Engineering Commons^™

Improved Sequence Embeddings For Peptide-Class I Mhc Binding Prediction, Patiphan Wongklaew

Chulalongkorn University Theses and Dissertations (Chula ETD)

The binding of a peptide antigen to a class I major histocompatibility complex (MHC) protein is considered an essential process by which the immune system monitors infected cells. This mechanism has facilitated the development of peptide-based vaccines aimed at stimulating the patient's immune response for cancer treat-ment. Accurate prediction of peptide-MHC binding is therefore crucial for prioritizing optimal peptides tailored to individual patients. Nevertheless, many MHC alleles lack sufficient experimental data on peptide-MHC binding, which potentially constrains the accuracy of existing prediction models. This study introduces an enhanced iteration of MHCSeqNet, an open-source deep learning model which utilizes sequence data …

การประมาณมวลชีวภาพของต้นไม้ด้วยเทคโนโลยีไลดาร์ร่วมกับภาพถ่ายดาวเทียมเพื่อหาค่าคาร์บอนเครดิต, พสธร ธีระกานตภิรัตน์

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้นำเสนอวิธีการประมาณมวลชีวภาพของต้นไม้และการคำนวณคาร์บอนเครดิตในพื้นที่จุฬาลงกรณ์มหาวิทยาลัย โดยใช้เทคโนโลยีไลดาร์จากโทรศัพท์มือถือ เก็บข้อมูลในรูปแบบแผนที่ความลึก เพื่อมาวิเคราะห์องค์ประกอบและคัดแยกชุดข้อมูลในส่วนของลำต้นในการสร้างชุดข้อมูลพอยต์คลาวด์ในรูปแบบสามมิติ และภาพถ่ายดาวเทียมเพื่อหาพื้นที่สีเขียวร่วมด้วย เพื่อวิเคราะห์เส้นผ่านศูนย์กลางระดับอก (DBH) ของต้นไม้ และสร้างความสัมพันธ์ระหว่าง DBH กับความสูงต้นไม้ ผลลัพธ์จะถูกนำไปคำนวณมวลชีวภาพต่อพื้นที่สีเขียว เพื่อคำนวณ การกักเก็บคาร์บอน และคาร์บอนเครดิตทั้งหมดในพื้นที่ศึกษา พร้อมทั้งเปรียบเทียบความแม่นยำระหว่างข้อมูลจากไลดาร์และการวัดจริง งานนี้มีเป้าหมายเพื่อสร้างวิธีการที่สะดวกและมีแบบแผนสำหรับการติดตามพื้นที่สีเขียวและคาร์บอนเครดิตในพื้นที่อื่นๆ ในอนาคต.

Improving Thai-Dialect Automatic Speech Recognition With Curriculum Learning, Artit Suwanbandit

Chulalongkorn University Theses and Dissertations (Chula ETD)

Automatic Speech Recognition (ASR) has been the foremost feature and integrated in many real-world usages for decades. The novel deep learning approaches prevail in the ASR field as a model architecture. However, ASR still struggles with low-resource data, especially in the Thai dialectal language. Transfer learning is the conventional approach in the machine learning field for escalating the performance of low-resource settings. In the scenario that additional fine-tuning tasks are not achievable, exerting all efficiency of target data is an accountability. To overcome stagnation, we proposed transfer-based curriculum learning for low-resource dialectal ASR, Phonemetically-induced subword Out-of-vocabulary rate (PhIS-OOV), and Model …

Grammatical Error Correction In Thai Sentences For Deaf Students, Supachan Traitruengsakul

Chulalongkorn University Theses and Dissertations (Chula ETD)

Deaf students encounter challenges in written communication due to errors such as insertion, deletion, disorder, misusage, and misspellings. Grammatical error correction (GEC) technology can help mitigate these issues. However, existing GEC models are primarily trained on online resources from second-language hearing learners. In contrast, sentences written by deaf students suffer from a variety of errors not typically found elsewhere. To address this issue, we create the Thai Deaf Corpus (TDC), focusing on identifying and analyzing errors among deaf students in grades 7-12 across four deaf schools. Additionally, we introduce a two-stage system for the Thai-GEC model, automatically detecting and correcting …

การสร้างเทสต์สคริปต์ของโรบอทเฟรมเวิร์คจากยูสเซอร์สตอรีและซีนาริโอ สำหรับการทดสอบเว็บแอปพลิเคชัน, กฤชวัฒน์ เวชสาร

Chulalongkorn University Theses and Dissertations (Chula ETD)

กระบวนการพัฒนาซอฟต์แวร์ด้วยสกรัมเฟรมเวิร์กและแนวคิดบีเฮฟเวียร์ดริเวนดีเวลลอปเมนต์ (BDD) เป็นกระบวนการที่ได้รับความนิยมในปัจจุบัน โดยความต้องการของผู้ใช้จะถูกเก็บรวบรวมและถ่ายทอดผ่านยูสเซอร์สตอรีและซีนาริโอ ซึ่งทำหน้าที่เป็นทั้งตัวอย่างและเกณฑ์การยอมรับ การแบ่งการพัฒนาออกเป็นช่วงเล็ก ๆ ช่วยลดเวลาและต้นทุนในการพัฒนา อย่างไรก็ตาม กระบวนการที่มีการพัฒนาเป็นช่วงสั้น ๆ อย่างต่อเนื่องนี้สร้างความท้าทายสำหรับการทดสอบ โดยเฉพาะการทดสอบเชิงถดถอย แม้ว่าการทดสอบแบบอัตโนมัติจะถูกนำมาใช้เพื่อลดภาระ แต่การเขียนเทสต์สคริปต์ด้วยตนเองยังคงมีความเสี่ยงที่จะเกิดข้อผิดพลาดและขาดประสิทธิภาพ ส่งผลให้การทดสอบเชิงถดถอยที่สำคัญมักถูกเลื่อนออกไปจนไม่สามารถตรวจพบข้อผิดพลาดได้ทันการณ์ งานวิจัยนี้นำเสนอแนวทางการแก้ปัญหาด้วยการสร้างเทสต์สคริปต์สำหรับโรบอทเฟรมเวิร์กโดยอัตโนมัติจากยูสเซอร์สตอรีและซีนาริโอ โดยใช้ XML เพื่อกำหนดโครงสร้างส่วนต่อประสานผู้ใช้ของหน้าเว็บ ช่วยให้สามารถจัดเตรียมเทสต์สคริปต์ได้ตั้งแต่ขั้นตอนการออกแบบ นอกจากตัวอย่างในซีนาริโอแล้ว ยังใช้ XML Schema Definition (XSD) เพื่อสร้างชุดข้อมูลสำหรับการทดสอบเพิ่มเติมโดยอัตโนมัติ การพารามิเตอร์ไรซ์เทสต์สคริปต์ช่วยรวบรวมกรณีทดสอบที่มีขั้นตอนร่วมกัน แต่ใช้ชุดข้อมูลต่างกันไว้ในสคริปต์เดียวกัน ซึ่งช่วยลดความซับซ้อนและภาระในการบำรุงรักษา เครื่องมือที่พัฒนาขึ้นตามแนวทางนี้สามารถลดความยุ่งยากในการจัดการเทสต์สคริปต์และเพิ่มประสิทธิภาพในการทดสอบเชิงถดถอยได้อย่างมีนัยสำคัญ และช่วยสนับสนุนการตรวจพบข้อผิดพลาดตั้งแต่ช่วงแรกของกระบวนการพัฒนา

Electrolyte/Electrode Interfacial Electrochemical Optimization Strategies For Durable Aqueous Zinc-Ion Batteries, Jingjing Niu

Chulalongkorn University Theses and Dissertations (Chula ETD)

Aqueous zinc ion batteries, as a type of secondary battery using inorganic aqueous solution as the electrolyte, are considered to be a strong contender in the field of future energy storage technologies due to their high theoretical electric capacity, high safety, low cost and environmental friendliness. However, aqueous zinc ion batteries also face some challenges. The first is the relatively low energy density, which requires further optimisation of electrode materials and battery structure. Secondly, the creation of zinc dendrites can reduce the cycling stability and safety of the battery. In addition, prolonging the cycle life is also a pressing issue. …

Development Of Janus Membrane From Electrospun Nanofiber For Membrane Distillation Processes, Michaela Olisha Lobregas

Chulalongkorn University Theses and Dissertations (Chula ETD)

The Janus membrane, characterized by surfaces with opposite wettability, represents an innovative design for membrane distillation (MD). This study introduces new methods for fabricating MD membranes with tailored surfaces to address fouling and wetting challenges in saline systems. A nanofibrous Janus membrane was developed, featuring an omniphobic substrate as the non-wetting layer and a hydrophilic top coating as the wetting layer. Using the dual-spinneret system for simultaneous electrospinning and electrospraying, a composite substrate membrane, comprising a loose network of PVDF nanofibers and PVDF-fluorinated TiO2 microclusters, was produced. This membrane exhibited high resistance to liquids, including water, glycerol, diiodomethane, and saline. …

Air Flow Behavior Assessment In Particulate Filtration Efficiency (Pfe) Tester Via Computational Fluid Dynamics (Cfd), Muhammad Amer

Chulalongkorn University Theses and Dissertations (Chula ETD)

Particulate Filtration Efficiency (PFE) assessment is critical for evaluating filter media. There are several PFE testing protocol depending on products and issuing countries, for example ASTM-F2299 standard for disposable medical masks, NIOSH-TEB-APR-STP-0059 for N and R series respirators, and ISO-29463 for HEPA filters. While the PFE testing principles of all standard are the same, their requirement, testing protocol and minute details are different. Air flow characteristic before and after filter media in testing chamber is very important to accuracy of PFE measurement, especially for ASTM F2299 standard. We have developed a custom PFE tester per ASTM F2299 using a simple …

Silica Dust Exposure Assessment Using Job-Exposure Matrix:A Case Of Sandstone Workers In Thailand, Chamaiphon Chari

Chulalongkorn University Theses and Dissertations (Chula ETD)

Respirable crystalline silica (RCS) has been classified as a human carcinogen by the International Agency for Research on Cancer (IARC). In Thailand, household sandstone workers were one of the most susceptible occupational groups to silicosis. However, a lack of current information about their work patterns and job-related exposures to respirable crystalline silica could hinder the success of tracking and surveillance programs. This study was conducted to explore working characteristics and patterns, and to evaluate the work diary data in the estimation of RCS exposure and health risk among household sandstone workers. All data were collected from a work diary record …

Measurement Of The Local Static Mechanical Pressure Of Earplugs, Luiz G.C. Melo, Ahmed S. Dalaq, Franck Sgard, Olivier Doutres, Laurianne Legroux, Eric Wagnac

Études primaires

Earplugs are used in various critical industrial sectors, such as construction, aviation, military and defense, transportation, and healthcare. However, they present inherent drawbacks, notably discomfort that can lead to inconsistent and incorrect use, thereby leaving a significant proportion of workers vulnerable to irreversible hearing damage, ranging from tinnitus to deafness. This discomfort is closely linked to a physical parameter known as static mechanical pressure (SMP), representing the pressure exerted by earplugs on the earcanal walls. Determining the SMP is crucial for developing earplugs that prioritize comfort. However, experimental studies in this area are scarce and there is no experimental set-up …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

Reevaluating The Paradox: Does Low-Dose Radiation From A-Bombs Affect Lifespan And Cancer Mortality?, Razieh Rashidfar, Zeynab Seyedi Sarhad, Seyed Mohammad Javad Mortazavi, Lembit Sihver

Informatics and Engineering Systems Faculty Publications

No abstract provided.

Disaggregating Longer-Term Trends From Seasonal Variations In Measured Pv System Performance, Chibuisi Chinasaokwu Okorieimoh, Brian Norton, Michael Conlon

Articles

Photovoltaic (PV) systems are widely adopted for renewable energy generation, but their performance is influenced by complex interactions between longer-term trends and seasonal variations. This study aims to remove these factors and provide valuable insights for optimising PV system operation. We employ comprehensive datasets of measured PV system performance over five years, focusing on identifying the distinct contributions of longer-term trends and seasonal effects. To achieve this, we develop a novel analytical framework that combines time series and statistical analytical techniques. By applying this framework to the extensive performance data, we successfully break down the overall PV system output into …

Influence Of Heat Treatment And Shielding Gas Environment On The Very High Cycle Fatigue Behavior Of 17-4 Precipitation Hardened Stainless Steel, Jade Welsh

UNF Graduate Theses and Dissertations

Additive manufacturing (AM) for stainless steel components has proved useful in a variety of industries ranging from aerospace to biomedical engineering. Many of these components are subject to very high cycle fatigue (VHCF) loading and must have tailored mechanical properties to ensure failures do not occur before a service life is reached. Testing the fatigue strength AM materials exposed to VHCF loading is paramount to compare with the expected life cycles of their wrought counterparts. In this thesis, AM 17-4 precipitation hardened (PH) stainless steel (SS) were fabricated using laser powder bed fusion (L-PBF) technique, while the wrought 17-4 PH …

Improving Risk Governance Strategies Via Learning: A Comparative Analysis Of Solar Radiation Modification And Gene Drives, Khara Grieger, Jonathan B. Wiener, Jennifer Kuzma

Faculty Scholarship

Stratospheric aerosol injection (SAI) and gene drive organisms (GDOs) have been proposed as technological responses to complex entrenched environmental challenges. They also share several characteristics of emerging risks, including extensive uncertainties, systemic interdependencies, and risk profiles intertwined with societal contexts. This Perspective conducts a comparative analysis of the two technologies, and identifies ways in which their research and policy communities may learn from each other to inform future risk governance strategies. We find that SAI and GDOs share common features of aiming to improve or restore a public good, are characterized by numerous potential ecological, societal, and ethical risks associated …

Matthew Gaber: Peekaboo, Matthew Gaber, Mohiuddin Ahmed, Helge Janicke

Research Datasets

Cyber-attacks continue to evolve, increasing in frequency and sophistication where Artificial Intelligence (AI) is becoming essential in detecting modern malware. However, the accuracy of AI in malware detection is dependent on the quality of the features it is trained with. Static and dynamic analysis of malware is limited by the widespread use of obfuscation and anti-analysis techniques employed by malware authors, where if an analysis environment is detected the malware will hide its malicious behavior. However, Dynamic Binary Instrumentation (DBI) allows deep and precise control of the malware sample, thereby facilitating the extraction of authentic features from sophisticated and evasive …

The Hydrodynamics Of Double-Layered Vegetation Partially Occupying Both Banks In An Open Channel Flow, Maheshwara Ihala Gallangage Gedara

Research Datasets

This data delves into the hydrodynamic intricacies within an open channel featuring continuous double-layered vegetation along both banks. Measurements are undertaken using nonintrusive laser diagnostics (LDV) to assess velocity, shear stress, and turbulence intensity. Two double-layered vegetation configurations, uniform growth (A1) and mixed growth (A2), were examined in conjunction with four aspect ratios (AR-6.67, 5.00, 4.00, and 2.63). The raw data were acquired using "BSA Flow Software v5.00", provided by DANTEC DYNAMICS, Denmark, and the extracted data used for the analysis was included in the Excel sheets for the purpose of sharing.

Enhancing Research Productivity: Seamless Integration Of Personal Devices And Hpc Resources With The Cybershuttle Notebook Gateway, Yasith Jayawardana, Dimuthu Wannipurage, Eroma Abeysinghe, Suresh Marru

Computer Science Faculty Publications

Scientists often utilize personal laptops and workstations for initial research stages and turn to high-performance computing (HPC) supercomputers for compute-intensive tasks. However, seamless transitions between these environments are vital for enhancing productivity and accelerating research progress. Our paper presents the Cybershuttle Notebook Gateway, an open-source framework crafted to streamline this transition, optimize resource utilization, and reduce time-to-science for researchers. Leveraging JupyterLab, the framework extends kernel mechanics for seamless provisioning and connection to remote HPC cluster kernels. We delve into its architecture, which separates user authentication, kernel provisioning, and remote file system access. Additionally, we highlight practical capabilities like analyzing network …

Enhanced Skin Cancer Diagnosis Through Grid Search Algorithm-Optimized Deep Learning Models For Skin Lesion Analysis, Rudresh Pillai, Neha Sharma, Sheifali Gupta, Deepali Gupta, Sapna Juneja, Saurav Malik, Hong Qin, Mohammed S. Alqahtani, Amel Ksibi

Computer Science Faculty Publications

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)-CNN1, CNN2, and CNN3-each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal …

Flexible Fitting Of Alphafold2-Predicted Models To Cryo-Em Density Maps Using Elastic Network Models: A Methodological Affirmation, Maytha Alshammari, Jing He, Willy Wriggers

Computer Science Faculty Publications

Motivation: This study investigates the flexible refinement of AlphaFold2 models against corresponding cryo-electron microscopy (cryo-EM) maps using normal modes derived from elastic network models (ENMs) as basis functions for displacement. AlphaFold2 generally predicts highly accurate structures, but 18 of the 137 models of isolated chains exhibit a TM-score below 0.80. We achieved a significant improvement in four of these deviating structures and used them to systematically optimize the parameters of the ENM motion model.

Results: We successfully refined four AlphaFold2 models with notable discrepancies: lipid-preserved respiratory supercomplex (TM-score increased from 0.52 to 0.69), flagellar L-ring protein (TM-score increased from 0.53 …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

Autonomous Strike Uavs For Counterterrorism Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

UAVs are becoming a crucial tool in modern warfare, primarily due to their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. The use of autonomous UAVs to conduct strike missions against highly valuable targets is the focus of this research. Due to developments in ledger technology, smart contracts, and machine learning, such activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of challenges and potential solutions for successful implementation of an autonomous UAV mission.

Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li

Computer Science Faculty Publications

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding …

Autonomous Strike Uavs In Support Of Homeland Security Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we …

Mosaic: A Prune-And-Assemble Approach For Efficient Model Pruning In Privacy-Preserving Deep Learning, Yifei Cai, Qiao Zhang, Rui Ning, Chunsheng Xin, Hongyi Wu

Computer Science Faculty Publications

To enable common users to capitalize on the power of deep learning, Machine Learning as a Service (MLaaS) has been proposed in the literature, which opens powerful deep learning models of service providers to the public. To protect the data privacy of end users, as well as the model privacy of the server, several state-of-the-art privacy-preserving MLaaS frameworks have also been proposed. Nevertheless, despite the exquisite design of these frameworks to enhance computation efficiency, the computational cost remains expensive for practical applications. To improve the computation efficiency of deep learning (DL) models, model pruning has been adopted as a strategic …

Speculative Anisotropic Mesh Adaptation On Shared Memory For Cfd Applications, Christos Tsolakis, Nikos Chrisochoides

Computer Science Faculty Publications

Efficient and robust anisotropic mesh adaptation is crucial for Computational Fluid Dynamics (CFD) simulations. The CFD Vision 2030 Study highlights the pressing need for this technology, particularly for simulations targeting supercomputers. This work applies a fine-grained speculative approach to anisotropic mesh operations. Our implementation exhibits more than 90% parallel efficiency on a multi-core node. Additionally, we evaluate our method within an adaptive pipeline for a spectrum of publicly available test-cases that includes both analytically derived and error-based fields. For all test-cases, our results are in accordance with published results in the literature. Support for CAD-based data is introduced, and its …

In Vivo Measurement Of Nadh Fluorescence Lifetime In Skeletal Muscle Via Fiber-Coupled Time-Correlated Single Photon Counting, Kathryn M. Priest, Jacob V. Schluns, Nathania Nischal, Colton L. Gattis, Jeffery C. Wolchok, Timothy J. Muldoon

Biomedical Engineering Faculty Publications and Presentations

Nicotinamide adenine dinucleotide (NADH) is a cofactor that serves to shuttle electrons during metabolic processes such as glycolysis, the tricarboxylic acid cycle, and oxidative phosphorylation (OXPHOS). NADH is autofluorescent, and its fluorescence lifetime can be used to infer metabolic dynamics in living cells. Fiber-coupled time-correlated single photon counting (TCSPC) equipped with an implantable needle probe can be used to measure NADH lifetime in vivo, enabling investigation of changing metabolic demand during muscle contraction or tissue regeneration. This study illustrates a proof of concept for point-based, minimally-invasive NADH fluorescence lifetime measurement in vivo. Volumetric muscle loss (VML) injuries were …

A Practical Framework For Component-Level Structural Health Monitoring Of The Gerald Desmond Bridge, Mehran Rahmani, Vesna Terzic, Andrea Calabrese, Brittany Cambell

Mineta Transportation Institute

Bridges serve as critical transportation infrastructure, but traditional maintenance inspection to ensure their safety is time-consuming, costly, and labor-intensive, especially for larger and more complex structures. This study presents a practical framework for the instrumentation, data acquisition, and remote condition assessment of the Gerald Desmond Bridge, California’s largest cable-stayed bridge. The framework aims to establish a foundation for real-time or near real-time remote health monitoring of the bridge’s critical elements. The study highlights the advantages of remote monitoring in terms of efficiency, cost-effectiveness, and early detection of damage.

Developing Herbal-Based Beverage Fermentation Using Saccharomyces Cerevisiae: The Physico-Chemical Properties, Siti Madihah Mohd Don, Mas Munira Rambli, Beston Faiek Nore

ASEAN Journal on Science and Technology for Development

Herbal-based fermented beverages are popular non-dairy products among consumers who seek healthy and immunity-supporting products. Amongst commonly used health supplements that are added to food and beverage would be probiotics, commonly Saccharomyces cerevisiae or yeast. This study aims to determine the physico-chemical properties of fermented beverages at different fermentation periods using lemongrass (Cymbopogon citratus DC. Stapf), ginger (Zingiber officinale Rosc.), turmeric (Curcuma longa L.), mint (Mentha), and moringa (Moringa oleifera Lam.). This study revealed a significant decrease (p ≤ 0.05) in pH, total soluble solids, and density in most fermented beverages after 24, 36 and 48 hours of fermentation. Low …