Analyzing the link between the COVID-19 pandemic and essential resources, and how Nigerian households adapt with various coping strategies. The Covid-19 National Longitudinal Phone Surveys (Covid-19 NLPS-2020), conducted while the Covid-19 lockdown was in effect, furnished the data we employ. The Covid-19 pandemic, our research demonstrates, has exposed households to shocks like illness, injury, agricultural disruptions, job losses, business closures, and the escalating costs of food and agricultural supplies. Access to fundamental needs for households is hampered severely by these negative shocks, showing different consequences based on the household head's gender and whether they live in a rural or urban community. In order to mitigate the impact of shocks on their access to fundamental needs, households adopt a diverse array of formal and informal coping strategies. county genetics clinic This paper's findings echo the growing body of evidence concerning the imperative of supporting households affected by negative shocks and the significance of formal coping strategies for households in developing countries.
This article's feminist analysis investigates the extent to which agri-food and nutritional development policies and interventions effectively confront gender inequality. Global policy analysis, coupled with project examples from Haiti, Benin, Ghana, and Tanzania, reveals a prevalent gender equality focus within policies and practices that often relies on a static, homogenous portrayal of food provisioning and marketing. Women's labor, often depicted in these narratives, frequently becomes a tool for interventions that prioritize funding their income generation and caregiving responsibilities, leading to household food and nutrition security. However, these interventions remain insufficient, as they neglect the underlying structural vulnerabilities that cause this burden, including the disproportionate work load and land access challenges, amongst other critical issues. We believe that policies and interventions should prioritize and consider the unique circumstances of local social norms and environmental conditions, and further examine how wider policies and developmental support systems affect social relationships in order to resolve the structural issues of gender and intersectional inequalities.
This study investigated the interconnectedness of internationalization and digitalization, employing a social media platform, within the early phases of internationalization for new ventures in an emerging economy. Cannabinoid Receptor agonist A longitudinal, multiple-case study approach was employed in the research. All investigated firms had operated on Instagram, the social media platform, from the moment they were initiated. Two rounds of in-depth interviews, combined with secondary data sources, served as the basis for data collection. To identify patterns and trends, the research employed thematic analysis, cross-case comparison, and pattern-matching logic. The research enhances the existing body of knowledge by (a) proposing a conceptual model of digitalization and internationalization in the initial stages of international expansion for small, nascent ventures from emerging economies leveraging a social media platform; (b) explicating the role of the diaspora in the internationalization of these enterprises and outlining the theoretical implications; and (c) offering a nuanced micro-perspective on how entrepreneurs utilize platform resources and mitigate associated risks during their enterprises' early domestic and international stages.
Supplementary material is integrated into the online version and is accessible at 101007/s11575-023-00510-8.
Supplementary material for the online version is accessible at 101007/s11575-023-00510-8.
This investigation, guided by organizational learning theory and institutional perspectives, delves into the dynamic relationship between internationalization and innovation in emerging market enterprises (EMEs), exploring the moderating role of state ownership. Our investigation, using a panel data set of Chinese listed companies from 2007 to 2018, uncovers that internationalization fuels innovation investment in emerging market economies, thus yielding higher levels of innovation output. International engagement thrives due to a high output of innovation, causing a compounding effect on innovation and internationalization. It is fascinating to observe that state ownership acts as a positive moderator for the link between innovation input and innovation output, but as a negative moderator for the relationship between innovation output and international expansion. Our paper further refines our understanding of the dynamic interplay between internationalization and innovation in emerging market economies (EMEs) through a combined lens. This comprehensive approach integrates knowledge exploration, transformation, and exploitation, while simultaneously considering the institutional aspect of state ownership.
Physicians' careful monitoring of lung opacities is vital, for misdiagnosis or confusion with other findings may lead to irreversible patient outcomes. Consequently, long-term scrutiny of lung regions characterized by opacity is recommended by medical professionals. Characterizing the regional structures of images and separating them from other lung pathologies can offer considerable relief to physicians. Deep learning methods offer a straightforward approach to the detection, classification, and segmentation of lung opacity. Employing a three-channel fusion CNN model, this study effectively detected lung opacity in a balanced dataset derived from public datasets. For the first channel, the MobileNetV2 architecture is selected; the InceptionV3 model is chosen for the second channel; and the VGG19 architecture is used in the third channel. The ResNet architecture facilitates the transfer of features from the preceding layer to the current layer. The straightforward implementation of the proposed approach results in considerable cost and time advantages for physicians. genetic assignment tests Accuracy results from the newly compiled dataset for classifying lung opacity are 92.52% for two classes, 92.44% for three classes, 87.12% for four classes, and 91.71% for five classes.
Protecting the safety of subterranean mining and safeguarding surface installations and nearby residences from the impact of sublevel caving demands a comprehensive investigation of the ensuing ground movement. This research investigated the failure behaviors of the surface and drift within the surrounding rock, employing data from in situ failure analyses, monitoring records, and geological parameters. To uncover the mechanism causing the movement of the hanging wall, the empirical results were merged with theoretical analysis. Horizontal displacement, a direct result of the in-situ horizontal ground stress, is vital to the movement of both the ground surface and underground passages. The ground surface exhibits accelerated motion in correspondence with drift failures. Deep-seated rock failure gradually radiates outward, ultimately affecting the surface. Steeply dipping discontinuities are responsible for the distinctive ground movement pattern observed in the hanging wall. Given the steeply dipping joints cutting through the rock mass, the rock surrounding the hanging wall can be visualized as cantilever beams, subjected to both the in-situ horizontal ground stress and the additional stress from caved rock laterally. A modified toppling failure formula can be generated by utilizing this model. Furthermore, a mechanism for fault slippage was put forth, alongside the stipulations necessary for such slippage to occur. A model for ground movement, derived from the failure mechanisms of steeply inclined separations, was formulated, encompassing the effect of horizontal in-situ stress, slippage along fault F3, slippage along fault F4, and the toppling of rock columns. Based on the singular ground movement mechanisms, the rock mass encircling the goaf is segregated into six zones, comprising a caved zone, a failure zone, a toppling-sliding zone, a toppling-deformation zone, a fault-slip zone, and a movement-deformation zone.
The detrimental effects of air pollution on public health and worldwide ecosystems are largely caused by various sources, including industrial activities, vehicle exhaust, and fossil fuel combustion. Air pollution, a significant contributor to climate change, also presents a serious threat to human health, causing respiratory ailments, cardiovascular issues, and potentially even cancer. A possible resolution to this problem has been suggested by the integration of diverse artificial intelligence (AI) and time-series models. Internet of Things (IoT) devices are used by these cloud-implemented models to forecast the Air Quality Index (AQI). The abundance of recent IoT-connected time-series air pollution data presents a hurdle for established models. IoT devices and cloud environments have been utilized in various ways to predict AQI. Through evaluating an IoT-Cloud-based model, this study aims to gauge its ability to predict AQI in the face of different meteorological conditions. In order to predict air pollution levels, a novel BO-HyTS approach was created, combining seasonal autoregressive integrated moving average (SARIMA) with long short-term memory (LSTM), subsequently optimized by Bayesian optimization. The forecasting process's accuracy is augmented by the proposed BO-HyTS model's ability to capture both linear and nonlinear properties in the time-series data. In parallel, several methods for forecasting air quality index (AQI) including classical time series analysis, machine learning techniques, and deep learning models, are applied to forecast air quality from time series data. Five statistical evaluation metrics are employed in order to evaluate the efficiency of the models. While the comparative analysis of diverse algorithms presents a challenge, a non-parametric statistical significance test—the Friedman test—is utilized for measuring the performance of machine learning, time-series, and deep learning models.