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The purpose of this paper is to examine franchisees’ business start-ups from an entrepreneurial perspective, adopting a process representative of entrepreneurship to examine opportunity identification and evaluation by franchisees and to analyse factors that influence this process.

A qualitative study was employed and data collected using semi-structured interviews with a sample of service industry franchisees in Macau.

The study identifies that social networks play a key role in opportunity identification and that franchisees’ goals influence the criteria used and information search activities undertaken while evaluating franchise opportunities.

The study makes two contributions to franchise literature. It identifies that social networks can serve as substitutes for lack of prior knowledge in franchise opportunity identification. It also identifies the interrelated nature of franchisees’ goals based on the activities and criteria used to evaluate franchise opportunities, and the importance of relational criteria when franchisees lack prior industry knowledge. It therefore also contributes to franchise/entrepreneurship literature by identifying the interrelated nature of the factors contributing to the dynamics of franchise chain growth.

Franchisors should explore how to better use franchisees’ social networks and identify the longer term goals of prospective franchisees to support market penetration and franchise chain growth. Franchisees are advised to use independent information sources to evaluate franchise opportunities using goal-informed objectives and demand and relational criteria.

The study presents a more comprehensive understanding of franchisees’ decision-making process when joining franchise chains by identifying the activities undertaken and criteria used to identify and evaluate franchise opportunities.

Genetic and environmental factors are associated with psychosis risk, but the latter present more tangible markers for prevention. We conducted a theoretical exercise to estimate the proportion of psychotic illnesses that could be prevented if we could identify and remove all factors that lead to increased incidence associated with ethnic minority status and urbanicity. Measures of impact by population density and ethnicity were estimated from incidence rate ratios [IRR] obtained from two methodologically‐similar first episode psychosis studies in four UK centres. Multilevel Poisson regression was used to estimate IRR, controlling for confounders. Population attributable risk fractions [PAR] were estimated for our study population and the population of England. We considered three outcomes; all clinically relevant ICD‐10 psychotic illnesses [F10‐39], non‐affective psychoses [F20‐29] and affective psychoses [F30‐39]. One thousand and twenty‐nine subjects, aged 18‐64, were identified over 2.4 million person‐years. Up to 22% of all psychoses in England (46.9% within our study areas) could be prevented if exposures associated with increased incidence in ethnic minority populations could be removed; this is equivalent to 66.9% within ethnic minority groups themselves. For non‐affective psychoses only, PAR for population density was large and significant (27.5%); joint PAR with ethnicity was 61.7%. Effect sizes for common socio‐environmental risk indicators for psychosis are large; inequalities were marked. This analysis demonstrates potential importance in another light: we need to move beyond current epidemiological approaches to elucidate exact socio‐environmental factors that underpin urbanicity and ethnic minority status as markers of increased risk by incorporating gene‐environment interactions that adopt a multi disciplinary perspective.

This study aims to explore the critical influencing factors that lead to the site selection failure of waste-to-energy (WtE) projects in China under the influence of the “Not In My Back Yard” (NIMBY) effect, which can provide references to improve the decision-making process of similar projects in the future.

The fuzzy decision-making trial and evaluation laboratory (DEMATEL) method was used to propose an analytical framework for exploring the critical influencing factors affecting the site selection failure of WtE projects. The causal relationship between different influencing factors is finally determined on the basis of the opinions of 12 experts from universities, government departments, consulting units, planning and design units, construction units and WtE enterprises.

Results showed that six crucial factors resulted in the site selection failure of WtE projects from the NIMBY effect perspective: “Insufficient public participation,” “Near the place of residence,” “Nonstandard government decision-making processes,” “Low information disclosure,” “Destroys the surrounding environment,” and “Imperfect compensation scheme.”

Results can determine the priorities and causal relationships among the various influencing factors. The decision-making optimization suggestions can provide reference for decision- makers, thereby possibly promoting the scientific and standardization of site selection decision process.

The purpose of this paper is to describe how fabricate solar cell based‐on porous silicon (PS) prepared by electrochemical etching process is fabricated and the effect of porosity layer on the solar cell performance is investigated.

The techniques used include SiO₂ thermal oxidation, ZnO/TiO₂ sputtering deposition and PS prepared by electrochemical etching. Surface morphology and structural properties of porous Si were characterized by using scanning electron microscopy. Photoluminescence and Raman spectroscopy measurements were also performed at room temperature. Current‐voltage measurements of the fabricated solar cell were taken under 80 mW/cm² illumination conditions. Optical reflectance was obtained by using optical reflectometer (Filmetrics‐F20).

Pore diameter and microstructure are dependent on anodization condition such as HF: ethanol concentration, duration time, temperature, and current density. On other hand, a much more homogeneous and uniform distribution of pores is obtained when compared with other wafer prepared with different electrolyte composition.

PS is found to be an excellent anti‐reflection coating against incident light when it is compared with another anti‐reflection coating and exhibits good light‐trapping of a wide wavelength spectrum which produce high efficiency solar cells (11.23 per cent).

The purpose of this paper is to determine the incidence of the use of section 135(1) of the Mental Health Act 1983 in a London borough and describe the main features of the population subject to that section.

Uses of section 135(1), hospital stay, and demographic data were gathered from service and patient records over one year. Means, medians, modes and standard deviation were calculated for interval data. Nominal data were cross-tabulated and the chi square test applied where appropriate. Study data were compared to census and national hospital data; the significance of proportional population differences were calculated using the Z-test.

In total, 63 uses of section 135(1) were recorded. It was primarily used with people with psychotic diagnoses (79 per cent), and was used predominantly in black populations, and among people aged 40-54. People admitted to hospital after section 135(1) use who had psychosis diagnoses had median spells in hospital that were double the corresponding national median.

Total uses of section 135(1) in the borough equated to 25 per cent of the national total for all section 135 admissions recorded in 2012/2013. Hospital statistics in England focusing on admissions alone may fail to reflect a more widespread use of this section. Further research is required to confirm and develop the findings of this small scale study.

The repeated use of this section is suggested as a marker for reviewing practice and resource allocation to prevent or shorten hospital admissions for people with psychosis diagnoses.

This paper highlights gaps in NHS data collection in England relevant to policy makers, mental health service providers, and the police service.

Demonstrates the application of spreadsheets in simulating queuing systems with arrivals from a finite population. The problem is referred to as the machine repair problem where the members of the queue are machines that are breaking down and the servers are the technicians repairing the broken machines. The total number of machines are finite and pre‐specified. The technique for the development of the simulation is illustrated with six machines. Describes the approach for developing a generalized simulation model with any number of machines.

This study aims to compare and evaluate the application of commonly used machine learning (ML) algorithms used to develop models for assessing energy efficiency of buildings.

This study foremostly combined building energy efficiency ratings from several data sources and used them to create predictive models using a variety of ML methods. Secondly, to test the hypothesis of ensemble techniques, this study designed a hybrid stacking ensemble approach based on the best performing bagging and boosting ensemble methods generated from its predictive analytics.

Based on performance evaluation metrics scores, the extra trees model was shown to be the best predictive model. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method. Finally, it was discovered that stacking is a superior ensemble approach for analysing building energy efficiency than bagging and boosting.

While the proposed contemporary method of analysis is assumed to be applicable in assessing energy efficiency of buildings within the sector, the unique data transformation used in this study may not, as typical of any data driven model, be transferable to the data from other regions other than the UK.

This study aids in the initial selection of appropriate and high-performing ML algorithms for future analysis. This study also assists building managers, residents, government agencies and other stakeholders in better understanding contributing factors and making better decisions about building energy performance. Furthermore, this study will assist the general public in proactively identifying buildings with high energy demands, potentially lowering energy costs by promoting avoidance behaviour and assisting government agencies in making informed decisions about energy tariffs when this novel model is integrated into an energy monitoring system.

This study fills a gap in the lack of a reason for selecting appropriate ML algorithms for assessing building energy efficiency. More importantly, this study demonstrated that the cumulative result of ensemble ML algorithms is usually always better in terms of predicted accuracy than a single method.

Flights are often delayed owing to emergencies. This paper proposes a cooperative slot secondary assignment (CSSA) model based on a collaborative decision-making (CDM) mechanism, and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.

Taking passenger delays, transfer delays and flight cancellation delays into account comprehensively, the total delay time is minimized as the objective function. The model is verified by a linear solver and compared with the first come first service (FCFS) method to prove the effectiveness of the method. An improved adaptive partheno-genetic algorithm (IAPGA) using hierarchical serial number coding was designed, combining elite and roulette strategies to find pareto solutions.

Comparing and analyzing the experimental results of various scale examples, the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time, and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.

Based on the actual situation, this paper considers the operation mode of flight waves. In addition, the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness, which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

The purpose of this paper is to investigate critical success factors (CSFs) that enhance integration between building information modelling (BIM) and lean construction (LC) practices on construction mega-projects. BIM and LC have gained momentum in the past decade.

The Delphi survey technique was used to gauge opinions of a panel of 16 experts through a two-round Delphi questionnaire survey. Panel responses were scrutinised using inferential and descriptive statistical techniques.

In total, 30 CSFs were identified in the literature. The top ranked factor out of 30 that supports LeanBIM synergy was “collaboration in design, construction works and engineering management”. Other top rated CSFs were centric on people, data and technology elements. The research findings are important for project stakeholders, organisations, contractors, engineers and local authorities who implement LC and BIM synergies in construction mega-projects.

The research findings are important for project stakeholders, organisations, contractors, engineers and local authorities who implement LC and BIM synergies in construction mega-projects. The research recommends further hands-on training to increase the integration of BIM and LC practices in the architecture, engineering and construction industry and to enrich the extant body of knowledge in construction of mega-projects.