Residential Patterns Research Articles

Evolution of residential segregation patterns in the Netherlands between 2015 and 2020

We investigate the evolution of residential segregation patterns in the Netherlands, with a focus on the population with a non-western migration background. Unlike previous research relying on predefined spatial structures, this study employs a regionalization approach to track the evolution of social enclaves in 82 municipalities from 2015 to 2020. Enclaves have become more mixed in municipalities with historically homogeneous social enclaves whereas in the other municipalities, they have become more homogeneous. In addition, we find a positive association between the increase in the share of population with a non-western migration background at the municipality level and the spatial growth of social enclaves. These insights contribute to a deeper understanding of residential segregation in the Netherlands, offering a valuable foundation for informed policymaking.

Matchmaking in Off-Grid Energy System Planning: A Novel Approach for Integrating Residential Electricity Demands and Productive Use of Electricity

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must be carefully evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis and energy system modeling to optimize decision making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends significantly on three factors: (1) The residential electricity consumption patterns, which are influenced by the socio-economic composition of the community; (2) The degree of flexibility of (i) PUE assets and (ii) operational preferences of the PUE user; and (3) The capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy systems can reduce the financial risk for operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project’s financial sustainability and is achieving the overall objectives of decentralized energy system planning.

Parking Occupancy Detection using Computer Vision Techniques

The surge in vehicle numbers on roads contributes significantly to traffic congestion and management challenges, particularly evident in developing nations like India where the influx of cars exceeds road and parking capacity. Addressing these issues necessitates the implementation of sophisticated parking management systems. This project focuses on two key objectives: detecting vehicle occupancy within marked parking slots and analyzing parking data. Using the parking lot near IIT Kanpur main gate as a reference, video data was collected for 14 consecutive days, enabling the evaluation of vehicle occupancy and parking patterns. Object detection algorithms such as Mask-RCNN and YOLO-v5 were employed to identify occupied parking spaces within the lot. Various methods, including HAAR cascade-based classifiers, DNN-based systems utilizing ResNet classifiers, and RCNN with IoU, were tested for detecting vehicles within allotted slots. The data collected was stored in CSV format for analysis. This project aims to provide insights into detecting parking space availability and analyzing parking data to optimize time and fuel efficiency. In the Mask-RCNN approach, pre-occupied spaces are denoted by red boxes, while green boxes represent available parking spots. Similarly, YOLOv5 was utilized to count cars in video frames and identify available parking spaces. The YOLO Annotation Toolbox facilitated the extraction of parking space coordinates from recorded video frames, which were then visualized in QGIS for further analysis.

Optimizing Energy Efficiency in Commercial Buildings through Advanced Building Strategies

The pressing issue of climate change and its correlation with greenhouse gas emissions has propelled a heightened emphasis on energy-efficient building practices, particularly within the commercial sector. This study delves into advanced design strategies aimed at maximizing energy efficiency while upholding quality and comfort standards. By scrutinizing a functioning commercial building in Gurugram, Haryana, data was gathered to construct an energy model using eQUEST. Analysis encompassed various building facets such as envelope components (e.g., walls, roofs, and windows), HVAC systems, lighting, and occupancy patterns. The outcomes furnish valuable insights into pivotal factors influencing energy efficiency and propose tailored strategies for integration into commercial building design. This methodical approach, informed by case evaluation, seeks to optimize energy performance and cost-effectiveness across diverse building types and climates.

Forty years of struggle in North Carolina: Workplace segregation and fatal occupational injury rates.

To assess workplace segregation in fatal occupational injury from 1992 to 2017 in North Carolina. We calculated occupational fatal injury rates within categories of occupation, industry, race, age, and sex; and estimated expected numbers of fatalities among Black and Hispanic male workers had they experienced the rates of White male workers. We also estimated the contribution of workforce segregation to disparities by estimating the expected number of fatalities among Black and Hispanic male workers had they experienced the industry and occupation patterns of White male workers. We assessed person-years of life-lost, using North Carolina life expectancy estimates. Hispanic workers contributed 32% of their worker-years and experienced 58% of their fatalities in construction. Black workers were most overrepresented in the food manufacturing industry. Hispanic males experienced 2.11 (95% CI: 1.86-2.40) times the mortality rate of White males. The Black-White and Hispanic-White disparities were widest among workers aged 45 and older, and segregation into more dangerous industries and occupations played a substantial role in driving disparities. Hispanic workers who suffered occupational fatalities lost a median 47 life-years, compared to 37 among Black workers and 36 among White workers. If Hispanic and Black workers experienced the workplace safety of their White counterparts, fatal injury rates would be substantially reduced. Workforce segregation reflects structural racism, which also contributes to mortality disparities. Root causes must be addressed to eliminate disparities.

REVIEWING THE ROLE OF ARTIFICIAL INTELLIGENCE IN ENERGY EFFICIENCY OPTIMIZATION

Artificial intelligence (AI) is revolutionizing the field of energy efficiency optimization by enabling advanced analysis and control of energy systems. This review provides a concise overview of the role of AI in enhancing energy efficiency. AI technologies, such as machine learning and neural networks, are being increasingly applied to optimize energy consumption in various sectors, including buildings, transportation, and industrial processes. These technologies analyze vast amounts of data to identify patterns and trends, enabling more precise control of energy systems and the prediction of energy demand. One of the key advantages of AI in energy efficiency optimization is its ability to adapt and learn from data, leading to continuous improvement in energy-saving strategies. AI algorithms can optimize energy consumption based on factors such as weather conditions, occupancy patterns, and energy prices, resulting in significant cost savings and environmental benefits. Furthermore, AI enables the integration of renewable energy sources into existing energy systems by predicting renewable energy generation and optimizing its use. This integration helps reduce reliance on fossil fuels and mitigates greenhouse gas emissions, contributing to a more sustainable energy future. However, the implementation of AI in energy efficiency optimization is not without challenges. These include data privacy concerns, the need for specialized skills to develop and deploy AI solutions, and the complexity of integrating AI systems into existing energy infrastructure. Addressing these challenges will be crucial for realizing the full potential of AI in energy efficiency optimization. In conclusion, AI holds great promise for enhancing energy efficiency by enabling more intelligent control and optimization of energy systems. By leveraging AI technologies, organizations can achieve significant energy savings, reduce costs, and contribute to a more sustainable and resilient energy future.
 Keywords: Role, AI, Energy, Efficiency, Optimization.

Through Time: Reconstructing Palaeolithic Occupations Through Use-Wear Analysis in the Middle Palaeolithic Site of Ciota Ciara (Borgosesia, Italy)

Lithic use-wear analysis, through defining site function and allowing reconstructing of patterns of human occupation, can contribute to our understanding of archaeological palimpsests. The Ciota Ciara cave represents an excellent case study for this methodology. Multidisciplinary research so far conducted on the materials recovered from the atrial sector of the cave distinguishes three archaeological units from a Middle Palaeolithic occupation of the site: stratigraphic units (SUs) 13, 14, and 15. Each unit is interpreted as referring to a period of numerous, superimposed episodes of human occupation, the characteristics of which we try to reconstruct and present in this work through use-wear studies. The functional analysis of lithic industries from the upper units (13 and 14) has already been published previously; here, we report corresponding new data from the lowest level, SU 15. By comparing the use-wear results from the three units and integrating the findings with data from the geoarchaeological, palaeontological, zooarchaeological, and technological studies, we attempt to reconstruct the different phases of human occupation represented in the site through time, contributing to current interpretations regarding settlement dynamics and human behaviour in the Middle Palaeolithic of north-western Italy.

Comparison of polypharmacy and potentially inappropriate medication use in older adults with and without dementia receiving residential medication management reviews.

Among residents who had a residential medication management review (RMMR), there is a lack of studies assessing exposure to polypharmacy and potentially inappropriate medications (PIMs) in people with dementia. This study compared the exposure to polypharmacy and PIMs in residents with dementia and without dementia receiving RMMR. A retrospective analysis was performed using data of 16,261 residents living in 343 Australian residential aged care facilities who had an RMMR in 2019. Medication use was assessed as polypharmacy (defined as ≥9 medications) and use of ≥1 PIMs using the 2019 updated Beers criteria. Dementia diagnosis was determined with ICD-10 coding from medical records. Descriptive analyses reported resident demographics and patterns of medication use. Pearson's χ2 tests and logistic regression analysis were conducted to compare medication exposure between residents with and without dementia. Among 16,261 residents, 6781 (42%) had dementia. Residents with dementia were significantly more likely to be exposed to polypharmacy and PIMs, compared to those without dementia (74% vs. 70% and 83% vs. 73%, p < .001 respectively). Residents with dementia had 1.31 times the odds of exposure to polypharmacy (adjusted OR: 1.31, 95% CI: 1.22-1.41, p < .001) and 1.88 times the odds of being prescribed ≥1 PIMs than people without dementia (adjusted OR: 1.88, 95% CI: 1.73-2.04, p < .001). In a study of residents receiving RMMR, polypharmacy and PIMs were highly common, and those with dementia were more likely to be exposed to inappropriate polypharmacy. There is a need for targeted deprescribing strategies to immediately address inappropriate prescribing in residents, particularly those living with dementia.

Optimizing microgrid efficiency: Coordinating commercial and residential demand patterns with shared battery energy storage

The optimization of energy systems within a multi-microgrid framework, enriched by shared Battery Energy Storage Systems (BESS), has emerged as a compelling avenue for enhancing the efficiency of distributed energy networks. In response to the increasing integration of BESS in modern energy systems, this study investigates the implications of incorporating BESS within connected residential-commercial Microgrids (MGs). Unlike previous studies that primarily focused on cost and reliability, this research fills a significant gap in the literature by investigating the optimization of load demands patterns. Specifically, we explore the impact of shared BESS on load demand patterns in commercial-residential MGs. The research introduces two innovative critical load metrics, peak-to-average ratio (PAR) and demand profile smoothness (DPS), to assess the influence of BESS on demand profiles. In addition, the study explores the integration of a Dynamic Thermal Rating (DTR) system, compared to traditional fixed thermal rating systems, to further optimize the performance and efficiency of connected residential-commercial MGs enriched by shared BESS. Three distinct case studies, each comprising a commercial MG (shopping mall, hotel, and office building) paired with a residential MG, were considered. Utilizing a Firefly Algorithm (FA) for optimization, the study determines the optimized BESS capacity for minimum total cost. The results highlight that the implementation of shared BESS, especially in collaboration between commercial and residential MGs, significantly reduces imported energy from the main grid, enhancing MG flexibility and resilience. While the economic benefits of shared BESS may not be substantial (up to 3.25 % cost reduction), the study underscores its contribution to more balanced and smoother load demand curves, with improvements in PAR (up to 15.63 %) and DPS (up to26.05 %). Moreover, considering DTR for transmission lines, instead of fixed thermal rating, improves the PAR and DPS up to 28.52 % and 41.06 % respectively. The findings of this study highlight the importance of considering load demand patterns in the design and operation of MGs and underscore the multifaceted benefits of shared BESS beyond economic considerations.

Time-series data clustering with load-shape preservation for identifying residential energy consumption behaviors

Categorizing residential energy demand patterns is a principal task for demand-side management (DSM) and energy-saving strategies. While deep learning (DL)-based clustering offers a promising alternative to conventional machine learning (ML), DL’s advantages and disadvantages over ML still remain unclear in identifying energy demand patterns. Moreover, prevalent DL-based clustering can suffer from catastrophic feature distortion when capturing load-shape information from energy-load data, leading to erroneous pattern identification. To address these issues, we propose integrating a load-shape preservation mechanism into representative DL-based clustering and investigate its effectiveness in categorizing energy demand patterns, compared to existing ML and DL. We experiment and compare the three clustering approaches using one-year residential energy-load data. Results show that the proposed DL, equipped with load-shape preservation, outperformed ML quantitatively and closely aligns with the baseline DL’s performance. This is particularly significant considering that the baseline DL prioritizes quantitative enhancements, sometimes compromising load-shape precision. Furthermore, the proposed DL discovered more diverse energy demand patterns than the baseline ML and DL, while producing more human-agreeable results. This finding underscores the pivotal role of load-shape preservation in enhancing data clustering and demand pattern recognition in the real-world. These benefits will facilitate personalized DSM interventions and foster residents’ energy-saving behaviors.

Dynamic analysis of major public health emergency transmission considering the dual-layer coupling of community–resident complex networks

Abstract We construct a dual-layer coupled complex network of communities and residents to represent the interconnected risk transmission network between communities and the disease transmission network among residents. It characterizes the process of infectious disease transmission among residents between communities through the SE2IHR model considering two types of infectors. By depicting a more fine-grained social structure and combining further simulation experiments, the study validates the crucial role of various prevention and control measures implemented by communities as primary executors in controlling the epidemic. Research shows that the geographical boundaries of communities and the social interaction patterns of residents have a significant impact on the spread of the epidemic, where early detection, isolation and treatment strategies at community level are essential for controlling the spread of the epidemic. In addition, the study explores the collaborative governance model and institutional advantages of communities and residents in epidemic prevention and control.

Transcription factor regulation of ribosomal RNA in hematopoiesis.

Ribosomal RNAs (rRNAs) are transcribed within nucleoli from rDNA repeats by RNA Polymerase I (Pol I). There is variation in rRNA transcription rates across the hematopoietic tree, and leukemic blast cells have prominent nucleoli, indicating abundant ribosome biogenesis. The mechanisms underlying these variations are poorly understood. The purpose of this review is to summarize findings of rDNA binding and Pol I regulation by hematopoietic transcription factors. Our group recently used custom genome assemblies optimized for human and mouse rDNA mapping to map nearly 2200 ChIP-Seq datasets for nearly 250 factors to rDNA, allowing us to identify conserved occupancy patterns for multiple transcription factors. We confirmed known rDNA occupancy of MYC and RUNX factors, and identified new binding sites for CEBP factors, IRF factors, and SPI1 at canonical motif sequences. We also showed that CEBPA degradation rapidly leads to reduced Pol I occupancy and nascent rRNA in mouse myeloid cells. We propose that a number of hematopoietic transcription factors bind rDNA and potentially regulate rRNA transcription. Our model has implications for normal and malignant hematopoiesis. This review summarizes the literature, and outlines experimental considerations to bear in mind while dissecting transcription factor roles on rDNA.

Intra‐urban residential mobility and segregation of foreigners in Rome

AbstractThis research studies the residential mobility of Italians and foreigners in Rome from 2002 to 2019. We examine the differences in residential mobility patterns for (1) Italians and foreigners, (2) foreign migrants by selected country of origin, and (3) the effect of intra‐urban mobility on residential segregation. Log‐linear models and segregation indexes are used to analyze unpublished microdata on residential mobility by neighbourhood from the population register of Rome. Results show that foreigners are more mobile than Italians and more prone to move outside the area where they live, but with an increasing process of remaining in their current locations over time. Also, similarities and differences emerge by country of origin. Finally, we find residential mobility has increased the spatial concentration and clustering of foreigners in Rome, whilst reducing the dissimilarity from Italians at the same time.

Energy-efficient operation of portable air cleaners based on real-time prediction of non-uniform concentrations of indoor air pollutants in open offices

In an open office environment with high occupant density and random occupancy patterns, excessive energy consumption is often observed because mechanical ventilation (MV) is usually designed and operated assuming a full-occupancy-based ventilation rate (VR). Considering the duo challenges of post-pandemic and climate change, this study proposes a real-time monitoring and optimization approach for operating portable air cleaners (PACs) to assist and reduce the energy consumption of the MV while maintaining the minimal VR and improving indoor air quality (IAQ). The approach was as follows. First, by assuming four VRs and 36 different emission sources, numerical simulations were conducted and validated based on an actual open office on non-uniform concentrations of a surrogate for indoor air pollutants (IAPs) throughout the breathing zone. Second, the pre- and post-purified IAP distributions were obtained using a limited number of sensors and trained by two artificial neural networks. Third, the real-time optimization of PACs’ on/off operation and placements was accomplished by the particle swarm optimization algorithm to balance energy output and improve IAQ simultaneously. Results showed that by deploying four sensors, the predictions of post-purifying concentrations were in acceptable accuracy (i.e., CV-RMSE <2.0%) within 30–40s. Using at most three PACs resulted in a significant decline in local IAP concentration levels. Meanwhile, an average reduction of total energy consumption of MV and PACs was 34.6% compared to using MV to reach the same levels. Overall, this study supports the use of PACs to assist MV in achieving energy efficiency and good IAQ.

Replacing the Scalpel With a Computer Mouse: An Evaluation of Time Spent on Electronic Health Record for Plastic Surgery Residents and Its Impact on Resident Training.

Following the integration of the electronic health record (EHR) into the healthcare system, concern has grown regarding EHR use on physician well-being. For surgical residents, time spent on the EHR increases the burden of a demanding, hourly restricted schedule and detracts from time spent honing surgical skills. To better characterize these burdens, we sought to describe EHR utilization patterns for plastic surgery residents. Integrated plastic surgery resident EHR utilization from March 2019 to March 2020 was extracted via Cerner Analytics at a tertiary academic medical center. Time spent in the EHR on-duty (0600-1759) and off-duty (1800-0559) in the form of chart review, orders, documentation, and patient discovery was analyzed. Statistical analysis was performed in the form of independent t tests and Analysis of Variance (ANOVA). Twelve plastic surgery residents spent a daily average of 94 ± 84 minutes on the EHR, one-third of which was spent off-duty. Juniors (postgraduate years 1-3) spent 123 ± 99 minutes versus seniors (postgraduate years 4-6) who spent 61 ± 49 minutes (P < 0.01). Seniors spent 19% of time on the EHR off-duty, compared with 37% for juniors (P < 0.01). Chart review comprised the majority (42%) of EHR usage, followed by patient discovery (22%), orders (14%), documentation (12%), other (6%), and messaging (1%). Seniors spent more time on patient discovery (25% vs 21%, P < 0.001), while juniors spent more time performing chart review (48% vs 36%, P = 0.19). Integrated plastic surgery residents average 1.5 hours on the EHR daily. Junior residents spend 1 hour more per day on the EHR, including more time off-duty and more time performing chart review. These added hours may play a role in duty hour violations and detract from obtaining operative skill sets.

Leveraging Electronic Health Record Data and Measuring Interdependence in the Era of Precision Education and Assessment.

The era of precision education is increasingly leveraging electronic health record (EHR) data to assess residents' clinical performance. But precision in what the EHR-based resident performance metrics are truly assessing is not fully understood. For instance, there is limited understanding of how EHR-based measures account for the influence of the team on an individual's performance-or conversely how an individual contributes to team performances. This study aims to elaborate on how the theoretical understandings of supportive and collaborative interdependence are captured in residents' EHR-based metrics. Using a mixed methods study design, the authors conducted a secondary analysis of 5 existing quantitative and qualitative datasets used in previous EHR studies to investigate how aspects of interdependence shape the ways that team-based care is provided to patients. Quantitative analyses of 16 EHR-based metrics found variability in faculty and resident performance (both between and within resident). Qualitative analyses revealed that faculty lack awareness of their own EHR-based performance metrics, which limits their ability to act interdependently with residents in an evidence-informed fashion. The lens of interdependence elucidates how resident practice patterns develop across residency training, shifting from supportive to collaborative interdependence over time. Joint displays merging the quantitative and qualitative analyses showed that residents are aware of variability in faculty's practice patterns and that viewing resident EHR-based measures without accounting for the interdependence of residents with faculty is problematic, particularly within the framework of precision education. To prepare for this new paradigm of precision education, educators need to develop and evaluate theoretically robust models that measure interdependence in EHR-based metrics, affording more nuanced interpretation of such metrics when assessing residents throughout training.

Proliferations of Gated Communities and the Fear of Crimes: An Empirical Study of Some Select Gated Communities in Zaria and Sabon Gari Local Government Areas, Kaduna State, Nigeria

Gated communities are becoming increasingly popular in Zaria and Sabon Gari Local Government Areas of Kaduna State. The objectives of the study were to investigate the reasons for the proliferation of gated communities, and their safety and security. The study adopted the Defensible Space Theory. This article which is an empirical endeavour used the qualitative method in collecting and analysing data. In-depth interviews were conducted to elicit primary data. Key findings of the study revealed that the proliferation of gated communities was a result of the high level of insecurity and the quest for a secure community. The study further discovered that informants interviewed in the gated communities feel safe and secure against all forms of criminal activities. The study recommends that the Kaduna State government should design a policy that will make gated communities a residential development pattern in urban areas like Zaria and Sabon Gari.

Patterns of Prairie Soil Preference and Occupancy for the Threatened Mazama Pocket Gopher in Washington

Patterns of Prairie Soil Preference and Occupancy for the Threatened Mazama Pocket Gopher in Washington

Travel Characteristics of Urban Residents Based on Taxi Trajectories in China: Beijing, Shanghai, Shenzhen, and Wuhan

With the advancement of urban modernization, more and more residents are flocking to large cities, leading to problems such as severe traffic congestion, uneven distribution of spatial resources, and deterioration of the urban environment. These challenges pose a serious threat to the coordinated development of cities. In order to better understand the travel behavior of metropolitan residents and provide valuable insights for urban planning, this study utilizes taxi trajectory data from the central areas of Beijing, Shanghai, Shenzhen, and Wuhan. First, the relationship between daytime taxi drop-off points and urban amenities is explored using Ordinary Least Squares (OLS). Subsequently, Geographically Weighted Regression (GWR) techniques were applied to identify spatial differences in these urban drivers. The results show that commonalities emerge across the four cities in the interaction between external transport stops and commercial areas. In addition, the average daily travel patterns of residents in these four cities show a trend of “three peaks and three valleys”, indicating the commonality of travel behavior. In summary, this study explores the travel characteristics of urban residents, which can help urban planners understand travel patterns more effectively. This is crucial for the strategic allocation of transport resources across regions, the promotion of sustainable urban transport, and the reduction in carbon emissions.

A Review on Parking Occupancy Prediction and Pattern Analysis

Parking occupancy prediction and pattern analysis is a crucial component of modern urban management systems. Utilizing advanced data analysis techniques, this project aims to develop a predictive model for forecasting parking occupancy levels and analyzing patterns within parking data. By leveraging machine learning algorithms and statistical methods, the project seeks to provide insights into parking behavior and optimize resource allocation in urban areas. The implementation of parking occupancy prediction and pattern analysis contributes to efficient urban planning, improved traffic management, and enhanced user experience. Through the integration of predictive analytics, decision-makers can anticipate parking demand, optimize parking space utilization, and alleviate congestion in urban areas.This project explores the application of data- driven approaches to address challenges in parking management, including predicting peak parking times, identifying trends in parking occupancy, and optimizing parking infrastructure. By harnessing the power of data analysis, the project aims to enhance urban mobility, reduce environmental impact, and improve overall quality of life. Keywords: Parking occupancy prediction, Pattern analysis, Urban management systems, Data analysis techniques, Machine learning algorithms, Traffic management, Urban planning.