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The global textile and apparel sector is critically important as an early phase in industrialization for many developing countries and as a provider of employment opportunities to thousands of low-income workers, many of them women. The goal of this book is to explore how the lifting of the Multi-fibre Arrangement/ Agreement on Textiles and Clothing (MFA/ATC) quotas has affected nine countries Bangladesh, Cambodia, Honduras, India, Mexico, Morocco, Pakistan, Sri Lanka, and Vietnam with the broader aim of better understanding the links between globalization and poverty in the developing world. Analyzing how employment, wage premiums, and the structure of the apparel industry have changed after the MFA/ATC can generate important lessons for policy makers for economic development and poverty reduction. This book uses in-depth country case studies as the broad methodological approach. In-depth country studies are important because countries are idiosyncratic: differences in regulatory context, history, location, trade relationships, and policies shape both the apparel sector and how the apparel sector changed after the end of the MFA. In-depth country studies place broader empirical work in context and strengthen the conclusions. The countries in this book were chosen because they represent the diversity of global apparel production, including differences across regions, income levels, trade relationships, and policies. The countries occupy different places in the global value chain that now characterizes apparel production. Not surprisingly, the countries studied in this book represent the diversity of post-MFA experiences. This book highlights four key findings: The first is that employment and export patterns after the MFA/ATC did not necessarily match predictions. This book shows that only about a third of the variation in cross-country changes in exports is explained by wage differences. While wage differences explain some of the production shifts, domestic policies targeting the apparel sector, ownership type, and functional upgrading of the industry also played an important role. Second, changes in exports are usually, but not always, good indicators of what happens to wages and employment. While rising apparel exports correlated with rising wages and employment in the large Asian countries, rising exports coincided with falling employment in Sri Lanka. Third, this book identifies the specific ways that changes in the global apparel market affected worker earnings, thus helping to explain impacts on poverty. Fourth, in terms of policies, the countries that had larger increases in apparel exports were those that promoted apparel sector upgrading; those that did not promote upgrading had smaller increases or even falling exports.

The number of accidents involving elderly individuals has been increasing with the increase of the aging population, posing increasingly serious challenges. Most accidents are caused by reduced judgment and physical abilities, which lead to severe consequences. Therefore, studies on support systems for elderly and visually impaired people to improve the safety and quality of daily life are attracting considerable attention. In this study, a road surface condition distinction method using reflection intensities obtained by an ultrasonic sensor was proposed. The proposed method was applied to movement support systems for elderly and visually impaired individuals to detect dangerous road surfaces and give an alarm. The method did not perform well in previous studies of puddle detection, because the alert provided by the method did not enable users to avoid puddles. This study extended the method proposed by previous studies with respect to puddle detection ability. The findings indicate the effectiveness of the proposed method by considering four road surface conditions. The proposed method could detect puddle conditions. The effectiveness of the proposed method was verified in all four conditions, since users could differentiate between road surface conditions and classify the conditions as either safe or dangerous.

This report evaluates the outcomes of World Bank Group support to Liberia from its post-war reengagement in 2003 through 2011. The country has moved from total disarray to a solid foundation for inclusive development. Although development has not moved forward as quickly as hoped, substantial progress has been made. Public finance and key institutions have been rebuilt; crucial transport facilities have been restored; and hospitals, schools, and universities are operating. The debilitating burden of massive external debt has been eliminated. Although the government deserves most of the credit, this success would not have been possible without external development and security partners, including the World Bank Group. Regarding outcomes, the rebuilding of public institutions has seen substantial progress, with important achievements in restoring public finances and reforming the civil service. Regarding the rehabilitation of infrastructure, the World Bank Group has helped improve the conditions of roads, ports, power supply, and water and sanitation. However, World Bank Group financial support has been relatively modest with regard to facilitating growth, but it has helped with policy advice and in filling gaps left by other partners. With regard to the three cross-cutting themes of Bank Group strategy, some effective programs were carried out, including capacity development at several core public finance-related agencies. However, the integration of these themes across World Bank Group interventions, which was the underlying intent, still needs a vision and better articulated strategy. Finally, the Bank and the International Monetary Fund led efforts to reduce Liberia's inherited external debt burden under the enhanced Highly-Indebted Poor Country Initiative and the Multi-lateral Debt Relief Initiative mechanisms.

The objective of the Nepal Investment Climate Assessment (ICA) is to evaluate the investment climate in Nepal in all its dimensions and promote policies to strengthen the private sector. The investment climate is made up of many dimensions that shape the opportunities for investments, employment creation, and growth of private firms. Such dimensions include factor markets, product markets, infrastructure services, and the macroeconomic, legal, regulatory, and institutional framework. The report's key finding is that while there are some niche sectors growing and expanding employment in Nepal (including tourism and certain educational and other services), there are many constraints to the investment climate in Nepal that are hindering the development and growth of the private sector. In particular, political instability, poor infrastructure, poor labor relations, poor access to finance, and declining exports plague Nepal's private sector. To overcome many of these issues and move forward, many reforms are needed. Given the extent of the challenge, effective public-private dialogue is required so that the government and the private sector can work in partnership to address these constraints. The pervasiveness and impact of political instability in Nepal makes the investment climate in the country comparable more to Afghanistan than other countries in the region or the comparator countries used in the analysis. While this comparison is unflattering, it is true. Political instability has stifled growth and limited Nepal's ability to exploit its hydropower and tourism potential. Interestingly, many firms do not perceive access to land and finance as major obstacles. This could be a reflection of lack of dynamism: Nepalese firms are simply not planning to invest, expand, and grow in their unstable and unpredictable environment. The peace dividend is not difficult to measure. As the surveys show, ending civil unrest alone would give back to enterprises 44 working days a year. The effects on economic activity, investment, growth, and job creation could be potentially huge.

Although average incomes in China have risen dramatically since the 1980s, concerns are increasing that the rural elderly have not benefited from growth to the same extent as younger people and the urban elderly. Concerns about welfare of the rural elderly combine spatial and demographic issues. Large gaps exist between conditions in coastal and interior regions and between conditions in urban and rural areas of the country. In addition to differences in income by geography, considerable differences exist across demographic groups in the level of coverage by safety nets, in the benefits received through the social welfare system, and in the risks of falling into poverty. This book aims to do two things: first, it provides detailed empirical analysis of the welfare and living conditions of the rural elderly since the early 1990s in the context of large-scale rural-to-urban migration, and second, it explores the evolution of the rural pension system in China over the past two decades and raises a number of issues on its current implementation and future directions. Although the two sections of the book are distinct in analytical terms, they are closely linked in policy terms: the first section demonstrates in several ways a rationale for greater public intervention in the welfare of the rural elderly, and the second documents the response of policy to date and options to consider for deepening the coverage and effects of the rural pension system over the longer term.

Deprescribing fall-risk increasing drugs (FRIDs) is promising for reducing the risk of falling in older adults. Applying appropriate deprescribing in practice can be difficult due to the outcome uncertainties associated with stopping FRIDs. The ADFICE_IT intervention addresses this complexity with a clinical decision support system (CDSS) that facilitates optimum deprescribing of FRIDs by using a fall-risk prediction model, aggregation of deprescribing guidelines, and joint medication management. The development process of the CDSS is described in this paper. Development followed a user-centered design approach in which users and experts were involved throughout each phase. In phase I, a prototype of the CDSS was developed which involved a literature and systematic review, European survey (n = 581), and semi-structured interviews with clinicians (n = 19), as well as the aggregation and testing of deprescribing guidelines and the development of the fall-risk prediction model. In phase II, the feasibility of the CDSS was tested by means of two usability testing rounds with users (n = 11). The final CDSS consists of five web pages. A connection between the Electronic Health Record allows for the retrieval of patient data into the CDSS. Key design requirements for the CDSS include easy-to-use features for fast-paced clinical environments, actionable deprescribing recommendations, information transparency, and visualization of the patient's fall-risk estimation. Key elements for the software include a modular architecture, open source, and good security. The ADFICE_IT CDSS supports physicians in deprescribing FRIDs optimally to prevent falls in older patients. Due to continuous user and expert involvement, each new feedback round led to an improved version of the system. Currently, a cluster-randomized controlled trial with process evaluation at hospitals in the Netherlands is being conducted to test the effect of the CDSS on falls. The trial is registered with ClinicalTrials.gov (date; 7-7-2022, identifier: NCT05449470).

Despite being a major public health problem, falls in the elderly cannot be detected efficiently yet. Many studies have used acceleration as the main input to discriminate between falls and activities of daily living (ADL). In recent years, there has been an increasing interest in using smartphones for fall detection. The most promising results have been obtained by supervised Machine Learning algorithms. However, a drawback of these approaches is that they rely on falls simulated by young or mature people, which might not represent every possible fall situation and might be different from older people's falls. Thus, we propose to tackle the problem of fall detection by applying a kind of novelty detection methods which rely only on true ADL. In this way, a fall is any abnormal movement with respect to ADL. A system based on these methods could easily adapt itself to new situations since new ADL could be recorded continuously and the system could be re-trained on the fly. The goal of this work is to explore the use of such novelty detectors by selecting one of them and by comparing it with a state-of-the-art traditional supervised method under different conditions. The data sets we have collected were recorded with smartphones. Ten volunteers simulated eight type of falls, whereas ADL were recorded while they carried the phone in their real life. Even though we have not collected data from the elderly, the data sets were suitable to check the adaptability of novelty detectors. They have been made publicly available to improve the reproducibility of our results. We have studied several novelty detection methods, selecting the nearest neighbour-based technique (NN) as the most suitable. Then, we have compared NN with the Support Vector Machine (SVM). In most situations a generic SVM outperformed an adapted NN.

Falls are the leading cause of injury-related mortality and hospitalization among adults aged [greater than or equal to] 65 years. An important modifiable fall-risk factor is use of fall-risk increasing drugs (FRIDs). However, deprescribing is not always attempted or performed successfully. The ADFICE_IT trial evaluates the combined use of a clinical decision support system (CDSS) and a patient portal for optimizing the deprescribing of FRIDs in older fallers. The intervention aims to optimize and enhance shared decision making (SDM) and consequently prevent injurious falls and reduce healthcare-related costs. A multicenter, cluster-randomized controlled trial with process evaluation will be conducted among hospitals in the Netherlands. We aim to include 856 individuals aged [greater than or equal to] 65 years that visit the falls clinic due to a fall. The intervention comprises the combined use of a CDSS and a patient portal. The CDSS provides guideline-based advice with regard to deprescribing and an individual fall-risk estimation, as calculated by an embedded prediction model. The patient portal provides educational information and a summary of the patient's consultation. Hospitals in the control arm will provide care-as-usual. Fall-calendars will be used for measuring the time to first injurious fall (primary outcome) and secondary fall outcomes during one year. Other measurements will be conducted at baseline, 3, 6, and 12 months and include quality of life, cost-effectiveness, feasibility, and shared decision-making measures. Data will be analyzed according to the intention-to-treat principle. Difference in time to injurious fall between the intervention and control group will be analyzed using multilevel Cox regression. The findings of this study will add valuable insights about how digital health informatics tools that target physicians and older adults can optimize deprescribing and support SDM. We expect the CDSS and patient portal to aid in deprescribing of FRIDs, resulting in a reduction in falls and related injuries.

Background: Gait recognition has been applied in the prediction of the probability of elderly flat ground fall, functional evaluation during rehabilitation, and the training of patients with lower extremity motor dysfunction. Gait distinguishing between seemingly similar kinematic patterns associated with different pathological entities is a challenge for the clinician. How to realize automatic identification and judgment of abnormal gait is a significant challenge in clinical practice. The long-term goal of our study is to develop a gait recognition computer vision system using artificial intelligence (AI) and machine learning (ML) computing. This study aims to find an optimal ML algorithm using computer vision techniques and measure variables from lower limbs to classify gait patterns in healthy people. The purpose of this study is to determine the feasibility of computer vision and machine learning (ML) computing in discriminating different gait patterns associated with flat-ground falls. Methods: We used the Kinect® Motion system to capture the spatiotemporal gait data from seven healthy subjects in three walking trials, including normal gait, pelvic-obliquity-gait, and knee-hyperextension-gait walking. Four different classification methods including convolutional neural network (CNN), support vector machine (SVM), K-nearest neighbors (KNN), and long short-term memory (LSTM) neural networks were used to automatically classify three gait patterns. Overall, 750 sets of data were collected, and the dataset was divided into 80% for algorithm training and 20% for evaluation. Results: The SVM and KNN had a higher accuracy than CNN and LSTM. The SVM (94.9 ± 3.36%) had the highest accuracy in the classification of gait patterns, followed by KNN (94.0 ± 4.22%). The accuracy of CNN was 87.6 ± 7.50% and that of LSTM 83.6 ± 5.35%. Conclusions: This study revealed that the proposed AI machine learning (ML) techniques can be used to design gait biometric systems and machine vision for gait pattern recognition. Potentially, this method can be used to remotely evaluate elderly patients and help clinicians make decisions regarding disposition, follow-up, and treatment.