Data Analysis Methods Research Articles

“Unless you’ve got a conviction, there’s not much you can do”: Rethinking long-term disruption and exit strategy in RASSO investigations

Purpose This paper aims to evaluate how five Operation Soteria Bluestone (OSB) participating forces integrated principles of procedural justice and proactive disruption of offending into their investigations, specifically during the closing stages of a case when considering exit strategy and longer-term prevention. Design/methodology/approach A mixed-methods approach was taken, including 32 semi-structured interviews with officers and 59 case reviews of recent rape and serious sexual offences (RASSO). Data analysis methods included qualitative thematic analysis of interviews, while data from reviewed case logs was extracted and analysed using a bespoke case review tool. Findings Increased workloads, preparation of evidence and relational/communication difficulties with the crown prosecution service (CPS) were identified as barriers to implementing long-term disruption options. Dedicated resourcing, training and support are needed to promote suspect-focused investigative strategy and wider uptake of long-term disruption methods. Early investigative approach was also found to have “snowball” effects on suspect exit strategy, with initial lack of suspect focus and preoccupation with victim credibility, impeding consideration of long-term disruption and safeguarding options. Findings also point to a disparity between promising practice in relation to procedural justice for victims and (a relative lack of) proactive policing of repeat suspects. Originality/value The study represents a novel contribution to research on policing RASSO, illustrating the scale and nature of the obstacles to changing how these offences are investigated, even when under intense scrutiny from policy makers. It also highlights how increased organisational investment and support is linked to positive changes in practice in some forces.

Some Views on Multi‐criteria Methods for Data Analysis

ABSTRACTMany data analysis methods actually combine optimization of several criteria. In this paper, a framework is offered for categorizing such multi‐criteria methods. In particular, it categorizes multiset and three‐way analysis methods as well as penalized methods and combinations thereof. The framework aims to stimulate critical evaluation of methods and reflection on the purpose of methods and, by signaling gaps, to help the development of new data analysis methods.

Environmental Monitoring Data Management and Analysis Based on 5G IoT

As environmental issues become increasingly severe, environmental monitoring has become a crucial measure for protecting and managing ecosystems. The high speed, low latency, and massive connectivity characteristics of 5G technology, combined with the widespread application of IoT technology, provide new solutions for environmental monitoring data management and analysis. This study explores the methods of environmental monitoring data management and analysis based on 5G IoT technology, designs a systematic environmental monitoring framework, and verifies its effectiveness through empirical research. The results show that the environmental monitoring system based on 5G IoT can efficiently, real-time, and reliably collect and analyze environmental data, providing strong support for environmental protection and management.

What has brain diffusion magnetic resonance imaging taught us about chronic primary pain: a narrative review.

Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. This review synthesizes findings from 58 articles that constitute the current research landscape, covering methods and key discoveries. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic primary pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the prospective clinical utility of diffusion MRI, such as its role in identifying diagnostic, prognostic, and therapeutic biomarkers. Furthermore, we address shortcomings and challenges associated with brain diffusion MRI in chronic primary pain studies, emphasizing the need for the harmonization of data acquisition and analysis methods. We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.

Analysis of Guangzhou city image perception based on weibo text data (2019–2023)

With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to “Guangzhou City” between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.

Analyzing marketing campaign effectiveness: a comparative approach using traditional and online data analysis methods

Advertising campaign analysis reports are considered an essential tool for marketing analytics. They are used to assess the effectiveness of the marketing activities carried out and to improve future activities. It is necessary to verify whether the actions taken - online and in the public space - align with the intentions and budget, whether they lead to achieving the objectives, and, if not, what the campaign errors are. Due to the ease of collecting and accessing data, analyzing online and social media advertising campaigns is a popular topic. With access to data on the number of clicks, the ad's reach, the number of interactions, and so on, one can move on to the next steps of analyzing the campaign to determine its effectiveness. Online marketing tools have a massive advantage over traditional media channels. When analyzing the results of advertising campaigns, it is necessary to approach the examination of the individual channels and then analyze which of them is the most profitable and in which to invest the most. However, traditional campaigns must be addressed in the analyses. Despite the limited data available, collecting relevant information and analyzing the traditional campaign is worth trying. In the case of conventional campaigns, we can mainly measure the amount of sales resulting from the campaigns. When dealing with an online campaign, we gain many additional indicators, such as the number of ad impressions, clicks, and conversions. In both cases, analysis tools may allow us to isolate factors that significantly influence the success or failure of a campaign and predict the effectiveness of a campaign with given characteristics.

Peptide clustering enhances large-scale analyses and reveals proteolytic signatures in mass spectrometry data

Recent advances in mass spectrometry-based peptidomics have catalyzed the identification and quantification of thousands of endogenous peptides across diverse biological systems. However, the vast peptidomic landscape generated by proteolytic processing poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present an algorithm that aggregates peptides into peptide clusters, reducing the dimensionality of peptidomics data, improving the definition of protease cut sites, enhancing inter-sample comparability, and enabling the implementation of large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wound infections and human clinical non-healing wounds. This revealed signature phenotype-specific peptide regions and proteolytic activity at the earliest stages of bacterial colonization. We validated the method on the urinary peptidome of type 1 diabetics which revealed potential subgroups and improved classification accuracy.

Institutions' dynamics and homeownership aspirations in Ethiopia

The desire to own a home is a common aspiration amongst many. The role of institutions either by enabling or constraining the achievement of the aspirations is very crucial. Unfortunately, the study investigating the impact of these institutional factors on homeownership aspirations was not adequately adequate. Thus, the researchers motivated to analyze the role of institutions for homeownership aspirations. Researchers used qualitative and quantitative data analysis methods to explore the relationship between mortgage access, housing market prices, and homeownership in Sheger City, Ethiopia. The study involved 384 participants, and the results were analyzed using descriptive statistics and multinomial regression. The findings suggest that mortgage access and housing prices play a significant role in shaping homeownership aspirations. However, it was apparent that banks prioritize the wealthy, resulting in the housing market neglecting the needs of low-income individuals and families, leading to unaffordable housing options and findings revealed institutions’ dynamics are accompanied by dysfunctions. The multinomial regression analysis revealed that the likelihood of owning a home over renting one increases when housing becomes more affordable which shows market dysfunctions affect aspirations to homeownership. Obtaining a mortgage is also critical, as it significantly improves the chances of aspiring to own a home. The authors suggest that the Municipality reconsider the affordability of mortgage services and housing prices to ensure that homeownership opportunities are distributed fairly.

An insight into effect of soil salinity on vegetation dynamics in the exposed seafloor of the Aral Sea

The desiccation of the Aral Sea has precipitated significant ecological degradation, resulting in the progressive development of vegetation on the exposed seafloor. Soil salinity emerges as a pivotal determinant in this ecological succession process. Employing a comprehensive methodology integrating multi-source datasets spanning from 1986 to 2023, this study elucidates the temporal changes in vegetation dynamics and soil salinity levels. Satellite imagery (Landsat-4/5/7/8), field soil samplings, hydrological and topographic data were analyzed to understand their interactions with regression analysis. The results reveal a consistent increasing trend in the Normalized Difference Vegetation Index (NDVI) across the exposed seabed since 1986. However, NDVI demonstrates a non-linear relationship with elevation in the North Aral Sea region. Interestingly, NDVI levels near an elevation of 42 m on the exposed seabed approximate those observed during the pre-recession period in the 1960s. Conversely, in the South Aral Sea region, NDVI demonstrates a linear upward trend with increasing elevation. Furthermore, the spatial distribution of soil salinity on the exposed seabed was delineated with linear regression analysis. It revealed water salinity levels at the time of sea recession can serve as a proxy for soil salinity in cases where direct soil data is unavailable. Through establishing a robust correlation between NDVI and soil salinity, the range of stable NDVI values on the exposed seabed was delineated. Lastly, three hypothetical scenarios of rising water levels were considered to evaluate changes in stable NDVI across different elevation gradients. If the water level returns to 45 m, the salt-desert area would decrease by 4.5 × 104 km2, accounting for 23 % of the total area in 1960. At this water level, it is anticipated that lake hydrological conditions and ecological environments may restore to those observed in 1981. This study provides a long-term perspective on environmental changes in the Aral Sea region by integrating multiple data sources and analytical methods. The predictive insights from the scenario analysis offer valuable guidance for future water management and ecological restoration efforts. Compared with previous studies, it presents a detailed and comprehensive picture of the interplay between vegetation dynamics and soil salinity, highlighting the critical impact of water level changes on the region's ecosystem.

Analysis of Costs and Revenue of Arabic Coffee Farming and its Sales System

This research aims to determine the production costs and revenues of Arabica coffee farming in the research area to determine the net income and income of Arabica coffee farming families in the research area; to determine the feasibility level of Arabica coffee farming in the research area; to find out how the Arabica coffee sales system is in the research area. Determining the research area was carried out purposively. The sampling method is a simple random sampling method with a sample size of 30 farmers. The data analysis methods used are primary data and secondary data. The research results obtained several conclusions: The total cost of Arabica coffee farming in the research area for one year is IDR 24,747,244.44/Farmer or IDR 38,969,822.66/Ha. Revenue from coffee farmingamounting to IDR 102,758,553.33/Farmer or IDR 160,581,337.78/Ha; The net income of Arabica coffee farming for one year is IDR.78,011,308.89/Farmer or Rp.118,834,031.78/Ha. The income of an Arabica coffee farming family for one year is IDR 98,325,542.22/Farmer or IDR 152,317,561.41/Ha; The RCR value for coffee farming in the research area is 4.27 or RCR > 1. This shows that coffee farming is still feasible to cultivate or develop in the research area. The Arabica coffee sales system in the research area is that Arabica coffee farmers sell their harvest to regional collectors, market it to district collectors, and then sell it to provincial collectors.

Pengaruh Gaya Hidup dan Motivasi Belanja Hedonis Terhadap Minat Beli Pengguna Aplikasi Shopee di Kota Banjarmasin

This study aims to examine the influence of lifestyle and hedonic shopping motivation on the buying interest of Shopee application users in Banjarmasin City.The research method used is quantitative method. The number of samples used by 106 respondents was taken by purposive sampling. Data collection using online questionnaires. Data analysis methods use descriptive statistics, instrumental testing, multiple linear analysis and hypothesis testing. This test uses the help of SPSS 26 statistical software.The results of this study show that lifestyle variables (X1) have a significant positive influence on buying interest variables. The hedonic shopping motivation variable (X2) has a positive influence on the buying interest variable. Based on simultaneous testing, lifestyle variables (X1) and hedonic shopping motivation variables (X2) on the buying interest of shopee application users together have a significant influence.

Problems of Studying the Urbanization Process for Students of the Educational Program "Geography" in Kazakhstan

The study of the urbanization process by students of the educational program "Geography" in Kazakhstan is topical in the context of rapid urban development and evolutions in the geographical space of the country. Various key problems were revealed in the process of the studies such as the need for in-depth research of the theoretical foundations of urbanization, limited access to current data and no work experience on modern methods of data processing and analysis. An online survey was conducted on the basis of the Google Forms and the survey questions were worked out to have the further study of urbanization processes and update knowledge in this field. The study applied a data analysis method using the SPSS Base software package. This method provided the opportunities to structure and process the survey results among 331 teachers and master’s students. The approach ensured an accurate analysis and interpretation of the data which enabled to identify key trends and consistent patterns in respondents’ answers. The results point to both positive and negative conclusions based on the data. The conclusions were made on having evaluated the results with the help of SWOT analysis.

A unified approach to long-term population monitoring of grizzly bears in the Greater Yellowstone Ecosystem

Long-term wildlife research and monitoring programs strive to maintain consistent data collections and analytical methods. Incorporating new techniques is important but can render data sets incongruent and limit their potential to discern trends in demographic parameters. Integrated population models (IPMs) can address these limitations by combining data sources that may span different periods into a unified statistical framework while providing a holistic view of population dynamics. We developed an IPM in a Bayesian framework for grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem. We coupled demographic data with multiple, independent population count data to link annual changes in abundance with vital rates over 4 decades (1983–2023). Abundance increased threefold from an estimated 270 individuals in 1984 to 1030 individuals in 2023. Parameter estimates indicated survival of bears ≥2 years of age was high, contributing to robust population growth during the 1980s (λ = 1.023 [50 % interquartile range = 0.993–1.082]) and 1990s (λ = 1.064 [1.023–1.103]). A slowing of population growth started around 2000 (2000s: λ = 1.030 [0.989–1.068]) and continued into the 2010s (λ = 1.021 [0.985–1.057]), due primarily to reductions in survival of bears <2 years of age. These findings corroborate previous research that identified density-dependent effects as a likely cause. The IPM framework provided greater certainty and understanding regarding the dynamic demographic characteristics of the population and serves as a powerful monitoring tool for this long-lived species. Implementation of the IPM allows timely dissemination of demographic data to help inform adaptive management strategies and policy decisions necessary for the continued management and conservation of this population. This robust and flexible monitoring system allows scientists to investigate the effects of a changing ecosystem on population dynamics, incorporate new data sources and statistical models, and respond to changes in monitoring needs for the population. We highlight the efficacy of the IPM in estimating and tracking demographic parameters for a long-lived species, while accommodating shifts in monitoring techniques and data collections typical of long-term wildlife conservation programs worldwide.

Enhancing Security through Intelligent Threat Detection and Response: The Integration of Artificial Intelligence in Cyber-Physical Systems

Cyber-Physical Systems (CPS) play a crucial role in critical industries such as manufacturing, transportation, energy, and healthcare by integrating the physical and digital worlds. However, the complexity and interconnectivity of CPS with the global network increase their vulnerability to cyber-attacks. This research explores the benefits of implementing artificial intelligence (AI) in the context of cyber-physical systems (CPS) to detect and respond to security threats. This study uses machine learning and deep learning techniques to analyze sensor data and system threats. The data analysis methods encompass predictive modeling and evaluating AI algorithms' performance in detecting threats. The research data is obtained from relevant literature reviews and secondary data analysis. The research findings indicate that integrating AI in CPS can enhance the success rate of threat detection, prompt response, and accuracy in threat identification. By enabling the system to learn from previous experiences, AI can reduce the number of false positives and false negatives while providing automated real-time responses to threats without human intervention. The research concludes that AI has great potential to enhance security in CPS by providing more efficient and effective solutions to address increasingly complex cyber threats. The findings of this study are expected to provide insights and recommendations that can be applied in developing CPS security systems in the future.

Board monitoring and corporate disclosure: the role of the institutional environment and firm-level governance

PurposeThis article aims to answer two research questions that remain controversial in the accounting and corporate governance literature: (1) how corporate disclosure is related to board monitoring and (2) how this link is affected by the institutional environment and firm-level governance.Design/methodology/approachThe study is based on S&amp;P data on corporate disclosure by Russian companies collected over 2002–2010 and supplemented by information from the SKRIN database. The dataset covers 125 non-financial companies, with 559 observations in total. We use three indicators of board monitoring: the percentage of non-executive directors, a dummy for two-tier boards, and a dummy for an audit committee. The firm’s governance is proxied by a dummy for single class stock, while the institutional environment is proxied by a dummy for ADRs/GDRs. We apply conventional methods of panel data analysis with several robustness checks, including the random- and fixed-effects models, 2SLS that addresses the potential endogeneity of board composition, alternative definitions of the dependent variable, and an extended list of controls.FindingsWe find a positive (complementary) relationship between the amount of disclosure and the proxies for board monitoring employed. This complementary relationship turns out to be the strongest among companies that have better internal governance but face a weaker institutional environment. There is little evidence of such complementarity under strong institutions.Practical implicationsThe findings may be of interest to investors and policymakers. As to the former, the results warn of firms that provide limited disclosure in the presence of strong corporate governance arrangements, such as independent boards, as these factors are not substitutes for each other. As to the latter, the results support comprehensive policies aimed at simultaneous improvements in both board governance and corporate disclosure in weak institutional settings.Originality/valueThis paper uses a unique setting and rich, partly proprietary data to extend the existing literature on the relationship between corporate disclosure and board monitoring, with an emphasis on the moderating role of the institutional environment and firm-level governance. It is also one of the very few studies of corporate disclosure in Russia, an important emerging economy of the early 2000s.

Comparison of the impact of dimensionality reduction and data splitting on classification performance in credit risk assessment

Credit risk assessment (CRA) plays an important role in credit decision-making process of financial institutions. Today, developing big data analysis and machine learning methods have marked a new era in credit risk estimation. In recent years, using machine learning methods in credit risk estimation has emerged as an alternative method for financial institutions. The past demographic and financial data of the person whose CRA will be performed is important for creating an automatic artificial intelligence credit score prediction model based on machine learning. It is also necessary to use features correctly to create accurate machine learning models. This article aims to investigate the effects of dimensionality reduction and data splitting steps on the performance of classification algorithms widely used in the literature. In our study, dimensionality reduction was performed using Principal Component Analysis (PCA). Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Naive Bayes (NB) algorithms were chosen for classification. Percentage splitting (PER, 66–34%) and k-fold (k = 10) cross-validation techniques were used when dividing the data set into training and test data. The results obtained were evaluated with accuracy, recall, F1 score, precision, and AUC metrics. German data set was used in this study. The effect of data splitting and dimension reduction on the classification of CRA systems was examined. The highest ACC in PER and CV data splitting was obtained with the RF algorithm. Using data splitting methods and PCA, the highest accuracy was observed with RF and the highest AUC with NB, with 13 PCs in which 80% of the variance was obtained. As a result, the data set consisting of a total of 20 features, expressed by 13 PCs, achieved similar or higher success than the results obtained from the original data set.

The Impact of Educational Informatics on School Management Decision-Making in the Context of Big Data

In the domain of private education, particularly vocational education, school management encounters a complex and variable environment with diverse demands. As the advancement of educational informatics progresses, schools have accumulated a substantial amount of data, including student learning behavior, teaching data, and classroom utilization statistics. However, the efficient utilization of these data for scientific management decisions to enhance the quality of education and resource efficiency presents an urgent challenge. The study of the impacts of educational informatics on school management decision-making is of significant importance. It aids administrators in comprehensively understanding and analyzing the allocation and usage of educational resources, thereby enhancing management efficiency and teaching quality and further promoting the development of educational informatics. Despite some progress in data analysis and decision-making methods within educational informatics, deficiencies remain. Traditional methods often overlook comprehensive considerations of spatiotemporal data, failing to accurately reflect the dynamic changes in educational resources. Existing decision-making methods, predominantly based on single-agent models, lack studies on multi-agent collaborative decision-making, resulting in suboptimal decision outcomes. This study comprises three main components: first, the data modeling of educational informatics based on a spatiotemporal data model; second, an intelligent decision-making framework for school management under a reinforcement learning mechanism; and third, the implementation of an intelligent school management decision-making method based on multi-agent reinforcement learning. Through these investigations, the scientific nature and efficiency of school management are enhanced, providing new ideas and methods for management decision-making in other educational domains.

Pengaruh Pelatihan, Motivasi dan Disiplin Kerja Terhadap Kinerja Karyawan PT Osi Electronics Batam

The objectives of this study are to identify and evaluate the impact of training on employee performance at PT OSI Electronics Batam, to identify and evaluate the impact of work motivation on performance, and to identify and evaluate the impact of training and work discipline on employee performance. In this study, an associative technique was applied. 228 employees of PT OSI Electronics Batam made up the study's population, and 145 were chosen as the study's sample from the total using the Slovin formula. In this study, questionnaires, document analyses, and interview approaches were employed to collect data (questionnaires). The Classical Assumption Test, Multiple Linear Regression, t test, f test, and coefficient of determination are data analysis methods used in research. processing data. The SPSS program was utilized in this study's data processing (version 25). According to the study's findings, training, work motivation, and work discipline individually or in combination positively affect employees at PT OSI Electronics Batam

Assessment of Relationship Between Planning of Time Resources and Quality of Education in Secondary Schools in Kaduna State, Nigeria

This study assessed the effectiveness of planning on quality of education in secondary schools in Kaduna State, Nigeria. The purpose of the study was to appraise the effectiveness of planning on the quality of education in secondary schools in Kaduna State, Nigeria. The population of the study was 616 while the sample size was 248. The instrument used was a questionnaire, which was validated and found to be reliable. Data analysis methods were descriptive and inferential statistics. Pearson Product Moment Correlation (PPMC) was used to test the null hypotheses at 0.05 level of significance. All the five hypotheses were rejected. The findings of the study revealed, among other things, that there is a significant relationship between planning of curriculum and quality education in secondary schools in Kaduna State. It was recommended, among others, that the curriculum should be effectively planned to enhance quality education in secondary schools in Kaduna State.

Increasing Student Learning Interest through Cooperative Learning

This study aims to explain the influence of cooperative learning on the increase in students' interest in learning in the context of education. A qualitative approach with the type of library research is used to explore the concept of cooperative learning and its impact on students' interest in learning. Through secondary data analysis and using Miles and Huberman data analysis methods, this study gained an in-depth understanding of how cooperative learning affects students' learning interests. The results of the study show that the application of the cooperative learning model results in a significant increase in students' interest in learning. Factors such as the integration of game elements, cooperation between students in teams, and an inclusive environment play a key role in stimulating greater interest in learning. Cooperative learning strategies not only improve academic achievement, but also create high motivation for students to understand the learning material better. Nonetheless, challenges such as an unsupportive environment and active student involvement need to be addressed through a precise and sustainable approach that involves collaboration between educators, students, and education stakeholders.