Qualified Data Research Articles

Mechanism-based and data-driven approach to developing the constitutive model of viscoelastic elastomers

Constitutive modeling of viscoelastic elastomers has been an active field for decades. In this work, we develop a mechanism-based and data-driven method to develop constitutive models of viscoelastic elastomers under large deformation. Based on the theory of finite deformation viscoelasticity, the feature of strain energy density function is utilized when we design the machine learning architecture, which allows for fast generation of qualified artificial data to train artificial neural networks (ANNs). According to the typical microstructures of elastomers, three groups of ANNs are established to determine the strain energy density functions of the hyperelastic and viscous polymer networks, which are further tested by experimental data of our own and those in the literature. The machine learning architecture also allows for flexible expansion of the ANN database to consider newly-developed elastomers. The developed constitutive model of the material automatically satisfies the laws of thermodynamics and can be easily implemented in finite element analysis for more complex structures and loading conditions. The developed numerical and experimental framework provides an efficient paradigm for constitutive modeling of viscoelastic elastomers.

Development of a minimum data set for a blockchain-based personal health records (PHRs), for patient/physician interaction in family medicine

BACKGROUND: The primary requirement for a capable patient health record (PHR) that can interact appropriately with the family medicine system and collect and share qualified data is a minimum data set (MDS) aligned with family medicine’s functions and goals. The aim of this research was to determine the MDS for a blockchain-based PHR system that can effectively interact with family medicine providers and collect and share qualified data. This MDS is intended to be applicable to all members of the community covered by family medicine. MATERIALS AND METHODS: This applied study was conducted in 2022 in a cross-sectional and descriptive approach in three phases. In the first phase, a content analysis related to the research objectives was conducted in scientific databases, search engines, and websites of the centers and scientific research organizations with publications and policy-making in this field. Consequently, 11 studies were selected for use in the second and third phases. In the second phase, to determine information needs, a researcher-developed questionnaire including 17 classes was given to 50 people under the cover of the family medicine plan in Shiraz city. By choosing one of the two options “Yes” or “No” by them, the necessary data classes were determined. In the third phase, the second researcher-developed questionnaire was designed and administered to 100 family physicians in Shiraz city. This questionnaire included the data elements corresponding to the data classes approved in the previous phase. The family physicians were asked to rate the importance of each data element using a Likert scale with five options, ranging from “very unimportant” to “very important.” The necessary data elements were determined based on these scores. RESULTS: In the first questionnaire, 16 of the 17 data classes received approval from individuals covered by the family medicine plan. Consequently, a questionnaire comprising 16 classes and 105 data elements was administered to the family physicians. Ultimately, the MDS was determined to include 16 classes and 72 data elements. CONCLUSIONS: Determining essential data elements, especially for patient/physician interaction in family medicine, should be such that they can be managed by the person while being comprehensive and providing sufficient help to the physician during the treatment process. This MDS can be used to interact with and refer PHRs to other physicians and specialists, as well as help interoperability between the PHR and other health systems, such as hospital information systems (HIS) and electronic health records (EHRs).

Genetic predisposition to impaired beta-cell function modifies the association between serum pyrethroid levels and the risk of type 2 diabetes: A gene-environment interaction study

Previous studies suggested that pyrethroid exposure was associated with elevated type 2 diabetes (T2D) risk, while it remains uncertain whether genetic predisposition modifies this association. A nested case-control study within the prospective Dongfeng-Tongji cohort comprised 1832 T2D cases, age- (±5 years) and sex-matched controls with qualified genotyping data. Serum pyrethroids were measured by gas chromatography-tandem mass spectrometry. Overall diabetes-related genetic risk score (GRS) or pathway-specific GRS, including unweighted GRSs (uGRS) and weighted GRSs (wGRS), was developed by genetic variants identified in Asian populations. Higher overall diabetes-related GRS and GRS specific to the pathway of impaired beta cell function (Beta-cell GRS) were associated with a higher incident T2D risk. Beta-cell uGRS significantly modified the association of serum permethrin (Pinteraction=0.04) and deltamethrin (Pinteraction=0.01) with T2D. Specifically, for each doubling increase in serum deltamethrin, the odds ratios (ORs) (95 % confidence intervals [CIs]) for T2D were 1.23 (0.98–1.56) and 0.91 (0.77–1.07) in the highest and lowest Beta-cell uGRS group, as well as 1.23 (1.02–1.47) and 0.95 (0.78–1.15) for Beta-cell wGRS group, respectively. When considering jointly, those with the highest deltamethrin levels and highest Beta-cell GRS had a substantially higher T2D risk, compared with the reference group (OR for uGRS: 3.79 [95 % CI: 2.03–7.07], Pinteraction=0.03 and 3.23 [95 % CI: 1.78–5.87], Pinteraction=0.05 for wGRS). Our findings suggested that genetic susceptibility to impaired beta-cell function should be considered for T2D prevention targeting pyrethroid exposure, particularly deltamethrin.

The safety and efficacy of third- and fourth-generation cryoballoons for atrial fibrillation: a systematic review and meta-analysis.

An increasing number of studies have shown that third (CB3)- and fourth-generation cryoballoons (CB4) have been used to treat various types of atrial fibrillation (AF), but previous research regarding the safety and efficacy of CB3 or CB4 ablation remains controversial. Therefore, a meta-analysis was performed to further evaluate the safety and efficacy of pulmonary vein isolation (PVI) using the CB3 and CB4 in the treatment of AF. We searched PubMed, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, Wanfang, China Science and Technology Journal Database, and Clinicaltrials.gov up to December 2023 for qualified trials and data extraction according to inclusion and exclusion criteria. All analyses were carried out using Review Manager 5.3 software. The meta-analysis included 13 observational studies consisting of 3,281 subjects and did not include a randomized controlled trial. Overall analyses indicated that the CB3 significantly reduced total procedure time [weighted mean difference (WMD) = -8.69 min, 95% confidence interval (CI) = -15.45 to -1.94 min, I2 = 93%], increased the PVI recording [relative risk (RR) = 1.24, 95% CI = 1.03-1.49, I2 = 90%], and increased the mean nadir temperature of overall PVs (WMD = 2.80°C, 95% CI = 1.08-4.51°C, I2 = 89%) compared with the CB2. Moreover, the CB4 significantly reduced the total procedure time (WMD = -14.50 min, 95% CI = -20.89 to -8.11 min, I2 = 95%), reduced the fluoroscopy time (WMD = -2.37 min, 95% CI = -4.28 to -0.46 min, I2 = 95%), increased the PVI recording (RR = 1.40, 95% CI = 1.15-1.71, I2 = 90%) compared with the CB2. Time-to-isolation, the success rate of PVI, AF recurrence, and complications in the CB3 and CB4 were not significantly different compared with the CB2. These findings demonstrated that the CB3 and CB4 tended to be more effective than the CB2 in the treatment of AF, with shorter procedure times, more PVI recording, and similar safety endpoints.

Development and application of Breadth-Depth-Context (BDC), a conceptual framework for measuring technology engagement with a qualified clinical data registry.

Despite the proliferation of dashboards that display performance data derived from Qualified Clinical Data Registries (QCDR), the degree to which clinicians and practices engage with such dashboards has not been well described. We aimed to develop a conceptual framework for assessing user engagement with dashboard technology and to demonstrate its application to a rheumatology QCDR. We developed the BDC (Breadth-Depth-Context) framework, which included concepts of breadth (derived from dashboard sessions), depth (derived from dashboard actions), and context (derived from practice characteristics). We demonstrated its application via user log data from the American College of Rheumatology's Rheumatology Informatics System for Effectiveness (RISE) registry to define engagement profiles and characterize practice-level factors associated with different profiles. We applied the BDC framework to 213 ambulatory practices from the RISE registry in 2020-2021, and classified practices into 4 engagement profiles: not engaged (8%), minimally engaged (39%), moderately engaged (34%), and most engaged (19%). Practices with more patients and with specific electronic health record vendors (eClinicalWorks and eMDs) had a higher likelihood of being in the most engaged group, even after adjusting for other factors. We developed the BDC framework to characterize user engagement with a registry dashboard and demonstrated its use in a specialty QCDR. The application of the BDC framework revealed a wide range of breadth and depth of use and that specific contextual factors were associated with nature of engagement. Going forward, the BDC framework can be used to study engagement with similar dashboards.

DETERMINANTS OF CONSUMER’ PREFERENCES FOR ROBOT SERVICE HOTEL: AN EXPERIMENT AMONG VIETNAMESE YOUNG TRAVELLERS

The study intends to determine prominent antecedents of Vietnamese young travelers’ intentions to book robot service hotels during and after the health pandemic. The study aims to leverage the context to uncover the guests’ preferences toward a secure option when a health crisis is salient. A quantitative research method was employed to collect qualified data. The extended TAM theory was employed to develop research hypotheses. SPSS and AMOS version 24 were used to analyze data and confirm the research hypotheses. The study found that guests’ booking intention toward robot service hotels during the health pandemic played a mediation role in linking the influence of social distancing concerns, subjective threat norms, perceived trust, and consumer cosmopolitanism on their intentions after the health pandemic. The study provides a valuable understanding of the determinants of Vietnamese young travelers’ preferences for robot-service hotels. Moreover, the study highlights the significance of situation in deciding guests’ intentions, and thus it is strongly recommended that practitioners take context into consideration when sharpening their strategies.

Buffeting reliability of high-rise bridge tower in mountain area based on CNN-BiLSTM

In recent years, the wind-induced vibration of structures has received a great deal of scholarly attention due to the increasing scale of large structures. In this study, an algorithm that combines a stochastic pseudo excitation method (SPEM), a convolutional neural network (CNN) and a bidirectional long short-term memory network (BiLSTM), named as SPEM-CNN-BiLSTM (SCBL), is proposed. The SCBL is a surrogate model consisting of three different computational modules, in which the SPEM can provide training samples for CNN-BiLSTM (CBL) network architecture, a convolutional layer extracts features of stochastic parameters and a BiLSTM handles time histories of bridge response. The accuracy of the SCBL prediction results is ensured by the set error threshold. The model also accounts for structural uncertainty in the inputs and is adapted during the training step to be more suitable for reliability analysis. According to the output qualified prediction data, the dynamic reliability based on the 99 % threshold is calculated by obtaining its probability density function, and the wind-induced vibration responses of uncertain bridge towers are studied. Firstly, the wind environment of the bridge tower was analyzed, and the average wind speed of 20 m/s among the bridge area was obtained by field measurements, and Computational Fluid Dynamics (CFD). Secondly, the proposed artificial neural network algorithm is applied to bridge jitter reliability analysis and compared with the traditionally recognized methods: the Monte Carlo method (MCM) and the stochastic pseudo excitation method (SPEM). The results of SCBL calculations are closer to the results of MCM than those of other methods. Additionally, the computational efficiency of the method is improved by 40 times over MCM and 2 times over SPEM. Finally, the data generated by the SCBL method will be used for reliability analysis. Uncertainties in structure, wind speed, and yaw angle all contribute to the uncertainty and dispersion of the stochastic response prediction. Among them, the uncertainty in wind speed has the greatest impact on the reliability.

Patient-reported symptomatic events do not adequately reflect atrial arrhythmia

BackgroundManagement of atrial fibrillation is frequently geared toward improving symptoms. Yet, the magnitude of symptom-rhythm discordance is not well known in the setting of monitoring by ambulatory electrocardiography (AECG). ObjectiveWe aimed to quantify the symptom-rhythm correlation (SRC) for atrial arrhythmia (atrial tachycardia/atrial fibrillation [AT/AF]) events. MethodsThis was a retrospective cohort analysis of AECG data at a tertiary care center. All AECGs of ≥7 days with at least 1 AT/AF were included. Patient-triggered symptoms included shortness of breath, tiredness, palpitations, dizziness, or passing out with or without concurrent AT/AF. SRC was calculated for each patient. In addition, AT/AF-symptom association was evaluated at the event level by multivariable mixed effects logistic regression. ResultsWe identified 742 patients with qualifying AECG data; mean age was 64 years, 50% were female, and 22% had heart failure. The mean CHA2DS2-VASc score was 2.5. There were 6289 symptomatic events and 6900 AT/AF episodes. Of symptomatic events, 1013 (16%) had shortness of breath, 839 (13%) tiredness, 2640 (42%) palpitations, 783 (12%) dizziness, and 93 (1%) passing out. Overall SRC was 0.39 (range, 0–1.0), but presence of AT/AF increased odds of symptoms by ∼8.3 times in adjusted analyses (P < .01). In multivariable analysis, prior AF rhythm control treatment and lower heart rate were associated with worse SRC (P < .01). ConclusionWhereas AT/AF events increase the chances of symptoms, there is poor overall correlation between symptomatic events and documented AT/AF. Patient factors and prior treatments influence SRC. An improved understanding of this relationship correlation is needed to optimize clinical outcomes and to improve the rigor of AF research.

Effects of long-term antipsychotic medication on brain instability in first-episode schizophrenia patients: a resting-state fMRI study

Early initiation of antipsychotic treatment plays a crucial role in the management of first-episode schizophrenia (FES) patients, significantly improving their prognosis. However, limited attention has been given to the long-term effects of antipsychotic drug therapy on FES patients. In this research, we examined the changes in abnormal brain regions among FES patients undergoing long-term treatment using a dynamic perspective. A total of 98 participants were included in the data analysis, comprising 48 FES patients, 50 healthy controls, 22 patients completed a follow-up period of more than 6 months with qualified data. We processed resting-state fMRI data to calculate coefficient of variation of fractional amplitude of low-frequency fluctuations (CVfALFF), which reflects the brain regional activity stability. Data analysis was performed at baseline and after long-term treatment. We observed that compared with HCs, patients at baseline showed an elevated CVfALFF in the supramarginal gyrus (SMG), parahippocampal gyrus (PHG), caudate, orbital part of inferior frontal gyrus (IOG), insula, and inferior frontal gyrus (IFG). After long-term treatment, the instability in SMG, PHG, caudate, IOG, insula and inferior IFG have ameliorated. Additionally, there was a positive correlation between the decrease in dfALFF in the SMG and the reduction in the SANS total score following long-term treatment. In conclusion, FES patients exhibit unstable regional activity in widespread brain regions at baseline, which can be ameliorated with long-term treatment. Moreover, the extent of amelioration in SMG instability is associated with the amelioration of negative symptoms.

Big Data Analytics: Future of Database

Abstract: Big data analytics is a revolutionary method for processing massive and complicated datasets, analysing them, and drawing important conclusions from them. Big data's introduction has caused a paradigm change in data management and analysis, giving businesses previously unheard-of opportunity to gain insightful information and make informed decisions. This study explores how big data analytics affects how well organization’s function, concentrating on the methods and tools applied across a range of sectors. We undertook an empirical analysis of case studies from businesses in the retail, healthcare, and financial sectors after conducting a thorough assessment of the literature. According to our research, using big data analytics leads to better decision-making, increased operational effectiveness, and a stronger competitive advantage. Data quality, analytics tools, and corporate culture are the main success determinants. However, issues with data privacy and the necessity for qualified data experts make the effective application of big data analytics difficult. This study adds to our understanding of big data analytics' revolutionary potential and offers useful information for practitioners and decision-makers who want to use data to their advantage.

Empowering Internal Control: A Case Study in Leveraging Data and Technologies

This essay responds to the PCAOB's call for insights into data analytics and emerging technologies in auditing by developing a Social Construction of Technology (SCOT) framework to embed data analytics and technologies to improve internal control performance. SCOT outlines four phases of institutionalization: problem definition, interpretative flexibility management, stabilization, and social construction. This paradigm is validated through a documented procedure of institutionalizing a data-driven analyticproject optimizing the purchase-to-payment process at a state university. The study used a value-focused thinking approach to observe and identify the values at stake in the decisions about how communication among IT, auditors, and management impacts the adoption of technologies. The collected evidence highlights the importance of identifying suitable algorithms and qualified data as the starting point for technology’s institutionalization. Additionally, the case study demonstrates mechanisms for preparingan integrated and transparent data platform, emphasizing the role of integrating relevant social groups and technology in circulating emerging technology. This study offers a practical roadmap about how to fit AI and technologies into an institutional social management ecosystem. It contributes to the literature by addressing the emerging technology usage trap and improving analytics performance, offering practical value through a social construct roadmap for institutionalizing emerging technologies.

Pelatihan Metodologi Penelitian Analisis Data Menggunakan Microsoft Excel dan SPSS di STIE Tribhakti Bekasi

Carrying out research is an obligation carried out by lecturers and students at universities. However, a good research implementation must of course be supported by an understanding of research methodology and the use of available and qualified data processing program applications. The results of interviews conducted by the community service team of the Institute of Business and Informatics Kwik Kian Gie indicate that thesis supervisors and students who compose theses at STIE TRIBHAKTI Bekasi really need training related to understanding research methodologies and using data processing programs in this case Excel and SPSS and how interpret the output of the processed results. Based on this background, a training on research methodology and data analysis using Excel and SPSS was held on Saturday, March 31, 2021 at 13.30 – 16.30. This activity is carried out online using Google Meet virtual media. The material presented includes univariate, bivariate, multivariate, and path analysis. One part of multivariate analysis is multiple regression. After the training was given, the thesis supervisors and students who wrote the thesis at STIE TRIBHAKTI benefited from increasing knowledge and understanding of research methodology and becoming more skilled in using Microsoft Excel and SPSS data processing applications, so that this can be a motivation that moves lecturers and students to do research well.

Poorer Outcomes in Bladder Cancer Patients With Diabetes: A Systematic Review and Meta-analysis Addressing Over 226,472 Bladder Cancer Patients

Purpose: Diabetes mellitus, a frequent comorbid condition in cancer patients, has been shown to increase risk of all-site cancer mortality. This relationship has not been systematically studied in bladder cancer patients. This systematic review and meta-analysis aimed to identify, evaluate, and synthesize available evidence on the relationship between history of diabetes and outcomes in bladder cancer patients.Materials and Methods: Systematic searches interrogated OVID MEDLINE, Embase, Web of Science, Google Scholar, and Cochrane Library to identify scholarly reports relating diabetes to all-cause mortality, bladder cancer-specific mortality, recurrence, and progression in bladder cancer patients. After critical review, meta-analysis was used to quantitively synthesize qualifying data and assess potential influence of publication bias, clinical heterogeneity, and residual confounding.Results: We synthesized data on over 226,472 patients treated with curative intent uniquely represented in 28 studies that met quality metrics. Having diabetes was positively associated with each outcome. Hazard ratio estimates were indistinguishable for mortality from any cause, 1.22 (95% confidence interval [CI], 1.12–1.33) and bladder cancer-specific mortality, 1.28 (95% CI 1.17–1.41) and notably stronger in patients with muscle-invasive and high-risk non–muscle-invasive bladder cancer, 1.32 (95% CI, 1.15–1.50) and 1.48 (95% CI, 1.06–2.06). Neither publication bias, systematic error, nor confounding by factors such as smoking or obesity is likely to explain the observed associations.Conclusions: Bladder cancer patients with diabetes experience elevated mortality that is not explained by diabetes-related comorbidities or complications. Future research should explore type, severity, and duration of diabetes in relation to unfavorable bladder cancer outcomes.

Pengaruh Deferred Tax Dan Tingkat Utang Terhadap Tax Avoidance Pada Perusahaan Yang Terdaftar Di Indeks Sri-Kehati 2021-2023

The aim of this research is to find the effect of deferred taxes and debt ratios on tax avoidance in companies listed on the SRI-KEHATI Index on the Indonesia Stock Exchange throughout the period 2021 to 2023. This research uses descriptive methodology with a quantitative approach. This research data comes from secondary data sources, specifically obtained from www.idx.com. The study encompasses a population of 25 companies, selected through purposive sampling methodology, This leads to the selection of a sample comprising 20 companies observed over a span of three years period (2021-2023) with 60 qualifying data points. Data analysis employs Utilizing descriptive statistical tests in conjunction with classical assumption tests, the study employs multiple linear regression analysis, Conducting hypothesis testing through the utilization of F and t tests, along with determination coefficient assessments.The findings from this study suggest that, in partial (t) terms, the Deferred Tax variable does not exert a statistically significant impact on tax avoidance. Furthermore, The Debt Ratio variable does not show any influence on Tax Avoidance. Simultaneously (F), both Deferred Taxes and Debt Levels together do not have a statistically significant effect on tax avoidance in companies listed on the SRI-KEHATI index. Keywords: deferred tax, debt ratio, tax avoidance

Brief Interventions for Suicidal Youths in Medical Settings: A Meta-Analysis.

Most youths who die by suicide have interfaced with a medical system in the year preceding their death, placing outpatient medical settings on the front lines for identification, assessment, and intervention. Review and consolidate the available literature on suicide risk screening and brief intervention with youths in outpatient medical settings and examine common outcomes. The literature search looked at PubMed, OVID, CINAHL, ERIC, and PsychInfo databases. Interventions delivered in outpatient medical settings assessing and mitigating suicide risk for youths (ages 10-24). Designs included randomized controlled trials, prospective and retrospective cohort studies, and case studies. Authors extracted data on rates of referral to behavioral health services, initiation/adjustment of medication, follow-up in setting of assessment, suicidal ideation at follow-up, and suicide attempts and/or crisis services visited within 1 year of initial assessment. There was no significant difference in subsequent suicide attempts between intervention and control groups. Analysis on subsequent crisis service could not be performed due to lack of qualifying data. Key secondary findings were decreased immediate psychiatric hospitalizations and increased mental health service use, along with mild improvement in subsequent depressive symptoms. The review was limited by the small number of studies meeting inclusion criteria, as well as a heterogeneity of study designs and risk of bias across studies. Brief suicide interventions for youth in outpatient medical settings can increase identification of risk, increase access to behavioral health services, and for crisis interventions, can limit psychiatric hospitalizations.

Prevalence of precancerous lesions and other cervical abnormalities among internally displaced women in Benue State Nigeria.

visual inspection is a low-cost screening strategy that can be used to prevent cervical cancer in women. These techniques can improve screening health outcomes for internally displaced women (IDW) who have poor sexual and reproductive health and rights' behaviors and outcomes. This study aimed to determine the prevalence of precancerous lesions and other clinical features using a visual inspection with acetic acid (VIA) technique during a cervical cancer screening campaign in two internally displaced people (IDP) camps in Benue State, Nigeria. this was a cross-sectional study of 166 IDW who voluntarily participated in the study during a VIA cervical cancer screening campaign in two IDP camps in Benue State, Nigeria the screening was done by a group of qualified and trained healthcare workers and data was collected using a structured, pretested questionnaire. a total of 99(60%) of the women had a first sexual experience at 16 years, while 78(47%) had more than 5 full-term pregnancies. Although only 72(43.4%) of the women acknowledged having more than one sexual partner, over 70% of the women stated that their sexual partner had another sexual partner. The prevalence of precancerous lesions among women was 10.8%. Smoking(p=0.003), age at menarche (p≤ 0.001) and sexual behaviors (p=0.009, p=0.004) were factors that had a statistically significant relationship with the presence of a precancerous lesion among the IDW. The study also highlights the high rate (95%) of cervicitis among the women and the relatively high rate (5.4%) of leukoplakia. the majority of IDW had sociodemographic and lifestyle characteristics that predisposed them to developing cervical cancer More targeted interventions aimed at improving the sociodemographic and lifestyle characteristics of IDW are recommended. In addition, there is a need to create awareness about cervical cancer among IDW and make screening available in camp facilities for early detection and management.

A Brief Survey on Semantic-preserving Data Augmentation

In the era of deep learning, it’s important to obtain enough qualified data for model training. For the labor-intensive and time-consuming characteristics of data collection and annotation, date augmentation has been widely employed in deep learning methods to mitigate over-fitting. It has been proven to be one of the most effective ways to ensure the performance of deep models. In the field of computer vision, the principal goal of data augmentation usually is enlarging both the scale and diversity of the dataset, and maintaining the quality of both the image and the annotation data simultaneously. Data augmentation methods at the raw input data level are intuitive to obtain semantic meaningful images. In this paper, we provide a brief survey on some data augmentation studies conducted at image level and briefly categorize the studies into two main classes: those based on image editing and those based on image generation. We also make an analysis of the semantic-preserving considerations within data augmentation techniques.

The impact of architecturally qualified data in deep learning methods for the automatic generation of social housing layouts

The objective of this work is to explore the impact of the data in the automatic generation of social housing layouts with different deep learning models. The design of Social Housing is a subfield of architecture that necessitates, from the architect, a high degree of precision in refining layouts to effectively address both the programmatic requirements and the aspiration to attain the utmost architectural quality to guarantee inhabitants comfort. This dual challenge stands to gain significantly from the incorporation of data-driven generative processes bolstered by machine learning techniques in general and deep learning ones in particular. The most widely used dataset for data-driven layout generation with deep learning methods is RPLAN, a generic floor plan dataset available online. Although it contains a significant amount of plans, it lacks any information about the architectural quality criteria used for the selection of the data. We conducted an initial analysis of the architectural consistency and key quality features of RPLAN. Upon finding several issues that raise doubts about its suitability for training social housing generation models, we proceeded to create PUBLICPLAN, a new dataset based on proposals submitted and evaluated in public architecture competitions in Spain over the past three years. It comprises 2446 distinct layouts extracted from 1279 proposals in Catalonia and the Balearic Islands. All of these layouts demonstrate high architectural standards. Then, we retrained the three most advanced existing models for generating interior plans using PUBLICPLAN and compared their performance to the same models trained with different subsets of RPLAN. Once the models were trained, we conducted several experiments comparing different models and data subsets and evaluating the impact of the quantity and quality of the data on the models' performance. Those experiments involved over two hundred architects and architecture students. We also examined the plausibility of the generated plans being synthetic with a survey asking to distinguish between human and machine generated layout plans. Our results showed that PUBLICPLAN had a significant impact compared to the alternatives with a similar number of cases. In the examination of the feasibility of human-generated plans, over 48.43% of architects and architecture students identified machine-generated plans as being of human origin. Moreover, in qualitative experiments conducted with an equivalent dataset, our qualified dataset PUBLICPLAN demonstrated a significantly superior human rating (72.13%) when evaluated by human assessors, as compared to models trained with RPLAN. This highlights the importance of using qualified datasets in deep learning for the generation of social housing layouts. We also identified challenges and priorities in the research field, both in the deep learning process and architectural design practices.

Artificial Intelligence Security through Input Data Quality Steps

In times of digital transformation, data generation constitutes a new type of asset, as it requires new forms of organizational learning, giving rise to new business models. The focus of all this transformation is Artificial Intelligence (AI), a subfield of Computer Science responsible for creating computational resources with capabilities similar to human reasoning for automated problem-solving. Elements of mathematics and engineering are used to reproduce aspects of human intelligence, as well as insights from other areas such as philosophy, mathematics, economics, neuroscience, psychology, computer engineering, control theory, cybernetics, and linguistics. Hence the name AI. Machines learn to speak, write, interpret data, and solve problems through AI, a tool that is now essential for Industry 4.0, as a transition from industrial society to the knowledge and digital economy. In turn, Generative AI uses multimodal tools to work with elements such as spoken language, images, sounds, body movements, etc. All this technology requires innovations in companies through the adoption of frameworks that enable the selection and storage of qualified data. This original article was developed based on extensive bibliographic research and scientific materials. Its objective is to demonstrate the relevance of data analysis as a way to apply compliance practices to AI. As a result of this research, there was a recognized need for greater attention to input data quality processes for generative AI training.

A Mediterranean Focus Overview of EU Marine Litter Data Management Performed in the Framework of the European Marine Observation and Data Network Chemistry

The Mediterranean Sea is an almost completely closed basin connecting several countries. Its configuration leads to its peculiarity and richness, but the intensive activities within the basin and along the coast aggravate the ecological conditions. The existing regulatory European Framework for environmental protection has already been in place through a series of legal instruments for almost 20 years. In this context, open science could play a fundamental role. The existing data must become findable, accessible, interoperable, and reusable (FAIR) to provide stakeholders and decision-makers with the instruments to understand how to improve the available information and support decisions based on the best set of existing information. Since 2009, the European Marine Observation and Data Network, EMODnet, has provided access to high-quality marine information supporting research and stakeholders’ mission and objectives. Data related to pollution are collated, validated, and published using standard protocols, formats, and vocabularies, thus becoming FAIR. For marine litter, a detailed and qualified data management system for macro- and microlitter in diverse compartments was structured. Some of these data and metadata were already used to calculate the first coastline litter baselines based on harmonized and FAIR datasets (2012–2016). The availability of these data related to the Mediterranean area is relevant, but additional work is required.