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Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.

This study aims to investigate the thermal fracture mechanism of moisture-preconditioned SAC305 ball grid array (BGA) solder joints subjected to multiple reflow and thermal cycling.

The BGA package samples are subjected to JEDEC Level 1 accelerated moisture treatment (85 °C/85%RH/168 h) with five times reflow at 270 °C. This is followed by multiple thermal cycling from 0 °C to 100 °C for 40 min per cycle, per IPC-7351B standards. For fracture investigation, the cross-sections of the samples are examined and analysed using the dye-and-pry technique and backscattered scanning electron microscopy. The packages' microstructures are characterized using an energy-dispersive X-ray spectroscopy approach. Also, the package assembly is investigated using the Darveaux numerical simulation method.

The study found that critical strain density is exhibited at the component pad/solder interface of the solder joint located at the most distant point from the axes of symmetry of the package assembly. The fracture mechanism is a crack fracture formed at the solder's exterior edges and grows across the joint's transverse section. It was established that Au content in the formed intermetallic compound greatly impacts fracture growth in the solder joint interface, with a composition above 5 Wt.% Au regarded as an unsafe level for reliability. The elongation of the crack is aided by the brittle nature of the Au-Sn interface through which the crack propagates. It is inferred that refining the solder matrix elemental compound can strengthen and improve the reliability of solder joints.

Inspection lead time and additional manufacturing expenses spent on investigating reliability issues in BGA solder joints can be reduced using the study's findings on understanding the solder joint fracture mechanism.

Limited studies exist on the thermal fracture mechanism of moisture-preconditioned BGA solder joints exposed to both multiple reflow and thermal cycling. This study applied both numerical and experimental techniques to examine the reliability issue.

This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R² = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.

This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R² = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.

The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R²” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.

The authors confirm the originality of this paper.

The built environment is taking enormous leaps towards its digitalization. Computer-aided tools such as building information modeling (BIM) are found in the forefront of this evolution, playing a critical role in creating the foundations for the upcoming development of smart low-carbon cities. However, the potential of BIM is still untapped – links will need to be created among the available and forthcoming methodologies under one integral operational system. The purpose of this paper is to present an integrated BIM-based life cycle-oriented framework for achieving sustainable constructions at the pre-construction phase. The developed framework represents an example of the approaches that the construction industry will need to adopt to integrate the different tools under an integrated smart city context.

The methodological approach follows the development of four same-volume different-configuration three-dimensional BIM designs, which are coupled with life cycle assessment (LCA) tools for establishing sustainable building design.

The results of this paper indicated that the choice of building design and shape can play a significant role in reducing the embodied energy and embodied carbon of buildings, achieving a reduction of up to 15% compared to a reference building of same volume and gross floor area.

The originality of this paper is found in its approach application by coupling three-dimensional BIM models with LCA data, the use of reinforcement detailing in an nD BIM study and the employment of country-specific LCA databases.

Exposure to a public health threat of significant proportions made current models inadequate to explain the failure phenomenon in small businesses. Hence, the need to reimagine the phenomenon. Borrowing from the principles of biology, this study extended theoretical and empirical perspectives on the failure phenomenon by unpacking its constituent elements and the measurement metrics using the regeneration lens.

Based on a cohort tracked over time, the study estimated the survival probabilities of small and medium-scale enterprises (SMEs) with and without regeneration using the Kaplan–Meier method. The study investigated the factors that predict enterprise regenerative capacity using the multivariate Cox proportional hazard ratios.

Rates of interruption in business activity, by month, ranged between 0% and 18% during the follow-up period. True mortality rates hovered between 0% and 4% over the same period. Over three in five SMEs that experienced interruption in business activity without ceasing operations regenerated at some point in time during the follow-up period. The survival probabilities beyond the follow-up period were 0.85 and 0.44 with and without regeneration effects, respectively. Fresh capital injection (+), the introduction of new/improved processes or products/services (+), perceived business outlook (+) and the presence of debt (−) influenced the capacity to regenerate.

The cohort was followed for only six months. There is a need to continue interrogating the failure phenomenon in other contexts over longer periods using the regeneration lens. Bringing on board academia, financial institutions and other SME-related ecosystem players will be strategic.

The approach provides a more nuanced understanding of the life and well-being of enterprises under conditions of disruption. Improving the precision and validity of failure-related statistics enhances their utility in policy and remediation-related discussions.

The results did not show significant differences in SME mortality rates between male and female-owned enterprises. The results provide further evidence that the failure phenomenon is ungendered. As such, financial institutions and the SME ecosystem at large must eliminate perceptual gender biases in the financing and other support to SMEs.

The study used the principles of biology to reimagine the failure phenomenon in small businesses. The approach breathes life into entrepreneurship research and policy.

The purpose of this study is to investigate whether and how inter-firm cooperation and firm age moderate the relationship between family ownership and productivity.

We first estimate the total factor productivity (TFP) of a large sample of Italian firms observed over the period 2010–2018 and then apply a Poisson random effects model.

TFP is, on average, higher for non-family firms (non-FFs) than for FF. Furthermore, inter-organizational cooperation and firm age mitigate the negative effect of family ownership. In detail, it is found that belonging to a network acts as a moderator in different ways according to firm age. Indeed, young FFs underperform non-FF peers, although the TFP gap decreases with age. In contrast, the benefits of a formal network are high for older FFs, suggesting that an age-related learning process is at work.

The study provides evidence that FFs can outperform non-FFs when they move away from Socio-Emotional Wealth-centered reference points and exploit knowledge flows arising from high levels of social capital. In the case of mature FFs, networking is a driver of TFP, allowing them to acquire external resources. Since FFs often do not have sufficient in-house knowledge and resources, they must be aware of the value of business cooperation. While preserving the familiar identity of small companies, networks grant FFs the competitive and scale advantages of being large.

Despite the wide but ambiguous body of research on the performance gap between FFs and non-FFs, little is known about the role of FFs’ heterogeneity. This study has proven successful in detecting age as a factor in heterogeneity, specifically to explain the network effect on the link between ownership and TFP. Based on a representative sample, the study provides a solid framework for FFs, policymakers and academic research on family-owned companies.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

This research attempts to examine bank dividend policy in Indonesia by applying the life cycle theory of dividends.

This research used secondary data gotten from two sources: banks’ annual financial statements from 2005 to 2019 and the number of observation samples was 510 from 42 banks. Random Effects Logit Model (RELM) is used to detect the influence of independent variables on Propensity to Pay Dividends (PPD) and Random Effects Tobit Model (RETM) is used to test the influence of independent variables on Dividend Payout Ratio (DPR).

The RELM results show that Retained Earnings to Total Equity (RE/TE), Retained Earnings to Total Asset (RE/TA) and bank age have a positive impact on the propensity to pay dividends (PPD) while bank growth (GRW) has a negative impact. The RETM results reveal that RE/TE, ROA and bank size have a positive impact on the dividend payout ratio (DPR) while GRW has a negative impact. This analysis also discovers that the capital adequacy ratio (CAR) and Non-performing Loans (NPL) is one important factor considered by banks in Indonesia in determining their dividend policy.

This study contributes to enriching literature in finance, especially in the life cycle theory of dividends. Also, it can be a guide to consider by investors before deciding to put their shares in banks in Indonesia.

Research on bank-specific life cycle theory is very difficult to find, especially in the Indonesian context, so this research can enrich the body of knowledge on dividend decisions.

Traditionally, life cycle assessment (LCA) has focused on environmental aspects, but integrating social aspects in LCA has gained traction among scholars and practitioners. This study aims to review key social life cycle assessment (SLCA) themes, namely, drivers and barriers of SLCA implementation, methodology and measurement metrics, classification of initiatives to improve SLCA and customer perspectives in SLCA.

A total of 148 scientific papers extracted from the Web of Science database were used and analyzed using bibliometric and content analysis.

The findings suggest that the existing research ignores several aspects of SCLA, which impedes positive growth in topical scholarship, and the study proposes a classification of SLCA research paths to enrich future research. This study contributes positively to SLCA by further developing this area, and as such, this research is a primer to gain deeper knowledge about the state-of-the-art in SLCA as well as to foresee its future scope and challenges.

The study provides an up-to-date review of extant research pertaining to SLCA.