Database Approach Research Articles

Blue Accounting Literacy Perspective on the Tourism Sector in Indonesia

This research aims to analyze the challenges, role of government, and understanding of stakeholders in managing marine and coastal resources in Indonesia through the Blue Accounting approach. This research uses qualitative methodology with literature analysis of twenty-five relevant articles. The data was analyzed to identify the main challenges, government role, and stakeholder understanding in implementing Blue Accounting. Management of marine and coastal resources in Indonesia faces various challenges, including inadequate infrastructure and low public awareness. The government's role is critical in developing regulations, coordinating conservation efforts, and involving stakeholders. Stakeholder understanding of Blue Accounting and multi-stakeholder collaboration have proven effective in overcoming these challenges and improving resource welfare. This research provides insight into the importance of strong regulation, public education, and collaboration between governments, communities, and the private sector in the management of marine and coastal resources. The practical implications are the need to develop a blue accounting model that is more integrated with information and communication technology to improve the effectiveness of environmental monitoring and management. The study emphasizes the importance of data-based Blue Accounting approaches and multi-stakeholder collaboration in marine and coastal resource management, which has not been much applied in previous research.

Implementasi Program Peningkatan Edukasi di bidang Lingkungan Hidup

The implementation of the Education Improvement Program in the field of Environment is a strategic effort to increase public awareness and knowledge about environmental issues. The program is designed to meet increasingly complex environmental challenges by providing an effective and sustainable education. This study evaluates the effectiveness of various environmental education methods, including seminars, workshops, and media campaigns. Through a data-based approach, the analysis was carried out to measure the impact of the program on changes in participants' attitudes and behaviors towards environmental issues. The results show that interactive and participatory methods are more successful in increasing community understanding and engagement. This finding is expected to be a reference for the development of better environmental education programs in the future.

On political interests and self-serving diversity: How state policymakers envision public service media

ABSTRACT Since 2012, diversity has been a declared pillar of Public Service Media (PSM) organizations worldwide – yet the term lacks conceptual clarity. This article offers an empirical analysis of how diversity is discussed and conceptualized in the process of legislating a new PSM law. Through reflexive thematic analysis of 160 transcripts of Israel’s parliament discussions during 2012–2018, we find that policymakers approach diversity as a crucial feature in PSM organizations, yet seem unable to produce an operationalizable definition of it. Our findings suggest diversity is easily manipulated for political interests – a phenomenon we coin as “self-serving diversity.” The Israeli case herein contributes a data-based approach to theorizing media diversity as a bendable ideology rather than a concrete embracive approach to society.

Perancangan Strategi Digital Marketing Pada Brand Sofia And Jenna Menggunakan Metode SOSTAC

The development of the Muslim Fashion Industry is increasingly rapid every year. This indicates that competition is getting tighter and the potential market for Muslim fashion in Indonesia continues to increase. Sofia and Jenna are one of the business actors operating in the Muslim fashion sector. Sofia and Jenna is a local brand that produces its own clothes (self-manufactured) which was founded in 2020. Even though it has been operating for several years, sales of Sofia and Jenna from January 2023 to October 2023 experienced fluctuations, in fact sales growth was still below Indonesian fashion industry. This is caused by several factors, namely low heart share and suboptimal use of digital marketing platforms, so this affects Sofia and Jenna's sales performance. For this reason, deeper observations were made regarding alternative solutions related to product, place, resource and promotion aspects. Of these four aspects, there is one aspect that shows the roots of the problem more than the other aspects, namely the promotion aspect. This research was conducted using the SOSTAC method which went through six stages, namely Situation Analysis, Objectives, Strategy, Tactics, Action, and Control. The aim of designing this digital marketing strategy is as a promotional medium and is expected to increase sales and marketing performance so that it can increase awareness of the Sofia and Jenna brand. Based on the digital marketing strategy design, this final project produced a design for the use of Social Media Optimization (SMO), Content Marketing, Partnership, Video Marketing, Linktree, User Generated Content (UGC), and Mobile Commerce Optimization tools. Then, the design is visualized using a Strategy Map. Through a structured and data-based approach, Sofia and Jenna are expected to be able to improve their position in the Muslim fashion market, as well as increase competitiveness and consumer loyalty towards the brand.

Identification of Pathogenic Copy Number Variants in Mexican Patients With Inherited Retinal Dystrophies Applying an Exome Sequencing Data-Based Read-Depth Approach.

Retinal dystrophies (RDs) are the most common cause of inherited blindness worldwide and are caused by genetic defects in about 300 different genes. While targeted next-generation sequencing (NGS) has been demonstrated to be a reliable and efficient method to identify RD disease-causing variants, it doesn't routinely identify pathogenic structural variant as copy number variations (CNVs). Targeted NGS-based CNV detection has become a crucial step for RDs molecular diagnosis, particularly in cases without identified causative single nucleotide or Indels variants. Herein, we report the exome sequencing (ES) data-based read-depth bioinformatic analysis in a group of 30 unrelated Mexican RD patients with a negative or inconclusive genetic result after ES. CNV detection was performed using ExomeDepth software, an R package designed to detect CNVs using exome data. Bioinformatic validation of identified CNVs was conducted through a commercially available CNV caller. All identified candidate pathogenic CNVs were orthogonally verified through quantitative PCR assays. Pathogenic or likely pathogenic CNVs were identified in 6 out of 30 cases (20%), and of them, a definitive molecular diagnosis was reached in 5 cases, for a final diagnostic rate of ~17%. CNV-carrying genes included CLN3 (2 cases), ABCA4 (novel deletion), EYS, and RPGRIP1. Our results indicate that bioinformatic analysis of ES data is a reliable method for pathogenic CNV detection and that it should be incorporated in cases with a negative or inconclusive molecular result after ES.

Landscape metrics as predictors of water-related ecosystem services: Insights from hydrological modeling and data-based approaches applied on the Arno River Basin, Italy

The study addresses the challenge of integrating complex landscape-hydrological interactions into predictive models for improved water resource management. The aim is to investigate the effectiveness of landscape metrics—quantitative indices measuring landscape composition and configuration—as predictors of WES in the Arno River Basin, Italy. Utilizing two hydrological models alongside a random forest algorithm, we assessed spatial and temporal variations in water yield, runoff, and groundwater recharge. The findings indicate that landscape metrics derived from high-resolution land use data significantly impact WES outcomes. Specifically, the models demonstrated average landscape metric importances of 16.8 % for spatial and 17.8 % for temporal predictions concerning runoff. For water yield, these averages were 32.9 % spatially and 43.5 % temporally, while groundwater modeling showed importances of 14.09 % spatially and 33.8 % temporally. Key landscape metrics identified include the core area index for broad-leaved forests and the perimeter-to-area ratio for non-irrigated agricultural areas as critical spatial and temporal predictors of water yield and groundwater recharge. Thresholds were observed, indicating landscape configurations that minimize hydrological variability. For instance, runoff variation is minimal when the landscape exhibits high forest fragmentation (over 1000 coniferous patches), low aggregation (aggregation index <75), and reduced connectivity (cohesion index under 80). Similarly, groundwater variation is minimized with decreased boundary length of vegetation patches (perimeter-to-area ratio <0.8), agricultural lands (perimeter-to-area ratio under 1), and the presence of low core agricultural areas (core area index above 8). The identified thresholds could inform land-use policies, such as targeted afforestation or crop diversification strategies, to optimize WES provision.

Transcriptomic signatures during normothermic liver machine perfusion correspond with graft quality and predict the early graft function

A better understanding of the molecular events during liver normothermic machine perfusion (NMP) is warranted to develop a data-based approach for the identification of biomarkers representative of graft quality and posttransplant outcome. We analysed the dynamic transcriptional changes during NMP and linked them to clinical and biochemical parameters. 50 livers subjected to NMP for up to 24h were enrolled. Bulk RNA sequencing was performed in serial biopsies collected pre and during NMP, and after reperfusion. Perfusate was sampled to monitor liver function. qPCR and immunohistochemistry were performed to validate findings. Molecular profiles were compared between transplanted and non-transplanted livers, and livers with and without early allograft dysfunction. Pathways related to immune and cell stress responses, cell trafficking and cell regulation were activated during NMP, while cellular metabolism was downregulated over time. Anti-inflammatory responses and genes involved in tissue remodelling were induced at later time-points, suggesting a counter-response to the immediate damage. NMP strongly induced a gene signature associated with ischemia-reperfusion injury. A 7-gene signature corresponds with the benchmarking criteria for transplantation or discard at 6h NMP (area under curve 0.99). CD274 gene expression (encoding programmed cell-death ligand-1) showed the highest predictive value. LEAP2 gene expression at 6h NMP correlated with impaired graft function. Assessment of gene expression markers could serve as a reliable tool to evaluate liver quality during NMP and predicts early graft function after transplantation. The research was supported by "In Memoriam Dr. Gabriel Salzner Stiftung", Tiroler Wissenschaftsfond, Jubiläumsfonds-Österreichische Nationalbank and MUI Start grant.

From GIS to Graphical Representation for Maintaining Connectivity of Wastewater Network Elements

Wastewater network management is a critical aspect of ensuring public health and environmental sustainability. Geographic Information Systems (GIS) are widely employed for managing and analyzing wastewater network data. A Shapefile is a model for representing vector data in GIS. In this model, different databases are used to store various types of geometric data, including points, lines, and polygons. For instance, the components of wastewater networks are stored in different databases. However, this approach presents difficulties in accurately depicting the connectivity among the wastewater network components that are physically interconnected in reality. To address this issue, this paper discusses the limitations of the separate databases approach for representing wastewater networks in Shapefile and proposes a novel graph-based approach. In this approach, each component of the network, such as manholes, structures, pumps, etc., is represented as a node in the novel graph, while the pipes represent the connections between them. By adopting the proposed representation, the real interconnected nature of the wastewater network can be effectively captured and visualized. The validation of this approach, using five real datasets, confirms its ability to connect the various components of the wastewater network via a graph-based representation.

Development of a High-Resolution Indoor Radon Map Using a New Machine Learning-Based Probabilistic Model and German Radon Survey Data.

Radon is a carcinogenic, radioactive gas that can accumulate indoors and is undetected by human senses. Therefore, accurate knowledge of indoor radon concentration is crucial for assessing radon-related health effects or identifying radon-prone areas. Indoor radon concentration at the national scale is usually estimated on the basis of extensive measurement campaigns. However, characteristics of the sampled households often differ from the characteristics of the target population owing to the large number of relevant factors that control the indoor radon concentration, such as the availability of geogenic radon or floor level. Furthermore, the sample size usually does not allow estimation with high spatial resolution. We propose a model-based approach that allows a more realistic estimation of indoor radon distribution with a higher spatial resolution than a purely data-based approach. A multistage modeling approach was used by applying a quantile regression forest that uses environmental and building data as predictors to estimate the probability distribution function of indoor radon for each floor level of each residential building in Germany. Based on the estimated probability distribution function, a probabilistic Monte Carlo sampling technique was applied, enabling the combination and population weighting of floor-level predictions. In this way, the uncertainty of the individual predictions is effectively propagated into the estimate of variability at the aggregated level. The results show an approximate lognormal distribution of indoor radon in dwellings in Germany with an arithmetic mean of , a geometric mean of , and a 95th percentile of . The exceedance probabilities for 100 and are 12.5% (10.5 million people affected) and 2.2% (1.9 million people affected), respectively. In large cities, individual indoor radon concentration is generally estimated to be lower than in rural areas, which is due to the different distribution of the population on floor levels. The advantages of our approach are that is yields a) an accurate estimation of indoor radon concentration even if the survey is not fully representative with respect to floor level and radon concentration in soil, and b) an estimate of the indoor radon distribution with a much higher spatial resolution than basic descriptive statistics. https://doi.org/10.1289/EHP14171.

Optimal Control of Material Microstructures

Abstract In this paper, we consider the optimal control of material microstructures. Such material microstructures are modeled by the so-called phase-field model. We study the underlying physical structure of the model and propose a data-based approach for its optimal control, along with a comparison to the control using a state-of-the-art reinforcement learning (RL) algorithm. Simulation results show the feasibility of optimally controlling such microstructures to attain desired material properties and complex target microstructures.

What influences the implementation of clinical guidelines related to enteral nutrition in the intensive care unit: A mixed-methods systematic review.

To systematically evaluate and analyse literature concerning the factors influencing the implementation of clinical practice guidelines related to enteral nutrition in the adult intensive care unit. Guidelines serve as crucial tools for guiding clinical practice. However, a significant gap persists between current clinical practice and guidelines pertaining to enteral nutrition. It is essential to identify the reasons behind this disparity to foster clinical transformation. A mixed-methods systematic review. A systematic search was conducted across PubMed, Embase, Medline, Cochrane, PsycINFO and CNKI databases to identify impediments and facilitators to the implementation of ICU clinical practice guidelines related to enteral nutrition. The types of studies included quantitative, qualitative and mixed-methods studies. The search spanned from January 2003 to January 2024 and was updated in May 2024. The quality assessment of the included literature was conducted using the Mixed-Methods Study Evaluation Tool (MMAT). Data analysis was performed using a data-based convergent integration approach. The protocol for this study was prospectively registered (PROSPERO2023, CRD42023483287). Twenty papers were finally included, and 65 findings were extracted, integrating a total of three categories, Category 1: healthcare provider factors, including three sub-themes: knowledge of guideline-related knowledge and awareness of guideline application; social/professional roles and identity domains; beliefs, attitudes and self-efficacy; collaboration, Category 2: practice environments, including two sub-themes: environmental factors and resource areas; systems and behavioural norms, Category 3: patient values and nutritional support preferences including two sub-themes: patient disease status and value orientation. Healthcare professionals should analyse obstacles and facilitators to guideline implementation from multiple perspectives, strengthen healthcare collaboration, improve education and training systems, correct misperceptions and increase awareness of evidence-based practice.

Effect of Culture Temperature on 2-Methylisoborneol Production and Gene Expression in Two Strains of Pseudanabaena sp.

The presence of the odorant 2-methylisoborneol (2-MIB) in drinking water sources is undesirable. Although 2-MIB production is known to be influenced by temperature, its regulation at the gene level and its relationship with Chlorophyll-a (Chl-a) at different temperatures remain unclear. This study investigates the impact of temperature on 2-MIB production and related gene expression in Pseudanabaena strains PD34 and PD35 isolated from Lake Paldang, South Korea. The strains were cultured at three temperatures (15, 25, and 30 °C) to examine cell growth, 2-MIB production, and mic gene expression levels. 2-MIB production per cell increased with higher temperatures, whereas mic gene expression levels were higher at lower temperatures, indicating a complex regulatory mechanism involving post-transcriptional and enzyme kinetics factors. Additionally, the relationship between Chl-a and 2-MIB involved in metabolic competition was analyzed, suggesting that high temperatures appear to favor 2-MIB synthesis more than Chl-a synthesis. The distinct difference in the total amount of the two products and the proportion of 2-MIB between the two strains partially explains the variations in 2-MIB production. These findings highlight the significant effect of temperature on 2-MIB biosynthesis in Pseudanabaena and provide a valuable background for gene data-based approaches to manage issues regarding 2-MIB in aquatic environments.

Beast DB: A Computational Electrocatalysis Database with Solvation, Grand-Canonical Applied Potential, and Beyond-DFT Reaction Energetics

Electrocatalytic reactions are key to decarbonizing the world economy, but progress in understanding electrocatalytic systems is often hindered by the diversity of conditions under which experiments are run and calculations are performed. This diversity is most often a result of experimental innovation to achieve better electrocatalyst performance, but nonetheless makes it difficult to establish clear baselines for electrocatalytic performance and to understand the fundamental factors that govern changes in performance. In order to make clear comparisons between different electrochemical configurations routine, we have constructed BEAST DB: the Beyond-DFT Electrochemistry with Accelerated and Solvated Techniques database. BEAST DB contains the most promising electrocatalysts for CO2reduction (CO2RR), the oxygen reduction reaction (ORR), the oxygen evolution reaction (OER), the hydrogen evolution reaction (HER), and nitrogen reduction reaction (NRR). For each electrocatalyst and chemistry, we have computed the adsorption energies, 𝛥Gads, and electronic properties for the most relevant catalyst facets and applied potentials. In this talk, we discuss general trends observed from this high-throughput database approach, and examine a few catalysts and chemistries in detail. Additionally, we describe our delta learning approach to learn high-fidelity reaction energetics from less expensive, lower-fidelity DFT calculations and applications of it to electrocatalytic systems. We illustrate how calculations of potential-dependent reaction energetics, as well as observables such as oxidation state and vibrational frequencies, allows for detailed comparisons to experiments such as X-ray absorption spectroscopy (XAS), Surface-enhanced infrared absorption spectroscopy (SEIRAS), and cyclic voltammetry. BEAST DB provides a wealth of information for both computationalists and experimentalists in the field of electrocatalysis, and provides a foundation for exploration of the underlying factors that govern electrocatalytic performance across a wide range of catalysts and chemistries.

The Peptidoglycan Recognition Protein 1 confers immune evasive properties on pancreatic cancer stem cells

ObjectivePancreatic ductal adenocarcinoma (PDAC) has limited therapeutic options, particularly with immune checkpoint inhibitors. Highly chemoresistant ‘stem-like’ cells, known as cancer stem cells (CSCs), are implicated in PDAC aggressiveness. Thus, comprehending...

Integrating human factors into the distribution model of goods and fast-moving consumer goods for effective inventory control

Multiple factors contribute to the occurrences of disruptions in workflow management within the e-commerce system that focuses on the distribution of goods and other fast-moving consumer goods (FMCG). These disruptions have particularly affected and increased lead times and caused frequent shipment delays. The causes of these disruptions, especially in the context of distribution of goods and FMCG towards inventory control, have been widely speculated upon in recent years. Given the recent interruptions, the study presented in this paper used a methodology called statistical model analysis (SMA) to study the impact of human factors (HFs) on the system performance in a warehouse distribution system. A case study was taken on a particular company called ABC and XYZ to model the impact of some specific HF such as job fatigue, and job rotation to name a few and to uncover the underlying reasons behind the ongoing disruptions within the distribution system. Specifically, the result of this study aims to provide a data-driven understanding of these issues and one of the contributions of this study is to enhance our understanding of the significance of these HFs, rather than focusing on the equipment and materials as seen in prior research. Through this data-based approach study, stakeholders in operations management, e-commerce, and the supply chain system would be well-informed in many ways to resolve the challenges faced by humans in the system towards enhancing their overall system performance.

Evolution of relative density and shear wave velocity in non-compacted embankment layers: Geological long-term monitoring

This study proposes a novel method for assessing the evolution of relative density and shear wave velocity that vary with depth and effective stress in non-compacted embankment layers. Embedded soil stiffness measurement devices were used to monitor the shear waves at each stage of filling at the test site, and comprehensive laboratory compaction tests were conducted using a floating ring oedometer cell to reproduce real field conditions with minimal friction. Assuming a constant soil unit weight γ with 7% water content, the effective stress σ’ is proportional to depth z (i.e., σ’ = γ·z). Field test results were compared with the shear wave velocity relationships determined in laboratory tests for four samples of differing relative density (30%, 50%, 70%, and 90%), indicating that the relative density at the bottom of the embankment varied between 15% and 48%. However, the constant soil unit weight applied in this study can underestimate the relative density of the embankment, causing a prediction error if the self-weight compaction of the embankment is not minute. Therefore, this study provides guidelines based on a physics-inspired and data-based approach and anticipates the relative density and shear wave velocity analyzed in the context of depth in non-compacted embankment layers. The physics-based data analyses suggested in this study can be used in first-order estimations to assess the relative density and shear wave velocity evolution in non-compacted embankment layers.

Cooperative Path Following Control in Autonomous Vehicles Graphical Games: A Data-Based Off-Policy Learning Approach

Cooperative Path Following Control in Autonomous Vehicles Graphical Games: A Data-Based Off-Policy Learning Approach

A realist review of the causes of, and current interventions to address 'missingness' in health care.

This protocol describes a realist review exploring the problem of "missingness" in healthcare, defined as the repeated tendency not to take up offers of care that has a negative impact on the person and their life chances. More specifically, the review looks at the phenomenon of patients missing multiple appointments in primary care in the UK - at the causal factors that influence how patients come to be "missing" in this way, and what interventions might support uptake and "presence" in healthcare. Background research informing this project suggests that a high rate of missed appointments predicted high premature death rates, and patients were more likely to have multiple long-term health conditions and experience significant socioeconomic disadvantage. Most research in this field focuses on population- or service-level characteristics of patients who miss appointments, often making no distinction between causes of single missed appointments and of multiple missed appointments. There have therefore been no interventions for 'missingness', accounting for the complex life circumstances or common mechanisms that cause people to repeatedly miss appointments. We use a realist review approach to explore what causes missingness - and what might prevent or address it - for whom, and in what circumstances. The review uses an iterative approach of database searching, citation-tracking and sourcing grey literature, with selected articles providing insight into the causal dynamics underpinning missed appointments and the interventions designed to address them. The findings of this review will be combined with the findings of a qualitative empirical study and the contributions of a Stakeholder Advisory Group (STAG) to inform the development of a programme theory that seeks to explain how missingness occurs, whom it affects and under what circumstances. This will be used to develop a complex intervention to address multiple missed appointments in primary care. CRD42022346006.

Precision Nutrition Management in Continuous Care: Leveraging AI for User-Reported Dietary Data Analysis.

A As health technology advances, this study aims to develop an innovative nutritional intake management system that integrates artificial intelligence technology and social media software to achieve precise analysis of patient-generated data and comprehensive management in continuous care. Our system is built on the Line Bot platform, allowing users to easily and intuitively obtain detailed analyses of their individual nutritional intake by reporting dietary information. While users report their dietary habits through the Line Bot, our AI model conducts real-time analysis of nutrient intake, providing personalized nutritional recommendations. This instantaneous feedback not only enhances user engagement in nutritional management but also aids in establishing healthy habits. Additionally, through integration with social media software, our system facilitates information sharing and community support among users, promoting the exchange of nutritional knowledge and mutual assistance. This study further explores the specific needs of patients with chronic diseases, collecting individual data on chronic conditions and total nutritional intake. Based on the nutritional intake guidelines proposed by the Health Promotion Administration in Taiwan, more precise nutritional management recommendations are provided to meet the unique health needs of each patient. This study introduces a comprehensive, patient-generated data-based approach for precision nutrition management in continuous care. By integrating artificial intelligence, social media software, and data analysis, our system not only offers effective tools for monitoring and managing patients' nutritional intake but also fosters interaction and support among patients, driving the implementation of continuous care practices.

Impact of Hypoglycemia on Hospitalized Patients With Hepatocellular Carcinoma.

Aims Hepatocellular carcinoma (HCC) is one of the common liver malignancies that presents a challenge to global healthcare. The impact and outcomes of hypoglycemia in HCC have not been studied in detail before. This study aimed to investigate the outcomes and prognosis associated with hypoglycemia in patients diagnosed with HCC, utilizing a large-scale database approach. Methods Using the Nationwide Inpatient Sample (NIS) database from 2017 to 2020, we conducted a comprehensive retrospective analysis to examine the incidence, risk factors, and clinical implications of hypoglycemia on HCC patients. The patients were divided into two groups: those with hypoglycemia and those without hypoglycemia. Univariate and multivariate logistic regression were used to conduct the analysis. STATA® version 17.0 software(StataCorp LLC, College Station, TX) was used for this purpose. Results Out of a total of 343,895 patients with HCC, the prevalence of hypoglycemia was present in 1.5% of this patient population. We found that hypoglycemia was common in the male population (68%). Compared with patients without hypoglycemia, patients who had hypoglycemia with HCC had higher mortality (42%, p-value < 0.05) and higher risks of secondary outcomes such as hepatic failure, spontaneous bacterial peritonitis (SBP), ascites, and portal vein thrombosis compared to patients who did not have hypoglycemia. The multivariate-adjusted odds ratio for hepatic failure was 2.7 (2.3-3.1), for SBPwas 2.9 (1.8-3.0), for ascites was 1.6 (1.4-1.9), and for portal vein thrombosis was 1.2 (0.9-1.4). Conclusion In conclusion, hypoglycemia in HCC is associated with increased mortality and worse outcomes.