Unattended baggage detection using deep neural networks

As the world becomes ever more attuned to potential security threats, the need for sophisticated surveillance system is increasing to monitor and detect any potential threats. Sophisticated surveillance system should functions as an intuitive “robotic eye” for accurate and real-time detection of threats. Unattended baggage has become a critical need for security personnel at airports, stations, malls, and in other public or crowded areas. However, an effective system for detection of objects like baggage and people with a real-time video input requires high processing power and storage to just process the video frames using the typical digital image processing technique. This will require a very high development cost and time in order to make the system work which is impractical for commercial use. Moreover, manual configuration is needed which is not flexible to be for multiple application. Therefore, the objective of this thesis is to improve the object detection accuracy and flexibility compared to existing digital image processing techniques. This proposed system uses deep neural networks approach through collection of datasets thus providing a more accurate detection and flexible application. Tensorflow framework is used as the deep neural network framework for the development of this system. This system utilizes the Single Shot multibox Detection detection algorithm to the ’MobileNet’ neural network architecture which is optimized to provide a promising performance even in embedded system. This project is developed by implementing the Tensorflow Object Detection Application Programming Interface (API). This method enables 4 main classes of detection which are suitcase, backpack, handbag and person. The datasets used for benchmarking are surveillance video sample that contain unattended baggage scenario used by most existing works like AVSS2007, PETS2006 and ABODA. The overall accuracy and flexibility of the proposed system improved up to 43% thus unattended baggage is able to be detected. The system is able to be applied in various environment due to the excellent flexibility of the system

Palm oil mill effluent as alternate carbon source for ammonia removal in wastewater treatment

To address high demand in searching for carbon sources alternatives in ammonia wastewater treatment, comparison among various carbon sources in term of pollutants reduction efficiency was essential to determine the most cost-effective carbon source selection for industry scale in bulk amount. This study focuses on investigating palm oil mill effluent (POME) as the alternate carbon source for supporting ammonia oxidizing bacteria (AOB) in ammonia removal of glove industrial wastewater treatment. Ammonia reduction efficiency was compared between POME with molasses, one of the most commonly used carbon sources. POME as carbon source in ammonia wastewater treatment had shown significant comparable reduction efficiency as compared to molasses. Furthermore, the study on various mixture ratios of POME-molasses had also shown further improvement in ammonia reduction efficiency. At the optimum ratio of 50:50 (v/v) POME-molasses as carbon source mixture, the ammonia reduction in the treatment system had achieved 53.11% reduction, which reduced ammonia content down to 10.49 mg/L NH3. In this study, the results suggested that POME showing great potential to be the new cost-effective carbon source alternative in industry scale treatment

Biosynthesis of agar in red seaweeds: a review

Agar is a jelly-like biopolymer synthesized by many red seaweeds as their major cell wall component. Due to its excellent rheological properties, it has been exploited commercially for applications in food, cosmetic, pharmaceutical, biomedical and biotechnology industries. Despite its multiple uses, the biosynthesis of this phycocolloid is not fully understood. The current knowledge on agar biosynthesis is inferred from plant biochemistry and putative pathways for ulvan and alginate biosynthesis in green and brown seaweeds, respectively. In this review, the gaps in our current knowledge on agar biosynthetic pathway are discussed, focusing on the biosynthesis of agar precursors, elongation of agar polysaccharide chain and side chain modification. The development of molecular markers for the screening of desired seaweeds for industrial exploitation is also discussed

Sulfated galactans from red seaweeds and their potential applications

Red seaweeds (Rhodophyta) produce a variety of sulfated galactans in their cell wall matrix and intercellular space, contributing up to 50-60 % of their total dry weight. These sulfated polysaccharides are made up of galactose disaccharides substituted with sulfate, methoxyl, pyruvic acid, or non-galactose monosaccharides (e.g. xylose, glucose and mannose). They are required by the Rhodophytes for protection against pathogen, desiccation, tidal waves and extreme changes in pH, temperature and salinity. Since ancient times, sulfated galactans from red seaweeds, such as agar and carrageenan, have been consumed as human foods and later being used in traditional medicine. Nowadays, some red seaweeds are cultivated and exploited for commercial uses in various fields. In this review, different types of sulfated galactans found in red seaweeds and their current and potential uses in food, biotechnology, medical and pharmaceutical industries are discussed

Factors affecting yield and gelling properties of agar

Agar, a gelatinous polysaccharide in the cell wall of many red algal species, is widely used as a gelling, thickening and stabilizing agent. The commercial value of seaweed is judged by their agar content and gel quality. Seaweed materials with higher agar yield and better gelling properties are desired due to the growing demand for agar in the global market. Agar biosynthesis in seaweeds is affected by genetic variations, developmental stages and environmental conditions, while different agar extraction techniques can also affect the yield and quality of agar. In this paper, the effects of different physiological states of seaweed, abiotic and biotic factors, seaweed storage and agar extraction techniques on the agar yield and gelling characteristics, are reviewed. This information is important as a guide for marine aquaculture of potential agarophytes and the possible effects of climate change on the stock of this natural resource

Development and Testing of Retrieval Augmented Generation in Large Language Models -- A Case Study Report

Purpose: Large Language Models (LLMs) hold significant promise for medical applications. Retrieval Augmented Generation (RAG) emerges as a promising approach for customizing domain knowledge in LLMs. This case study presents the development and evaluation of an LLM-RAG pipeline tailored for healthcare, focusing specifically on preoperative medicine. Methods: We developed an LLM-RAG model using 35 preoperative guidelines and tested it against human-generated responses, with a total of 1260 responses evaluated. The RAG process involved converting clinical documents into text using Python-based frameworks like LangChain and Llamaindex, and processing these texts into chunks for embedding and retrieval. Vector storage techniques and selected embedding models to optimize data retrieval, using Pinecone for vector storage with a dimensionality of 1536 and cosine similarity for loss metrics. Human-generated answers, provided by junior doctors, were used as a comparison. Results: The LLM-RAG model generated answers within an average of 15-20 seconds, significantly faster than the 10 minutes typically required by humans. Among the basic LLMs, GPT4.0 exhibited the best accuracy of 80.1%. This accuracy was further increased to 91.4% when the model was enhanced with RAG. Compared to the human-generated instructions, which had an accuracy of 86.3%, the performance of the GPT4.0 RAG model demonstrated non-inferiority (p=0.610). Conclusions: In this case study, we demonstrated a LLM-RAG model for healthcare implementation. The pipeline shows the advantages of grounded knowledge, upgradability, and scalability as important aspects of healthcare LLM deployment.Comment: N

Diagnosing Sarcopenia with AI-Aided Ultrasound (DINOSAUR)—A Pilot Study

Background: Sarcopenia has been recognized as a determining factor in surgical outcomes and is associated with an increased risk of postoperative complications and readmission. Diagnosis is currently based on clinical guidelines, which includes assessment of skeletal muscle mass but not quality. Ultrasound has been proposed as a useful point-of-care diagnostic tool to assess muscle quality, but no validated cut-offs for sarcopenia have been reported. Using novel automated artificial intelligence (AI) software to interpret ultrasound images may assist in mitigating the operator-dependent nature of the modality. Our study aims to evaluate the fidelity of AI-aided ultrasound as a reliable and reproducible modality to assess muscle quality and diagnose sarcopenia in surgical patients. Methods: Thirty-six adult participants from an outpatient clinic were recruited for this prospective cohort study. Sarcopenia was diagnosed according to Asian Working Group for Sarcopenia (AWGS) 2019 guidelines. Ultrasonography of the rectus femoris muscle was performed, and images were analyzed by an AI software (MuscleSound® (Version 5.69.0)) to derive muscle parameters including intramuscular adipose tissue (IMAT) as a proxy of muscle quality. A receiver operative characteristic (ROC) curve was used to assess the predictive capability of IMAT and its derivatives, with area under the curve (AUC) as a measure of overall diagnostic accuracy. To evaluate consistency between ultrasound users of different experience, intra- and inter-rater reliability of muscle ultrasound parameters was analyzed in a separate cohort using intraclass correlation coefficients (ICC) and Bland–Altman plots. Results:The median age was 69.5 years (range: 26–87), and the prevalence of sarcopenia in the cohort was 30.6%. The ROC curve plotted with IMAT index (IMAT% divided by muscle area) yielded an AUC of 0.727 (95% CI: 0.551–0.904). An optimal cut-off point of 4.827%/cm2 for IMAT index was determined with a Youden’s Index of 0.498. We also demonstrated that IMAT index has excellent intra-rater reliability (ICC = 0.938, CI: 0.905–0.961) and good inter-rater reliability (ICC = 0.776, CI: 0.627–0.866). In Bland–Altman plots, the limits of agreement were from −1.489 to 1.566 and −2.107 to 4.562, respectively. Discussion: IMAT index obtained via ultrasound has the potential to act as a point-of-care evaluation for sarcopenia screening and diagnosis, with good intra- and inter-rater reliability. The proposed IMAT index cut-off maximizes sensitivity for case finding, supporting its use as an easily implementable point-of-care test in the community for sarcopenia screening. Further research incorporating other ultrasound parameters of muscle quality may provide the basis for a more robust diagnostic tool to help predict surgical risk and outcomes.</p

Improve Space and Manpower Utilisation

See also the data for the project http://ink.library.smu.edu.sg/researchdata/17/</p