Wireless sensor networks (WSNs) are a critical research area with numerous practical applications. WSNs are utilized in real-life scenarios, including environmental monitoring, healthcare, industrial automation, smart homes, and agriculture. As WSNs advance and become more sophisticated, they offer limitless opportunities for innovative solutions in various fields. However, due to their unattended nature, it is essential to develop strategies to improve their performance without draining the battery power of the sensor nodes, which is their most valuable resource. This paper proposes a novel sink mobility model based on constructing a bipartite graph from a deployed wireless sensor network. Using the bipartite graph’s properties, the mobile sink node can visit stationary sensor nodes in an optimal way to collect data and transmit it to the base station. We evaluated the proposed approach through simulations using the NS-2 simulator to investigate the performance of wireless sensor networks when adopting this mobility model. Our results show that using the proposed approach can significantly enhance the performance of wireless sensor networks while conserving the energy of the sensor nodes.

Purpose: This study aimed to describe the molecular epidemiology and seasonality of human rhinovirus (HRV) in chronic obstructive pulmonary disease (COPD) and its association with COPD exacerbations in Abu Dhabi, the United Arab Emirates (UAE). Methods: Sputum specimens were collected for analysis from all COPD patients who visited a medical center from November 2021 to October 2022. The real-time quantitative polymerase chain reaction (qPCR) test was used to detect HRV. Results: Of the 78 COPD patients included in the study, 58 (74%) pa-tients presented with one or more exacerbation episodes. The incidence of COPD exacerbation peaked over the winter and substantially decreased during the summer. HRV positivity in pa-tients during exacerbation (E1) was 11/58 (19%) and 15/58 (26%) two weeks after the exacerbation episode (E2). There was no significant difference in the HRV load in these patients. No statistically significant difference was observed in the detection of HRV during exacerbation compared to patients with stable COPD. Conclusion: This is the first study to assess the association between HRV detection by qPCR and COPD exacerbations in the UAE. The high sensitivity of the detec-tion technology helped collect reliable epidemiologic data. Few studies have provided similar Middle East data. This study's pattern of COPD exacerbations and HRV detection parallels that of temperate countries. This information can help with future, more extensive surveillance of res-piratory viruses in the UAE and the Middle East and their association with COPD exacerbations.

Peer-to-peer (P2P) content distribution or file sharing system aims to facilitate the dissemination of large files over unreliable networks. Network coding is a new transmission technique that has captured the interest of researchers because of its ability to increase throughput and robustness of the network, and decrease the download time. In this survey paper, we extensively summarize, assess, compare, and classify the most recently used techniques to improve P2P content distribution systems performance using network coding. To the best of our knowledge, this survey is the first comprehensive survey that specifically focuses on the performance of network coding based P2P file sharing systems.

Recent studies have demonstrated silk fibroin’s ability to extend the shelf life of foods by mitigating the hallmarks of spoilage, namely oxidation and dehydration. Due to the potential for this protein to become more widespread, its safety was evaluated comprehensively. First, a bacterial reverse mutation test (Ames test) was conducted in five bacterial strains. Second, an in vivo erythrocyte test was conducted with Sprague Dawley rats at doses up to 1,000mg/kg-bw/day. Third, a range-finder study was conducted with Sprague Dawley rats at the highest consumption amount given solubility and oral gavage volume constrains (500mg/kg-bw/day). Fourth, a 28-day study in Sprague Dawley rats was conducted at the 500mg/kg-bw/day amount. Fifth, an in vitro pepsin digestion assay was performed to assess the potential for protein allergenicity. Sixth, allergenic potential was further assessed using liquid chromatography-mass spectroscopy for detection of allergenic insect proteins. Seventh, the protein sequences were subjected to bioinformatic analyses. Together, these studies raise no mutagenic, carcinogenic, toxicological, or allergenic concerns with the oral consumption of silk fibroin.

Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

This paper presents an innovative multi-agent, data-driven reinforcement learning (RL) approach to develop and utilize the thermal equivalent network model that represents the motor's thermal dynamics. A multi-agent reinforcement learning is designed and trained to adjust the model parameters using data from several motor driving cycles. To ensure the incoming driving cycle matches the historical data before employing the pre-trained RL agents, offline statistical analysis and clustering techniques are developed and used. Numerical simulations highlighted the RL agent's ability to develop strategies that effectively address the variability of driving cycles. The proposed RL framework showed its capability to accurately reflect the motor's thermal behavior under various driving conditions.

Face recognition is one of the most active research fields of computer vision and pattern recognition, with many practical and commercial applications including identification, access control, forensics, and human-computer interactions. However, identifying a face in a crowd raises serious questions about individual freedoms and poses ethical issues. Significant methods, algorithms, approaches, and databases have been proposed over recent years to study constrained and unconstrained face recognition. 2D approaches reached some degree of maturity and reported very high rates of recognition. This performance is achieved in controlled environments where the acquisition parameters are controlled, such as lighting, angle of view, and distance between the camera-subject. However, if the ambient conditions (e.g., lighting) or the facial appearance (e.g., pose or facial expression) change, this performance will degrade dramatically. 3D approaches were proposed as an alternative solution to the problems mentioned above. The advantage of 3D data lies in its invariance to pose and lighting conditions, which has enhanced recognition systems efficiency. 3D data, however, is somewhat sensitive to changes in facial expressions. This review presents the history of face recognition technology, the current state-of-the-art methodologies, and future directions. We specifically concentrate on the most recent databases, 2D and 3D face recognition methods. Besides, we pay particular attention to deep learning approach as it presents the actuality in this field. Open issues are examined and potential directions for research in facial recognition are proposed in order to provide the reader with a point of reference for topics that deserve consideration.

To investigate the process and chemistry of reaction zone formation, we conducted detailed petrographic observations and major-trace element analysis of rocks and minerals of lherzolite xenoliths from basanites of Tumusun volcano (Baikal Rift Zone). The reaction zones gradually disappear from contact toward center of xenoliths. This indicates their formation as a result of lherzolite-basanite interaction. The influence of basanite melt on major-trace element composition of secondary minerals of reaction zones is notable only at distance up to 100-200 μm from the contact. The major-trace element composition of secondary clinopyroxenes from orthopyroxene reaction zone indicates their formation from a melt formed by dissolution of orthopyroxene and influenced by element diffusion from basanite melt. Inside xenoliths the secondary minerals have Mg# values equal to or higher than Mg# of primary minerals. The secondary clinopyroxenes inherit their depleted or enriched REE pattern from primary pyroxenes. The major-trace element variations in secondary clinopyroxenes testify melt heterogeneity. Secondary clinopyroxene has slightly higher LILE and similar abundances of other trace elements compared to unreacted part of clinopyroxene grain. This is consistent with model developed from experimental studies: due to the interaction with basanite, incongruent dissolution of orthopyroxene occurs to form a melt which circulates in lherzolite and leads to pyroxenes and spinel dissolution. Non-dissolved centers of minerals are homogenous and similar in major-trace element composition to primary minerals. The process of peridotite-basanite interaction is not isochemical: even the centers of large xenoliths become enriched in LILE.

Background: The presence of a skilled health professional at delivery is critical to reduce infant and maternal mortality. Health development plans and strategies, especially in developing countries, consider equity in access to maternal health care services as a priority. This study aimed to measure and analyze the inequality in the use of skilled births attendance services in Mauritania. The study identifies the inequality determinants and to explore its changes over the period 2007–2015. Methods: The concentration curve, concentration index, decomposition of the concentration index and Oaxaca-type decomposition technique were performed to measure socio-economics related inequalities in skilled birth attendance services utilization and identify contribution of different determinants to such inequality as well as the changes in inequality overtime using data from Mauritania Multiple Indicator Cluster Surveys (MICS) 2007, 2011 and 2015. Results: The concentration index for skilled birth attendance services utilization dropped from 0.6324 [P < 0.001] in 2007 to 0.5852 [P < 0.001] in 2015. Prenatal care, household wealth Status and urban-rural location made the biggest contributions to socio-economic related inequalities. Decomposition of the concentration index and Oaxaca-type decomposition revealed changes prenatal care, rural-urban location made positive contributions to decline in inequality. However, alternation in household wealth score, woman’s age, her education level and the number of living children pushed the equality toward deterioration. Conclusion: Clearly, the pro-rich inequality in skilled birth attendance is high in Mauritania despite a slight decrease during the study period. Policy actions on eliminating urban-rural and wealth index disparity should target increase access to skilled birth attendance. Multisectoral Policy actions is needed to improve social determinants of health and to remove health system bottlenecks including socio-economic empowerment of women and girls, enhancing availability and affordability of Reproductive and Maternal Health commodities, improving availability of obstetrical providers in rural area, promoting a better distribution and quality of health infrastructure particularly health posts and health centers, and replacing user fees by an equitable, efficient and sustainable financing scheme under an universal health coverage vision.

This study aimed to reveal the creative thinking of primary school female teachers in Al-Ahsa Governorate, in addition to identifying differences in light of the variables of experience and specialization. The researchers followed the descriptive method. The study was applied to a sample of (340) female teachers. The Creative Thinking Scale, which was adapted by researcher Hawzam Salman (2021), was used. The study concluded that the female teachers of primary stage in Al-Ahsa Governorate have creative thinking at a high degree; in addition, there were no differences in light of the variables of experience and specialization.

Anode materials based on TiO2 nanoparticles of different morphologies were prepared using the hydrothermal method and characterized by various techniques, such as X-ray diffraction (DRX), scanning electron microscopy (FE-SEM), and N2 absorption. The TiO2 nanoparticles prepared were used as anode materials for lithium-ion batteries (LIBs), and their electrochemical properties were tested using discharging/charging measurements. The results showed that the initial morphology of the nanoparticles plays a minor role in battery performance after the first few cycles and that better capacity was achieved for TiO2 nanobelt morphology. The sharp drop in the specific capacity of LIB during their first cycles is examined considering changes in the morphology of TiO2 particles and their porosity properties in terms of shape and connectivity.

Remanufacturing is a way for product recovery initiative and maintaining sufficient production flow to satisfy customer demand through circular economy. Recently, manufacturers have been progressively incorporating remanufacturing processes, making their supply chain vulnerable to disruptions. Among the disruptions that usually occur is the shortage in spare parts supply, which causes unexpected delay in production and possible loss of sales. In the face of potential disruption, it is essential for remanufacturing facilities to manage their supply chains in an optimum manner as to decrease the adverse impact of disruptions to their business. In this study, a two-stage production-inventory system is analysed by developing a cost-minimisation model focusing on the recovery schedule after a disruption occurrence in sourcing spare parts for a remanufacturer’s production cycle. The developed model was solved using the branch and bound algorithm. Through numerical experiments, the results indicate that the optimal recovery schedule and the number of recovery cycles are significantly dependant on disruption time, lost sales and backorder costs. Moreover, a sensitivity analysis shows that the lost sales option seems to be more effective than the backorder option in optimising the system’s overall cost. The proposed model could assist managers in deciding the optimal production strategy while providing interesting managerial insights into vital spare parts recovery issues when disruption strikes.

Mallard (Anas platyrhynchos) are currently one of the most popular species in rare bird breeding in several southern provinces of China, but there are very few studies on the gut microbial communities of domestic and wild mallards. In this study, 16S rRNA gene high-throughput sequencing technology was used to compare the composition and diversity of gut microbial communities in domestic and wild mallards. Alpha diversity analysis revealed significant differences in the gut microbial communities of domestic and wild mallards. Beta diversity analysis showed that the two groups of stool samples were mostly separated on the principal coordinate analysis (PCOA) plot. In domestic mallards, Firmicutes (67.97%) was the most abundant bacterial phylum, followed by Proteobacteria (24.48%), Bacteroidetes (3.13%), Fusobacteria (2.21%), and Actinobacteria (1.10%). The dominant bacterial phyla in wild mallards were Firmicutes (79.02%), Proteobacteria (12.85%), Fusobacteria (3.37%), and Bacteroidetes (2.79%). At the genus level, a total of 10 dominant genera (Streptococcus, Enterococcus, Clostridium, Lactobacillus, Soilbacillus, Bacillus, Acinetobacter, Comamonas, Shigella, and Cetobacterium) with an average relative abundance greater than 1% were detected in the fecal samples of both groups. The average relative abundance of five potential pathogens (Streptococcus, Enterococcus, Acinetobacter, Comamonas, and Shigella) was higher in domestic mallards than in wild mallards. The enrichment of pathogenic bacteria in the intestinal tract of domestic mallards should be of sufficient concern.

Mallard (Anas platyrhynchos) are currently one of the most popular species in rare bird breeding in several southern provinces of China, but there are very few studies on the gut microbial communities of domestic and wild mallards. In this study, 16S rRNA gene high-throughput sequencing technology was used to compare the composition and diversity of gut microbial communities in domestic and wild mallards. Alpha diversity analysis revealed significant differences in the gut microbial communities of domestic and wild mallards. Beta diversity analysis showed that the two groups of stool samples were mostly separated on the principal coordinate analysis (PCOA) plot. In domestic mallards, Firmicutes (67.97%) was the most abundant bacterial phylum, followed by Proteobacteria (24.48%), Bacteroidetes (3.13%), Fusobacteria (2.21%), and Actinobacteria (1.10%). The dominant bacterial phyla in wild mallards were Firmicutes (79.02%), Proteobacteria (12.85%), Fusobacteria (3.37%), and Bacteroidetes (2.79%). At the genus level, a total of 10 dominant genera (Streptococcus, Enterococcus, Clostridium, Lactobacillus, Soilbacillus, Bacillus, Acinetobacter, Comamonas, Shigella, and Cetobacterium) with an average relative abundance greater than 1% were detected in the fecal samples of both groups. The average relative abundance of five potential pathogens (Streptococcus, Enterococcus, Acinetobacter, Comamonas, and Shigella) was higher in domestic mallards than in wild mallards. The enrichment of pathogenic bacteria in the intestinal tract of domestic mallards should be of sufficient concern.

Aquaculture is one of the fastest-growing animal food-producing agricultural industries in the world and proper performance of fish in morphological, physiological and immunological aspects is important for fish production and sustainable expansion of aquaculture. But several inhibitors like disease, pathogen, and adverse environment can overpower these performances. At present, antibiotics in preventing these inhibitors have been seen as becoming favorable to those inhibitors. So, Bacillus, an important group of probiotic bacteria can be an alternative to these antibiotics in aquaculture. Bacillus has been seen used in different experiments, mainly as a supplement in feed at various concentrations. Bacillus showed effective results like improved growth with minimum cost, improvement in reproduction, hematology, improved immune response and disease, and stress resistance as well as better proximate composition in different fish species. Application of Bacillus strains has proven efficient in improving water quality by reducing ammonia and nitrite toxicity, harmful algal blooms and utilization of H⁺ ion. Larger application of probiotic Bacillus instead of the hazardous synthetic chemicals would promote eco-friendly low-input sustainable aquaculture for food and nutritional security of the increasing world population. So many more experiments should be conducted in commercially important fishes for better growth and health of fishes which will certainly increase fish

The World Health Organization estimated that around 66 thousand HBV are caused by needlestick injuries annually. Healthcare students should be aware of HBV transmission route and preventive measures. This study assessed the knowledge, attitudes, and practices toward HBV among Jordanian healthcare students and its associated factor. A cross-national study conducted from March to August. The participants were asked to complete the questionnaire. It had four sections: participants' sociodemographic, knowledge, attitudes, and practices about HBV. 2322 participants were enrolled, 67.9% were females, 26.4% were medical student, 35.9% were in the 3rd year. 40% of the participants held a high level of knowledge and attitude. 63.9% of participants had good practices toward HBV. Medical students in the last year of study, encountered HBV patients , had better knowledge. Male students from medicine college, encountered HBV patients and had extra HBV courses showed better attitude. High practice level was associated with being dentistry student, at 5th year, encountered HBV patient and extras HBV courses. This study demonstrated insufficient knowledge and attitudes toward HBV, the practices level toward HBV among healthcare students was promising. Subsequently, public health efforts should modify the knowledge and attitude gaps to reinforce awareness and minimizing risks of the infection.

Computer vision is a rapidly developing field that focuses on highly sophisticated picture analysis, manipulation, and comprehension. Its objective is to analyze what is happening in front of a camera and utilize that understanding to control a computer or robotic system or to present users with fresh visuals that are more enlightening or appealing than the original camera images. Computer vision technologies make it feasible for new user interfaces, augmented reality gaming, biometrics, automobile, photography, movie creation, Web search, and many more applications. This essay seeks to explain how computer vision can be utilized to play blackjack successfully.

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

The stability between the limited available energy resources and their consumption is an important concern of the modern world. The study deals with the development of the methods for measuring the energy performance of different renewable and non-renewable resources used for electricity generation with the help of an energy performance indicator called the Primary Energy Factor (PEF). This study takes account of all energy stages from the resources extraction to its transportation, conversion, combustion and then finally to utilization. The study conducted a comparative approach to calculate the PEF values of electricity generated in South Korea from the period of 1980 to 2017. Four different methodologies namely Eurostat methodology, Life cycle methodology, Modified Eurostat methodology, and upper-end methodology have been used. A set of Programs has been written in MATLAB to calculate the i. PEF values of electricity generated. ii. Impact of different renewable and non-renewable energy sources on PEF values. iii. Future trend of PEF values of electricity in South Korea At the national and local level, this study will help the government and other law-making institutions to make policies regarding annual purchase consumptions of energies by different industrial, residential, public and transportation sectors and at the Global level, the implementation of this study will be the catastrophe of maintaining the stability between the available energy resources, and consumptions. This, in turn, will lead to suitable living conditions for future generations

Gout is the most common form of inflammatory arthritis. Hyperuricaemia is the pre-requisite for gout and is influenced by variable modifiable and non-modifiable risk factors. Clinical features unique for gout are due to deposition of monosodium urate (MSU) crystal in articular and extra articular tissues. Among various treating agents, anti-inflammatory drugs and urate lowering therapies (ULT) are used widely and successfully, however, non-medicinal means are also effective in the disorder. In their updated guidelines, ACR (2012) and EULAR (2016) recommended both medicinal and non-medicinal approaches that could be used in treating gout, though some of the recommendations are based on lower level of evidence. Moreover, researchers’ continued effort in finding new gout managing agents appear promising, for example, role of Lesinurad in gout management (CLEAR1, CLEAR2). In this new synthesis the author is aimed to provide updated information on gout management based on a systematic review including published work within last ten years between 2008 and 2018 and for this purpose, using ‘clinical trials in gout management’ string, published worked searched in PubMed database from 1st September 2018 to 30 October 2018. Besides the recent ACR and EULAR evidence based management guidelines, the author reviewed another 91 (total 93) articles to make this new draft – 39 articles describe role of pharmacological agents and 54 describe different gout risks, pharmacokinetics/pharmacodynamics of ULT, association between raised sUA level and renal impairment, efficacy of non-pharmacological agents in reducing sUA. According to published work, anti-inflammatory agent is the most appropriate drug group in mitigating inflammatory symptoms of gout, though they often adversely affect over other vital 2 organs with impaired function. Besides ULT, uricase analogues are also found useful in non refractory gout. Since anti-inflammatory agents and ULT contraindicate in some clinical conditions, intra-articular steroid and or adrenocorticotropic hormone (ACTH) are appropriate alternatives instead. However, head-to-head comparison between different NSAIDs, NSAID and prednisolone, NSAID and colchicine are yet to perform. Use of combined anti-inflammatory preparations in gout is also based on lower level of evidence. Regarding effective maximum dose and long-standing impact of ULT on vital organs we are yet to reach a conclusion. Likewise, non-medicinal approaches are widely using in achieving target sUA level, though some of them are based on biased study outcomes and or study with inadequate power, requiring further analysis. Among non-pharmacological approaches, life-style modification, restriction of purine rich diets, avoidance of gout inciting agents are important, but inconclusive. Educating patients’ about diseases, risk factors, available treatment options and side effects from them are also important in terms of achieving sUA level, nevertheless too much counseling sometimes could be worthless.

This work is aiming to motivation on the prospect of evolving new thiazole dyes with respectable application properties, expected pharmacological activities. Curcumin Coupling with diverse diazonium salts of 2-amino thiazole derivatives as 2-aminobenzothiazole, 2- amino-5-phenylthiazole, 2 amino-5-methylthiazole and 2 amino-5-nitrothiazole to produce novel azo dyes. All synthesised dyes were completely confirmed their structures via elemental and spectroscopic techniques. The synthesised thiazole derivatives were examined for their “antimicrobial, anticancer and antioxidant” activities. All of the synthesized dyes were applied on synthetic fabrics as polyester and successively their dyeing properties, “light, washing, perspiration, rubbing and sublimation” fastness were evaluated. Prepared dyestuffs are suitable for dyeing polyester fabrics. It was initiate that all of prepared dyes own extraordinary colour hue, along with respectable fastness properties. Also the synthesised thiazole derivatives display moral pharmacology activity.

Hypnotizability is a cognitive trait associated with differences in the brachial artery flow-mediated dilatation of individuals with high hypnotizability (highs) and low hypnotizability scores (lows). The study investigated possible hypnotizability-related cerebrovascular differences. Among 24 healthy volunteers the Stanford Hypnotic Susceptibility Scale Form A, identified 13 medium-to-lows (med-lows), 11 medium-to-highs (med-highs), 1 medium hypnotizable. Hypnotizability did not influence the significant changes produced by the trail making task (TMT), mental arithmetic task (MAT), hyperventilation (HVT) and rebreathing (RBT) on Heart rate (HR), arterial blood pressure (ABP) and partial pressure of end-tidal CO2 (PETCO2) and oxygenation (TOI); but moderated the correlations between the changes occurring during tasks with respect to basal conditions (Δ) in ABP and PETCO2 with MCAv. In HVT, med-lows exhibited a significant correlation between ΔMCAv and ΔPETCO2, med-highs showed a significant correlation between ΔABP and ΔMCAv; a significant correlation between ΔTHI and ΔTOI was observed in medium lows. Cerebrovascular reactivity (CVR) and conductance (ΔCVCi) were significantly correlated with ΔMCAv in med-lows during HVT and RBT. Findings represent the first assessment of hypnotizability-related differences in the mechanisms controlling the middle cerebral artery flow velocity, cerebrovascular reactivity and conductance in response to hyperventilation and rebreathing.

Approximately 85 million people's houses are scattered all over Indonesia, and almost all are in strong earthquake areas. In every earthquake, the houses are generally damaged or collapsed. Therefore, those houses must be strengthened to make them earthquake resistant. This paper discusses a gradual strengthening of existing houses using ferrocement bandaging. The gradual strengthening is introduced due to limited funding of the people. It also serves as an educational tool to educate people to be self-sufficient in building their earthquake-resistant houses. The first step, maybe the sleeping room shall be strengthened so that if there is an earthquake during night-time, people will be safe, and if there is an earthquake during the daytime, people can immediately run to that particular room. A global analysis is made of a sample house shaken by Palu, Central Sulawesi earthquake 2018, and West Sumatra earthquake 2009, with one room strengthened to show that the strengthened room can survive the earthquakes. Then the analysis is continued gradually to the other rooms until the masonry house is fully strengthened by ferrocement bandaging. The results show that the masonry house strengthened by ferrocement layers is earthquake resistant.

Background: During esophagogastroduodenoscopy performed with colonoscopy, gastric and duodenal erythema, erosions, and ulcerations are often observed. This investigation was designed to review the prevalence of gastroduodenal lesions in patients who underwent wireless capsule endoscopy using standard bowel cleansing preparations, but no endoscopy or sedation. Methods: A retrospective analysis was conducted on patients referred for capsule endoscopy. Records and capsule reports were reviewed for patient demographics, preparation prescribed, procedural indications, and gastroduodenal findings. Preparations studied included polyethylene glycol lavage (PEG), PEG plus bisacodyl (PEG+bis), bisacodyl (bis), oral sulfate solution (OSS) and no prep. Results: Among the 1236 records, 498 (40.3%) were men and 738 (59.7%) were women. The mean age was 57 years +/- 17.8 years SD. The percentage of patients with lesions after any bowel preparation was 52.7% for gastric lesions and 23.6% for duodenal lesions. The percentage of patients with gastroduodenal lesions was 58.3% with prep, compared to 38.2% without prep. These findings were statistically significant with RR 1.53 [1.19-1.94] (p-value=0.00004). This difference was more pronounced in the OSS group RR 1.65 [1.29-2.1] and bisacodyl groups RR 1.64 [1.25-2.15] compared to the PEG group RR 0.95 [0.7-1.3]. Conclusion: This study showed that patients undergoing wireless capsule endoscopy who received bowel preparations had a significant increase in gastric and duodenal lesions. Of the preparations studied, OSS was associated with the greater number of gastroduodenal lesions, while PEG was the least associated with lesions with an occurrence similar to the non-prep group. The clinical significance of these lesions remains undetermined.

This study aims to explore significant present and future trends in accounting and corporate sustainability by examining publications from the Web of Science database spanning from 2010 to 2023. With a particular focus on research related to the digital business environment, the study employs bibliometric analysis grounded in the resource-based view theory to investigate the evolution of auditing and digital accounting research. Utilizing the VOSviewer tool, the study analyzes keyword relationships and their geographical distribution based on a comprehensive examination of 700 publications from the Web of Science. The findings highlight the transformative impact of auditing and digital accounting on business operations in response to rapid digitization. Key clusters identified shed light on digital transformation and the intersection between information technology and corporate management, revealing the evolving research landscape. The study discusses both practical and theoretical implications arising from these findings.

In this study, the olive pomace was considered because of its importance in the environment. So, this study carried out to estimate the total nitrogen content and the fatty acids profile of olive pomace. The olive pomace samples were collected from Irbid, Jarash, Ajloun and Mafraq during the harvesting season 2014 to study the biochemical characteristics of fatty acid composition and the total nitrogen (TN) content. The total nitrogen content was determined by standard Kjeldahl method and the fatty acid profile was detected by gas chromatography (GC). The results show that the total nitrogen content ranged from (0.39 ± 0.0) to (0.62 ± 0.02) with statistically significant difference suggesting that the composition of matrices and their percentage may be responsible for composition of amendments. The calculated total protein percentage ranged between (2.43 ± 0.00) to (3.87 ± 0.17). Also, 13 different fatty acids were quantitatively profiled and quantitated. Oleic acid (C 18:1) was found to be the highest percentage of all other fatty acids and ranged between (59.03%) and (63.81%), moreover the C 18:1/C 18:2 (oil quality) was calculated and C.V% showed variation meaning that nutritional implication could affect the oxidative stability of oils. In conclusion, OP by-product could give a sustainable and alternative-cheap source for fertilizers, pharmaceutical industries, cosmetics and other industries.

The 3GPP release 16 integrates TSN functionality into 5G and standardizes various options for TSN time synchronization over 5G such as transparent mode and bridge mode. The time domains for the TSN network and the 5G network are kept separate with an option to synchronize either of the networks to the other. The TSN time synchronization over 5G is possible either by using the IEEE 1588 generalized Precision Time Protocol (gPTP) based on UDP/IP multicast or via IEEE 802.1AS based on Ethernet PDUs. The INET and Simu5G simulation frameworks, which are both based on the OMNeT++ discrete event simulator, are widely used for simulating TSN and 5G networks. The INET framework comprises the 802.1AS based time synchronization mechanism and Simu5G provides the 5G user plane carrying IP PDUs. We modified the 802.1AS based synchronization model of INET so that it works over UDP/IP. With that it is possible to synchronize TSN slaves (connected to 5G UEs), across a 5G network, with a TSN master clock, present within a TSN network, that is connected to the 5G core network. Our simulation results show that 500 microseconds synchronization accuracy can be achieved with the corrected asymmetric propagation delay of uplink and downlink between the gNodeB (gNB) and the User Equipment (UE). Futhermore, the synchronization accuracy can be improved if the delay difference between uplink and downlink is known.

Respiratory transmission is the primary route of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Angiotensin I converting enzyme 2 (ACE2) is the known receptor of SARS-CoV-2 surface spike glycoprotein for entry into human cells. A recent study reported absent to low expression of ACE2 in a variety of human lung epithelial cell samples. Three bioprojects (PRJEB4337, PRJNA270632 and PRJNA280600) invariably found abundant expression of ACE1 (a homolog of ACE2 and also known as ACE) in human lungs compared to very low expression of ACE2. In fact, ACE1 has a wider and more abundant tissue distribution compared to ACE2. Although it is not obvious from the primary sequence alignment of ACE1 and ACE2, comparison of X-ray crystallographic structures show striking similarities in the regions of the peptidase domains (PD) of these proteins, which is known (for ACE2) to interact with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Critical amino acids in ACE2 that mediate interaction with the viral spike protein are present and organized in the same order in the PD of ACE1. In silico analysis predicts comparable interaction of SARS-CoV-2 spike protein with ACE1 and ACE2. In addition, this study predicts from a list of 1263 already approved drugs that may interact with ACE2 and/or ACE1, potentially interfere with the entry of SARS-CoV-2 inside the host cells and alleviate the symptoms of Coronavirus disease (COVID-19).

Globally, rice is utilized as staple food, belongs to the family Poaceae. From the past few decades under variable climate conditions, it greatly affected by the bacterial leaf blight (BLB) disease caused by a bacterium Xanthomonas oryzae pv. oryzae (Xoo). Scientists studied causes of the disease and found more than 61 isolates of Xoo. About 39 Xa genes were noted that cause race specifically resistance either individually or in pairs against Xoo. The canvas on the characterization of these genes is still unrevealed. In this study, amino acid sequences of Xa15, Xa19, Xa20 and Xa21 were mined and used for motif-domain identification, characterization, and comparative analysis. It includes screening of physical and chemical characteristics, sequence comparison to find sequence similarity between them and their phylogenetic comparison with other Xa genes and other species based upon LRR and S_TKc domains to find evolutionary relationship among them. The comparison-based modeling was performed and assessed by different tools to gain better understanding and structural evaluation. The results showed identified domains are specific in function, each domain involved in resistance against biotic and abiotic stresses through regulating different cellular processes. This study also revealed high similarity (>98% sequence identity) between these genes and encode a similar leucine-rich repeat receptor kinase like protein. It will optimize the breeding programs as it will be useful for the selection of effective genes to produce resistance in rice varieties against the specific strains of Xoo that will be more effective against BLB than the other Xa genes.

We consider the problem of calibrating distance measurement of Light Detection and Ranging (lidar) sensor without using additional hardware, but rather exploiting assumptions on the environment surrounding the sensor during the calibration procedure. More specifically we consider the assumption of calibrating the sensor by placing it in an environment so that its measurements lie in a 2D plane that is parallel to the ground, and so that its measurements come from fixed objects that develop orthogonally w.r.t. the ground, so that they may be considered as fixed points in an inertial reference frame. We moreover consider the intuition that moving the distance sensor within this environment implies that its measurements should be such that the relative distances and angles among the fixed points above remain the same. We thus exploit this intuition to cast the sensor calibration problem as making its measurements comply with this assumption that “fixed features shall have fixed relative distances and angles”. The resulting calibration procedure does thus not need to use additional (typically expensive) equipment, nor deploying special hardware. As for the proposed estimation strategies, from a mathematical perspective we consider models that lead to analytically solvable equations, so to enable deployment in embedded systems. Besides proposing the estimators we moreover analyse their statistical performance both in simulation and with field tests, reporting thus the dependency of the MSE performance of the calibration procedure as a function of the sensor noise levels, and observing that in field tests the approach can lead to a ten-fold improvement in the accuracy of the raw measurements.

The operations of digitization, transmission and storage of medical data, particularly images require increasingly effective encoding methods not only in terms of compression ratio and flow of information but also in terms of visual quality. At first, there was DCT (discrete cosine transform) then DWT (discrete wavelet transform) and their associated standards in terms of coding and image compression. After that, the 2nd generation wavelets seeks to be positioned and confronted to the image and video coding methods currently used. It is in this context that we suggested a method combining bandelets and SPIHT (set partitioning in hierarchical trees) algorithm. There are two main reasons for our approach: the first lies in the nature of the bandelet transform to take advantage by capturing the geometrical complexity of the image structure. The second reason stems in the suitability of encoding the bandelet coefficients by the SPIHT encoder. Quality measurements shows that in some cases (for low bit rates) the performances of the proposed coding compete with the well-established ones and opens up new application prospects in the field of medical imaging.

Integrating artificial intelligence (AI) and natural language processing (NLP) technologies with project-based learning experiences in Science,Technology, Reading, Engineering, Arts, and Mathematics (STREAM) education offers the potential to enhance student engagement, critical thinking,problem-solving, and interdisciplinary learning. This paper presents a comprehensive approach to implementing AI and NLP technologies, such as ChatGPT, in STREAM project-based learning experiences, highlighting the potential advantages and challenges of this innovative educational approach. The proposed method encompasses personalized learning pathways, AI-powered research assistance, collaborative AI tools, real-time feedback, virtual mentoring, advanced simulation, and objective assessment. We also discuss the implications of this integration for educators, institutions, and students, along with recommendations for future research and development in this emerging field.

Advanced manufacturing techniques, aimed at implants with high dependability, flexibility, and low manufacturing costs, are crucial in meeting the growing demand for high-quality products like biomedical implants. Incremental sheet forming is a promising flexible manufacturing approach for rapidly prototyping sheet metal components using low-cost tools. Titanium and its alloys are used to shape most biomedical implants because of its superior mechanical qualities, biocompatibility, low weight, and great structural strength. The poor formability of titanium sheets at room temperature, however, limits their widespread use. The goal of this research is to show that gradual sheet formation of a titanium biomedical implant is possible. The possibility of creative and cost-effective concepts for the manufacturing of such complicated shapes with significant wall angles is explored in this study. A numerical simulation based on finite element modeling as well as a design process tailored to metal forming is used to complete the development. The mean of uniaxial tensile tests with a constant strain rate was used to study the flow behavior of the studied material. To forecast the crack, the obtained flow behavior was modeled using the behavior model and failure model.

Internet users have significantly increased as a result of the spread of Internet of Things (IoT) technologies and 5G networks. But these developments also make people more susceptible to cybercrime. Intrusion detection systems (IDSs), which protect against cyber threats and facilitate early response, have emerged as crucial security measures to handle this expanding risk. This study intends to present a comprehensive review of IDS, how it interacts with machine learning (ML), and develop a suitable approach for attack detection in 5G and IoT environments. To accomplish this, we leverage the AWID dataset, which is the first wireless traffic dataset specifically designed for security purposes, focusing on the IEEE 802.11 standard and developed to the AWID3 dataset. In this research, we suggest a powerful machine-learning framework for wireless system intrusion detection. We perform evaluations in three stages, covering scenarios for multiple nominal classes, multiple numeric classes, and binary classes. In order to improve the performance of the intrusion detection model, we also use feature selection approaches. Additionally, we offer a model that incorporates the outcomes of three feature selection techniques, highlighting how crucial it is to comprehend the features present in wireless datasets. Our experiments demonstrate how a machine learning-based approach can detect attacks with a high level of accuracy. In particular, the boosted decision tree performs best when overlapping feature selection procedures, whereas the Logistic Regression approach obtains the maximum accuracy of 99% in the first two phases. By providing a comprehensive framework for identifying attacks in 5G and IoT contexts using machine learning approaches, this research makes a contribution to the field of intrusion detection. The results underline how important it is to comprehend wireless dataset characteristics and highlight the possibility of ML-based methods for attaining highly accurate intrusion detection.

In the digital era, enhancing the heritage resources of a tourist route means describing and networking them to promote their visibility and attractiveness. Prior studies mainly assess mountainous region’s natural resources through multi-indicator descriptive evaluation. This study aims to develop a comprehensive measurement of indicator’s value while assessing the tourism development potential of mountainous rural area. It concerns the main tourist route of the UNESCO M'Goun Geopark, located in the central High Atlas of Morocco. It also involves analyzing the criteria for choosing this destination by segmenting tourists' consumption experiences. Statistical analysis of the collected data reveals five dimensions of destination choice and four visitor groups. This article proposes to discuss findings and their applicability in terms of tourism destination management and decision-making, which can help local governments to rationally allocate tourism resources and land use.

The rise in Internet users has brought with it the impending threat of cybercrime as the Internet of Things (IoT) increases and the introduction of 5G technologies continues to transform our digital world. It is now essential to protect communication networks from illegal intrusions in order to guarantee data integrity and user privacy. In this situation, machine learning techniques used in data mining have proven to be effective tools for constructing intrusion detection systems (IDS) and improving their precision. We use the well-known AWID3 dataset, a comprehensive collection of wireless network traffic, to investigate the effectiveness of machine learning in enhancing network security. Our work primarily concentrates on the Krack and Kr00k attacks, which target the most recent and dangerous flaws in IEEE 802.11 protocols. Through diligent implementation, we were able to successfully identify these threats using an IDS model that is based on machine learning. Notably, the resilience of our method was demonstrated by our Ensemble classifier’s astounding 99% success rate in detecting the Krack attack. The effectiveness of our suggested remedy was further demonstrated by the high accuracy rate of 96.7% displayed by our Neural Network-based model in recognizing instances of the Kr00k attack. Our research shows the potential for considerably boosting network security in the face of new threats by leveraging the capabilities of machine learning and a diversified dataset. Our findings open the door for stronger, more proactive security measures to protect IEEE 802.11 networks’ integrity, resulting in a safer online environment for all users.

According to the risk investigations of being involved in an accident, alcohol-impaired driving is one of the major causes of motor-vehicles accidents. Preventing highly intoxicated persons from driving would potentially save many lives. This paper proposes a lightweight in-vehicle alcohol detection that processes the data generated from 6-alcohol sensors (MQ-3 Alcohol Sensors) using an optimizable shallow neural network (O-SNN). The experimental evaluation results exhibit a high-performance detection system scoring a 99.8% of detection accuracy with a very short inferencing delay of 2.22 µ seconds. Hence, the proposed model can be efficiently deployed and used to discover in-vehicle alcohol with high accuracy and low inference overhead as a part of the driver alcohol detection system for safety (DADSS) system aiming at massive deployment of alcohol sensing systems that could potentially save thousands of lives annually.

Despite the scientific and technological development, libraries of Jordanian universities suffer from an obvious lack of digital information resources, because for many reasons, the most important of these is the non-application of intellectual property legislation (IPL) related to these resources. This study investigated the reality of the application of intellectual property legislation related to digital information resources at the libraries of public universities in Jordan. The population study consisted of all employees of departments of digital information resources. The study sample was (74) employees. Texts of regulations for the intended libraries and the Jordanian copyright law No. (22) for the year (1992) were reviewed and analyzed. In addition, a questionnaire was developed regarding the actual application of intellectual property legislation relating to digital resources at the university libraries in Jordan. Results revealed that the application of IP legislation relating to digital resources at the libraries of public universities in Jordan was moderate and that the supervising to the libraries of universities from the National Library regarding the protection of intellectual property rights of digital resources was moderate. The study recommended the application of intellectual property legislation related to digital resources in the intended libraries, this could be achieved through including intellectual property legislation in the regulations of public services in the intended libraries, to provide for the terms of protecting IP rights for digital resources, improving the legislation, developing of the Jordanian copyright law to be compatible with digital information resources, providing the necessary support for these libraries, and emphasizing the role of the National Library.

Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.

Keywords: Barriers; Facilitators; Sepsis; Sepsis Six Guidelines; Nurses.

The main purpose of study is to identify the impact of COVID-19 pandemic on the green financing of banks and non-bank financial institutions (NBFIs) in an emerging economy such as Bangladesh. Also, this study shows the green banking activities of the banks and NBFIs during the pandemic. To analyze the impact of the pandemic on green financing, secondary data were obtained from the quarterly and annual reports of Bangladesh Bank (BB) on green financing as well as the annual reports and websites of 61 banks and 34 NBFIs in Bangladesh for the period 2021–2019. Subsequently, the study deployed dependent t-test statistics, growth rate (year-on-year), descriptive statistics, relative percentage changes, and varying tables and graphs to analyze the obtained secondary data. The empirical findings revealed that during the COVID-19 pandemic, there was an increase in green finance for all banks and NBFIs compared to before the epidemic, indicating that the pandemic had no negative impact on the total green finance growth of all banks and NBFIs. On the other hand, compared to the pre-pandemic period, bank-wise growth in green financing was higher for state-owned commercial banks (SOCBs), specialized banks (SDBs), and private commercial banks (PCBs) but lower for foreign-owned commercial banks (FCBs) during the COVID-19 epidemic. This suggests that the pandemic does not affect the expansion of green finance by SOCBs, SDBs and PCBs but significantly impacted the growth of green financing by FCBs. Furthermore, the research findings showed that the total outstanding and classified loans within the green finance investment decrease for both banks and NBFIs during the COVID-19 pandemic. The results indicated that the Bangladeshi banks’ level of automation towards green banking were satisfactory during the pandemic. Therefore, major policy implications for the green economic recovery by the government, BB, and managers of the banks and financial institutions in emerging economies like Bangladesh were discussed.

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, Internet of Things (IoT) has earned a wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack tending to be treated as a normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide a comprehensive development of a new intelligent and autonomous deep learning-based detection and classification system for cyber-attacks in IoT communication networks leveraging the power of convolutional neural networks, abbreviated as (IoT-IDCS-CNN). The proposed IoT-IDCS-CNN makes use of the high-performance computing employing the robust CUDA based Nvidia GPUs and the parallel processing employing the high-speed I9-Cores based Intel CPUs. In particular, the proposed system is composed of three subsystems: Feature Engineering subsystem, Feature Learning subsystem and Traffic classification subsystem. All subsystems are developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the NSL-KDD dataset which includes all the key attacks in the IoT computing. The simulation results demonstrated more than 99.3% and 98.2% of cyber-attacks’ classification accuracy for the binary-class classifier (normal vs anomaly) and the multi-class classifier (five categories) respectively. The proposed system was validated using k-fold cross validation method and was evaluated using the confusion matrix parameters (i.e., TN, TP, FN, FP) along with other classification performance metrics including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning based IDCS systems in the same area of study.

This work aims to describe the early life history and the length frequency distribution of larval stages of three mesopelagic species collected near coral reef areas in the Red Sea. Larvae were collected monthly using a plankton net (500 µ) from three coastal coral reef areas surrounding Sharm El-Shekikh on the Egyptian Red Sea coast between January and December 2015. The identification of larvae was based on the author’s experience and other literature. The larvae of this species were divided into preflexion, flexion, and postflexion based on the flexion of the notochord and they were also categorized according to their size into relevant size classes. Mesopelagic fishes were represented in the collection by four species belonging to four families; Vinciguerria mabahiss (Family: Phosichthyidae), Benthosema pterotum (Family: Myctophidae), Astronesthes martensii (Family: Stomiidae; subfamily: Astronesthidae) and Trichiurus sp. (Family Scombridae). In general, a total of 3678 larvae were collected, of which 1191 belonged to mesopelagic species. The most abundant species was V. mabahiss with 677 larvae that constituted 18% of total larvae collected. The second most abundant species was B. pterotum that was represented by 485 larvae (13%). A. martensii was represented by the lowest number of larvae (29 larvae, 2%). Most larvae of the three species were in preflexion stage and more abundant in the cooler months of the year between November and April. The high number of preflexion larvae at the time may indicate the spawning of the three species in the colder times of the year.

This paper brings to fruition recently found \cite{migdal2023exact} exact reduction of decaying turbulence in the Navier-Stokes equation in $3 + 1$ dimensions to a Number Theory problem of finding the statistical limit of the Euler ensemble. We reformulate the Euler ensemble as a Markov chain and show the equivalence of this formulation to the \textbf{quantum} statistical theory of $N$ fermions on a ring, with an external field related to the random fractions of $\pi$. We find the solution of this system in the turbulent limit $N\to \infty, \nu \to 0$ in terms of a complex trajectory (instanton) providing a saddle point to the path integral over the density of these fermions. This results in an analytic formula for the observable correlation function of vorticity in wavevector space. This is a full solution of decaying turbulence from the first principle without approximations or fitted parameters. The energy spectrum decays as $k^{-\frac{7}{2}}$. We compute resulting integrals in \Mathematica{} and present effective index $n(t) = -t\partial_t \log E $ for the energy decay as a function of time Fig.\ref{fig::NPlot}. The asymptotic value of the effective index in energy decay $n(\infty) = \frac{5}{4}$, but the universal function $n(t)$ is neither constant nor linear.

In the present article, the application of an artificial neural network (ANN) model is studied, whose function is the development of plastic instability maps of a medium carbon microalloyed steel during the hot forging process. And, secondly, we proceed to create another neural network capable of providing the recrystallized grain size in the steady state phase resulting from the deformation by hot forging, thus creating a methodology that can be applied to different types of processes and materials. In order to achieve this objective, we start from the experimental data of a medium carbon microalloy steel, obtained by hot compression tests, for strain rates that vary between 10-4 and 3 s-1 and in a range of temperatures between 900 oC and 1150 oC. These experimental data will be used to train the neural network that is intended to be created in this work. Once trained, it will be verified if the results of the test correspond to the experimental data and the fluency curves will be obtained. Finally, the processing maps will be developed applying the Dynamic Materials Model (DMM), according to which the safe domains to forge the material and the plastic instability domains are delineated, to be avoided during the forging process. The maps obtained by means of RNA will be compared with the experimental ones and it will be possible to verify that the optimal regions of forging in the maps obtained by RNA coincide with those obtained by means of experimental data. In addition, a study of the influence of the microstructure on the behavior of steel during hot forming will be carried out, since the experimental tests are carried out at austenitizing temperature, so the microstructure is different in each test.

This study explores the impact of multi-user interaction, group work, and simulations within virtual worlds on language learning and the development of intergroup empathy. Two distinct research projects were conducted, involving a total of 241 participants aged 19-45. The language learning study engaged 116 participants in diverse interactive experiences, while the intercultural study had 125 participants collaborating in multicultural groups and participating in perspective-taking simulations. Both studies employed qualitative data collection methods, including surveys, interviews, and observations. The findings suggest that the combination of networking strategies, collaborative learning, and simulations within virtual worlds contributes to improvements in learners' language proficiency, confidence, and empathy towards diverse social groups. Participants reported increased motivation and engagement, attributed to the immersive and interactive nature of the virtual environments. The studies highlight the importance of collaboration and reflection in facilitating language acquisition and intercultural understanding. Technical challenges were identified as potential barriers to implementation. The results demonstrate the potential of virtual worlds to enhance language education and foster empathy in diverse societies, offering valuable insights for educators and researchers. However, the findings may be limited by the specific contexts and sample sizes of the studies, warranting further research to explore the generalizability and long-term impact of virtual world interventions. not exaggerate the main conclusions.

Inadequate data linking geology and health in Developing Countries contributes immensely to the challenges to identify sources and causes of many of the emerging diseases. Deficiencies and toxicities of trace elements generally impact human and animal health. The review of the geology of Ghana suggests the presence of oxides and sulphide minerals that are released into the natural environment during the geological process of weathering which introduce both essential and, potentially harmful elements. Of great concern is the fact that majority of the Ghanaian population eat locally cultivated food and expect to be nourished from the diet. Furthermore, archived reports on Ghana rural drinking water indicates most of the aquifers are enriched in As and F while deficient in Mg. Medical geology, the emerging discipline that attempts to address the environmental health issues emanating from geological processes is known in developed nations but not much of its activities are recognized in Ghana. This review has identified the concentrations of a number of elements in different geological settings and have linked these concentrations with health issues. There is therefore a need for medical geologists to work together with other disciplines to devise preventive as well as mitigative techniques in addressing many geology related health issues in Ghana.

The emergence of blockchain know-how currently presents the opportunity for the health sector to adopt such technologies in electronic health records. Blockchain assists in maintaining and sharing the relevant medical records of the patient with the relevant group of healthcare providers and the hospital. Numerous specific applications include traceability of drug and patient monitoring or Electronic Health Records (EHR). While Blockchain assists in maintaining and sharing the relevant medical records of the patient with the relevant group of healthcare providers and the hospital, it is important to note that the moral consciousness of the healthcare professionals is the main guide of the moral consciousness is ethics. This paper presents an overview of the application of blockchain in the healthcare and medical sector, highlighting the specific challenges and concerns. The study adopted a systematic review of secondary literature in answering the research question.

We proposed a so-called Beta Kumaraswamy Burr Type X distribution which gives the extension of the Kumaraswamy-G class of family distribution. Some properties of this proposed model were provided, like: the expansion of densi- ties and quantile function. We considered the Bayes and maximum likelihood methods to estimate the parameters and also simulate the model parameters to validate the methods based on dierent set of true values. Some real data sets were employed to show the usefulness and exibility of the model which serves as generalization to many sub-models in the elds of engineering, medical, survival and reliability analysis.

We proposed a so-called Beta Kumaraswamy Burr Type X distribution which gives the extension of the Kumaraswamy-G class of family distribution. Some properties of this proposed model were provided, like: the expansion of densities and quantile function. We considered the Bayes and maximum likelihood methods to estimate the parameters and also simulate the model parameters to validate the methods based on different set of true values. Some real data sets were employed to show the usefulness and flexibility of the model which serves as generalization to many sub-models in the field of engineering, medical, survival and reliability analysis.

Solid spent nuclear fuel from nuclear power plants has 3.4% fission products (80-160amu), contributing to over 99.8% radioactivity. On the other hand, liquid high-level radioactive waste (HLRW) from spent fuel reprocessing has 98.9% bulk elements (0-60amu) with 0.1% radioactivity. A separation mechanism on the mass categories as groups presents unique opportunities in managing HLRW for the long term with a considerable cost reduction. This paper proposes a thermal plasma-based separation system incorporating atmospheric pressure plasma torches for HLRW mass separation into low-resolution mass groups. Several engineering issues, such as waste preparation, waste injection into the plasma and waste collecting after mass separation, need to be addressed. Using COMSOL Multiphysics simulation, the generic system can be studied using noble gas mass separation and further analyze the mass filter capabilities. This paper provides the history of plasma-based mass separation. Functional modelling of a thermal plasma mass separation system is proposed under atmospheric pressure. Finally, aspects of mass separation simulation using noble gas Argon and Helium inside the plasma mass separation system were studied in COMSOL Multiphysics.

New antibacterial agents are needed to overcome the increasing number of infectious diseases caused by pathogenic microorganisms due to the emergence of multi-drug resistant strains. In this context, halogens, especially Iodine is known since ages for its antimicrobial activity. Therefore, especially triiodides encapsulated in organometallic complexes can be helpful as new agents against microorganisms. The aims of this work was to study the biological activity of [Na(12-Crown-4)₂]I₃ against gram positive Streptococcus pyogenes, Streptococcus faecalis, the spore forming bacteria Bacillus subtilis and gram negative bacteria Escherichia coli, Proteus mirabilis, Pseudomonas aeruginosa and Klebsiella pneumoniae, as well as the yeast Candida albicans. The antimicrobial and antifungal activities of the triiodide were determined by zone of inhibition plate studies. [Na(12-Crown-4)₂]I₃ exhibited potent antimicrobial activity on gram positive Streptochocci and the yeast C. albicans. Furthermore, the gram negative bacteria P. aeruginosa and K. pneumoniae were less effectively inhibited, while E. coli and P. mirabilis proved to be even resistant.

Currently, face recognition technologies are the most widely used methods for verifying 1an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face spoofing attacks, in which a photo or video of an authorized person’s face is used to get access to services. Based on a combination of Background Subtraction (BS) and Convolutional Neural Networks (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face spoof detection algorithm. This algorithm includes a Fully Connected (FC) classifier with a Majority Vote (MV) algorithm, which uses different face spoof attacks (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the Face Anti-Spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results by our proposed approach are better than those obtained by state of the art methods. On the REPLAY-ATTACK database, we were able to attain a Half Total Error Rate (HTER) of 0.62% and an Equal Error Rate (EER) of 0.58%. It was possible to attain an EER of 0% on both the CASIA-FASD and the MSU FAS databases.

Computer vision is a fast-expanding discipline focusing on analyzing, altering, and comprehending images at a high level. Its goal is to figure out what's going on in front of a camera and use that knowledge to manage a computer or robotic system or to show people new visuals that are more instructive or attractive than the original camera photos. Video surveillance, biometrics, automotive, photography, movie production, Web search, medicine, augmented reality gaming, new user interfaces, and many other applications are all possible with computer vision technologies. This paper aims to describe how computer vision will be used to play a winning game of blackjack.

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more Artificial Intelligence (AI). Deep learning is used remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layer and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

This research paper presents a deep learning approach to early detection of skin cancer using image augmentation techniques. The authors propose a two-stage image augmentation technique that involves the use of geometric augmentation and generative adversarial network (GAN) to classify skin lesions as either benign or malignant. This research utilized the public HAM10000 dataset to test the proposed model. Several pre-trained models of CNN were employed, namely Xception, Inceptionv3, Resnet152v2, EfficientnetB7, InceptionresnetV2, and VGG19. Our approach achieved accuracy, precision, recall, and F1-score of 96.90%, 97.07%, 96.87%, 96.97%, respectively, which is higher than the performance achieved by other state-of-the-art methods. The paper also discusses the use of SHapley Additive exPlanations (SHAP), an interpretable technique for skin cancer diagnosis, which can help clinicians understand the reasoning behind the diagnosis and improve trust in the system. Overall, the proposed method presents a promising approach to automated skin cancer detection that could improve patient outcomes and reduce healthcare costs.

Green public space immensely provides ecological soundness (ES), social resilience, social inclusiveness, mental health, self-esteem, and cultural prosperity from privileged class to lower ones. This research aims at estimating the effect of social services, safety, and infrastructural amenity in green space (parks) on mental health building self-esteem for elderly population. Data were collected from four parks out of 21 in Dhaka selected by systematic random sampling. Two-hundred and three elderly participants were picked up for survey using snowball sampling. The survey was conducted in a pre-structured questionnaire consisted of 40 close-ended questions. Data was analyzed applying ordinal logistic model, multivariate regression analysis, and Partial Least Square (PLS) -Path Modeling. The study explored several key findings-1) Infrastructural amenities impacted social services of parks; 2) Social services contributed positively to mental health boosting up self-esteem for elderly population. 3) Likewise, mental health for elderly population was affected by safety measure of green space. Dhaka requires constructing age-friendly parks to promote elderly mental health.

Sulfotransferases (SULTs) family plays a significant role in the biotransformation of a variety of xenobiotics and endogenous compounds by which carcinogenesis and mutagenicity of different malignancies are increasingly affected. Recent data identified various genetically polymorphic SULTs enzymes with significant variations in the enzyme activity. This study aimed to investigate the impact of SULT1A1 gene polymorphism and and its potential risk on females with breast cancer in Jordan using a PCR-RFLP and Sanger Sequencing methods. The analysis showed that 24.7% of the patients and 25.3% of the controls were homozygous for the SULT1A1*1 allele (SULT1A1*1/SULT1A1*1) compared to 8.8% and 5.7% homozygous for the SULT1A1*2 allele (SULT1A1*2/SULT1A1*2) for patients and controls respectively. Most of the patients and controls were heterozygous for SULT1A1*1 allele (SULT1A1*1/SULT1A1*2) with rates of 66.5% and 69.0% in patients and controls respectively. In addition, the frequencies of the mutant SULT1A1*2 allele were 0.42 and 0.4 in the patient and control groups respectively. No significant difference in genotype and allele distribution was noted between the breast cancer and control groups. The risk of breast cancer in individuals carrying the SULT1A1*2 allele was determined by combining the SULT1A1*1/SULT1A1*2 and ULT1A1*2/SULT1A1*2 genotypes. No association was observed between SULT1A1 polymorphism and breast cancer incidence (P = 0.63; OR, 0.93; 95% CI, 0.68–1.26). However, SULT1A1*2 allele was found to increase the risk of breast cancer by 1.26-fold.

Abstract Background COVID-19 vaccination program among Adults in India is one of the highly successful vaccination drives globally. Indian children aged 15–18 years old became eligible for COVID-19 vaccination on 3rd January, 2022 followed by 12-14 years old children on 16th March, 2022. As parents are primary decision-makers for their children, we aimed to assess parents' perceptions and intentions regarding COVID-19 vaccination for children in India. Aim To assess parental perceptions and intention to get COVID-19 vaccination for their children (aged<18 years), Materials and Methods A cross-sectional anonymous web-based survey was designed. Healthcare workers of a tertiary care institute in Eastern U.P. having any children aged <18 years were the study population. They were recruited through snowball sampling and were sent the study questionnaire in Google form through email and WhatsApp. Cross-tabulation was performed by parents' intention to vaccinate their children against COVID-19 virus with socio-demographic characteristics and their risk perception toward COVID-19, trust in the healthcare system, and their history of vaccine hesitancy behavior routine childhood immunization. Bi-variate analysis was performed to find out the predictors of child vaccination intention among the parents. Results A total of 388 healthcare workers parents (HCWPs) having A child < 18 years of age completed the survey. Mean age of the parents were 40.28 ±11.34 years with majority(97.0%) completing recommended 2 doses of COVIID-19 vaccine. Around 91% of the parents agreed that COVID-19 vaccines are important for child’s health whereas about 89% agreed to the statement that COVID-19 vaccines are effective. A total 356 (91.7%) participants were willing to vaccinate their child against COVID-19, 91.3% of them liked to do so as soon as possible. The factors significantly associated with the stated likelihood of child vaccination among the participants were younger age, female gender, urban residence, absence of any chronic illness, lower monthly income, history of COVID-19 in child and the child/children completed with routine childhood vaccines. Parental concerns mainly centered around vaccine need, safety and side effects. Conclusion The healthcare worker parents(HCWPs) have good knowledge and positive attitude towards COVID-19 vaccination for children. Around 92% of them were willing to vaccinate their children with COVID vaccine currently, which implies very low vaccine hesitancy among the HCWPs . Further longitudinal studies are required to be conducted to assess the trend of parental acceptance of COVID vaccine.

The keyless systems have replaced the old fashion methods of inserting physical keys in the keyhole to, i.e., unlock the door, because they are inconvenient and easy to be exploited by the threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, Keyless systems are susceptible to being compromised by a thread actor who intercepts the transmitted signal and performs a reply attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system remotely. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

The number of artificially ripened pineapples is outnumbered than the naturally ripened pineapples. However, there is a lack of understanding between artificially ripened and naturally ripened pineapples. Thus the inquiry was anticipated to explore the physicochemical changes and organoleptic acceptability of the naturally ripened and artificially ripened pineapples. Farmers used different chemicals such as calcium carbide, ethylene, besides growth hormones to reduce production loss. Here we evaluated the content of moisture, ash, protein, fat, crude fiber, reducing sugar, total sugar, titratable acidity, sucrose, and vitamin C in both naturally ripened and artificially ripened pineapples. Artificially ripened pineapples showed a significantly lower vitamin C than naturally ripened ones, but arsenic content was nil in both samples. In the case of color and appearance, there was no significant difference between the two samples, but in case of the other organoleptic properties, such as flavor, sweetness, sourness, the natural one was more acceptable. Thus naturally ripened pineapples are more beneficial to consumers than artificially ripened ones.

In this study, the effects of grain size and gradation of riprap, overtopping flow depth and downstream slope of embankment on scouring and deposition characteristics at the downstream toe of the embankment was investigated. For the experiment, three different downstream slopes (1:2, 1:3, and 1:4), three different overflow depths (1, 2, and 3 cm), and three different sizes (d₅₀ of 16.41 mm, 8.48 mm, and 3.39 mm herein referred as No.5, 6 and 7 respectively) of riprap particles, respectively, were used in the laboratory. The experimental results demonstrated that the scour depth and deposition height increased with increasing energy head for each downstream slope condition. Among the three particle sizes, for No. 5 size particle shows the lowest scour depth and highest deposition height. For the 1:2 slope, the No. 5 particle size could resist scour 62% and 75% less than that of the No. 6 and No. 7 particles, respectively. For the 1:3 slope case, this was 31% and 46%, and for the 1:4 slope case, this was 39% and 49% less than the No. 6 and No. 7 size particles, respectively.

1-[(4-ethyl-2-phenyl-4,5-dihydro-1,3-oxazol-4-yl)methyl]-4-phenyl-1H-1,2,3-triazole (Ph4) and1-[(4-ethyl-2-phenyl-4,5-dihydro-1,3-oxazol-4-yl)methyl]-5-phenyl-1H-1,2,3-triazole (Ph5) are new isomers of the triazole derivative family, were synthesized and tested on the corrosion of mild steel in molar hydrochloric acid molar media using weight loss, electrochemical polarization and impedance spectroscopy. Then the experimental results were confirmed by quantum chemical calculations using DFT at B3LYP /6-31G (d,p). The compound Ph4 is the best inhibitor and its inhibitory efficiency increased with increasing concentration and reaching 95% at 10⁻³ M. Polarization curves studies show that both compounds tested are mixed-type inhibitors. Nyquist curves presented a single capacitive loop, their diameter increases progressively with both inhibitors concentration. The change of the substitution phenyl from position 5 to position 4 in the triazole ring increases the inhibitory effect of the triazole compounds. The effect of temperature on the corrosion behavior of iron indicates that the inhibitory efficiency of the two inhibitors decreases with increasing temperature in the range of 308 to 338K. DFT study is in good correlationwith the experimental results.

Immune checkpoint blockade (ICB)-based therapy is revolutionizing cancer treatment by fostering successful immune surveillance and effector cell responses against various types of cancers. However, patients with HER2+ cancers are yet to benefit from this therapeutic strategy. Precisely, several questions regarding the right combination of drugs, drug modality, and effective dose recommendations pertaining to the use of ICB-based therapy for HER2+ patients remain unanswered. In this study, we use a mathematical modeling-based approach to quantify the growth inhibition of HER2+ breast cancer (BC) cell colonies (ZR75) when treated with anti-HER2; trastuzumab (TZ) and anti-PD-1/PD-L1 (BMS-202) agents. Our data show that a combination therapy of TZ and BMS-202 can significantly reduce the viability of ZR75 cells and trigger several morphological changes. The combination decreased the cell’s invasiveness along with altering several key pathways, such as Akt/mTor and ErbB2 compared to monotherapy. In addition, BMS-202 causes dose-dependent growth inhibition of HER2+ BC cell colonies alone, while this effect is significantly improved when used in combination with TZ. Based on the in-vitro monoculture experiments conducted, we argue that BMS-202 can cause tumor growth suppression not only by mediating immune response but also by interfering with the growth signaling pathways of HER2+ BC. Nevertheless, further studies are imperative to substantiate this argument and to uncover the potential crosstalk between PD-1/PD-L1 inhibitors and HER2 growth signaling pathways in breast cancer.

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients’ long-term survival. Several computer-based methods have recently been proposed in the pursuit of diagnosing skin lesions at their early stages. Despite achieving some level of success, there still remains a margin of error that the machine learning community considers to be an unresolved research challenge. This study presents a novel framework for the classification of skin lesions. The framework incorporates deep features to generate a highly discriminant feature vector, while also maintaining the integrity of the original feature space. Recent deep models including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2 are employed in our study for the purpose of feature extraction. Additionally, transfer learning is leveraged to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models is combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection using a novel entropy-controlled grey wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques is employed to initially incorporate the feature vector with a high level of information and subsequently eliminate redundant and irrelevant feature information. The efficacy of our design is substantiated through the evaluation on three benchmark dermoscopic datasets, namely PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation is conducted, including a rigorous comparison with established techniques in the field.

This study reports the design of a dielectric resonator (DR) metasurface-based terahertz biosensor for rapid and accurate detection of different kinds of viruses (H1N1, SARA-COV-2, etc.). The characterization of the sensor is done by analyzing its absorbance properties with and without the presence of the H1N1 virus layer of different concentrations and electrical properties in the frequency range of 2.4 THz to 3 THz. A full-wave EM model of the sensor is designed using CST MWS software. The geometrical parameters of the proposed biosensor, as well as its dielectric constant of the virus swab, are varied to achieve good performance with an absorption level close to 1 (100%). A significant change in the resonating pick-up frequency owing to the variation in the effective refractive index of the realized sensor ensures the detection and classification of the different concentrations of the same or different viruses. The presented design could be used for the rapid detection of viruses with the added advantages of contactless, label-free, and accurate identification of different concentrations of viruses.

Canola is the major oilseed crop of Canada. The de-oiled material is an important by-product due to its rich phenolic profile and high protein content. This co-processing stream from canola is primarily utilized as animal feed but represents an invaluable source of nutraceuticals. Microwave-assisted solvent extraction (MAE), as a green extraction method, has received considerable attention in recent times. The ease of use and application of many solvents at the same time makes the MAE one of the best methods for studying multiple solvents at the same time. The formation of canolol, from sinapine and sinapic acid, is primarily dependant on temperature which favors the decarboxylation reaction. Hence, MAE using green extractants can be used to enhance the yield of canolol. This study examined the effects of different pre-treatment temperature-time combinations of 140, 150, 160, and 170℃ for 5, 10, 15, 20 and 30 minutes on the extraction of canolol and other canola endogenous phenolic compounds. Three antioxidant assays assessed the antioxidant activity of the different extracts obtained by MAE confirming the microwave as a novel and versatile instrument for enhancing the yield of canolol. Improvements in the antioxidant activity of the different extracts further established the efficacy of the current method for isolating important natural phenolic derivatives for utilization by the nutraceutical industry.

Background Hokkaido is the northernmost, least populous, and coldest of the Japanese islands. It was the first prefecture to be affected by COVID-19, while Kanagawa is home to one of the most populous areas of Japan, namely the Tokyo metro area. The Japanese government responded early during the pandemic by identifying infected patients, contact tracing, and performing PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. The government has also been publishing information about each individual who tested positive for the virus. Both Hokkaido and Kanagawa started recording COVID-19 cases in the winter of 2020 and have detailed records of thousands of patients, thus providing an invaluable resource for the transmission and behavior of the virus. Methods The current study analyzed the COVID-19 registry data from the Hokkaido and Kanagawa prefectures. The Hokkaido registry contained 1,269 cases (674 (53%) females and 595 (47%) males) recorded between February 14 and July 22, 2020. The Kanagawa registry had 3,123 cases (1,346 (43%) females and 1,777 (57%) males. The final data contained a total of 4,392 cases (2,020 (46%) females and 2,372 (54%) males). By leveraging the information on viral transmission paths available in the registry data, we performed exponential random graph model (ERGM) network analysis to examine demographic and symptomological homophilies of the SARS-CoV-2 viral transmission networks. Results We observed age, symptomatic, and asymptomatic homophilies in both prefectures. Furthermore, those patients who contracted the virus through secondary or tertiary contacts were more likely to be asymptomatic than those who contracted it from primary infection cases. The transmission networks showed that transmission occurred significantly in healthcare settings, as well as in families, although the size of the networks was small in the latter. Most of the transmissions stopped at the primary and secondary levels and no transmission beyond quaternary was observed. We also observed a higher level of asymptomatic transmission in Kanagawa than in Hokkaido. Conclusions Symptom homophilies are an important component of COVID-19 and suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can thus result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Moreover, environmental variables such as temperature and humidity may also be playing an important role in the overall pathogenesis of the virus.

The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

Background: The Internet has become an essential component of college students' lives, serving as a tool for academic pursuits, daily activities, and social interactions with individuals and the global community. Productive internet use is associated with student success, while pathological internet use can negatively impact psychosocial performance. This study aimed to assess the impact of internet addiction on the cognitive function, mood, and physical activity of college students. Study design: The study was based on a correlational observational study design. Settings: The study’s participants were taken using a convenience sampling method from the Manav Rachna International Institute of Research and Studies campus, Faridabad, Haryana, having a student strength of more than two thousand. Methods: With a response rate of 69.93%, one hundred male and female college students who could understand the self-administered questionnaires and obtained the internet addiction test (IAT) scores equal to or above twenty participated (aged 18-25 years) in this study. The participants were assessed for the study’s outcomes, such as the internet addiction (viz. average, moderate, and excessive) cognition, mood, and physical activity level, using an internet addiction test questionnaire (IATQ), cognitive failure questionnaire (CFQ), the profile of mood state questionnaire (POMS), and international physical activity questionnaire (IPAQ), respectively. A categorical analysis with mean scores was carried out for all the outcomes. The correlation between internet dependency and cognitive abilities, emotional state, and levels of physical activity among the participants was ascertained using Pearson's correlation coefficient. The threshold of significance was set to 95%, meaning any p-value < 0.05 was considered significant. Results: The study’s outcome variable- internet addiction, revealed a moderate positive correlation with cognition (r=0.793; p=0.001) and mood (r=0.703; p=012), while a moderate negative correlation with physical activity (r=-0.681; p=003). Conclusion: The study concluded that internet addiction is positively linked to cognition and mood disruption; however, it negatively affects physical activities among college students. An awareness program should be executed on “Internet addiction and its effects on college students’ Cognition, Mood, and Physical Activity” and advocating Internet use in a limited manner for getting aroused, feeling better, and avoiding physical inactivity.

Global pandemic COVID-19 is in stage 4 of widespread local transmission in Bangladesh- the country which did not have a noteworthy health policy and legislative structures to combat COVID-19 like a pandemic. Early strategic planning and groundwork for evolving and established challenges are crucial to assemble resources and react in an appropriate timely manner. This article, therefore, focuses on the public perception of comparative lockdown scenario analysis and how they may affect the sustainable development and the strategic management regime of COVID-19 pandemic in Bangladesh socioeconomically. Response from 159 respondents was collected via a purposive sampling survey method through a questionnaire, and 54 statements were collected for scenario analysis. Datasets were analyzed through a set of statistical techniques including Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the lockdown scenarios and response strategies to be formulated. A strong significant positive relationship was observed between people who will start moving towards regular life and the formal and informal economic activities will be started in lockdown scenario 1(r=0.671, p<0.01). The scenario one describes how the death and infection rate will increase if Govt withdraw the partial lockdown before 40 to 50 days. Scenario 2 outlines people’s movement will enable low-level community transmission of COVID-19 with the infection and death rate will increase slowly (r=0.540, p<0.01). Moreover, there will be less supply of necessities of daily use with a price hike (r= 0.680, p<0.01). Scenario 3, full lock down will reduce the community transmission and death from COVID-19 (r=0.545, p<0.01). Moreover, along with the other problems gender discrimination and gender-based violence will increase rapidly (r=0.661, p<0.01). Form regression analysis, due to full lockdown, the formal and informal business, economy and education sector will be hampered severely (R=0.695), there was a strong association between the loss of livelihood and unemployment rate will increase due to business shutdown (p<0.01) and poor communities both in urban and rural areas will be affected severely (p<0.01).All these will further aggravate the humanitarian needs of the most vulnerable groups in the country in the coming months to be followed which needs to be dealt with proper response plans. It will undoubtedly affect the targets of global sustainable development goals (SDGs) of 2030 and all other development targets.

Advances in reconfigurable liquid-based reconfigurable antennas are enabling new possibilities to fulfil the requirements of more advanced wireless communication systems. In this review, a comparative analysis of various state-of-the-art concepts and techniques for designing reconfigurable antennas using liquid is presented. First, the electrical properties of different liquids at room temperature commonly used in reconfigurable antennas are identified. This is followed by a discussion of various liquid actuation techniques in enabling high frequency reconfigurability. Next, liquid-based reconfigurable antennas in literature used to achieve the different types of reconfiguration will be critically reviewed. These include frequency-, polarization-, radiation pattern- and compound reconfigurability. The current concepts of liquid-based reconfigurable antennas can be classified broadly into three basic approaches: altering the physical (and electrical) dimensions of antennas using liquid, applying liquid-based sections as reactive loads; and implementation of liquids as dielectric resonators. Each concept and their design approaches will be examined, outlining their benefits, limitations, and possible future improvements.

In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.

Purpose The gradual increase in geriatric issues and global imbalance of the ratio between patients and healthcare professionals has created a demand for intelligent systems with the least error-prone diagnosis results to be used by less medically trained persons and save clinical time. This paper aims at investigating the development of an image-based colourimetric analysis. The purpose of recognising such tests is to support wider users to begin a colourimetric test to be used at homecare settings, telepathology, etc. Design/methodology/approach The concept of an automatic colourimetric assay detection is delivered by utilising two cases. Training Deep Learning (DL) models on thousands of images of these tests using transfer learning, this paper i) classifies the type of the assay, and ii) classifies the colourimetric results. Findings This paper demonstrated that the assay type can be recognised using DL techniques with 100% accuracy within a fraction of a second. Some of the advantages of the pre-trained model over the calibration-based approach are robustness, readiness and suitability to deploy for similar applications within a shorter period of time. Originality/value To the best of our knowledge, this is the first attempt to provide Colourimetric Assay Type Classification (CATC) using DL. Humans are capable to learn thousands of visual classifications in their life. Object recognition may be a trivial task for humans, due to photometric and geometric variabilities along with the high degree of intra-class variabilities it can be a challenging task for machines. However, transforming visual knowledge into machines, as proposed, can support non-experts to better manage their health and reduce some of the burdens on experts.

The substrate N1, N3, N5 -tris(2-hydroxyphenyl)benzene-1,3,5-tricarboxamide (Sensor A) was prepared in the reaction of 1,3,5-benzenetricarboxylic acid (trimesic acid) and o-aminophenol in ethanol. The prepared organic sensor fulfills the chemiluminescent requirements including luminophore, spacer, and suitable binding receptor that distress the probe's luminescent features, providing selective and sensitive detection of mercury and iron ions in aqueous solutions. The sensor selectively detects mercury and iron ions in a water matrix containing various metal ions including sodium, calcium, magnesium, zinc, and nickel. Strong and immediate binding was observed between mercury ions and the substrate at pH 7.0 with a binding affinity toward Hg (II) enhanced by nine folds higher than that observed for iron sensor binding affinity, which makes the substrate a distinctive luminescence sensor for mercury detection at ambient conditions. The sensor shows a linear response toward Hg (II) in the concentration range from 4.2 x 10-5 to 2.0 x 10-8 M with a limit of detection of 1.0 x 10-8 M. Further, Sensor A provides linear detection for iron ions in the range from 1.5 x 10-3 to 1.5 x 10-8 M. The measured adsorption capacity of Sensor A toward mercury ions ranged from 1.25 to 1.97 mg/g and the removal efficiency from water samples reached 98.8% at pH 7.0. The data demonstrate that Sensor A is an excellent probe for detecting and removing mercury ions from water bodies.

Background: The medical workforce plays a pivotal role in advancing human health, particularly within the healthcare system of Saudi Arabia. While the government-employed healthcare providers form the central structure of the system and offer free healthcare services, the private healthcare sector is also witnessing significant growth. In parallel, the field of child neurology has experienced notable transformations in recent years, with continued expansion. This expansion brings forth a range of challenges for both current and future pediatric neurologists, necessitating careful consideration and proactive measures to address them. Aim of the study: This study aims to analyze the current challenges faced by the pediatric neurology workforce. Methods: In May 2023, a community-based exploratory study was conducted involving 82 practicing pediatric neurologists as well as pediatric neurology trainees across the 13 regions of Saudi Arabia. A comprehensive 26-item questionnaire, divided into four sections, was utilized to assess the participants. Results: The survey received responses from a total of 82 pediatric neurologists in Saudi Arabia (response rate 55.03%), with 38 (46.34%) being male and 44 (53.66%) being female. The mean age was 33±1.225 years. The majority of participants practiced in major cities such as Riyadh and Jeddah. Around two-thirds had published three or fewer research articles. Approximately 30% were enrolled in a fellowship program. Nearly 50% of pediatric neurologists experienced some form of delay in obtaining their first job, ranging from 1 to 36 months. Conclusion: The landscape of the pediatric neurology workforce is currently witnessing noteworthy demographic shifts. With a majority of practitioners concentrated in major cities, there exists an ongoing demand for qualified professionals in peripheral areas. The study underscores the real-life challenges faced by pediatric neurologists, particularly the delay in securing employment after graduation, and underscores the critical importance of addressing these persistent issues along the journey of pediatric neurology.

The development of quantum computers seeks software developers' attention regarding security in the era of Information Technology, software security is the primary goal for our quantitative assessment of software security in the development cycle of software. Security assessment of software is identifying the key security factors of the software. A security elective provides ex-tensive strategies and calculations to ensure product safety. The security assessment is the key factor in surveying, administering, and controlling security to further enhance the nature of safety. It should be acknowledged that assessing security early on in the development process is beneficial in identifying worms, hazards, flaws, and threats. The definition and portrayal of Quantum Computing (QC) in software security will be discussed in this study. Researchers use cryptography calculations to secure our financial institutions, medical devices, military weaponry, planes, ships, vehicles, and pilots. Here authors of this study use the Fuzzy Technique for Order Preference by Similarity to Ideal Situation (FTOPSIS) to quantitatively assess the weight/rank of the quantum enable security alternatives like (Diffie-Hellman key-exchange algorithm, Quantum key distribution algorithm, Deutsch-Jozsa Algorithm, Special Deutsch-Jozsa Algorithm, Grover’s Algorithm and Quantum key distribution algorithm in GHZ state) with security factors like (Confidentiality, Integrity, Authenti-cation, Privacy, Reliability, Maintainability, Authorization, Integrity, Possessions, and Availability). Additionally, they critically analyze and select the six alternatives of quantum-based security algorithms. The nature of safety infers the ability to execute a thing on time in this exploration study, specifically 'software security'.

Wireless Sensor Networks (WSN) are the core of Internet of Things and require cryptographic protection due to the increase number of attacks. Cryptographic methods for WSN should be fast and consume low power as these networks consist of battery-powered devices and constrained microcontrollers. NTRU, the fastest and secure public key cryptosystem, uses high degree polynomials, and is susceptible to the lattice basis reduction attack (LBRA). CPKC, proposed by NTRU authors, works on integers modulo $q$ and is easily attackable by LBRA since it uses small numbers for the sake of the correct decryption. Herein, RCPKC, a random congruential public key cryptosystem working on integers modulo $q$ is proposed, such that the norm of a two-dimensional vector formed by its private key is greater than $\sqrt{q}$. RCPKC works similar to NTRU, and it is a secure version of insecure CPKC. RCPKC specifies a range from which the random numbers shall be selected, and it provides correct decryption for valid users and incorrect decryption for an attacker using LBRA by Gaussian lattice reduction. Because of its resistance to LBRA, RCPKC is more secure. Simultaneously, due to the use of big numbers instead of high degree polynomials, RCPKC is about 24 (7) times faster in encryption (decryption) than NTRU. Also, RCPKC is more three times faster than the most effective known NTRU variant, BQTRU. Compared to NTRU, RCPKC reduces energy consumption at least seven times that allows increasing life-time of unattended WSN more than seven times.

In view of intensified disasters and fatalities caused by natural phenomena and geographical expansion, there is a pressing need for a more effective environment logging for a better management and urban planning. This paper proposes a novel utility computing model (UCM) for structural health monitoring (SHM) that would enable dynamic planning of monitoring systems in an efficient and cost-effective manner. The proposed UCM consists of network-attached data drive that stores data from SHM logger, population count system and Geographic Information System (GIS) enhanced with a Cloud IoT data backup, display, and analysis server. The UCM using this data and data from building information systems applies a simple machine learning algorithm to generate real-time structure health and suggests re-planning of SHM units. The health of structure varies dynamically with disturbances created by higher occupancy and structure density per zone. The proposed SHM-UCM is unique in terms of its capability to manage heterogeneous SHM resources. This was tested in a case study on Qatar University (QU) in Doha Qatar, where it looked at where SHM nodes are distributed along with occupancy density in each building. This information was taken from QU simulated occupation and zone calculation models and then compared to ideal SHM system data. Results show the effectiveness of the proposed model in logging and dynamically planning SHM.

BPA is a commonly used compound in many industries and has versatile applications in polycarbonate plastics and epoxy resins production. BPA is classified as endocrine-disrupting chemical which can hamper fetal development during pregnancy and may have long term negative health outcomes in humans. Dietary sources, main route of BPA exposure, can be contaminated by the migration of BPA into food during processing. The global regulatory framework for using this compound in food contact materials is currently not harmonized. This review aims to outline, survey, and critically evaluate BPA contamination in meat products, including level of BPA and/or metabolites present, exposure route, and recent advancements in the analytical procedures of these compounds from meat and meat products. The contribution of meat and meat products to the total dietary exposure of BPA ranges between 10 and 50% depending on the country and exposure scenario considered. From can lining materials of meat products, BPA migrates towards the solid phase resulting higher BPA concentration in solid phase than the liquid phase of the same can. The analytical procedure is comprised of meat sample pre-treatment, followed by cleaning with SPE, and chromatographic analysis. Considering several potential sources of BPA in industrial and home culinary practices, BPA can also accumulate in non-canned or raw meat products. Very few scientific studies have been conducted to identify the amount in raw meat products. Similarly, analysis of metabolites and identification of the origin of BPA contamination in meat products is still a challenge to overcome.

Work done by us shows that rough topology is a good tool for dealing with things that happen in the real world. The concept of ”basis” has been used to show out the deciding elements of the recent outbreak of COVID-19 that was reported all over the world.

Let $G$ be a group with identity $e$ and $R$ be a commutative $G$-graded ring with nonzero unity $1$. Graded semi-primary and graded $1$-absorbing primary ideals have been investigated and examined by several authors as generalizations of graded primary ideals. However, these three concepts are different. In this article, we characterize graded rings over which every graded semi-primary ideal is graded $1$-absorbing primary and graded rings over which every graded $1$-absorbing primary ideal is graded primary.

A cognitive radio sensor network (CRSN) based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and also different from the conventional SG that uses a static resource allocation technique to allocate resources to sensor nodes and communication devices in the SG network. Due to the challenges associated with competitive sensor nodes and communication devices in accessing and utilizing radio resources, the need for dynamic radio resource allocation (RRA) has been proposed as a solution for allocating radio resources to sensor nodes in a CRSN based smart grid ecosystem (network). These challenges include energy/power constraints, poor quality of service (QoS), interference, delay, spectrum efficiency issues, and excessive spectrum hand-offs. Hence, the optimization of resource allocation criteria, such as energy efficiency, throughput maximization, QoS guarantee, fairness, priority, interference mitigation/avoidance, etc., will go a long way in addressing the problems of RRA in a CRSN based SG. Consequently, this work explores RRA in CRSNs for SGs. Various resource allocation schemes, as well as its architecture in a CRSN for SG environment, are presented. The work reported in this paper introduces a model called the “guaranteed network connectivity channel allocation” for throughput maximization (GNC-TM) and optimal spectrum band determination in RRA for improved throughput criteria in CRSNs for SGs. The results show that the model outperforms the existing protocol in terms of throughput and error probability. Finally, the contribution to knowledge and future research direction, such as energy efficiency and hybrid energy harvesting schemes are highlighted.

This paper aims to unify q-derivative/q-integral and h-derivative/h-integral in a single definition, this will be called q−h-derivative/q−h-integral. These notions are further extended on finite interval [a,b] in the form of left and right q−h-derivatives and q−h-integrals. Some inequalities are studied for q−h-integrals which are directly connected with well known results. In diverse fields of science and engineering the theory based on q-derivatives/q-integrals and h-derivatives/h-integrals can be unified by using the concept of q−h-derivative/q−h-integral.

Aims: Angiotensin-converting enzyme 2 (ACE2) plays an important role in the entry of coronaviruses into host cells. This paper described how carnosine, a naturally occurring supplement, can be an effective drug candidate for coronavirus disease (COVID-19) on the basis of molecular docking and modeling to host ACE2 co-crystallized with COVID-19 spike protein. Methods: First, the starting point was ACE2 inhibitors and their structure-activity relationship (SAR). Next, chemical similarity (or diversity) and PubMed searches made it possible to repurpose and assess approved or experimental drugs for COVID-19. In parallel, at all stages, authors performed bioactivity scoring to assess potential repurposed inhibitors at ACE2. Finally, investigators performed molecular docking and modeling of the identified drug candidate to host ACE2 co-crystallized with COVID-19 spike protein. Results: Carnosine emerged as the best known drug candidate to match ACE2 inhibitor structure. Preliminary docking was more optimal to ACE2 than the known typical angiotensin-converting enzyme 1 (ACE1) inhibitor (enalapril) and quite comparable to known or presumed ACE2 inhibitors. Viral spike protein elements binding to ACE2 were retained in the best carnosine pose in SwissDock at 1.75 Angstroms. Out of the three main areas of attachment expected to the co-crystallized protein structure, carnosine bind with higher affinity to two compared to the known ACE2 active site. LibDock score was 92.40 for site 3, 90.88 for site 1, and inside the active site 85.49. Conclusion: Carnosine has promising inhibitory interactions with host ACE2 co-crystallized with COVID-19 spike protein and hence could offer potential mitigating effect against current COVID-19 pandemic.

Background: Eukaryotes whole-genome sequencing is crucial for species identification, gene detection and protein-annotation. Oxford Nanopore sequencing serves as an affordable and rapid platform for sequencing eukaryotes, however the relatively higher error rates require computational and bioinformatic efforts to produce more accurate genome assemblies. Here, we evaluated the effect of read correction tools on eukaryotes genome completeness, gene detection and protein-annotation. Methods: Reads generated by ONT of four eukaryotes, C. albicans, C. gattii, S. cerevisiae, and P. falciparum, were assembled using minimap2 and underwent three rounds of read correction using flye, medaka and racon. The generates consensus FASTA files were compared for total length (bp), genome completeness, gene detection, and protein-annotation by QUAST, BUSCO, BRAKER1 and InterProScan, respectively. Results: genome completeness was dependent on assembly method rather than read correction tool, however, medaka performed better than flye and racon. Racon significantly performed better than flye and medaka in gene detection, while both racon and medaka significantly performed better than flye in protein-annotation. Conclusion: We show that three rounds of read correction significantly affects gene detection and protein-annotation which are dependent on assembly quality in preference to assembly completeness.

Egyptians face a significant challenge in meeting the growing population's water needs for agriculture and food production. To address this, policymakers and researchers have implemented the National Great Project for Lining and Rehabilitation of All Open Canals of the Irrigation Network to reduce irrigation water losses through seepage, evaporation, and evapotranspiration. The current field study evaluates water losses from the Al Maanna canal network in Assiut governorate, Middle Egypt, using empirical formulas and field ponding methods. The obtained results indicated that the Moleth-Worth formula is more compatible and gives results that are in good agreement with the field measurements carried out. The estimated seepage losses from the existing, distorted Al Maanna canal network using the Moleth-Worth formula and field ponding method were about 2.07 and 2.20 million m3/month, respectively. Also, the calculated maximum evaporation and evapotranspiration losses were about 0.086 and 1.133 million m3/month, respectively. Consequently, the total water losses might be about 3.42 million m3/month, representing nearly 13.63% of the Al Maanna canal’s total discharge. After completing the lining process, seepage losses from lined canals were determined to be only about 0.472 million m3/month using the ponding method, while evaporation and evapotranspiration losses will be significantly decreased after canal rehabilitation. Hence, lining the Al Maanna canal network can decrease the total water losses by about 84%, which is sufficient to recommend and encourage the lining processes where the value of the gained quantities of water exceeds all the implementation expenses, in addition to other moral, cultural, and environmental gains.

Objective: To investigate commonalities and variations in the learning outcomes, curriculum content, assessment methods and competencies in undergraduate orthodontic curricula globally. Methods: This scoping review followed the updated methodological guidance proposed by the Joanna Briggs Institute and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A search on electronic databases PubMed, Scopus and Embase was conducted for the last 25 years. Google Scholar was used to identify eligible unpublished and grey literature. Results: The total number of reports identified was 231. After removal of 62 duplicates, 169 reports were included in the title and abstract screening. Finally, 17 studies were included in the review which included 13 cross-sectional surveys, three expert panel proceedings and one discussion paper. Marked variations were reported in undergraduate orthodontic curricula and competency assessments at the level of individual countries, regionally as well as globally. The challenges of imparting competency in orthodontic treatment during undergraduate dental education are also acknowledged. Conclusion: Lack of consistency in undergraduate orthodontic education is evidenced by several Delphi studies to develop consensus on orthodontic teaching in undergraduate programs. A common message emanating from the available studies on undergraduate orthodontic education seems to emphasize a focus on assessment and diagnosis of orthodontic treatment needs of patients and a basic understanding of contemporary treatment options to facilitate patient referral.

Portable Document Format (PDF) files are one of the most universally used file types. This has fascinated hackers to develop methods to use these normally innocent PDF files to create security threats via infection vectors PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF Malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine-learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PFD files from malware PFD files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern-inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight-accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at high detection performance and low detection overhead.

The purpose of this study is to present a systematic review of the literature on public blockchain consensus algorithms. Blockchain consensus algorithms have gain much popularity in last few years especially in the cryptocurrency field. Based on a systematic review of the relevant literature, we provide a classification of blockchain consensus algorithms, philosophy behind creation of blockchain consensus algorithms and as well as the rewards and incentive strategies of various public blockchain consensus algorithms. On the basis of these results, the research gaps and future work directions are identified for further study.

The residential built form, including open space, provides the physical environment for social interaction. Understanding urban open space, including semi-public and public domains, through the lens of physical accessibility and visual permeability can potentially facilitate the building of a sense of community contributing to a better quality of life. Using an inner-city suburb in Perth, Western Australia as a case study, this research explores the importance of physical accessibility patterns and visual permeability for socialising in semi-public and public domains, such as the front yard and the residential streets. It argues that maintaining a balance between public and private inter-relationship in inner city residential neighbourhoods is important for creating and maintaining a sense of community.

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

Asthma is a chronic respiratory disease that impairs breathing. Management of asthma presents a significant challenge due to its inherent variability; that is, its symptoms can substantially differ among individuals, thereby complicating the prediction and management of exacerbations. Furthermore, individuals with asthma often have unique triggers that precipitate symptoms or attacks. The identification of these triggers can often prove to be a challenging, and at times, an impractical attempt. To address this, our research proposes a practical, personalized alert system, predicated on individual lung function tests conducted under varying environmental conditions classified by air-quality sensors. To validate this concept, we conducted an observational pilot study involving healthy individuals. We recruited twelve healthy participants and monitored their responses across a broad spectrum of environments, characterized by varying air quality, temperature, and humidity conditions. The lung function for each participant, assessed using peak expiratory flow (PEF) values, was recorded in each of these environments. Our results highlighted substantial variability in pulmonary responses to different environments. Utilizing these insights, we proposed a personalized alarm system that provides real-time air-quality monitoring and issues alerts when environmental conditions may potentially become unfavorable. We also explored the feasibility of employing basic machine learning techniques to predict PEF values in the aforementioned environmental conditions. This proposed system has the potential to empower individuals in actively safeguarding their respiratory health and mitigating discomfort caused by environmental conditions, especially in cases of asthma patients. By enabling timely and personalized interventions, the system aims to provide individuals with the necessary tools to minimize exposure to asthma’s possible triggers.

The COVID-19 pandemic situation, disease intensity, weak healthcare facilities, unawareness, and misinformation led people to fear and anxiety in Bangladesh. This study intended to get peoples’ perception on psychosocial, socio-economic and environmental crisis amidst the pandemi. An online questionnaire was surveyed nationwide (respondents no.1066). Datasets were analyzed through the Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the psychosocial, socio-economic and environmental parameters. A significant association between fear of COVID-19 with struggling healthcare system (p<0.05) was found. Also, negative association between fragile health system and government’s ability to deal with the pandemic (p<0.05) revealing poor governance. Again, a positive association of shutdown and social distancing with fear of losing life, and due to lack of health treatment (p<0.05) reveals that shut down hampers normal activities which may lead to mental and economic stress. However, a positive association of socio-economic impact of the shutdown with poor people’s suffering, the price hike of basic need, hamper of formal education (p<0.05) may lead to severe socio-economic and health crisis. There is a possibility of climate-induced disaster during/after the pandemic, which will create severe food insecurity (p<0.01). Daily wage earners and poors will suffer most by food and nutritional deficiency, and the country may face huge economic burden. Proper risk assessment and communications is needed to alleviate fear and anxiety. Thus, financial support and mental boosting is required.

This study aimed to evaluate and compare the mechanical properties of three CAD-CAM monolithic esthetic zirconia (Ceramill® Zolid FX Multilayer (ZF), IPS e.max® ZirCAD MT Multi (ZM), and KATANATM STML (KS)) with a CAD-CAM lithium disilicate glass-ceramic (IPS e.max® CAD LT (MC)) as control. 80 samples (20/group) were milled, sintered, and subjected to a three-point bending test (3-PBT) to measure flexural strength and elastic modulus; Vickers indentations were used to measure microhardness. The numerical outcomes were analyzed using one-way ANOVA with a statistical significance set at <0.05. Zirconia showed superior mechanical properties compared with lithium disilicate (P<0.05). ZM had the highest values of flexural strength (P<0.05), KS and ZM showed significantly higher elastic modulus values than ZF (P<0.05), and ZF and KS microhardness values were significantly higher than ZM (P<0.05). Monolithic multi-layered zirconia had enhanced mechanical properties when compared with lithium disilicate and can be a predictable replacement in clinical applications.

The rising importance of Big Data in modern information analysis is supported by vast quantities of user data, but it is only possible to collect sufficient data for all tasks within certain data-gathering contexts. There are many cases where a domain is too novel, too niche, or too sparsely collected to adequately support Big Data tasks. To remedy this, we have created ADG Engine that allows for the generation of additional data that follows the trends and patterns of the data that’s already been collected. Using a database structure that tracks users across different activity types, ADG Engine can use all available information to maximize the authenticity of the generated data. Our efforts are particularly geared towards data analytics by identifying abnormalities in the data and allowing the user to generate normal and abnormal data at custom ratios. In situations where it would be impractical or impossible to expand the available dataset by collecting more data, it can still be possible to move forward with algorithmically expanded data datasets.

Residents’ perceptions on ecosystem services (ESs) provided by urban green spaces (UGSs) are vital for developing sustainable cities. However, there has been limited research on this issue in Malaysia. The aim of this paper is to assess Malaysians’ perceptions on ESs provided UGSs and how their perceptions are influenced by different socio-demographic factors. We used a structured online questionnaire and obtained 645 responses on 16 ESs from regulatory, cultural, and supporting ES categories. Descriptive statistics and binary logistic regressions were used for data analysis. In general, city dwellers have high positive perceptions on ESs provided by UGSs. Their perceptions for global climate regulation, noise buffering, pest regulation, spiritual, and research and education services would likely to significantly increase as they grow older. Households with moderate income have significantly greater perceptions of microclimate regulation, air filtration and aesthetic services. Having non-tertiary level education would significantly lower their perceptions of global climate and microclimate regulation, spiritual, aesthetics, and recreational services. Tertiary educated residents with non-environmental degrees also showed similar associations. Infrequent visits to UGSs would likely to significantly reduce the perception of global climate regulation, noise buffering, runoff reduction, erosion control, spiritual, and aesthetics services. People living far (>5km) from the UGSs also have significantly lower perceptions of microclimate regulation, air filtration services. Suggestions are made to incorporate ESs related content in pre-university curriculum and establishing small parks in neighborhoods to improve knowledge and uses of UGSs. These findings would assist urban planners designing environmentally sound urban policies and thereby improving human wellbeing in the cities.

Nowadays, the Internet of Things (IoT) devices and applications have rapidly expanded worldwide due to their benefits in improving the business environment, industrial environment, and people's daily lives. However, the IoT devices are not immune to malicious network traffic, which causes potential negative consequences and sabotages IoT operating devices. Therefore, developing a method for screening network traffic is necessary to detect and classify malicious activity to mitigate its negative impacts. Therefore, this research proposes a predictive machine learning model to detect and classify network activity in an IoT system. Specifically, our model distinguishes between normal and anomaly network activity. Furthermore, it classifies network traffic into five categories, normal, Mirai attack, denial of service (DoS) attack, Scan attack, and man-in-the-middle (MITM) attack. Five supervised learning models were implemented to characterize their performance in detecting and classifying network activities for IoT systems. This includes models shallow neural networks (SNN), decision trees (DT), bagging trees (BT), support vector machine (SVM), and k-nearest neighbor (kNN). The learning models were evaluated on a new and broad dataset for IoT attacks, the IoTID20 dataset. Besides, a deep feature engineering process was applied to the dataset to improve the accuracy of the learning models. Our experimental evaluation exhibited an accuracy of 100% recorded for the detection using all implemented models and an accuracy of 99.4%-99.9% recorded for the classification process.