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Recounts the merger of Jerusalem’s Neighbourhood Self Management Organizations (NSMOs) and Community Service Organizations (CSOs) into the Joint Community Neighbourhood Management Organization (JCNM). Refers to literature on institutional theory and the process of decentralization. Focuses then on the NSMOs and CSOs, providing some background information on how and why they were originally set up and what their goals were. Compares the differences between the organizations, as well as the basic principles they have in common – hence the merge into one organization. Discusses the problems the JCNM has faced over the years, including issues such as identify, legitimacy, composition of board of directors, professional or political values, and restructuring the organization. Draws a quadrant model of patronage and centralization, suggesting that the NCNM aims to occupy a particular quadrant but is being hampered in doing so because policy makers prefer to keep things the way they are.

This paper aims to utilize institutional theory to examine the impact of the 2008-2010 regulatory reforms on compliance with mandatory disclosures by savings and credit co-operatives (SACCOs) in Kenya.

Two-stage least squares panel regression approach is utilized to analyse data covering 1,272 firm-year observations for 212 SACCOs over a six-year period, 2008-2013. An analysis of the pre- and post-regulation impacts on compliance with mandatory disclosure requirements is also performed.

The results, which are in support of the institutional theory, reveal that licensed SACCOs engage in higher compliance with mandatory disclosures, and this improves from the pre- to the post-regulation period. The results show that SACCOs under inquiry engage in lower compliance with mandatory disclosure requirements, especially in the post-regulation period. The findings also reveal a significant and positive association between SACCO size, co-operative governance and compliance with mandatory disclosure requirements.

The study focuses on transition-level SACCOs in a single country. An extension into other jurisdictions with nascent, transitional and mature SACCOs would provide greater insights into the impact of disclosure regulation. Further, the study uses a self-constructed disclosure checklist which is subject to coding errors and biases.

The findings highlight the need for SACCO regulators and accounting professional body to devise incentives to improve the level of compliance with required disclosures.

The study contributes to the dearth of evidence on the efficacy of the introduction of mandatory disclosure requirements in a developing country where compliance is problematic because of difficulties with enforcement.

One of the most significant changes in monetary economics at the beginning of the twenty-first century has been the virtual disappearance of what was once a dominant focus, the role of money in monetary policy, and parallelly, the disappearance of the liquidity preference-money supply (LM) curve. Economists used to consider monetary policy with the help of the LM curve as part of the analytical framework which captures the demand for money. However, the workhorse model of modern monetary theory and policy, the New Keynesian Dynamic Stochastic General Equilibrium (DSGE) framework, only comprises the dynamic investment-savings (IS) curve, the New Keynesian (NK) Phillips curve, and a monetary policy rule. The monetary policy rule is generally known as the Taylor rule. It relates the nominal interest rate to the output-gaps and inflation-gaps, but typically not to either the quantity or the growth rate of money. This change in the modern monetary model reflects how the central banks make monetary policy now. This study provides a detailed discussion on the role of money in monetary policy formulation in the context of the NK and the New Monetarist perspectives. The pros and cons of abandonment of money or the LM curve from monetary policy models have been discussed in detail.

Significant evidence in the literature points to money demand instability and therefore inaccurate forecasting. In view of this issue, this chapter seeks to use a method, innovative for money demand literature, that is, the machine learning model to predict money demand. Specifically, this chapter uses Random Forest Regression to predict money demand using monthly data in the Indian context over the period April-1996 to December-2018 using the variables usually used in literature. The chapter finds that in money demand prediction, the Random Forest Regression performs fairly well. The results are also compared to traditional models and it is found that the Random Forest Regression model has the potential to enhance the prediction of money demand over what traditional models predicts.

The purpose of this paper is to provide an effective way to assess landslide risk quantitatively. Quantitative assessment plays an important role in mitigating the landslide risk and developing a landslide risk-based warning system. However, efficient risk assessment on the large deformation failure process of slope with spatially variable soils is a challenging problem.

Combining the Monte Carlo simulation (MCS) and the higher-order material point method – the B-spline Material Point Method (BSMPM) – the concept of MC-BSMPM to assess the landslide risk quantitatively is proposed in this paper. The overall dynamic evolution of soil slope failure has been simulated by the BSMPM, and the probability density function of the sliding duration, the sliding kinematic energy, the sliding mass and the sliding distance of the landslide are obtained based on the MCS. Through the four risk assessment parameters of the sliding duration, the sliding kinematic energy, the sliding mass and the sliding distance, the landslide risk could be assessed quantitatively.

It is found that the post-failure behavior of the landslide conforms well to a normal distribution as the soil physical parameter is in a normal distribution. The variation of soil’s shear strength affects the dynamic motion of the landslide greatly.

The result shows that the landslide hazard cannot be estimated comprehensively by the deterministic BSMPM, while the landslide risk could be more clearly understood and quantitatively assessed with more details by the proposed method, which demonstrates that the MC-BSMPM method is an effective way to assess the landslide risk quantitatively.

This study aims to examine whether and how gender diversity on corporate boards is associated with voluntary nonfinancial disclosures, particularly water disclosures.

This study uses corporate water information disclosure data from Chinese listed firms between 2010 and 2018 to conduct regression analyses to examine the association between female directors and water information disclosure.

Empirical results show that female directors have a significantly positive association with corporate water information disclosure. Additionally, internal industry water sensitivity of firms moderates this significant relationship.

This study determined that female directors can promote not only water disclosure but also positive corporate water performance, reflecting the consistency of words and deeds of female directors in voluntary nonfinancial disclosures.

The crystalline quality of wider direct band gap semiconductor (3.4 eV) h-GaN epilayer grown on Si (111) is evaluated by different growth approaches and by using different interlayer's. The investigations of GaN epilayer crystal quality for the template of converted porous GaN layer formed by novel nitridation process of thin (2 and 0.5 μm) GaAs layer on Si (111) and on C⁺ ion implanted very thin SiC layer formed on Si (111) and grown ambient effect are made. Epilayer grown on thinner non-isoelectronic converted SiC templates is found to broaden its PL line width whereas epilayer grown on porously converted GaN layer fromed from iso- electronic GaAs (111) layer on Si (111) is found narrow line width. H₂ ambient grown film better crystalline quality and higher PL Ex. peak energy is found as compared to N₂ ambient grown film. Low temperature PL measurement, similarity between defect related donor-acceptor peaks (DAP) to defect related yellow band luminescence at the room temperature PL measurement is also found. Grown epilayer different characterization reveals better crystalline quality h-GaN is achieved by using thin iso-electronic GaAS interlayer on Si (111) with H₂ grown ambient.

The sustainable performance of manufacturing companies may commence with employees’ eco-friendly initiatives. The management is responsible for employee green initiatives, requiring the human resource department to develop and implement green strategies. Therefore, it is essential to investigate how green human resource management enhances sustainable performance among manufacturing personnel. This study aims to determine if the green knowledge management and green innovation of manufacturing employees serve as serial mediators in the relationship between green human resource management and sustainable performance. Additionally, if artificial intelligence facilitated the relationship between green human resource management, green knowledge management and sustainable performance.

The data were gathered from manufacturing firm managers and analyzed using partial least squares structural equation modeling.

The results indicated a notable and positive correlation between green human resource management and sustainable performance, green knowledge management and green innovation. Green knowledge management and green innovation exhibited significant positive correlations with sustainable performance. Additionally, green knowledge management and green innovation facilitated the connection between green human resource management and sustainable performance. This relationship was serially mediated by green knowledge management and green innovation. Furthermore, artificial intelligence moderated the relationship between green human resource management, green knowledge management and sustainable performance.

This study suggests that management can use study findings in decision-making to improve firms’ sustainable performance.

This study provides novel empirical evidence by investigating the mediation roles of green knowledge management and green innovation between green human resource management and sustainable performance through the lens of the natural resource orchestration theory.

This study aims to satisfy the thermal management of gallium nitride (GaN) high-electron mobility transistor (HEMT) devices, microchannel-cooling is designed and optimized in this work.

A numerical simulation is performed to analyze the thermal and flow characteristics of microchannels in combination with computational fluid dynamics (CFD) and multi-objective evolutionary algorithm (MOEA) is used to optimize the microchannels parameters. The design variables include width and number of microchannels, and the optimization objectives are to minimize total thermal resistance and pressure drop under constant volumetric flow rate.

In optimization process, a decrease in pressure drop contributes to increase of thermal resistance leading to high junction temperature and vice versa. And the Pareto-optimal front, which is a trade-off curve between optimization objectives, is obtained by MOEA method. Finally, K-means clustering algorithm is carried out on Pareto-optimal front, and three representative points are proposed to verify the accuracy of the model.

Each design variable on the effect of two objectives and distribution of temperature is researched. The relationship between minimum thermal resistance and pressure drop is provided which can give some fundamental direction for microchannels design in GaN HEMT devices cooling.

At airport security checkpoints, baggage screening is aimed to prevent transportation of prohibited and potentially dangerous items. Observing the projection images generated by X-rays scanner is a critical method. However, when multiple objects are stacked on top of each other, distinguishing objects only by a two-dimensional picture is difficult, which prompts the demand for more precise imaging technology to be investigated for use. Reconstructing from 2D X-ray images to 3D-computed tomography (CT) volumes is a reliable solution.

To more accurately distinguish the specific contour shape of items when stacked, multi-information fusion network (MFCT-GAN) based on generative adversarial network (GAN) and U-like network (U-NET) is proposed to reconstruct from two biplanar orthogonal X-ray projections into 3D CT volumes. The authors use three modules to enhance the reconstruction qualitative and quantitative effects, compared with the original network. The skip connection modification (SCM) and multi-channels residual dense block (MRDB) enable the network to extract more feature information and learn deeper with high efficiency; the introduction of subjective loss enables the network to focus on the structural similarity (SSIM) of images during training.

On account of the fusion of multiple information, MFCT-GAN can significantly improve the value of quantitative indexes and distinguish contour explicitly between different targets. In particular, SCM enables features more reasonable and accurate when expanded into three dimensions. The appliance of MRDB can alleviate problem of slow optimization during the late training period, as well as reduce the computational cost. The introduction of subjective loss guides network to retain more high-frequency information, which makes the rendered CT volumes clearer in details.

The authors' proposed MFCT-GAN is able to restore the 3D shapes of different objects greatly based on biplanar projections. This is helpful in security check places, where X-ray images of stacked objects need to be distinguished from the presence of prohibited objects. The authors adopt three new modules, SCM, MRDB and subjective loss, as well as analyze the role the modules play in 3D reconstruction. Results show a significant improvement on the reconstruction both in objective and subjective effects.