Search results for: trauma network

Authors: Namugenyi Ephrance Eunice, Julianne Sansa Otim, Marco Zennaro, Stephen D. Wolthusen

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The Internet of Things (IoT) is a network of connected data processing devices, mechanical and digital machinery, items, animals, or people that may send data across a network without requiring human-to-human or human-to-computer interaction. Each component has sensors that can pick up on specific phenomena, as well as processing software and other technologies that can link to and communicate with other systems and/or devices over the Internet or other communication networks and exchange data with them. IoT is increasingly being used in fields other than consumer electronics, such as public safety, emergency response, industrial automation, autonomous vehicles, the Internet of Medical Things (IoMT), and general environmental monitoring. Consumer-based IoT applications, like smart home gadgets and wearables, are also becoming more prevalent. This paper presents the main IoT deployment areas for environmental monitoring in developing regions and the backhaul options suitable for them. A detailed review of each of the list of papers selected for the study is included in section III of this document. The study includes an overview of existing IoT deployments, the underlying communication architectures, protocols, and technologies that support them. This overview shows that Low Power Wireless Area Networks (LPWANs), as summarized in Table 1, are very well suited for monitoring environment architectures designed for remote locations. LoRa technology, particularly the LoRaWAN protocol, has an advantage over other technologies due to its low power consumption, adaptability, and suitable communication range. The prevailing challenges of the different architectures are discussed and summarized in Table 3 of the IV section, where the main problem is the obstruction of communication paths by buildings, trees, hills, etc.

Keywords: communication technologies, environmental monitoring, Internet of Things, IoT deployment challenges

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Authors: Nadir Atayev, Mehman Hasanov

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Future spacecraft will require a structural change in the way data is transmitted due to the increase in the volume of data required for space communication. Current radio frequency communication systems are already facing a bottleneck in the volume of data sent to the ground segment due to their technological and regulatory characteristics. To overcome these issues, free space optics communication plays an important role in the integrated terrestrial space network due to its advantages such as significantly improved data rate compared to traditional RF technology, low cost, improved security, and inter-satellite free space communication, as well as uses a laser beam, which is an optical signal carrier to establish satellite-ground & ground-to-satellite links. In this approach, there is a need for high-speed and energy-efficient systems as a base platform for sending high-volume video & audio data. Nano Satellite and its branch CubeSat platforms have more technical functionality than large satellites, wheres cover an important part of the space sector, with their Low-Earth-Orbit application area with low-cost design and technical functionality for building networks using different communication topologies. Along the research theme developed in this regard, the output parameter indicators for the FSO of the optical communication transceiver subsystem on the existing CubeSat platforms, and in the direction of improving the mentioned parameters of this communication methodology, 3D optical switch and laser beam controlled optical transponder with 2U CubeSat structural subsystems and application in the Low Earth Orbit satellite network topology, as well as its functional performance and structural parameters, has been studied accordingly.

Keywords: cubesat, free space optics, nano satellite, optical laser communication.

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Authors: Medagedara Kaushalya Sewwandi Supun Gunarathne

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This research explores the underlying motivations and consequences of individuals changing their sexual orientation as a response to sexual violence. The primary objective of the study is to unravel the psychological, emotional, and social factors that drive individuals, akin to Celie in Alice Walker’s ‘The Color Purple’, to contemplate and undergo changes in their sexual orientation following the trauma of sexual violence. Through an analytical and qualitative approach, the study employs in-depth textual and thematic analyses to scrutinize the complex interplay between sexual orientation and violence within the selected text. Through a close examination of Celie’s journey and experiences, the study reveals that her decision to switch sexual orientation arises from a desire for a more favorable and benevolent relationship driven by the absence of safety and refuge in her previous relationships. By establishing this bond between sexual orientation and violence, the research underscores how sexual violence can lead individuals to opt for a change in their sexual orientation. The findings highlight Celie’s transformation as a means to seek solace and security, thus concluding that sexual violence can prompt individuals to alter their sexual orientation. The ensuing discussion explores the implications of these findings, encompassing psychological, emotional, and social consequences, as well as the societal and cultural factors influencing the perception of sexual orientation. Additionally, it sheds light on the challenges and stigma faced by those who undergo such transformations. By comprehending the complex relationship between sexual violence and the decision to change sexual orientation, as exemplified by Celie in ‘The Color Purple’, a deeper understanding of the experiences of survivors who seek a safe haven through altering their sexual orientation can be attained.

Keywords: sexual violence, sexual orientation, refuge, transition

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Authors: S. Shafiee Ardestani, A. Najafi, N. Valizadeh, E. Payani, H. Karimian

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Objectives: Treatment of mild traumatic brain injury in emergency ward patients with any type of traumatic intracranial hemorrhage is flexible. The aim of this study is to assess the clinical outcomes of mild traumatic brain injury patients who had acute traumatic intracranial hemorrhage on initial emergency ward neuroimaging. Materials-Methods: From March 2011 to November 2012 in a retrospective cohort study we enrolled emergency ward patients with mild traumatic brain injury with Glasgow Coma Scale (GCS) scores of 14 or 15 and who had stable vital signs. Patients who had any type of intracranial hemorrhage on first head CT and repeat head CT within 24 hours were included. Patients with initial GCS < 14, injury > 24 hours old, pregnancy, concomitant non-minor injuries, and coagulopathy were excluded. Primary endpoints were neurosurgical procedures and/or death and for discharged patients, return to the emergency ward during one week. Results: Among 755 patients who were referred to the emergency ward and underwent two head CTs during first 24 hours, 302 (40%) were included. The median interval between CT scans was 6 hours (ranging 4 to 8 hours). Consequently, 135 (45%) patients had subarachnoid hemorrhage, 124 (41%) patients had subdural hemorrhage, 15 (5%) patients had epidural hemorrhage, 28 (9%) patients had cerebral contusions, and 54 (18%) patients had intra-parenchymal hemorrhage. Six of 302 patients died within 15 days of injury. 200 patients (66%) have been discharged from the emergency ward, 25 (12%) of whom returned to the emergency ward after one week. Conclusion: Discharge of the head trauma patients after a repeat head CT and brief period of observation in the emergency ward lead to early discharge of mild traumatic brain injury patients with traumatic ICH without adverse events.

Keywords: clinical outcomes, emergency ward, mild traumatic intracranial hemorrhage, Glasgow Coma Scale (GCS)

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Authors: Hang Yang, Jichao Li, Kewei Yang, Tianyang Lei

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Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis.

Keywords: data mining, industrial system, multivariate time series, anomaly detection

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Authors: Shahed Vahedi, Mohd Salmi Md Noorani

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This paper investigates cluster synchronization phenomena between community networks. We focus on the situation where a variety of dynamics occur in the clusters. In particular, we show that different synchronization states simultaneously occur between the networks. The controller is designed having an adaptive control gain, and theoretical results are derived via Lyapunov stability. Simulations on well-known dynamical systems are provided to elucidate our results.

Keywords: cluster synchronization, adaptive control, community network, simulation

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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Authors: Shayli Varasteh Moradi, Wayne A. Johnston, Dejan Gagoski, Kirill Alexandrov

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The demand for a rapid and more efficient technique to identify protein-protein interaction particularly in the areas of therapeutics and diagnostics development is growing. The method described here is a rapid in vitro protein-protein interaction analysis approach based on AlphaLISA technology combined with Leishmania tarentolae cell-free protein production (LTE) system. Cell-free protein synthesis allows the rapid production of recombinant proteins in a multiplexed format. Among available in vitro expression systems, LTE offers several advantages over other eukaryotic cell-free systems. It is based on a fast growing fermentable organism that is inexpensive in cultivation and lysate production. High integrity of proteins produced in this system and the ability to co-express multiple proteins makes it a desirable method for screening protein interactions. Following the translation of protein pairs in LTE system, the physical interaction between proteins of interests is analysed by AlphaLISA assay. The assay is performed using unpurified in vitro translation reaction and therefore can be readily multiplexed. This approach can be used in various research applications such as epitope mapping, antigen-antibody analysis and protein interaction network mapping. The intra-viral protein interaction network of Zika virus was studied using the developed technique. The viral proteins were co-expressed pair-wise in LTE and all possible interactions among viral proteins were tested using AlphaLISA. The assay resulted to the identification of 54 intra-viral protein-protein interactions from which 19 binary interactions were found to be novel. The presented technique provides a powerful tool for rapid analysis of protein-protein interaction with high sensitivity and throughput.

Keywords: AlphaLISA technology, cell-free protein expression, epitope mapping, Leishmania tarentolae, protein-protein interaction

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Authors: Sergio A. Navarro Tuch, Martin Rogelio Bustamante Bello, Leopoldo Julian Lechuga Lopez

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In recent years, Mexico’s population has seen a rise of different physiological and mental negative states. Two main consequences of this problematic are deficient work performance and high levels of stress generating and important impact on a person’s physical, mental and emotional health. Several approaches, such as the use of audiovisual stimulus to induce emotions and modify a person’s emotional state, can be applied in an effort to decreases these negative effects. With the use of different non-invasive physiological sensors such as EEG, luminosity and face recognition we gather information of the subject’s current emotional state. In a controlled environment, a subject is shown a series of selected images from the International Affective Picture System (IAPS) in order to induce a specific set of emotions and obtain information from the sensors. The raw data obtained is statistically analyzed in order to filter only the specific groups of information that relate to a subject’s emotions and current values of the physical variables in the controlled environment such as, luminosity, RGB light color, temperature, oxygen level and noise. Finally, a neural network based control algorithm is given the data obtained in order to feedback the system and automate the modification of the environment variables and audiovisual content shown in an effort that these changes can positively alter the subject’s emotional state. During the research, it was found that the light color was directly related to the type of impact generated by the audiovisual content on the subject’s emotional state. Red illumination increased the impact of violent images and green illumination along with relaxing images decreased the subject’s levels of anxiety. Specific differences between men and women were found as to which type of images generated a greater impact in either gender. The population sample was mainly constituted by college students whose data analysis showed a decreased sensibility to violence towards humans. Despite the early stage of the control algorithm, the results obtained from the population sample give us a better insight into the possibilities of emotional domotics and the applications that can be created towards the improvement of performance in people’s lives. The objective of this research is to create a positive impact with the application of technology to everyday activities; nonetheless, an ethical problem arises since this can also be applied to control a person’s emotions and shift their decision making.

Keywords: data analysis, emotional domotics, performance improvement, neural network

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Authors: Khadeeja Munawar, Shamsul Haque

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The claim that traumatic events influence the recalled memories and mental health has received mixed empirical support. This study examines the memories of a sample drawn from Pakistan, a country that has witnessed many life-changing socio-political events, wars, and natural disasters in 72 years of its history. A sample of 210 senior citizens (Mage = 64.35, SD = 6.33) was recruited from Pakistan. The aim was to investigate if participants retrieved more memories related to past traumatic events using a word-cueing technique. Each participant reported ten memories to ten neutral cue words. The results revealed that past traumatic events were not adversely affecting the memories and mental health of participants. When memories were plotted with respect to the ages at which the events happened, a pronounced bump at 11-20 years of age was seen. Memories within as well as outside of the bump were mostly positive. The multilevel logistic regression modelling showed that the memories recalled were personally important and played a role in enhancing resilience. The findings revealed that despite facing an array of ethnic, religious, political, economic, and social conflicts, the participants were resilient, recalled predominantly positive memories, and had intact mental health. The findings have clinical implications in Cognitive Behavioral Therapy (CBT). The patients can be made aware of their negative emotions, troublesome/traumatic memories, and the distorted thinking patterns and their memories can be restructured. The findings can also be used to teach Memory Specificity Training (MEST) by psycho-educating the patients around changes in memory functioning and enhancing the recall of memories, which are more specific, vivid, and filled with sensory details.

Keywords: cognitive behavioral therapy, memories, mental health, resilience, trauma

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Authors: Z. S. Haidar

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Millions of teeth are removed annually, and dental extraction is one of the most commonly performed surgical procedures globally. Whether due to caries, periodontal disease, or trauma, exodontia and the ensuing wound healing and bone remodeling processes of the resultant socket (hole in the jaw bone) usually result in serious deformities of the residual alveolar osseous ridge and surrounding soft tissues (reduced height/width). Such voluminous changes render the placement of a proper conventional bridge, denture, or even an implant-supported prosthesis extremely challenging. Further, most extractions continue to be performed with no regard for preventing the onset of alveolar osteitis (also known as dry socket, a painful and difficult-to-treat/-manage condition post-exodontia). Hence, such serious resorptive morphological changes often result in significant facial deformities and a negative impact on the overall Quality of Life (QoL) of patients (and oral health-related QoL); alarming, particularly for the geriatric with compromised healing and in light of the thriving longevity statistics. Despite advances in tissue/wound grafting, serious limitations continue to exist, including efficacy and clinical outcome predictability, cost, treatment time, expertise, and risk of immune reactions. For cases of dry socket, specifically, the commercially available and often-prescribed home remedies are highly-lacking. Indeed, most are not recommended for use anymore. Alveogyl is a fine example. Hence, there is a great market demand and need for alternative solutions. Herein, SockGEL/PLUG (patent pending), an innovative, all-natural, drug-free, and injectable thermo-responsive hydrogel, was designed, formulated, characterized, and evaluated as an osteogenic, angiogenic, anti-microbial, and pain-soothing suture-free intra-alveolar dressing, safe and efficacious for use in fresh extraction sockets, immediately post-exodontia. It is composed of FDA-approved, biocompatible and biodegradable polymers, self-assembled electro-statically to formulate a scaffolding matrix to (1) prevent the on-set of alveolar osteitis via securing the fibrin-clot in situ and protecting/sealing the socket from contamination/infection; and (2) endogenously promote/accelerate wound healing and bone remodeling to preserve the volume of the alveolus. The intrinsic properties of the SockGEL/PLUG hydrogel were evaluated physical-chemical-mechanically for safety (cell viability), viscosity, rheology, bio-distribution, and essentially, capacity to induce wound healing and osteogenesis (small defect, in vivo) without any signaling cues from exogenous cells, growth factors or drugs. The proposed animal model of cranial critical-sized and non-vascularized bone defects shall provide new and critical insights into the role and mechanism of the employed natural bio-polymer blend and gel product in endogenous reparative regeneration of soft tissues and bone morphogenesis. Alongside, the fine-tuning of our modified formulation method will further tackle appropriateness, reproducibility, scalability, ease, and speed in producing stable, biodegradable, and sterilizable thermo-sensitive matrices (3-dimensional interpenetrating yet porous polymeric network) suitable for the intra-socket application. Findings are anticipated to provide sufficient evidence to translate into pilot clinical trials and validate the innovation before engaging the market for feasibility, acceptance, and cost-effectiveness studies.

Keywords: hydrogel, nanotechnology, bioengineering, bone regeneration, nanogel, drug delivery

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Authors: Hozaifa Zaki, Ghada Soliman

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In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Mubarak Suliman, Mohamed Hassan

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Detection of downed conductors occurring with high fault resistance (reaching kilo-ohms) has always been a challenge, especially in countries like Saudi Arabia, on which earth resistivity is very high in general (reaching more than 1000 Ω-meter). The new approaches for the detection of resistive and high impedance faults are based on the analysis of the fault current waveform. These methods are still under research and development, and they are currently lacking security and dependability. The other approach is communication-based solutions which depends on voltage measurement at the end of overhead line branches and communicate the measured signals to substation feeder relay or a central control center. However, such a detection method is costly and depends on the availability of communication medium and infrastructure. The main objective of this research is to utilize the available standard protection schemes to increase the probability of detection of downed conductors occurring with a low magnitude of fault currents and at the same time avoiding unwanted tripping in healthy conditions and feeders. By specifying the operating region of the faulty feeder, use of tripping curve for discrimination between faulty and healthy feeders, and with proper selection of core balance current transformer (CBCT) and voltage transformers with fewer measurement errors, it is possible to set the pick-up of sensitive earth fault current to minimum values of few amps (i.e., Pick-up Settings = 3 A or 4 A, …) for the detection of earth faults with fault resistance more than (1 - 2 kΩ) for 13.8kV overhead network and more than (3-4) kΩ fault resistance in 33kV overhead network. By implementation of the outcomes of this study, the probability of detection of downed conductors is increased by the utilization of existing schemes (i.e., Directional Sensitive Earth Fault Protection).

Keywords: sensitive earth fault, zero sequence current, grounded system, resistive fault detection, healthy feeder

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Recep Duzsoz, Ozlem Gormez, Umit Memis, Selma Demer, Hikmet Orhan

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Stylohyoid ligament is a connective tissue extending from apex of the styloid process to small horn of the hyoid bone. The normal length of styloid process ranges from 20 to 30 mm and measurements more than 30 mm is named stylohyoid ligament calcification (SLC). Fluorosis is a health problem that arises in individuals who intake large amounts of fluor long periods of time. The aim of this study was to investigate the effects of fluorosis on SLC. This study has been conducted on 100 patients who had SLC detected on panoramic radiograph. The study group was consisted of 50 patients with dental fluorosis and control group was consisted of 50 patients without dental fluorosis. Length and thickness of SLC were measured and the type of SLC was determined on panoramic radiographs. There was no statistically significant differences between the study and control group for SLC length, thickness and type. The thickness of left and right SLC of severe dental fluorosis group was statistically significant higher than moderate dental fluorosis group (p < 0,05). Cervicopharyngeal trauma, tonsillectomy, endocrine disease in menopause, persistent mesenchymal tissue, mechanical stress have reported as etiology of SLC in the literature and studies are still ongoing. It was reported that fluorosis as a factor on calcification of some ligaments in body (posterior longitudunal ligament, ligamentum flavum and transverse atlantal ligament) previously but relationship between fluorosis with SLC was not investigated. Our study is unique because it is the first study on SLC thickness measurements on panoramic radiographs and the relationship between fluorosis and SLC to our knowledge. According to the obtained results, it is thought that fluorosis may have an effect on SLC in thickness due to the relationship between dental fluorosis severity with SLC thickness and this study will contribute to the progress of the future studies.

Keywords: calcification, fluorosis, ligament, stylohyoid

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Authors: Abdallah Al-ZoubI, Mamoun Dmour

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The spectrum of IoT devices is becoming widely diversified, entering almost all possible fields and finding applications in industry, health, finance, logistics, education, to name a few. The IoT active endpoint sensors and devices exceeded the 12 billion mark in 2021 and are expected to reach 27 billion in 2025, with over $34 billion in total market value. This sheer rise in numbers and use of IoT devices bring with it considerable concerns regarding data storage, analysis, manipulation and protection. IoT Blockchain-based systems have recently been proposed as a decentralized solution for large-scale data storage and protection. COVID-19 has actually accelerated the desire to utilize IoT devices as it impacted both demand and supply and significantly affected several regions due to logistic reasons such as supply chain interruptions, shortage of shipping containers and port congestion. An IoT-blockchain system is proposed to handle big data generated by a distributed network of sensors and controllers in an interactive manner. The system is designed using the Ethereum platform, which utilizes smart contracts, programmed in solidity to execute and manage data generated by IoT sensors and devices. such as Raspberry Pi 4, Rasbpian, and add-on hardware security modules. The proposed system will run a number of applications hosted by a local machine used to validate transactions. It then sends data to the rest of the network through InterPlanetary File System (IPFS) and Ethereum Swarm, forming a closed IoT ecosystem run by blockchain where a number of distributed IoT devices can communicate and interact, thus forming a closed, controlled environment. A prototype has been deployed with three IoT handling units distributed over a wide geographical space in order to examine its feasibility, performance and costs. Initial results indicated that big IoT data retrieval and storage is feasible and interactivity is possible, provided that certain conditions of cost, speed and thorough put are met.

Keywords: IoT devices, blockchain, Ethereum, big data

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Authors: Vivian Khamis

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Exposure to war and forced migration have been widely linked to child subsequent adaptation. What remains sparse is research spanning multiple risk and protective factors and examining their unique and relative implications to difficulties in mental health among refugee girls. This study investigated the mechanisms through which posttraumatic stress disorder (PTSD), emotion dysregulation , neuroticism, and behavioral and emotional disorders in Syrian refugee girls is impacted by exposure to war traumas, age, and other risk and protective factors such as coping styles, family relationships, and school environment. The sample consisted of 539 Syrian refugee girls who ranged in age from 7 to 18 years attending public schools in various governorates in Lebanon and Jordan. Two school counselors carried out the interviews with children at school. Results indicated that war trauma, older age, and a combination of negative copying style associated with conflict in the family could lead to an overall state of emotion dysregulation, neuroticism, behavioral and emotional disorders, and PTSD in refugee girls. On the other hand, lapse of time since resettlement in host country, positive copying style, cohesion, and expressiveness in the family would lead to more positive mental health status, including lower levels of emotion dysregulation, neuroticism, behavioral and emotional disorders, and PTSD . Enhanced understanding of the mechanistic role of risk and protective factors in contributing to difficulties in mental health in refugee girls may contribute to the development of effective interventions to target the psychological effects of the refugee experience.

Keywords: refugee girls, PTSD, emotion dysregulation, neuroticism, behavioral and emotional disorders

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Authors: Alejandra Betancur Sánchez, Carmen Elena Zapata Sánchez, Juan Bautista López Ortiz

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Adverse effects and increased air pollution has raised concerns about regulatory policies and has fostered the development of new air quality standards; this is due to the complexity of the composition and the poorly understood reactions in the atmospheric environment. Toxic compounds act as environmental agents having various effects, from irritation to death of cells and tissues. A toxic agent is defined an adverse response in a biological system. There is a particular class that produces some kind of alteration in the genetic material or associated components, so they are recognized as genotoxic agents. Within cells, they interact directly or indirectly with DNA, causing mutations or interfere with some enzymatic repair processes or in the genesis or polymerization of proteinaceous material involved in chromosome segregation. An air pollutant may cause or contribute to increased mortality or serious illness and even pose a potential danger to human health. The aim of this study was to evaluate the effect on the viability and the genotoxic potential on the cell lines CHO-K1 and Jurkat and peripheral blood of particulate matter PM T lymphocytes 2.5 obtained from filters collected three monitoring stations network air quality Aburrá Valley. Tests, reduction of MTT, trypan blue, NRU, comet assay, sister chromatid exchange (SCE) and chromosomal aberrations allowed evidence reduction in cell viability in cell lines CHO-K1 and Jurkat and damage to the DNA from cell line CHOK1, however, no significant effects were observed in the number of SCEs and chromosomal aberrations. The results suggest that PM2.5 material has genotoxic potential and can induce cancer development, as has been suggested in other studies.

Keywords: PM2.5, cell line Jurkat, cell line CHO-K1, cytotoxicity, genotoxicity

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Authors: Guncel Ozturk

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Background: Rhinoplasty is the most commonly performed cosmetic operations in plastic surgery. Maneuvers used in rhinoplasty lead to a firm and stiff nasal tip in the early postoperative months. This unnatural stability of the nose may easily cause distortion in the reshaped nose after severe trauma. Moreover, a firm nasal tip may cause difficulties in performing activities such as touching, hugging, or kissing. Decreasing the stability and increasing the mobility of the nasal tip would help rhinoplasty patients to avoid these small but relatively important problems. Methods: We use delivery approach with closed rhinoplasty and changed positions of intranasal incisions to reach a dynamic and mobile nose. A total of 203 patients who had undergone primary closed rhinoplasty in private practice were inspected retrospectively. Posterior strut flap that was connected with connective tissues in the caudal of septum and the medial crurals were formed. Cartilage of the posterior strut graft was left 2 mm thick in the distal part of septum, it was cut vertically, and the connective tissue in the distal part was preserved. Results: The median patient age was 24 (range 17-42) years. The median follow-up period was15.2 (range12-26) months. Patient satisfaction was assessed with the 'Rhinoplasty Outcome Evaluation' (ROE) questionnaire. Twelve months after surgeries, 87.5% of patients reported excellent outcomes, according to ROE. Conclusion: The soft tissue connections between that segment and surrounding structures should be preserved to save the support of the tip while having a mobile tip at the same time with this method. These modifications would access to a mobile, non-stiff, and dynamic nasal tip in the early postoperative months. Further and prospective studies should be performed for supporting this method.

Keywords: closed rhinoplasty, dynamic, mobile, tip

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Authors: U. V. Okpala

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Flood is an overflow of water that submerges or ‘drowns’ land. In developing countries it occurs as a result of blocking of natural and man-made drainages and poor maintenance of water dams/reservoirs which seldom give way after persistent heavy down pours. In coastal lowlands and swamp lands, flooding is aided mainly by blocked channels and indiscriminate sand fling of coastal swamp areas and natural drainage channel for urban development/constructions. In this paper, the causes of flood and possible scientific, technological, political, economic and social impacts of flood disaster on the environment a case study of Anambra State have been studied. Often times flooding is caused by climate change, especially in the developed economy where scientific mitigating options are highly employed. Researchers have identified Green Houses Gases (GHG) as the cause of global climate change. The recent flood disaster in Anambra State which caused physical damage to structures, social dislocation, contamination of clean drinking water, spread of water-borne diseases, shortage of crops and food supplies, death of non-tolerant tree species, disruption in transportation system, serious economic loss and psychological trauma is a function of climate change. There is need to encourage generation of renewable energy sources, use of less carbon intensive fuels and other energy efficient sources. Carbon capture/sequestration, proper management of our drainage systems and good maintenance of our dams are good option towards saving the environment.

Keywords: flooding, climate change, carbon capture, energy systems

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Authors: A. Maghoul, A. Rostampouryasouri, MR. Maghami

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The development of an urban design and power network planning has been gaining momentum in recent years. The integration of renewable energy with urban design has been widely regarded as an increasingly important solution leading to climate change and energy security. Through the use of passive strategies and solar integration with Urban Building Energy Modeling (UBEM) tools, architects and designers can create high-quality designs that meet the needs of clients and stakeholders. To determine the most effective ways of combining renewable energy with urban development, we analyze the relationship between urban form and renewable energy production. The procedure involved in this practice include passive solar gain (in building design and urban design), solar integration, location strategy, and 3D models with a case study conducted in Tehran, Iran. The study emphasizes the importance of spatial and temporal considerations in the development of sector coupling strategies for solar power establishment in arid and semi-arid regions. The substation considered in the research consists of two parallel transformers, 13 lines, and 38 connection points. Each urban load connection point is equipped with 500 kW of solar PV capacity and 1 kWh of battery Energy Storage (BES) to store excess power generated from solar, injecting it into the urban network during peak periods. The simulations and analyses have occurred in EnergyPlus software. Passive solar gain involves maximizing the amount of sunlight that enters a building to reduce the need for artificial lighting and heating. Solar integration involves integrating solar photovoltaic (PV) power into smart grids to reduce emissions and increase energy efficiency. Location strategy is crucial to maximize the utilization of solar PV in an urban distribution feeder. Additionally, 3D models are made in Revit, and they are keys component of decision-making in areas including climate change mitigation, urban planning, and infrastructure. we applied these strategies in this research, and the results show that it is possible to create sustainable and energy-efficient urban environments. Furthermore, demand response programs can be used in conjunction with solar integration to optimize energy usage and reduce the strain on the power grid. This study highlights the influence of ancient Persian architecture on Iran's urban planning system, as well as the potential for reducing pollutants in building construction. Additionally, the paper explores the advances in eco-city planning and development and the emerging practices and strategies for integrating sustainability goals.

Keywords: energy-efficient urban planning, sustainable architecture, solar energy, sustainable urban design

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Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

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Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes a distinct detection framework based on the one stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: roads, defect detection, visualization, deep learning

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Authors: Tania Breton, Dorina Simeonov, Shauna MacEachern

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Ontario’s 10 Year Mental Health and Addictions Strategy has included the establishment of 18 Service Collaborative across the province; cross-sector tables in a specific region coming together to explore mental health and addiction system needs and adopting an intervention to address that need. The process is community led and supported by implementation teams from the Centre for Addiction and Mental Health (CAMH), using the framework of implementation science (IS) to enable evidence-based and sustained change. These justice initiatives are focused on the intersection of the justice system and the mental health and addiction systems. In this presentation, we will share the learnings, achievements and challenges of implementing innovative practices to the mental health and addictions needs of Ontarians within the justice system. Specifically, we will focus on the key points across the justice system - from early intervention and trauma-informed, culturally appropriate services to post-sentence support and community reintegration. Our approach to this work involves external implementation support from the CAMH team including coaching, knowledge exchange, evaluation, Aboriginal engagement and health equity expertise. Agencies supported the implementation of tools and processes which changed practice at the local level. These practices are being scaled up across Ontario and community agencies have come together in an unprecedented collaboration and there is a shared vision of the issues overlapping between the mental health, addictions and justice systems. Working with ministry partners has allowed space for innovation and created an environment where better approaches can be nurtured and spread.

Keywords: implementation, innovation, early identification, mental health and addictions, prevention, systems

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Authors: Samira Kalantary, Sara Taghipour, Mansoure Ghias Abadi

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The Internet of Things (IOT) is a technological revolution that represents the future of computing and communications. IOT is the convergence of Internet with RFID, NFC, Sensor, and smart objects. Fog Computing is the natural platform for IOT. At present, the IOT as a new network communication technology has rapidly shifted from concept to application under fog computing virtual storage computing platform. In this paper, we describe everything about IOT and difference between cloud computing and fog computing.

Keywords: cloud computing, fog computing, Internet of Things (IoT), IOT application

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Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: Rafiu Babatunde Ibrahim, Richard Oluseyi Taiwo, Abiodun Toheeb Akintunde

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The relationship between road transport infrastructure and commercial activities in Nigeria has been a topical issue and identified as one of the crucial components for economic development in the country. This study examines road transport infrastructure and commercial activities along selected roads in Ede, Nigeria. The study assesses the characteristics of the selected roads, the condition of road infrastructure, the degree of road network connectivity, maintenance culture for the road infrastructure as well as commercial activities along identified roads in the study area. Stratified Sampling Techniques were used to partition the study area into core, Intermediate and Suburb Township zones. Roads were also classified into Major, Distributor and Access Roads. Field observation and measurement, as well as a questionnaire, were used to obtain primary data from 246 systematically sampled respondents along the roads selected, and they were analyzed using descriptive and inferential statistics. The study revealed that most of the roads were characterized by an incidence of potholes. A total of 448 potholes were observed, where Olowoibida Road accounted for (19.0%), Federal Polytechnic Road (17.4%), and Back to Land Road (16.3%). The majority of the selected roads have no street lights and are of open drainage systems. Also, the condition of road surfaces was observed to be deteriorating. Road network connectivity of the study area was found to be poorly connected with 11% using the alpha index and 40% of Gamma index. It was found that the tailoring business (39) is predominant on major roads and Distributor Roads, while petty trading (35) is dominant on the access road. Results of correlation analysis (r = 0.242) show that there is a low positive correlation between road infrastructure and commercial activities; the significant relationships have indeed explained how important it is in influencing commercial activities across the study area. The study concluded by emphasizing the need for the provision of more roads and proper maintenance of the existing ones. This will no doubt improve the commercial activities along the roads in the study area.

Keywords: road transport, infrastructure, commercial activities, maintenance culture

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Authors: Tahir Khaleeq, Patrick Lancaster, Keji Fakoya, Pedro Ferreira, Usman Ahmed

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Introduction: Virtual fracture clinics (VFC) have been shown to be a safe and cost-effective way of managing outpatient referrals to the orthopaedic department. During the coronavirus pandemic there has been a push to reduce unnecessary patient contact whilst maintaining patient safety. Materials and Methods: A protocol was developed by the clinical team in collaboration with Advanced Physiotherapy Practitioners (APP) on how to manage common musculoskeletal presentations to A&E prior to COVID as part of routine service development. Patients broadly triaged into 4 categories; discharge with advice, referral to VFC, referral to face to face clinic or discussion with on call team. The first 9 months of data were analysed to assess types of injury seen and outcomes. Results: In total 2489 patients were referred to VFC from internal and external sources. 734 patients were discharged without follow-up and 182 patients were discharged for physiotherapy review. Only 3 patients required admission. Regarding follow-ups, 431 patients had a virtual follow-up while 1036 of patients required further face to face follow up. 87 patients were triaged into subspecialty clinics. 37 patients were felt to have been referred inappropriately. Discussion: BOA guidelines suggest all patients need to be reviewed within 72 hours of their orthopaedic injury. Implementation of a VFC allows this target to be achieved and at the same time reduce patient contact. Almost half the patients were discharged following VFC review, the remaining patients were appropriately followed up. This is especially relevant in the current pandemic where reducing unnecessary trips to hospital will benefit the patient as well as make the most of the resources available.

Keywords: virtual fracture clinic, lockdown, trauma and orthopaedics, Covid- 19

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Authors: Ciaran Smiddy, Fergus Nugent, Karen Fitzmaurice

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A measure of prescribing and administration of Antimicrobial Prophylaxis before and during Covid-19(2019 vs. 2021) was desired to assess how these were affected by Covid-19. Antimicrobial Prophylaxis was assessed for 60 patients, under 3 Orthopaedic Consultants, against local guidelines. The study found that compliance with guidelines improved significantly, from 60% to 83%, but Appropriate use of Vancomycin reduced from 37% to 29%.

Keywords: antimicrobial stewardship, prescribing, spinal surgery, vancomycin

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Authors: Chia-Chen Hsieh, Fei-Hsiu Hsiao

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Objective: To examine the effects of supportive care interventions on depressive symptoms in patients with lung cancer. Methods: The databases of Cochrane Central Register of Controlled Trials (CENTRAL), Ovid EMBASE, PubMed, and Chinese Electronic Periodical Services (CEPS) were searched from their inception until September 2015. We included the studies with randomized controlled trial design that compared standard care with supportive care interventions using psychotherapeutic or exercises approach. The standardized mean differences (SMD) (Cohen’s d) were calculated to estimate the treatment effects. The Cochrane Risk of Bias Tool was used for quality assessment and subgroup analysis was conducted to identify possible sources of heterogeneity. Results: A total of 1472 patients with lung cancer were identified. Compared with standard care, the overall effects of all supportive care interventions significantly reduced depressive symptoms (SMD = -0.74 with 95% CI = -1.07 to -0.41), and the effect was maintained at the 4th, 8th, and 12th weeks of follow-up. Either psychotherapy combined with psychoeducation or exercise alone produced significant improvements in depressive symptoms, while psychoeducation alone did not. The greater improvements in depressive symptoms occurred in lung cancer patients with severe depressive symptoms at baseline, total duration of interventions of less than ten weeks, and intervention provided through face-to-face delivery. Conclusions: Psychotherapy combined with psychoeducation can help patients manage the causes of depressive symptoms, including both symptom distress and psychological trauma due to lung cancer. Exercise can target the impaired respiratory function that is a cause of depressive symptoms in lung cancer patients.

Keywords: supportive care intervention, depressive symptoms, lung cancer, meta-analysis

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