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Local Economic Development (LED) is the main anchor through which economic development is achieved by building entrepreneurial capacities and improving opportunities for economic growth and citizens’ quality of life, especially in rural settlements. In Ghana, the implementation of LED is under the Ministry of Local Government, Decentralization and Rural Development (MLDGRD) through the Metropolitan, Municipal, and District Assemblies (MMDAs) at the local level. The implementation of LED contributes significantly to Ghana’s economic growth, business creation, and employment generation. LED is therefore identified as essential to sustainable development and poverty reduction in rural settlements in Ghana. However, the challenges of poor implementation of the LED policy are financial constraints to implement LED activities at the local level, performance action of Public-Private Partnership (PPP) towards LED, and limited exploration of the sanitation value chain LED efforts at the local level. The poor capacity of small-medium micro enterprises (SMMEs) and smallholder farmers have often affected the successful implementation of LED activities at the local level. Hence, this paper seeks to identify the determinants for resilience in Local Economic Development in Ghana. The paper further provides an overview of the LED challenges in Ghana. Finally, this paper recommends appropriate theoretical frameworks that integrate the determinants for the resilience of Local Economic Development to address the identified challenges of LED, leading to poverty reduction in Ghana.

The product functions (PFs) extracted by local mean decomposition (LMD) of the noisy signal contain obvious energy‐concentrated pulses. As a result, the conventional amplitude threshold filtering used in wavelet transform (WT)‐based and empirical mode decomposition (EMD)‐based de‐noising methods is no longer applicable. To address this issue, an improved signal de‐noising method is proposed by using the multi‐level local mean decomposition (ML‐LMD), the superposition and recombination (SR) of high‐order PFs, the outlier detection, and waveform smoothing (OD‐WS) to remove noise by eliminating the pulse components. The proposed method's superior noise reduction performance is demonstrated through theoretical analysis and experimental verification. Compared to well‐known methods like WT‐based and EMD‐based de‐noising, the results show that the proposed method has significant comparative advantages in reducing noise in rolling bearing signals. Considering the dual pulse characteristics of the high‐order PFs obtained from the LMD of the noisy signal, an improved signal de‐noising method is proposed by using the multi‐level local mean decomposition (ML‐LMD), the superposition and recombination (SR) of high‐order PFs, the outlier detection and waveform smoothing (OD‐WS) for noise removal by eliminating the pulse components.

This paper analyses the level of fiscal decentralization and structural characteristics of local public finances in Republic of Serbia with focus on local public investments. Share of central government expenditures in consolidated government spending of 83%, indicates relatively high degree of fiscal centralization. In spite of significant rise in local public revenues in the last decade public investments remained low - amounting to 1% of GDP, which is significantly below EU and Central and Eastern Europe average (1.4 and 1.5% GDP, respectively). Our results indicate large variation in relative size of public investments across LSGs. Most local public investments are focused on roads maintenance administrative infrastructure, while investments in environment and education are low. To tackle local disparities in terms of quality of local infrastructure and to foster economic convergence, development of planning and implementation capacities and introduction of systemic incentives for local public investments should be considered.

This article assesses the role of local councils as a conduit for democratic consolidation through the examination of the legislative performance of the members of a South Korean metropolitan city council. We collected data on ordinance proposals in Busan Metropolitan Council from 2006 to 2018 (the 5th to 7th Councils) and analysed, first, the effects of individual attributes of local council members on legislative performance through negative binomial model analysis and, second, the effects of legislative networks on council members' performance. Three findings contribute to the literature: first, the number of proposed ordinances by council members increased over time, while those by the mayor decreased in the same period, suggesting an erosion of executive dominance of policymaking in local councils. Second, female and newly elected council members are most active in legislative proposals, which underlines that these members are more connected to the electorate than long-serving incumbents. Third, network analyses show increasingly diverse and multi-centred communities behind ordinance proposals; this suggests a move from personalistic politics to institutionalised politics.

Purpose Coopetition, namely, the interplay between cooperation and competition, has received a good deal of interest in the business-to-business marketing literature. Academics have operationalised the coopetition construct and have used these measures to test the antecedents and consequences of firms collaborating with their competitors. However, business-to-business marketing scholars have not developed and validated an agreed operationalisation that reflects the dimensionality of the coopetition construct. Thus, the purpose of this study is to develop and validate a multi-dimensional measure of coopetition for marketing scholars to use in future research. Design/methodology/approach To use a highly cooperative and highly competitive empirical context, sporting organisations in New Zealand were sampled, as the key informants within these entities engaged in different forms of coopetition. Checks were made to ensure that the sampled entities produced generalisable results. That is, it is anticipated that the results apply to other industries with firms engaging in similar business-to-business behaviours. Various sources of qualitative and quantitative data were acquired to develop and validate a multi-dimensional measure of coopetition (the COOP scale), which passed all major assessments of reliability and validity (including common method variance). Findings The results indicated that coopetition is a multi-dimensional construct, comprising three distinct dimensions. First, local-level coopetition is collaboration among competing entities within a close geographic proximity. Second, national-level coopetition is cooperation with rivals within the same country but across different geographic regions. Third, organisation-level coopetition is cooperation with competitors across different firms (including with indirect rivals), regardless of their geographic location and product markets served. Indeed, organisation-level coopetition extends to how companies engage in coopetition in domestic and international capacities, depending on the extent to which they compete in similar product markets in comparison to industry rivals. Also, multiple indicators were used to measure each facet of the coopetition construct after the scale purification stage. Originality/value Prior coopetition-based investigations have predominately been conceptual or qualitative in nature. The scarce number of existing scales have significant problems, such as not appreciating that coopetition is a multi-dimensional variable, as well as using single indicators. In spite of a recent call for research on the multiple levels of coopetition, there has not been an agreed measure of the construct that accounts for its multi-dimensionality. Hence, this investigation responds to such a call for research by developing and validating the COOP scale. Local-, national- and organisation-level coopetition are anticipated to be the main facets of the coopetition construct, which offer several avenues for future research.

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

This paper evaluates the potential fiscal effects on cities of the coronavirus-induced recession. We provide estimates of revenue shortfalls in fiscal year 2021, as compared to the trajectory prior to the recession. Our analysis is based on data for 150 fiscally standardized cities, fiscal units designed to take account of variations across central cities in governmental structure. We forecast revenues from all of the major revenue sources of cities, including property, sales and income taxes, fees and charges, and intergovernmental aid. We investigate two scenarios, “less severe” and “more severe,” depending on assumptions about fiscal pressures at the state level and the elasticities of the various revenue sources. Our average predictions are for a shortfall in revenues of 5.5 percent under the less severe scenario and 9 percent under the more severe scenario. We predict wide variation across cities, depending on differences in revenue structures and the fiscal condition of states going into the recession. The hardest hit cities face revenue losses of 15 percent or more. We also compare revenue pressure to cost pressures from the coronavirus and find that a number of cities will experience large revenue shortfalls and high additional costs.

Integrated crop-livestock systems have been shown to improve nutrient cycling, particularly by re-coupling nitrogen and carbon cycles. Yet the number of mixed crop–livestock farms has been falling steadily in Europe. Integration between crops and livestock at the local level, through exchanges between already specialised farms, is rarely implemented. Given the lack of knowledge on new ways to maintain or to reintegrate crops and livestock from the farm up to the local level, concrete guidelines are needed. In this paper, we developed a transversal analysis of three complementary case studies regarding development of crop–livestock integration at the farm and beyond farm level. To this aim, we reviewed three French case studies in which participatory approaches were used to design scenarios of crop–livestock integration. When crop–livestock integration disappears from the farm level due to labour organisation, exchanges between specialised crop farmers and livestock farmers is a way to redevelop such integration at the local level. Transversal analysis of case-studies allowed us to suggest guidelines for further research regarding the design of agroecological crop–livestock integration. Articulating options of change at farm level and collective level allows to consider the appropriate level of design and trade-offs between (1) farm and beyond farm level, and (2) social, environmental and economic dimensions Considering these different levels of organisation is needed to identify possible pathways to and policy incentives for integrated crop–livestock systems. Developing specific Decision Support Systems and participative research is needed to conceive locally adapted scenarios of crop-livestock integration.

Road accident is one of the main causes of death and injury worldwide in this fast-paced modern world. Many developing countries, including Malaysia, are facing serious road accident problems. Forecasting road accident cases has become an important step towards setting the road safety target. Hence, this study aims to develop forecasting models and forecast future trends of monthly road accident cases in Malaysia. The data set on monthly number of accident cases from January 2001 to December 2019 was provided by Polis Diraja Malaysia (PDRM). Box-Jenkins and State space models were developed using the data under study. The models were then evaluated based on in-sample and out-sample evaluation using lowest root mean square error, mean absolute percentage error and mean absolute error. The results show that the basic structural state space model with trend and seasonal component was the most appropriate model in forecasting road accident cases in Malaysia. The 10-year ahead forecast from January 2020 to December 2030 shows that monthly road accident cases in Malaysia have a constant inclining pattern for each year. It is hoped that the finding from this study could become a reference for the authorities of Malaysia in making recommendations in order to improve road safety and reduce road traffic accidents in Malaysia.

Image-text retrieval aims to search related results of one modality by querying another modality. As a fundamental and key problem in cross-modal retrieval, image-text retrieval is still a challenging problem owing to the complementary and imbalanced relationship between different modalities (i.e., Image and Text) and different granularities (i.e., Global-level and Local-level). However, existing works have not fully considered how to effectively mine and fuse the complementarities between images and texts at different granularities. Therefore, in this paper, we propose a hierarchical adaptive alignment network, whose contributions are as follows: (1) We propose a multi-level alignment network, which simultaneously mines global-level and local-level data, thereby enhancing the semantic association between images and texts. (2) We propose an adaptive weighted loss to flexibly optimize the image-text similarity with two stages in a unified framework. (3) We conduct extensive experiments on three public benchmark datasets (Corel 5K, Pascal Sentence, and Wiki) and compare them with eleven state-of-the-art methods. The experimental results thoroughly verify the effectiveness of our proposed method.