Additional Data Sources Research Articles

Law Omission in Muslim Society: Inquiring Citizen Rights in the Administration of Islamic Family Law in Madura Indonesia

Understanding on the law omission seems controversial; some view it only as an interruption of normal life, and some discern it as an administrative violation. Differences in the understanding may produce difficulties in enforcing the administration of Islamic family law. In line with this context, while mapping forms of the law omission in various community groups, this study analyses reasons for differences of the understanding of it. This study was based on data collected from interviews, FGDs and Google Forms on three different community groups with religious structural characteristics in 4 regencies on Madura Island. This study revealed that social differences and social classes in a society have become a force that structures the society’ views or understanding of the law omission. Differences of the understanding have consequences for the levels of difficulties in enforcing the Islamic family law, especially in the administrative context, such as in cases of registration and dispensation of marriages. Structural differences that exist in a religious society become important to differentiate factors in an interpretation of a reality of life. This paper suggests to expand the observed cases and to provide additional data sources to enable an in-depth and comprehensive understanding of the impact of the law omission in the field of administration of Islamic family law.

Analyzing the Spatial Interaction Characteristics of Urban Area Shared Bicycle Systems: A Case Study of Beijing’s Central Area

Shared bicycle systems play a crucial role in promoting sustainable urban transportation, addressing challenges such as traffic congestion and air pollution. Understanding the spatiotemporal patterns of shared bike usage is essential for optimizing bike-sharing infrastructure and improving transportation planning. In this study, we analyzed 2.4 million records of shared bicycle data to explore the spatial distribution, interaction patterns, and flow dynamics within Beijing’s urban central area. We found that bike distribution peaks during commuting hours, particularly in central regions with employment centers. Complex networks are an important method for studying travel flows. Through a spatial interaction network, we identified key streets with high node strength and popularity, often concentrated in central areas. They experience heavy shared bicycle use during peak hours due to their employment-centric location. Conversely, peripheral areas see increased usage in the evenings, reflecting distinct commuting patterns. The morning exhibits higher positive central values compared to the evening, while negative values show the opposite trend. Based on these findings, we recommend enhancing bike infrastructure in high-density areas with bike lanes and ample shared bikes during peak hours. Implementing mixed-use zoning policies in the central region can reduce traffic congestion. Expanding shared bike services to peripheral regions can promote equitable access. This research underscores the importance of considering spatial and temporal factors in urban transportation planning. Future work should incorporate additional data sources, explore environmental impacts, and analyze usage in different seasons and special events, further contributing to sustainable urban mobility development.

H - 51 Neuropsychological Evaluation of Non-Fluent English Speakers: a Case Study.

Sometimes patients are referred for neuropsychological evaluation who do not speak English as their first language. In many cases, we have declined evaluating patients who were not fluent in English, and when we have evaluated bilingual patients, have qualified the limitations of our interpretations. However, there are no neuropsychologists providing services in languages besides English in the state of Oklahoma, and most patients never received services. Patient is 83-year-old woman with five years of progressive decline in memory, unable to manage instrumental activities of daily living, presenting with family history of Alzheimer's disease, and neuroimaging negative for atrophy or cerebrovascular disease. Patient was not fluent in English. Efforts to find a neuropsychologist able to evaluate in Spanish were unsuccessful, so testing was modified, and measures were administered by a bilingual psychometrist. Attention, orientation, and memory were impaired (MMSE = 14). Verbal fluency was intact in Spanish. Auditory memory was not assessed due to unavailability of Spanish measures. Visual memory was impaired, consistent with figure copy. WAIS-IV was administered in English, with instructions in Spanish. Patient was diagnosed with possible Alzheimer's disease. Recommended PET scan or spinal tap for Alzheimer's disease biomarkers and reevaluation in Spanish in one year. Since neuropsychological evaluation is not available in every language, evaluation in English and highlighting the limitations of our findings may meet the ethical principle of beneficence, if we consider the impact of language and additional sources of data, such as collateral report of the patient's caregivers, and medical tests.

Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast

Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.

Multiple Object Detection on Surveillance Videos for Improving Accuracy Using Enhanced Faster R-CNN

Computer vision is a dynamic and rapidly evolving field within the broader domain of artificial intelligence. Within surveillance monitoring systems, one of the central tasks is object detection, which involves identifying and localizing objects of interest in video sequences to provide safety and security of the people. Detection of multiple objects is a challenging task in video sequences which interprets less accuracy and false Bounding box regression. In this paper, enhanced faster R-CNN model is proposed and trained to compute regional proposal through Convolutional layers on the different scene of the sequences in term of lighting, motion capture related to spatial analysis. These enhancements could encompass architectural improvements, novel training strategies, or the incorporation of additional data sources to improve the model's overall performance. Proposed model is experimented on pedestrian video gives an improved accuracy detection rate than single detector techniques.

Understanding Design Approach of the Workplace Through Value Creation and Satisfaction

The study explored how a newly designed facility met the needs of the workers. Professional office workers of a recently renovated office space comprised the sample for this study. A questionnaire and an interview were the methods of data collection. Additional data sources included over 200 photographs illustrating various new building design elements. Data analysis included descriptive findings as well as thematic analysis ultimately describing the phenomenon. Participants described building-design-driven elements that impacted satisfaction. Factors such as behavioral, psychological, and physiological well-being; personal values; as well as organizational values, culture, and image also influenced satisfaction. The findings suggested that the interior design of an office space drives satisfaction based on several perspectives. We found that the key drivers of satisfaction, in addition to personal needs being met, were impacted by behavioral, psychological, and physiological well-being, personal values, as well as organizational culture and values. Constructs of the social theory and Personality Adjusted Model (PAM) were also implicated suggesting the previous school of thought and understanding of perceived satisfaction related to the office space and the workplace have changed. Based on the findings, we suggested a framework to explore new space planning and design approaches based on several perspectives.

A deep learning approach for transgender and gender diverse patient identification in electronic health records

BackgroundAlthough accurate identification of gender identity in the electronic health record (EHR) is crucial for providing equitable health care, particularly for transgender and gender diverse (TGD) populations, it remains a challenging task due to incomplete gender information in structured EHR fields. ObjectiveUsing TGD identification as a case study, this research uses NLP and deep learning to build an accurate patient gender identity predictive model, aiming to tackle the challenges of identifying relevant patient-level information from EHR data and reducing annotation work. MethodsThis study included adult patients in a large healthcare system in Boston, MA, between 4/1/2017 to 4/1/2022. To identify relevant information from massive clinical notes, we compiled a list of gender-related keywords through expert curation, literature review, and expansion via a fine-tuned BioWordVec model. This keyword list was used to pre-screen potential TGD individuals and create two datasets for model training, testing, and validation. Dataset I was a balanced dataset that contained clinician-confirmed TGD patients and cases without keywords. Dataset II contained cases with keywords. The performance of the deep learning model was compared to traditional machine learning and rule-based algorithms. ResultsThe final keyword list consists of 109 keywords, of which 58 (53.2%) were expanded by the BioWordVec model. Dataset I contained 3,150 patients (50% TGD) while Dataset II contained 200 patients (90% TGD). On Dataset I the deep learning model achieved a F1 score of 0.917, sensitivity of 0.854, and a precision of 0.980; and on Dataset II a F1 score of 0.969, sensitivity of 0.967, and precision of 0.972. The deep learning model significantly outperformed rule-based algorithms. ConclusionThis is the first study to show that deep learning-integrated NLP algorithms can accurately identify gender identity using EHR data. Future work should leverage and evaluate additional diverse data sources to generate more generalizable algorithms.

Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models

This study investigates the application of various machine learning models for land use and land cover (LULC) classification in the Kerch Peninsula. The study utilizes archival field data, cadastral data, and published scientific literature for model training and testing, using Landsat-5 imagery from 1990 as input data. Four machine learning models (deep neural network, Random Forest, support vector machine (SVM), and AdaBoost) are employed, and their hyperparameters are tuned using random search and grid search. Model performance is evaluated through cross-validation and confusion matrices. The deep neural network achieves the highest accuracy (96.2%) and performs well in classifying water, urban lands, open soils, and high vegetation. However, it faces challenges in classifying grasslands, bare lands, and agricultural areas. The Random Forest model achieves an accuracy of 90.5% but struggles with differentiating high vegetation from agricultural lands. The SVM model achieves an accuracy of 86.1%, while the AdaBoost model performs the lowest with an accuracy of 58.4%. The novel contributions of this study include the comparison and evaluation of multiple machine learning models for land use classification in the Kerch Peninsula. The deep neural network and Random Forest models outperform SVM and AdaBoost in terms of accuracy. However, the use of limited data sources such as cadastral data and scientific articles may introduce limitations and potential errors. Future research should consider incorporating field studies and additional data sources for improved accuracy. This study provides valuable insights for land use classification, facilitating the assessment and management of natural resources in the Kerch Peninsula. The findings contribute to informed decision-making processes and lay the groundwork for further research in the field.

Height Above Nearest Drainage (HAND) as a Model for Rapid Flood Inundation Mapping Based on Remote Sensing and Geographic Information Systems in the Kapuas Sintang Sub Watershed

This study aims to map the flood inundation and the extent of the inundation in the study area using the HAND model. The data used in this study is DEM. The DEM is used to generate a hydrologic framework, including flow accumulation, drainage network, flow direction, elevation, and flow distance. The method used in this study is the HAND descriptor. The analysis in this study used spatial hydrological analysis and hypsometric analysis using zonal statistical tables in ArcGIS. Based on the results of the analysis of height above the nearest drainage it is known that the Kapuas Sintang sub-watershed has five classes of inundation, namely very high inundation, high inundation, moderate inundation, low inundation, and no inundation. Very high, high, and moderate inundation classes are spread over three sub-districts, namely Sintang, Dedai, and Tempunak sections. Sintang District has the widest distribution, followed by Dedai District and Tempunak District is the narrowest. Prediction of inundation area and flood area with HAND can be used to improve the new mapping model without involving additional data sources. The HAND model is a nice and simple tool that is useful for inundation studies as well as in inundation area prediction.

A First Step towards Developing a Decision Support System Based on the Integration of Environmental Monitoring Activities for Regional Water Resource Protection

The topic of diffuse pollution is of particular interest from technical, scientific, and administrative management points of view. Diffuse pollution is defined as the contamination or chemical, physical, or biological alterations of environmental matrices caused by diffuse sources and not attributable to a single origin. In this study, various sources of diffuse pollution such as nitrates, pesticides, metals, and plastics were analysed. This was aimed at the implementation of a decision support system able to represent the state of environmental matrices degradation, with particular attention to water resources, and to make decisions evaluating similar environmental contexts. The potential of the developed system makes it possible to identify areas with the same environmental characteristics, referring to the various activities that create diffuse pollution and areas with the same pressure values on the environmental matrices. The system provides the political decision-maker with greater awareness of the environmental state, thus enabling him to apply more accurate land management policies. The created system, based on open-source software, which can be implemented with additional available data sources, is characterised by a data processing workflow that provides output information at the municipal level, so that it can be managed both by mayors and regional managers who are able to share the same information with all.

THE IMPACT OF ORGANIZATIONAL CULTURE ON EMPLOYEE PERFORMANCE: THE CASE OF UNIVERSITY OF TECHNOLOGY AND APPLIED SCIENCES-IBRA IN OMAN

Organizational culture is one of the most important factors affecting the performance of employees in various organizations. A system of values and beliefs adopted by members of the organization affects their performance either positively or negatively. This study aims to investigate the factors of organizational culture (decision-making, organizational environment & reward and recognition system) on employee job performance at UTAS- Ibra in Oman, also the study will examine the effect of the organizational culture on employee job performance at UTAS- Ibra. The method used in this study to collect data is a survey questionnaire. For the purpose of this study, 114 completed questionnaires using stratified sampling method from all academic departments and Educational Technology Center at UTAS-Ibra received and analyzed through SPSS program and smart PLS-SEM. Moreover, the study used secondary data from previous studies in different books and journals as additional sources of data. The results indicate that decision-making and the organizational environment factors of organizational culture have a positive relationship with performance. While, reward and recognition system had a negative impact on employee performance. Based on the results of the study, the study recommending that officials in UTAS-Ibra and the university should strengthen the reward and recognition system so that an integrated system is designed to make employees work in a stimulating environment for work, which results in increasing their performance.

Trends for Percutaneous Tracheostomy in Italian Acute Care Setting over a 5-Year Period.

Background: Tracheostomy is a widespread procedure usually performed with a percutaneous approach for prolonged mechanical ventilation. Little is known about the population-based trends for percutaneous tracheostomies (PT). The aim of this study was to evaluate the order to analyze the characteristics, rates, and costs of PTs performed in Italy from 2009 to 2014. Methods: We analyzed 102,646 PTs performed in Italy between 2009 and 2014. We obtained the data of patients from the section of the discharge report of the Italian Ministry of Health (National Archive for Hospital Discharge Form, Ministry of Health) about age, gender, length of stay (LOS), hospital types, and hospital region for code 541 and 542 for the years 2009, 2010, 2011, 2012, 2013 and 2014. Our additional source of data was the Annual Discharge Reports of the Italian Ministry of Health. Results: In this study, including 102,646 PTs performed from 2009 to 2014, we found that (1) the rates of PTs significantly decreased over time; (2) PTs were mostly performed in patients aged less than 65 years and hospitalized in ICUs for less than 40 days; and (3) the costs of PTs severely decreased over time, with a breakpoint between 2011 and 2012. Conclusions: Percutaneous tracheostomy is still a procedure frequently performed in the setting of acute care. Although percutaneous tracheostomy still results in high medical care reimbursement, it is a safe and cost-saving procedure.

Physics-Informed, Data-Driven Model for Atmospheric Corrosion of Carbon Steel Using Bayesian Network.

Atmospheric corrosion is a significant challenge faced by the aviation industry as it considerably affects the structural integrity of an aircraft operated for long periods. Therefore, an appropriate corrosion deterioration model is required to predict corrosion problems. However, practical application of the deterioration model is challenging owing to the limited data available for the parameter estimation. Thus, a high uncertainty in prediction is unavoidable. To address these challenges, a method of integrating a physics-based model and the monitoring data on a Bayesian network (BN) is presented herein. Atmospheric corrosion is modeled using the simulation method, and a BN is constructed using GeNie. Moreover, model calibration is performed using the monitoring data collected from aircraft parking areas. The calibration approach is an improvement over existing models as it incorporates actual environmental data, making it more accurate and applicable to real-world scenarios. In conclusion, our research emphasizes the importance of precise corrosion models for predicting and managing atmospheric corrosion on carbon steel. The study results open new avenues for future research, such as the incorporation of additional data sources to further improve the accuracy of corrosion models.

Drone-based photogrammetry for riverbed characteristics extraction and flood discharge modeling in taiwan’s mountainous rivers

Flooding is a natural hazard that poses considerable risks to the mountainous regions of Taiwan. Accurate flood modeling can be used for flood prevention, but it requires detailed knowledge of components of riverbed characteristics, which can be challenging to investigate due to the complex terrain in Taiwan's mountains. To overcome these challenges, this study employed an automated grain sizing (AGS) photogrammetry technique using unmanned aerial vehicle (UAV) based orthophotos to extract riverbed characteristics. The AGS model was developed and calibrated in the Linbien River in Western Taiwan, and then the calibrated AGS model was used to extract riverbed characteristics for calculating Manning's roughness coefficient (n value) for flood modeling in the Beinan River in Eastern Taiwan. The study assessed the applicability of AGS in flood modeling using two scenarios: (i) traditional one sample (S1) and (ii) three UAV-based samples (S2) within each cross-section. The model's accuracy was evaluated using the coefficient of determination (R2), root-mean-squared error (RMSE), and mean absolute error (MAE). The results indicated only 3.7% to 9.9% differences of mean representative grain diameters between AGS and PCS. In flood discharge modeling, the R2, RMSE, and MAE of S1 were 0.9948, 106.40 m3/s, and 60.32 m3/s, respectively, while for S2, these values were 0.9994, 34.94 m3/s, and 19.08 m3/s, respectively. The results demonstrate that UAV-based AGS has the potential to enhance flood discharge modeling performance in Taiwan's mountainous rivers, leading to future research focused on refining sampling techniques and incorporating additional data sources for improved AGS-based applications.

Национальные интересы США, Великобритании и Австралии в соглашении AUKUS

The article focuses on the study of the goals of the member countries of the new security organization AUKUS in the Indo-Pacific region. The work carries out an event analysis of the activities of AUKUS, to clarify the role of new organization in the region and differences from existing security organizations. Particular attention is paid to the analysis of national documents in the field of security and related discussions in the parliaments of the AUKUS member states, as a result of which detailed matrices of interests for the United States, Great Britain and Australia were obtained. It is noteworthy that in a number of cases the allies assessed each other’s inter-ests in their own analytical documents, which serves as an additional source of data on the true objectives of AUKUS. The study also analyzes and assesses the strategic environment in the Indo-Pacific region to clarify the pattern of changes in the field of security. The results obtained can be used to simulate the development of the security situation in the short and mid-term perspective.

USING LONG SHORT-TERM MEMORY NETWORKS FOR NATURAL LANGUAGE PROCESSING

The problem of emotion classification is a complex and non-trivial task of language interpretation due to the natural language structure and its dynamic nature. The significance of the study is in covering the important issue of automatic processing of client feedbacks, collecting opinions and trend-catching. In this work, a number of existing solutions for emotion classification problem were considered, having their shortcomings and advantages illustrated. The evaluation of performance of the considered models was conducted on emotion classification on four emotion classes, namely Happy, Sad, Angry and Others. The model for emotion classification in three-sentence conversations was proposed in this work. The model is based on smileys and word embeddings with domain specificity in state of art conversations on the Internet. The importance of taking into account the information extracted from smileys as an additional data source of emotional coloring is investigated. The model performance is evaluated and compared with language processing model BERT (Bidirectional Encoder Representations from Transformers). The proposed model achieved better performance at classifying emotions comparing to BERT (having F1 score as 78 versus 75). It should be noted, that further study should be performed to enhance the processing by the model of mixed reviews represented by emotion class Others. However, modern performance of models for language representation and understanding did not achieve the human performance. There is a variety of factors to consider when choosing the word embeddings and training methods to design the model architecture.

Evaluating the Potential of Crowdsourced Data to Estimate Network-Wide Bicycle Volumes

This research integrated and evaluated emerging user data sources (Strava Metro, StreetLight, and hybrid docked/dockless bike share) of bicycle activity data with conventional “static” demand determinants (land use, built environment, sociodemographics) and measures (permanent and short-duration counts) to estimate annual average daily bicycle traffic (AADBT). We selected six locations (Boulder, Charlotte, Dallas, Portland, Bend, and Eugene) covering varied urban and suburban contexts and specified three sets of Poisson regression models: city-specific models, an Oregon pooled model, and all cities pooled. Static variables, Strava, and StreetLight complemented one another, with each additional data source tending to improve the model performance. Sites with lower volumes were more difficult to predict, with considerable error in even the best-performing models. City-specific models in general exhibited improved fit and prediction performance. Expected prediction error increased by a factor of about 1.4 when using Strava or StreetLight alone, but without static adjustment variables, to predict AADBT. Combining Strava plus StreetLight, but without static variables, increased error slightly less; by 1.3 times. We also found that transferring the model specifications from one year to the next without re-estimating the model parameters resulted in a 10% to 50% increase in error rates across models, so such transfer is not recommended. The findings from this study indicate that rather than replacing conventional bike data sources and count programs, old “small” data sources will likely be very important for big data sources like Strava and StreetLight to achieve their potential for predicting AADBT.

Seabed Classification From Multispectral Multibeam Data

Given the recent increase in the availability of multispectral multibeam echosounder data, this work aims to identify suitable processing and classification methodologies for seabed classification based on such data. We propose a complete processing and classification pipeline and investigate the adequacy of state-of-the-art classification algorithms to perform seabed classification based on multispectral backscatter data alone, and when additional data sources are considered. Starting from raw acquisition data, we generate region-wide multispectral backscatter composite images through noise removal, inpainting/gap-filling and mosaicking. Ground truth data from in situ seabed samples are used. We have tried different classification methods, including random forests, support vector machines, and multilayer perceptrons, with the latter providing the best results. Quantitative and qualitative evaluation on five surveys indicate high classification performance based only on multispectral backscatter data, while additional features, like bathymetry, bathymetric positional index (BPI), or positional encoding, offer limited gains. We offer a web service for seabed classification from multispectral multibeam echosounder data to further support and increase interest in the topic.

OPENHERITAGE3D: BUILDING AN OPEN VISUAL ARCHIVE FOR SITE SCALE GIGA-RESOLUTION LIDAR AND PHOTOGRAMMETRY DATA

Abstract. LiDAR and photogrammetry are common mechanisms for the documentation of cultural heritage sites. Their outputs provide foundational primary sources for research and important engineering decisions contributing to the on-going conservation of sites and structures. Unfortunately, due to the complex nature of these data, they are rarely shared with stakeholders in their full forms. Raw data is routinely transferred to portable hard drives and forgotten at a project’s end. Digital heritage documentation, often acquired with great effort and cost, is at risk of loss (UNESCO 2003). When these data are shared, they are rarely presented or formatted in ways which enable their widespread re-use. As processing tools improve at a rapid pace, and offer new pipelines for improved reconstruction and fusion with additional data sources, it is important to preserve the data in a way which maintains all functionality and interoperability between processing platforms. It's not enough to share data or to simply make them "available." Data must be technologically and intellectually intelligible (and useful). A new system framework is required to ensure their integrity and wide-spread utility, and to democratize these data for re-use by the diverse communities of stakeholders with whose purposes fall outside the narrow scopes of a 3D documentation project’s original goals. In this paper we present OpenHeritage3D.org as a platform and framework for an open visual archive seeking to provide authoritative and democratized access to site-scale lidar and photogrammetry through data curation, file-sharing, and web-visualization systems with granular segmentation and data conversion capabilities.

Prediction of Continuous Emotional Measures through Physiological and Visual Data

The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments.