Data Analysis Approach Research Articles

Comparative assessment of costs of household trips to healthcare facilities in contiguous riverine regions in Rivers State, Nigeria

Physical access to neighbourhood facilities for utilization depends on distance, time and cost factors in relation to location characteristics. In this study the costs of accessing healthcare facilities in island riverine contiguous regions of Rivers State, Nigeria were considered. The study employed the survey research design following quantitative approach for data collection and analyses. Furthermore, the multistage sampling technique employed and 30% (66) communities of total communities of 223 in the study area was purposively determined. Also sample size of (56,131) households was obtained from total population of 305, 289 of the study area as provided by 1991 National Population Census data by application of Krejcie and Morgan Model (1970) principle and 5 persons average household considered. Finally, the random sampling technique was used in selection of communities for administration of questionnaires in the study area. A total of 382 respondents (household heads) were interviewed including key informants (government agencies and professionals). Other survey tools used were photographs, observations, Stock-watch and Geographic Positioning System (GPS) Speedometer. 328 questionnaires were retrieved from the field and considered valid for analysis with the aid of Statistical Package for Social Sciences (SPSS). Hypotheses were tested by the application of the multi regression model. It was found in traveling for healthcare, an aggregate of 21.4% of household members in the region monthly made trips in communities with and without PHC facilities with multi-modal transport systems at varied costs.

Machine Learning-Based Spectral Analyses for Camellia japonica Cultivar Identification

Camellia japonica is a plant species with high cultural and biological relevance. Besides being used as an ornamental plant species, C. japonica has relevant biological properties. Due to hybridization, thousands of cultivars are known, and their accurate identification is mandatory. Infrared spectroscopy is currently recognized as an accurate and rapid technique for species and/or subspecies identifications, including in plants. However, selecting proper analysis tools (spectra pre-processing, feature selection, and chemometric models) highly impacts the accuracy of such identifications. This study tests the impact of two distinct machine learning-based approaches for discriminating C. japonica cultivars using near-infrared (NIR) and Fourier transform infrared (FTIR) spectroscopies. Leaves infrared spectra (NIR—obtained in a previous study; FTIR—obtained herein) of 15 different C. japonica cultivars (38 plants) were modeled and analyzed via different machine learning-based approaches (Approach 1 and Approach 2), each combining a feature selection method plus a classifier application. Regarding Approach 1, NIR spectroscopy emerged as the most effective technique for predicting C. japonica cultivars, achieving 81.3% correct cultivar assignments. However, Approach 2 obtained the best results with FTIR spectroscopy data, achieving a perfect 100.0% accuracy in cultivar assignments. When comparing both approaches, Approach 2 also improved the results for NIR data, increasing the correct cultivar predictions by nearly 13%. The results obtained in this study highlight the importance of chemometric tools in analyzing infrared data. The choice of a specific data analysis approach significantly affects the accuracy of the technique. Moreover, the same approach can have varying impacts on different techniques. Therefore, it is not feasible to establish a universal data analysis approach, even for very similar datasets from comparable analytical techniques.

P1330 The gut microbiome at the onset of inflammatory bowel disease: A systematic review of the literature and unified bioinformatic synthesis of sequencing data from >1000 treatment naïve patients

Abstract Background There are few studies of the gut microbiome in new-onset IBD. We present a novel secondary bioinformatic re-analysis (BA) of sequence outputs mapped to latest microbial taxonomy derived from systematic review. Methods MEDLINE and Embase searches were performed for microbiome studies in pre-treatment IBD. Appraisal was completed with ROBINS-E. Available 16S rRNA sequence datasets were downloaded and missing datasets requested. Integrated datasets were run through a unified pipeline based in QIIME2 supplemented by phyloseq and DESeq2. Results From 12,264 studies, 10,804 abstracts and 217 full texts were screened. 31 studies were eligible; 18 met the requirements for BA. Most were low risk for bias, except uncontrolled confounding. Methodology varied extensively. Considering only amplicon data; samples originated from 1,743 unique individuals (including 678 Crohn’s disease (CD), 399 ulcerative colitis (UC), 123 ‘other’ IBD, 130 healthy, 405 symptomatic, and 8 familial controls) derived from 15 countries. DNA was extracted using 15 different kits, sequencing performed on 4 separate platforms targeting 10 different 16S rRNA hypervariable regions. There was a paucity of adult data, particularly mucosal biopsies. Bacterial alpha diversity was reduced: in faeces in paediatric UC vs symptomatic controls (SC), in adult CD and UC vs HC and in paediatric SC vs HC (Figure 1). In paediatric biopsies in CD vs SC, CD vs UC, SC vs UC (Figure 2). Beta-diversity BA by Bray-Curtis demonstrated clear distinctions between faecal and mucosal biopsy communities, least evident in UC. Faecal communities separated by geography, though less evidently in biopsies. Differential abundance analysis revealed enrichment of oral genera across sample types (Fusobacterium, Peptostreptococcus, Neisseria in CD; Haemophilus, Aggregatibacter, Peptostreptococcus, Porphyromonas, Neisseria, Gemella in UC). Short-chain fatty acid (SCFA) producers were diminished but less consistently across IBD phenotype and sample type. Conclusion This work demonstrates the utility of a combined systematic review/secondary data analysis approach to published microbiome datasets. Whilst synthesis remains challenging due to the methodological inconsistencies within these studies, this approach better accounts for these variations and presents a new way to consider this data. A reduction in bacterial diversity remains core to the IBD microbiome, though with more complexity than previously considered (SC<UC in paediatric biopsies). Microbial community structure changes by sample type and geography. Enrichment of oral bacteria is consistent across IBD subtypes and sample types suggesting their importance in the disease. A deficit in SCFA producers may also be important.

Comparative Analysis of the Incidence, Diagnosis and Treatment of Prostate Cancer between China and the United States

In the context of globalization and an aging population, prostate cancer has emerged as a significant threat to male health, with its incidence and mortality rates on the rise worldwide. Particularly in comparison between China and the United States, two representative countries, there are notable differences in the incidence, diagnosis, and treatment of prostate cancer due to variations in medical resource allocation, diagnostic techniques, lifestyles, and cultural perceptions. This article aims to compare and analyze the current status of prostate cancer in China and the U.S., explore the key factors influencing these differences, and provide references for the development of prostate cancer prevention and control strategies in China. We employ multi-dimensional data collection and analysis approaches including literature review, case-control studies, and statistical analysis. We focus on the epidemiological data, clinical practice guidelines, patient quality of life, and medical economic burden to comprehensively dissect the differences and their causes in prostate cancer management between China and the U.S. Our findings highlight the disparities between the two countries in the aspects of early screening, treatment preference, and long-term follow-up mechanisms, aiming to uncover the underlying reasons behind these differences and propose corresponding improvement suggestions. Conclusions drawn from our analyses indicate that the U.S. is relatively advanced in early diagnosis and personalized treatment of prostate cancer, benefiting from a more comprehensive healthcare system and advanced medical technologies. Although China has made a significant progress in recent years, it still faces many challenges due to the uneven distribution of medical resources, and there is space for improvement in public health awareness. The significance of our analysis lies in providing empirical evidence for formulating more scientifically sound and reasonable strategies for the prevention and control of prostate cancer in China, which will help promote further development and improvement in this field.

A functional data analysis approach for modelling frequency‐modulated tonal sounds in animal communication

Abstract Frequency modulation (FM) is believed to play a major role in encoding information in tonal vocal communication. However, most studies aimed at investigating acoustic variability rely on the manual measurement of acoustic parameters, and the implementation of multivariate techniques, which represent only partially sound FM. Thus, innovative modelling approaches able to capture local FM dynamics are needed for tonal sounds modelling. These kinds of methodological developments in bioacoustics might be key for further understanding animal behaviour and ecology. We propose the application of a functional data analysis (FDA) approach to model extracted FM patterns, which entails the transformation of acoustic signals into continuous functions. We describe a Raven‐to‐R FDA workflow for modelling tonal sounds and we highlight two of its potential applications for classification aims and FM analysis in relation to behavioural, social, and environmental factors. For illustrative purposes, the approach performance was tested on FM patterns (contours) of signature whistles emitted by bottlenose dolphins (Tursiops truncatus), to investigate their acoustic variability and potential information content. Our results show that tonal sounds are inherently of functional nature and can be treated as such for FM analysis. We found that building the FDA sound curves using 25 B‐spline basis functions was the most appropriate setting for the functionalisation of dolphin whistles. Our approach was able to accurately reconstruct the FM patterns of the modelled tonal sounds and even to capture their fine‐scale modulations, with a precision of 80%. Functional clustering showed an accuracy up to 89% for short whistles. Estimated functional coefficients of the regression model suggested that different contextual factors have a significant effect on the FM patterns of SWs. The adoption of an FDA approach appears to be quite promising for the study of frequency‐modulated tonal sounds, both for classification aims and in‐depth FM analysis. The limitations and advantages of the approach were discussed. We developed and tested a new methodology able to fill knowledge gaps in animal communication, and we anticipate it will lead to advances in the ethological and ecological understanding of wild animal populations.

Exploring the Intersection of Mental and Reproductive Health Among Women Living with HIV in Spain: A Qualitative Secondary Data Analysis.

Background/Objectives: Globally, girls and women make up over half of those living with Human Immunodeficiency Virus (HIV), facing unique reproductive and mental health challenges. An HIV diagnosis impacts motherhood desires and increases trauma, stigma, and depression risks. Addressing these overlapping vulnerabilities with tailored, comprehensive healthcare is essential. This study aims to qualitatively explore the complex interplay between mental health and reproductive decision making among women living with HIV (WLWH). Methods: A secondary data analysis approach was employed, utilizing qualitative data from a wider study. Theoretical sampling was used to recruit a sample of WLWH attending the HIV clinic at a public hospital in Badalona (Barcelona, Spain). Interviews took place in a private and quiet space between May 2019 and January 2020. To perform secondary data analysis, Braun and Clarke's thematic analysis was used. Results: Twenty-six women living with HIV were interviewed, with an average age of 39.3 years. The participants had diverse nationalities and faced economic challenges. Following data analysis, four main themes were identified: Emotional Fragmentation and Reproductive Self-Perception after the HIV Diagnosis; Coping Strategies in Reproductive Decision Making; Impacting Emotional Resilience and Motherhood Decisions; and Emotions, Mental Health, and Desire for Motherhood. The themes revealed the profound emotional impact of HIV on their identity, mental health, and reproductive decision making, affecting motherhood aspirations. Conclusions: Women living with HIV face psychological challenges in reproductive decision making, including anxiety and stigma. The study highlights their resilience and emphasizes incorporating hope-based strategies into HIV care, advocating for integrated and mental health-focused approaches to improve support and outcomes.

Transformer enabled multi-modal medical diagnosis for tuberculosis classification

Recently, multimodal data analysis in medical domain has started receiving a great attention. Researchers from both computer science, and medicine are trying to develop models to handle multimodal medical data. However, most of the published work have targeted the homogeneous multimodal data. The collection and preparation of heterogeneous multimodal data is a complex and time-consuming task. Further, development of models to handle such heterogeneous multimodal data is another challenge. This study presents a cross modal transformer-based fusion approach for multimodal clinical data analysis using medical images and clinical data. The proposed approach leverages the image embedding layer to convert image into visual tokens, and another clinical embedding layer to convert clinical data into text tokens. Further, a cross-modal transformer module is employed to learn a holistic representation of imaging and clinical modalities. The proposed approach was tested for a multi-modal lung disease tuberculosis data set. Further, the results are compared with recent approaches proposed in the field of multimodal medical data analysis. The comparison shows that the proposed approach outperformed the other approaches considered in the study. Another advantage of this approach is that it is faster to analyze heterogeneous multimodal medical data in comparison to existing methods used in the study, which is very important if we do not have powerful machines for computation.

Migrant churches and reverse mission: Missiological gaps and missionary trap

The migration of Zimbabweans in search of better opportunities has spurred the growth and spread of Zimbabwean Churches within diaspora communities. Currently, many Zimbabwean churches like other African Churches have crossed continental borders, setting up branches in Europe and North America. This phenomenon of African Churches engaging with diasporic communities has been described by scholars as a ‘Reverse Mission’. A critical question that emerges is: To what degree is the establishment of African churches in other nations and continent an indicative of a reverse mission? The case study focuses on two Zimbabwean churches that have established branches in South Africa and other countries both within and beyond the African continent. The selection of the two churches is based on their origins that trace back to South Africa, rendering them pertinent examples for discussion in the context of reverse mission. The research identified ‘missiological gaps’ leading to a ‘missionary trap’ in the context of reverse mission, as the two Zimbabwean Churches face new frontiers. The article discusses how Zimbabwean churches, along with other African churches in the diaspora, created obstacles that impeded the dissemination of the gospel because of their insufficient cross-cultural training and experience. The researcher utilised descriptive phenomenological and interpretive phenomenological approaches for data collection and analysis, respectively.Intradisciplinary and/or interdisciplinary: The article is framed within the context of reverse mission, missiology, and the history of African Christianity. It contends that the African church should reassess its evangelistic strategies as it transcends national and international borders.

Language of assurance: The use of discourse markers in insurance policy documents

The comprehensibility of insurance policy documents is an important issue but often overlooked. The lack of comprehensibility of the policy can cause misunderstandings and even disputes between insurance providers and policyholders. In the realm of legal texts of which insurance policy is a part of, discourse markers play an important role in enhancing or impeding the comprehensibility of the contents. The role of discourse markers within insurance policies has not been extensively studied. Thus, there is a significant research gap in exploring how these linguistic elements contribute to the comprehensibility of insurance policies, ergo, affecting readers’ comprehension. The study employed a mixed-method approach of quantitative and qualitative data analysis to examine the use of discourse markers in insurance policy documents and how they contribute to the policy's comprehensibility. The quantitative analysis involved the frequency counts of the use and distribution of the discourse markers in the policy documents. The qualitative analysis involved the analysis of data from a semi-structured interview that gauged the policyholders’ perceptions on their comprehension of the policy documents concerning the use of discourse markers. 839 discourse markers were identified based on six categories of discourse markers: contrastive, elaborative, inferential, temporal, conditional, and reference. Each category plays a specific role in shaping the narrative and ensuring the clarity of contractual details. The interview data indicate that familiar and common discourse markers could facilitate understanding, but complex and unfamiliar discourse markers might detract comprehension and readability. The findings pointed out that the challenges of understanding the legal documents are observable across various groups of policyholders. Insurers can resolve this by using more common and familiar discourse, making it more inclusive and comprehensible for all consumers.

The Continuous Fragility Index of Statistically Significant Findings in Studies Based on High Levels of Evidence Comparing Interventions for Femoroacetabular Impingement Syndrome.

Critical analysis of studies with high level of evidence has relied on the significance set by the reported P values. However, this strategy steers readers toward categorical interpretation of the data; therefore, a more comprehensive approach of data analysis is warranted. The continuous fragility index (CFI) allows for frailty interpretation of any given study's continuous outcome results. To calculate the CFI of high-level quality studies reporting significant continuous outcomes on comparison of treatment modalities for management of femoroacetabular impingement syndrome. Meta-analysis; Level of evidence, 2. Three databases (PubMed, Embase, Cochrane) were queried from inception to February 2024 utilizing Boolean operators to combine variations of the following search terms: "femoroacetabular impingement, randomized controlled trials or prospective cohort." Studies were included if level of evidence 1 or 2, and a statistically significant outcome was reported for any continuous outcome. CFI calculation was performed for all significant outcomes to obtain a study-specific mean CFI and also for the primary outcome of each study. Mean CFI was also calculated for outcomes reported to be significant in >3 studies. Additionally, multivariable linear regression was utilized for assessment of variables associated with achievement of a higher CFI. Thirteen studies totaling 1316 patients were included for analysis: 11 level of evidence 1 and 2 level of evidence 2 studies. A total of 48 outcomes reaching significance were extracted, with 8 representing primary outcomes. Study-specific mean (SD) CFI was 8 (9.3), whereas primary outcome mean CFI was 12.5 (12). In 4 of the 13 studies, the number of hips lost to follow-up was greater than the study CFI. Outcome-specific mean CFI was obtained for 5 outcomes reported in >3 studies. Multivariable linear regression revealed that larger sample size and greater journal impact factor had a significant positive association with a higher overall CFI value (P < .05). The mean (SD) number of patient outcome events needed to reverse the significance of a continuous outcome (ie, CFI) was 8.0 (9.3). Nearly one-third of studies had a CFI less than the reported loss to follow-up, reflecting the need for better patient compliance to attain less fragile statistical results. Larger sample size and greater journal impact factor were both predictive of a higher CFI.

The Opportunities for Professional Growth When Using Knowledge of Students’ Prior Science Ideas in the Teaching of Evolution and Genetics: A Self-Study

This inquiry sought to investigate the opportunities and potential challenges of engaging in a self-study approach as a strategy for enhancing professional growth during my teaching of the topic of evolutionary genetics to 24 twelfth-grade students. I had, for many years, experienced pedagogical deficits and shortcomings when teaching evolutionary genetics despite my professional knowledge from my teacher training. I always struggled to a) represent this content in ways that make it comprehensible to my students, b) motivate my students to accept this topic, and c) address students’ misconceptions. Vygotsky’s social constructivism of knowledge and the idea of pedagogical reasoning and action as propounded by Shulman were the key theoretical lenses that guided this inquiry. Collected data included pre- and post-intervention data, students’ conceptions of evolutionary genetics ideas, lesson transcripts, my reflections, pedagogical actions and reasoning during my teaching, and notes on collaborative activities with critical friends. The collected data was analysed from a quantitative and qualitative approach. Thematic analysis for emerging themes was done from a deductive to inductive data analysis approach. This study established that through the idea of opening up for professional feedback from critical friends, one ought to make one’s pedagogical reasoning and actions public. This opening up makes one ‘vulnerable’ to severe criticism or ‘attack' by critical friends. However, despite this setback, it offers unlimited professional growth opportunities beyond one’s personal biases, interpretations, and teacher training expertise on key issues of practice. The ‘negative’ and positive critiques from others are powerful critical reflection points for enhancing professional development. However, through engaging in open feedback collaborative sessions, a practitioner experiences internal tensions between the 'inner egoistic voice' and the probing 'outer voice'. Thus, the ability to objectively reconcile one’s inner voices in the wake of the usually unfriendly outer voices is a crucial starting point for one to realise professional growth as a practitioner.

A human-centered data analysis approach for long-term assessment of human health quality

Internet of Things (IoT)-based health monitoring is a key focus in Healthcare 4.0 for continuously tracking Vital signs and other health biometrics. While IoT technologies like sensors, connectivity, hardware, software, and batteries have been extensively studied, there has been a lack of emphasis on evaluating human-centered health capabilities using long-term data collected by the Internet of Medical Things (IoMT). This paper tackles the problem of human-centered Vital signs-based health long-term assessment by proposing a hybrid approach that includes model-free multi-profile monitoring, optimization models, control charts, and profile capability indices. In this new approach, a curves dissimilarity index-based profile monitoring method is employed to address the complexity and outlier elimination issues in the traditional regression model-based profile monitoring approach, and a mixed-integer linear mathematical model is developed to assign human-centered tolerance to the dissimilarity index of each Vital sign profile. Also, control charts are employed to monitor the health of individuals according to each Vital sign, and overall. In addition, the profile capability index and non-confirming probability values are interpreted as long-term health measures. The efficacy of the newly proposed method is demonstrated in a simulation study based on an individual’s six Vital signs generated data set using MATLAB, CPLEX, and MINITAB.

The Impact of Mother’s Education on Their Children’s Academic Performance: A Study Based on the Secondary Schools in Vavuniya District, Sri Lanka

Mother has crucial role in their children’s education such as motivation, facilitation, healthcare, school involvement. Mother’s education level has a significant relationship on their children education. Purpose of the study is identify the relationship mother education level on students’ achievement of students in the schools of Vavuniya District. The study was adopted descriptive survey design. The data collection and analysis approach was a mixed method using qualitative and quantitative techniques. Data were collected through a closed-ended questionnaire consisting of 100 students and their mothers selected from a total of 725 students studying in four different schools in a backward area under Vavuniya district using a random sampling technique. The data were collected through a questionnaire consisting of completely closed-ended questions Mother’s education was significantly associated with student learning activities at schools Mother’s education was significantly associated with parent involvement at school. Using mother’s education as an indicator of cultural capital, the findings suggest mothers with more capital in the form of a degree are more involved than mothers with lower levels of education. Parent Volunteering. As hypothesized, mother’s level of education was correlated with parent volunteering. The effects were stronger for mothers without collegiate level education. Mothers with a college degree or higher were more likely to be involved with volunteering. Mother’s level of education significantly correlated with group activities for less than a high school diploma and a college degree or higher

Supporting Smart Manufacturing in the Space Industry: A Case Study

ABSTRACTIn the era of Industry 4.0, the New Space Economy, often called Space 4.0, has taken center stage in the satellite industry. The advent of mega‐constellations, which entails mass satellite production, necessitates state‐of‐the‐art manufacturing methods. Leveraging Industry 4.0 technologies like the Internet of Things (IoT) and Big Data analytics show great potential for improving the manufacturing, assembly, integration, and testing (MAIT) cycle. This paper focuses on how Industry 4.0 principles enhance data analysis and automation in space manufacturing, illustrated through a case study at an aerospace company, with a focus on the composite sandwich panel manufacturing line. We introduce two key contributions. First, an interactive dashboard is proposed to enhance data analytics capabilities, offering real‐time access to insights and key performance indicators (KPIs) for operators and data analysts, and enabling the exploration of customized metrics. This facilitates comprehensive analysis of the entire MAIT process, supporting trend detection, anomaly identification, and areas for improvement to facilitate data‐driven decision‐making. Second, we present two strategies to tackle the challenges posed by the constrained number of attempts to insert installations on sandwich panels. These strategies are founded on a proposed data analytics approach rooted in Markov chain principles. This approach aids operators in making informed decisions on whether to proceed with additional attempts or discard the insert. By calculating the probability of successful insertions in future attempts, our approach can suitably enhance resource usage and production timelines. The proposed approach is evaluated through stress testing, where three processes insert 212,000 sensor records into Kafka queues at varying throughputs, monitored via Metricbeat for system resource usage. Results show low CPU usage (below 20%), consistent network throughput, and stable average data insertion times after initial peaks, demonstrating the architecture's scalability and efficiency.

“HOW DOES THAT MAKE YOU FEEL?” USING NETWORK SCIENCE TO ESTIMATE AFFECT DYNAMICS ACROSS ADULTHOOD

Abstract Research on affect dynamics has dramatically changed in the past two decades. With the proliferation of ecological momentary assessment and online data collection, methodological and analytical approaches have greatly expanded. One approach is the estimation of emotion networks which can be done through a few lines of R code but requires a great deal of planning and interpretation by the researcher. Network estimation models individual emotions as distinct (i.e., sad or angry) and dimensionally dependent (i.e., sad and angry). This technique can test the independent influence of each emotion and unique associations between every pair of emotions. Critically, network structures (i.e., magnitude of co-fluctuation, independence of items) can be compared both within-individuals and between groups. Our previous findings demonstrate the feasibility and utility of estimating emotional reactivity across the lifespan. Using a video clip elicitor and a sample of 531 people (178 younger, 175 middle-aged, and 178 older adults), emotion networks were used to verify mean affective reactivity before and after the clip while also testing distinct co-occurrences (i.e., mixed emotions). To further demonstrate the usefulness of this approach, we have undertaken a database project that evaluates affective blending in response to 48 video clips. Mirroring the methodology from above, a sample of younger, middle-aged, and older adults rated their emotions before and after each video clip they watched. After summarizing overall reactivity findings, we will discuss how emotion networks offer great utility for disentangling unique and co-occurring emotions and their importance in emotional aging research.

THE ROLE OF PANCASILA EDUCATION TEACHERS IN OVERCOMING BULLYING IN CLASS VLLL STUDENTS AT SMPN 2 WEST KARAWANG

This research falls within the category of descriptive qualitative studies. This research was carried out at SMPN 2 West Karawang. The subjects in this research were Pancasila Education teachers and class VIII students. Methods such as observation, interviews, and documentation are employed in data gathering approaches. In data analysis approaches, data reduction, data display, and the derivation of conclusions are encompassed. Based on the results of this research, bullying for students that occurs in class is students who mock and make fun of their friends by calling their friends by their parents' names. The role of Pancasila Education teachers in overcoming bullying is by reprimanding students and advising students so that students do not bully again. The role of Pancasila Education teachers in overcoming bullying can provide classical and individual guidance. Pancasila Education Teachers can coordinate with students' parents to advise students who bully. Keywords: The Role of Teachers, Overcoming Bullying, Pancasila Education

Geography, Trajectories, and Controls of Coastal Water Quality: More Rapid Improvement in the Shallow Zone of the Chesapeake Bay.

Many coastal ecosystems have suffered from cultural eutrophication and dead zones. In the Chesapeake Bay, water quality degradation is manifested in low dissolved oxygen, poor water clarity, and decreased submerged aquatic vegetation acreage. This research combines long-term monitoring data, science-based assessment methods, and novel data analysis approaches (i.e., machine learning) toward understanding the geography, trajectories, and controls of water quality in the Chesapeake Bay, which provides an example for the assessment and management of complex coastal ecosystems. Results showed that the attainment of water quality standards has improved in both the deep zone and the shallow zone since 1985, but the shallow zone has improved more rapidly. In addition, the attainment trajectory has been affected by mainly external drivers (i.e., nutrient reductions) and, to a lesser extent, internal drivers (i.e., water temperature and stratification). Reductions in nutrient loads would improve attainment, whereas warming and stratification would decrease attainment. Furthermore, scenario analyses demonstrated the importance of managing both nitrogen and phosphorus loads. Overall, the deep zone and the shallow zone showed different trajectories and controls, emphasizing the importance of geographical targeting with management actions.

Analisis Kejahatan Ekonomi dalam Praktik Pinjaman Online: Dampak terhadap Masyarakat dan Strategi Penanggulangan

Technological developments have triggered the emergence of online loan services (Pinjol) that offer easy access to finance. However, illegal practices in this sector, such as fraud, misuse of personal data, and intimidation, pose serious social and economic impacts for society. This study aims to analyze the forms of economic crime in online lending, its impact on society, and effective countermeasure strategies. The descriptive-analytical research method is used with the approach of literature study and empirical data analysis. The results of the study show that the practice of illegal online lending causes financial losses, psychological pressure, and damages trust in the financial system. Weak regulations and low digital literacy in the community exacerbate this situation. Countermeasures include strengthening regulations, law enforcement based on the concepts of Strict Liability, Absolute Liability, and Vicarious Liability, as well as increasing people's financial literacy. In conclusion, a collaborative approach between the government, OJK, law enforcement, and the public is urgently needed to create a safe and responsible online lending ecosystem.

Identifying the Sources of Exchange Rate Exposure: Integrating Rolling Window Regression and Panel Data Analysis Approaches

This article attempts to identify the various sources governing the phenomenon of exchange rate exposure for 336 firms from 2012 to 2022. The study employs the two-step procedure to achieve the desired objective. In the first step, dynamic and time-varying exchange rate exposure coefficients have been estimated using rolling window regression, whereas, in the second step, a fixed-effects model of panel data analysis with Driscoll and Kraay’s (1998) standard errors has been estimated to explore the factors affecting the exchange rate exposure. The study finds a positive relationship between exchange rate exposure and the size of a firm and the level of debt a firm possesses, indicating that corporate hedging in India has not effectively reduced exchange rate risk. Likewise, the firm’s growth, asset utilization, and profitability are the fundamental drivers of growth, whereas export revenue decreases the exchange rate exposure. The study suggests that managers must aim to increase exports to act as an effective tool for hedging the exchange rate risk. Also, financial managers should judiciously use the debt component in the capital structure to avoid adversely affecting the firm’s value.

The Impact of the Inherent Language Complexity on Academic Performance: Using Data Analytic Approach to Profile Diverse Student Learning

The study examines how language complexity in engineering courses affects students' academic performance to create a more inclusive learning environment. While math is crucial, language proficiency also impacts success, often overlooked. We aim to assess language complexity in chemical engineering courses, proposing a methodology to gauge curriculum progression from basic knowledge to problem-solving and design. Chemical engineering courses typically start with core concepts and advance to practical application. Our goal is to establish a method reflecting this progression and identify areas for improvement. Using natural language processing (NLP), we analyze course materials and final exams, defining features like word frequency and syntactic complexity. Performance data from a decade and 1100 graduates validate our analysis, showing increased language complexity in higher-level courses. International students initially outperform citizens, but this diminishes in advanced courses. Our study pioneers a framework for assessing course language difficulty, aiding curriculum evaluation and student support. By integrating NLP and data analytics, it identifies diverse learning challenges, enabling more inclusive education. Future research should expand to include more courses and disciplines, enhancing educational equity and effectiveness.