Residential Patterns Research Articles

Gendered processes of recruitment to elite higher educational institutions in mid‐twentieth century Britain

AbstractThis article uses rare and detailed data on matriculants to the University of Oxford during the middle decades of the twentieth century as a prism through which to consider gendered processes of recruitment to elite institutions. The article makes four key claims. First, the broader shifts in middle‐class women's labour market participation in the mid‐century are reflected in patterns of maternal occupation among matriculants, shifting from being predominantly housewives to professionals across the period. Related to this, the fathers of matriculants had similar professions whether their child was male or female, but mothers’ professions varied much more between male and female students. There was much more variation between mothers and fathers of students who attended what we term ‘elite’ schools. Finally, across the mid‐twentieth century, the number of male students from elite schools declined significantly, whereas the number of women students who had attended an ‘elite’ school was much steadier. Given the centrality of Oxford for processes of elite recruitment, these trends in their matriculants will have far wider implications for who gets access to elite positions in the decades after these shifts were occurring, revealing in some ways the continuity of class privilege and the increasingly salient role of mother's occupation in processes of elite reproduction.

A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning

The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.

Occupant behavioral adjustments and thermal comfort with torso and/or foot warming in two cold indoor environments

Personal warming systems have been shown to improve occupant thermal comfort in cold indoor environments. Currently, there are almost no published comprehensive studies on the examination of occupant behavioral patterns in relation to the use of personal warming systems. Thus, this study presented a human trial study to investigate the effects of torso and foot warming on thermal comfort of young female occupants in two cold indoor environments (15 °C and 17 °C), based on a water warming suit (WWS) and HYB (a WWS combined with heated socks). Besides, occupant behavior adjustments regarding water temperature setpoints for WWS and electrical heating levels for heated socks were thoroughly investigated. WWS and HYB improved occupant thermal comfort at 15 °C and 17 °C, with ≥85.7 % of ‘No Change’ OTPVs (overall thermal preference votes) observed during the last 20 min of exposure. Foot heating can significantly reduce heating demand on the upper torso, both in terms of water setting temperatures and total heating time. HYB exhibited a 25.4 % and 43.3 % shorter total duration with water setting temperatures of ≥40 °C compared to WWS at 15 °C and 17 °C, respectively. Further, occupants have employed a trial-and-error adjustment approach to determine the heating temperature/level that offers optimal thermal comfort in the cold. Lastly, the use of two PWS combinations has the potential to reduce energy consumption for indoor heating by 62.9 %, 32.1 %, and 53.7 % in cities such as San Francisco, Phoenix, and Minneapolis (baseline heating setpoint: 21.5 °C).

Evaluation of Deep Learning-Based Non-Intrusive Thermal Load Monitoring

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems.

Occupational patterns of opioid-related harms comparing a cohort of formerly injured workers to the general population in Ontario, Canada.

The role of work-related injuries as a risk factor for opioid-related harms has been hypothesized, but little data exist to support this relationship. The objective was to compare the incidence of opioid-related harms among a cohort of formerly injured workers to the general population in Ontario, Canada. Workers' compensation claimants (1983-2019) were linked to emergency department (ED) and hospitalization records (2006-2020). Incident rates of opioid-related poisonings and mental and behavioural disorders were estimated among 1.7 million workers and in the general population. Standardized incidence ratios (SIRs) and 95% confidence intervals (CI) were calculated, adjusting for age, sex, year, and region. Compared to the general population, opioid-related poisonings among this group of formerly injured workers were elevated in both ED (SIR = 2.41, 95% CI = 2.37-2.45) and hospitalization records (SIR = 1.54, 95% CI = 1.50-1.59). Opioid-related mental and behavioural disorders were also elevated compared to the general population (ED visits: SIR = 1.86, 95% CI = 1.83-1.89; hospitalizations: SIR = 1.42, 95% CI = 1.38-1.47). Most occupations and industries had higher risks of harm compared to the general population, particularly construction, materials handling, processing (mineral, metal, chemical), and machining and related occupations. Teaching occupations displayed decreased risks of harm. Findings support the hypothesis that work-related injuries have a role as a preventable risk factor for opioid-related harms. Strategies aimed at primary prevention of occupational injuries and secondary prevention of work disability and long-term opioid use are warranted.

Transforming Public Spaces in Post-Socialist China’s Danwei Neighbourhoods: The Third Dormitory of the Party Committee of Shandong Province

The urban residential pattern in China experienced two significant transitions during the second half of the 20th century. The first happened in the 1960s, based on the Soviet model, when a large-scale community model was built led by government enterprises and institutions under a planned economy; the second was in 1998 when the real estate market-led socialised community model emerged after the reform of commercial housing. The former is characterised by the integration of supervisory units, service units, and property owners: Residents enjoy the right to use the residences and supporting services provided by their affiliated institutions, while for the latter case, supervisory units, service units, and property owners are separate. New conflicts have been found in Danwei neighbourhoods with the housing commercialisation reform. This research focuses on the Third Dormitory of the Party Committee of Shandong Province as a case study to analyse the transformation of public space in the Danwei neighbourhood during the post-socialist era. Through archival research, interviews, and observation, this research has found that two forces that celebrate marketisation by the new residents and resist marketisation by the original residents coexist in the Third Dormitory. Unregulated spatial practices have resulted from the incomplete control of the owners of public space by the provincial government office. This research offers an example of public space transformations in Danwei neighbourhoods, which have undergone incomplete marketisation. The reflections on the Third Dormitory provide references for future neighbourhood management and policy-making.

Energy-efficiency oriented occupancy space optimization in buildings: A data-driven approach based on multi-sensor fusion considering behavior-environment integration

Buildings contribute significantly to global energy consumption. Optimizing internal building space layout is an essential approach for reducing energy consumption. However, proactively improving energy efficiency by building space design is still challenging requiring comprehensive consideration of complex interactions between indoor environment and occupant behavior, which is less studied previously. Considering behavior-environment integration, this study proposes a data-driven approach based on multi-sensor fusion for energy-efficiency oriented occupancy space optimization in buildings. Firstly, time series data including indoor environment and occupant behavior were collected based on multi-sensor fusion. Then, a data-integrated Convolutional Neural Network (CNN) model was developed for occupancy state classification. Based on obtained occupant schedules, space occupancy patterns of users were extracted using hierarchical clustering, and space optimization was further conducted for energy efficiency improvement. Finally, energy consumption was predicted with random forest regression after space optimization, and the impact of occupancy space optimization on energy efficiency can be evaluated. The proposed method was successfully applied in an academic office building on a campus in Wuhan, China, which helped achieve energy consumption reduction by 23.5 %. This study presents a promising path towards sustainable energy goals in building design, which serves as advanced guidance in the management of building energy performance.

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.