Search results for: adjusted network

Authors: Iman Shabanzadeh

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The economic growth and the special model of development in China have caused sensitivity in the public opinion of the world regarding the nature of this growth and development. In this regard, the Chinese have tried to put an end to such alarming procedures by using all the tools at their disposal and seek to present a peaceful and cooperative image of themselves. In this way, one of the most important diplomatic tools that Beijing has used to reduce the concerns caused by the Threat Theory has been the use of soft power resources and its tools in its development policies. This article begins by analyzing the concept of soft power and examining its foundations in international relations, and continues to examine the components of soft power in its Chinese version. The main purpose of the article is to figure out about the position of West Asia in China's soft power strategy and resources China use to achieve its goals in this region. In response to the main question, the paper's hypothesis is that soft power in its Chinese version had significant differences from Joseph Nye's original idea. In fact, the Chinese have imported the American version of soft power and adjusted, strengthened and, in other words, internalized it with their abilities, capacities and political philosophy. Based on this, China's software presence in West Asia can be traced in three areas. The first source of China's soft power in this region of West Asia is cultural in nature and is realized through strategies such as "use of educational tools and methods", "media methods" and "tourism industry". The second source is related to political soft power, which is applied through the policy of "balance of influence" and the policy of "mediation" and relying on the "ideological foundations of Confucianism". The third source also refers to China's economic soft power and is realized through three tools: "energy exchanges", "foreign investments" and "Belt-Road initiative". The research method of this article is descriptive-analytical.

Keywords: soft power, cooperative power, china, west asia

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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: Hsien Hwa Kuo, Lin Wen Chun, Lin Wen Chun, Hsien Wen Kuo

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Objective: Nurses represent an important occupational group frequently affected by wrist musculoskeletal disorders (WMSDs) due to a heavy workload, working shifts, poor posture, giving shots, making beds, lifting patients, bending their waist and insufficient rest time every day. However, lack of research reported nurses whether workload in household correlated with the severity of WMSDs. Methods: 550 nurses from a hospital in Taoyuan were interviewed using a modified standardized Nordic Musculoskeletal (NMQ) questionnaire including the demographic information, workplace condition and nine body parts of musculoskeletal disorders. Results: 17.9% and 23.9% of severity and symptoms in WMSDs among nurses with children were significant higher than among nurses without children (1​2.4% and 15.9%). Based on multiple logistic regression models adjusted for age, work duration, job title and body mass index (BMI), we found that heavy workload in hospital had higher odds ratio (OR) of the severity and symptoms of WMSD among nurses with children (OR= 8.67 and OR= 4.30, p<0.05) compared to nurses without children (OR= 1.94 and OR= 1.70). Conclusion: The severity and symptoms of WMSDs among nurses significantly correlated with workload in hospital among nurses with children. If women are at greater risk because of the combination of their work and family demands, synergistic effect of WMSDs was found among nurses. Comment: Women's domestic work, especially once they become mothers, they invest more time and energy caring for children, helping others, and doing housework. Thus domestic work, per se, may be a risk factor for wrist musculoskeletal problems, and, more importantly, it may constrain women's ability to protect themselves from the effects of their paid work. If nurses with more domestic work periodically make efforts to physical activity or modify inappropriate posture, their WMSDs symptoms will be alleviated.

Keywords: musculoskeletal disorders, nurse, NMQ, WMSDs

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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: Rafooneh Mokhtarshahi Sani

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This study explores how the residents of a conserved urban area have used goods and ideas as resources to maintain an enviable architectural identity. Whereas conserved urban quarters are seen as role model for maintaining architectural identity, the article describes how their residents try to give a contemporary modern image to their homes. It is argued that despite the efforts of authorities and decision makers to keep and preserve the traditional architectural identity in conserved urban areas, people have already moved on and have adjusted their homes with their preferred architectural taste. Being through such conflict of interests, have put the future of architectural identity in such places at risk. The thesis is that, on the one hand, such struggle over a desirable symbolic status in identity formation is taking place, and, on the other, it is continuously widening the gap between the real and ideal identity in the built environment. The study then analytically connects the concept of symbolic status to current identity debates. As an empirical research, this study uses systematic social and physical observation methods to describe and categorize the characteristics of settlements in Walled City of Famagusta, which symbolically represent the modern houses. The Walled City is a cultural heritage site, which most of its urban context has been conserved. Traditional houses in this area demonstrate the identity of North Cyprus architecture. The conserved residential buildings, however, either has been abandoned or went through changes by their users to present the ideal image of contemporary life. In the concluding section, the article discusses the differences between the symbolic status of people and authorities in defining a culturally valuable contemporary home. And raises the question of whether we can talk at all about architectural identity in terms of conserving the traditional style, and how we may do so on the basis of dynamic nature of identity and the necessity of its acceptance by the users.

Keywords: symbolic status, architectural identity, conservation, facades, Famagusta walled city

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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: Jesus D. Cortes Gil, Fernanda Ewerling, Leonardo Ferreira, Aluisio J. D. Barros

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Background: The sustainable development goals call for inclusive, equitable, and quality learning opportunities for all. This is especially important for children, to ensure they all develop to their full potential. We studied the prevalence and inequalities of suspected delay in child development in 63 low- and middle-income countries. Methods and Findings: We used the early child development module from national health surveys, which covers four developmental domains (physical, social-emotional, learning, literacy-numeracy) and provides a combined indicator (early child development index, ECDI) of whether children are on track. We calculated the age-adjusted prevalence of suspected delay at the country level and stratifying by wealth, urban/rural residence, sex of the child, and maternal education. We also calculated measures of absolute and relative inequality. We studied 330.613 children from 63 countries. The prevalence of suspected delay for the ECDI ranged from 3% in Barbados to 67% in Chad. For all countries together, 25% of the children were suspected of developmental delay. At regional level, the prevalence of delay ranged from 10% in Europe and Central Asia to 42% in West and Central Africa. The literacy-numeracy domain was by far the most challenging, with the highest proportions of delay. We observed very large inequalities, and most markedly for the literacy-numeracy domain. Conclusions: To date, our study presents the most comprehensive analysis of child development using an instrument especially developed for national health surveys. With a quarter of the children globally suspected of developmental delay, we face an immense challenge. The multifactorial aspect of early child development and the large gaps we found only add to the challenge of not leaving these children behind.

Keywords: child development, inequalities, global health, equity

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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: Chi Nguyen Thi Quynh, Steven Schilizzi, Atakelty Hailu, Sayed Iftekhar

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Territorial Use Rights for Fisheries (TURFs) have been emerged as a promising tool for fisheries conservation and management. However, illegal fishing has undermined the effectiveness of TURFs, profoundly degrading global fish stocks and marine ecosystems. Conservation and management of fisheries, therefore, largely depends on effectiveness of enforcing fishing regulations, which needs co-enforcement by fishers. However, fishers tend to resist monitoring participation, as their views towards monitoring scheme design has not been received adequate attention. Fishers’ acceptability of a monitoring scheme is likely to be achieved if there is a mechanism allowing fishers to engage in the early planning and design stages. This study carried out a choice experiment with 396 fishers in Vietnam to elicit fishers’ preferences for monitoring scheme and to estimate the relative importance that fishers place on the key design elements. Preference heterogeneity was investigated using a Scale-Adjusted Latent Class Model that accounts for both preference and scale variance. Welfare changes associated with the proposed monitoring schemes were also examined. It is found that there are five distinct preference classes, suggesting that there is no one-size-fits-all scheme well-suited to all fishers. Although fishers prefer to be compensated more for their participation, compensation is not a driving element affecting fishers’ choice. Most fishers place higher value on other elements, such as institutional arrangements and monitoring capacity. Fishers’ preferences are driven by their socio-demographic and psychological characteristics. Understanding of how changes in design elements’ levels affect the participation of fishers could provide policy makers with insights useful for monitoring scheme designs tailored to the needs of different fisher classes.

Keywords: Design of monitoring scheme, Enforcement, Heterogeneity, Illegal Fishing, Territorial Use Rights for Fisheries

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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: 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: Anza-Vhudziki Mboyi, Ilunga Kamika, Maggy Momba

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The aim of this study was to ascertain the survival limit and capability of commonly found wastewater protozoan (Aspidisca sp, Trachelophyllum sp, and Peranema sp) and bacterial (Bacillus licheniformis, Brevibacillus laterosporus, and Pseudomonas putida) species to remove COD while exposed to commercial nanomaterials under varying pH conditions. The experimental study was carried out in modified mixed liquor media adjusted to various pH levels (pH 2, 7 and 10), and a comparative study was performed to determine the difference between the cytotoxicity effects of commercial zinc oxide (nZnO) and silver (nAg) nanomaterials (NMs) on the target wastewater microbial communities using standard methods. The selected microbial communities were exposed to lethal concentrations ranging from 0.015 g/L to 40 g/L for nZnO and from 0.015 g/L to 2 g/L for nAg for a period of 5 days of incubation at 30°C (100 r/min). Compared with the absence of NMs in wastewater mixed liquor, the relevant environmental concentration ranging between 10 µg/L and 100 µg/L, for both nZnO and nAg caused no adverse effects, but the presence of 20 g of nZnO/L and 0.65 g of nAg/L significantly inhibited microbial growth. Statistical evidence showed that nAg was significantly more toxic compared to nZnO, but there was an insignificant difference in toxicity between microbial communities and pH variations. A significant decrease in the removal of COD by microbial populations was observed in the presence of NMs with a moderate correlation of r = 0.3 to r = 0.7 at all pH levels. It was evident that there was a physical interaction between commercial NMs and target wastewater microbial communities; although not quantitatively assessed, cell morphology and cell death were observed. Such phenomena suggest the high resilience of the microbial community, but it is the accumulation of NMs that will have adverse effects on the performance in terms of COD removal.

Keywords: bacteria, biological treatment, chemical oxygen demand (COD) and nanomaterials, consortium, pH, protozoan

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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: Jin Young Kim, Kwon Kyoo Kang

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The SGR1 (STAYGREEN1) protein is a critical regulator of plant leaves in chlorophyll degradation and senescence. The functions and mechanisms of tomato SGR1 action are poorly understood and worthy of further investigation. To investigate the function of the SGR1 gene, we generated a SGR1-knockout (KO) null line via clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated gene editing and conducted RNA sequencing and gas chromatography tandem mass spectrometry (GC-MS/MS) analysis to identify the differentially expressed genes. The SlSGR1 (Solanum lycopersicum SGR1) knockout null line clearly showed a turbid brown color with significantly higher chlorophyll and carotenoid content compared to wild-type (WT) fruit. Differential gene expression analysis revealed 728 differentially expressed genes (DEGs) between WT and sgr1 #1-6 line, including 263 and 465 downregulated and upregulated genes, respectively, for which fold change was >2, and the adjusted p-value was <0.05. Most of the DEGs were related to photosynthesis and chloroplast function. In addition, the pigment, carotenoid changes in sgr1 #1-6 line was accumulated of key primary metabolites such as sucrose and its derivatives (fructose, galactinol, raffinose), glycolytic intermediates (glucose, G6P, Fru6P) and tricarboxylic acid cycle (TCA) intermediates (malate and fumarate). Taken together, the transcriptome and metabolite profiles of SGR1-KO lines presented here provide evidence for the mechanisms underlying the effects of SGR1 and molecular pathways involved in chlorophyll degradation and carotenoid biosynthesis.

Keywords: tomato, CRISPR/Cas9, null line, RNA-sequencing, metabolite profiling

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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: Julius Adebayo John

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The objective of this study is to compare the use of medicinal plants and Conventional medicine intervention in the management of HBV among Ibadan populace. A purposive sampling technique was used to administer questionnaires at 2 places, namely, the University College Hospital and Total Healthcare Diagnostic Centre, Ibadan, where viral loads are carried out. A EuroQol (EQ – 5D) was adopted to collect data. Descriptive and inferential analyses were performed. Also, ANOVA, Correlation, charts, and tables were used. Findings revealed a high prevalence of HBV among female respondents and sample between ages 26years to 50years. Results showed that the majority discovered their health status through free HBV tests. Analysis indicated that the use of medicinal plant extract is cost-effective in 73% of cases. Rank order utility derived from medicinal plants is higher than other interventions. Correlation analysis performed for the current health status of respondents were significant at P<0.01 against the intervention management adopted (0.046), cost of treatment (0.549), utility (0.407) at P<0.00, duration of the treatment (0.604) at P<0.01; viral load before treatment (-0.142) not significant at P<0.01, the R2 (72.2%) showed the statistical variance in respondents current health status as explained by the independent variables. Respondents gained quality-adjusted life-years (QALYs) of between 1year to 3years. Suggestions were made for a public-private partnership effort against HBV with emphasis on periodic screening, viral load test subsidy, and free vaccination of people with –HBV status. Promoting phytomedicine through intensive research with strong regulation of herbal practitioners will go a long way in alleviating the burdens of the disease in society.

Keywords: medicinal plant, HBV management interventions, utility, QALYs, ibadan metropolis

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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: Chimuanya Osuji, Kido Uyamasi, Morgan Bradley

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Introduction: Obesity is a major risk factor for many chronic diseases, with higher rates occurring among certain populations. Even though disparities in obesity rates exist for those with disabilities, few studies have assessed the association between disability and obesity status. This study aims to examine the association between type of disability and obesity status among US adults during the Covid-19 pandemic (2019-2021). Methods: Data for this cross-sectional study was obtained from the 2019, 2020 and 2021 NHIS. Multinomial logistic regressions were used to assess the relationship between each type of disability and obesity status (reference= normal/underweight). Each model adjusted for demographic, health status and health-related quality of life variables. Statistical analyses were conducted using SAS version 9.4. Results: Of the 82,632 US adults who completed the NHIS in 2019, 2020, and 2021. 8.9% (n= 7,354) reported at least 1 disability-related condition. Respondents reported having a disability across vision (1.5%), hearing (1.5%), mobility (5.3%), communication (0.8%), cognition (2.4%) and self-care (1.1%) domains. After adjusting for covariates, adults with at least 1 disability-related condition were about 30% more likely to have moderate-severe obesity (AOR=1.3; 95% CI=1.11, 1.53). Mobility was the only disability category positively associated with mild obesity (AOR=1.16; 95% CI=1.01, 1.35) and moderate/severe obesity (AOR=1.6; 95% CI=1.35, 1.89). Individuals with vision disability were about 35% less likely to have mild obesity (AOR=0.66; 95% CI=0.51, 0.86) and moderate-severe obesity (AOR=0.66; 95% CI= 0.48, 0.9). Individuals with hearing disability were 28% less likely to have mild obesity (AOR=0.72; 95% CI= 0.56, 0.94). Individuals with communication disability were about 30% less likely to be overweight (AOR=0.66; 95% CI=0.47, 0.93) and 50% less likely to have mild obesity (AOR=0.45; 95% CI= 0.29, 0.71). Individuals with cognitive disability were about 25% less likely to have mild obesity and about 35% less likely to have moderate-severe obesity. Individuals with self-care disability were about 30% less likely to be overweight. Conclusion: Mobility-related disabilities are significantly associated with obesity status among adults residing in the United States. Researchers and policy makers should implement obesity intervention methods that can address the gap in obesity prevalence rates among those with and without disabilities.

Keywords: cognition, disability, mobility, obesity

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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: Jiashu Shen, Guoting Zhang, Zhicheng Wang, Yu Wang, Yun Liang, Siyu Zou, Fan Yang, Kun Tang

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Objectives: This study aims to enhance the understanding of paid blood donors’ characteristics in Chinese population and devise strategies to protect these paid donors. Background: Paid blood donation was the predominant mode of blood donation in China from the 1970s to 1998 and caused several health and social problems including largely increased the risk of infectious diseases with nonstandard operation in unhygienic conditions. Methods: This study utilized the cross-sectional data from the China Kadoorie Biobank with about 0.5 million people from 10 regions of China from 2004 to 2008. Multivariable logistic regression was performed to examine the associations between socio-demographic factors and paid blood donation. Furthermore, a stratified analysis was applied in education level and annual household income by rural and urban areas. Results: The prevalence of paid blood donation was 0.50% in China and males were more likely to donate blood than females (Adjusted odds ratio (AOR) =0.81, 95%Confident Intervals (CI): 0.75-0.88). Urban people had much lower odds than rural people (AOR =0.24, 95%CI: 0.21-0.27). People with a high annual household income had lower odds of paid blood donation compared with that of people with low income (AOR=0.37, 95%CI: 0.31-0.44). Compared with people who didn’t receive school education, people in a higher level of education had increased odds of paid blood donation (AOR=2.31, 95%CI: 1.94-2.74). Conclusion: Paid blood donors in China were associated with those who were males, living in rural areas, with low annual household income and educational background.

Keywords: China Kadoorie Biobank, Chinese population, paid blood donation, socio-demographic factors

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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: Kaisy Xinhong Ye, Su Lin Lim, Jialiang Li, Lei Feng

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background: Multiple healthful dietary patterns have been linked with dementia, but limited studies have looked at the role of diet in cognitive health in Asians whose eating habits are very different from their counterparts in the west. This study aimed to derive a dietary pattern that is associated with the risk of cognitive impairment (CI) in the Singaporean population. Method: The analysis was based on 719 community older adults aged 60 and above. Dietary intake was measured using a validated semi-quantitative food-frequency questionnaire (FFQ). Reduced rank regression (RRR) was used to extract dietary pattern from 45 food groups, specifying sugar, dietary fiber, vitamin A, calcium, and the ratio of polyunsaturated fat to saturated fat intake (P:S ratio) as response variables. The RRR-derived dietary patterns were subsequently investigated using multivariate logistic regression models to look for associations with the risk of CI. Results: A dietary pattern characterized by greater intakes of green leafy vegetables, red-orange vegetables, wholegrains, tofu, nuts, and lower intakes of biscuits, pastries, local sweets, coffee, poultry with skin, sugar added to beverages, malt beverages, roti, butter, and fast food was associated with reduced risk of CI [multivariable-adjusted OR comparing extreme quintiles, 0.29 (95% CI: 0.11, 0.77); P-trend =0.03]. This pattern was positively correlated with P:S ratio, vitamin A, and dietary fiber and negatively correlated with sugar. Conclusion: A dietary pattern providing high P:S ratio, vitamin A and dietary fiber, and a low level of sugar may reduce the risk of cognitive impairment in old age. The findings have significance in guiding local Singaporeans to dementia prevention through food-based dietary approaches.

Keywords: dementia, cognitive impairment, diet, nutrient, elderly

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Authors: M. Dezyani, R. Ezzati bbelvirdi, M. Shakerian, H. Mirzaei

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Sodium Hexametaphosphate (SHMP) is commonly used as an Emulsifying Salt (ES) in process cheese, although rarely as the sole ES. It appears that no published studies exist on the effect of SHMP concentration on the properties of process cheese when pH is kept constant; pH is well known to affect process cheese functionality. The detailed interactions between the added phosphate, Casein (CN), and indigenous Ca phosphate are poorly understood. We studied the effect of the concentration of SHMP (0.25-2.75%) and holding time (0-20 min) on the textural and Rheological properties of pasteurized process Cheddar cheese using a central composite rotatable design. All cheeses were adjusted to pH 5.6. The meltability of process cheese (as indicated by the decrease in loss tangent parameter from small amplitude oscillatory rheology, degree of flow, and melt area from the Schreiber test) decreased with an increase in the concentration of SHMP. Holding time also led to a slight reduction in meltability. Hardness of process cheese increased as the concentration of SHMP increased. Acid-base titration curves indicated that the buffering peak at pH 4.8, which is attributable to residual colloidal Ca phosphate, was shifted to lower pH values with increasing concentration of SHMP. The insoluble Ca and total and insoluble P contents increased as concentration of SHMP increased. The proportion of insoluble P as a percentage of total (indigenous and added) P decreased with an increase in ES concentration because of some of the (added) SHMP formed soluble salts. The results of this study suggest that SHMP chelated the residual colloidal Ca phosphate content and dispersed CN; the newly formed Ca-phosphate complex remained trapped within the process cheese matrix, probably by cross-linking CN. Increasing the concentration of SHMP helped to improve fat emulsification and CN dispersion during cooking, both of which probably helped to reinforce the structure of process cheese.

Keywords: Iranian processed cheese, emulsifying salt, rheology, texture

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Authors: Takeo Shibata, Arihito Endo, Akemi Kunimatsu, Chika Hiraga, Yoko Shimizu

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Introduction: Yuzawa Town, located in the Niigata prefecture of Japan, is famous for its hot springs. A health promotion program, Yuzawa family health plan, was initiated in 2002. It has been held for fifteen years. We evaluated the profiles of brushing teeth in adults. Subjects: 368 questionnaires were corrected from people who live in Yuzawa town. The range of age was between nineteen and sixty-four years old. Methods: Mann-Whitney’s U test and Kruskal-Wallis test were used to evaluate significant differences in frequencies of brushing teeth per a day. Chi-square test and the adjusted residuals were used to evaluate when they brush their teeth. Results: Women showed greater frequencies of brushing teeth per a day than men. No difference was shown by age. Construction workers showed fewer frequencies of brushing teeth. Specialized technicians, clerical workers, and housewives showed greater frequencies. People who know Yuzawa family health plan, take a regular life, or take a breakfast every day showed greater frequencies. People who think not healthy, don’t care a balance of foods, don’t take yearly health check-up, or smoke showed fewer frequencies. After breakfast, women and specialized technicians showed greater frequencies, and construction workers and self-employed workers showed fewer frequencies. After lunch, clerical workers and specialized technicians showed greater frequencies. There was no significant difference at after waking up, after dinner, and before going to bed. Construction workers showed a lower rate of having a marital partner and having information of health. Conclusion: Gender and occupational differences were shown in frequencies of brushing teeth per a day. A promotion of teeth brushing for male, especially construction workers and self-employed workers, is needed.

Keywords: health promotion, Yuzawa family health plan, brushing teeth, occupational difference

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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: Michael Moeti, Khuliso Sigama, Thapelo Samuel Matlala

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One of the principal challenges that the world is confronted with is insecurity. The crime rate is increasing exponentially, and protecting our physical assets especially in the motorist industry, is becoming impossible when applying our own strength. The need to develop technological solutions that detect and report theft without any human interference is inevitable. This is critical, especially for vehicle owners, to ensure theft detection and speedy identification towards recovery efforts in cases where a vehicle is missing or attempted theft is taking place. The vehicle theft detection system uses Convolutional Neural Network (CNN) to recognize the driver's face captured using an installed mobile phone device. The location identification function uses a Global Positioning System (GPS) to determine the real-time location of the vehicle. Upon identification of the location, Global System for Mobile Communications (GSM) technology is used to report or notify the vehicle owner about the whereabouts of the vehicle. The installed mobile app was implemented by making use of python as it is undoubtedly the best choice in machine learning. It allows easy access to machine learning algorithms through its widely developed library ecosystem. The graphical user interface was developed by making use of JAVA as it is better suited for mobile development. Google's online database (Firebase) was used as a means of storage for the application. The system integration test was performed using a simple percentage analysis. Sixty (60) vehicle owners participated in this study as a sample, and questionnaires were used in order to establish the acceptability of the system developed. The result indicates the efficiency of the proposed system, and consequently, the paper proposes the use of the system can effectively monitor the vehicle at any given place, even if it is driven outside its normal jurisdiction. More so, the system can be used as a database to detect, locate and report missing vehicles to different security agencies.

Keywords: CNN, location identification, tracking, GPS, GSM

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