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This study aims to evaluate the efficiency of various techniques for enhancing indoor air quality (IAQ) in construction. It analyzed the alterations in the concentration of indoor air pollutants over time for each product employed in controlling pollution sources and removing it, which included eco-friendly substances and adsorbents. The study will provide more precise and dependable data on the effectiveness of these control methods, ultimately supporting the creation of more efficient and sustainable approaches for managing indoor air pollution in buildings.

The research investigates the impact of eco-friendly materials and adsorbents on improving indoor air quality (IAQ) in Dubai's tall apartment buildings. Field experiments were conducted in six units of The Gate Tower, comparing the IAQ of three units built with “excellent” grade eco-friendly materials with three built with “good” grade materials. Another experiment evaluated two adsorbent products (H and Z) in the Majestic Tower over six months. Results indicate that “excellent” grade materials significantly reduced toluene emissions. Adsorbent product Z showed promising results in pollutant reduction, but there is concern about the long-term behavior of adsorbed chemicals. The study emphasizes further research on household pollutant management.

The research studied the effects of eco-friendly materials and adsorbents on indoor air quality in Dubai's new apartments. It found that apartments using “excellent” eco-friendly materials had significantly better air quality, particularly reduced toluene concentrations, compared to those using “good” materials. However, high formaldehyde (HCHO) emissions were observed from wood products. While certain construction materials led to increased ethylbenzene and xylene levels, adsorbent product Z showed promise in reducing pollutants. Yet, there is a potential concern about the long-term rerelease of these trapped chemicals. The study emphasizes the need for ongoing research in indoor pollutant management.

The research, while extensive, faced limitations in assessing the long-term behavior of adsorbed chemicals, particularly the potential for rereleasing trapped pollutants over time. Despite the study spanning a considerable period, indoor air pollutant concentrations in target households did not stabilize, making it challenging to determine definitive improvement effects and reduction rates among products. Comparisons were primarily relative between target units, and the rapid rise in pollutants during furniture introduction warrants further examination. Consequently, while the research provides essential insights, it underscores the need for more prolonged and comprehensive evaluations to fully understand the materials' and adsorbents' impacts on indoor air quality.

The research underscores the importance of choosing eco-friendly materials in new apartment constructions for better IAQ. Specifically, using “excellent” graded materials can significantly reduce harmful pollutants like toluene. However, the study also highlights that certain construction activities, such as introducing furniture, can rapidly elevate pollutant levels. Moreover, while adsorbents like product Z showed promise in reducing pollutants, there is potential for adsorbed chemicals to be rereleased over time. For practical implementation, prioritizing higher-grade eco-friendly materials and further investigation into furniture emissions and long-term behavior of adsorbents can lead to healthier indoor environments in newly built apartments.

The research offers a unique empirical assessment of eco-friendly materials' impact on indoor air quality within Dubai's rapidly constructed apartment buildings. Through field experiments, it directly compares different material grades, providing concrete data on pollutant levels in newly built environments. Additionally, it explores the efficacy of specific adsorbents, which is of high value to the construction and public health sectors. The findings shed light on how construction choices can influence indoor air pollution, offering valuable insights to builders, policymakers and residents aiming to promote public health and safety in urban living spaces.

Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.

In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.

The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types

This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This study aims to systematise the methodology used in comparative urban planning law and propose primary contexts for comparison in planning law.

This study undertook a review of comparative law methodology discourse and sought to establish connections between the discourse and the field of planning law.

This study argues for establishment of a realistic goal for comparative planning law by focusing on the planning law's modifiability. The goal of comparison in planning law should not be to find universally desirable principles or better solutions. Rather, the goal should be to identify a motive for devising a solution. This is because it is not only difficult to establish legal values that are universally applicable to planning law but also inappropriate to determine superiority of planning laws that have been developed over time by each jurisdiction’s sovereignty and policies on land use. When determining comparable systems for analysis among legal systems that are functionally equivalent, it is important to consider the context of land use relations alongside the comparative analysis to be done. To set realistic goals, the context should not be extended indefinitely but be systematised. Based on the foundational relationship underlying planning law, including the tension between planning authorities and property owners, this study presents five specific contexts for comparative analysis: “Strength of Property Rights,” “Level of Judicial Intervention,” “Plan- or Development-led System,” “Allocation of Planning Power” and “Level of Participation.” Examination of these contexts will allow better understanding of the similarities and differences among different systems and practical application of the results of comparative studies.

This study presents a novel approach to systematising the methodology and framework of comparative planning law.

This study aims to focus on the optimal green R&D of a capital-constrained supply chain under different channel power structures as well as the impact of capital constraint, financing cost, channel power structure and cost-reducing efficiency on green R&D and supply chain profitability.

A two-echelon supply chain is considered. The upstream firm engages in green R&D but has capital constraints that can be overcome by external financing. Green R&D is beneficial to reduce production costs and increase consumer demand. Based on whether or not the upstream firm is capital constrained and dominates the supply chain, four models are developed.

Capital constraints significantly lower green R&D and supply chain profitability. Transferring leadership from the upstream to the downstream firms leads to higher green R&D levels and downstream firm profitability, whereas the upstream firm's profitability is increased (decreased) if green R&D investment efficiency is high (low) enough. Greater financing costs reduce green R&D and downstream firm profitability; however, the upstream firm's profitability under the model in which it functions as the follower increases if the initial capital is sufficient. More importantly, empirical analysis based on practice data is used to verify the theoretical results reported above.

This study reveals how upstream firms in supply chains decide green R&D decisions in situations with capital constraints, providing managers and governments with an understanding of the impact of capital constraint, channel power structure, financing cost and cost-reducing efficiency on supply chain green R&D and profitability.

The major contributions are the exploration of supply chain green R&D by taking into consideration channel power structures and cost-reducing efficiency and the validation of theoretical results using practice data.

Since the outbreak of the COVID-19 pandemic, most organizations have experienced a sudden and unprecedented drop in revenue and productivity. However, the pandemic did not exclusively negatively impact organizations; rather, it resulted in both negative and positive effects. To delve into the multi-level process through which organizational outcomes change from negative to positive indicators, this study focuses on organizational resilience as a theoretical concept to overcome pandemic-related turmoil.

The authors conducted a multi-level analysis based on grounded theory with a sample of 30 healthcare employees who worked in hospitals and were simultaneously enrolled in a part-time master of business administration (MBA) program at a university in the Midwest. Of the 30 participants, 21 were from a single university hospital (UH), and the remaining 9 participants were from other hospitals (non-UH).

The authors analyzed the data and incorporated three existing perspectives of organizational resilience (attribute, process and multi-level views) into an integrated model. The authors identified 25 first-order concepts and 8 second-order themes and categorized them into 4 aggregate dimensions at different unit levels: organizational field, leadership, operation and individual units.

A resilient hospital operates as a cohesive system, with entities at various levels – from individuals and teams to the broader organization – collaborating seamlessly to foster resilience. Top management team (TMT) should persistently communicate with employees to provide information about the current crisis and clear strategic directions to reduce employees' fear and prevent anomie stemming from future uncertainty. Managers should not only be concerned about employees' physical safety from infection and psychological safety from isolation but also encourage employees to elicit meaningfulness from their work. Furthermore, TMT and human resource (HR) teams should adapt human resource management (HRM) practices to allow for flexibility and optimism in employee roles.

In this study, the authors utilized a qualitative methodology with grounded theory in order to develop a comprehensive model that holds theoretical, methodological and practical significance. Theoretically, the authors' novelty lies in the synthesis of three distinct perspectives: attribute, process and multi-level. The authors merged these approaches into a unified model, identifying precursors of resilience at different levels. Methodologically, the authors focused on hospitals as target samples, which were the foremost and representative organizations severely confronting the crisis and turmoil brought by the pandemic. The authors documented organizations' experiences amidst the crisis as they unfolded in real time rather than in hindsight. This approach highlights the immediacy and significance of the authors' research in the realm of crisis management. Practically, the authors' findings illuminate that organizational resilience can be developed through a collaborative effort. It emerges from coordinated interactions across various organizational actors, from employees and middle managers to the TMT.

The vibration of the rails is a significant source of railway rolling noise, often forming the dominant component of noise in the important frequency region between 400 and 2000 Hz. The purpose of the paper is to investigate the influence of the ground profile and the presence of the train body on the sound radiation from the rail.

Two-dimensional boundary element calculations are used, in which the rail vibration is the source. The ground profile and various different shapes of train body are introduced in the model, and results are observed in terms of sound power and sound pressure. Comparisons are also made with vibro-acoustic measurements performed with and without a train present.

The sound radiated by the rail in the absence of the train body is strongly attenuated by shielding due to the ballast shoulder. When the train body is present, the sound from the vertical rail motion is reflected back down toward the track where it is partly absorbed by the ballast. Nevertheless, the sound pressure at the trackside is increased by typically 0–5 dB. For the lateral vibration of the rail, the effects are much smaller. Once the sound power is known, the sound pressure with the train present can be approximated reasonably well with simple line source directivities.

Numerical models used to predict the sound radiation from railway rails have generally neglected the influence of the ground profile and reflections from the underside of the train body on the sound power and directivity of the rail. These effects are studied in a systematic way including comparisons with measurements.

This study aims to explore the management learning during a long-term crisis like a pandemic. The paper addresses both what health-care managers have learnt during the COVID-19 pandemic and how the management learning is characterized.

The paper is based on a qualitative case study carried out during the COVID-19 pandemic at two different public hospitals in Sweden. The study, conducted with semi-structured interviews, applies a combination of within-case analysis and cross-case comparison. The data were analyzed using thematic deductive analysis with the themes, i.e. sensemaking, decision-making and meaning-making.

The COVID-19 pandemic was characterized by uncertainty and a need for continuous learning among the managers at the case hospitals. The learning process that arose was circular in nature, wherein trust played a crucial role in facilitating the flow of information and enabling the managers to get a good sense of the situation. This, in turn, allowed the managers to make decisions meaningful for the organization, which improved the trust for the managers. This circular process was iterated with higher frequency than usual and was a prerequisite for the managers’ learning. The practical implications are that a combined management with hierarchical and distributed management that uses the normal decision routes seems to be the most successful management method in a prolonged crisis as a pandemic.

The gained knowledge can benefit hospital organizations, be used in crisis education and to develop regional contingency plans for pandemics.

This study has explored learning during the COVID-19 pandemic and found a circular process, “the management learning wheel,” which supports management learning in prolonged crises.

Site supervision features largely influence the productivity status of construction operational processes. This study aims to use a case study containing mixed methods to test the site supervisory traits in applying mathematical theories to construction operations for directing supervisory capabilities under various operational characteristics.

A total of 62 construction site supervisors were trained as part of a new apprenticeship programme. Through literature reviews and expert consultations, grading criteria were designed with various degrees of descriptions and score ratings. The supervisory attributes were evaluated under seven competency element characteristics mapped with the relevant learning domains.

The results demonstrate a detailed sectional view of performance ratings of supervisors under different characteristics of competency factors with the validity, reliability, applicability and generalisability assurance of the research findings using relevant statistical tests and expert evaluations.

Though the research applications were engaged directly with the construction industry in the Sri Lankan setting, other developing countries and emerging industries can also employ equivalent tactics to attain similar outcomes in their industry-based operations.

The research findings have led to producing a new guide that makes significant impacts on deciding the capability levels in construction supervisory attributes while executing problem-solving applications in construction planning and operational processes. Accordingly, the findings push to open a gate to intake advanced cognitive attributes towards addressing the industry's knowledge gap on how the problem-solving-based apprenticeship protocols need to be linked with the supervision features.

Introduction: The birth of a preterm child requires hospitalization in a neonatal intensive care unit (NICU), which is a very stressful experience for parents. Aim: To determine the stress level of parents of preterm babies admitted to intensive and sub-intensive units in two hospitals in Northern Italy and its association with their sociodemographic variables and the clinical conditions of their newborns.

The sampling was non-probabilistic and included parents of preterm babies admitted to intensive and/or sub-intensive care for at least 10 days. Instruments: (1) information deduced from the clinical record of preterm newborns; (2) sociodemographic determinants of parents' well-being deduced from a questionnaire; (3) parental stress scale: neonatal intensive care unit (PSS:NICU), which measures the perception of parents about stressors from the physical and psychological environment of the NICU.

Results: A total of 104 parents of 59 hospitalized preterm babies participated in the study. The average parental stress level was 1.87 ± 0.837. The subscale score that got higher was parent-infant relationship subscale. Concerning the infant characteristics, the birth weight of the babies and the length of their hospitalization affected the parents' stress level. Looking at parents' sociodemographic characteristics instead, the greater predictors were gender, age and occupational social class.

The parental role alteration caused by infant premature birth and consequent hospitalization is a major stressor for parents and in particular for mothers. The variables that resulted positively associated with higher stress in parents of preterm infants hospitalized are specific parental characteristics, including not adequately or previously studied ones, and infant characteristics.