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This study aims to investigate the feasibility of proposed microgrid (MG) that comprises photovoltaic, wind turbines, battery energy storage and diesel generator to supply a residential building in Grindelwald which is chosen as the test location.

Three operational configurations were used to run the proposed MG. In the first configuration, the electric energy can be vended and procured utterly between the main-grid and MG. In the second configuration, the energy trade was performed within 15 kWh as the maximum allowable limit of energy to purchase and sell. In the third configuration, the system performance in the stand-alone operation mode was investigated. A whale optimization technique is used to determine the optimal size of MG in all proposed configurations. The cost of energy (COE) and other measures are used to evaluate the system performance.

The obtained results revealed that the first configuration is the most beneficial with COE of 0.253$/KWh and reliable 100%. Furthermore, the whale optimization algorithm is sufficiently feasible as compared to other techniques to apply in the applications of MG.

The value of the proposed research is to investigate to what extend the integration between MG and main-grid is beneficial economically and technically. As opposed to previous research studies that have focused predominantly only on the optimal size of MG.

This study aims to investigate the daily performance of the proposed microgrid (MG) that comprises photovoltaic, wind turbines and is connected to the main grid. The load demand is a residential area that includes 20 houses.

The daily operational strategy of the proposed MG allows to vend and procure utterly between the main grid and MG. The smart metre of every consumer provides the supplier with the daily consumption pattern which is amended by demand side management (DSM). The daily operational cost (DOC) CO2 emission and other measures are utilized to evaluate the system performance. A grey wolf optimizer was employed to minimize DOC including the cost of procuring energy from the main grid, the emission cost and the revenue of sold energy to the main grid.

The obtained results of winter and summer days revealed that DSM significantly improved the system performance from the economic and environmental perspectives. With DSM, DOC on winter day was −26.93 ($/kWh) and on summer day, DOC was 10.59 ($/kWh). While without considering DSM, DOC on winter day was −25.42 ($/kWh) and on summer day DOC was 14.95 ($/kWh).

As opposed to previous research that predominantly addressed the long-term operation, the value of the proposed research is to investigate the short-term operation (24-hour) of MG that copes with vital contingencies associated with selling and procuring energy with the main grid considering the environmental cost. Outstandingly, the proposed research engaged the consumers by smart meters to apply demand-sideDSM, while the previous studies largely focused on supply side management.

This study investigates the causal relationship and mechanisms between the development of digital finance and household carbon emissions. Its objective is to explore how digital finance can influence the carbon footprint at the household level, aiming to contribute to the broader understanding of financial innovations' environmental impacts.

The research combines macro and micro data, employing input-output analysis to utilize data from the China Household Finance Survey (CHFS) for the years 2013, 2015, 2017, and 2019, national input-output tables, and Energy Statistical Yearbooks. This approach calculated CO2 emissions at the household level, including the growth rate of household carbon emissions and per capita emissions. It further integrates the Peking University Digital Financial Inclusion Index of China (PKU-DFIIC) for 2012–2018 and corresponding urban economic data, resulting in panel data for 7,191 households across 151 cities over four years. A fixed effects model was employed to examine the impact of digital finance development on household carbon emissions.

The findings reveal that digital finance significantly lowers household carbon emissions. Further investigation shows that digital transformation, consumption structure upgrades, and improved household financial literacy enhance the restraining effect of digital finance on carbon emissions. Heterogeneity analysis indicates that this mitigating effect is more pronounced in households during the nurturing phase, those using convenient payment methods, small-scale, and urban households. Sub-index tests suggest that the broadening coverage and deepening usage of digital finance primarily drive its impact on reducing household carbon emissions.

The paper recommends that China should continue to strengthen the layout of digital infrastructure, leverage the advantages of digital finance, promote digital financial education, and facilitate household-level carbon emission management to support the achievement of China's dual carbon goals.

The originality of this paper lies in its detailed examination of the carbon reduction effects of digital finance at the micro (household) level. Unlike previous studies on carbon emissions that focused on absolute emissions, this research investigates the marginal impact of digital finance on relative increases in emissions. This method provides a robust assessment of the net effects of digital finance and offers a novel perspective for examining household carbon reduction measures. The study underscores the importance of considering heterogeneity when formulating targeted policies for households with different characteristics.

This chapter proposes a protocol based on blockchain technology applied to corporate social responsibility (CSR). The first part discusses the characteristics associated with CSR actions and the main difficulties its development faces, such as transparency, security, fault tolerance, among others. Subsequently, the authors describe the characteristics and concepts related to blockchain-based developments to later describe our framework for the control and development of CSR actions based on blockchain. Herein, the authors also describe how to publicly and privately identify the participating elements of CSR and the operations and resources necessary for the implementation and operation of the proposed protocol.

Purpose: Technological innovations and frameworks that provide a framework for unification have evolved to improve information exchange across organisational units and information security. These integration technologies share and communicate information using defined protocols and different data. Service-oriented architecture (SOA) is a significant emerging approach that enables modular design solution construction.

Methodology: These designs are beneficial when many apps operating on different architectures and networks need to connect. A well-defined strategy and company-specific guidelines are essential for ensuring the firm’s systematic adoption of such an architecture. The critical components of MASSOASCM ‘(Multi-Agent System Service Oriented Architecture Supply Chain Management’ are a multi-agent system (MAS), a service-oriented structure, and supplier management. The MASSOASCM model has been made, and a production unit has been made to show how it works.

Findings: It has been stated that it saves development costs, and inventory management, all of which are critical concerns in any company. Our goal is to create an inventory control approach that relies on MAS and SOA but also a simulation that demonstrates how it works and may enhance Supply Chain Management (SCM) productivity in a production plant.

Practical Implications: The SCM implementation comprises three different services: SCM, SOA, and MAS. These facilities are constructed, maintained, planned, and implemented individually before being brought together collectively using MAS and SOA techniques.

Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.

As the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.

The empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.

With these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.

To address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

The purpose of this paper is to present a novel rapid prototyping (RP) approach and verifying its feasibility. This alternative solution is to bring several merits from both selective laser sintering and laminated object manufacturing.

The phenolic resin coated sand is used in this method. It could be cured at an appropriate temperature and be invalidated at a higher one. Therefore, the fabrication flows from laser cutting along slice profiles to a bulk curing heating after stacking up. Finally, the workpiece may be detached out of the excess material. Experiments and modeling on laser scanning are conducted to optimize the processing parameters, which, along with the direct slicing strategy, guarantee the part performance.

A novel prototyping system is developed comprising the software package and prototyping machine, through which several specimens are fabricated. The results show the feasibility of the proposed RP method.

This research brings the applicability of a hybrid solution: profile invalidation RP.

Gang affiliation is strongly associated with youth crime. Although gang prevention, intervention and suppression programmes have been used to reduce affiliation and manage youth gang‐related activities, the effectiveness of these approaches is questionable. Further, comprehensive programmes supporting disengagement from gangs that also address the actual criminal behaviours of gang‐affiliated youth are rare. Arguably, these are necessary if the goal of intervention is to reduce criminal behaviour and support disengagement from gangs. This paper aims to address these issues.

This study sought to elucidate the criminogenic needs of gang‐ and nongang‐affiliated youth offenders (n=165) using two commonly used risk/need assessment instruments, the structured assessment of violence risk in youth (SAVRY) and the youth level of service/case management inventory (YLS/CMI).

The results revealed that gang‐ and nongang‐affiliated youth offenders had similar criminogenic need profiles except for one difference on an item measuring peer delinquency.

Gang‐affiliated youth offenders have comparable criminogenic needs to other youth offenders. These needs require intervention if a reduction in crime is desired, and since gang‐affiliated youth offenders are more likely to re‐offend than those that are nongang‐affiliated, these results also suggest that there may be additional needs, beyond those assessed by the SAVRY and YLS/CMI, which should be investigated and considered in rehabilitation programmes.

Few studies have directly compared the risk and needs profiles between gang‐ and nongang‐affiliated youth offenders using standardised risk assessment measures; this study may be relevant to professionals working in the juvenile justice and offender rehabilitation arenas.

This paper attempts to investigate what kind of firms is more likely to use capital structure to signal; and in particular to investigate the impacts of corporate ownership structures on firms' capital structure signalling decisions.

The paper develops theoretical models and then uses OLS multiple regression, piecewise regression and logistic regression analysis on a set of data derived from 327 UK firms listed in the FTSE ALL share index to test the hypotheses.

The empirical results show that capital structure is not homogeneously used as a signalling tool; and firms with insider ownerships less or equal to 1.14 per cent are more likely the signallers.

Although other variables have been examined, this paper focuses on the impacts of insider ownership on capital structure signalling. Further work is required to investigate other variables that are mentioned but they are outside the scope of this paper.

This paper provides useful practical insights to both managers and investors to help them better understand and interpret firms' capital structure signals.

Before this paper, most people commonly agreed that capital structure contains signalling values. However, the findings suggest that it is not always the case.