The debate on relocating Indonesia's national capital from Jakarta stems from critical issues such as overpopulation, social inequality, environmental degradation, and natural disaster risks. These challenges highlight the need to reassess Jakarta's viability as the nation's administrative center. This study evaluates Indonesia's readiness to address the complexities of relocation by analyzing Jakarta's socio-economic, political, cultural, and geographical conditions. Using a systematic literature review (SLR) with a qualitative approach, the research explores key questions: Do Jakarta's conditions necessitate relocation? What challenges might arise from the move? How prepared is Indonesia to tackle these challenges? The SLR process includes defining questions, sourcing literature from reputable databases, applying inclusion/exclusion criteria, and synthesizing data for analysis. Findings reveal Jakarta's multifaceted challenges, including social disparities, environmental degradation, disaster risks, and governance issues, which emphasize the urgency of considering relocation. However, the study also identifies significant hurdles, such as high costs, logistical complexities, potential social conflicts, and environmental risks at the new capital site. Relocating the capital is a strategic and complex undertaking that requires meticulous planning. Indonesia must weigh Jakarta's current issues, address potential relocation challenges, and ensure readiness for risk mitigation and sustainable development. Comprehensive and thoughtful planning is essential to achieve a successful and balanced transition.

Management and efficiency have a fundamental impact on the performance of public hospitals, as well as on their philanthropic mission. Various studies have shown that the financial weaknesses of these entities affect the planning, setting of goals and objectives, monitoring, evaluation and feedback necessary to improve health systems and guarantee accessibility as an inalienable right. This study aims to analyze the management and efficiency of third-level and/or high-complexity hospitals in Colombia, through a statistical model that uses financial analysis and key performance indicators (KPIs) such as ROA, ROE and EBITDA. A non-experimental cross-sectional design is used, with an analytical-synthetic, documentary, exploratory and descriptive approach. The results show financial deficiencies in the hospitals evaluated; hence it is recommended to make adjustments in the operating cycle to increase efficiency rates. In addition, the use of the KPIs ROA and ROE under adjusted models is suggested for a more precise analysis of the financial ratios, since these adequately explain the variability of each indicator and are appropriate to evaluate hospital management and efficiency, but not in EBITDA ratio, hence the latter is not recommended to evaluate hospital efficiency reliably. This study provides relevant information for public health policy makers, hospital managers and researchers, in order to promote the efficiency and improvement of health services.

Urban planning is critical to managing rapid urban growth, particularly in African regions experiencing high urbanization rates. This study focuses on Bol, Lake Chad Province, a city facing significant challenges due to inadequate planning frameworks compounded by recurrent humanitarian and climate crises. It fills an empirical gap by analyzing how local planning mechanisms respond to these socio-environmental complexities, with a focus on the interplay between institutional structures, legislative frameworks, and resource allocation. The study assesses urban planning practices in Bol to identify challenges and opportunities, with the aim of improving institutional effectiveness, aligning policies with realities, and integrating climate resilience strategies. Using a qualitative methodology, it combines field surveys, stakeholder interviews, and document analysis, using SWOT (Strengths, Weaknesses, Opportunities, Threats) and PESTEL (Political, Economic, Sociocultural, Technological, Environmental, Legal) frameworks for data analysis. The findings reveal that ineffective institutions, poor inter-sectoral coordination, outdated legislative frameworks and resource constraints hamper sustainable urban development in Bol. To address these issues, the study proposes to strengthen local institutional capacities, foster stakeholder collaboration, and modernize urban planning policies through participatory approaches. The study highlights the need to integrate resilience strategies into urban settings to mitigate climate change impacts and improve governance. These measures not only address immediate challenges, but also advance urban planning theory and provide a basis for future research on adaptation strategies in crisis-prone regions. This study offers practical insights for policy makers and contributes to developing more sustainable and resilient urban planning systems in similar contexts.

Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.

Inflammation of the lungs, called pneumonia, is a disease characterized by inflammation of the air sacs that interfere with the exchange of oxygen and carbon dioxide. It is caused by a variety of infectious organisms, including viruses, bacteria, fungus, and parasites. Pneumonia is more common in people who have pre-existing lung diseases or compromised immune systems, and it primarily affects small children and the elderly. Diagnosis of pneumonia can be difficult, especially when relying on medical imaging, because symptoms may not be immediately apparent. Convolutional neural networks (CNNs) have recently shown potential in medical imaging applications. A CNN-based deep learning model is being built as part of ongoing research to aid in the detection of pneumonia using chest X-ray images. The dataset used for training and evaluation includes images of people with normal lung conditions as well as photos of people with pneumonia. Various preprocessing procedures, such as data augmentation, normalization, and scaling, were used to improve the accuracy of pneumonia diagnosis and extract significant features. In this study, a framework for deep learning with four pre-trained CNN models—InceptionNet, ResNet, VGG16, and DenseNet—was used. To take use of its key advantages, transfer learning utilizing DenseNet was used. During training, the loss function was minimized using the Adam optimizer. The suggested approach seeks to improve early diagnosis and enable fast intervention for pneumonia cases by leveraging the advantages of several CNN models. The outcomes show that CNN-based deep learning models may successfully diagnose pneumonia in chest X-ray pictures.