High Occupancy Research Articles

Chinese students’ willingness to study abroad: The personal and familial influencing factors

Background: Studying abroad is no longer a new phenomenon. The popularity of Chinese students studying abroad is well known and the enthusiasm for studying abroad among students has been consistently strong for many years, especially with the continuous development of the Chinese economy. Aims: This paper will examine personal and family factors which influence the willingness to study abroad among Chinese university students. The application of these influencing factors can potentially have an impact in the formulation of government-related policies, the establishment and development of study abroad agencies, and the decision process among students when choosing to study abroad. Methods: The current study utilized a self-report approach to examine the potential impact of several personal and family factors on the willingness to study abroad. Results: Utilizing multivariate analysis and statistical analysis modeling, it was found that willingness to study abroad is greatest among female students with moderate to high grades, who are from urban areas and non-agricultural households, with fathers in high status occupations, mothers with higher levels of education, and higher family income. These factors embody the intergenerational transmission of benefits associated with family capital.

Progressing a river health assessment framework to tropical waters

Diverse anthropogenic activities have threatened the valuable tropical biodiversity and freshwater reserves. Despite that, there is little concern in the Brazilian legislation about the ecological integrity of aquatic ecosystems. We proposed a framework based on two joint indices, allowing the creation of a management tool. The first is the Tropical Water Healthy Index (TWHI), a river index that integrates pressures on aquatic ecosystems, their conditions, and societal/governmental response. The second is the Suitability of the Class of Use (SCU), which measures whether rivers’ conditions are in accordance with their classes of use proposed for the legal Brazilian water framework. Our results showed that most sites had good river health conditions, with the worst conditions occurring in areas with high urban occupations followed by agricultural areas. The second index indicated that few streams were poorly or incompatible with their classes of use, suggesting their classifications were based on current conditions but not future intentions. The force of these joint analyses was shown in the Melchior River stretch, which was classified as compatible with SCU, but the TWHI was critical. This is good to show that we simultaneously have a problem with the legislation and to indicate something that needs to be done quickly for managers. Therefore, we built a management model that can assess the real situation of aquatic ecosystems and define efficient management strategies, producing alerts for the decision-maker.

Wheat Topoisomerase VI Positively Regulates the Biosynthesis of Cuticular Wax and Cutin.

Lipophilic cuticles mainly composed of wax mixtures and cutin matrices seal the plant epidermis and control plant development and environmental adaptation. Although cuticle-associated traits have been selected in the breeding of agronomically important cereal bread wheat, the biosynthesis of wheat cuticular wax and cutin remains poorly understood. Herein, wheat topoisomerase VI was characterized as an essential activator of cuticular wax and cutin biosynthesis. Knock-down of wheat TaTOP6A, TaTOP6B, TaRHL1, or TaBIN4 gene encoding component of topoisomerase VI resulted in decreased loads of leaf cuticular wax and cutin, as well as increased leaf cuticle permeability. Moreover, TaCYP86A2 was identified as a key component of the wheat cutin biosynthetic machinery. Reduction of wheat TaCYP86A2 expression led to decreased cutin accumulation and enhanced cuticle permeability. In addition, TaTOP6A, TaTOP6B, TaRHL1, or TaBIN4 was shown to enrich at the promoter regions of the wax biosynthesis gene TaKCS1 and the cutin biosynthesis gene TaCYP86A2. Importantly, chromatin at TaKCS1 and TaCYP86A2 promoters is marked by high nucleosome occupancy and low histone acetylation in TaTOP6A-, TaTOP6B-, TaRHL1-, or TaBIN4-silenced wheat leaves. These results collectively support that wheat topoisomerase VI positively regulates the biosynthesis of cuticular wax and cutin probably via maintaining a permissive chromatin state at TaKCS1 and TaCYP86A2 genes.

Visual software defect prediction method based on improved recurrent criss-cross residual network

Purpose This study aims to solve the problems of large training sample size, low data sample quality, low efficiency of the currently used classical model, high computational complexity of the existing concern mechanism, and high graphics processing unit (GPU) occupancy in the current visualization software defect prediction, proposing a method for software defect prediction termed recurrent criss-cross attention for weighted activation functions of recurrent SE-ResNet (RCCA-WRSR). First, following code visualization, the activation functions of the SE-ResNet model are replaced with a weighted combination of Relu and Elu to enhance model convergence. Additionally, an SE module is added before it to filter feature information, eliminating low-weight features to generate an improved residual network model, WRSR. To focus more on contextual information and establish connections between a pixel and those not in the same cross-path, the visualized red as integer, green as integer, blue as integer images are inputted into a model incorporating a fused RCCA module for defect prediction. Design/methodology/approach Software defect prediction based on code visualization is a new software defect prediction technology, which mainly realizes the defect prediction of code by visualizing code as image, and then applying attention mechanism to extract the features of image. However, the challenges of current visualization software defect prediction mainly include the large training sample size and low sample quality of the data, and the classical models used today are not efficient, and the existing attention mechanisms have high computational complexity and high GPU occupancy. Findings Experimental evaluation using ten open-source Java data sets from PROMISE and five existing methods demonstrates that the proposed approach achieves an F-measure value of 0.637 in predicting 16 cross-version projects, representing a 6.1% improvement. Originality/value RCCA-WRSR is a new visual software defect prediction based on recurrent criss-cross attention and improved residual network. This method effectively enhances the performance of software defect prediction.

Development of a MAPS-based upstream tracker for the LHCb upgrade II

The LHCb Upstream Tracker is a silicon strip tracking detector that started operation in the Run 3 of the LHC. To meet the requirements imposed by high occupancy and data rate at the HL-LHC, a new Upstream Pixel (UP) tracker is proposed for LHCb Upgrade II based on CMOS pixel sensors. A preliminary detector layout is presented. The design of a sensor in a 55 nm high-voltage CMOS technology is introduced and further development is in process. Performance studies of UP with simulation are also ongoing.

Impact of COVID-19 on Airbnb occupancy rate in Bandung, Indonesia

The emergence of sharing economy, especially accommodation platform has enable people to rent and capitalize on their private properties by renting to others. However, with the covid-19 pandemic and proximity to first-tier city it is crucial to examine how these sharing accommodation properties survives.The paper investigates how urban neighbourhoods, Airbnb characteristics and urban mobility restrictions determine rental occupancy rates in Bandung, a second-tier city, during the COVID-19 pandemic. This paper examines; (i) the dynamics of determinant factors of occupancy rates before and after the pandemic and (ii) the impact of mobility and spatial proximity to first-tier city on sharing accommodations’ occupancy rates. This study uses econometric and spatial analysis with more than 2200 Airbnb property listings registered between 2016 and 2020 in Bandung city.Our study found a shift of preferences in a more urban direction and electronic word-of-mouth (eWOM) variables continuing with a slight decline in the pandemic. Comparison between before and during the pandemic suggests the persistent of high occupancy rate concentration only in in private listings, and located near to tourist attractions and a variety of amenities. Our finding calls for urban policies that limits the geographical distribution of short-term rental (STR) properties to avoid wider gentrification, safety risks, and pressure to the local housing supply.

Deep Learning Models for Vision-Based Occupancy Detection in High Occupancy Buildings

Deep Learning Models for Vision-Based Occupancy Detection in High Occupancy Buildings

CFD Evaluation of Respiratory Particle Dispersion and Associated Infection Risk in a Coach Bus with Different Ventilation Configurations

The COVID-19 pandemic has underscored the urgency of understanding virus transmission dynamics, particularly in indoor environments characterized by high occupancy and suboptimal ventilation systems. Airborne transmission, recognized by the World Health Organization (WHO), poses a significant risk, influenced by various factors, including contact duration, individual susceptibility, and environmental conditions. Respiratory particles play a pivotal role in viral spread, remaining suspended in the air for varying durations and distances. Experimental studies provide insights into particle dispersion characteristics, especially in indoor environments where ventilation systems may be inadequate. However, experimental challenges necessitate complementary numerical modeling approaches. Zero-dimensional models offer simplified estimations but lack spatial and temporal resolution, whereas Computational Fluid Dynamics, particularly with the Discrete Phase Model, overcomes these limitations by simulating airflow and particle dispersion comprehensively. This paper employs CFD-DPM to simulate airflow and particle dispersion in a coach bus, offering insights into virus transmission dynamics. This study evaluates the COVID-19 risk of infection for vulnerable individuals sharing space with an infected passenger and investigates the efficacy of personal ventilation in reducing infection risk. Indeed, the CFD simulations revealed the crucial role of ventilation systems in reducing COVID-19 transmission risk within coach buses: increasing clean airflow rate and implementing personal ventilation significantly decreased particle concentration. Overall, infection risk was negligible for scenarios involving only breathing but significant for prolonged exposure to a speaking infected individual. The findings contribute to understanding infection risk in public transportation, emphasizing the need for optimal ventilation strategies to ensure passenger safety and mitigate virus transmission.

Spinal Injuries and Spine Care in the US Military Health System (2001-Present).

Literature Review. The goal of this study was to provide a comprehensive outline of spinal injuries that may transpire over the course of military service from traumatic to repetitive stress injuries and chronic sequalae. We considered studies that assessed spinal injuries in the combat and non-combat settings as reported in the literature over the last 15-20 years. Military service places servicemembers under substantial physical demands, while also exposing them to dangerous, unpredictable environments. As a result, servicemembers are at an increased risk of spinal injuries from combat-related trauma and other causes. They may have different care needs and recovery profiles when compared to civilians with spinal disorders. We performed a review of the available literature on spinal injuries and spinal care in the Military Health System from 2001-present. The studies discussed in this review were primarily focused on the conflicts in both Iraq and Afghanistan from over ten years ago and do not fully capture the present-day advancements in military technology that may have an impact on the potential for spinal injuries. The long-term effects of sustained military service and the relative influence of high demand versus sedentary military occupations on the development of spinal disorders remains poorly understood. Given the changing nature of military service, both with respect to the demographic in uniform and the ever-evolving nature of modern combat, we believe that only a long-term prospective observational study dedicated to the surveillance of spinal problems could effectively answer these questions. Further research into the present-day characterization of spinal injuries is warranted given the advancements in both military technology and spine care that have occurred over the last ten years.

BATS: Bat‐aggregated time series—A python‐based toolkit for landscape‐level monitoring of free‐tailed bats via weather radar

Abstract The US operates a system of 160 S‐band Doppler weather radars known as NEXRAD (NEXt generation weather RADar) that continuously monitors the airspace around the majority of the United States and outlying territories. These radars detect and track birds, insects, and bats. Free‐tailed bats (Genus Tadarida) provide considerable ecosystem services through their voracious insect consumption; but their movements and ecosystem service provision have historically been difficult to track/study in space and time. We introduce ‘BATS’, a Python toolkit that streamlines the process of downloading, classifying, and aggregating time series of free‐tailed bats across large landscapes. BATS retrieves data from NOAA's weather radar data repositories and classifies the processed radar data using a pre‐trained ML trained to detect and classify radar echoes associated with free‐tailed bats. We trained various machine learning approaches at classifying pixels containing free‐tailed bats and compared the effectiveness across approaches. With an AUC of 0.963, the neural network approach is highly effective in identifying free‐tailed bats in NEXRAD data over our study sites in California and Texas. Furthermore, BATS is capable of quickly distilling 6 months of radar data from a single tower (3.5 Tb) into a single 15 Mb‐sized map of bat occurrence, contingent on available computing resources. BATS will help scientists and stakeholders identify areas of high bat occupancy at the landscape level over long periods of time. This ability has the potential to increase our understanding of the economic and agricultural value of these species.

Waterless structures in the Protein Data Bank.

The absence of solvent molecules in high-resolution protein crystal structure models deposited in the Protein Data Bank (PDB) contradicts the fact that, for proteins crystallized from aqueous media, water molecules are always expected to bind to the protein surface, as well as to some sites in the protein interior. An analysis of the contents of the PDB indicated that the expected ratio of the number of water molecules to the number of amino-acid residues exceeds 1.5 inatomic resolution structures, decreasing to 0.25 at around 2.5 Å resolution. Nevertheless, almost 800 protein crystal structures determined at a resolution of 2.5 Å or higher are found in the current release of the PDB without any water molecules, whereas some other depositions have unusually low or high occupancies of modeled solvent. Detailed analysis of these depositions revealed that the lack of solvent molecules might be an indication of problems with either the diffraction data, the refinement protocol, the deposition process or a combination of these factors. It is postulated that problems with solvent structure should be flagged by the PDB and addressed by the depositors.

An international comparison of Long-term Care Facility outcomes during the COVID-19 pandemic

Abstract Background The COVID-19 pandemic has had huge repercussions for public health internationally and different countries had different outcomes based on preconceived policy interventions. Lessons influencing future policy have been learned, notably how best to manage vulnerable populations, particularly those living in long-term care facilities (LTCFs). In Ireland LTCF residents accounted for 56% of deaths during the first wave of the pandemic and it is imperative to establish the causes of this. International research has highlighted how factors including county incidence rate, facility size, occupancy, LTCF ownership and compliance with health inspection regulations influenced LTCF COVID-19 outcomes. The aim of this research is to explore the impact of these factors in an Irish context and compare to best international standards. Methods The main outcome considered was deaths per 100 beds. Data on crude mortality were available for 580 Irish LTCFs up to 28 May 2020. Data regarding occupancy, ownership, facility size and compliance with regulations were obtained for these LTCFs from a novel dataset of 1130 available LTCF regulatory inspection reports between 2018- 2022. Regression analysis was carried out to show how LTCF mortality was predicted by examined variables. Results The most significant predictor of deaths from COVID-19 in Irish LTCFs resulting from this research was LTCF occupancy (Odds ratio:1.029, significance <0.001). Regional community incidence, in concordance with international literature was also an important factor. Other stated predictors in this model were either non-significant or only marginally significant. These results are analysed in conversation with international research of the same variables. Conclusions To protect LTCF residents for potential future pandemics, attention should be paid to monitoring and suppressing local incidence rates and moves should be implemented towards designing smaller facilities with individual rooms for vulnerable residents. Key messages • Irish long-term care pandemic mortality was predicted by high occupancy of facilities and high regional COVID-19 incidence. • Comparing International COVID-19 outcomes in long-term care facilities can provide guidelines for the future.

Comparing the South African and Brazilian student uprisings (2015–2016): Similarities, Circulations, and Distance

ABSTRACT South Africa and Brazil are notorious for having some of the highest levels of (racialised) inequality on the planet, and dysfunctional education systems failing millions in their aspirations for a better life. Over 2015-16, student protests erupted across Brazilian and South African schools and universities, challenging these conditions through strikes, occupations, on-campus and street protests, and intellectual production. This paper builds on a World-Systems theoretical framework to understand the similarities, circulation, and distance between the two contemporaneous movements. In Brazil, the ‘Primavera Secundarista' high school occupations dealt primarily with local political dynamics, such as school closures, and intersectional forms of oppression. In South Africa, the ‘Fallist' university mobilisations demanded free, decolonised education, alongside addressing issues of outsourcing and institutional racism. In the first section, I give a comparative overview of these student movements’ similarities, situating them in their context and highlighting the key concerns and political and pedagogical practices of student activists in each. While these movements did not generally engage with each other, there were some important points of interaction, discussed in the second section. While highlighting these points of connection, I also advance an argument in the third section for why there was nevertheless relatively little direct engagement between these contemporaneous movements that otherwise had so much in common. The points of connection and disconnection between these similar movements offers insights about the functioning of the contemporary World-System and the modes of transnational circulation between movements therein, with lessons for transnational understandings of social movements, especially South-South movements.

Binding profiles for 961 Drosophila and C. elegans transcription factors reveal tissue-specific regulatory relationships.

A catalog of transcription factor (TF) binding sites in the genome is critical for deciphering regulatory relationships. Here we present the culmination of the efforts of the Model Organism ENCyclopedia Of DNA Elements (modENCODE) and the model organism Encyclopedia of Regulatory Networks (modERN) consortia to systematically assay TF binding events in vivo in two major model organisms, Drosophila melanogaster (fly) and Caenorhabditis elegans (worm). These datasets comprise 605 TFs identifying 3.6M sites in the fly and 356 TFs identifying 0.9 M sites in the worm and represent the majority of the regulatory space in each genome. We demonstrate that TFs associate with chromatin in clusters termed "metapeaks", that larger metapeaks have characteristics of high occupancy target (HOT) regions, and that the importance of consensus sequence motifs bound by TFs depends on metapeak size and complexity. Combining ChIP-seq data with single cell RNA-seq data in a machine learning model identifies TFs with a prominent role in promoting target gene expression in specific cell types, even differentiating between parent-daughter cells during embryogenesis. These data are a rich resource for the community that should fuel and guide future investigations into TF function. To facilitate data accessibility and utility, all strains expressing GFP-tagged TFs are available at the stock centers for each organism. The chromatin immunoprecipitation sequencing data are available through the ENCODE Data Coordinating Center, GEO, and through a direct interface that provides rapid access to processed data sets and summary analyses, as well as widgets to probe the cell type-specific TF-target relationships.

Multi-lane cellular automata model considering bus-HOV lane

To quantify the impacts of high occupancy vehicles (HOV) entering the exclusive bus lane on road traffic efficiency, the car following and lane changing characteristics of bus, HOV and non-HOV were analyzed when setting the bus-HOV lane. Using the theory of cellular automata (CA) and combining the control rules of the bus-HOV lane, the lane occupied by a vehicle and the type of a vehicle regarded as constraints were introduced into the asymmetric lane changing rules, and then a multi-lane CA model was established for the car following and lane changing of multi-type vehicles considering the bus-HOV lane. MATLAB software was used to accomplish numerical simulation to analyze the specific effects of the proportion of HOV, bus departure frequency and bus stop spacing on the traffic flow parameters and their relationships. The results show that: when the proportion of HOV is less than 0.5 on a three-lane road segment, setting the bus-HOV lane can not only improve road traffic efficiency but also ensure the speed of bus operation under the condition of high initial space occupancy; when the proportion of HOV exceeds 0.5 , it is difficult to improve road traffic efficiency by restricting non-HOV from entering the bus-HOV lane; when the initial space occupancy is greater than 0.2 , the higher the bus departure frequency is, the greater the decline of traffic volume of the bus-HOV lane is; when there are 5 bus routes to dispatch a bus every 3 minutes, compared with the low initial space occupancy, the traffic volume of the bus-HOV lane under the condition of the high initial space occupancy reduces by about 30%; the bus stop spacing more significantly affects the traffic efficiency of the bus-HOV lane, the longer the bus stop spacing is, the higher the frequency of HOV lane changing is, the more the increase of the congestion segments on the bus-HOV lane and the adjacent normal lane is, thus the traffic efficiency of the whole road is influenced.

A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets

The airport apron is a high-risk area for aircraft collisions due to its heavy operational load and high aircraft density. Currently, existing quantitative models for apron collision risk provide limited consideration and classification of risk areas. In response, this paper proposes a Petri net-based method for assessing aircraft collision risk. The method predicts the probability of aircraft reaching different areas at different times based on operational data, enabling the calculation of collision risks within the Petri net framework. This approach highlights areas with potential collision risks and provides a classification evaluation. Subsequently, aircraft path re-planning is carried out to reduce collision risks. The model simplifies the complex operations of the apron system, making the calculation process clearer. The results show that, during the mid-phase of aircraft taxiing, there is a significant deviation between the actual and ideal positions of aircraft. Areas with high taxiway occupancy are more prone to collision risks. On peak days, due to relatively high flight volumes, the frequency of collision risks is 14% higher than on regular days, with an average risk increase of 23.3%, and the risks are more concentrated. Therefore, reducing collision risks through path planning becomes more challenging. It is recommended to focus attention on areas with high taxiway occupancy during peak periods and carefully plan routes to ensure apron safety.

Physiology-based pharmacokinetic model with relative transcriptomics to evaluate tissue distribution and receptor occupancy of anifrolumab.

Type I interferons contribute to the pathogenesis of several autoimmune disorders, including systemic lupus erythematosus (SLE), systemic sclerosis, cutaneous lupus erythematosus, and myositis. Anifrolumab is a monoclonal antibody that binds to subunit 1 of the type I interferon receptor (IFNAR1). Results of phase IIb and phase III trials led to the approval of intravenous anifrolumab 300 mg every 4 weeks (Q4W) alongside standard therapy in patients with moderate-to-severe SLE. Here, we built a population physiology-based pharmacokinetic (PBPK) model of anifrolumab by utilizing the physiochemical properties of anifrolumab, binding kinetics to the Fc gamma neonatal receptor, and target-mediated drug disposition properties. A novel relative transcriptomics approach was employed to determine IFNAR1 expression in tissues (blood, skin, gastrointestinal tract, lungs, and muscle) using mRNA abundances from bioinformatic databases. The IFNAR1 expression and PBPK model were validated by testing their ability to predict clinical pharmacokinetics over a large dose range from different clinical scenarios after subcutaneous and intravenous anifrolumab dosing. The validated PBPK model predicted high unbound local concentrations of anifrolumab in blood, skin, gastrointestinal tract, lungs, and muscle, which exceeded its IFNAR1 dissociation equilibrium constant values. The model also predicted high IFNAR1 occupancy with subcutaneous and intravenous anifrolumab dosing. The model predicted more sustained IFNAR1 occupancy ≥90% with subcutaneous anifrolumab 120 mg once-weekly dosing vs. intravenous 300 mg Q4W dosing. The results informed the dosing of phase III studies of anifrolumab in new indications and present a novel approach to PBPK modeling coupled with relative transcriptomics in simulating pharmacokinetics of therapeutic monoclonal antibodies.

Analysis of A Two-queue Discrete-time Model with Random Alternating Service Under High Occupancy in One Queue

Analysis of A Two-queue Discrete-time Model with Random Alternating Service Under High Occupancy in One Queue

Assessing evacuation risks in prominent historical mosques: An integrated quantitative and qualitative approach via the HM-ERI framework

Historical mosques, with their unique architectural features and high occupant densities, pose distinct challenges for safe and efficient evacuation. This study introduces the Historical Mosque Evacuation Risk Index (HM-ERI), a novel and comprehensive framework specifically designed to assess evacuation risks in these culturally significant structures. The HM-ERI model integrates three quantitative criteria derived from agent-based simulations and seventeen qualitative criteria assessed through on-site evaluations, generating two dimensionless risk scores: HM-ERIQN (quantitative risk) and HM-ERIQL (qualitative risk). Analysisof 12 prominent 15th and 16th century mosques in Turkey revealed a spectrum of evacuation risk levels. The mosques' HM-ERIQN values ranged from 0.27 (low risk) to 0.80 (high risk) in the range of 0–1, indicating variations in potential congestion and evacuation efficiency based on simulated total evacuation times ranging from 2 min 46.3 s to 8 min 18.5 s. HM-ERIQL values, reflecting the severity of qualitative factors on a 0–1 scale such as the lack of alternative exits, improper placement of shoe racks, complex building layouts, and limited emergency preparedness, fell between 0.46 and 0.73, indicating moderate to high risks.By integrating expert judgment and utilizing data from both simulations and on-site observations, the HM-ERI framework provides a practical tool for identifying high-risk historical mosques and informing targeted interventions to enhance evacuation safety. This research contributes to developing enhanced evacuation standards for these culturally significant structures, enabling improved safety evaluations and guiding effective preservation strategies that prioritize human life and cultural heritage.

Bouncing Back: Hospital Reliance On Transitional Care Beds – A Disservice To Patients? A Review Of Readmission Rates

Abstract Background The Health Service Executive (HSE) developed an Urgent and Emergency Care (UEC) Operational Plan in 2023. One UEC action was an aim to transfer “clinically appropriate” patients to alternative care settings e.g. Transitional Care Beds (TCBs) to relieve pressure caused by high occupancy rates in acute hospitals. Inclusion criteria for TCB use is ill defined and we sought to evaluate their usage. Methods Retrospective analysis of characteristics and readmission rates (RAR) of patients discharged from a tertiary hospital to TCB from Oct 1st, 2023 - Jan 1st, 2024. Results 158 patients were discharged from hospital to TCBs. 49% (n=77) were female, mean [SD] age 77.82 [10.09] years. The median length of stay (LOS) was 15 days. 50% (n=79) were discharged from surgical services, 40.5% (n=64) from medical teams, 8% (n=13) from oncology services, 7% (n=11) from geriatric medicine and 1% (n=2) from ED. 27% (n=43) had a “fall” documented as their discharge diagnosis. 32% (n=51) of patients were readmitted within 90 days, 11% (n=18) within 30 days and 4% (n=6) within 14 days. 30% (n=15) were readmitted directly from TCB. Average time between discharge and readmission was 44.7 days. Median LOS on readmission was 10 (IQR 15.5 days). 9% (n=14) had an eventual discharge to long term care (LTC) either from a subsequent admission or transitioned directly from TCB. 6% (n=10) of patients died. Conclusion Of those readmitted, one third were directly from TCBs suggesting discharge to TCB may have been premature and patient selection inappropriate subsequently resulting costly readmissions. Although interventions are needed to relieve capacity pressure in hospitals, greater emphasis needs to be placed on access for patients to designated rehabilitation programmes. We should aim to support older adults following acute hospital admissions by carrying out comprehensive geriatric assessment to maximise independence and reduce likelihood of hospital readmission or transition to LTC.