The 4ICEBA2023 aims to connect researchers and scientists from Vietnam and around the world, particularly from Asian countries, in the fields of engineering, physics, microelectronics, semiconductors, and electronic engineering. Our focus is on their

This study investigates the radiation shielding properties of construction materials, with a focus on iron oxide (Fe₂O₃)-enhanced heavy concrete. It covers a range of Fe₂O₃ concentrations from 0% to 12.9% in concrete formulations and uses M200-grade standard samples as references. The study observes a linear increase in sample density as Fe₂O₃ content rises. A gamma transmission measurement system utilizing NaI(Tl) detectors assesses shielding effectiveness. It measures linear attenuation coefficients for concrete samples at 59.54 keV (²⁴¹Am), 661.7 keV (¹³⁷Cs), and 1332 keV (⁶⁰Co) energy levels. Precise measurements result from careful calibration of detectors and radiation sources, ensuring a focused gamma ray beam. The findings establish a direct correlation between linear attenuation coefficients and Fe₂O₃ content at each energy level. Notably, samples with the highest Fe₂O₃ concentration exhibit significant increases in attenuation coefficients, such as 19.6% (59.54 keV), 7.75% (661.7 keV), and 13.5% (1332 keV), compared to standard samples. These insights suggest the potential use of iron oxide-enhanced heavy concrete as effective radiation shielding in construction applications.

Amid the rapid advancements in technology, the Internet of Things (IoT) has become a pivotal element in the realm of wireless communication. The sub-1GHz network, in comparison to its higher frequency counterparts for IoT endeavors, offers distinct advantages, including extended range and reduced power consumption. Recognizing the critical role of phase-locked loops (PLLs) in enhancing data transceiver and communication systems, this study presents a charge-pump phase-locked loop (CPPLL) designed specifically for sub-1G IoT deployments. Utilizing a 1.0 V input voltage alongside a modest 20 MHz reference frequency, our PLL architecture is developed utilizing 45 nm technology from the NCSU process. According to simulations conducted on the Cadence Virtuoso platform, our CPPLL design achieves an output frequency range of 467.3 to 877.2 MHz and an RMS jitter of 150 ps, demonstrating its potential effectiveness for sub-1G IoT systems.

Gamma-ray scattering is a powerful method in the non-destructive testing field. Many researches related to gamma-ray scattering is being used in the world. Gamma-ray scattering can be used to determine thickness, structure as well as components in a material. Along with computer science, application of computer science in many scientific fields may constitute good achievements such as precision and speed of data analysis. In this paper, Machine learning is being used in gamma-ray scattering to determine thickness of material based on gamma-ray spectrum. To provide a dataset for machine learning, Monte Carlo was used for Ti, Mn, Fe, Co, Cu, Zn samples from 1mm to 50mm. In Machine learning, 8^th-degree polynomial regression method is used.

Since mammography was introduced in the middle of the 20th century, it has been the preferred method for detecting breast cancer, thereby significantly impacting the prognosis and survival rates of affected individuals. However, the diagnostic accuracy of mammographic images can be compromised by the presence of noise, low contrast, indistinct features, and poor differentiation from surrounding tissue. This paper presents a comprehensive approach that addresses the limitations of mammograms by combining wavelet-based denoising and morphological transformation for denoising and enhancing mammographic images. This work reduces the impact of noise artifacts while preserving image features by applying various shrinkage thresholds to the subbands of the stationary wavelet transform. Subsequently, a novel morphological filtering operation is applied to further enhance contrast, suppress noise, refine edges, and remove unwanted artifacts, all while emphasizing relevant structures. The experimental results outperformed existing methods in terms of quantitative metrics such as PSNR, SSIM and MSE. Notably, it also achieved superior performance compared to the common enhancing method, CLAHE. This proposed method is a promising new approach for denoising and enhancing mammograms. It has the potential to contribute to earlier and more accurate breast cancer diagnosis, thereby advancing the fields of women's health and cancer detection.

Water is essential for human survival and ecosystem health. Therefore, robust mechanisms must be implemented to monitor drinking and environmental water quality. Timely warnings about water contamination are critical for protecting public and environmental health. The Internet of Things (IoT) enables remote monitoring, data collection, and analysis of water quality. This study aims to develop a smart water quality monitoring system using Web and Mobile applications. The system measures five key water quality parameters (temperature, pH, turbidity, dissolved oxygen, and salinity). Sensors are calibrated in the lab and field to ensure data accuracy. The system provides real-time water quality assessments and early warning of contamination events to safeguard drinking and environmental water resources.

Rice is a staple food crop in Vietnam, with a majority of production centered in the Mekong River Delta, especially in An Giang province. Ensuring sustainable rice cultivation under climate change requires an understanding of how projected shifts may impact rice agroecosystems. Simulation modeling provides important insights for forecasting climate change effects on key factors like pests, diseases, yield, evapotranspiration, and water requirements. The warning model of 90-100-day rice crop insect pests and diseases is programmed in FORTRAN based on rice growth stages, pest and disease species, and meteorological factors. Results show certain pests proliferate while others manifest only at specific stages. Climate change models predict decreased pest pressure but also reduced yields in Vietnam, indicating a need for adapted management strategies. Separate rice yield modeling using ORYZA2000 reveals stable autumn-winter harvests but rainfall-vulnerable winter-spring and summer-autumn yields. Emission scenarios also predict declining yields by mid-century in studied regions. Modeling of evapotranspiration and water requirements with CROPWAT 8.0 demonstrates the highest evapotranspiration in summer-autumn and greatest water needs in winter-spring. Climate change scenarios project increasing evapotranspiration and water requirements through 2099, threatening rice production. In summary, complex meteorological interactions influence rice pests, diseases, yield, evapotranspiration, and water requirements in An Giang province. Climate change models consistently predict challenges to rice cultivation from pests, yield reductions, and rising resource demands.

This paper focuses on improving mixing efficiency through the design and investigation of a micromixer that utilizes a micro-T sinusoidal geometry with a microelectrode cavity, employing numerical simulation. This work entails analyzing active mixers to enhance mixing efficiency by investigating the effects of microelectrode capacity, the placement of electrode pairs, the number of electrode pairs, and variations in voltage. The findings from the examinations indicate that the exceptional mixing efficiency of 85.7% is achieved through the utilization of the micro-T sinusoidal structure featuring a microelectrode cavity. Integrating the microelectrode cavity into the liquid structure significantly enhanced mixing by making the liquid in the microchannel charged and disturbing the contact surface. The use of numerous microelectrode cavities in the microchannel enhanced the performance of the micromixer, resulting in a reduction in mixing time. Furthermore, the research thoroughly investigated the quantity in the microchannel, uncovering that an optimal increase in a certain parameter enhances mixing efficiency. This proposal introduces a structure that holds promise for enhancing the mixing quality of the micro-T sinusoidal structure. Moreover, this study signifies remarkable progress, delivering enhanced efficiency at lowered costs for future applications.

Mangrove forests provide important coastal protection services by attenuating waves, reducing erosion, and mitigating storm surge. Based on the measured wave data in 2014-2015, the study calculated wave attenuation in the Cu Lao Dung mangrove forest in Soc Trang Province, Vietnam using the WAPROMAN model. WAPROMAN simulates wave transformation processes and mangrove characteristics to predict changes in wave height. Based on Cu Lao Dung site, the model was parameterized using local bathymetry, topography, mangrove stand structure, and wave climate data. Simulations were conducted for variable mangrove widths, densities, stem diameters, and wave heights. Results showed significant wave attenuation was most influenced by mangrove width, substantially dampening waves and reducing erosion potential.

This research focuses on harnessing excess heat energy in Aquaponic greenhouses-a system combining vegetable and fish cultivation with water circulation, where a perpetual temperature difference exists between the greenhouse air and the water in hydroponic vegetable tubes. Our objective is to convert this temperature difference into electric power for using in wireless IoT sensing systems. Nineteen thermoelectric generators (TEG-12708) were evaluated in a laboratory environment to determine their internal resistance and corresponding load resistance for experimental energy surveys in greenhouses. The maximum output power reached 699.38 µW when the temperature difference across the TEG was 3°C. Furthermore, we successfully demonstrated a sensing system driven by the realistic ambient temperature environment by integrating the TEG with other electronic components, including a DC-DC converter, power management circuit, supercapacitor, and sensors. Under aqua-greenhouse conditions, the harvested temperature difference across the TEG was 3.5°C, corresponding to the TEG's output power of 0.55 mW. The output of the DC-DC converter could reach over 8 V. This work opens up new opportunities for TEGs to harness ambient aqua-greenhouse energy and convert it into usable electricity for wireless IoT sensing systems.