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This study aims to create an efficient approach to validate building energy simulation models amidst challenges from time-intensive data collection. Emphasizing precision in model calibration through strategic short-term data acquisition, the systematic framework targets critical adjustments using a strategically captured dataset. Leveraging metrics like Mean Bias Error (MBE) and Coefficient of Variation of Root Mean Square Error (CV(RMSE)), this methodology aims to heighten energy efficiency assessment accuracy without lengthy data collection periods.

A standalone school and a campus facility were selected as case studies. Field investigations enabled precise energy modeling, emphasizing user-dependent parameters and compliance with standards. Simulation outputs were compared to short-term actual measurements, utilizing MBE and CV(RMSE) metrics, focusing on internal temperature and CO₂ levels. Energy bills and consumption data were scrutinized to verify natural gas and electricity usage against uncertain parameters.

Discrepancies between initial simulations and measurements were observed. Following adjustments, the standalone school 1’s average internal temperature increased from 19.5 °C to 21.3 °C, with MBE and CV(RMSE) aiding validation. Campus facilities exhibited complex variations, addressed by accounting for CO₂ levels and occupancy patterns, with similar metrics aiding validation. Revisions in lighting and electrical equipment schedules improved electricity consumption predictions. Verification of natural gas usage and monthly error rate calculations refined the simulation model.

This paper tackles Building Energy Simulation validation challenges due to data scarcity and time constraints. It proposes a strategic, short-term data collection method. It uses MBE and CV(RMSE) metrics for a comprehensive evaluation to ensure reliable energy efficiency predictions without extensive data collection.

Energy-efficiency measures have always been important when renovating aging building stock. For property owners, window intervention is a recurring issue. Replacement is common to reduce operational heating energy (OHE) use, something many previous building renovation studies have considered. Maintaining rather than replacing windows has received less attention, especially for multi-residential buildings in a subarctic climate where there is great potential for OHE savings. The objective was to assess the life cycle (LC) climate impact and costs of three window maintenance and replacement options for a 1980s multi-residential building in subarctic Sweden.

The options’ embodied and operational impacts from material production, transportation and space heating were assessed using a life cycle assessment (LCA) focusing on global warming potential (LCA-GWP) and life cycle costing (LCC) with a 60-year reference study period. A sensitivity analysis was used to explore the impact of uncertain parameters on LCA-GWP and LCC outcomes.

Maintaining instead of replacing windows minimized LC climate impact and costs, except under a few specific conditions. The reduced OHE use from window replacement had a larger compensating effect on embodied global warming potential (E-GWP) than investment costs, i.e. replacement was primarily motivated from a LC climate perspective. The LCA-GWP results were more sensitive to changes in some uncertain parameters, while the LCC results were more robust.

The findings highlight the benefits of maintenance over replacement to reduce costs and decarbonize window interventions, challenging property owners’ preference to replace windows and emphasizing the significance of including maintenance activities in future renovation research.

The purpose of this study is to examine crisis effects on customer satisfaction and underlying drivers by adding a new set of data to previous research. The core questions are: are the findings from Hallencreutz and Parmler (2019, 2021) sustained or can new customer demands, needs, expectations and behaviours be traced in the wake of the ongoing crisis?

A first study covering 2005–2017 was completed in 2018, published online in 2019 and in print in 2021 (Hallencreutz and Parmler, 2021). This new study adds the years 2018–2023 to the data set and reuses the partial least squares (PLS) approach to structural equation models, also known as PLS path modelling.

This additional study sustains the results from the initial study (Hallencreutz and Parmler, 2019, 2021). The variable product quality has been substituted by service quality as one of the most crucial drivers for customer satisfaction together with brand image, and the current state of permacrisis has not changed that.

The study is built on Swedish data from the EPSI Rating Initiative (Eklöf and Westlund 2002) covering customer perceptions in banking, insurance (life and non-life), telco (mobile operators, broadband and Pay-tv) and energy (trade, distribution and heating) over the years 2005–2023.

The study emphasizes the importance of understanding how customer satisfaction drivers evolve over time in different industries and societal sectors, especially during crises. This additional study sustains the paradigm shift in the studied industries – product quality has been substituted by service quality as one of the most crucial drivers for customer satisfaction, and the current state of economic downturn has not changed that.

Society will have to learn to live with political and economic instability and unpredictability for the foreseeable future. To recognize the increasing value deriving from firms’ intangible assets while providing flawless deliveries seems to be a way forward in troublesome times. This is also a catalyst for existing societal trends: the necessary reforms to master sustainable transformations will require an ongoing adaptation process, with both winners and losers across continents.

The world has coped with a global pandemic, and Europe is currently experiencing a humanitarian, political and economic crises caused by a war in Ukraine. This extended period of global instability and insecurity could be called a permacrisis (Collins dictionary, 2022). This study offers a unique quantitative analysis built on Swedish data from EPSI Rating initiative.

Reynolds-averaged Navier–Stokes (RANS) models often perform poorly in shock/turbulence interaction regions, resulting in excessive wall heat load and incorrect representation of the separation length in shockwave/turbulent boundary layer interactions. The authors suggest that this can be traced back to inadequate numerical treatment of the inviscid fluxes. The purpose of this study is an extension to the well-known Harten, Lax, van Leer, Einfeldt (HLLE) Riemann solver to overcome this issue.

It explicitly takes into account the broadening of waves due to the averaging procedure, which adds numerical dissipation and reduces excessive turbulence production across shocks. The scheme is derived based on the HLLE equations, and it is tested against three numerical experiments.

Sod’s shock tube case shows that the scheme succeeds in reducing turbulence amplification across shocks. A shock-free turbulent flat plate boundary layer indicates that smooth flow at moderate turbulence intensity is largely unaffected by the scheme. A shock/turbulent boundary layer interaction case with higher turbulence intensity shows that the added numerical dissipation can, however, impair the wall heat flux distribution.

The proposed scheme is motivated by implicit large eddy simulations that use numerical dissipation as subgrid-scale model. Introducing physical aspects of turbulence into the numerical treatment for RANS simulations is a novel approach.

A wide gap exists between the innovation and development of self-monitoring, analysis and reporting technology (SMART) technologies and the actual adoption by older adults or those caring for them. This paper aims to increase awareness of available technologies and describes their suitability for older adults with different needs. SMART technologies are intelligent devices and systems that enable autonomous monitoring of their status, data analysis or direct feedback provision.

This is a scoping review of SMART technologies used and marketed to older adults or for providing care.

Five categories of SMART technologies were identified: (1) wearable technologies and smart tools of daily living; (2) noninvasive/unobtrusive technology (i.e. passive technologies monitoring the environment, health and behavior); (3) complex SMART systems; (4) interactive technologies; (5) assistive and rehabilitation devices. Technologies were then linked with needs related to everyday practical tasks (mainly applications supporting autonomous, independent living), social and emotional support, health monitoring/managing and compensatory assistance rehabilitation.

When developing, testing or implementing technologies for older adults, researchers should clearly identify concrete needs these technologies help meet to underscore their usefulness.

Older adults and caregivers should weigh the pros and cons of different technologies and consider the key needs of older adults before investing in any tech solution.

SMART technologies meeting older adult needs help support both independent, autonomous life for as long as possible as well as aiding in the transition to assisted or institutionalized care.

This is the first review to explicitly link existing SMART technologies with the concrete needs of older adults, serving as a useful guide for both older adults and caregivers in terms of available technology solutions.

The energy performance gap (EPG) in building construction has been one of the major barriers to the realization of environmental and economic sustainability in the built environment. Although there have been a few studies addressing this issue, studying this topic with a special focus on the project delivery process has been almost overlooked. Hence, this study aims to address the EPG in building construction through the lens of collaborative and life cycle-based project delivery.

In order to realize the objective of this study, the development of a theoretical framework based on the literature review was followed by a qualitative study in which 21 semi-structured interviews were conducted with Finnish project professionals representing clients, design/planning experts, constructors and building operation/maintenance experts to explore their views on the topic under study.

The findings reveal the project delivery-related causes of EPG in building construction. Moreover, the obtained results present a collaborative and life cycle-based delivery model that integrates project and product (i.e. building) life cycles, and it is compatible with all types of contractual frameworks in building construction projects.

Although the findings of this study significantly contribute to theory and practice in the field of collaborative and sustainable construction project delivery, it is acknowledged that these findings are based on Finnish professionals’ input, and expanding this research to other regions is a potential area for further studies. Moreover, the developed model, although validated in Finland, needs to be tested in a broader context as well to gain wider generalizability.

The obtained results reveal the significance and impact of collaborative and life cycle-based project development and delivery on the realization of environmentally sustainable building construction.

The information systems (IS) literature has indicated the importance of data analytics capabilities (DAC) in improving business performance in organizations. The literature has also highlighted the roles of organizations’ data-related resources in developing their DAC and enhancing their business performance. However, little research has taken resource quality into account when studying DAC for business performance enhancement. Therefore, the purpose of this paper is to understand the impact of resource quality on DAC development for business performance enhancement.

We studied DAC development using the resource-based view and the IS success model based on empirical data collected via 19 semi-structured interviews.

Our findings show that data-related resource (including data, data systems, and data services) quality is vital to the development of DAC and the enhancement of organizations’ business performance. The study uncovers the factors that make up each quality dimension, which is required for developing DAC for business performance enhancement.

Using the resource quality view, this study contributes to the literature by exploring the role of data-related resource quality in DAC development and business performance enhancement.

This study explores the work practices of managers who increased working from home during the pandemic to determine what, if any, impact there was on the conditions for vertical leadership development.

The project utilized a survey approach. Each of the participants completed an anonymous online questionnaire using Google Forms. The questionnaire included four sections. The first section included informed consent and required participants to agree before completing the questionnaire. Participants provided general demographic information in the second section, including gender, age, race, job title, company size, average project team size and industry. The third section asked if there had been any change in their work location following the pandemic. The last section asked participants about their work practices.

This study demonstrates that managers continued to be engaged in vertical leadership development activities while working from home. It also suggests that managers faced challenges working from home following the COVID-19 pandemic, which were prime vertical leadership development opportunities.

To capitalize on these opportunities, organizations can more intentionally support the development of their remote staff.

Prior research and theory have shown that transformational leadership can be developed through leader development programs (LDPs). To explain how this happens, research suggests a strong connection between an individual’s leader self-concept and their leadership behavior that can be manipulated through LDPs. The purpose of this article is to delve deeper into this phenomenon to understand how development occurs and to better understand how an LDP can improve leadership behaviors by influencing one’s self-concept and ultimately reduce the training transfer gap. As a broader goal, this article contributes to the existing literature on the value proposition of investing in LDPs.

We conducted an explanatory sequential design mixed-methods study to measure the development of participants and the impact of the program. The quantitative phase of the study used self-report surveys to measure the LDP’s impact on participants’ transformational leader behavior (TLB) and concept of themselves regarding leadership, their leader self-views (LSVs). The study also measured the relationship between LSVs and later enactment of leadership behaviors as the second hypothesis. The subsequent qualitative study was designed to understand the mechanisms that might explain the quantitative results.

The study’s empirical findings indicate a positive relationship between LSVs (efficacy, self-awareness and identity) and distal leader development outcomes (TBLs). The study’s findings also provide support for the claim that LDPs convert knowledge and skills into TLB, expanding an individual’s capacity to be effective in leadership roles and processes (Day & Dragoni, 2015). Qualitative results also show LDPs improve leader behavior by influencing their self-efficacy via providing tools and knowledge as well as building experimental mindset.

By showing LDPs' long term impacts, this paper demonstrates why short-term leadership development programs are worth investing.

The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.

Taking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.

By comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.

The Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.