Urban morphologies in the global south are shaped by a complex interplay of historical imprints, from colonial legacies and ethnic tensions to waves of modernization and decolonization efforts. This study delves into the urban morphology of Hangzhou during the late 19th and early 20th centuries, unraveling its transformative patterns steered by a convergence of spatial politics, economic forces, and cultural dynamics. Drawing upon a unique blend of historical map restoration techniques, we unearth pivotal morphological nuances that bridge Hangzhou’s transition from its pre-modern fabric to its modern-day urban layout. We uncover key shifts such as the movement from intricate street layouts to systematic grids, the strategic integration of public spaces like West Lakeside Park, and the city’s evolving urban epicenter mirroring its broader socio-political and economic narratives. These insights not only spotlight Hangzhou’s distinct urban journey in the context of ethnic conflicts, Western influences, and decolonization drives but also underscore the value of context-sensitive urban morphological research in the global south. Our findings emphasize the criticality of synergizing varied methodologies and theoretical perspectives to deepen our comprehension of urban transitions, sculpt place identities, and invigorate public imagination in global urban planning.

To gain a deep understanding of maintenance and repair planning, investigate the weak points of the distribution network, and discover unusual events, it is necessary to trace the shutdowns that occurred in the network. Many incidents happened due to the failure of thermal equipment in schools. On the other hand, the most important task of electricity distribution companies is to provide reliable and stable electricity, which minimal blackouts and standard voltage should accompany. This research uses seasonal time series and artificial neural network approaches to provide models to predict the failure rate of one of the equipment used in two areas covered by the greater Tehran electricity distribution company. These data were extracted weekly from April 2019 to March 2021 from the ENOX incident registration software. For this purpose, after pre-processing the data, the appropriate final model was presented with the help of Minitab and MATLAB software. Also, average air temperature, rainfall, and wind speed were selected as input variables for the neural network. The mean square error has been used to evaluate the proposed models’ error rate. The results show that the time series models performed better than the multi-layer perceptron neural network in predicting the failure rate of the target equipment and can be used to predict future periods.

Remote sensing technologies have revolutionized forestry analysis by providing valuable information about forest ecosystems on a large scale. This review article explores the latest advancements in remote sensing tools that leverage optical, thermal, RADAR, and LiDAR data, along with state-of-the-art methods of data processing and analysis. We investigate how these tools, combined with artificial intelligence (AI) techniques and cloud-computing facilities, enhance the analytical outreach and offer new insights in the fields of remote sensing and forestry disciplines. The article aims to provide a comprehensive overview of these advancements, discuss their potential applications, and highlight the challenges and future directions. Through this examination, we demonstrate the immense potential of integrating remote sensing and AI to revolutionize forest management and conservation practices.

Tidal sea level variations in the Mediterranean basin, although altered and amplified by resonance phenomena in confined sub-basins (e.g., Adriatic Sea), are generally confined within 0.5 meters and exceptionally up to 1.5 meters. Here we explore the possibility of retrieving sea level measurements using data from GNSS antennas on duty for ground motion monitoring and analyze the spectral outcomes of such distinctive measurements. We estimate one year of GNSS data collected on the Mediterranean coasts in order to get reliable sea level data from all publicly available data and compare it with collocated tide gauges. A total of eleven stations were suitable for interferometric analysis (as of 2021), and all were able to supply centimeter-level sea level estimates. The spectra in the tidal frequency windows are remarkably similar to tide gauge data. We find that the O₁ and M₂ diurnal and semidiurnal tides and MK₃, MS₄ shallow sea water tides may be disturbed by aliasing effects.

The silver nanoparticles (AgNPs) exhibit unique and tunable plasmonic properties. The size and shape of these particles can manipulate their localized surface plasmon resonance (LSPR) property and their response to the local environment. The LSPR property of nanoparticles is exploited by their optical, chemical, and biological sensing. This is an interdisciplinary area that involves chemistry, biology, and materials science. In this paper, a polymer system is used with the optimization technique of blending two polymers. The two polymer composites polystyrene/poly (4-vinylpyridine) (PS/P4VP) (50:50) and (75:25) were used as found suitable by their previous morphological studies. The results of 50, 95, and 50, 150 nm thicknesses of silver nanoparticles deposited on PS/P4VP (50:50) and (75:25) were explored to observe their optical sensitivity. The nature of the polymer composite embedded with silver nanoparticles affects the size of the nanoparticle and its distribution in the matrix. The polymer composites used are found to have a uniform distribution of nanoparticles of various sizes. The optical properties of Ag nanoparticles embedded in suitable polymer composites for the development of the latest plasmonic applications, owing to their unique properties, were explored. The sensing capability of a particular polymer composite is found to depend on the size of the nanoparticle embedded in it. The optimum result has been found for silver nanoparticles of 150 nm thickness deposited on PS/P4VP (75:25).

This study explores the marginalization of a poor fishing community in Gwadar, Pakistan. The study provides an insight into how different levels of power, such as hidden, visible/pluralist, and invisible ideological powers, are used in policy arenas to hinder fishers’ access to participatory spaces, decision-making, and resource use. By employing Gaventa’s power cubes analytical model, we analyze fishers’ experiences and prevailing scenarios. Qualitative research methods were used to collect data, including in-depth interviews and participant observation. The finding shows that the interests of the fishing community in fishery policies and ongoing development projects are excluded both with intention and unintentionally. The exclusion of the local fisher community from key spaces brings interruptions and transformations that influence their lives. Due to this, they are induced to join insurgent groups to confront exclusion-based policies in Gwadar, Pakistan.