysis in Smart Home

Deep learning technology can improve sensing efficiency and has the ability to discover potential patterns in data; the efficiency of user behavior recognition in the field of smart homes has been further improved, making the recognition process more intelligent and humanized. This paper analyzes the optical sensors commonly used in smart homes and their working principles through case studies and explores the technical framework of user behavior recognition based on optical sensors. At the same time, CiteSpace (Basic version 6.2.R6) software is used to visualize and analyze the related literature, elaborate the main research hotspots and evolutionary changes of optical sensor-based smart home user behavior recognition, and summarize the future research trends. Finally, fully utilizing the advantages of cloud computing technology, such as scalability and on-demand services, combining typical life situations and the requirements of smart home users, a smart home data collection and processing technology framework based on elderly fall monitoring scenarios is designed. Based on the comprehensive research results, the application and positive impact of optical sensors in smart home user behavior recognition were analyzed, and inspiration was provided for future smart home user experience research.

Alternative Approach to the Segmentation of Fishing Fleets

The appropriate segmentation of fishing fleets is controversially discussed in fisheries research and management and a variety of approaches has been introduced. The present approach, developed in a pilot study funded by the European Commission – Data Collection Framework (DCF), introduces a standardized multivariate approach for characterizing fisheries fleet segments by hierarchical agglomerative cluster analysis (HAC) of their catch composition. We chose data from 2021 of the Romanian fishing fleet as the basis of our analysis. Statistical analyses were performed using the program RStudio V3.6.1 by running the fleet segmentation package script. The specific indices, tests, and visual validation methods of the package were applied to determine the optimal number of clusters. The procedure was finalized by a post-hoc validation of the clustering result to identify the actual fleet segments. From the basic data, 6 fleet segments for Vessels using active and passive gears (PMP) were highlighted, representing 52 boats, where it was noted that fishing at Rapa whelk (RPW) prevailed with 91.94% of the total catches on the segment, respective 5 fleet segments for Vessels using passive gears (PG) only for vessels 12m, representing 78 vessels where the main catches or recorded at European anchovy (ANE) 25.16%, turbot (TUR) 13.08%, horse mackerel (HMM) 8.75%, thus 130 vessels from two classes of different gears in total. We detected mied fishing, especially on various assemblages of demersal and pelagic fish, as well as target fishing on demersal and pelagic fish, Rapa whelk, and mussels. For a better understanding of the approach, further research is needed.

Protocol for metadata and image collection at diabetic foot ulcer clinics: enabling research in wound analytics and deep learning

BackgroundThe escalating impact of diabetes and its complications, including diabetic foot ulcers (DFUs), presents global challenges in quality of life, economics, and resources, affecting around half a billion people. DFU healing is hindered by hyperglycemia-related issues and diverse diabetes-related physiological changes, necessitating ongoing personalized care. Artificial intelligence and clinical research strive to address these challenges by facilitating early detection and efficient treatments despite resource constraints. This study establishes a standardized framework for DFU data collection, introducing a dedicated case report form, a comprehensive dataset named Zivot with patient population clinical feature breakdowns and a baseline for DFU detection using this dataset and a UNet architecture.ResultsFollowing this protocol, we created the Zivot dataset consisting of 269 patients with active DFUs, and about 3700 RGB images and corresponding thermal and depth maps for the DFUs. The effectiveness of collecting a consistent and clean dataset was demonstrated using a bounding box prediction deep learning network that was constructed with EfficientNet as the feature extractor and UNet architecture. The network was trained on the Zivot dataset, and the evaluation metrics showed promising values of 0.79 and 0.86 for F1-score and mAP segmentation metrics.ConclusionsThis work and the Zivot database offer a foundation for further exploration of holistic and multimodal approaches to DFU research.

Community-powered change in the Omaha metropolitan area: Examining a digital approach to mental health stigma reduction during the COVID-19 pandemic

ABSTRACT Mental health stigma is associated with a range of negative health outcomes, and there is a need for more information on how to apply evidence-based digital strategies to various geographic areas. This case study reviews the creation and implementation of a community-based participatory digital media campaign that uses a collective impact framework to address mental health stigma reduction in the Omaha metropolitan area. The campaign was delivered during the COVID-19 pandemic and consisted of various components, including two social media campaigns which delivered evidence-based messaging through user-generated content from local individuals: Spokesimals Midwest paired images of local pets with educational mental health messages, while WhatMakesUs featured personal testimonials of people’s experiences with mental health. Campaign reach was amplified by engagement with community-based organizations and local influencers. Within the first 10 months, WhatMakesUs and Spokesimals Midwest received 412 testimonials and pet submissions. The campaign partnered with 47 community-based organizations, and 50 influencers achieved over 27,000 engagements and a reach of 173.5K on social media. The positive metrics from the first 10 months of the campaign show promise in the use of this model to impact mental health stigma in the Midwestern United States.

A Neural Separation Algorithm for the Rounded Capacity Inequalities

The cutting plane method is a key technique for successful branch-and-cut and branch-price-and-cut algorithms that find the exact optimal solutions for various vehicle routing problems (VRPs). Among various cuts, the rounded capacity inequalities (RCIs) are the most fundamental. To generate RCIs, we need to solve the separation problem, whose exact solution takes a long time to obtain; therefore, heuristic methods are widely used. We design a learning-based separation heuristic algorithm with graph coarsening that learns the solutions of the exact separation problem with a graph neural network (GNN), which is trained with small instances of 50 to 100 customers. We embed our separation algorithm within the cutting plane method to find a lower bound for the capacitated VRP (CVRP) with up to 1,000 customers. We compare the performance of our approach with CVRPSEP, a popular separation software package for various cuts used in solving VRPs. Our computational results show that our approach finds better lower bounds than CVRPSEP for large-scale problems with 400 or more customers, whereas CVRPSEP shows strong competency for problems with less than 400 customers. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) [RS-2023-00259550] and the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) [2022-0-01032, Development of Collective Collaboration Intelligence Framework for Internet of Autonomous Things]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0310 .

Population Administration Services and Village Government Performance Analysis

This study aims to provide a thorough examination of Population Administration Services and its impact on the performance of the Banjarbendo Sidoarjo Village Government. Utilizing qualitative descriptive research methods, including interviews, observation, and documentation, the research follows Agus Dwiyanto's analytical framework for data collection, reduction, presentation, and conclusion. The results reveal a commendable performance in implementing Population Administration Services, showcasing high village government productivity. The assessment of service quality, encompassing nine essential elements, demonstrates overall effectiveness. However, challenges are identified, including internal issues related to information technology readiness and external constraints such as aligning service schedules with community preferences. This research contributes valuable insights to public service enhancement, emphasizing the need for adaptive strategies in addressing internal and external obstacles. Highlight : Comprehensive Analysis: The study provides an in-depth examination of Population Administration Services, offering a holistic perspective on its implementation and impact on rural governance. Service Quality Evaluation: Through interviews, the research evaluates service quality based on nine crucial elements, providing insights into the effectiveness of Population Administration Services. Challenges and Implications: Identified internal and external constraints, such as information technology readiness and service scheduling, highlight the need for adaptive strategies, contributing to the ongoing discourse on public service enhancement Keywords: Population Administration Services, Village Government Performance, Rural Governance, Public Service Enhancement, Qualitative Descriptive Research.

A LDA-Based Social Media Data Mining Framework for Plastic Circular Economy

The mass production of plastic waste has caused an urgent worldwide public health crisis. Although government policies and industrial innovation are the driving forces to meet this challenge, trying to understand public attitudes may improve the efficiency of this process. Social media has become the main ways for the public to obtain information and express opinions and feelings. This motivated us to mine the perceptions and behavioral responses towards plastic usage using social media data. In this paper, we proposed a framework for data collection and analysis based on mainstream media in the UK to obtain public opinions on plastics. An unsupervised machine learning model based on Latent Dirichlet Allocation (LDA) has been employed to analyse and cluster the topics to deal with the lack of annotation of the data contents. An additional dictionary method was then proposed to evaluate the sentiment of the comments. The framework also provides tools to visualise the model and results to stimulate insightful understandings. We validated the framework's effectiveness by applying it to analyse three mainstream social media, where 6 first-level topic categories and 13 second-level topic categories from the comment texts related to plastics have been identified. The results show that public sentiment towards plastic products is generally stable. The spatiotemporal distribution of each topic's sentiment is highly correlated with the number of occurrences.

BERETTYÓÚJFALUN ÉLŐ FOGYATÉKOS GYERMEKET NEVELŐ CSALÁDOK ÉLETKÖRÜLMÉNYEI

Bass (2004) examined the living conditions of families raising severely disabled children in the framework of national data collection. His research results reflect the circumstances nearly twenty years ago. The present study aims is to compare the research results of Bass to the present living conditions of interviewees who live in Berettyóújfalu in the 2020s, revealing the changes which have occurred in their lifestyle, family relationships, and future plans in the course of the past years. We used a qualitative interview to reveal the characteristics of the living conditions of ten families living in Berettyóújfalu. According to our research results, there are positive tendencies in the families’ social situation, relationship system or in the economic activity of the parents, but they still face hardship and drawbacks in accessing public services and ensuring their child’s long-term future.

A Report on Investment Differs from Gambling: A Comprehensive Study on Technical Analysis in Investment Decision-Making

This research paper conducts a thorough examination with primary objectives focused on acquiring a comprehensive understanding of scrutinizing portfolio analysis, navigating the complexities of the stock market, addressing risk management considerations, and exploring the psychological facets of trading, the study aims to provide valuable insights for investors and researchers. Employing a descriptive research design, the paper establishes a structured framework for data collection, analysis, and interpretation, ensuring the reliability and validity of the study's outcomes. The research emphasizes technical analysis, specifically delving into candlestick patterns, and systematically evaluates indicators, oscillators, and trading strategies prevalent in financial markets. Distinguishing investing from trading and illuminating the divergence from speculative activities like gambling are additional focal points. In terms of data collection, the paper opts for a secondary method, utilizing existing information from literature, articles, and databases. This pragmatic choice aligns with considerations of accessibility, cost-effectiveness, and time efficiency, facilitating an in-depth analysis of the company's financial landscape and investment dynamics. Ultimately, the research contributes a nuanced understanding of financial markets and investment strategies, offering insights crucial for investors, analysts, and researchers navigating the complex terrain of corporate finance.

How could businesses with anchor positioning contribute to community wellbeing? A study of supermarket community support actions in the UK

Challenging economic and social conditions undermine community wellbeing across the UK. Using desk research and case studies, we examined how one business sector, UK supermarkets, support local communities through community-orientated support actions (COSAs) as part of Corporate Social Responsibility commitments. A theory of change was developed drawing on community wellbeing theory and community asset-based practices, providing a framework for data collection and development of detailed hypotheses for particular COSAs and resources invested. Findings present the results of analysis according to context-mechanism-outcome configurations representing scenarios for the mobilisation of: community colleague's time; donations of money/goods; and store space. Contextual enablers and barriers are also identified, with a view to supporting practice within and across business sectors as well as possible collaborations with state and voluntary sectors to enhance the contribution of COSAs to local communities in the future. Five areas of more ‘transformative’ actions which would require more concerted and collaborative efforts were identified: collaboration in the wider system; mobilising physical store assets; actions on household food security; economic interventions and empowering the local community; alongside possible system levers for doing so.