Analysis Tools Research Articles

Design and implementation of breast cancer prediction system based on machine learning

With the increasing incidence of breast cancer worldwide, early diagnosis and treatment of breast cancer has become the key to improving patient survival and quality of life. As a powerful data analysis tool, machine learning is increasingly widely used in the medical field, especially in disease prediction and assisted diagnosis. This paper aims to design and implement a machine learning-based breast cancer prediction system to improve the early diagnosis rate of breast cancer and reduce medical costs. Through an in-depth analysis of the global incidence of breast cancer and the current application of machine learning in the medical field, this study clarified the importance of breast cancer prediction and the problems existing in the existing prediction system. This paper further discusses the theoretical basis of machine learning in breast cancer detection, evaluates the advantages and disadvantages of commonly used machine learning algorithms, and reviews the latest research progress in this field at home and abroad. In the part of system design and implementation, the architecture design, data flow and processing process of the prediction system, as well as the method of data preprocessing and feature selection are introduced in detail. In addition, this paper also constructs a machine learning model suitable for breast cancer prediction, and carries out systematic implementation and testing through the actual development environment. In the discussion section, the applicability of machine learning model in breast cancer prediction is analyzed, the causes of model inefficiency are discussed, and the corresponding solutions are proposed. Finally, the paper summarizes the content of the full text, points out the limitations of the research, and puts forward the direction of future research. The results of this study not only provide a new technical means for the early diagnosis of breast cancer, but also provide valuable experience for the application of machine learning in the medical field.

Chronic Stress Indicator: A Novel Tool for Comprehensive Stress Analysis.

Extensive research has highlighted the strong association between chronic stress and negative health outcomes. This relationship is influenced by various factors, including sociobehavioral, environmental, and genetic and epigenomic forces. To comprehensively assess an individual's stress levels, we propose the development of the Chronic Stress Indicator (CSI), a novel comprehensive multifaceted tool that incorporates key biological, anthropometric, behavioral, and socioeconomic factors. The objective of this study is to assess the effectiveness of the CSI compared to Allostatic Load (AL), a type of chronic stress, in identifying health issues related to stress. The objective of this research is to evaluate the performance of the Chronic Stress Indicator (CSI) versus Allostatic Load (AL) in detecting adverse health outcomes within the U.S. demographic aged 20-49. The information used for this study was sourced from the National Health and Nutrition Examination Survey (NHANES), carried out from 2001 to 2004. Logistic regression modeling was employed to calculate odds ratios and confidence intervals. The Wilcoxon rank-sum test was employed to assess differences in means, whereas the chi-square test, accompanied by Cramer's V statistic, was used to examine the association among categorical variables. Additionally, the relationship between continuous variables was analyzed using Pearson's correlation coefficient. Our association tests show that the length of occupation activity and health status were among the strongest associations to CSI risk. Based on our logistic regression models, age and sex were found to be significant factors in determining AL. We also found that age, smoking, and longest occupation activity were significant factors of CSI risk. These findings suggest a need for individuals to limit smoking as it may lead to higher overall stress despite its common use as a coping mechanism for stress. We should also review the level of occupational activity a job has before continuously working on it as this may also lead to higher cumulative stress.

Use of GIS and RS Tools for Analysis of Land Use Land Cover and Land Surface Temperature: A Case Study of District Khairpur, Pakistan

Land use and land cover (LULC) monitoring plays an important role in sustainable planning and management of resources. This research focuses on analysing historical LULC changes over the Khairpur district of Sindh province of Pakistan. Remote Sensing and Geographic Information System (RS & GIS) tools have been employed using the maximum likelihood algorithm of the supervised classification method. Temporal data of the latest high-resolution satellite imagery provided by NASA GLCF (Global Land Cover Facility), Landsat 8 Operational Land Imager (OLI), and Landsat 4, 5 Thematic Mapper (TM) for 2001, 2010, 2015, and 2020 were acquired from Earth Explorer of United States Geological Survey (USGS). The study area is classified into four major classes namely Barren Land, Vegetation, Built-up areas, and Water Bodies. Analysis revealed that for 2001 and 2005 there was no change in barren land area, whereas in 2010 slight decrease in barren land was found which further decreased for the analysis period of 2020, overall barren land class decreased at a rate of 07% (i.e., 74% to 67%) during the analysis period. Also, vegetation and water bodies classes experienced variations throughout the analysis period, however built-up areas showed constant increase throughout the analysis period where built-up area classes increased from 9% to 15%. Whereas land surface temperature (LST) throughout the analysis period showed increasing trends, for both high ranges and low ranges. It was analysed that the areas having dominancy of the barren land class experienced the highest temperature ranges whereas low temperature ranges were discernible in the areas having vegetation covers. It can be concluded from the study that barren land covers the major area of the study area and there is an increase in built-up areas which can further enhance the LST in the study area.

A 30 YEAR (1991-2020) RETROSPECTIVE MORTALITY REVIEW OF GIRAFFIDS (GIRAFFA SPP. AND OKAPIA JOHNSTONI) IN MANAGED CARE.

Identifying common causes of mortality in zoo giraffe (Giraffa spp.) and okapi (Okapia johnstoni) provides an opportunity to help improve welfare and population management for these endangered species. Mortality reports from 1,024 giraffe and 95 okapi in zoos were compiled from the Species 360 Zoological Information Management Software (ZIMS) utilizing the Morbidity & Mortality Analysis tool. Thirty years of mortality reports (1991-2020) were evaluated to help identify trends and evaluate the impacts, if any, of changes over time in husbandry and management practices. The most common causes of death for giraffe from 1991 to 2015 were neonatal issues (234/845, 27.7%), trauma (213/845, 25.2%), noninfectious disease (190/845, 22.5%), and infectious disease (188/845, 22.2%). In comparison, the most common causes of mortality for giraffe from 2016 to 2020, were noninfectious disease (78/179, 43.6%), trauma (39/179, 21.8%), neonatal issues (39/179, 21.8%), and infectious disease (17/179, 9.5%). The most common cause of death for okapi from 1991 to 2015 were neonatal issues (29/64, 45.3%), infectious disease (13/64, 20.3%), noninfectious disease (11/64, 17.2%), and trauma (10/64, 15.6%). In comparison, the most common cause of death for okapi from 2016 to 2020 was noninfectious disease (15/31, 48.4%), neonatal issues (8/31, 25.8%), and infectious disease (5/31, 16.1%). The results suggest that zoo giraffids have had a relative decrease in mortality from infectious diseases in recent years, whereas death from noninfectious causes has increased significantly. Trauma-related giraffe mortalities and neonatal mortality in both giraffe and okapi, although decreasing in prevalence between time periods, continue to be important causes of death in zoos. This is the first descriptive mortality review for the Giraffidae family and provides data on potential giraffe and okapi health issues that zoos could proactively address.

Preparatory activity and the expansive null-space.

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

THE EFFECT OF INFLUENCER MARKETING, VIRAL MARKETING AND CUSTOMER ENGAGEMENT ON PURCHASE INTENTION AT BLOODS STORE IN PURWOKERTO

This research investigates the multifaceted influence of contemporary marketing methodologies, namely influencer marketing, viral marketing, and customer engagement, on consumers' purchase intentions. This research used SmartPLS statistical analysis tool version 3.0. This research uses an online questionnaire with a total of 150 respondent from students of the Faculty of Economics and Business at Jenderal Soedirman University, University of Muhammadiyah Purwokerto, and State Islamic University Prof. KH. Saifuddin Zuhri Purwokerto that have purchase intention in Blood Store Purwokerto. Based on the findings of this research, it shows that influencer marketing, viral marketing, and customer engagement has a positive and significant impact on the purchase intention. By understanding the synergistic effects of these marketing approaches, businesses can strategically tailor their campaigns to optimize customer engagement and enhance purchase intentions, ultimately fostering greater success in today's competitive market landscape.This research investigates the multifaceted influence of contemporary marketing methodologies, namely influencer marketing, viral marketing, and customer engagement, on consumers' purchase intentions. This research used SmartPLS statistical analysis tool version 3.0. This research uses an online questionnaire with a total of 150 respondent from students of the Faculty of Economics and Business at Jenderal Soedirman University, University of Muhammadiyah Purwokerto, and State Islamic University Prof. KH. Saifuddin Zuhri Purwokerto that have purchase intention in Blood Store Purwokerto. Based on the findings of this research, it shows that influencer marketing, viral marketing, and customer engagement has a positive and significant impact on the purchase intention. By understanding the synergistic effects of these marketing approaches, businesses can strategically tailor their campaigns to optimize customer engagement and enhance purchase intentions, ultimately fostering greater success in today's competitive market landscape.

SWOT Analysis: Implementation of National Education Standard Policy to Improve the Quality of High School Graduates

The competence of quality graduates cannot be separated from the relationship between each educational standard that is a reference for management, governance, and education. The purpose of this analysis is to examine the implementation of national education standard policies as guidelines for improving the quality of education governance based on the quality of graduates who are ready to compete in the world of work both nationally and at the global level. The approach used in this analysis is descriptive-qualitative, which involves processing data obtained through literature studies in the form of reference books, scientific journals, official documents, digital sites, and official information that can be accessed online. The results of the study on the topic above conclude that SWOT analysis can be used as an alternative management analysis tool to identify aspects of strengths, challenges, opportunities, and threats to improve the quality and competence of graduates in accordance with the policies of national education standards. The results of the SWOT analysis recommend alternative strategies that can be developed and followed up as a form of implementation for improving the quality of graduates.

Developing a mobile application with geo-analytic tools to perform real estate valuation within smart cities

Urban land management policies and applications require comprehensive information about real estate development dynamics and value criteria. In this study, geo-analytic tools and a mobile app were produced for residential property valuation in smart cities. Geo-analytical tools were automated to process and use datasets extracted from the databases and ‘Smart Real Estate’ mobile app using data sets produced by the developed geo-analytics tools shares map services automatically. In this way, users can get real-time location data via web services by triggering geographic analysis tools at certain intervals. It can manage and analyse big geographic data to share smart city services about real estate to citizens.

Exploring the Dynamic Relationship: How Exchange Rates Drive Company Valuation through Foreign Portfolio Investments

This study aims to analyze the influence of economic growth and exchange rates on firm value with foreign portfolio investment as an intervening variable at PT Bank Central Asia Tbk. for the period 2011-2020. Quarterly secondary data from 2011-2020 were utilized. The data were then processed using EViews 11 Version analysis tool. The analysis employed descriptive statistics, linear tests, and classical assumption tests. Hypothesis testing was conducted using Partial Test (T-Test). The results of the partial test (T-Test) indicate that economic growth, exchange rates, and foreign portfolio investment insignificantly influence firm value. However, regarding the intervening variable, foreign portfolio investment does not mediate the influence of economic growth (GDP) on firm value, either directly or indirectly, and foreign portfolio investment also does not directly mediate the influence of exchange rates on firm value. Nonetheless, exchange rates directly affect foreign portfolio investment, thus reinforcing the relationship between exchange rates and firm value. Therefore, the fifth hypothesis is accepted.

Evaluating Students’ User Experience on Student Management Information Systems

Studies evaluating students’ UX of applications that will influence their continuance use of educational systems in Higher Educational Institutions (HEIs) have not been sufficiently addressed in the African region, specifically in Ghana. Thus, conducting a study on students’ UX of systems in HEIs will enhance students’ interest in continuing to use such systems. Therefore, this study examines students’ user experience (UX) and how it impacts their continued use of the Student Management Information System (SMIS). The study proposed a research model by integrating user experience questionnaire (UEQ) constructs with continuance intention to use. The study adopted an online questionnaire to collect data from 415 students at Koforidua Technical University (KTU). The partial least square-structural equation model (PLS-SEM) method was used to evaluate the proposed model’s reliability, validity, and relationship among the constructs. The UEQ data analysis tool was used in analysing the data. The study’s findings showed that attractiveness, perspicuity, efficiency, stimulation, and novelty significantly influenced students’ continuance intention to use the SMIS. However, dependability did not significantly affect students’ continuance intention to use the SMIS. Also, the study’s findings on the benchmark results showed that attractiveness, perspicuity, and stimulation were categorized as good, while dependability, efficiency, and novelty were categorized as excellent. These findings offer valuable insights for UX designers and developers aiming to create engaging and intuitive SMIS solutions. Also, it will provide students feedback on system UX that can be incorporated into future release updates. Furthermore, this research contributes significantly to the understanding of student UX with SMIS in developing country HEIs and its impact on continued usage.

Elastoplastic constitutive model of gravelly soil and its validation through numerical analysis of the Kobe Port Island liquefaction

The liquefaction of gravelly soil has been demonstrated in many earthquakes, but the constitutive model to predict its liquefaction behavior is limited. The dilatancy equation plays an important role in establishing elastoplastic models of geotechnical materials. Recognizing the unique dilatancy characteristics of gravelly soil, this study introduces an improved nonlinear dilatancy equation into the generalized plasticity model. Consequently, a unified elastoplastic constitutive model is proposed, aimed at accurately predicting both the monotonic and cyclic behaviors of saturated gravelly soil under diverse drainage conditions. A series of drained and undrained triaxial tests of gravelly soil under monotonic and cyclic loadings are carried out to validate the model performance. In addition, the model performance is also examined through monotonic and liquefaction tests of gravelly soil obtained from prior publications. Furthermore, the proposed model is implemented into the nonlinear finite element program GEODYNA and applied to the numerical simulation of the Kobe Port Island liquefaction to better validate the capabilities of the proposed model. From the comprehensive analysis of triaxial tests and numerical simulations, the proposed model performs well in predicting mechanical behavior of gravelly soil and can provide a powerful tool for seismic liquefaction analysis of gravelly soil sites.

A Neural Modelling Tool for Non-Linear Influence Analyses and Perspectives of Applications in Medical Research

Neural network models are often used to analyse non-linear systems; here, in cases of small datasets, we review our complementary approach to deep learning with the purpose of highlighting the importance and roles (linear, non-linear or threshold) of certain variables (assumed as causal) in determining the behaviour of a target variable; this also allows us to make predictions for future scenarios of these causal variables. We present a neural tool endowed with an ensemble strategy and its applications to influence analyses in terms of pruning, attribution and future predictions (free code issued). We describe some case studies on climatic applications which show reliable results and the potentialities of our method for medical studies. The discovery of the importance and role (linear, non-linear or threshold) of causal variables and the possibility of applying the relationships found to future scenarios could lead to very interesting applications in medical research and the study and treatment of cancer, which are proposed in this paper.

Investigation of Rosa species by an optimized LC-QTOF-MS/MS method using targeted and non-targeted screening strategies combined with multivariate chemometrics.

Plants of the Rosa genus are renowned for their pronounced and pleasant aroma and colors. The aim of this work was to develop a novel liquid chromatographic triple quadrupole time-of-flight tandem mass spectrometric (LC-QTOF-MS/MS) method for the investigation of the bioactive fingerprint of petals of different genotypes belonging to Rosa damascena and Rosa centifolia species. Central composite design (CCD) of response surface methodology (RSM) was used for the optimization of the LC-QTOF-MS/MS method. The method was validated and target, suspect, and non-target screening workflows were applied. Statistical analysis and chemometric tools were utilized to explore the metabolic fingerprint of the Rosa species. RSM revealed that the optimal extraction parameters involved mixing 11 mg of sample with 1mL of MeOH:H2O (70:30, v/v). Target analysis confirmed the presence of 11 analytes, all of which demonstrated low limits of quantification (LOQs; as low as 0.048 ng mg-1) and sufficient recoveries (RE: 85%-107%). In total, 28 compounds were tentatively identified through suspect analysis. Non-target analysis enabled the generation of robust OPLS-DA and HCA models that classified the samples according to their species with 100% accuracy. A novel LC-QTOF-MS/MS method was developed and applied in the analysis of 47 R.centifolia and R.damascena flowers belonging to different genotypes.

Uncovering Hidden Dependencies: Constructing Intelligible Path Witnesses using Dataflow Analyses

The lack of sound, concise and comprehensive error reports emitted by a static analysis tool can cause increased fixing cost, bottleneck at the availability of experts and even may undermine the trust in static analysis as a method. This paper presents novel techniques to improve the quality of bug reports for static analysis tools that employ symbolic execution. With the combination of data and control dependency analysis, we can identify the relevance of particular code snippets that were previously missing from the report. We demonstrated the benefits of our approach by implementing an improved bug report generator algorithm for the Clang Static Analyzer. After being tested by the open source community our solution became enabled by default in the tool.

Predicting Skill Decay: A Practical Application of the Skill Decay Analysis Tool

This paper describes a skill decay prediction model that was applied to a safety critical task conducted by rail workers. The tool is easy to use and promotes skill maintenance for operators in environments that have an increased risk of skill decay. A skill decay analysis tool called the User Decision Aid was adapted for use in a large, transportation organisation. The tool is an easy-to-use method that has proven useful in guiding training to prevent the degradation of skill and knowledge in various tasks. It produces skill decay curves, addressing questions such as how frequently should an operator receive training or undertake practice in an activity, to remain skilled and competent in that activity? The skill decay tool provides useful benefits to organisations as it allows the judicious allocation of training resourcing, and can guide the design of tasks.

Exploring the entrepreneurial landscape of university-industry collaboration on public university spin-off creation: A systematic literature review

Research into the factors influencing university-industry collaboration on public university spin-offs creation has focused on management, entrepreneurship, technology and innovation. This research began with a careful systematic literature review of 4427 scientific papers published in the last ten years (2014–2023) and accessible in the prestigious Web of Science core collection. A quantitative methodology was used, complemented by the use of the visual analysis tool Posit PBC™, formerly known as R Cloud Studio. This comprehensive approach facilitated the seamless ingestion of raw data into Biblioshyni, which is a web-based platform specialised in bibliometric analysis. This review has revealed compelling trends, particularly in terms of increasing diversity, the emergence of United Kingdom as a major player, and the central role of university-industry collaboration. Our systematic review identified influential authors in the field, including the prolific contributions of scholars such as D. Radicid, S. Ropers, Y. Li and R. Owen. We also identified important research institutions, such as Utrecht University in Netherlands, Lund University in Sweden and The University of Manchester in the UK. In addition, we have shown that countries such as the United Kingdom, China and USA have made a significant contribution to the volume of publications. The results highlighted a marked increase in the phenomenon of univeristy spin-offs over the past decade, as evidenced by the exponential growth in both publication output and citation rates. This empirical revelation was underpinned by a rigorous exploration of the Web of Science database, using a carefully crafted set of keywords. Thirty-seven pivotal studies were selected for in-depth review through a sophisticated selection process that adhered to the rigorous standards of the PRISMA methodology. The aim of this review is to improve understanding and encourage deeper exploration of spin-off-based public universities through collaboration between academia and industry.

SERVICE QUALITY AND CUSTOMER LOYALTY: THE ROLE OF SATISFACTION AND TRUST IN INDONESIAN SHARIA BANK

This study determines the effect of service quality on customer loyalty with satisfaction and trust as mediating variables in Indonesia sharia banks during the Covid-19 pandemic period. This study used 300 respondents of sharia bank customers in Indonesia. The data were analyzed using a partial least square with the SmartPLS 3.0 analysis tool. Service quality has a positive effect on customer loyalty, service quality has a positive effect on customer satisfaction, service quality has a positive effect on trust, satisfaction has a positive effect on customer loyalty, trust has a positive effect on customer loyalty, satisfaction is able to mediate the effect of service quality on customer loyalty, and trust is able to mediate the effect of service quality on loyalty. This study adopts two mediating variables, namely satisfaction and trust in the effect of service quality on customer loyalty. The results present partial mediation of satisfaction on the influence of service quality on customer loyalty and partial mediation of trust on the influence of service quality on customer loyalty.

Buddhist heritage landscape evolution of Mount Wutai: a path dependence approach

An adequate understanding about the evolutionary process, mechanism and influencing factors of cultural landscapes is crucial for their efficient preservation and sustainable development. The theoretical framework of self-reinforcing sequence in path dependence analysis appears applicable but has not yet been well explored as a tool for broader historical landscape analysis like the evolution of cultural landscapes that may last thousands of years. Within such a framework, an empirical case of Buddhist heritage landscape of Mount Wutai is used to account for the institutional genesis, reproduction and change associated with landscape evolution over time. The findings suggest the framework provides an essential toolkit to reveal the specific ways in which path dependence shapes the evolutionary trajectories of cultural landscapes and, based on that, to detect management options that steer development and change of cultural landscapes into a direction that aligns with desired outcomes.

Overexpression of HbGRF4 or HbGRF4-HbGIF1 Chimera Improves the Efficiency of Somatic Embryogenesis in Hevea brasiliensis.

Transgenic technology is a crucial tool for gene functional analysis and targeted genetic modification in the para rubber tree (Hevea brasiliensis). However, low efficiency of plant regeneration via somatic embryogenesis remains a bottleneck of successful genetic transformation in H. brasiliensis. Enhancing expression of GROWTH-REGULATING FACTOR 4 (GRF4)-GRF-INTERACTING FACTOR 1 (GIF1) has been reported to significantly improve shoot and embryo regeneration in multiple crops. Here, we identified endogenous HbGRF4 and HbGIF1 from the rubber clone Reyan7-33-97, the expressions of which dramatically increased along with somatic embryo (SE) production. Intriguingly, overexpression of HbGRF4 or HbGRF4-HbGIF1 markedly enhanced the efficiency of embryogenesis in two H. brasiliensis callus lines with contrasting rates of SE production. Transcriptional profiling revealed that the genes involved in jasmonic acid response were up-regulated, whereas those in ethylene biosynthesis and response as well as the S-adenosylmethionine-dependent methyltransferase activity were down-regulated in HbGRF4- and HbGRF4-HbGIF1-overexpressing H. brasiliensis embryos. These findings open up a new avenue for improving SE production in rubber tree, and help to unravel the underlying mechanisms of HbGRF4-enhanced somatic embryogenesis.

Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification.

The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients.