Technological University, Mandalay

This paper provides an initial assessment of Myanmar’s PM0.1 (small to nanoparticulate matter) levels. In Mandalay, the second-largest city in Myanmar, there are two distinct seasons: wet and dry. In 2019, a Nano-sampler with PM10/2.5/1.0/0.5/0.1 stages was used to collect ambient particles. The first was from March, and the second was from December, both in 2019. The PM2.5 and PM10 levels exceeded the World Health Organization Air Quality Guidelines in both periods. The PM0.1 level ranged from 10 to 23 µg/m3, with an average of 15.21 ± 4.85 µg/m3. The Char-EC/Soot-EC ratios in PM0.1 (0.79–1.57) indicate a mixed combustion regime, with a slight dominance of biomass burning. However, values close to unity suggest substantial contributions from fossil fuel combustion, particularly diesel emissions, reflecting a dual-regime aerosol system in the study area. On the other hand, local and transboundary biomass burning emissions affect both fine particles (PM0.5−1.0 and PM1.0−2.5) and coarse particles (PM> 10 and PM2.5−10). In December, emissions from the area may be more important than smoke from biomass burning in other parts of Myanmar. However, long-range transport from South Asia and the west side of Mandalay is important for elevated PM levels during March. This outcome will help Myanmar and other developing nations affected by burning tropical biomass transition to long-term air quality management.

Landslides are a recurrent and damaging hazard in the mountainous terrain of Chin State, Myanmar, driven by steep slopes, fragile lithology, and intense monsoonal rainfall. This study develops a regional-scale landslide susceptibility mapping framework by integrating cloud-based geospatial processing in Google Earth Engine with the Analytical Hierarchy Process (AHP) for multi-criteria decision analysis. A landslide inventory was compiled through visual interpretation of high-resolution satellite imagery and used for model validation. Sixteen landslide-conditioning factors representing topography (slope, aspect, curvature), hydrology (drainage density, topographic wetness index, stream power index, distance to rivers), climate (mean annual precipitation), geology/structure (lithology, geomorphology, lineament density), land surface characteristics (land use/land cover, Normalized Difference Vegetation Index, Normalized Difference Water Index, soil texture), and anthropogenic influence (distance to roads) were derived from open datasets within Google Earth Engine (GEE). Each factor was standardized into five susceptibility classes and weighted using AHP; the consistency ratio (CR = 0.014) indicated acceptable pairwise judgments. The weighted linear combination produced a landslide susceptibility index (LSI), which was classified into five susceptibility zones. Validation using receiver operating characteristic (ROC) analysis on an independent sample of 183 points yielded an area under the curve (AUC) of 0.768, Accuracy = 0.699, F1-Score = 0.751, and Cohen's Kappa = 0.38, demonstrating acceptable predictive capability. High and very high-susceptibility areas mainly coincide with steep slopes, dissected terrain, dense drainage and lineament networks, weak lithologies, and proximity to roads and rivers. The resulting susceptibility map provides actionable information for land-use planning and risk reduction in data-scarce mountainous regions and can be readily transferred to other areas using the same GEE-based workflow.

DC-DC buck converters are widely employed in power electronic systems to provide efficient voltage regulation for various applications. However, the converter performance is significantly influenced by the duty cycle, which determines the output voltage and current characteristics. This study presents a comparative analysis of the voltage and current responses of a DC-DC buck converter operating at duty cycles of 0.5 and 0.25 using MATLAB/Simulink. The objective is to evaluate the effect of duty cycle variation on the converter's dynamic and steady-state performance. A simulation model was developed and tested under identical operating conditions, and the resulting output waveforms were analyzed. The results indicate that increasing the duty cycle enhances both the average output voltage and load current, while a lower duty cycle reduces the output magnitude. The simulated responses closely agree with the theoretical principles of buck converter operation, demonstrating the effectiveness of MATLAB/Simulink for performance evaluation and design optimization.

Automated classification of bone fractures has become a cornerstone of modern emergency radiology, significantly enhancing diagnostic speed and precision. This study evaluates the comparative efficacy of three leading deep learning frameworks ResNet50, MobileNetV3, and Vision Transformer (ViT) using a diverse dataset that includes various fracture modalities, healthy X-rays, and non-radiological images.The experimental data reveals that the Vision Transformer (ViT) attained the highest diagnostic accuracy at 95%, marginally outperforming MobileNetV3 and ResNet50, which both achieved 94%. While all three models demonstrated flawless reliability (100%) in identifying Forteen Classes Bone categories, their performance diverged when analyzing complex fracture patterns.

Threshold voltage variation affects circuit reliability, power efficiency, switching speed, and device performance. The process design corner, the concentration of doping, the potential of the surface, the length of the channel, the thickness of the oxide, the fluctuation of random dopants, the temperature, and other characteristics all have an impact on threshold voltage. Although a wide range of factors is employed, process design corner analysis is most effective in digital electronics because it demonstrates how modifications to the process have a direct impact on the rate at which transistors transition between logic states. The behaviors and threshold voltages of p-channel metal oxide semiconductor field-effect transistors (PMOS) and n-channel metal oxide semiconductor field effect transistors (NMOS) are examined based on the transfer (input) and drain (output) characteristic curves with different drain-to-source and gate-to-source voltages. Understanding the behavior of PMOS and NMOS transistors in the cutoff, linear, and saturation regions is crucial for the design and analysis of analog and digital circuits. The transfer and drain characteristic curves are utilized to analyze the threshold voltage (Vth) using several process design corners, including monte carlo (mc), typical test (tt), fast-fast (ff), slow-slow (ss), fast-slow (fs), and slow-fast (sf). Cadence Virtuoso software has been used to numerically investigate the electrical properties of the p-channel and n-channel MOSFETs. With the lowest threshold voltage value among several process design corners, the fast-fast (ff) process design corner enables metal oxide semiconductor field-effect transistors (MOSFETs) to switch on and off more quickly.