Effects of mobile phone electromagnetic radiation on thyroid glands and hormones in Rattus norvegicus brain: An analysis of thyroid function, reactive oxygen species, and monocarboxylate transporter 8.

The aim of this study was to investigate the effects of mobile phone electromagnetic radiation (MP-EMR) on the thyroid glands and hormones in Rattus norvegicus brain in term of thyroid function, reactive oxygen species (ROS), and monocarboxylate transporter 8 (MCT8) concentration. Forty rats were divided into different groups: control (without EMR exposure), EMR1 (120-min/day exposure), EMR2 (150-min), and EMR3 (180-min). The levels of serum thyroid stimulating hormone (TSH), thyroxine (T4), and malondialdehyde (MDA) and brain and MCT8 were measured using enzyme-linked immunosorbent assay. One-way analysis of variance followed by the Duncan test was used to analyze the data. Our data indicated that the levels of serum TSH and T4 in all the EMR groups were lower significant postexposure compared to the control with P < 0.01 (EMR1 and EMR2) and P < 0.001 (EMR3), suggesting hypothyroidism due to MP-EMR exposure. Increased MDA and decreased MCT8 levels were also observed following the intervention; however, the changes in both concentrations were notably significant after being subjected to 150-min and 180-min of exposure. In conclusion, a significant reduction in TSH, T4, and MCT8 levels indicated thyroid dysfunction due to MP-EMR exposure.

Knowledge management strategy for managing disaster and the COVID-19 pandemic in Indonesia: SWOT analysis based on the analytic network process.

Indonesia has significant expertise in disaster management due to its disaster geography. Collective expertise and knowledge are valuable resources for lowering disaster risk and enhancing disaster resilience. Additionally, in the current pandemic situation, a clearer understanding of COVID-19 is growing, which could make a difference in how effectively we respond to this and future pandemics. Therefore, it is crucial to record and maintain information related to the event in order to handle any crisis and COVID-19 pandemic appropriately. The goal of this study is to explore KM implementation approaches for handling disasters and the COVID-19 pandemic in Indonesia. In order to collect data for this study, 20 experts were interviewed and 30 experts participated in a Focus Group Discussion (FGD). SWOT analysis was utilised in this study to find different KM implementation strategies. The Analytic Network Process (ANP) was used to prioritize several previously discovered strategies. The study finds that the approach which must be prioritised is to ensure that knowledge products can be accessed by the public, and they must include the community (family) as subjects in establishing knowledge management methods (not only the government or institutions).

BreaCNet: A high-accuracy breast thermogram classifier based on mobile convolutional neural network.

The presence of a well-trained, mobile CNN model with a high accuracy rate is imperative to build a mobile-based early breast cancer detector. In this study, we propose a mobile neural network model breast cancer mobile network (BreaCNet) and its implementation framework. BreaCNet consists of an effective segmentation algorithm for breast thermograms and a classifier based on the mobile CNN model. The segmentation algorithm employing edge detection and second-order polynomial curve fitting techniques can effectively capture the thermograms' region of interest (ROI), thereby facilitating efficient feature extraction. The classifier was developed based on ShuffleNet by adding one block consisting of a convolutional layer with 1028 filters. The modified Shufflenet demonstrated a good fit learning with 6.1 million parameters and 22 MB size. Simulation results showed that modified ShuffleNet alone resulted in a 72% accuracy rate, but the performance excelled to a 100% accuracy rate when integrated with the proposed segmentation algorithm. In terms of diagnostic accuracy of the normal and abnormal test, BreaCNet significantly improves the sensitivity rate from 43% to 100% and specificity of 100%. We confirmed that feeding only the ROI of the input dataset to the network can improve the classifier's performance. On the implementation aspect of BreaCNet, the on-device inference is recommended to ensure users' data privacy and handle an unreliable network connection.

Knowledge creation elements for enhancing community resilience towards disaster: A Delphi study.

Knowledge capacity plays a vital role in building community resilience to disasters. However, the problem is that there is no resilience framework that integrates the knowledge creation process. This article introduces a new framework for increasing community resilience based on knowledge creation theory (KCT). This research aims to define the elements that support the Knowledge Creation for Community Resilience (KCCR) and to gain consensus from experts on these factors. This study was conducted using semi-structured interviews with five panellists and three rounds of Delphi technique to determine the assessment of 26 factors (including six additional factors) that have been identified by experts (30, 18 and 11 experts in rounds I, II and III, sequentially). The data analysis was carried out in several stages, and included Spearman's Rank Correlation Coefficient, consensus appraisal and interrater agreement (IRA) statistical evaluation. The result of the agreement level (AL) analysis shows that the majority of the constructs (96.15%) are in the 'moderate strong' category. This study shows that there is a significant consensus (with IRA index [a wg(1)] ranging from 0.529 to 1), and panellists confirm the significance of all the key constructs. Consensus was gained from experts on seven elements that support the KCCR. This study establishes a systematic, operational and multidimensional KCCR framework that combines the concepts of knowledge creation, community resilience and disaster preparedness. This framework can be used as a qualitative instrument or guidance to build community resilience based on knowledge creation and a quantitative tool for measuring community resilience in facing disasters.

Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition.

Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.

Cross-spectral iris recognition using phase-based matching and homomorphic filtering.

In cross-spectral iris recognition, different spectral bands are used to obtain rich information of the human iris. Previous studies on cross-spectral iris recognition are based primarily on feature-based approaches, which are prone to the changes in parameters in the feature extraction process, such as spatial position and iris image acquisition conditions. These parameters can degrade iris recognition performance. In this paper, we present a phase-based approach for cross-spectral iris recognition using phase-only correlation (POC) and band-limited phase-only correlation (BLPOC). A phase-based iris recognition system recognizes an iris using the phase information contained in the iris image; therefore, its performance is not affected by feature extraction parameters. However, the performance of a phase-based cross-spectral iris recognition is strongly influenced by specular reflection. Different illumination conditions may produce different iris images from the same subject. To overcome this challenge, we integrate a photometric normalization technique -homomorphic filtering- with phase-based cross-spectral iris recognition. The experimental results reveal that the proposed technique achieved an excellent matching performance with an equal error rate of 0.59% and a genuine acceptance rate of 95%.

Effective and fast binarization method for combined degradation on ancient documents.

Document image binarization is a challenging task because of combined degradation in a document. In this study, a new binarization method is proposed for binarizing an ancient document with combined degradation. The proposed method comprises the following four stages: histogram analysis, contrast enhancement, local adaptive thresholding, and artifact removal. In histogram analysis, a new approach is applied to establish a uniform background. Next, the image contrast is enhanced using a new contrast enhancement, and then the document is binarized using a novel local adaptive thresholding. Artifacts from the binarization process are removed in the artifact removal stage. Finally, an experiment is conducted using one private and four public datasets and by simulating the proposed method with and without contrast enhancement. The results showed that the proposed method is faster and more effective compared to other state-of-the-art procedures for binarizing ancient documents.

A secure online image trading system for untrusted cloud environments.

In conventional image trading systems, images are usually stored unprotected on a server, rendering them vulnerable to untrusted server providers and malicious intruders. This paper proposes a conceptual image trading framework that enables secure storage and retrieval over Internet services. The process involves three parties: an image publisher, a server provider, and an image buyer. The aim is to facilitate secure storage and retrieval of original images for commercial transactions, while preventing untrusted server providers and unauthorized users from gaining access to true contents. The framework exploits the Discrete Cosine Transform (DCT) coefficients and the moment invariants of images. Original images are visually protected in the DCT domain, and stored on a repository server. Small representation of the original images, called thumbnails, are generated and made publicly accessible for browsing. When a buyer is interested in a thumbnail, he/she sends a query to retrieve the visually protected image. The thumbnails and protected images are matched using the DC component of the DCT coefficients and the moment invariant feature. After the matching process, the server returns the corresponding protected image to the buyer. However, the image remains visually protected unless a key is granted. Our target application is the online market, where publishers sell their stock images over the Internet using public cloud servers.