CCNs are specific type of matricellular proteins, which are essential signaling molecules, and play multiple roles in multicellular eukaryotes. This family of proteins consists of six separate members in mammals. The architecture of CCN proteins is multimodular and comprises four distinct motifs. CCN proteins achieve their specific physiological functions by binding to integrin receptors. The CCN family has been implicated in both cure and disease with impacts on biological interactions, such as cell adhesion, chemotaxis and migration, mitogenesis, cell survival, angiogenesis, differentiation, tumorigenesis, immune functions, chondrogenesis, and wound healing. Breast cancer is the most commonly diagnosed cancer worldwide and the leading cause of cancer mortality among women triggered by atypical expression of CCNs. A favorable or unfavorable association between various CCNs has been reported in patients with breast carcinomas. Aberrant expression of CCN1 intensifies the proliferation of epithelial cells that line the lobes and ducts of the breast. Evidence also shows that the expression of CCN2 can ameliorate tumor growth and metastasis. However, CCN3 (NOV), CCN5 (WISP-2), and CCN6 (WISP-3) are consistent with neoplastic development and metastasis repression. Particular CCN members can develop tumors and cancer progression, whereas others can competitively counter the processes. Several studies have been conducted on CCN proteins and cancer in recent years. In our study, we intend to provide an overview of those research works while keeping breast carcinoma on focus. We believe that the importance of the CCN protein family in breast cancer should be reconsidered.

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.

Due to an increase of the monocrystalline PV systems installations, for a drive towards the sustainable smart cities in the hot arid developing country such as Pakistan, pose challenges of the performance and degradation issues. Monocrystalline PV module efficiencies are declining and damaging under the continuous exposure to higher surface day-time temperatures in the different parts of the country. A MATLAB simulations were performed based on the validated mathematical approach. This paper investigates the hot arid surface temperature impacts on the performance of PV modules during the summer and winter seasons in Pakistan. The investigations are performed examining the power generating efficiency of the PV system. This paper also investigates the influence of installations of PV-system in the North, South, East and West regions of Pakistan. It was examined that the northern areas of Pakistan are more suitable for maintaining the long-term durability of the PV system. Investigations are performed for the peak summer and peak winter days. During summer months, cooling strategies have to be implemented to overcome the heating effects whilst reducing degradation effect on installed PV-system.

Delivery of drugs through the nasal route provides an alternative, yet highly efficacious modality for administering both large and small pharmaceutic molecules. This method proves particularly advantageous for drugs that are unstable during oral administration, as they undergo significant degradation in the gastrointestinal tract or metabolization through the first-pass effect in the liver. Nasal administration stands as a viable alternative to parenteral therapy and is also suitable for long-term medical management. The nasal mucosa, characterized by high vascularity and permeability, facilitates rapid pharmaceutical absorption, ensuring a swift onset of therapeutic action. Despite its noninvasive nature, the nasal delivery technique finds broad applicability for both local treatment and systemic therapy, as the drug directly enters systemic circulation. This article explores the feasibility of a novel spray delivery mechanism for intranasal drug deposition. The study aims to determine whether users can correctly follow the state-of-the-art application instructions for a nasal spray with an optimal nozzle placement angle (namely, 12-15°) at the nostril while maintaining sufficient safety and efficacy, and without engaging in preventable usage errors that could harm the user. Additionally, the study evaluates the effectiveness of device labeling, instructions for use, and a short video in supporting the correct application of the nasal spray at the optimal angle (12-15°) and assesses whether users can differentiate the product from others with similar appearances. We have also performed physical spray tests to experimentally evaluate drug penetration to the nasopharynx (a key target site for viral infections) for two different nozzle placements – one for current package instructions coming with over-the-counter spray products and the other for the optimal spray usage procedure to enhance targeted drug delivery. Human Factors (HF) validation studies were also conducted, instructing users to follow the application instructions of the nasal spray bottle with the nozzle placed at the optimal angle to target the nasopharynx. The experimental tests consistently confirmed improved penetration with the 12-15° nozzle placement, with visual cues proving effective when used in tandem with written instructions for guiding the study participants to adhere to the recommended usage procedures.

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.

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.

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

Long-term durability of the vacuum edge-seal plays a significant part in retrofitting triple vacuum glazing (TVG) to existing buildings in achieving towards zero-energy buildings (ZEB) target. Vacuum pressure decrement with respect to time between panes affect the thermal efficiency of TVG. This study reports a 3D finite element model, with validated mathematical methods and comparison, for the assessment of the influence of vacuum pressure diminution on the thermal transmittance (U value) of TVG. The centre-of-pane and total U values of TVG calculated to be 0.28 Wm⁻²K⁻¹ and 0.94 Wm⁻²K⁻¹ at the cavity vacuum pressure of 0.001 Pa. The results suggests that a rise in cavity pressure from 0.001 Pa to 100 kPa increases the centre-of-pane and total U values from 0.28 Wm⁻²K⁻¹ and 0.94 Wm⁻²K⁻¹ to 2.4 Wm⁻²K⁻¹ and 2.58 Wm⁻²K⁻¹, respectively. The temperature descent on the surfaces of TVG between hot and cold sides’ increases by decreasing the cavity vacuum pressure from 50 kPa to 0.001 Pa. To maintain the cavity vacuum pressure of 0.001 Pa for over 20 years of life span in the cavity of 10 mm wide edge sealed triple vacuum glazing, non-evaporable getters will maintain the cavity vacuum pressure that will enable the long-term durability to TVG.

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.

A flat-plate unglazed solar water heater (SWH) with polymer thermal absorber was developed and tested. Polymer thermal absorber as a viable alternative to metal thermal absorber that is found in most SWH systems. The performance of this polymer SWH system is measured based on inlet and outlet water temperature, water flowrate, ambient air temperature and solar irradiance. The polymer thermal absorbers are hollow Polyvinyl Chloride (PVC) pipes with 20mm external diameter and 3 mm thick, painted in black to enhance radiation absorption. The pipes are arranged in a rectangular spiral inward-outward opposing-flow (RSioof). The collector pipes are placed in a 1 m2 square enclosure with bottom insulation and reflective surface for better radiation capture. Water circulation is a closed loop with a 16-liter uninsulated storage tank, driven by a pump and controlled by valves to maintain a mass flow rate of 0.00031 to 0.00034 kg/s. The test was conducted under partially clouded sky from 9 am to 5 pm, with solar irradiance between 105 and 1003 W/m2 and 27-36oC ambient air temperature. The SHW produced outlet hot water at 65oC by mid-day and maintained the storage temperature at 63oC until the end of test duration. Photo-thermal energy conversion was recorded with 23% maximum value. Results indicate that a flat plate solar water heater with polymer thermal absorber in RSioof design can be an effective alternative to the metal thermal absorber solar water heater and can be further optimized with glazing and tube sizing.

Bangladesh has a history of species introduction from different geographic regions. The country was a major trade route during the early-modern era and was under British colonial rule until 1947s. Many species of plants and animals are either domesticated or cultivated at different times that were brought into the country by the settlers, seamen, and traders. The deliberate preferences of fast-growing, high-yielding exotics in recent decades also threaten the existence of native species and their genetic resources in the country. Here we provide an overview of the invasive alien species in Bangladesh, likely pathways of their introduction, their impacts on ecosystem and people, and strategies for their effective management and regulation. Many exotic plants and animals both terrestrial and aquatic have found to be invasive in the country with negative impacts on local ecosystems, biodiversity, and livelihoods. Collectively, those species possess serious threats to country’s agriculture, forestry, and fisheries sector. Although initially introduced to favor primary productivity or other environmental benefits, many of these are now regarded as obnoxious pest or weed in Bangladesh. A comprehensive list of invasive species both for native and exotics and a framework to characterize them is also absent in the country. We recommend a separate statutory body and appropriate rules and policies for the introduction, monitoring, and management of alien species in the country. Community awareness, advocacy, surveillance, capacity building of relevant government staff and agreement with neighboring countries for transboundary management of invasive alien species is also necessary.

Thresholding is considered as a statistical-decision making theory which can lessen the average error incurred in allocating pixels to two or more groups. The traditional Bayes decision rule can be applied with the prior knowledge of the Probability Density Function (PDF) of each class. It is surmised that a threshold resulting in the best class separation is the optimal one. In this paper, Otsu’s thresholding for image segmentation has been implemented. The well-known Otsu’s method is to learn a threshold that can maximize the between-class variance or equivalently make light of the within-class variance of the entire image. At first, a color image of a tree is taken. After that, the image is transformed into a grayscale image. Then in the first part, two-level thresholding is conducted, and later on, three-level thresholding is also applied. Again, two-level thresholding, as well as three level thresholding, are also applied to some other images. Finally, the comparison is made between two level thresholding and three level thresholding

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.

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.

This study provides the bibliographical analysis of the articles produced by various authors from the year 1965 to 2017. Bibliometric approach is used to analyze data that is provided through standard statistical calculations. Findings indicated that, the highest rate of researches were produced during 1980s, with the average publication of 28.6 articles each year. Findings further indicated that the ratio of male population was high in producing articles, where most of the articles were based on single authorship. The analysis of the trend in terms of article type indicated researchers high level interest in producing biographies during 90s, which was then shifted to the production of articles related to library management after the beginning of 2000. Following the findings of this study, it is suggested to encourage researchers to explore diverse topics that lacked the interest of different authors.

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 study introduces a novel nanocomposite coating composed of PANI/CeO2 nanocomposite films, aimed at addressing corrosion protection needs. Analysis through FTIR spectra and XRD patterns confirms the successful formation of the nanocomposite films. Notably, the PANI/CeO2 nanocomposite films exhibit a hydrophilic nature. The bandgap energy of the PANI composite film measures at 3.74 eV, while the introduction of CeO2 NPs into the PANI matrix reduces the bandgap energy to 3.67 eV. Furthermore, the electrical conductivity of the PANI composite film is observed to be 0.40 S.cm-1, with the incorporation of CeO2 NPs leading to an increase in electrical conductivity to 1.07 S.cm-1. To evaluate its efficacy, electrochemical measurements were conducted to assess the corrosion protection performance. Results indicate a high protection efficiency of 92.25% for the PANI/CeO2 nanocomposite film.

Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced classification model based on One class SVM (OCSVM) classifier that can identify any deviation from the normal memory dump file patterns and detect it as malware. The proposed model integrates OCSVM and Principal Component Analysis (PCA) for increased model sensitivity and efficiency. An up-to-date dataset known as “MALMEMANALYSIS-2022” was utilized during the evaluation phase of this study. The accuracy achieved by the traditional one-class classification (TOCC) model was 55%, compared to 99.4% in the one-class classification with PCA (OCC-PCA) model. Such results have confirmed the increased performance achieved by the proposed model.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

This paper addresses the pressing concern of corporate tax evasion recognized by several nations, employing insights from public choice theory and tax compliance theory. The research aims to evaluate the ramifications of corporate tax evasion on public revenue, public expenditures, tax rates, and the prevailing budget deficit. Methodologically, the study utilizes questionnaire data and employs structural equation modeling for analysis. The findings indicate that tax evasion is significantly influenced by multiple factors, encompassing tax income, public expenditures, tax rates, and the budget deficit. The article offers a comprehensive exploration of both theoretical frameworks and practical implications, contributing valuable insights to the ongoing discourse on corporate tax evasion and its multifaceted impact on fiscal dynamics.

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.

The goal of the current study was to discover a novel potential probiotic strain of Lactobacillus spp. with anti-Escherichia coli activity from locally produced yogurt in Tongi, Gazipur, Bangladesh, compare its antagonistic activity with a commercial probiotic mixture of several strains, and approve a novel method for confirming the viability and relative abundance of the microbial community in a probiotic mixer. We carried out 16S sequencing, 16S metagenomics, Transmission Electron Microscopy (TEM) analysis, and other in vitro laboratory experiments to reach this objective. The strain TY-11 was identified as Lactobacillus delbrueckii subsp. indicus (16S sequence accession number OQ652026). It was gram-positive, anaerobic, lactose fermenting, and round-ended rod that typically measured 0.7 to 1.3 µm by 2.2 to 9 µm. In addition to having seven probiotic characteristics, it also showed an antagonistic impact on six different pathogens, but what's more noteworthy is that E. coli was the pathogen it inhibited most strongly (inhibition zone diameter was 18.88±0.18 mm). The most important and ground-breaking finding of this work was determining the probiotic features of a new probiotic strain, TY-11, whose antibacterial activity was virtually as effective as that of the probiotic combination with three different strains. Furthermore, the results of 16S high throughput sequencing and the conventional plate count method demonstrated a strong correlation (0.999) at the genus level, indicating that the use of both of these approaches in combination may be a practical way to assess the relative abundance of the microbial community and their viability in commercially available probiotic blends.

The paper portrays predictive models for dissolved oxygen (DO) levels in an urban lake using common water quality parameters like Temperature, pH, Conductivity and ORP at a time. Data were sampled using three real-time, industry-standard sensors, OPTOD, CTZN, and PHEHT, and then interpolated using the ArcGIS kriging technique. Correlation studies were analyzed through the ML algorithm, the correlation study signified a highly positive correlation between DO and other water parameters and the model was corroborated by R-score in order to create the linear regression model. In addition, an artificial neural network- a machine learning method using the Levenberg-Marquardt algorithm was developed to build a model to predict the do as well. Then, the performance of the models was validated and also the R² accuracy was checked of the predicted data against the actual data. Thus, the appropriateness of the ANN model for the forecasting of investigated attributes is indicated by the fact that the discrepancy between the forecasted and real ANN model is significantly lesser than that of the regression model. However, the model can be used to reveal DO data from unknown urban lake water.

This paper is purposed to delineate the current situation around Bangladesh as well as impacts of Coronavirus Disease-2019 (COVID-19) around the country and how the people over here are encountering this threatening pandemic. COVID-19 is an international epidemic that has got rapid wide-spread throughout different countries of the world to date. After its first outbreak in China different continents started to make sense and get aware against COVID-19 though, due to its special strategy of transmission several countries have been quite deteriorated preventing it. All the countries are moving at their best forward to find out any solution so that whole world could get rid of this horrifying situation as soon as possible. The authors here have reported an overview of how the outbreak of COVID-19 had put its commence in Bangladesh and to date how people over here have been tracing the way to tackle this havoc. Also, the changes that have brought around due to the crisis have offered us some fertile lessons that are enunciated here by the authors. To conclude, special considerations are anticipated to be highlighted pertaining COVID-19 outbreak in Bangladesh.

This paper addresses the conceptual exploration of various issues that form an integral part of the ethical dimensions surrounding the use of artificial intelligence (AI) in the field of marketing. We critically review some of the main ethical challenges AI poses to humanity: privacy, data security, bias, transparency, and accountability. Therefore, we will discuss the delicate balance between exploiting the transformation potential of AI for personally designed marketing strategies and ethical imperatives to safeguard the rights of consumers to retain trust. Then, the present regulatory landscape responding to these ethical challenges will be assessed by building on effective regulation, like the GDPR, existing proposed legislative frameworks, and industry guidelines. To underscore the role of an ongoing dialogue between marketers, technologists, ethicists, and regulators for developing a responsible AI ecosystem in marketing. Further, strategic recommendations for implementing company-based ethical AI marketing practices. The further strong guidelines provide enhancement transparency to consumers and investment in research to remove biases from algorithms. It also underscores principal scopes of further research and development, focusing on the need for new-age solutions to guide the ethical quagmires that AI-led marketing throws up. We suggest that integration should be done proactively and collaboratively. Thus, if attention is paid more to ethics and multidisciplinary dialogue, only such hyperbolic promises can be realized by AI-driven marketing. There are such promises through which AI-driven marketing will meet business goals and be huge at societies' values.

Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven Machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

This work proposes a hollow-core photonic crystal fiber-based edible oil sensor in the terahertz (THz) range (e.g., 1.0THz ≤ f ≤ 3.0THz) and different sensing characteristics are numerically analyzed. The suggested sensor’s performance was assessed by means of COMSOL Multiphysics, a commercial program that uses the finite element approach. The computational results indicate that the relative sensitivity is 85.591%, 84.648%, 82.625%, 82.683%, and 79.161%, respectively, at f = 2.2 THz, for several types of sunflower oil, mustard oil, coconut oil, olive oil and palm oil; and the corresponding effective areas are 7.22×10-8 um2, 7.09×10-8 um2, 6.83×10-8 um2,7.09×10-8 um2, 6.5231 ×10-8 um2. In addition, the effective material loss for sunflower oil, muster oil, coconut oil, olive oil, and palm oil has been found to be 0.02561 cm-1, 0.027054 cm-1, 0.030322 cm-1, 0.028854 cm-1 ,0.035427cm-1 respectively. Moreover, the proposed sensor also has low confinement loss are 1.55×10-8 dB/m, 1.63×10-8 dB/m, 1.31×10-8 dB/m, 1.99×10-8 dB/m, 4.0345×10-8 dB/m.This proposed sensor can be fabricated using extrusion and 3D-printing technologies, and due to its augmented detecting capabilities, it can be a vital part of oil sensing devices implemented in real life such as industry fields.

This paper reports on the optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation systems for cattle farming in rural areas of Bangladesh. Bangladesh is a lower middle-income country with declining rates of poverty among its 160 million people due to persistent economic growth in conjunction with balanced agricultural improvements. Most of the rural households adopt a mixed farming system by cultivating crops and simultaneously rearing livestock. Among the animals raised, cattle are considered as the most valuable asset for the small/medium-scale farmers in terms of their meat and milk production. Currently, along with the major health issue, the COVID-19 pandemic is hindering the world’s economic growth and has thrust millions into unemployment; Bangladesh is also in this loop. However, natural disasters such as COVID-19 pandemic and floods, largely constrain rural smallholder cattle farmers from climbing out of their poverty. In particular, small and medium-scale cattle farmers face many issues that obstruct them from taking advantage of market opportunities and imposing a greater burden on their families and incomes. An appropriate measure can give a way to make those cattle farmers’ businesses both profitable and sustainable. Optimization of thin-film coating assisted self-sustainable off-grid hybrid power generation system for cattle farming is a new and forward-looking approach for sustainable development of the livestock sector. In this study, we design and optimize a thin-film coating assisted hybrid (photovoltaic-battery-generator) power system by using the Hybrid Optimization of Multiple Energy Resources (HOMER, Version 3.14.0) simulation tool. An analysis of the results has suggested that the off-grid hybrid system is more feasible for small and medium-scale cattle farming systems with long-term sustainability to overcome the significant challenges faced by smallholder cattle farmers in Bangladesh.

Achieving Universal Health Coverage (UHC) is a strategic objective of the Jordanian Government and has been prioritized in its strategies and plans. However, there are several challenges affecting primary health care in Jordan and the health system in general that prevent Jordan from achieving UHC. This paper highlights the importance of team-based care in the form of Family Health Teams (FHTs) to realize Jordan’s goal of achieving UHC. FHTs are a team-based approach that brings together diverse professionals to provide a comprehensive, efficient, patient-centered primary care system that meets the changing needs of Jordan's population and refugees. However, the implementation of FHT may encounter obstacles, including individual, organizational and institutional, and external barriers. To overcome such obstacles, several actions and processes need to be taken, including political commitment and leadership, implementing good governance and policy frameworks, allocating resources and funding, multisectoral collaboration, and engagement of communities and stakeholders. The successful implementation of FHTs requires participation from government officials, parliamentarians, civil society, and influential community, religious, and business leaders. A strategic policy framework, effective oversight, coalition building, regulation, attention to system design, and accountability are also essential. In conclusion, adopting the FHT approach in Jordan's Primary Health Care system offers a promising path towards achieving UHC, improving healthcare access, quality, and efficiency while addressing the unique challenges faced by the country's healthcare system.

Objective: SARS-Cov-2 infection or COVID-19 is a global pandemic. From the time of identification to till, multiple clinical symptoms and parameters have been identified by the researchers of various countries and regions regarding the diagnosis and presentations of COVID-19 disease. In this manuscript, we investigated the primary symptoms and basic hematological presentations of SARS-CoV-2 infection among the Bangladeshi patients. Methodology: We have collected the disease history of mild to moderate degree of COVID-19 patients; hematological and biochemical on admission reports of moderate degree COVID-19 patients. All of them were tested positive for SARS-CoV-2 by RT-PCR in different institutes in Bangladesh. Results: According to this study though COVID-19 patients in Bangladesh commonly presented with fever, cough, fatigue, shortness of breath, and sore throat, but symptoms like myalgia, diarrhea, skin rash, headache, Abdominal pain/cramp, nausea, vomiting, restlessness, and a higher temperature of >100⁰F have a greater presentation rate and more frequent than other published studies. CRP and Prothrombin time was found to increase in all the patients. Serum ferritin, ESR, SGPT, and D-Dimer were found increased among 53.85%, 80.43, 44%, and 25% patients respectively. 17.39% of the patients had leukocytosis and neutrophilia. 28.26% of patients presented with lymphocytopenia. 62.52% of patients had mild erythrocytopenia. Conclusion: Despite some similarities, our study has evaluated a different expression in presenting symptoms in the case of COVID-19 patients in Bangladesh. CRP, Prothrombin time, serum ferritin, ESR, SGPT, D-Dimer, erythrocytopenia, and lymphocytopenia can be initial diagnostic hematological findings and assessment for prognosis COVID-19 disease. Also, gender variations have a different scenario of clinical and laboratory appearance in this region.

Bangladesh's railway system mostly uses typical manual railway crossing technique or boom gates through its 2,955.53 km rail route all over the country. The accidents are frequently happening in the railway crossings due to not having obstacle detectable and quickly operating gate systems, and also for fewer safety measures in the railway crossing. Currently, there are very few automatic railway crossing systems (without obstacle detectors) available, however, all of them are dependent on the national power grid without a backup plan for any emergency cases. Bangladesh is still running a bit behind in the power generation of its consumption, hence it is not possible to have a continuous power supply at all times all over the countryside. We aim to design and develop a smart railway crossing system with an obstacle detector to prevent common types of accidents in the railway crossing points. We design to use two infrared (IR) sensors to operate the railway crossing systems which will be controlled by the Arduino Uno. This newly designed level crossing system will be run with the help of sustainable renewable energy which is cost-effective, eco-friendly, and apply under the national green energy policy towards achieving sustainable development in Bangladesh as a part of the global sustainable goal to face climate change challenges. We have summarized the simulated results of several renewable energy sources including a hybrid system and optimized the Levelized Cost of Energy (LCOE), and the payback periods.

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Many researches have been conducted to detect it from MRI images through various deep-learning approaches. However, the problem of the availability of medical data and the privacy of the patients still exists. To mitigate this issue in Alzheimer’s disease detection, we have implemented the federated approach which is found to be more efficient, robust, and consistent compared to the conventional approach. For this, we needed deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilized the publicly available two datasets (OASIS, and ADNI), and designed various cases to evaluate the performance of the federated approach. In almost all of them, the federated approach achieved better accuracy and sensitivity. In distinguishing between two classes equally, the capability of the models was better in the found model when trained with federated approach compared to the conventional approach. In this approach. MobileNet, which is a low-cost transfer learning architecture, achieved the highest 95.24%, 81.94%, and 83.97% accuracy in OASIS, ADNI, and merged (ADNI+OASIS) test set, which was way higher than the achieved performance in Conventional approach. Moreover, in this approach, only the weights of the model were shared which keeps the original MRI images in their respective hospital or institutions, mitigating the privacy concern in the healthcare domain.

Like the most of crops, especially sugarcane needs to be kept upright until it is harvested. Because, lodging of sugarcane has a significant negative effect on cane yield and sugar content of sugarcane. To keep sugarcane upright earthing up is an utmost intercultural operation. In Bangladesh, most of the operations for sugarcane cultivation including earthing up are generally done in traditional method using human labor which increases production costs as well as reduces the income of sugarcane growers. So, a cost effective two-wheeled tractor (2WT) mounted earthing up machine was developed in Bangladesh Sugarcrop Research Institute (BSRI), Pabna to reduce drudgery and cost of sugarcane production. Field tests were conducted in experimental sugarcane field at BSRI and technical and economic performances of the developed earthing up machine were also carried out based on the field test. Average effective field capacity and field efficiency were found 0.16 ha/hr and 77.41 %, respectively for the developed earthing up machine. The earthing up machine was not found economically viable when it is used only for earthing up operation. Besides, when the 2WT was being used as the main driver for other activities including earthing up operation, then the earthing up machine becomes economically beneficial with net cash flow, net present value, internal rate of return, benefit-cost ratio and payback period of BDT 148497/ha, BDT 23184, 3%, 3.81:1 and approximately 1 year, respectively. On the contrary, considering cost of only earthing up tool without 2WT, then it was found economically beneficial with net cash flow, internal rate of return, net present value, benefit-cost ratio and payback period of BDT 16428/ha, BDT 3053, 4.7%, 2.71:1 and approximately 2 years, respectively. Since 2WT is commonly used for versatile farming purposes. Therefore, versatile use of 2WT as prime mover for other machines including earthing up machine can make earthing up machine economically viable and beneficial for sugarcane growers.

Introduction. Streptococcus pneumoniae infections are a major cause of mortality and morbidity worldwide. In Jordan, pneumococcal conjugate vaccines (PCVs) are not included in the national vaccination program. Due to the current availability of several PCVs including PCV-10, PCV-13 and PCV-15, along with PCV-20, currently undergoing pediatric approvals globally, the decision to introduce PCVs and their selection should be based on valid local data on the common serotypes of Streptococcus pneumoniae. Methods: This cross-sectional study aimed to identify the frequency of serotypes of Streptococcus pneumoniae in children aged below 5 years hospitalized with invasive pneumococcal diseases (IPD) including pneumonia, septicemia and meningitis during the study’s duration in representative areas of Jordan. Serotyping for culture-positive cases was based on the capsular reaction test, known as the Quellung reaction. qPCR was conducted on blood samples of patients with lobar pneumonia identified via X-ray or on cerebrospinal fluid for those with a positive latex agglutination test for Streptococcus pneumoniae. Results. This study was based on the analysis of the serotypes of 1015 Streptococcus pneumoniae cases among children younger than the age of 5; 1006 cases with pneumonia, 6 cases with meningitis, and 3 cases with septicemia. Only 23 culture-positive cases were identified, in comparison to 992 lobar pneumonia cases which were PCR-positive but culture-negative, with a PCR positivity rate of 92%. Serotypes 6B, 6A, 14 and 19F were the most common serotypes identified in the study, with prevalence rates of 16.45%, 13.60%, 12.12% and 8.18%, respectively. PCV-10, PCV-13, PCV-15 and PCV-20 coverage rates were 45.32%, 61.87%, 64.14% and 68.47%, respectively. Discussion: To the best of our knowledge, this is largest prospective study from the Middle East and one of the largest studies worldwide showing the serotypes of Streptococcus pneumoniae. It reveals the urgency for the introduction of PCV vaccination in Jordan, utilizing recently developed vaccines with broader serotype coverage.

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.

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.

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.

As an uncommon cause of spinal cord infarction, anterior spinal cord syndrome can manifest with motor paralysis, loss of pain, and temperature sensation distal to the site of the lesion. The main pathogenesis of this syndrome is the disruption of blood flow in the anterior spinal artery. Mortality and morbidity differ with the etiology of the syndrome. So knowing the etiology of blood flow disruption is essential for patient management. This review article highlights the important clinical manifestation of Anterior spinal artery syndrome. Also describes etiology, pathogenesis, diagnosis, prognosis, possible management, and complications.

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).

One of the major concerns in structural health management (SHM) is the early detection of a growing crack. Using this, future damage due to crack propagation can be mitigated or eliminated by implementing proper repair and maintenance. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks. The research presented in this paper focuses on SHM using AE. A novel AE signal analysis approach was proposed in order to detect crack initiation and assess small crack lengths. Experimental investigation indicated that initiation of a crack could be identified through the statistical analysis of the resulting features of the AE signals. A probabilistic AE-based model for small fatigue crack sizing was developed and the uncertainties of the model were estimated. In addition, a probabilistic model validation approach was implemented to confirm accurate estimation of the results. The outcome of this research can be used to evaluate the integrity of structures under fatigue loading. The proposed approach can also be applied as an approach to manage health and assess prognosis of structures.

Coarse-grained molecular dynamics simulations are employed to investigate the spatiotemporal evolution of vesicles (polymersomes) by the self-assembly of randomly distributed amphiphilic BAB triblock copolymers with hydrophilic A and hydrophobic B blocks in an aqueous solution. The vesiculation pathway consists of several intermediate structures, such as an interconnected network of copolymer aggregates, a cage of cylindrical micelles and a lamellar cage. The cage-to-vesicle transition occurs at a constant aggregation number and practically eliminates the hydrophobic interfacial area between the B block and solvent. Molecular reorganization under-lying the sequence of morphology transitions from a cage-like aggregate to a vesicle is nearly isentropic. The end-to-end distances of isolated copolymer chains in solution and those within a vesicular assembly follow log normal probability distributions. This is attributed to the pre-ponderance of folded chain configurations in which the two hydrophobic end groups of a given chain stay close to each other. However, the probability distribution of end-to-end distances is broader for chains within the vesicle as compared to that of a single chain. This is due to the swelling of the folded configurations within the hydrophobic bilayer. Increasing the hydropho-bicity of the B block reduces the vesiculation time without qualitatively altering the self-assembly pathway.

In this paper, we compile the fractional power series method and the Laplace transform to design a new algorithm for solving the fractional Volterra integro-differential equation. For that, we assume the Laplace power series (LPS) solution in terms of power q=1m,m∈Z+, where the fractional derivative of order α=qγ for which γ∈Z+. This assumption will help us to write the integral, the kernel, and the nonhomogeneous terms as a LPS with the same power. The recurrence relations for finding the series coefficients can be constructed using this form. To demonstrate the algorithm's accuracy, the residual error is defined and calculated for several values of the fractional derivative. Two strongly nonlinear examples are discussed to provide the efficiency of the algorithm. The algorithm gains powerful results for this kind of problem. Under Caputo meaning the obtained results are illustrated numerically, and graphically. Geometrically, the behavior of the solution declares that the changing of the fractional derivative parameter values' in their domain alters the style of the attained solution in a symmetrical meaning and fully coinciding to the ordinary derivative value'. From these simulations, the results report that the recommended novel algorithm is a straightforward, accurate, and superb tool to generate analytic-approximate solutions for Integral, and integro-differental equations of fractional order.

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.

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.

Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

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.

The speed-density or flow-density relationship is a fundamental aspect of traffic flow theory. Single-regime models often face challenges in consistently fitting empirical data under diverse traffic conditions. This paper addresses the inaccuracies of single-regime models, attributing them not only to their functional forms but also to data dispersion and sample selection bias. To mitigate these issues, the data is segmented into day and night periods, leading to the creation of separate models for each. Calibration results, conducted on data from two freeways, Tehran-Karaj and Tehran-Qom, reveal distinct challenges, with the former exhibiting high dispersion and the latter showing low dispersion. Surprisingly, single-regime models demonstrate notable effectiveness when applied to distinct day and night data. Further analysis, categorizing data into day and night, results in reduced errors (20-60% improvement in R-Square), emphasizing the potential for enhanced accuracy by considering distinct regimes. Overall, the study underscores the significance of regime-specific considerations for accurate traffic flow modeling. Upon thorough evaluation, the study highlights the superiority of the neural network Multi-layer Perceptron (MLP) over traditional models. The research discusses the implications of incorporating day and night parameters into the models, emphasizing their potential for accuracy enhancement.

Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths. Classically, liver transplantation (LT) can be curable for HCC tumors within the Milan criteria. Bridging strategies to reduce the dropouts from LT waiting lists or/and to downstage patients who are beyond Milan criteria are widely utilized. We conducted a literature-based review to evaluate the role of systemic therapies as a bridging treatment to liver transplantation (LT) in HCC patients. Tyrosine Kinase Inhibitors (TKIs) can be used as a bridging systemic therapy to LT in patients with contraindication to locoregional liver-directed therapies. Immune Checkpoint Inhibitors (ICIs) treatment can be utilized either as a monotherapy or as a combination therapy with Bevacizumab or TKIs prior to LT. Acute rejection post liver transplantation is a concern in the context of ICIs treatment. Thus, a safe ICI washout period before LT and cautious post-LT immunosuppression strategies are required to reduce post-LT rejections and to optimize clinical outcomes. Nevertheless, prospective clinical trials are needed to establish definitive conclusions about the utility of systemic therapy as a bridging modality prior to LT in HCC patients.

Background: Fine-needle aspiration cytology (FNAC) is widely used to diagnose and monitor thyroid nodules. The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) is the standard for interpreting FNAC specimens. The risk of malignancy in Bethesda III nodules also known as Atypia of Undetermined Significance (AUS) varies significantly throughout several studies published worldwide. This retrospective study examines the risk of cancer in thyroid FNAC categorized as Bethesda III, as identified in the final histopathology of thyroidectomy specimens at a single endocrine surgery center. Methods: This retrospective cohort analysis included 1038 consecutive patients who underwent elective thyroid surgery with complete follow up data between January 2020 and March 2024. Preoperative data on clinical and pathological characteristics have been collected. The final histopathology report from the thyroidectomy specimen was compared to the results of the preoperative FNAC on nodules that were judged to be Bethesda category III. Statistical methods were performed using SPSS version 29. Results: A total of 670 ultrasound-guided FNACs (64.5%) performed during the study period were included in the final analysis. The study population was predominantly female, represented by 79.6% of patients with a mean age of 42.5 (SD 12.1), while 20.4% were male and significantly older with mean age 45.13 years (p = 0.02). The FNAC inadequacy rate was 5.1%, which was associated with a high risk of malignancy (6 out of 34, 17.6%). Out of the total sample size of 170 patients classified as group III, 57 were found to have malignancies in final surgical histopathology, representing 33.5% of the cases within this category. The secondary gender-related outcome analysis showed that female patients classified under the Bethesda II category had a significantly higher risk of malignancy, with a rate of 21.2%, compared to males who had a malignancy rate of 3.4% in the same Bethesda category (p = 0.001, chi square test). However, female had more better prognostic non-invasive tumors than male (p = 0.02, chi square test). Conclusion: The study's results indicate that Bethesda categories II and III are associated with a higher risk of malignancy in comparison to the reports of the first and third editions of the TBSRTC, particularly for female patients classified under category II.

In dry season paddy farming, the alternate wetting and drying (AWD) irrigation improves water productivity, paddy production, and has the potential to decrease greenhouse gas (GHG) such as methane (CH4) and nitrous oxide (N2O) emissions when compared to continuous flooding (CF). However, there is a lack of research in Bangladesh on the effects of water management on CH4 and N2O emissions. During November 2017–April 2018, participatory on-farm trials were conducted at Feni and Chattogram districts of Bangladesh. Total 105 farmers comprising 20-hectare of land (62 farmers at Feni and 43 farmers at Chattogram district, each location having 10 hectare of land). We compared irrigation water and cost reductions, paddy yield, and CH4 and N2O emissions from paddy fields irrigated using AWD and CF irrigation methods. The CH4 and N2O emissions were determined using the Cool Farm Beta-3 methodology, and the global warming potential (GWP) was estimated using the Intergovernmental Panel on Climate Change-2014 standard approach. The mean results of randomly selected 30 farmers from two locations (15 of each) showed that AWD remarkably decreased irrigation water consumption by about 24% and increased water productivity by 224%. We estimated 23% savings for irrigation costs in AWD. By this time, AWD improved paddy production by 3% over CF. The AWD irrigation resulted in a 47% reduction in cumulative CH4 emissions having a lower CH4 emission factor (0.74 kg ha-1 day-1) than CF (1.39 kg ha-1 day-1). There was no obvious difference in N2O emission between AWD and CF. When compared to CF, AWD decreased the overall GWP by 27% and lowered the GHG intensity by 42%. The CH4 and N2O emissions did not differ substantially between Feni and Chattogram.

There is a case about a system comprising two queues of M/M(a)/1/K with a due date with FCFS service policy in each queue. Using the methods of simulation and mathematical analysis, the solution diagrams were illustrated for the fixed due date Ɵ=1 second at the phase of due date up to the commencement of the service, limited capacity K₁=5 and K₂=4, for both scenarios of different service management rate resulted. For the analytical solution, first, a SAN model from the system was created in the Mobius tool. For simulation, a java discrete systems simulation code was used. Adjustment of the initial values, selection of the scenarios for service rate, the number of the consumers in the simulation, etc. were all carried out in the simulation code.

Water scarcity is a significant challenge facing Jordan today. It is a challenge in all areas that use water, and it has become certain that water is the critical factor in the population/resources equation. The water resources in Jordan have become limited with the noticeable increase in the population, mostly resulting from forced migrations in neighboring countries because of the wars and until our time. The rising natural population growth rate, along with the massive influx of refugees, has turned into a state of disproportion between the daily consumption of the population and the amount of water available. That Jordan shares most of its surface water resources with neighboring countries has exacerbated the situation. The current use of water already exceeds its renewable supply. Excessive withdrawal from aquifers, which leads to a lowering of the water table and deterioration in water quality, covered the deficit. This paper focuses on assessing the water situation in Jordan, mainly evaluating this problem and the solution being considered, the true basis for a sustainable water solution requires awareness by the population, and several governmental and non-governmental organizations are actively involved in educating residents about water shortages. The most important and 'actionable' elements of comprehensive water solutions are discussed in this paper, and these elements exist, develop extra water supplies, water harvesting, water desalination, proper wastewater reuse in the agricultural sector, and reduce the demand for drinking water. This research provides specific recommendations to address the shortage of water resources in the Kingdom and highlights the importance of water conservation and optimal use.

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.

Silencing of the fragile X mental retardation 1 (FMR1) gene and consequently lack of synthesis of FMR protein (FMRP) are associated with fragile X syndrome, which is one of the most prevalent inherited intellectual disabilities. FMRP is a multifunctional protein involved in many cellular functions in nearly all subcellular compartments under normal conditions and under conditions of cellular stress in both neuronal and non-neuronal cell types. This is achieved through its trafficking signals, nuclear localization signal (NLS), nuclear export signal (NES), and nucleolar localization signal (NoLS), as well as its RNA and protein binding domains, and is modulated by various post-translational modifications such as phosphorylation, ubiquitination, sumoylation, and methylation. This review summarizes recent advances in understanding the interaction networks of FMRP with a special focus on FMRP stress-related functions, including stress granule formation, mitochondrion and endoplasmic reticulum plasticity, ribosome biogenesis, cell cycle control, and DNA damage response.

The eutrophication of open water bodies, especially in coastal water area, has received intensive public focuses in recent years. Biological treatment of organic pollutants in industrial and domestic wastewater requires microbial flora to participate. The environmental impact of these microbes, particularly in regard to the spread of antibiotic resistance genes and pathogenicity in coastal water, remains largely unknown. We initiate studies to examine antibiotic resistance genes (ARGs) in microbial flora and study the mode of antibiotic spread in the coastal waters of Bohai Bay, North China by using high performance liquid chromatography-mass spectrum analysis and quantitative polymerase chain reaction. The metabolic features of bacteria related to the presence of ARGs in the coastal area showed the enrichment of Pseudomonadale species with triple resistance properties (ampicillin, kanamycin and gentamycin) at the estuary site locating adjacent to a coastal sewage treatment plant where both type I and type II polyketide synthase genes and b-lactam type antibiotics are detected. Pseudomonadale order is phylogenetically belonged to Proteobacteria Phylum, Gamma-proteobacteria Class and contains Pseudomonadaceae family and Pseudomonas genus. QPCR analysis suggests that the percent occupation of Pseudomonadale species to total Proteobacteria in the examined stations is close to several sampling stations close to sewage treatment plants, where consistent release of organic wastes occurred.

A convolutional neural network (CNN) is sometimes understood as a black box in the sense that while it can approximate any function, studying its structure will not give us any insights into the nature of the function being approximated. In other terms, the discriminative ability does not reveal much about the latent representation of a network. This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient. We also ask the question, "Do different input classes have a relationship or are they unrelated?" For instance, could there be an overlapping set of highly active neurons to identify different classes? Could there be a set of neurons that are useful for one input class whereas misleading for a different one? Intuition answers these questions positively, implying the existence of a structured set of neurons inclined to a particular class. Knowing this structure has significant values; it provides a principled way for identifying redundancies across the classes. Here the interpretability profiling has been done by evaluating the correspondence between individual hidden neurons and a set of human-understandable visual semantic concepts. We also propose an integrated gradient-based class-specific relevance mapping approach that takes the spatial position of the region of interest in the input image. Our relevance score verifies the interpretability scores in terms of neurons tuned to a particular concept/class. Further, we perform network ablation and measure the performance of the network based on our approach.

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.

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.

The musculoskeletal system consisting of bones and muscles have been recognized as endocrine organs secreting hormones that are involved in regulating metabolic and inflammatory pathways. Obesity and type 2 diabetes (T2D) are associated with several musculoskeletal system complications. We hypothesized that an interaction exists between adipomyokines namely, irisin and METRNL, and various bone markers in individuals with obesity and T2D. A total of 228 individuals were enrolled in this study, including 124 non-diabetic and 104 T2D. A Multiplex assay was used to assess the level of various bone markers namely Osteoactivin, Syndecan, osteoprotegerin (OPG) and osteonectin/SPARC. Our data shows elevated levels of Osteoactivin, Syndecan, OPG and SPARC in T2D as compared to non-T2D individuals (p ≤ 0.05). Using Spearman’s correlation, irisin was positively correlated only with Osteoactivin and OPG (p &lt; 0.05). Similarly, a positive association was observed between METRNL and Osteoactivin (p &lt; 0.05). The strong positive association shown in our study between irisin, METRNL and various bone markers emphasises the strong interaction between these organs. This suggests that a dysregulation in the functional interaction between these molecules could play a possible role in the development of bone and muscle related complications that are associated with obesity and T2D.

Abstract: Colistin is a last-resort antimicrobial for treating multidrug-resistant Gram-negative bacteria. Phe-notypic colistin resistance is highly associated with plasmid-mediated mobile colistin resistance (mcr) genes. mcr-bearing Enterobacteriaceae have been detected in many countries, with the emergence of colistin-resistant pathogens a global concern. This study assessed the distribution of mcr-1, mcr-2, mcr-3, mcr-4, and mcr-5 genes with the phenotypic colistin resistance in isolates from diarrheal infants and children in Bangladesh. Bacteria were identified using the API-20E biochemical panel and 16s rDNA gene sequencing. Polymerase chain reac-tions detected mcr gene variants in the isolates. Their susceptibilities to colistin were determined by agar dilu-tion and E-test by minimal inhibitory concentration (MIC) measurements. Over 30.0% (69/225) of isolates showed colistin resistance by agar dilution assessment (MIC> 2.0 μg/mL). Overall, 15.5% of isolates carried mcr genes (7, mcr-1; 17, mcr-2; 13, mcr-3; and co-occurrence occurred in 2 isolates). Clinical breakout MIC val-ues (≥ 4 μg/mL) were associated with 91.3% of mcr-positive isolates. The mcr-positive pathogens include twenty Escherichia spp., five Shigella flexneri, five Citrobacter spp., two Klebsiella pneumoniae, and three Pseudo-monas parafulva. mcr-genes appeared to be significantly associated with phenotypic colistin resistance phe-nomena (p=0.000), with 100% colistin-resistant isolates showing MDR phenomena. Age and sex of patients showed no significant association with detected mcr variants. Overall, mcr-associated colistin-resistant bacte-ria have emerged in Bangladesh, which warrants further research to determine their spread and instigate ac-tivities to reduce resistance.

The novel Ionized Gas Thermoelectric Generator (IG-TEG) system that has been studied theoretically showing capabilities to continually extracting energy from the thermal energy of the ambient air, at low temperatures within the standard room temperature and below. This system does not need a temperature gradient in order to work, unlike the other TEGs that use Seebeck effect, and therefore this new system can be utilized for cooling purposes, by extracting energy instead of wasting energy in compressing the gas for cooling. This novel system was designed based on Static Ratchet Potential (SRP), which is known as a spatially asymmetric electric potential produced by an array of positive and negative electrodes. The ratchet potential produces electrical current from random Brownian Motion of charged particles that are driven by thermal energy. Ratchet potential was studied and investigated by several researches in the fields of sensing and energy harvesting. The main ratchet potential system parameter is the particles transportation, this parameter was studied under the condition of flashing ratchet potentials, and was analyzed based on several methods. In this study, a different approach is pursued to estimate particles transportation, by evaluating the charged particles distribution, and applying the other conditions of the SRP.

According to many researchers, Abstraction is the basis of mathematics, computing, counting devices, and computer science and engineering. What is more, all of the above deal with complexity management in some way, and abstraction is the most basic mechanism of complexity management.Generative software development - whether in the sense of empowering humans by machine to create software or in the sense of reusing products - has been and is one of the serious concerns and goals of software engineering. The interesting thing is that in both views of generativity, the main issue is still, in a way, complexity management: whether this complexity management is to achieve diversity and reuse management (Czarnecki’s approach) or to Structuring from existing structures (the approach of Alexander and his followers in an object-oriented society).In this article, we will first look at complexity and its various definitions. The definitions that show, despite the different perspectives on complexity in different disciplines and domains, all point in one direction. We will conclude that complexity is rooted in multiplicity. In the following, we will formally define complexity. In the following discussion, we will look at the generative patterns of software development, and then we will look at the complexity management patterns at seven levels.In this article, the author has tried to maintain a comprehensive approach to complexity and to consider the approaches of different domains to complexity.

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.

Diabetes mellitus (DM) is one of the principal manifestations of metabolic syndrome and its prevalence with modern lifestyle is increasing incessantly. Chronic hyperglycemia can induce several vascular complications that were referred to be the major cause of morbidity and mortality in DM. Although several therapeutic targets have been identified and accessed clinically, the imminent risk of DM and its prevalence are still ascending. Substantial pieces of evidence revealed that histone deacetylase (HDAC) isoforms can regulate various molecular activities in DM via epigenetic and post-translational regulation of several transcription factors. To date, 18 HDAC isoforms have been identified in mammals that were categorized into 4 different classes. Classes I, II, and IV are regarded as classical HDACs, which operate through a Zn-based mechanism. In contrast, class III HDACs or Sirtuins depend on nicotinamide adenine dinucleotide (NAD+) for their molecular activity. Functionally, most of the HDAC isoforms can regulate β cell fate, insulin release, insulin expression and signaling, and glucose metabolism. Moreover, the roles of HDAC members have been implicated in the regulation of oxidative stress, inflammation, apoptosis, fibrosis, and other pathological events, which substantially contribute to diabetes-related vascular dysfunctions. Therefore, HDACs could serve as the potential therapeutic target in DM towards developing novel intervention strategies. This review sheds light on the emerging role of HDACs/isoforms in diabetic pathophysiology and emphasized the scope of their targeting in DM for constituting the novel interventional strategies for metabolic disorders/complications.

Introduction: Transportation logistics play a pivotal role in facilitating both individual mobility and supply chain operations on a global scale. However, conventional transportation systems have contributed significantly to urban congestion and environmental degradation. In response to these challenges, there is a growing momentum towards the adoption of intelligent transportation systems (ITS) and sustainable transportation solutions. This research aims to explore the potential of renewable energy sources to power electric vehicles (EVs) and flying automobiles, thereby accelerating the transition towards a low-carbon future. Objectives: The primary objectives of this research are as follows: - Investigate the role of renewable fuels in powering both EVs and flying cars to reduce reliance on fossil fuels and mitigate carbon emissions. - Analyze the dynamic growth and sustainability implications of advancements in battery technology and alternative renewable energy sources, such as Hydrogen Fuel Cells. - Evaluate the challenges and opportunities associated with the integration of flying vehicles into existing transportation infrastructure, focusing on safety, airspace control, and infrastructure development. - Review insights and guidance to stakeholders on promoting sustainable transportation solutions for EVs and flying cars through the utilization of renewable energy sources and the reduction of carbon emissions from the literature. Methodology: This research employs a mixed-method approach, combining literature review, and causal loop diagram analysis. The literature review provides a comprehensive understanding of current trends, challenges, and opportunities in sustainable transportation and renewable energy technologies. A causal loop diagram is utilized to visualize the dynamic interactions between various factors influencing the adoption of EVs and flying cars, including technological advancements, policy interventions, and market dynamics. Results and Discussion: The findings of this research highlight the significant potential of renewable energy sources, such as solar, wind, and hydrogen, in powering both EVs and flying cars. Advances in battery technology and hydrogen fuel cells are identified as key drivers in accelerating the transition towards sustainable transportation systems. However, challenges remain in terms of infrastructure development, safety regulations, and airspace management for flying vehicles. Nevertheless, there is a noticeable advancement in reducing carbon emissions and reliance on fossil fuels with the widespread adoption of EVs. By harnessing renewable energy and implementing effective policy measures, stakeholders might able to promote the widespread adoption of sustainable transportation solutions, thereby contributing to environmental protection and societal well-being. Conclusion: Literally, this research underscores the importance of sustainable transportation solutions in addressing urban congestion, environmental pollution, and energy security concerns. By leveraging renewable energy sources and embracing technological advancements, such as EVs and flying cars, stakeholders pave the way for a more sustainable and resilient transportation system. However, concerted efforts are needed to overcome existing challenges and ensure the successful integration of these technologies into existing infrastructure. Through collaborative partnerships and innovative initiatives, a low-carbon future for transportation logistics can be achieved, benefiting both the environment and society as a whole.

Large-span post-tensioned (PT) beams play a crucial role in maximizing the benefits of post-tensioning techniques. Bonded and unbonded systems are prevalent, with the latter being more widespread in the United States. While bonded systems are advantageous for creating long spans when multiple tendons are grouped in ducts, limited literature exists on their demolition. In a case study, this paper addresses the unique challenge of demolishing large-span bonded post-tensioned beams due to a building's functional change. Emphasizing insights for engineers, it explores the use of cutting and dismantling methods, considering the presence of prestressed cables. The demolition process is distinctive due to the presence of numerous pre-stressed cables along the beams, necessitating a specialized and cautious cutting approach. This is accomplished through the use of a drilling technique that selectively distresses the tendons, ensuring they are not all affected simultaneously. An intriguing observation discussed in this paper pertains to the occurrence of horizontal cracks accompanied by loud sounds following the drilling process, offering insights from the design perspective of PT systems. The paper details an innovative method for safely demolishing large-span bonded PT beams, using ground-penetrating radar and computer models to navigate structural complexities and ensure nearby structures' safety.

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.

Globalization, knowledge economy, information technology advancements, and the rise of learning societies contribute to the concept of a learning city. The study examines the economic indicators of Yazd city, Iran, in the context of learning city development. A descriptive-analytical approach with a survey method and cluster sampling was employed. Data analysis involved Kolmogorov-Smirnov normality test, single-sample T-test, simple linear regression, and Kendall's W test. The normality test confirmed data normality. The T-test indicated that experts perceived the current state of most variables in Yazd as undesirable. Regression analysis revealed a significant positive association between the economic index and independent variables. Kendall's W test prioritized areas based on economic indicators, with the top 7 and bottom 5 areas identified for focused interventions. The study emphasizes the need to prioritize areas within Yazd based on their economic indicators. This prioritization informs strategies for enhancing social and legal aspects of Yazd's transformation into a learning city. Further research should explore effective methods to cultivate a full-fledged learning environment in the city.

As with many of the non-deterministic search algorithms, particularly those are analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much faster.

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.

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.

COVID-19 is a severe global problem, and AI can play a significant role in preventing losses by monitoring and detecting infected persons in early-stage. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel method for increasing the classification accuracy of convolutional networks. We implemented our method using the ResNet50V2 network and a modified feature pyramid network alongside our designed architecture for classifying the selected CT images into COVID-19 or normal with higher accuracy than other models. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient identification phase, the system correctly identified almost 234 of 245 patients with high speed. We also investigate the classified images with the Grad-CAM algorithm to indicate the area of infections in images and evaluate our model classification correctness.

This research project focuses on the optimization of the hybrid energy system together with the assistance of thin-film coatings aiming to achieve self-sustainable food and crop storage facilities which will run effectively with its own generated energy. An infrastructure will be designed and constructed that will comprise a hybrid power generation system accompanied by thin-film coated semitransparent and non-transparent construction materials for energy saving. Thin-film low emissivity (Low-E) type coatings will assist the transparent or semitransparent construction materials to reflect most of the infrared (IR-mostly heat) and UV spectra of sunlight without interrupting the visible spectrum and will lead to saving energy consumption by reducing the heat and lighting during day time

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.

Abstract. The remarks by the Supreme Leader of the Islamic Revolution on the toll of the famines that afflicted occupied Iran in World War I and World War II indicate a desire to incorporate these calamities in Iran’s national narrative and collective memory. His criticism of famine historiography and stated toll numbers, and the unfolding catastrophic famine in Gaza, justify and necessitate a fresh look at each famine’s historiography and toll. This article re-estimates the toll of each famine by including many overlooked and neglected primary sources and in ways not previously done. It builds a trajectory of Iran’s demographic development from 1810 to 1944, and derives the toll of each famine. The World War II famine and typhus epidemics had claimed 5.5 million. World War I famine was also more deadly, though the 1869-73 famine was not far behind. At least 15.5 million Iranians died in the two world wars, a fact nowhere reflected in the history of the conflicts. Iran’s 1944 population of 12 million was unchanged from that of 1850 and a century of population growth obliterated

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.

Duct-dependent lesions are classically divided into duct dependent pulmonary circulation which involves severe right sided obstructive lesions such as pulmonary atresia or critical stenosis, and duct dependent systemic circulation which is characterized by malformation of the left side of the heart, and whose severest spectrum is hypoplastic left heart syndrome. Dextro-transposition of the great arteries remains in gray zone between being included in some old classifications as a duct dependent pulmonary circulation lesion or a new category of duct-dependent or independent mixing lesion. We hypothesize that re-classifying duct dependent congenital defects into duct dependent perfusion and oxygenation offers a model to fit D-TGA into the same category of right sided obstructive lesions and reflects the clinical spectrum in a more comprehensive way.

Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.

Considering the significant advancements in autonomous vehicle technology, research in this field is of interest to researchers. To automatic parking, controlling steer angle, gas hatch, and brakes need to be learned. Due to the increase in the number of cars and road traffic, car parking space has decreased. Its main reason is information error. Because the driver does not receive the necessary information or receives it too late, he cannot take appropriate action against it. This paper uses two phases: the first phase, for goal coordination, was used genetic algorithms and the Cuckoo search algorithm was used to increase driver information from the surroundings. Using the Cuckoo search algorithm and considering the limitations, it increases the driver’s level of information from the environment. Also, by exchanging information through the application, it enables the information to reach the driver much more quickly and the driver reacts appropriately at the right time. The suggested protocol is called the MODM-based solution. Here, the technique is assessed through extensive simulations performed in the NS-3 environment. Based on the simulation outcomes, it is indicated that the parking system performance metrics are enhanced based on the detection rate, false-negative rate, and false-positive rate.

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.

Nowadays, one of the biggest concern of human being is greenhouse gas emission, especially carbon dioxide emission in developed and under-developing countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO₂ emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay. The studied region is modelled based on the available input data in terms of Million tons including oil (Million tons), gas (Million tons oil equivalent), coal (Million tons oil equivalent), $R_{ew}$ (Million tons oil equivalent), and Gross Domestic Product (GDP) in terms of billion US dollars.} Moreover, the available patents in the fields of climate change mitigation in six Latin American countries namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela, and Uruguay has been reviewed and analyzed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies which account for 60% and 72% respectively.

The adverse relationship between viral hepatitis and pregnancy in developing countries was seen as a reflection of retrospective biased hospital-based data collection by the West. However, the discovery of HEV from an epidemic of non-A, non-B hepatitis in Kashmir and documenting increased incidence and severity of hepatitis E in pregnancy from a house-to-house survey unmasked the unholy alliance. Among the family of HEV’s, genotype (gt)-1, with a unique ORF4-encoded protein enhancing viral polymerase activity and viral replication, is the sole HEV that shows this adverse relationship. The epidemics caused by HEV-gt1 and not by HEV-gt2 show adverse relationship with pregnancy. The pathogenesis of the unholy alliance is complex and at present not well understood. Possibly multiple factors play a role in causing severe liver disease in the mother including: infection, replication and damage to the maternal-foetal interface by HEV-gt1; vertical transmission of HEV to foetus causing severe foetal/neonatal hepatitis; and combined viral and hormone related immune dysfunction of diverse nature in the mother promoting viral replication. Management is multidisciplinary and needs a close watch for the development and management of ALF. Preliminary data suggest beneficial maternal outcomes by early termination of pregnancy in patients with lower grades of encephalopathy.

In this work, some numerical radius inequalities based on the recent Dragomir extension of Furuta's inequality are obtained. Some particular cases are also provided.

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.

Background: The purpose of this study is to carry out bioinformatic analysis of lncRNA data obtained as a result of genomic analysis of kidney tissue samples taken from rats with nephrotoxicity induced by methotrexate (MTX) and from rats without pathology and modeling with tree-based machine learning method. Another aim of the study is to identify potential biomarkers for the diagnosis of nephrotoxicity and to provide a better understanding of the nephrotoxicity formation process by providing the interpretability of the model with explainable artificial intelligence methods as a result of the modeling. Methods: To identify potential indicators of drug-induced nephrotoxicity, 20 female Wistar Albino rats were separated into two groups: nephrotoxicity and control. Kidney tissue samples were collected from the rats, and genomic, histological, and immunohistochemical analyses were performed. The data set obtained as a result of genomic analysis was modeled with Random Forest (RF), one of the tree-based methods. Modeling results were evaluated with sensitivity (Se), specificity (Sp), balanced accuracy (B-Acc), negative predictive value (Npv), accuracy (Acc), positive predictive value (Ppv), and F1-score performance metrics. The Local Interpretable Model-Agnostic Annotations (LIME) method was used to determine the lncRNAs that could be biomarkers for nephrotoxicity by providing the interpretability of the RF model. Results: The outcomes of the histological and immunohistochemical analyses done in the study supported the conclusion that MTX use caused kidney injury. According to the results of the bioinformatics analysis, 52 lncRNAs showed different expression in the groups. As a result of modeling with RF for lncRNAs selected with Boruta variable selection, the B-Acc, Acc, Sp, Se, Npv, Ppv, and F1-score were 88.9%, 90%, 90.9%, 88.9%, 90.9%, 88.9% and 88.9%. respectively. lncRNAs with id rnaXR_591534.3 rnaXR_005503408.1, rnaXR_005495645.1, rnaXR_001839007.2, rnaXR_005492056.1 and rna_XR_005492522.1 the lncRNAs with the highest variable importance values produced from RF modeling can be used as nephroxicity biomarker candidates. Also, according to the LIME results, the high level of lncRNAs with id rnaXR_591534.3 and rnaXR_005503408.1 especially increased the possibility of nephrotoxicity. Conclusions: With the possible biomarkers obtained as a result of the analyses made within the scope of this study, it can be ensured that the procedures for the diagnosis of drug-induced nephrotoxicity can be carried out easily, quickly and effectively.

Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

background: The Islamic Republic of Iran has been under sanctions since 1979. These sanctions were further reinforced between the years 2005 and 2012. This study utilizes available data from Iran post-sanctions to review the impact of economic sanctions on both the mental health and the quality of life of Iranian citizens.Methods: A comprehensive scoping review was conducted to identify studies examining the impact of sanctions on health in Iran. Searches were initiated in 2012 using PubMed/Medline, Embase, Web of Science, Scopus, and the Scientific Information Database (Iranian Database) with keywords such as sanctions, implications, mental health, quality of life, civilian mental health, and Iran. Subsequent to the screening process, secondary reports and letters to the editor were excluded. Only original studies—those collecting data via observation, interviews, surveys, experiments, or literature reviews—were retained for analysis. In addition to these search strategies, reference scanning was undertaken to identify additional articles pertinent to the scope of the current study. This thorough approach facilitated the discovery of relevant and significant articles exploring the effects of sanctions against Iran on the mental health and overall well-being of its civilian population. Following the search, duplicate articles were meticulously identified and removed by the authors. Data were subsequently extracted and summarized, focusing on study characteristics and the observed effects of sanctions on civilian mental health and quality of life in Iran.Result: Out of 165 items identified in the preliminary search, ten articles met the inclusion criteria. These selected articles highlighted the pronounced effects of sanctions on various health-related domains.Conclusion: Since 2012, Iran has been subject to intensified sanctions. There is evidence to suggest that the economic sanctions, imposed by Western nations, and have exerted a negative and detrimental impact on the health of individual Iranians.