Chapter 1: Introduction Chapter 2: Microeconomic Tools for Health Economics Chapter 3: Statistical Tools for Health Economics Chapter 4: Economic Efficiency and Cost Benefit Analysis Chapter 5: Production of Health Chapter 6: The Production, Cost, and Technology of Health Care Chapter 7: Demand for Health Capital Chapter 8: Demand and Supply for Health Insurance Chapter 9: Consumer Choice and Demand Chapter 10: Asymmetric Information and Agency Chapter 11: The Organization of Health Insurance Markets Chapter 12: Managed Care Chapter 13: Nonprofit Firms Nonprofit Firms Chapter 14: Hospitals and Long-term Care Chapter 15: The Physician's Practice Chapter 16: Health Care Labor Markets and Professional Training Chapter 17: The Pharmaceutical Industry Chapter 18: Equity, Efficiency and Need Chapter 19: Government Intervention in Health Care Markets Chapter 20: Government Regulation: Principal Regulatory Mechanisms Chapter 21: Social Insurance Chapter 22: Comparative Health Care Systems and Health System Reform Chapter 23: Health System Reform Chapter 24: The Health Economics of Bads Chapter 25: Epidemiology and Economics: HIV/AIDS in Africa
In today's rapidly evolving world, the continuous development of knowledge and skills is essential. Accessible Internet and a variety of online platforms for distance learning allow individuals to learn throughout their lives, adapting to the changing demands of the labor market. Online educational platforms allow students to study at any time and from anywhere, which is especially important for people with limited access to traditional education due to geographic, temporal or other reasons. The introduction of artificial intelligence technologies into educational systems facilitates the automation of numerous tasks and enables structured feedback, allowing students to quickly identify and correct their mistakes. This contributes to a deeper understanding of the material and improves learning outcomes. The availability of web-oriented educational systems allows institutions to serve a large number of students simultaneously, expanding their audience without significant additional costs. This is also relevant for distance learning in cases of unexpected disruptions. This article explores how AI can automate the administration and evaluation of dictations in distance learning. The system was developed using JavaScript, Next.js, CSS, Tailwind CSS, and TypeScript, along with cloud-based solutions such as Google Cloud Storage. AI-driven technologies were incorporated, including Google Text-to-Speech for generating dictations in a natural-sounding audio format, Google Diff-Match-Patch for real-time text comparison, and Google Vertex AI for detailed feedback and error analysis. The developed system provides teachers with tools for creating, managing, and evaluating dictations. A convenient interface for working with dictations has been developed for students, including a feedback mechanism based on artificial intelligence. The developed web-oriented learning system, owing to AI integration, represents an innovative approach to personalized language learning, and also shows the potential of AI and cloud technologies in the formation of future educational practices.
BackgroundPopulation aging is a significant global issue. In China, the widening urban-rural disparity in population aging has contributed to problems such as the hollowing out of rural areas and a decline in the labor force. This situation, in turn, has subjected older adults in rural areas to significant psychological pressure. Thus their mental health has been widely emphasized. This study is dedicated to analyzing the linkages between mental health, attitudes toward aging, and their labor force participation among rural Chinese older adults in order to promote the construction of an age-friendly social system.MethodsBased on data from the 2020 China Longitudinal Aging Study (CLASS), this study selected rural household residents aged 60 and above as its research subjects. Correlation analysis examined the relationships among labor force participation, mental health among older adults, and attitudes toward aging. OLS regression model was used to investigate the impact of labor force participation on mental health. Stepwise regression and Bootstrap mediation analysis were applied to test the mediating role of attitudes toward aging. Propensity Score Matching was utilized to address endogeneity issues, ensuring the robustness of the findings.Results(1) This study finds that labor participation significantly promotes the mental health of rural older adults, manifested explicitly in reduced depressive symptoms. However, this beneficial effect exhibits a threshold, resulting in a “U-shaped” impact where the influence of labor participation first increases and then decreases. (2) Mechanism analysis further reveals that labor participation enhances older people’s mental health by shaping positive attitudes toward aging. (3) Notably, the mediating mechanism of positive aging attitudes transforms labor participation from a potential health risk in old age into a key protective factor for mental well-being.ConclusionResearch findings confirm that Depression Scales serve as crucial indicators for assessing mental health among older adults. Labor participation is a significant psychological protective factor for rural seniors in China. Moderate labor effectively alleviates depressive symptoms and enhances overall mental well-being in this demographic, whereas excessive or relentless labor yields adverse effects. Mechanism analysis indicates that labor participation helps older adults develop positive attitudes toward aging, thereby buffering and mediating the process by which potential health risks are transformed into psychological protective factors. This empirical study provides crucial evidence for policymakers to optimize rural labor force policies and establish intervention systems for active aging.
David H. B. Große, Eva M. Buhl, Alexander Croon
et al.
Plant virus nanoparticles (VNPs) can be used to generate versatile, functionalized nanomaterials, but they contain replicative genomic DNA whose impact on the environment must be assessed carefully. In contrast, plant virus-like particles (VLPs) lack replicative genomic information and are therefore non-infectious. However, the production of VLPs in plants is associated with challenges such as low yields and a purification process that is difficult to scale up. To address these issues, we used cell-free lysates derived from Nicotiana tabacum BY-2 cells to produce tobacco mosaic virus (TMV)-like particles. The objective of this approach was to generate VLPs with and without encapsulated RNA. SDS-PAGE and Western blotting indicated the accumulation of abundant quantities of the viral coat protein, with yields of up to 3.56 ± 0.62 mg per ml of cell-free lysate confirmed by enzyme-linked immunosorbent assay before purification. The yield of purified TMV VLP is comparable to that obtained from plants due to the low assembly efficiency, with assembled VLPs yielding up to 0.70 ± 0.16 mg per ml of lysate. Notably, this yield is approximately 23 times higher than that obtained through microbial expression systems, obviating the need for the labor-intensive processes typically associated with plant-based methods. Transmission electron microscopy revealed that VLPs assembled in the lysate at pH 7, and remained stable at up to pH 8.5. Cell-free expression therefore offers a rapid, straightforward and cost-effective method for the production of plant VLPs at high yields, and establishes a foundation for the on-demand production of functionalized VLPs under controlled and reproducible conditions.
Research objectives and hypothesis/research questions
The aim of this article is to analyze the theoretical foundations of coaching and to present the practical application of this method in the process of individual transformation. The author discusses key theoretical concepts that constitute the foundation of coaching, such as self-reflection and the development of competencies. Furthermore, the article presents a case study illustrating the real-life application of coaching techniques and their impact on the transformation process of the participant. The findings indicate that coaching, as an interactive and reflective process, can significantly contribute to personal growth, increased motivation, and the achievement of both life and professional goals.
Research methods
The research was conducted using a mixed methods methodology. The case study was carried out based on the structured interview technique, which involves analyzing a specific situation in which a structured interview was employed as a tool to support the coaching process.
Main results
Coaching, as a process of development and analysis, has the potential to support lasting and profound changes in individuals’ lives. This article explores how coaching influences personal development by stimulating reflection, fostering conscious decision-making, and facilitating change in both personal and professional domains. The significance of the coach-client relationship is emphasized, along with the role of an individualized approach in achieving the desired outcomes.
Implications for theory and practice
The continuation of research and the further development of coaching applications are crucial for the advancement and refinement of this field. This enables a more precise and tailored application of this form of support in various contexts, such as professional and personal development.
Management. Industrial management, Management information systems
Les auteurs examinent la capacité des syndicats et de la négociation collective d’entreprise à améliorer les conditions de travail dans les usines de confection approvisionnant les marques et distributeurs multinationaux. Ils montrent, à partir de données recueillies par le programme Better Work au Bangladesh, au Cambodge, à Haïti, en Indonésie, en Jordanie, au Nicaragua et au Viet Nam, que leur rôle peut être positif, même dans les pays qui restreignent la liberté syndicale et la négociation collective. Ils confirment ainsi en partie que l’implication des institutions et acteurs locaux permet d’améliorer le respect des normes du travail du côté production des chaînes d’approvisionnement mondiales.
The precision, stability, and performance of lightweight high-strength steel structures in heavy machinery is affected by their highly nonlinear dynamics. This, in turn, makes control more difficult, simulation more computationally intensive, and achieving real-time autonomy, using standard approaches, impossible. Machine learning through data-driven, physics-informed and physics-inspired networks, however, promises more computationally efficient and accurate solutions to nonlinear dynamic problems. This study proposes a novel framework that has been developed to estimate real-time structural deflection in hydraulically actuated three-dimensional systems. It is based on SLIDE, a machine-learning-based method to estimate dynamic responses of mechanical systems subjected to forced excitations.~Further, an algorithm is introduced for the data acquisition from a hydraulically actuated system using randomized initial configurations and hydraulic pressures.~The new framework was tested on a hydraulically actuated flexible boom with various sensor combinations and lifting various payloads. The neural network was successfully trained in less time using standard parameters from PyTorch, ADAM optimizer, the various sensor inputs, and minimal output data. The SLIDE-trained neural network accelerated deflection estimation solutions by a factor of $10^7$ in reference to flexible multibody simulation batches and provided reasonable accuracy. These results support the studies goal of providing robust, real-time solutions for control, robotic manipulators, structural health monitoring, and automation problems.
The labor shortage in Albania is quickly emerging as a significant challenge for the workforce. The substantial emigration of Albanians seeking opportunities in developed European countries has exacerbated this issue. Factors such as low wages, a lack of motivation, and various structural deficiencies within the labor market have also contributed to the growing crisis. Companies are not only struggling to attract new talent but also face the considerable challenge of retaining their existing employees. A robust performance management system is crucial for cultivating employee loyalty and satisfaction. However, many of these systems currently lack effectiveness and flexibility, leading to increased dissatisfaction among staff. This paper aims to explore the intricacies of the labor shortage and its significant impact on performance management systems. By gathering data through questionnaires and interviews across various sectors, we will employ qualitative analyses to uncover meaningful insights. Our research suggests that companies facing labor shortages experience enhanced efficiency and adaptability in their performance management systems, enabling them to respond more quickly to the constantly evolving market conditions. This adaptability is not only beneficial but essential for thriving in today’s economic environment. Furthermore, throughout this paper, artificial intelligence techniques were carefully applied to systematically organize the overall structure and refine the relevant methodologies, ensuring clarity and coherence in the presentation of ideas.
This paper examines the impact of racial discrimination in hiring on employment, wages, and wealth disparities between black and white workers. Using a labor search-and-matching model with racially prejudiced and non-prejudiced firms, we show that labor market frictions sustain discriminatory practices as an equilibrium outcome. These practices account for 57% of the racial unemployment gap, 48% of the average wage gap, and 16% of the median wealth gap. Discriminatory hiring also increases unemployment and wage volatility for black workers, increasing their labor market risks over the business cycle. Eliminating prejudiced firms reduces these disparities and improves the welfare of black workers as well as the overall economic welfare.
Accurate and timely modeling of labor migration is crucial for various urban governance and commercial tasks, such as local policy-making and business site selection. However, existing studies on labor migration largely rely on limited survey data with statistical methods, which fail to deliver timely and fine-grained insights for time-varying regional trends. To this end, we propose a deep learning-based spatial-temporal labor migration analysis framework, DHG-SIL, by leveraging large-scale job query data. Specifically, we first acquire labor migration intention as a proxy of labor migration via job queries from one of the world's largest search engines. Then, a Disprepant Homophily co-preserved Graph Convolutional Network (DH-GCN) and an interpretable temporal module are respectively proposed to capture cross-city and sequential labor migration dependencies. Besides, we introduce four interpretable variables to quantify city migration properties, which are co-optimized with city representations via tailor-designed contrastive losses. Extensive experiments on three real-world datasets demonstrate the superiority of our DHG-SIL. Notably, DHG-SIL has been deployed as a core component of a cooperative partner's intelligent human resource system, and the system supported a series of city talent attraction reports.
Embedded systems continue to rapidly proliferate in diverse fields, including medical devices, autonomous vehicles, and more generally, the Internet of Things (IoT). Many embedded systems require application-specific hardware components to meet precise timing requirements within limited resource (area and energy) constraints. High-level synthesis (HLS) is an increasingly popular approach for improving the productivity of designing hardware and reducing the time/cost by using high-level languages to specify computational functionality and automatically generate hardware implementations. However, current HLS methods provide limited or no support to incorporate or utilize precise timing specifications within the synthesis and optimization process. In this paper, we present a hybrid high-level synthesis (H-HLS) framework that integrates state-based high-level synthesis (SB-HLS) with performance-driven high-level synthesis (PD-HLS) methods to enable the design and optimization of application-specific embedded systems in which timing information is explicitly and precisely defined in state-based system models. We demonstrate the results achieved by this H-HLS approach using case studies including a wearable pregnancy monitoring device, an ECG-based biometric authentication system, and a synthetic system, and compare the design space exploration results using two PD-HLS tools to show how H-HLS can provide low energy and area under timing constraints.
With the increasing awareness of privacy and the deployment of legislations in various multi-agent system application domains such as power systems and intelligent transportation, the privacy protection problem for multi-agent systems is gaining increased traction in recent years. This article discusses some of the representative advancements in the filed.
Pyeonghwa Kim, Charis Asante-Agyei, Isabel Munoz
et al.
What does work and career success mean for those who secure their work using digital labor platforms? Traditional research on success predominantly relies on organizationally-centric benchmarks, such as promotions and income. These measures provide limited insights into the evolving nature of work and careers shaped at the intersection of digital labor platform technologies and workers' evolving perspectives. Drawing on data from a longitudinal study of 108 digital labor platform workers on Upwork, we (1) identify seven dimensions of success indicators that reflect workers' definitions of success in platform-mediated work and careers, (2) delineate three dimensions of digital labor platforms mediating workers' experiences of success and (3) examine the shifting perspectives of these workers relative to success. Based on these findings, we discuss the implications of platform-mediated success in workers' labor experiences, marked by platformic management, standardization, precarity and ongoing evolution. Our discussion intertwines CSCW scholarship with career studies, advancing a more nuanced understanding of the evolving perspectives on success in platform-mediated work and careers.
Erfan Mehdipour Abadi, Hamid Varmazyari, Masoud H. Nazari
This paper introduces a novel learning-based Stochastic Hybrid System (LSHS) approach for detecting and classifying various contingencies in modern power systems. Specifically, the proposed method is capable of identifying hidden contingencies that cannot be captured by existing sensing and monitoring systems, such as failures in protection systems or line outages in distribution networks. The LSHS approach detects contingencies by analyzing system outputs and behaviors. It then categorizes them based on their impact on the SHS model into physical, control network, and measurement contingencies. The stochastic hybrid system (SHS) model is further extended into an advanced closed-loop framework incorporating both system dynamics and observer-based state estimation error dynamics. Machine learning methods within the LSHS framework are employed for contingency classification and rapid detection. The practicality and effectiveness of the proposed methodology are validated through simulations on an enhanced IEEE-33 bus system. The results demonstrate that the LSHS framework significantly improves the accuracy and speed of contingency detection compared to state-of-the-art methods, offering a promising solution for enhancing power system contingency detection.
John D. Lawson, William C. Bridges, Jeffrey W. Adelberg
Difficult-to-root plants often perform poorly during acclimatization and in vitro rooting can increase the survival and quality of plants. The influence of auxin application and mineral nutrition on in vitro rooting and subsequent effects on plant quality in eight <i>Prunus</i> genotypes were investigated. Microshoots were rooted in vitro on Murashige and Skoog (MS), ½ MS, Driver and Kuniyuki (DKW), or New Prunus Medium (NPM) media formulations in combination with 15 µM indole-3-butyric acid (IBA), 4-day 15 µM IBA pulse, 1 mM 30 s quick-dip, or IBA-free treatments. Shoots were observed pre- and post-acclimatization to determine rooting methods to maximize quality and minimize labor. A genotype-specific response to auxin application was observed with seven of eight genotypes achieving 100% survival when paired with the recommended IBA treatment. Peaches performed best when treated with 4-day IBA pulse or 30 s quick-dip. Rooting of <i>P. cerasifera</i>, it’s hybrid to <i>P. persica,</i> and <i>P. munsoniana</i> all benefitted from IBA application. Shoots rooted with 15 µM IBA were smaller and lower quality in most genotypes. DKW maximized size and quality in six genotypes. Better shoots and larger root systems during in vitro rooting produced better plants in the greenhouse with no detrimental effect of callus growth. Rooting techniques to maximize plant quality while reducing labor are specified.
Junn Yong Loo, Ze Yang Ding, Vishnu Monn Baskaran
et al.
Most works on joint state and unknown input (UI) estimation require the assumption that the UIs are linear; this is potentially restrictive as it does not hold in many intelligent autonomous systems. To overcome this restriction and circumvent the need to linearize the system, we propose a derivative-free Unknown Input Sigma-point Kalman Filter (SPKF-nUI) where the SPKF is interconnected with a general nonlinear UI estimator that can be implemented via nonlinear optimization and data-driven approaches. The nonlinear UI estimator uses the posterior state estimate which is less susceptible to state prediction error. In addition, we introduce a joint sigma-point transformation scheme to incorporate both the state and UI uncertainties in the estimation of SPKF-nUI. An in-depth stochastic stability analysis proves that the proposed SPKF-nUI yields exponentially converging estimation error bounds under reasonable assumptions. Finally, two case studies are carried out on a simulation-based rigid robot and a physical soft robot, i.e., robots made of soft materials with complex dynamics to validate effectiveness of the proposed filter on nonlinear dynamic systems. Our results demonstrate that the proposed SPKF-nUI achieves the lowest state and UI estimation errors when compared to the existing nonlinear state-UI filters.
Abstract Background Older Syrian refugees in Lebanon are a marginalized population with under-recognized health needs. The inclusivity of this population within the humanitarian response is poorly understood. This study aims to identify the unique needs of older Syrian refugees in the context of recent concurrent crises in Lebanon, and explore the extent to which they are being met and prioritized by local and international aid agencies. Methods We conducted in-depth interviews with a snowball sample of 26 stakeholders from 11 organizations operating in the health, nutrition, and water, sanitation, and hygiene sectors. Data analysis followed principles of thematic analysis. Results Concurrent political, economic, and public health crises in host country promoted income insecurity among older refugees and increased dependency on younger relatives, leading to food insecurity, neglect, and poor health outcomes, including the sequelae of untreated non-communicable diseases. Mental illness was perceived to be exacerbated by Covid-19 related challenges, including social isolation, uncertainty about the future, and additionally due to feelings of guilt related to economic dependence and fundamental exclusion from labor force participation. Despite their vulnerability, older refugees are overlooked by the humanitarian response, which may be related to a lack of data. Pervasive medication shortages in the setting of the economic collapse, as well as inaccessible physical environments and competing interests were all identified as major barriers to care. Conclusions Older Syrian refugees in Lebanon experience dual vulnerability that is acutely exacerbated in the setting of concurrent crises. Sociopolitical, economic, and cultural barriers promote social exclusion and may confer an increased risk of income and food insecurity in this population, with significant implications for health. Humanitarian aid agencies operating in the context of fragmented, under-resourced health systems are currently unable to sufficiently address multi-faceted needs of this community. We recommend moving away from a donor-dependent model of aid by allocating resources toward strengthening inclusive national health systems that emphasize preventative care. We further call for age-disaggregation of routine data and normalization of data sharing among stakeholders in the academic and public health sectors in order to develop evidence-based initiatives that can meet the needs of this under-served community.
Special situations and conditions, Medical emergencies. Critical care. Intensive care. First aid