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Systematic efforts to study students' use of informal learning spaces are crucial for determining how, when and why students use such spaces. This case study provides an example of an effort to evaluate an informal learning space on the basis of students' usage of the space and the features within the space.

Use of heatmap camera technology and a semi-structured interview with a supervisor of an informal learning space supported the mixed-methods evaluation of the space.

Findings from both the heatmap outputs and semi-structured interview suggested that students' use of the informal learning space is limited due to the location of the space on campus and circumstances surrounding students' day-to-day schedules and needs.

Findings from both the heatmap outputs and semi-structured interview suggested that students' use of the informal learning space is limited due to the location of the space on campus and circumstances surrounding students' day-to-day schedules and needs. These findings are actively contributing to the authors’ institution’s efforts surrounding planning, funding and design of other informal learning spaces on campus.

While most research on instructors' and students' use of space has taken place in formal classrooms, some higher education scholars have explored ways in which college and university students use informal spaces around their campuses (e.g. Harrop and Turpin, 2013; Ramu et al., 2022). Given the extensive time students spend on their campuses outside of formal class meetings (Deepwell and Malik, 2008), higher education institutions must take measures to better understand how their students use informal learning spaces to allocate resources toward the optimization of such spaces. This mixed-methods case study advances the emerging global discussion on how, when and why students use informal learning spaces.

High levels of interior water vapour lead to condensation and black mould that in turn represent significant risks to residential properties and their occupants. Beliefs about window opening are good predictors of the degree to which householders will actually open windows to purge their homes of water vapour, including water vapour that they themselves generate. The present study tested if a short information-giving intervention could enhance householders’ beliefs that foster window opening as purge ventilation and, in turn, lead to greater window opening.

Data were collected from 242 UK householders with robust psychometrically sound measures embedded in an online self-report survey that also presented the intervention information.

The intervention led participants, and males in particular, to have significantly greater concerns about condensation and mould and significantly less concerns about heat loss costs arising from opening windows, and these altered beliefs in turn predicted a greater intention to open windows in the future.

By sharing simple information, surveyors and other building professionals can help householders take the simple step of opening their windows and so reduce the threats that condensation and mould present to themselves and their homes.

This is the first study to test (1) a time-based model that predicted the intervention would have a positive effect on specific window opening attitudes and that those new attitudes would in turn affect window opening intentions, and (2) if the intervention had different effects on men and women.

Meetings with colleagues are an essential activity in workplace collaboration. The iterative nature of collaborative work demands spaces that team members can access quickly and easily. Creating suitable meeting spaces will become more critical if the hybrid work model continues and the workplace environment becomes the hub for face-to-face collaborative time, learning and training. Workspace and fit-out is expensive so it is crucial that the investment in meeting spaces supports employees’ collaboration activities.

This paper presents a case study of a corporate organisation undertaken in New Zealand to investigate how employees from two business units use their workspace to collaborate within their own team and across other teams in their organisation. The study uses ethnographic techniques, including participant observation and in-depth face-to-face interviews.

The findings show that the frequency and nature of small group work in collaboration was underestimated in the initial planning of the new workspace. Although participants found the design and fit-out of the formal meeting rooms supportive of collaborative work, the meeting rooms were in high demand, and it was difficult to find a room at short notice. The breakout spaces were confusing because they lacked key design attributes identified by the participants as conducive to small group work. Design shortfalls together with fit-out features perceived as supportive of collaborative work are identified.

The research reports on employees’ perceptions and experiences across two functionally diverse business units, reflecting their different needs and concerns.

Existing research on how professionals manage after-hours connectivity to work has been dominated by studies on the strategies/practices individuals develop. In these studies, mobile technology is perceived as a tool or an enabler that supports otherwise human-centric connectivity decisions. This view sees technology as separate or external to the organisation, missing out on its nuanced role in shaping connectivity decisions. Our study aims to bring technology back into the sociomaterially imbricated context of connectivity and to unpack its parameters.

Drawing on data collected from documents and semi-structured interviews, we adopt the framework of “sociomaterial imbrications” (Leonardi, 2011) to understand the social and material parameters that influence connectivity management practices at two different academic institutions in Saudi Arabia.

The study identifies a set of social and material parameters (organisational, individual, technological and situational) that imbricate to shape, collectively and not individually, professionals’ connectivity management practices. Connectivity decisions to change practice (such as decisions of where, when or why to connect) or technology (how to connect) are not as distinct as they appear but originate from, and are founded on, imbricated sociomaterial parameters. Our study further suggests that connectivity decisions are shaped by individuals’ perceptions of sociomaterial imbrications, but decisions are not solely idiosyncratic. The context within which connectivity decisions are taken influences the type of decisions made.

Connectivity management emerged from sociomaterial imbrications within a context constitutive of four interacting parameters: organisational, technological, situational and individual. Decisions around the “how” and the “what” of connectivity – i.e. the practice of connectivity and its underpinning technology – originate from how people perceive sociomaterial imbrications as enabling or constraining within a context. Individual perceptions account for changes in practice and in technology, but the context they find themselves in is also important. For instance, we show that professionals may perceive a certain technology as affording, but eventually they may use another technology for communications due to social norms.

The purpose of this study is to describe peer support work in a men’s mental health unit from a lived experience and service user’s perspective. The intertwining of process (a lived experience perspective) and subject (the therapeutic value of peer support) leads to greater knowledge and insight into peer support for people with mental health problems.

This service user narrative draws on the extracts from a reflective journal of interactions and conversations with people with mental health problems as well as feedback from service users and staff about the value of peer support. These methods allow a first-person, service user’s, reflective and narrative account of peer support work.

Peer support work, particularly hearing voices sessions, are found to be highly therapeutic and worthwhile. They promote insight and create feelings of safety and hope in what can sometimes be a frightening and hostile ward environment. Peer support provides emotional and practical support. Sharing stories and experiences of mental illness with people leads to trust, feelings of being valued, heard and accepted as well as better experiences of care and being seen as a person first. Due to their shared experiences, peer support workers are able to befriend people with mental health problems on the ward. Peer support work bridges the gap and vacuum of care between people with mental health problems and staff. It compensates for understaffing to provide more holistic and person-centred care and support.

Lived experience/ service user perspectives and narratives on peer support are rare, particularly in a hospital setting. This article provides a rich, perhaps overlooked and hidden narrative on the nature of peer support work. People with mental health problems, like Ben, are often excluded from society, health and social care, education, employment and research. This narrative opens up a pathway to understanding peer support from a service user perspective.

The authors' work aims to identify the employer-specific drivers of the college (or university) wage gap, which has been identified as one of the major determinants of the dynamics of overall wage and income inequality in the past decades. The authors focus on three employer-level features that can be associated with asymmetries in the employment relation orientation adopted for college and non-college-educated employees: (1) size, (2) the share of standard employment and (3) the pervasiveness of incentive pay schemes.

The authors' establishment-level analysis (data from the Basic Survey on Wage Structure (BSWS), 2005–2018) focusses on Japan, an economy characterised by many unique economic and institutional features relevant to the aims of the authors' analysis. The authors use an adjusted measure of firm-specific college wage premium, which is not biased by confounding individual and establishment-level factors and reflects unobservable characteristics of employees that determine the payment of a premium. The authors' empirical methods account for the complexity of the relationships they investigate, and the authors test their baseline outcomes with econometric approaches (propensity score methods) able to address crucial identification issues related to endogeneity and reverse causality.

The authors' findings indicate that larger establishment size, a larger share of regular workers and more pervasive implementation of IPSs for college workers tend to increase the college wage gap once all observable workers, job and establishment characteristics are controlled for. This evidence corroborates the authors' hypotheses that a larger establishment size, a higher share of regular workers and a more developed set-up of performance pay schemes for college workers are associated with a better capacity of employers to attract and keep highly educated employees with unobservable characteristics that justify a wage premium above average market levels. The authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.

The authors' contribution to the existing knowledge is threefold. First, the authors combine the economics and management/organisation literature to develop new insights that underpin the authors' testable empirical hypotheses. This enables the authors to shed light on employer-level drivers of wage differentials (size, workforce composition, implementation of performance-pay schemes) related to many structural, institutional and strategic dimensions. The second contribution lies in the authors' measure of the “adjusted” college wage gap, which is calculated on the component of individual wages that differs between observationally identical workers in the same establishment. As such, the metric captures unobservable workers' characteristics that can generate a wage premium/penalty. Third, the authors provide empirical evidence on how three relevant establishment-level characteristics shape the heterogeneity of the (adjusted) college wage observed across organisations.

Occupant behavior can lead to considerable uncertainties in thermal comfort and air quality within buildings. To tackle this challenge, the use of probabilistic controls to simulate occupant behavior has emerged as a potential solution. This study seeks to analyze the performance of free-running households by examining adaptive thermal comfort and CO2 concentration, both crucial variables in indoor air quality. The investigation of indoor environment dynamics caused by the occupants' behavior, especially after the COVID-19 pandemic, became increasingly important. Specifically, it investigates 13 distinct window and shading control strategies in courtyard houses to identify the factors that prompt occupants to interact with shading and windows and determine which control approach effectively minimizes the performance gap.

This paper compares commonly used deterministic and probabilistic control functions and their effects on occupant comfort and indoor air quality in four zones surrounding a courtyard. The zones are differentiated by windows facing the courtyard. The study utilizes the energy management system (EMS) functionality of EnergyPlus within an algorithmic interface called Ladybug Tools. By modifying geometrical dimensions, orientation, window-to-wall ratio (WWR) and window operable fraction, a total of 465 cases are analyzed to identify effective control scenarios. According to the literature, these factors were selected because of their potential significant impact on occupants’ thermal comfort and indoor air quality, in addition to the natural ventilation flow rate. Additionally, the Random Forest algorithm is employed to estimate the individual impact of each control scenario on indoor thermal comfort and air quality metrics, including operative temperature and CO2 concentration.

The findings of the study confirmed that both deterministic and probabilistic window control algorithms were effective in reducing thermal discomfort hours, with reductions of 56.7 and 41.1%, respectively. Deterministic shading controls resulted in a reduction of 18.5%. Implementing the window control strategies led to a significant decrease of 87.8% in indoor CO2 concentration. The sensitivity analysis revealed that outdoor temperature exhibited the strongest positive correlation with indoor operative temperature while showing a negative correlation with indoor CO2 concentration. Furthermore, zone orientation and length were identified as the most influential design variables in achieving the desired performance outcomes.

It’s important to acknowledge the limitations of this study. Firstly, the potential impact of air circulation through the central zone was not considered. Secondly, the investigated control scenarios may have different impacts on air-conditioned buildings, especially when considering energy consumption. Thirdly, the study heavily relied on simulation tools and algorithms, which may limit its real-world applicability. The accuracy of the simulations depends on the quality of the input data and the assumptions made in the models. Fourthly, the case study is hypothetical in nature to be able to compare different control scenarios and their implications. Lastly, the comparative analysis was limited to a specific climate, which may restrict the generalizability of the findings in different climates.

Occupant behavior represents a significant source of uncertainty, particularly during the early stages of design. This study aims to offer a comparative analysis of various deterministic and probabilistic control scenarios that are based on occupant behavior. The study evaluates the effectiveness and validity of these proposed control scenarios, providing valuable insights for design decision-making.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

This systematic review paper aims to investigate seasonal ambient change’s impact on the incidence of falls among older adults.

The population, exposure, outcome (PEO) structured framework was used to frame the research question prior to using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis framework. Three databases were searched, and a total of 12 studies were found for inclusion, and quality appraisal was carried out. Data extraction was performed, and narrative analysis was carried out.

Of the 12 studies, 2 found no link between seasonality and fall incidence. One study found fall rates increased during warmer months, and 9 of the 12 studies found that winter months and their associated seasonal changes led to an increase in the incidence in falls. The overall result was that cooler temperatures typically seen during winter months carried an increased risk of falling for older adults.

Additional research is needed, most likely examining the climate one lives in. However, the findings are relevant and can be used to inform health-care providers and older adults of the increased risk of falling during the winter.