This paper presents an overview of the smart electro-clothing systems (SeCSs) targeted at health monitoring, sports benefits, fitness tracking, and social activities. Technical features of the available SeCSs, covering both textile and electronic components, are thoroughly discussed and their applications in the industry and research purposes have been highlighted. In addition, it also presents the developments in the associated areas of wearable sensor systems and textile-based dry sensors. As it became evident during the literature research, such a review on SeCSs covering all relevant issues has not been presented before. This paper will be particularly helpful for new generation researchers investigating the design, development, function and comforts of the sensor integrated clothing materials.

The aroused quest to reduce the delay at interconnect level is the main urge of this paper to come across a configuration of Carbon Nanotube (CNT) bundle namely squarely packed bundle of composite CNTs. The approach, demonstrated in this paper, adapts the composite bundle to adopt for high speed Very Large Scale Integration (VLSI) interconnect with technology sizing down. To reduce the delay of the proposed configuration of composite CNT bundle, the behavioral change of Resistance (R), Inductance (L) and Capacitance (C) has been observed with respect to both width of the bundle and diameter of the CNTs in the bundle. Consequently, the performance of the modified bundle configuration is compared with previously developed configuration namely squarely packed bundle of dimorphic MWCNTs in terms of propagation delay and crosstalk delay at local, semiglobal and global level interconnect. The proposed bundle configuration is ultimately enacted as better one for 32nmand 16nmtechnology node and suitable for 7nmas well.

Originating in the Mediterranean, B. oleracea is grown all over the world and is highly valued for its nutritional value and variety in cooking. Its susceptibility to pests, diseases, and environmental stressors, however, poses significant challenges to sustaining consistent crop quality and productivity. The BASIC PENTACYSTEINE gene significantly impacts plant growth and responses to abiotic stresses. Nevertheless, little is known about its precise role and the underlying biological processes in B. oleracea. Eleven BPC genes in B. oleracea were identified by a thorough genome-wide investigation and categorized into three categories according to their structural traits and gene motifs. There were eight different chromosomes on which these BoBPCs were located. Comparative synteny analysis was used to reveal the evolutionary traits of potatoes, Arabidopsis, and B. oleracea. The BoBPC family genes in B. oleracea were found to have specific cis-elements linked to growth, stress responses, and hormone regulation when their promoter regions were studied. Cuticular wax synthesis expression revealed that all BoBZR1 genes, from wild to mutant, expressed at varied amounts in distinct types. Moreover, distinctive BoBPC gene expression patterns were found in the seven distinct tissues that were studied. Our thorough analysis shows that the BoBPC family is widely involved in B. oleracea developmental processes. The present findings provide an established theoretical basis for further exploration into the composition and roles of BoBPC in B. oleracea.

The Arabian Desert is characterised by very low rainfall and high evaporation, yet on its northeastern edge in central Iraq are 210 springs along the Abu Jir lineament, which represents the western depositional margin of a foreland basin infilled by the floodplain sediments of the Tigris and Euphrates Rivers; there is little evidence of faulting. The springs discharge from gently east-dipping Paleocene-Eocene limestones, either where groundwater flowpaths intersect the ground surface or where groundwater flow is forced to the surface by confining aquitards. Calculated annual recharge to the aquifer system across the Arabian Desert plateau (130-500 million m3) is enough to sustain the Abu Jir springs provided groundwater extraction is not excessive. The hydrochemistry of the springs is determined by evaporation, rainfall composition (high SO4 concentrations are due to dissolution of wind-blown gypsum in the rainfall), and plant uptake of Ca and K (despite the sparse vegetation). Limestone dissolution has relatively little impact; many of the springs are undersaturated with respect to calcite and lack tufa/travertine deposits. The springs at Hit-Kubaysa contain tar and high levels of H2S that probably seeped upwards along subvertical faults from underlying oil reservoirs; the presence of hydrocarbons makes the water reducing and converts the dissolved SO4 to H2S.

Coronavirus disease 2019 (COVID-19) has been first appeared in Wuhan, China but its fast transmission, led to its widespread prevalence in various countries and make it a global concern. In addition, lack of a definitive treatment is another concern that needs to be attention. Researchers have come up with several options, which are not certain, but protease inhibitor and some antiviral agent are in the forefront. In this study a virtual screening procedure employing docking of different databases including 1615 FDA approved drugs was used to identify new potential small molecule inhibitors for protease protein of COVID-19. The docking result indicates that among all, simeprevir (Hepatitis C virus (HCV) NS3/4A protease inhibitor) could fit well to the binding pocket of protease and because of some other positive features including ADME profile, might be a helpful treatment option for COVID-19.

Prochlorococcus, a cyanobacteria genus of the smallest and most abundant oceanic phototrophs, encompasses ecotype strains adapted to high-light (HL) and low-light (LL) niches. To elucidate the adaptive evolution of this genus, we analyzed 40 Prochlorococcus marinus ORFeomes, including two cornerstone strains, MED4 and NATL1A. Employing deep learning with robust statistical methods, we detected new protein family distributions in the strains and identified key genes differentiating HL and LL strains. HL strains harbor genes (ABC-2 transporters) related to stress resistance, such as DNA repair and RNA processing, while LL strains exhibit unique chlorophyll adaptations (ion transport proteins, HEAT repeats). We report variable, depth-dependent endogenous viral elements in the 40 strains. We constructed the ORFeomes of MED4 and NATL1A covering 99 % of the annotated protein-coding sequences of the two species, totaling 3976 cloned, sequence-verified ORFs. These comparative genomics analyses, paired with MED4 and NATL1A ORFeomes, will facilitate future genotype-to-phenotype mappings and systems biology exploration of Prochlorococcus ecology.

While DNA serves as the fundamental genetic blueprint for an organism, it is not a static entity. Gene expression, the process by which genetic information is utilized to create functional products like proteins, can be modulated by a diverse range of environmental factors. Epigenetic mechanisms, encompassing DNA methylation, histone modification, and microRNAs, play a pivotal role in mediating the intricate interplay between the environment and gene expression. Intriguingly, alterations in the epigenome have the potential to be inherited across generations. Ethanol abuse poses significant health issues worldwide. Ethanol has the capability to induce changes in the epigenome, which can be inherited by offspring, thus impacting them even in the absence of direct ethanol exposure. This review article reviewed the alterations caused by ethanol on the epigenome. We thoroughly gathered all relevant papers investigating the behavioral and molecular consequences of parental ethanol exposure on their offspring. In conclusion, the comprehensive analysis of the literature supports the notion that ethanol exposure induces lasting epigenetic alterations, which can influence the behavior and health of future generations. This knowledge emphasizes the significance of addressing the potential transgenerational effects of ethanol and highlights the importance of preventive measures to minimize the adverse impact on offspring.

Respiratory syncytial virus (RSV) is a leading cause of acute lower respiratory infections in young children worldwide. RSV-associated deaths in children are underreported in Bangladesh. We analyzed hospital-based surveillance data on severe acute respiratory infections (SARI) in under-five children before (August 2009–February 2020) and during the COVID-19 pandemic (March 2020–March 2022). Using the World Health Organization definition, we identified SARI cases in 14 tertiary-level hospitals. Nasopharyngeal and oropharyngeal swabs were collected for real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) testing of six respiratory viruses, including RSV. SARI deaths during the pandemic (2.6%, 66) were higher than pre-pandemic (1.8%, 159; p<0.001). Nearly half of pandemic deaths (47%) had underlying respiratory viruses, similar to the pre-pandemic rate (45%). RSV detection in deaths was consistent pre-pandemic (13%, 20/159) and during the pandemic (12%, 8/66). Children aged <6 months constituted 57% (16) of RSV-related deaths. Evaluating interventions like maternal vaccination and infant monoclonal antibody prophylaxis is crucial to address RSV, a major contributor to under-five SARI deaths.

The aim of this study was to determine the impact of information technology in management of risks in the capital market-listed company is in Tehran Stock Exchange. The purpose of the present study is an applied descriptive approach. The target population for the survey, companies that from 2009 to the first half of 2015 have been a member of the Tehran Stock Exchange, through Cochranʼs sample size of 140 companies, respectively. We used cluster sampling method. In order to collect data from two questionnaires: risk management questionnaire Foakeh (2013) has 38 items and a standard questionnaire Chanvyas (2006) has 40 items, the whole five-item Likert scale questionnaire is above has been used. Data gathered through the questionnaire, sign the application was 21spss. For inferential analysis of the variables and to analyze the data from different statistical tests and regression was used Kolmogorov-Smirnov test. The results showed that information technology on risk management and its dimensions (primary market risk, market risk and the risk of secondary non-financial) impact.

The aim of the present research to identify the differences between the social acceptance, attention, and emotional and social loneliness feeling in women suffering from Alzheimer's and normal. The sample was analyzed in this study included 39 women age 51 to 69 years with the scope of that 19 of them with Alzheimer's disease (AD) and 18 people had normal cognitive function and healthy in the city of Gorgan in the winter 1395 in the study. Identification of Alzheimer's sufferers and healthy based on the diagnosis of the physician and cognitive status questionnaire (SPMSQ) and they were asked to answer the social acceptance of the scale questionnaire Marlow and Craven, affective and social adults alone feel scale (SELSA-S), Spotlight questionnaire (FAQ). The data using analysis of variance, multivariate factor test method (MANOVA) were studied. The results of this test indicate that it is totally focused attention between petty scale, the Spotlight focused on the notion of social acceptance of social interaction and social-emotional feeling lonely scale in women suffering from Alzheimer's and normal according to the analysis of the one-way variance of a significant difference in the context of manova level (P < 0.001). But in the other scale retail spotlight focused on his research, a significant difference was not observed. According to the results of the research in general can be found stating that to provide family-centered care and the implementation of the health-welfare interventions by relevant organs with different educational, supportive, emotional and consultation approaches, can be effective on health promotion of public health and the actual need in the community.

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.

Lead (Pb) is a toxic environmental heavy metal that induces serious clinical defect on all organs with the nervous system being the primary target. Curcumin is the main active constituent of turmeric rhizome (Curcuma longa) with strong antioxidant and anti-inflammatory properties. This study is aimed at evaluating the therapeutic potentials of curcumin on Pb-induced neurotoxicity. Thirty six male Sprague Dawley rats were randomly assigned into five (5) groups with 12 rats in the control (normal saline) and 6 rats for the lead treated group (LTG) (50 mg/kg lead acetate for 4 weeks), recovery group (RC) (50 mg/kg lead acetate for 4 weeks), treatment group 1 (Cur100) (50 mg/kg lead acetate for 4 weeks, followed by 100 mg/kg curcumin for 4 weeks) and treatment group 2 (Cur200) (50 mg/kg lead acetate for 4 weeks, followed by 200 mg/kg curcumin for 4 weeks) groups each. All experimental groups received the oral treatments through orogastric-tube on alternate days. Motor function was assessed using horizontal bar method while Pb concentration in the cerebellum of the rats were evaluated using ICP-MS techniques. Pb-administered rats showed significant decrease in motor scores, SOD activity with increase MDA levels and Pb concentration in their cerebellum with marked alterations in the histological architecture of the cerebellar cortex layers. However, treatment with curcumin improved the motor score, reduced Pb concentration in the cerebellum and ameliorates the markers of oxidative stress as well as restored the histological architecture of the cerebellum. The results this in study suggested that curcumin attenuates Pb-induced neurotoxicity via inhibition of oxidative stress and chelating activity.

Lead (Pb) is a toxic environmental heavy metal that induces serious clinical defect on all organs with the nervous system being the primary target. Curcumin is the main active constituent of turmeric rhizome (Curcuma longa) with strong antioxidant and anti-inflammatory properties. This study is aimed at evaluating the therapeutic potentials of curcumin on Pb-induced neurotoxicity. Thirty six male Sprague Dawley rats were randomly assigned into five (5) groups with 12 rats in the control (normal saline) and 6 rats for the lead treated group (LTG) (50 mg/kg lead acetate for 4 weeks), recovery group (RC) (50 mg/kg lead acetate for 4 weeks), treatment group 1 (Cur100) (50 mg/kg lead acetate for 4 weeks, followed by 100 mg/kg curcumin for 4 weeks) and treatment group 2 (Cur200) (50 mg/kg lead acetate for 4 weeks, followed by 200 mg/kg curcumin for 4 weeks) groups each. All experimental groups received the oral treatments through orogastric-tube on alternate days. Motor function was assessed using horizontal bar method while Pb concentration in the cerebellum of the rats were evaluated using ICP-MS techniques. Pb-administered rats showed significant decrease in motor scores, SOD activity with increase MDA levels and Pb concentration in their cerebellum with marked alterations in the histological architecture of the cerebellar cortex layers. However, treatment with curcumin improved the motor score, reduced Pb concentration in the cerebellum and ameliorates the markers of oxidative stress as well as restored the histological architecture of the cerebellum. The results this in study suggested that curcumin attenuates Pb-induced neurotoxicity via inhibition of oxidative stress and chelating activity.

Nowadays, more than 180 million people believe that the Holy Qur'an was sent by the Almighty Allah through Prophet Muhammad in 610-632 A.D in Mecca and Medina. However, the word Mecca (Q: 48:24) is mentioned only once in the entire Holy Qur'an, which is not related to the birth of the Prophet or any worship. Conventional Islam is mostly based on the hearsay of some Islamic narrators published in the eighth and ninth centuries. To think deeply and conduct research on the verses of the Holy Quran has been mentioned (special urge) directly and indirectly by this Holy Scripture itself many times but it would not be wrong to say that there is no scholarship on the verses of the Quran based on the research method described in the Holy Quran until the present century. Therefore, this paper has been dared to carry out by combining the methods of research followed in the Holy Quran and the academically accepted research methodology. The analysis shows that the results of almost fourteen hundred years of mythological history of conventional Islamic narrators are opposites. First of all, the proper name of the Prophet revealed by the Holy Quran is Ahmed, but Muhammad is an adjective and there are many such names. Secondly, this prophet was not born in Mecca, KSA, nor in 570/571 A.D, but in the Beqaa Valley of Lebanon, between the third and fourth centuries. Thirdly, the Muslim Hajj (pilgrimage) of Mecca is celebrated entirely according to the Lahwal Hadith (idle tales) (Q: 31:6) in the language of the Holy Quran. Originally the place designated for pilgrimage was in the Beqaa Valley, Lebanon. This article will have a double influence on the Muslim world, that is, Islamic scholars will think again deeply and research new ways of scholarship of the Holy Quran, instead of based on the hearsay of idle tales (Lahwal Hadith). On the contrary, those who are stubborn Islamic narrators, since they have already rejected the Holy Quran, will not hesitate to violently attack the researchers and they will not be positively motivated by this paper. This article specifically urges Islamic scholars to conduct in-depth studies on the most important issues of Islam using the Holy Quranic research method.

Melioidosis, or Whitmore's disease, is an infectious disease initiated by a bacteria known as Burkholderia pseudomallei. This bacteria is commonly found in contaminated soil and water. In Malaysia, the mortality from melioidosis infection was reported to be higher than in other infectious diseases. The research on melioidosis is still limited in Malaysia but slightly increasing. The article's objective was to seek all the melioidosis research related to mortality in Malaysia. The information from the selected papers was then abstracted into two sections; Section 1: Review of literature and Section 2: Findings on mortality from melioidosis in Malaysia. In Section 1, the information on transmission and clinical features of melioidosis, demographic, severity of melioidosis, a clinical signs of melioidosis, diagnosis of melioidosis, management of melioidosis, the economic burden of melioidosis, and incidence of mortality from melioidosis were discussed in this paper. Meanwhile, in Section 2, the findings on mortality of melioidosis in Malaysia were discussed according to eight published articles. It is important to manage patients with melioidosis with appropriate treatment and management to reduce severe complications, high fatality rate, and relapse. More published research on melioidosis will provide input to the clinicians on a more detailed understanding of how to improve the diagnosis of melioidosis and the prognosis factors of this disease.

Particulate Organic Carbon (POC) plays a crucial role in the marine carbon cycle, influencing carbon flux and many important ocean biogeochemical processes in the Bay of Bengal. The purpose of this study was to examine the seasonal and annual variability of POC in the Bay of Bengal (BoB) region and its correlation with Sea Surface Temperature (SST) and Chlorophyll-a (Chlor_a). This study uses level-3 POC, Chlor_a, and SST data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite. The surface POC concentration in the Bay of Bengal declined at a rate of -1.30 mgm^-3y^-1 over a ten-year period (2003-12) and ranged from 86.48 mgm^-3 to 110.93 mgm^-3 with an average of (93.74±7.50 mgm^-3). Seasonally, the mean POC was highest (97.40 mgm^-3) during the post-monsoon (October-November) and lowest (79.36 mgm^-3) in the pre-monsoon (March-May). Seasons were observed to have statistically significant differences in POC concentration in the study area (F_(3,2155) = 6.298; p <0.05). A strong positive correlation between POC and Chlorophyll was observed whereas a week positive correlation between POC and SST was observed. POC was observed to be very prominent in post-monsoon (97.40 mgm^-3) and in northeast-monsoon (97.14 mgm^-3) and moderate to low in southwest (81.12 mgm^-3) and pre-monsoon (79.36 mgm^-3). The Bay of Bengal’s annual mean POC:Chlor_a ratio ranged from 97.56 mgm^-3 to 332.89 mgm^-3, indicating a significant impact of biological processes on POC composition. To see how POC fluctuation affects biogeochemical processes in the Bay of Bengal, deeper investigation is required.

Equine herpesvirus type 1 (EHV-1) causes rhinopneumonitis, abortion and neurological outbreaks (equine herpesvirus myeloencephalopathy, EHM) in horses. EHV-1 also causes lethal encephalitis in laboratory small animals such as mice and hamsters experimentally. EHV-1 ORF76 is a homologue of HSV-1 US9, which is a herpesvirus kinase. Starting with an EHV-1 bacterial artificial chromosome clone of neuropathogenic strain Ab4p (pAb4p BAC), we constructed an ORF76 deletion mutant (Ab4p∆ORF76) by replacing ORF76 with the rpsLneo gene. Deletion of ORF76 had no influence on replication, cell-to-cell spread in cultured cells or replication in primary neuronal cells. In western blots of EHV-1-infected cell lysates, an EHV-1 Us9-specific polyclonal antibody detected multiple bands ranging from 35 to 42-kDa. In a CBA/N1 mouse infection model following intranasal inoculation, the parent and Ab4p∆ORF76 revertant caused the same histopathology in the brain and olfactory bulbs. The parent, Ab4p∆ORF76 and revertant mutant replicated similarly in the olfactory mucosa, although Ab4p∆ORF76 was not transported to the olfactory bulbs and was unable to infect the CNS. These results indicated that the ORF76 (US9) plays an essential role in the anterograde spread EHV-1.

Background: Studies have tried to identify predictors of functional markers of keratoconus progression, whereas structural outcomes of collagen cross-linking have not been properly investigated. Objective: find and set up a combination of diagnostic modalities and measurements to reliably reflect CXL efficiency. Material and methods: We retrospectively reviewed the medical histories of 107 KC patients (131 eyes) who underwent CXL in a medical center from January 2018 to December 2022. The dataset included preoperative examinations and follow-up results, which provided a total of 796 observations. The cases of other corneal diseases, repeated CXL, pregnancy, and observations without follow-up examinations were excluded. Results: The study findings revealed a significant drop in the central and corneal thickness during 14.01±9.98 months of observations. Linear and polynomial equations suggested different trends in pachymetry change after CXL. The linear model showed corneal thinning, and according to the polynomials, pachymetry findings return to baseline values two years after CXL. Conclusion: The most reliable prognosis of postoperative corneal thickness is achieved with the models trained on keratometry readings and topography indices. BAD indices are also reliable predictors of the corneal thickness after CXL. A combination of the aforementioned structural parameters and their derivatives can reliably predict CXL efficiency.

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.

In this paper, new types of M-fractional wave solutions of mathematical physics model named as truncated M-fractional (1+1)-dimensional non-linear simplified Modified Camassa-Holm model are achieved by applying the modified simplest equation (MSE), Sardar sub-equation and generalized Kudryashov techniques. The gained solutions containing dark, bright, periodic and mixed wave solitons. Effect of fractional order derivative is also discussed. Achieved wave solitons are verified by Mathematica tool. Few of the gained wave solitons are also described through 2-dimensional, 3-dimensional and contour graphs through Mathematica tool. The gained solutions are helpful for the further development of concerned model. Finally, these techniques are simple, fruitful and effective to deal with non-linear FPDEs.

The prohibitive costs of small-scale solar photovoltaic (PV) racks decreases PV adoption velocity. To overcome these costs challenges, an open hardware design method is used to develop two novel variable tilt racking designs. These are the first stilt mounted racking designs that allow manual change of tilt angle from zero to 90 degrees by varying the length of cables. The racks are designed using the calculated dead, wind and snow loads for Canada as conservative design for most of the rest of the world. Structural capacities of the wooden members are then ascertained and resisting bending moment, shear force, tensile force, and compressive force for them is calculated. A structural and truss analysis is performed to ensure that racking design with-stands the applicable forces. Moreover, implications of changing the tilt angle on the wooden members/cables used to build the system are also determined. The systems offer significant economic savings ranging from 1/3rd to 2/3rd the capital expenses of the commercially available alternatives. In addition, the racking designs are easy-to-build and require minimal manufacturing operations, which increases their accessibility. The stilt-mounted designs can be employed for agrivoltaic settings while allowing farm workers shaded ergonomic access to perform planting, weeding, and harvesting.

Stevia rebaudiana Bertoni, a perennial herb, has garnered significant attention for its diterpene glycosides, natural sweeteners approximately 250 to 300 times sweeter than sucrose. These glycosides, concentrated in the plant's leaves, not only serve as a low-calorie alternative to traditional sweeteners but also exhibit notable antimicrobial properties, making Stevia a promising candidate for both the food and pharmaceutical industries. In order to study the effect of mycorrhiza fungi on some growth characteristics of the stevia herb under inoculum conditions with Arbuscular mycorrhizal fungi(AMF)Arbuscular mycorrhizal fungi, a factorial experiment based on randomized complete block design with four replications was conducted at University of Mohaghegh Ardabili. The first factor was Imma & Angel and Novella nutrient solutions; the second was planting bed including leaf composts, vermicompost, and biolan (peat & perlite); and third factor involved inoculation with mycorrhiza (Glomus Musea). The control fungi indicated that the highest percentage of stevioside (16.45%) was related to vermicompost planting bed under conditions of invocation with mycorrhizal in harvest for the Imma and Angel nutrient solution. The maximum percentage of Rebaudioside A (7.57%) was related to vermicompost planting bed under conditions of invocation with mycorrhizal in harvest for the Novella nutrient solution. Further, the greatest levels of Rebaudioside C were obtained under the same conditions, with the difference that in the case of this glycoside, the leaf composts planting bed showed a higher percentage. Then, the highest leaf fresh weight, leaf dry weight, and number of main branches were obtained for the Novella nutrient solution in vermicompost planting bed under inoculation with mycorrhiza. Finally, Mycorrhizal inoculation in vermicompost beds with the Novella nutrient solution resulted in the greatest plant height. Conversely, the Imma & Angel nutrient solution in leaf compost beds, when combined with mycorrhizal inoculation, produced the tallest plants. The positive impact of AMF on plant growth was evident in the quantitative and qualitative improvements observed across various measured parameters.

Individuals with BPD grapple with unstable relationships, marked by extreme emotional swings and interpersonal conflicts. Emotional instability, a distorted self-image, and impulsivity further complicate matters, often leading to self-harm and suicide attempts as desperate coping mechanisms. Genetics and adverse childhood experiences contribute to BPD's development, shaping brain function and emotional regulation. Diagnosis involves meticulous assessments, paving the way for personalized treatments like dialectical behavior therapy and, in severe cases, hospitalization. Early intervention, professional support, and societal understanding are pivotal for positive outcomes, and challenging mental health stigma.

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.

Curcumin has restrained clinical applications due to poor bioavailability. This study aimed to synthesize cockle shell-derived calcium carbonate (aragonite) nanoparticles (CSCaCO3NP) for delivery of curcumin and to evaluate its kinetic release in vitro. CSCaCO3NP was synthesized and conjugated with curcumin (Cur-CSCaCO3NP) using a simple top down approach and characterized for its physicochemical properties as a potential curcumin carrier. In vitro release profile was assessed using dialysis bag membrane method. The release data were fitted to Korsmeyer-Peppas, Zero order and Higuchi models to evaluate the mechanism of release pattern. A spherical shaped CSCaCO3NP with a surface area of 14.48±0.1 m2/g, average mean diameter size of 21.38±2.7 nm and a zeta potential of -18.7 mV was synthesized which has a high loading content and encapsulation efficiency. The FT-IR and XRD revealed less observable changes on the peaks after conjugation. In vitro kinetic release profile demonstrated sustained release and best fitted to the Higuchi equation model. The results of this study showed the capacity of the synthesized CSCaCO3NP to encapsulate curcumin efficiently with a stable release in vitro. This could give an insight and supportive ideas on the potentials of CSCaCO3NP for curcumin delivery. Therefore, CSCaCO3NP holds future prospects in preclinical framework to enhance curcumin efficacy for oral therapeutic applications.

Previous studies demonstrate the critical importance of non-coding RNAs interfacing with chromatin-modifying machinery resulting in promoter-enhancer-based gene regulation and raise the possibility that many other enhancer-like RNAs may operate via similar mechanisms. Critically, more than 80% of the disease-linked variations identified in genome-wide studies are located in the non-coding regions of genomes, especially non-coding RNA, suggesting non-coding RNAs are relevant to disease. Thus, a critical path forward for understanding non-coding RNAs' role, especially long non-coding RNAs, is to understand the genomic regions' transcriptional regulation, especially non-coding regions. Here, we developed a user-friendly R package called SomaGene for studying and identifying enhancer-like non-coding RNAs with enriched somatic mutations in the cancer genome. SomaGene accepts different genomic variants (whole genome/exome somatic point mutations, structural variations, copy number variations) to identify those RNAs that significantly mutated in diseases (e.g., cancer). It then uses multiple publicly available genomics and epigenetics datasets including ENCODE epigenomics annotations, FANTOM5 tissue-specific expression profiles, disease-associated genome-wide association SNPs, and tissue-specific eQTL pairs to identify those RNAs with potentially enhancer function. SomaGene, as a powerful R package, can provide the opportunity to cancer scientists to study the roles of non-coding RNAs in different cancer genomes.

Corporate identity has played a vital role in the Halal industry to create a strong foundation for its reputation in the long run. This paper used two modeling analyses for two different data. The first analysis aims to predict the relationship between Corporate Identity Management between Internal Brand and Employee Brand Support (consequence) mediated by CIM using partial-least squares-structural equation modeling (PLS-SEM). A total of 206 employees from Malaysia’s Halal food SMEs took part in the survey. The second analysis to identify topic proportions patterns using the topic modeling approach on Halal brand identity Machine learning approach of knowledge mining was applied for this analysis. Metadata of 1,091 articles were mined from the Scopus database on Halal studies across all social sciences fields. The result revealed that there was a partial relationship between Internal Brand (antecedent) and Employee Brand Support (consequence) mediated by CIM. This finding was supported by the identified topic proportions patterns from the topic modeling approach that pointed out a weak topic proportion on the Halal brand identity discussion globally, interestingly the result also found that the Halal brand identity discussion involving the Halal food industry is almost non-existed in Malaysia’s Halal food SMEs. The contributions of this paper were apparent in three major areas which are methodology, theoretical and future suggestions. The utilization of two different model analyses was able to confirm the consistency of the major findings with the literature review which opens up more possibilities for future researchers.

This paper presents a comprehensive study on the design and aerodynamic optimization of a hybrid surveillance drone, conducted within the academic framework of Abu Dhabi University. The research primarily focuses on the comparison between flying wing and conventional aircraft layouts, with an emphasis on reducing drag coefficients and enhancing stall behavior through integrated design strategies. Utilizing SST k-omega viscous model (CFD) simulations, the study evaluates the aerodynamic performance of the drone model, analyzing lift, drag, and pitching moment coefficients against existing models. The paper further explores winglet selection and propeller dynamics, aiming to optimize the lift-to-drag ratio and achieve desired lift-to-weight ratios through careful consideration of propeller-wing interactions. Findings from the study indicate a notable improvement in aerodynamic efficiency, with the new drone model achieving a maximum lift coefficient (Cl, max) of 0.746, a minimum drag coefficient (Cd, min) of 0.039, and a peak lift-to-drag ratio (Cl/Cd) of 8.507. The research contributes significantly to the fields of Aerospace Engineering and Mechanical Engineering, providing a foundation for future advancements in drone design and application.

This paper explores the influence of GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) on the start-up intention through the mediating roles of entrepreneurship educational outcomes; entrepreneurship cognition, entrepreneurship competencies, and innovation spirit of students in higher education institutions in the Middle East. The proposed conceptual framework was empirically tested with partial least squares structural equation modeling using 166 survey responses from fourth-year undergraduate students enrolled in business administration programs in four higher education institutions in the Middle East region. The findings reveal that ChatGPT significantly promotes start-up intention, while ChatGPT has insignificant influence on entrepreneurship educational outcomes. Conversely, the SimVenture platform negatively influences start-up intention. Additionally, the SimVenture platform significantly improves entrepreneurship educational outcomes; thereby entrepreneurship educational outcomes directly influence start-up intention. Furthermore, the mediation paths show ChatGPT has an insignificant indirect influence on start-up intention through the mediating roles of entrepreneurship educational outcomes, while the SimVenture platform exhibits a notable indirect influence on startup intentions through improved entrepreneurship educational outcomes. These findings seek to provide insights for higher education institutions in the Middle East region in leveraging GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) to enhance entrepreneurial knowledge, awareness, competencies, and the spirit of innovation, which leads to promoting students’ intentions to start their businesses. Theoretically, the study contributes to filling the gap in the existing literature between AI tools and entrepreneurship education. This study is among the first to empirically explore the influences of GenAI chatbot (ChatGPT) and AI-based simulation (SimVenture) on entrepreneurship educational outcomes and the intention of students to embark on entrepreneurial ventures. It highlights the need for understanding AI technologies as distinct pedagogical tools.

The study employed simulation-based measurement approaches to evaluate and compare the performance of conventional networks to IRS-enhanced systems in terms of upstream spectral efficiency. The study discovered that applying IRS greatly improves coverage, or transmitting potency, for 6G services.

There is need to address the challenges of organ shortage, through development of tissues and organs with alternatives to those of the allograft-kind. This illustrates the quest behind novel biofabrication strategies such as 3D bio-printing, which is necessary to create artificial multi-cellular tissues/organs. Several findings have been reported in this review. First, the role of ECM components in tissue regenerative medicine is presented. Different ECM components such as collagen, gelatin, elastin, fibronectin, laminins and glycosaminoglycans are concisely examined for their tissue regenerative medicine applications. Next, current state of research on extrusion-based 3D bio-printing techniques and their limitations are reviewed. For example, we show that cell viability is still a challenge with extrusion, while the use of natural polymers such as collagen in improving composites’ mechanical properties is limited. Lastly, we examine unresolved research questions necessary to advance the present state of research in the field.

Brain plasticity is the ability of the brain to change and adapt in response to experience or learning. Its hallmarks are developmental flexibility, complex interactions of genetic and environmental influences, and structure-functional changes comprising neurogenesis, axonal sprouting, and synaptic remodeling. Herein, we review brain plasticity association with non-coding RNAs (ncRNAs). Studies on brain plasticity have important practical implications. Numerous cerebral ncRNAs are linked with neurological disorders, which makes them putative targets for RNA-based diagnostics and therapeutics since molecular characteristics of brain plastic change may reveal disease course and rehabilitative potential of the patient. A new insight into the concept of brain plasticity will provide perspectives on functional recovery following brain damage. The knowledge of this phenomenon will enable physicians to exploit cerebral plastic potential and regulate eloquent networks with timely interventions. Future studies will provide pathophysiologic mechanisms of brain plasticity at macro- and microscopic levels to advance rehabilitation strategies and improve the quality of life in neurological disorders.

Currently, the use of recycled waste plastic in road construction is not just considered ecofriendly but also considered advantageous for improving road performance over time. This research explores using recycled plastic bags, made of low-density polyethylene (LDPE), in asphalt mixtures to improve road performance and manage waste. The study aims to assess the effects of incorporating different amounts of shredded waste plastic into asphalt mixes and determine the optimal percentage. The aggregate used was obtained from Sargodha quarry Pakistan and asphalt used was 60/70 grade obtained from national refinery limited Pakistan. Using the Marshall mix design, the optimum bitumen content (OBC) was found to be 4.5% of the asphalt mix weight. Thirty samples were tested with waste plastic proportions of 6%, 8%, 10%, 12%, and 14% of OBC weight, along with a standard mix. Tests on stability, bulk density, flow, and air voids showed that a 10% plastic content significantly enhances asphalt performance, increasing stability by around 25%. The study recommends using 10% plastic by OBC weight in asphalt mixes for better performance and suggests further research on optimal incorporation techniques of waste materials in asphalt mixtures.

Globally, the fuel demand is increasing rapidly and the conventional method to produce fuel is toxic which causes environmental degradation and climate crisis. Therefore, biodiesel emerges as the substitute to overcome the conventional fuel problems because biodiesel is renewable and non-toxic. Biodiesel synthesis is the process of using fats and oils to produce biodiesel ester and glycerol. Biodiesel can be produced using edible and non-edible feedstock but using the edible feedstocks problems like fuel vs food, nutritional value and economic considerations were raised. Therefore, non-edible feedstocks are being used to overcome problems arising from edible feedstock. As non-edible feedstocks can easily be grown anywhere on land, doesn’t cause imbalance to food economy and has low cost. Many non-consumable seeds like Mahua, Jatropha, Castor, Neem etc. are used to produce biodiesel. Now a days, researchers are using nanoparticles to enhance the stability, quality, and properties of biodiesel. In future, researchers can use algae and microbial oils for production of biodiesel. Research can also use biojet fuel in such a way that it reduces the carbon emission. In future, production of biodiesel can be integrated into circular economy models to reduce environmental waste and impact. In this review we will discuss the production methods for biodiesel, catalysts for biodiesel production, and non-edible feedstock properties of biodiesel.

Recently, the Third Generation Partnership Project (3GPP) introduced the New Radio (NR) technology for Vehicle-to-everything (V2X) communication to enable delay-sensitive and bandwidth-hungry applications in vehicular communication. The NR system is strategically crafted to complement the existing Long Term Evolution (LTE) Cellular-Vehicle-to-Everything (C-V2X) infrastructure, particularly to support advanced services such as the operation of automated vehicles. It is widely anticipated that the 5th generation (5G) NR system will surpass LTE C-V2X in terms of achieving superior performance in scenarios characterized by high throughput, low latency, and enhanced reliability, especially in the context of congested traffic conditions and a diverse range of vehicular applications. This article will provide a comprehensive literature review on vehicular communications from Dedicated Short Range Communication (DSRC) to NR V2X. Subsequently, it delves into a detailed examination of the challenges and opportunities inherent in NR V2X technology. Finally, we proceed to elucidate the process of creating and analyzing an open-source 5G NR V2X module in network simulation-3 (ns-3) and then demonstrate the NR V2X performance in terms of different key performance indicators implemented through diverse operational scenarios.

Renal artery thrombosis is a sporadic serious clinical condition which potentially cause renal infarction. Diagnosis of renal infarction can be delayed or missed due to non specific clinical presentation and overlapping appearance of medical and surgical phenomena. Early diagnosis supported by biochemical and radiological findings while appropriate management potentially improve morbidity and mortality. Persistent abdominal or flank pain with raised LDH and proteinuria on background of thromboembolism risk factors supports the diagnosis. Despite the rarity of the disease rapid identification with prompt medical or endovascular intervention could prevent irreversible renal parenchymal damage.

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.

Data on immunogenicity, immune response persistency, and safety of COVID-19 boosters in patients with comorbidities are limited. Therefore, we aimed to evaluate and compare three different boosters in individuals who received two doses of the BBIBP-CorV vaccine including underlying diseases and healthy cases as control. One hundred forty subjects including 63 ones with at least one of three underlying diseases (UD) (including obesity, hypertension, and diabetes mellitus) and 77 healthy ones (HC) who had received a booster dose (either PastoCovac Plus or PastoCovac or BBIBP-CorV) were enrolled. The presence of SARS-CoV-2 antibodies was assessed before the booster injection and 28, 60, 90, and 180 days after it. Moreover, the adverse events (AEs) were recorded on days 7 and 21 post-booster shot for evaluating safety outcomes. Significantly increased titers of anti-spike, anti-RBD, and neutralizing antibodies were observed in both UD and HC groups 28 days after the booster dose, although the titer rise of anti-spike IgG and anti-RBD IgG was insignificantly inferior in the UD group compared to the HC group. All antibodies’ titers declined, with no significant differences between the two groups over time. Notably, all specific antibodies persisted up to 180 days; particularly the neutralizing antibody in both groups. Furthermore, no significant difference in antibody levels was observed between each UD subgroup and the HC group, except for neutralizing antibodies in the hypertension sub-group. PastoCovac Plus and PastoCovac boosters induced higher antibodies’ fold rise in UD individuals than BBIBP-CorV booster recipients. Safety outcomes did not show any serious AEs after the booster injection. The overall incidence of AEs post the booster injection was higher in the UD group than the HC group. Furthermore, the highest systemic AEs rate was reported in the UD group receiving the BBIBP-CorV booster. In conclusion, administration of COVID-19 boosters can equally induce robust and persistent humoral immune responses in individuals with or without UD primarily vaccinated with 2-doses of the BBIBP-CorV. Protein-based boosters with higher antibodies' fold rise and lower AEs in individuals with comorbidities might be considered a better choice for these individuals.

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.

Films obtained from a main biopolymer phase of fish gelatin (GelA, 3% w/w) encapsulated with cinnamon essential oil (CEO, 0.03 – 0.48% v/w) and loaded with bacterial cellulose nanocrystals (BCNCs, 0.06% w/w) at pH = 3.5 were characterized. CEO-GelA/BCNCs films were prepared by casting and analyzed in terms of thickness, light transmittance (TT) and haze (H), surface hydrophobicity, tensile properties, chemical composition, and water solubility (WS). All tested films displayed outstanding optical properties (TT &gt; 89.4%), with haze slightly exceeding a 3% value only when the highest CEO loading was used during nanoemulsion preparation. The CEO plasticizing effect yielded an increase in the elongation at break (EAB, from 0.84% up to 3.79%), which corresponded to a decrease in tensile strength (TS, from 8.98 MPa down to 1.93 MPa). The FT-IR spectra of films revealed a good degree of interaction among single nanoemulsion components via hydrogen bonding. The intrinsic hydrophobic nature of the CEO negatively impacted the WS (from 52.08% down to 8.48%) of the films. Results obtained in this work confirmed the possibility to produce packaging systems from renewable sources to be potentially used in the form of edible films/coatings for preserving water-sensitive food products, both vegan-based (fruits/vegetables), and animal-based (meat/seafood).

Aim: The purpose of this examination is determining predictors to oral health behaviors predict in Iranian students in district 1 Tehran based on the health belief model with added commitment to plan construct. Methods: This cross-sectional study were randomly selected conducted on 351 eligible four grade female students in the first district of Tehran, Iran in 2017. The random Multi‑stage random cluster sampling method was used to recruit students. The inclusion criteria were being graded, four female students (aged 9-11 years), or education at the fourth grade of one of the elementary schools studied in the first district of Tehran and, The health of the student from a physical and psychological of view. Logistic regression analysis was used to identify the variables that predict oral health behaviors. To assess the predictors of oral health behaviors, all the expanded Health Belief Model constructs with the construct of “Commitment to Plan of Action” from “Health Promotion Model were examined as risk factors to see if they influence on the probability of brushing behavior and dental floss behavior occurrence and were interpreted through odds ratio (OR). SPSS version 19 was used to analyze the data. Results: Totally 308 four grade female students with mean age of 9.32 ± 0.81 years old took part in the study. The total 31.8% of the students reported that they were brushing behavior less than twice a day and 55.2% students claimed, use of dental floss behavior once a week or less than once a day. The results indicated that perceived self-efficacy (OR=1.46, 95% CI=0.57-3.78, P<0.001), Commitment to plan (OR=1.13, 95% CI=1.04-1.23, P<0.001) and Cues to action (OR=1.42, 95% CI=1.14–1.76, P=0.002) were the significant predicting variables which is the key factor of brushing twice a day, and use of dental floss once a day or more (OR=1.02, 95% CI=0.23-3.53, P=0.003). Conclusion: This study has shown the effectiveness of the health belief model with added commitment to plan construct to predict oral health behavior in female students. So, it seems that the model as a framework for designing training programs to improve students to improve oral health behavior can be used.

Wear phenomena on natural stone of the cultural heritage are induced from various sources. Consequently, the development of multifunctional protective materials which combine two or more useful properties is an effective strategy to address the synergistic effects of various wear mechanisms. A multifunctional coating, consisting of a silane-based precursor and zinc oxide (ZnO) nanoparticles (NPs), is produced and tested for the protection of limestone. The hybrid coating combines the following three properties: superhydrophobicity, including water-repellency, photocatalytic self-cleaning and biocidal activity. The relative concentration of the NPs (0.8% w/w), used for the suggested composite coating, is carefully selected according to wetting studies, colourimetric measurements and durability (tape peeling) tests. The non-wetting state is evidenced on the surface of the composite coating by the large contact angle of water drops (≈153°) and the small contact angle hysteresis (≈5°) which gives rise to the physical self-cleaning scenario (lotus effect). The photocatalytic chemical self-cleaning is shown with the removal of methylene blue, induced by the UV-A radiation. Moreover, it is shown that the suggested coating hinders the incubation of E. coli and S. aureus as the inhibition were 94.8 and 99.9 %, respectively. Finally, preliminary studies revealed the chemical stability of the suggested coating.

The goal of the investigation is to increase coverage, which is ensure better transmission around IoT services using IRS within a two-tier 6G system comprising of one micro cell tier running beneath a macro cellular tier. As a result, the study tested and compared the reliability of traditional connectivity with IRS-enhanced connectivity in terms of upstream end-to-end latency using computer-based measuring methods. The study concluded that the implementation of IRS greatly improves coverage, which means transmission effectiveness, for 6G services.

Spinal Cord Injury in children can be a devastating condition in children with profound implications for their overall health and quality of life. In this review we aim to provide a concise overview of the key aspects associated with SCI in pediatric population. Firstly, we discuss the etiology and epidemiology of SCI in children, highlighting the diverse range of causes. We explore the unique anatomical and physiological characteristics of the developing spinal cord that contribute to the specific challenges faced by pediatric patients. Next, we delve into the clinical presentation and diagnostic methods, emphasizing the importance of prompt and accurate diagnosis to facilitate appropriate interventions. Furthermore, we approach the multidisciplinary management of pediatric SCI, encompassing acute medical care, surgical interventions, and ongoing supportive therapies. Finally, we explore emerging research and innovative therapies in the field and we emphasize the need for continued advancements in understanding and treating SCI in children to improve their functional independence and overall quality of life.

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.

The term Air Quality (AQ) pertains to the standard of air, which has a direct impact on the health and well-being of individuals. It is crucial to maintain high Air Quality (AQ), for better health and productivity. Though most of people spend more time indoors nowadays, it is not to be overlooked that the pollutant concentration in indoor air is directly linked to the outdoor air quality. Due to the changes in occupancy patterns, outdated maintenance of ventilation systems, and structural flaws in buildings, the indoor air is significantly polluted; pollutants emitted outdoors enter indoor air through open windows, ventilation systems and infiltration. In this paper, we have proposed a new technique, DTMCPM (discrete-time Markov chain (DTMC) model for the analysis and forecasting of AQ, using the power method). Specialized sensors and equipment to measure different pollutants, such as particulate matter, ozone, nitrogen dioxide, sulfur dioxide, benzene and toluene, are used to collect data by the Institute of Pakistan Air Quality Monitor in various cities; the data is converted to US EPA standard for all pollutant species. This data is used to calculate the transition matrix, steady-state values and mean return rate for the analysis of IAQ. The calculated and actual return rates have been compared and the proposed model is found to have a low average prediction error of 2.356%.

For a considerable length of time synthetic composts are utilized to satisfy the dirt necessity of supplements and yield, however huge measure of these substance manures are hazardous for condition, advantageous microorganisms, creatures, and people also. In this way, natural inviting and savvy biofertilizers are utilized. Biofertilizer are the substances which contain microorganisms those microorganisms might be growths, microscopic organisms, and protozoa which have capacity to build ripeness of soil by Nitrogen obsession, Phosphorous solubilization, and Iron sequestration. These cycles convert insoluble type of supplements into solvent structure and make it accessible to the foundations of plant which effectively take them up and use them. There are assortment of the yields whose profitability can be expanded by applying biofertilizer, for example, rice, oat, and other grain crops. In this audit we experience the method of utilization of biofertilizers, and how the assistance the plants and in which they help.

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).

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.

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.

With the advent of digitization, the integration of advanced technologies in operations and projects across all sectors is in progress. Accordingly, efforts are being made to utilize cutting-edge technologies to improve the circularity of the built environment. This study aimed to review the existing applications and limitations of building information modeling (BIM) tools for circular economy (CE) implementation from this perspective. A literature review was conducted to provide an overview of the use of BIM tools for conducting life cycle assessments, energy analysis, waste management, and formulation of material passports for buildings. It was found that BIM tools, which are available across all life cycle phases of building, have a great potential to improve the circularity of the sector. The overview provided on the use of various BIM tools may help stakeholders of the built environment understand the role of BIM for CE adoption in the sector.

High antioxidants level in food is gradually becoming popular because of the enhanced risk of oxidative stress in humans. Bread wheat is rich in vital antioxidants but its major bioactive compounds are not available for the human. This study was conducted with the aim to enhance the phytochemical constituents and antioxidative activity of wheat grains by fermenting it with the use of Bacillus subtilis KCTC 13241. The antioxidative potential was determined by DPPH (2,2-diphenyl-1-picryl- hydrazyl) and ABTS (3-ethyl-benzothiazo- line-6-sulfonic acid) radical scavenging assay as well by the concentration of amino acids, flavonoids, minerals, carbohydrates, and phenolic compounds. Different varieties showed different free radical scavenging potential on fermentation, which was significantly high with respect to their corresponding unfermented wheat varieties. The highest potential was found in a fermented wheat variety named Namhae and this combination can be used in the pharmaceutical and food industries.

We introduce the notion of generalized contraction on dualistic partial metric spaces. A fixed point theorem for mappings satisfying above mentioned contraction is obtained. Some consequences of our result are obtained. We construct an example to demonstrate the effectiveness of our result among the corresponding results in partial metric spaces. Our results provide substantial generalizations and improvements of several well known results existing in the comparable literature. We discuss an application of our fixed point results to show the existence of solution of functional equations.

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.

Thermal plasma technique is becoming prominent in the treatment of variety of waste ranging from municipal solid waste, incinerator residue, hospital waste, electronics waste and industrial sludge. Application of the new treatment technology to petroleum sludge requires information on the nature and characteristics of the sludge that will be use to optimize the treatment system. In this investigation, petroleum sludge obtained from Petronas Melaka was characterized for its physical and chemical features. Proximate and ultimate analysis as well as determination of elemental composition were carried out. The sludge was found to contain high moisture (78.91%), low ash (5.06%), low volatiles (5.52%) and high fixed carbon (10.51%). The sludge has a TOC of 54.48% and HHV of 23.599MJ/kg. Despite the high moisture content, the higher heating value (HHV) is high when compared to literature values. The high value of HHV may be associated with the high fixed carbon, low ash content and high value of TOC. The apparent density of the sludge is slightly lower. Fourteen heavy metals are detected in significant quantities. Proper waste management that will safely dispose the sludge is required. The waste disposal technique should take into cognizant the possibility of leaching of heavy metals into ground water on one hand and the gasification of lighter ones on the other.

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.

Sustainable development and education in sustainable development (ESD) are crucial and interconnected topics under wide discussion today. Young adults need to be educated in how to help bring about change. Thus, the dynamic relationship between school and home is important. In education, use could be made of English language classes, especially since English is a global language that enables access to the sustainability literature and facilitates discussion in global conferences and meetings. Consequently, English as a foreign language (EFL) students could gain confidence from being able to understand and use the terminology of, for example, sustainability. In this paper, the use of competence-based language teaching (CBLT) is proposed as a means of introducing sustainability topics into EFL writing classes at a university in Saudi Arabia. Students’ competence in sustainable development is therefore considered based on their ability to write about suggested sustainable development topics, measured on a scale for five dimensions of ESD proposed by Michelsen and Fischer (2017). The results demonstrate that the students possessed competence in sustainable development and intended to participate in its implementation. Thus, recommendations are made for the stakeholders in the education sector to include sustainable development topics as an initial offering in the curricula of various specialties using the CBLT approach.

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.

Different materials prepared by appropriate synthesis methods and applying different post syn-thesis processes could be used as hole transport layer in perovskite solar cells. In the present work annealing was employed to enhance physical properties of copper oxide (CuO) thin films fabricated via spin coating technique and the effects of annealing on their optical, morphological, electrical and structural properties were investigated. The XRD findings revealed the polycrystalline structure with large crystallite size. Optical spectra showed larger transmission beyond the visible region, reduced absorption, and a decrease in bandgap energy from 1.93 eV to 1.81 eV. The Ag phonon modes at 299.22 and 304 cm-1 shown by the Raman shift confirmed the development of the CuO pure phase. FTIR spectra showed the Cu-O stretching band at 759, 764 and 446 cm−1. SEM revealed the agglomeration of nano crystallites with large grains on the surface. The carrier type conductivity of annealed CuO thin films was determined via two-point hot probe technique. Low resistivity values were determined via four-point probe method. These findings indicate that CuO thin films may be successfully used as a companion with an n-type layer in p-n junction solar cells, and a hole transport layer in perovskite solar cells.

Structural design optimization (SDO) plays a pivotal role in enhancing various aspects of construction projects, including design quality, cost-efficiency, safety, and structural reliability. Recent endeavors in academia and industry have sought to harness the potential of Building Information Modeling (BIM) and optimization algorithms to optimize SDO and improve design outcomes. This review paper aims to synthesize these efforts, shedding light on how SDO contributes to project coordination. Furthermore, the integration of sustainability considerations and the application of innovative technologies and optimization algorithms in SDO necessitate more interactive early-stage collaboration among project stakeholders. This study offers a comprehensive exploration of contemporary research in integrated SDO employing BIM and optimization algorithms. It commences with an exploratory investigation, employing both qualitative and quantitative analysis techniques following the PRISMA systematic review methodology. Subsequently, an open-ended opinion survey was conducted among construction industry professionals in Europe. This survey yields valuable insights into the coordination challenges and potential solutions arising from technological shifts and interoperability concerns associated with widespread SDO implementation. These preliminary steps of systematic review and industry survey furnish a robust knowledge foundation, enabling the proposal of an intelligent framework for automating early-stage sustainable structural design optimization (ESSDO) within the construction sector. The framework ESSDO addresses the challenges of fragmented collaboration between architects and structural engineers. This proposed framework seamlessly integrates with the BIM platform, i.e., Autodesk Revit for architects. It extracts crucial architectural data and transfers it to the structural design and analysis platform, i.e., Autodesk Robot Structural Analysis (RSA), for structural engineers via the visual programming tool Dynamo. Once the optimization occurs, optimal outcomes are visualized within BIM environments. This visualization elevates interactive collaborations between architects and engineers, facilitating automation throughout the workflow and smoother information exchange.

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

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

This study investigated the relationship between environmental, social, and governance (ESG) disclosure and the performance of Saudi Arabian companies. We analysed panel data from the 100 non-financial companies listed on the Saudi stock exchange (Tadawul) from 2017 to 2022. Using fixed effects, random effects, and generalised method of moments (GMM) models to account for endogeneity concerns, we examined the impact of ESG disclosure on the return on assets (ROA), return on equity (ROE), and Tobin’s Q. An ESG index was constructed through principal component analysis of individual environmental, social, and governance scores. Our results indicate a significant positive relationship between ESG disclosure and companies’ key performance variables across all models. These findings are consistent with stakeholder theory and signalling theory, suggesting that comprehensive ESG practices can lead to better financial performance and serve as a positive signal to stakeholders. The study also reveals sector-specific differences, with non-manufacturing firms showing stronger positive relationships between ESG disclosure and performance measures compared to manufacturing firms. Additionally, we find that firm size, age, and liquidity are important factors influencing the ESG–performance relationship. This research contributes to the growing literature on ESG and corporate performance in emerging markets, offering valuable insights for policymakers, investors, and corporate practitioners in Saudi Arabia’s evolving sustainable business landscape. Our findings underscore the importance of ESG disclosure in driving sustainable and responsible business practices in the region.

The consumption and sale of fresh-cut products and salads have been growing tremendously in the present era. Therefore, the microbial safety of such products is of great concern. In the current study, a survey of general microbiological safety of fresh-cut produce and salads at quick-service restaurants (QSR) was undertaken across the Kingdom of Saudi Arabia. These findings were compared with microbiological criteria for foodstuffs by Saudi standards, metrology, and quality organization SASO-GSO-1016. Of the 82 samples of fresh-cut produce, 7% of samples were found to be unsatisfactory or beyond the acceptable limits. TPC count was unsatisfactory at 22%, coliform at 48%, and Staphylococcus aureus at 4%. For 108 samples for fresh salads, 11% of samples were found to be unsatisfactory or beyond the acceptable limits,13%, 27%, 4%, and 27% of samples showed an unsatisfactory range of TPC, coliforms, S. aureus, and Escherichia coli, respectively. The fresh-cut produce and salads were microbiologically safe in the central region compared to the eastern region followed by the western region. The relatively higher count was found in green pepper, mixed vegetables, and lettuce followed by fresh-cut onions and coleslaw salads. No Salmonella was detected in both fresh-cut produce and salads. The restaurants should be more stringent in their processing to ensure the consumer safety. Washing and sanitization of produce is the only way to reduce the diffusion of food borne pathogens.

Photonic sensors are innovative devices that leverage the principles of light-matter interaction to enable precise and efficient detection of various physical parameters. These sensors operate by exploiting the interaction between photons, the fundamental particles of light, and matter. The light propagates through an optical waveguide, and a portion of the electromagnetic field extends beyond the waveguide surface, forming the evanescent field. This evanescent field interacts with the surrounding medium, allowing for highly sensitive detection of changes in the refractive index or properties of nearby substances. The underlying concept involves the modulation of light properties, such as intensity, wavelength, or phase, when it interacts with a target substance or undergoes changes in the surrounding environment. The advancements in photonic sensor technology have paved the way for enhanced sensitivity, selectivity, and miniaturization, making them invaluable tools in diverse industries. The ability to harness light-matter interactions for sensing purposes not only facilitates highly sensitive measurements but also allows for non-intrusive and remote monitoring, contributing to the evolution of smart and connected systems. In this overview, the material platforms and waveguide structures are explored that can make highly sensitive photonic devices applied to gas and biosensing applications.

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (WMC) and a Directional Charger (DC) are deployed to transmit wireless energy to the sensor node to improve the network's throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by joint scheduling of MC and DC. We prove we can transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selects a total horizon of equipped with DCs, which selects the horizon of each specific area with based on its antenna orientation. DC's orientation is scheduled for each time slot. Furthermore, multiple MCs are used to transmit energy for sensor nodes which cannot be covered by DCs. We divided the Rechargeable Wireless Sensor Network into several zones via the Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtain the optimal charging location of MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research is to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network can increase, and the end delay can be maximized with joint energy transmission. Extensive simulation results demonstrate that our RWSNs are designed significantly to improve network lifetime over the baseline method.

City zones have become increasingly overcrowded as a result of the extensive population widening ratio and the swift relocation of people from villages. The traffic monitoring process is a major issue in these areas due to the massive traffic flow on the roads. This research proposed a cluster-based improved authentication and communication scheme for an Intelligent Transportation System in Vehicular AdHoc Networks (VANETs). Our primary objective is to optimize resource sharing in vehicular communication. We enhanced the reliability, scalability, and stability of fast-moving VANETs by introducing cluster-based routing schemes for Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. We used a third-party certification authority for vehicle authentication to address security and privacy concerns. Our proposed schemes support minimizing the end-to-end (E2E) delay and route request, in addition to reducing link failure. Our schemes’ leading yield includes throughput enhancement, TCP Socket Initialization time minimization, TCP handshake response speedup, and DNS lookup improvement. The schemes are centred on short-range peer-to-peer (P2P) wireless communication in a 400-meter radius cluster. This includes innovative P2P wireless communications on VANET using minimized resources. The proposed schemes deliver a secure authentication mechanism with a securely generated vehicle authentication key provided by a certification authority. Furthermore, we have developed RESTful APIs in vehicular communication for implementation purposes, and also offered and implemented algorithms for resource sharing regarding V2V and V2I communication. Ultimately, we evaluated the performance of our experiments.

Vaccination against SARS-CoV-2 has significantly contributed to the recent pandemic control. COVID-19 vaccines are available with different platforms and the primary clinical trials results presented acceptable safety profile of the approved vaccines. Nevertheless, the long-term assessment of the adverse events or rare conditions need to be investigated. The present systematic review, aimed at classification of Iranian case reports following COVID-19 immunization. To achieve this goal, the related published case reports were explored via PubMed, Web of Science and Google scholar according to PRISMA guideline and available up to 14th Dec, 2022. Out of 437 explored studies, the relevant data were fully investigated which totally led to 40 studies including 64 case reports with a new onset of a problem. The cases were then classified according to the various items such as the type of adverse event manifestations and COVID-19 vaccine. The reported COVID-19 vaccines in the studied cases included Sinopharm, AstraZeneca, and COVAXIN. The results showed that the adverse events presented in 8 different categories from which cutaneous problems accounted as the most prevalent manifestations (43.7%) in which rare diseases were also screened such as Steven-Johnson syndrome, Morphea and Toxic Epidermal Necrolysis. Notably, almost 60% of the cases had no comorbidities. Moreover, the obtained data revealed nearly half of the incidences occurred after the first dose of injection and the mean duration of improvement after the symptom onset was 18.72±24.69 days. 73% of all the cases were either significantly improved or fully recovered. Although the advantages of COVID-19 vaccination is undoubtedly significant, the high risk individuals including those with a history of serious disease or comorbidities immunodeficiency conditions should be vaccinated with the utmost caution.

Artificial neural networks are highly efficient tools for numerous kinds of tasks, which include forecasting, regression, and recognizing patterns in classification and pattern recognition. An artificial neural network is a model for processing information using essential components called artificial neurons that are inspired by the human brain. Two significant aspects of delayed neural networks dynamic activity are their stability and the synchronization phenomenon. This work presents a bidirectional associative memory (BAM) incommensurate fractional-order delay neural network model. The incommensurate fractional orders are calculated within the stability region through the corresponding eigenvalues and their points of equilibrium. The involved neuron states are synchronized with each other for the calculated incommensurate stable fractional orders. The reduction of the time delay will give more relaxation time for the state variable $w_4(t)$ to be stable as compared to the state variable $w_3(t)$ due to the induction of extra activation functions. The theoretical findings of this research have significance for providing guidance and controlling the dynamic behavior of artificial neural networks.

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.

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.

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.

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

The 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.

Drinking water supplies in the developing world often serve as a biosphere for various organisms. Viral gastroenteritis is a neglected area of research in Pakistan, there is no data for the prevalence of enteric viruses in drinking water of the largest city of Karachi. The present study aimed to provide a survey of the existence of enteric viruses: human adenovirus (HAdV), human enteroviruses (hEV), and genotype A rotavirus (GARV) in tap water. Using a simple PCR approach, we detected 20%, 43%, and 23% of HAdV, hEV, and GARV in tap water respectively. We have also shown an overall quality of tap water at the pumping station and consumer tap. Moreover, we assessed the efficiency of small-scale water treatment methods for the removal of viruses.

Construction 4.0 is witnessing exponential growth in Digital Twin (DT) technology developments and applications, revolutionizing the adoption of Building Information Modelling (BIM) and other emerging technologies used throughout the lifecycle of the built environment. BIM provides technologies, procedures, and data schemas representing building components and systems. At the same time, DT enhances this with real-time data for cyber-physical integration, enabling live asset monitoring and better decision-making. Despite being in the early stages of development, DT applications have rapidly progressed in the AEC sector, resulting in a diverse literature landscape due to the various technologies and parameters involved in fully developing the DT technology. The intricate complexities inherent in digital twin advancements have confused professionals and researchers. This confusion arises from the nuanced distinctions between the two technologies, i.e., BIM and DT, causing a convergence that hinders realizing their potential. To address this confusion and lead to a swift development of DT technology, this study presents a holistic review of the existing research focusing on the critical components responsible for developing DT applications in the construction industry. The study identifies five crucial elements: technologies, maturity levels, data layers, enablers, and functionalities. Additionally, it identifies research gaps and proposes future avenues for streamlined DT developments and applications in the AEC sector. Future researchers and practitioners can target data integrity, integration and transmission, bi-directional interoperability, nontechnical factors, and data security to achieve mature digital twin applications for AEC practices. This study highlights the growing significance of DTs in construction and provides a foundation for further advancements in this field to harness its potential to transform built environment practices.

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.

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.

The fourth industrial revolution has resulted in the digitalization of projects and operations across all sectors. Accordingly, efforts are being made to utilize advanced technologies to improve the energy efficiency of the building sector. This study has reviewed the current application and limitations of cutting-edge technologies in this regard. An overview of the use of the Internet of Things (IoT), artificial intelligence (AI), digital twin (DT), and building information modeling (BIM) for energy efficiency of buildings has been provided. It has been found that the use of Industry 4.0 technologies, during the construction and operational phase of buildings, has a great potential to reduce energy consumption and emissions of the sector. This study may help stakeholders of the built environment to understand the role of industry 4.0 tools for energy efficiency of the sector.

Biogeochemical models estimate soil organic carbon (SOC) sequestration, crop growth, and yield. The DeNitrification and DeComposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice-wheat rotation under organic/inorganic fertilizations with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC under-predicted harvestable grain C-biomass of rice by 29.22% to 42.14% and over-simulated grain C-biomass of wheat by 55.01% with equal amounts of NPK and animal manure applied under CT. However, after calibration by adjusting default values of soil/crop parameters, DNDC simulated harvestable grain C-biomass of both crops very close to observed values (only -2.81% to -6.17% less). DNDC also predicted effects of nutrient management practices on grain C-biomass of rice/wheat under CT/RT using d-index (0.76 to 0.96) and the calculated root mean squared error (RMSE of 165.36 to 494.18 kg C ha-1). DNDC simulated SOC trends for rice-wheat using measured values of several statistical indices. Regression analysis between modeled and observed SOC dynamics was significant with R2 ranging from 0.35 to 0.46 (p < 0.01), and intercept ranging from 0.30 to 1.34 (p < 0.65). DNDC demonstrated that combined inorganic and organic fertilization may result in higher C-biomass and more SOC sequestration in rice-wheat systems.

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

The efficiency of a new composite material of the layered double hydroxide (LDH) of ZnMgAl and rice husk biochar (RHB) for the removal of Cu(II) and Pb(II) ions from synthetic wastewater was investigated in this study. The images of scanning electron microscope showed extremely fine crystalline LDH particles decorated on the rough surface of the RHB while the succesful formation of the composite adsorbent (LDH/RHB) was confirmed by the corresponding energy dispersive X-ray and the Fourier-transform infrared spectroscopy. An equilibrium contact time of 30 and 15 min for Cu2+ and Pb2+, respectively, was proposed for the optimum performance of the batch adsorption process. The dose of the LDH/RHB adsorbent was optimized at 0.4 g yielding maximum adsorption capacities of 117 and 124 mg g-1 for Cu2+ and Pb2+, respectively, with corresponding maximum removal efficiencies of nearly 94% and 99%. The initial meta concentrations was optimized at 50 or 60 mg L-1 corresponding to maximum removal efficiencies or adsorption capacities. For the changing initial metal’s concentration, the removal efficiencies remained unchanged for up to 50 mg L-1 and decreased by about 50% by increasing the initial concentrations from 50 to 100 mg L-1. The adsorption capacities observed a linear increasing trend by increasing the initial concentrations from 5 to 60 mg L-1 and remained unchanged afterward up to 100 mg L-1. A solution pH of 6.0 yielded optimum results with an increasing trend in adsorption capacities and percentage removal by changing the solution pH from 2.0 to 7.0. Based on the best-fit of the pseudo second-order kinetic model to the experimental data, the chemisorption was suggested to be the controlling mechanism of adsorption. The fitting of the Langmuir model suggested a monolayer sorption of Cu2+ and Pb2+ in addition to a heterogeneous adsorption as supported by the fitting of the Temkin isotherm. The application of the Dubinin–Radushkevich isotherm proposed a physical adsorption (mean free energy of adsorption less than 8 kJ mol−1) of both heavy metal ions on the surface of the LDH/RHB adsorbent.

Bifenthrin is a synthetic pyrethroid insecticide. It is used worldwide against different agricultural pests. Nervous system (NS) is the primary target organ for the pyrethroid group of insecticide which causes hyper excitation. The aim of this study to determine the effects of bifenthrin on the brain of rabbits. The animals were treated for 14 days with three different doses (1/5th, 1/10th and 1/15th of LD50) of bifenthrin. Neurotransmitters and Histopathological alterations were observed at the end of the experiments. Neurochemical parameters were checked 24 hours after the last dose. Significant changes in the level of biogenic amines were observed. The levels of epinephrine (EPN), dopamine (DA), 3, 4 dihydroxyphenylacetic acid (DOPAC) were decreased as compared to controls group with the increase in insecticide. However, norepinephrine (NE) and homovanillic acid (HVA) levels were found to be significantly higher than those of the controls. A significant reduction in the activity of the acetyl cholinesterase enzyme was found in the frontal cortex’s erythrocyte, plasma and brain of rabbit with the increase in administered bifenthrin. The oxidative stress showed significantly decrease in the activity of glutathione peroxidase (GPx), superoxide dismutase (SOD), catalase (CAT) and glutathione S- transferase (GST) with the increase in dose of bifenthrin. Histopathological study of brain tissues showed normal GC (glial cells) and NC (neuronal cells) in the frontal cortex of brain tissue of control groups while the treated groups showed evacuation (odema), elongation of pyramidal nucleus, glial cell damage and brain haemorrhage.

The comfort of human life depends on the quality, size, and reliability of the infrastructure projects. In the infrastructure systems, rapid growth is found, where the economic and sustainable impact has become a topic of significant concern for policies and government officials. To achieve con-straints of sustainable development, all the policies and actions over the infrastructure project's life cycle must be assessed. Decision-makers have adopted approaches for economic, social, and en-vironmental initiatives through Life Cycle Assessment (LCA) and Life Cycle Cost Analyses (LCCA) of infrastructure projects. The purpose of this review is to highlight the impact of per-forming LCA and LCCA in infrastructure projects. To achieve this goal, a systematic literature review methodology is adopted in which renowned databases, i.e., Web of Science, Science Direct, Emerald and Scopus were selected to extract the relevant literature. Using the PRISMA approach, 1251 publications were identified which were then filtered and 55 documents were included in the final review. In the extracted publications most, researchers were biased toward LCA and LCA individually, whereas few focused on integrated LCA and LCCA. The researchers assessed the costs and impact associated with the infrastructure project while there were less focused on the environmental cost. Besides this, techniques of economic, social, and environmental growth of infrastructure projects have been emphasized during the design phase because of substantial relations between infrastructure design and operation management. Moreover, a conceptual framework has been developed that will assist the decision-makers to consider the effects of LCA and LCCA on various aspects of the infrastructure project and how it impacts sustainability. In the last, a case study was performed to assess the developed framework with the incorporation of environmental impact cost.

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.

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.

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.

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.

As urban populations and transport demand expand, Pakistani metropolitan centers face traffic congestion and environmental challenges. This research aims to assess carpooling (CP) to alleviate traffic congestion, fuel demand, and pollution. This study was conducted in Islamabad, a fast-growing city with a high percentage of personal cars in Pakistan, to assess the travelers’ perceptions, and public acceptance of CP and determine the effects on traffic congestion, fuel consumption, and CO2 emissions. The investigation of travelers' perceptions towards CP was conducted through a stated preference questionnaire survey. A total of 700 responses from one million population of Islamabad were collected as per Sloven's Formula. SPSS and Smart PLS software were used for data analysis. Various factors influencing peoples’ travel perceptions, mode choice, and tendency to carpool, were categorized to evaluate the relationship between travelers’ perception and the tendency to carpool in the presence of mode choice as a mediating variable. Confirmatory factor analysis was conducted in SMART PLS to refine the number of scale items for each variable. One-way ANOVA, step-wise and mediated multiple regression analysis was conducted in SPSS to check the direct and mediating relationship between different variables. Moreover, the probability of reducing traffic congestion and assessing fuel reduction resulting from CP was also analyzed. The findings imply that travelers’ mode choice acts as a mediator between the relationship between travelers’ perception and tendency to carpool. Furthermore, results predicted that CP might reduce 0.25 million cars on the road, about 33.6% of the total private vehicles. Thereby, saving up to 546,810 barrels per year, about 3.42% of Pakistan's total petrol consumption. The findings of this study could be helpful for transport planners, environmentalists, and policymakers to implement carpool systems. Thus, reducing traffic congestion and harmful CO2 emissions and maintaining a sustainable environment.

Blockchain innovation stands out enough to be noticed and adopted in various countries and organizations around the world. Many businesses, including finance, medical services, inventory networks, security, libraries, and the internet of things, are currently under attack. For the benefit of the blockchain, many businesses incorporate blockchain technology into their frameworks. Despite its solidarity, blockchain faces a few challenges in security, protection, adaptability, and other areas. This paper examines the forward leap in blockchain innovation, as well as its applications and challenges. While many blockchain papers focus on digital currencies, IoT, and security, this paper focuses on the overall best in a class of blockchain innovation, its new twists and turns, and choices, particularly in areas other than cryptographic forms of money. The investigators' goal is to provide a thorough audit of the cryptography underlying blockchain to better understand the innovation. The examiners also conduct general research on people and venture blockchains, as well as future exploration opportunities and their implications for blockchain innovation.

Gas sensing is crucial for detecting hazardous gases in industrial environments, ensuring safety and preventing accidents. Additionally, it plays a vital role in environmental monitoring and control, helping to mitigate pollution and protect public health. Integrated photonic gas sensors are important due to their high sensitivity, rapid response time, and compact size, enabling precise recognition of gas concentrations in real-time. These sensors leverage photonic technologies, such as waveguides and resonators, to enhance performance over traditional gas sensors. Advancements in materials and fabrication techniques could further improve their efficiency, making them invaluable for environmental monitoring, industrial safety, and healthcare diagnostics. In this review, we delved into photonic gas sensors that operate based on the principles of evanescent field absorption (EFA) and wavelength interrogation methods. These advanced sensing mechanisms allow for highly sensitive and selective gas detection, leveraging the interplay of light with gas molecules to produce precise measurements.

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.

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.

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

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

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