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

Let R be a commutative graded ring with unity, S be a multiplicative subset of homogeneous elements of R and P be a graded ideal of R such that P\bigcap S=\emptyset In this article, we introduce several results concerning graded S-prime ideals. Then we introduce the concept of graded weakly S-prime ideals which is a generalization of graded weakly prime ideals. We say that P is a graded weakly S-prime ideal of R if there exists s\in S such that for all x, y\in h(R), if 0\neq xy\in P, then sx\in P or sy\in P. We show that graded weakly S-prime ideals have many acquaintance properties to these of graded weakly prime ideals.

Non-Vitamin K Oral Anticoagulants (NOACs) have revolutionized anticoagulant therapy, offering improved safety and efficacy over traditional agents like warfarin. This review comprehensively examines the dual roles of NOACs—Apixaban, Rivaroxaban, Edoxaban, and Dabigatran—not only as anticoagulants but also as modulators of inflammation via Protease-Activated Receptor (PAR) signaling. We highlight the unique pharmacotherapeutic properties of each NOAC, supported by key clinical trials demonstrating their effectiveness in preventing thromboembolic events. Beyond their established anticoagulant roles, emerging research suggests that NOACs influence inflammation through PAR signaling pathways, implicating factors such as FXa and Thrombin in the modulation of inflammatory responses. This review synthesizes current evidence on the anti-inflammatory potential of NOACs, exploring their impact on inflammatory markers and conditions like atherosclerosis and diabetes. By delineating the mechanisms by which NOACs mediate anti-inflammatory effects, this work aims to expand their therapeutic utility, offering new perspectives for managing inflammatory diseases. Our findings underscore the broader clinical implications of NOACs, advocating for their consideration in therapeutic strategies aimed at addressing inflammation-related pathologies. This comprehensive synthesis not only enhances understanding of NOACs' multifaceted roles but also paves the way for future research and clinical applications in inflammation and cardiovascular health

In this age of global warming, academics and policymakers are increasingly concerned about firm environmental sustainability success. Therefore, this study aims to investigate whether Environmental, Social and Governance (ESG) performance impacts sustainability performance through the mediating effect of firm innovation. To this end, Structural Equation Modeling (SEM) was deployed to analyze data collected from the employees of manufacturing industries in an emerging economy like Bangladesh. The results revealed that ESG performance significantly enhances the innovation and sustainability performance of manufacturing industries, indicating that the higher the ESG performance of a firm, the greater its innovation and sustainability performance. Furthermore, the results confirmed that firm innovation performance fully mediates the relationship between ESG initiatives and sustainability performance. The findings of this study provide policymakers and industry authorities with valuable insight into the role of ESG and innovation performance in improving sustainability performance. Specifically, the study sheds knowledge on how firm ESG initiatives and innovation performance impact sustainability performance in the manufacturing sector of an emerging economy like Bangladesh.

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.

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.

Establishment of protected areas (PAs) is one of the key global conservation strategies that currently cover approximately 15% of the earth’s land surface. Globally, PA networks are designed to curb the growing anthropogenic pressures in areas with high biological diversity. Despite the importance of PAs in conserving the vanishing biodiversity and unique habitats, many of them are in critical condition due to poor governance thus functioning below the expected level. Moreover, in many developing countries, the PA coverage is below the global standard. Recognizing their contemporary role in conservation, governments have recently agreed to expand the global PA coverage to 17% by the year 2020 (Aichi target 11). This book with eight chapters from different regions of the world provides an overview of the PAs governance, institutional mechanisms, conservation benefits, limitations and challenges associated with their respective policy discourse, integrated management, and functional attributes. Protected areas expect to to play an important role in the long rn in conservation and protection of biodiversity and ecosystems particularly in countries where population pressure and habitat loss are high. Regular intervention, political commitment, and effective governance are essential for the sustainability of PAs across the world. Here, we also attempted to shed some light on future development clues for the sustainable management and monitoring of PAs worldwide.

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.

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.

This analysis examines the wide range of occupational and environmental health risks that affect people in diverse settings. This article emphasizes the importance of conducting exposure and risk assessments to safeguard public health by analyzing various hazards, including arsenic, oil accidents, lead, welding fumes, and mycotoxins. Various methodologies are utilized in these research articles to conduct the study, emphasizing hazard identification, exposure assessment, and risk characterization. The results uncover harmful substances, vulnerable demographics, and occupational duties linked to increased hazards. The report suggests various mitigation strategies, such as targeted interventions, enhanced community efforts, and improved exposure estimates. Notwithstanding the constraints identified in certain studies, the review emphasizes the necessity for continuous investigation to improve assessments of exposure, comprehend the cumulative effects, and examine the wider societal consequences. The significance of sector-specific risk assessments, protective measures, and ongoing monitoring is underscored to ensure mental and physical health protection.

Microplastic contamination, defined as plastic particles less than 5 mm in size, has become a major global environmental concern. This study synthesizes findings from carefully chosen studies undertaken in a variety of geographical areas and ecosystems, offering light on the multifaceted issues posed by microplastic pollution. These articles show the global prevalence of microplastics in both land and marine ecosystems. Seasonal fluctuations in microplastic abundance highlight the cyclical nature of this pollution, with larger amounts being reported during the summer months. Tourism, industrialization, and urbanization have all been highlighted as significant contributors to microplastic contamination, stressing the impact of man-made influences. Wastewater treatment plants (WWTPs) have been identified as hotspots for microplastic release into aquatic habitats, emphasizing the need for enhanced treatment procedures. Because microplastics can build up in the food chain, it underlines the possible health problems connected with microplastic pollution for both aquatic creatures and human consumers. Longitudinal studies, geographical variety, in-depth health and ecological impact evaluations, standardization, and effective mitigation methods should be prioritized in future studies. To address this worldwide issue, interdisciplinary collaboration among scientists, health professionals, politicians, and industrial stakeholders is required.

The important topic of climate change is discussed in this article, with a focus on the historical background and present effects. It draws attention to the differences between climate change and global warming and attributes the faster pace of climate change to human activity, specifically the release of greenhouse gases into the atmosphere. Agricultural practices, deforestation, and the combustion of fossil fuels are examined in conjunction with natural contributors like volcanic emissions. There is discussion of the far-reaching effects of climate change, such as increased temperatures, rising sea levels, harsh weather, and effects on water supplies. The article emphasizes how crucial it is to mitigate and adapt to the effects of climate change by putting in place climate-resilient infrastructure, switching to renewable energy sources, and lowering greenhouse gas emissions. While recognizing the difficulties involved, it examines several approaches, including nuclear energy, wind power, carbon capture and storage, and green building techniques. To successfully address the global challenge of climate change, the conclusion emphasizes the urgent need for joint efforts by scientists, environmentalists, communities, and governments.

Lump sum contracts are specific contracts used in the construction industry to give owners financial certainty and decrease risk while incentivizing contractors to finish projects on schedule and within budget. This article discusses the usage of lump sum contracts in construction projects, covering the types of projects appropriate for lump sum contracts, critical considerations for entering a lump sum contract, and the tender and preliminary expenses involved. The article also covers lump sum contracts' benefits and drawbacks and how owners and contractors allocate risk. Finally, the difficulties of implementing lump sum contracts, such as potential disagreements over scope changes and quality issues, are investigated. In general, lump sum contracts can be a valuable tool for managing construction projects, but their execution necessitates careful planning and collaboration to ensure successful completion.

Good governance in natural resource management (NRM) is one of the most challenging issues in developing countries that often inappropriately embedded in national policies and political agendas. It is, in fact, even more important for countries like Bangladesh with exceptionally high pressure and dependence on its natural resources for sustaining rural livelihoods. Globally, nowadays, good governance is considered as one of the key factor for achieving the goal of sustainable development and biodiversity conservation. Bangladesh, of late has responded to that global zeal by involving local communities in the management of country’s declining forest and other natural resources. The colonial legacy of the forestry sector of Bangladesh was planned and, managed as interim projects through donors’ prescriptions. Thus, institutions, management processes and conservation outcomes were problematic. The conventional approach adopted by colonial and post-colonial regimes for forest management also proved to be inefficient due to its top-down management system. The absolute dependency on donor support, and their prescription sometimes worsened the situation both ecologically and socially. Global, regional and local trends supported the need for a different dimension in the governance paradigms. The introduction of a pluralistic approach, known as co-management in protected areas (PAs) is an example of an attempt whereby shared governance mechanism are implemented to attain the desired goals of conservation that will also address the livelihoods and aspirations of communities living in and around PAs of the country. However, in designing future forest and PA regimes the concern of the external aid support and attached conditions remain a reality that needs to be addressed. Adequate attention should be given to our vanishing biodiversity, culture and community livelihoods through devising an appropriate governance mechanism recognizing and supporting local rights, access and participation in the environmental management. It is now time to mainstream the adhoc nature of governance according to our national conservation strategy and policy frameworks in order to achieve the goals and objectives of the Bangladesh NRM sector addressing the human and community right of people in the specific context of forest protected areas management.

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.

The spectral properties of a one-dimensional photonic crystal bounded by a resonant absorbing nanocomposite layer with the near-zero permittivity have been studied. The problem of calculating the transmittance, reflectance, and absorptance spectra of such structures at the normal and oblique incidence of light has been solved. It is shown that, depending on the permittivity sign near zero, the nanocomposite is characterized by either metallic or dielectric properties. For the first time, the possibility of simultaneous formation of the Tamm plasmon polariton at the photonic crystal/metallic nanocomposite interface and the localized state similar to the defect mode with the field intensity maximum inside the dielectric nanocomposite layer is demonstrated. Specific features of field localization at the Tamm plasmon polariton and defect mode frequencies are analyzed.

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

N6-methyladenosine (m6A) is an RNA modification wherein N6-position of adenosine is methylated. It is one of the most prevalent internal modifications on RNA and regulates various aspects of RNA metabolism. M6A is deposited by m6A methyltransferases, removed by m6A demethylases and recognized by reader proteins, which modulate splicing, export, translation and stability of the modified mRNA. Recently evidence suggests that various classes of non-coding RNAs (ncRNAs) including microRNAs (miRNAs), circular RNAs (circRNAs), and long con-coding RNAs (lncRNAs) are also targeted by this modification. Depending on the ncRNA species, m6A may affect processing, stability, or localization of these molecules. The m6A modified ncRNAs are implicated in a number of diseases including cancer. In this review, I summarize the role of m6A modification in the regulation and function of ncRNAs in tumor development. Moreover, the potential applications in cancer prognosis and therapeutics is discussed.

In this paper, the integrated performance of a modular biomass boiler with an existing industrial Rankine steam heat and power cycle and a supplementary supercritical-CO2 (sCO2) Brayton cycle is analyzed. The aim is to leverage the high efficiency supplementary sCO2 cycle to increase net generation and energy efficiency from the existing biomass boiler. Two sCO2 heater configurations situated within the flue gas flow path are investigated, namely a single convective-dominant heater, and a dual heater configuration with a radiative and a convective heater. A quasi-steady-state 1D model was developed to simulate the integrated cycle, including detailed component characteristics for the Rankine and Brayton cycles. The model solves the mass, energy, momentum, and species balance equations. The system is analyzed for three cases: (i) the existing Rankine cycle without the sCO2 integration, (ii) with the single convective-dominant sCO2 heater configuration, and (iii) the dual sCO2 heater configuration. The results show the required rate of overfiring for the sCO2 configurations, with a 15.3% increase in fuel flowrate resulting in an additional 21.2% in net power output. The model quantifies the impact of the sCO2 heaters, with reduced heat uptakes for downstream boiler heat exchangers. Furnace water wall heat uptake increased due to overfiring, offsetting the reduced heat uptakes at downstream evaporative heat exchangers. The dual configuration has more impact on Rankine cycle operation due to the radiative sCO2 heater placement in front of the second superheater, absorbing some of the direct radiation from the furnace.

In the construction business, which is known for its intricate and strategic nature, it is crucial to have efficient risk management in place. This is because various internal and external elements can cause disruptions to projects. Although the industry has had a historically negative reputation for risk analysis, the methodical process of identifying, analyzing, and managing risks has been more widely recognized and respected. This paper examines the historical progression of construction risk management, highlighting the need to identify and classify risks promptly. The study examines various risk factors related to finance, law, environment, and logistics. The study explores various risk management strategies, such as contract management, risk distribution, evaluation, mitigation, and sharing. The article discusses the significant impact of risk management software, such as Procore, PlanGrid, e-Builder, Autodesk BIM 360, and Oracle Primavera, on improving project results. The study finishes by highlighting the importance of proactive risk management in protecting against financial losses and maintaining a reputation in the construction business.

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.

The increasing expansion of the use of renewable distributed generation (RDG) technologies in electrical networks such as solar plants or wind farms and others has changed the nature of the distributed system and transformed it from the traditional system into an efficient system. This system has led to various technical effects in the host networks, with regard to voltage, electrical energy losses, fault levels, stability and electrical energy quality. These effects are not generic to any network; It can be positive, negative or neutral depending on the structure of the studied network, its operational condition and the various technology of the distributed generators. This research aims to quantitatively correct the effects of (RDG) in terms of voltage and electrical energy losses in the host networks. , since efficient distribution systems contain generators of a special nature and often contain bidirectional flux energy, it was necessary to construct a mathematical model of the distribution system hosting RDG units, which is fundamentally different from the traditional model of defining the reference electrical distribution bus bar. In order to achieve the systematic analysis, evaluation criteria and indicators were determined and mathematical formulas for them were completed. Based on the accomplished mathematical basis, an algorithm designed in such a way that it can be applied to any multi-voltage distribution system and to different distributed regenerative generation techniques is proposed. To demonstrate the algorithm's capabilities, it was tested on typical distribution networks using Factory Power DigSilent software, which is somewhat similar to MATLAB but more suitable for large capacity systems.

Visually impaired people encounter many impediments and challenges in their lives such as related to their mobility, education, communication, use of technology, and others. This paper reports the results of an online survey to understand the requirements and challenges blind and visually impaired people face in their daily lives regarding the availability and use of digital devices. The survey was conducted among the blind and visually impaired in Saudi Arabia using digital forms. A total of 164 people responded to the survey most of them using the VoiceOver function. People were asked about the use of smart devices, special devices, operating systems, object recognition apps, indoor and outdoor navigation apps, virtual digital assistive apps, the purpose (navigation, education, etc.) of and difficulty in using these apps, the type of assistance needed, the reliance on others in using the assistive technologies, and the level of satisfaction from the existing assistive technologies. The majority of the participants were 18 – 65 years old with 13% under 18 and 3% above 65. Sixty-five percent of the participants were graduates or postgraduates and the rest only had secondary education. White Cane, mobile phones, Apple iOS, Envision, Seeing AI, VoiceOver, and Google Maps were the most used devices, technologies, and apps used by the participants. Navigation at 39.6% was the most reported purpose of the special devices followed by education (34.1%) and office jobs (12.8%). The information from this survey along with a detailed literature review of academic and commercial technologies for the visually impaired was used to establish the research gap, design requirements, and a comprehensive understanding of the relevant landscape, which in turn was used to design smart glasses called LidSonic for visually impaired.

Hydrogels have gained significant attention as vehicles for transdermal delivery systems. These polymeric-structured substances have been implemented in pharmaceutical and cosmetic applications. Many factors are employed in their preparation. Light, pH, temperature and specific molecules ligands are commonly used in the production of hydrogels. The Transdermal delivery system (TDD) is a widely used route, used to deliver actives/drugs via the skin. Hydrogels as a TDD have been shown to be efficient in enhancing the efficacy in delivering the actives and drugs. The aim of this review is to discuss the hydrogels application in both dermatological therapeutic and cosmetic applications, covering the types, and properties of hydrogel polymer networks. Furthermore, this review presents one of the most extensively used polymers (Hyaluronic acid HA) in this field and discusses the properties, preparation methods and applications of HA hydrogel patches as a delivery system, including methods of loading the actives and the releasing of these actives from them.

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.

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.

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 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.

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.

Modern day construction is widely influenced using concrete-steel composite columns. A lot of research on concrete-steel composite columns is being carried out around the world. The rapid growth in concrete-steel composite construction has widely decreased the use of conventional reinforced cement concrete construction (R.C.C), and the steel construction practices. The concrete-steel composite construction has obtained an extensive receiving around the globe. As Pakistan is a developing country, so, it is relatively a new concept for its construction industry when compared with the developed countries around the globe. Although, the R.C.C construction is suitable and economical for construction of framing systems of low-rise buildings, however, the increased dead load, span restrictions, less stiffness and risky formwork makes it uneconomical and unviable when it comes across the construction of intermediate to high-rise buildings. This research is an effort to learn the cost effectiveness, increased or decreased stiffness, and change on the functionality of the composite construction for intermediate to high-rise buildings constructed in Pakistan. A Base + Ground + 11 storey commercial building is selected for this study. A comparison is made between conventional R.C.C columns structure and concrete-encased composite columns structure. The equivalent static nonlinear analysis is performed using ETABS 2017 software. Although, for Base + Ground + 11 storey building, the construction cost of concrete-encased composite structure is 7.7% more than the conventional R.C.C columns structure, but the concrete-encased composite structure will have 13.013% more constructed floor area. This increased floor area helps to settle the cost difference between the two structures.

The Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to dis-cover holistic, multi-perspective (e.g., local, cultural, national, and international) and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media & Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months starting March 2021 to August 2022 to showcase public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. Discovering system parameters are re-quired to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The proposed approach improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles.

Increasing genetic diversity in crop plants has been used for chemical mutagenesis. Through the application of various mutagenic agents, over 430 new varieties have been derived as rice mutants (Oryza sativa L.) Chemical mutagens such as ethyl methane sulphonate (EMS), diepoxybutane derivative (DEB), sodium azide, and gamma ray, x-ray, and quick neutron irradiation have been commonly used to induce a large number of functional variations in rice and others crops. Among chemical mutagens, ethyl methane sulfonate (EMS) is the alkylating agent most widely used in plants because it induces nucleotide substitutions to be extremely frequent, as detected in various genomes. In this study, seeds of potential genotype of the popular variety, (Oryza sativa L. Super Basmati variety) were treated with EMS at concentrations of 0.25%, 0.50%, 0.75%, 1% and 1.5%. Various measurements on the M1 generation determined EMS sensitivity. As concentration of applied EMS increased, will decrease in germination, shoot length, root length, plant height, productive tillers, Panicle Length, Total Spikelet, sterile spikelet and fertility under field conditions were observed in M1 generation as compared to the non-treatment control. Emergence, shoot length, root length, plant height, productive tillers, Panicle Length, Total Spikelet, sterile spikelet and fertility also decreased with increases in EMS mutagenesis in an approximately linear fashion. The LD₅₀ values were observed based on growth reduction of seedlings after EMS treatment with 0.25% and 0.50% on the rice variety (Oryza sativa L. spp.).

This article investigates the features of heat and mass transfer for the steady two-dimensional Williamson nanofluid flow across an exponentially stretched surface depending on suction/injection. The boundary conditions incorporate the impacts of the Brownian motion and thermophoresis boundary. The analysis of heat transfer is carried out for the two cases of prescribed exponential order surface temperature (PEST) and prescribed exponential order heat flux (PEHF). The ongoing flow problem is mathematically modeled under the basic laws of motion and heat transfer. The similarity variables are allowed to transmute the governing equations of the problem into a similarity ordinary differential equation (ODEs). The solution of this reduced non-linear system of ODEs is supported by the Homotopy analysis method (HAM). The combination of HAM arrangements is acquired by plotting the h-curve. In order to evaluate the influence of several emergent parameters, the outcomes are presented numerically and are plotted diagrammatically as a consequence of velocity, temperature and concentration pro les.

A layout of urban waste fired zero emission power plant is described in this paper. The principle of layout, which comes from similar coal-fired plants retrieved from the literature, integrates gasification with a power generation section, and implements two parallel conversion processes, one supplied by the heat of the gasifier consisting of a thermoacousticmagnetohydrodynamic (TAMHD) generator, while in the second one the syngas is treated in order to obtain almost pure hydrogen, which is fed to fuel cells. The CO2 deriving from the oxidation of Carbon base is stocked in liquid form. The novelty of the proposed layout lies in the fact that the entire conversion is performed without solid moving parts. The resulting plant avoids any type of emissions in the atmosphere, increases mechanical efficiency as compared to traditional plants, thanks to the absence of moving parts, nonetheless, resolving at its root the ever-increasing waste-related pollution problems.

Wind energy is becoming an essential source of power for countries which have the aim to reduce greenhouse gases emission and mitigate the effects of global warming. The Wind Turbines (WTs) installed around the globe is increasing significantly every year. The dramatic increase in wind power has encountered quite a few challenges, among which the major issues are availability and reliability. The unexpected failure in WTs Gearbox (GB) ultimately increases the Operation and Maintenance (O&M) cost. The identification of faults in the earlier stages before it turns to catastrophic damage to other components of WT is crucial. This research deals with the prediction of WT failures by using a Supervisory Control and Data Acquisition (SCADA) system. The main aim is to forecast the temperature of the WTs GB to predict the impending overheating of the GB at an early stage. The earlier prediction will help to optimize the maintenance period and to save maintenance expenses and, even more important, generate warnings in due time to avoid major damages or even technical disasters. In the proposed method we compared six different machine learning (ML) models based on error and accuracy of prediction. The bagging regressor is the best ML model, which results in the mean square error of 0.33 and R of 99.8 on training data. The bagging regressor is then used to predict the fault in the WT GB, which detected the anomalous behavior of WT GB 59 days earlier than the actual failure. This model also detects the extremely unusual behavior of the GB 9 days earlier than a complete failure.

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).

The cross-linked microspheres were prepared and loaded with Favipiravir SARS-CoV-2 antiviral drug, by copolymerization of chitosan (CS) with a polysaccharide extracted from fresh pomegranate peels. Moreover, glutaraldehyde (Glu) has been used as chemical cross-linker ,and sodium hexametaphosphate (SHMP) as physical cross-linker. The extracted polysaccharide was analyzed and different techniques have been used and the analyses leads to the fact that it is pectin .The surface morphology of the prepared microspheres was studied use scanning electron microscope where the size and the shape factor (S) of Glu microspheres have shown high values(74.27μm) and (0.852) respectively means their surfaces are tends to roughness, whereas the SHMP microspheres have shown size particle(20.47μm)and shape factor (0.748) which gives indication that the SHMP microspheres have smooth surfaces. The swelling studies have shown that Glu microspheres have higher degree of swelling which means SHMP microspheres are more compact. The prepared microspheres have shown more Loading percentage of Favipiravir antiviral drug in SHMP microspheres(37%w/w)in comparison with Glu microspheres(35%w/w) where the electrostatic interaction between the Favipiravir ions and SHMP anions help for more loading. The microspheres prepared under different types of cross-linking have shown initially burst release of Favipiravir and followed by a step of controlled release for a certain time where its period depends on the pH of the release media. Both Glu and SHMP cross-linked microspheres have shown high controlled release time in buffered release solution of pH 7.4 and for a less periods in pH 1.3 and pH 9.4 which may related to the type of electrostatic interactions between drug and polymer system and their reactions with release solution ions.

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.

This article aims to reflect upon the relevance of Decolonization methodologies with the Theses on Feuerbach. Somehow, all the Indigenous scholars started from new Marxist like Paulo Freire, Frantz Fanon, but not from classic Marx. To us, the German Ideology of young Marx only resembles the pioneering sources of Indigenous methodology. This discussion is thus a reflection of our studies and a philosophic endeavor to talk about the marginal people of the world, and the scholars who engaged in and with the oppressed. However, we are not prepared to turn our attention away from all the vastness of Marx to a collection of potentially equally relevant to Indigenous methodology. This article concludes that the Theses on Feuerbach is the core of Marxist archaeology of knowledge or philosophy as a whole and has been wading in the Indigenous paradigm.

The sustainability of the planet and its inhabitants is in dire danger and is among the highest priorities on global agendas such as the Sustainable Development Goals (SDGs) of the United Nations (UN). Solar energy -- among other clean, renewable, and sustainable energies -- is seen as essential for environmental, social, and economic sustainability. Predicting solar energy accurately is critical to increasing reliability and stability, and reducing the risks and costs of the energy systems and markets. Researchers have come a long way in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracies, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weathers due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for Sustainable Energy), a novel deep learning-based auto-selective approach and tool that, instead of generalising a specific model for all climates, predicts the best performing deep learning model for GHI forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyse the tool in great detail through a range of metrics and methods for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Forecast Skills (FS), Relative Forecasting Error, and the normalised versions of these metrics. The proposed auto-selective approach can be extended to other research problems such as wind energy forecasting and predict forecasting models based on different criteria (in addition to the minimum forecasting error used in this paper) such as the energy required or speed of model execution, different input features, different optimisations of the same models, or other user preferences.

Telemedicine is the use of Information and Communication Technology (ICT) for clinical health care from a distance. The exchange of radiographic images and electronic patient health information/records (ePHI/R) for diagnostic purposes has the risk of confidentiality, ownership identity, and authenticity. In this paper, a data hiding technique for ePHI/R is proposed. The color information in the cover image is used for key generation, and stego-images are produced with ideal case. As a result, the whole stego-system is perfectly secure. This method includes the features of watermarking and steganography techniques. The method is applied to radiographic images. For the radiographic images, this method resembles watermarking, which is an ePHI/R data system. Experiments show promising results for the application of this method to radiographic images in ePHI/R for both transmission and storage purpose.

Advancements in technology, policies, and cost reductions have led to rapid growth in wind power production. One of the major challenges in wind energy production is the instability of wind power generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning are better than traditional methods, like numerical weather prediction, statistical models, and machine learning models. This is more true for short-term prediction. Since there is a relationship between methods, climates, and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper proposes a novel model that combines the variational mode decomposition method with a long short-term memory model, developed for next-hour wind speed prediction in a hot desert climate, such as the climate in Saudi Arabia. We compared the proposed model performance to two other hybrid models, six deep learning models, and four machine learning models using different feature sets. Also, we tested the proposed model on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill between 61% to 74% based on mean absolute error, 64% to 72% based on root mean square error, and 59% to 68% based on mean absolute percentage error for locations in Saudi Arabia.

To protect power lines, distance relays are used which can rely on a modified distance resistance. But usually the operating range of these relays changes as the network conditions change (network topology, load value, output value, etc.) and leads to false trips, By using methods that can process information and recognize patterns, for example, the use of micro and intelligent processor algorithms can use the new relays with high precision and thus provide adequate protection. In this study, a distance relay was modeled using a neural network, and it was observed that the neural relay had higher accuracy than the conventional relay. In addition to detecting the fault and its location, type and phase of the fault, three-stage simultaneous protection can be performed. As a result, the number of linear relays can be reduced by using relays based on neural technologies. An MLP (multilayer perceptron) neural network is used to model a sequence of distances.

This study investigates the impact of Foreign Direct Investment (FDI) on economic growth and examines the causality between FDI and economic growth in Bangladesh during 1972-2013. Gross Domestic Product (GDP), export performance (EXP), Foreign Direct Investment (FDI), and Gross Fixed Capital Formation (GFCF) are considered to capture the objective of the study. The study methodology includes some systematic steps. As the data used in the study is time-series in nature, the author employs unit root tests, and in this case, Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests are used. Then Johansen’s cointegration test, Granger causality test, regression with Newey-West Standard Error and Vector Error Correction Model (VECM) are applied. By using the ADF and PP test the study reveals that the variables of four-time series are integrated of I (1) i.e. they are stationary at first difference. Regression analysis result demonstrates that FDI has a positive effect on economic growth. The Granger Causality test discloses that there is a unidirectional relationship between FDI and economic growth. But the VECM estimation finds that in the long run FDI negatively affects economic growth.

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.

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

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

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

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

The Aminofunctional starch/Carboxylated nitrile butadiene rubber (ANS/XNBR) latex films were prepared with control (0), 5, 10, 15, and 20 phr of ANS loadings. The films were subjected to soil burial test for 8 weeks, which recovered every two weeks to quantify the degraded properties. The control (week 0) and biodegraded films were subjected to physical tests (crosslink density, tensile, and tear strength tests) and biodegradation quantification (mass loss retention, and water vapor transmission rates). The optimum physical properties of ANS/XNBR films were subjected to further assessment of its degradation properties which include morphological (optical and scanning electron microscopes (SEM)), Fourier transform infrared (FTIR) analysis, and thermogravimetric analysis (TGA) respectively. Overall, mechanical properties of biodegraded ANS/XNBR latex films decreased as biodegradation period elapses. The biodegradation assessment via water vapor transmission rates and mass loss analyses indicates the high degree of biodegradation rates were observed with higher loading of ANS/XNBR latex films. Morphological analyses via optical microscope shows the transformation of the films surface opacity during biodegradation test. SEM images depicted the microorganism remains on the film surfaces. For FTIR analysis, the most significant changes in the spectras of the films appeared in the region of 790–3000 cm⁻¹. TGA thermograph shows the thermal retention of the materials decreased, as the biodegradation period elapses for optimum ANS/XNBR latex films. From the results obtained, ANS/XNBR latex films shows optimum degradation and mechanical properties at 10 phr filler loading.

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.

Vehicle driveability is one of the important vehicle attributes in range-extender electric vehicles due to the electric motor torque characteristics at low-speed events. The process of validating and rectifying vehicle driveability attributes is typically utilised by a physical vehicle prototype that can be expensive and required several design iterations. In this paper, a model-based energy method to assess vehicle driveability is presented based on a high-fidelity 49 degree-of-freedom powertrain and vehicle systems. Multibody dynamics components were built according to their true centre of gravity relative to the vehicle datum for providing an accurate system interaction. The work covered a frequency at less than 20 Hz. The results that consisted of the component frequency domination are structured and examined to identify the low-frequency sensitivity based on different operating parameters such as a road surface coefficient. An energy path technique was also implemented on the dominant component by decoupling its compliances to study the effect on the vehicle driveability and low-frequency response. The outcomes of the research provided a good understanding of the interaction across the sub-systems levels. The powertrain rubber mounts were the dominant components that controlled the low-frequency contents (< 15.33 Hz) and can change the vehicle driveability quality.

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

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.

Farmers’ conventional tillage (CT) and residue removal practices in rice-maize systems in South Asia’s Eastern Gangetic Plain (EGP) are input-intensive, costly and soil degradative. We conducted a rice-maize-mungbean (R-M-MB) system experiment with six tillage and three residue management treatments in Bangladesh representing the EGP. Maize yields were significantly (p≤0.05) higher under permanent (PB) or fresh (FB) beds and strip tillage (ST) than CT but no differences in mungbean yields. Rice yields under PB, FB and CT were similar, but significantly higher than under zero or minimum tillage and ST. Yields of all crops increased significantly (p≤0.05) with residue retention compared to no retention. Total system productivity was highest under PB followed by FB and ST. Compared with CT, gross margins in PB, FB and ST increased by 18, 13 and 11%, and soil organic matter (SOM) and total N contents across tillage treatments increased by 11-16% and 12-24%, respectively. After three years, SOM and total N and available P and S contents increased significantly (p≤0.05) by residue retention. Results demonstrate the potential of PB, FB and ST with residue retention, for improving the productivity, profitability and soil health under R-M-MB systems in Bangladesh and similar soils in the EGP.

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.

Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

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.

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 was conducted in the Dhanu River and adjacent waters at Mithamain upazila (sub-district) under Kishoreganj district of Bangladesh to prepare a check list of available native fishes with their availability status, conservation perspectives, habitat preferences, population trends and intimidations. Data were collected monthly by direct field survey, focus group discussions, and personal interviews with fishers by using a semi-structured questionnaire and a pictorial check list of fish species. A total of 91 indigenous fish species of 59 genera belonging to 29 families under 11 orders were documented where 17.58% species was abundantly available, 27.47% was commonly available, 31.87% was moderately available and 23.08% was rarely available. Cypriniformes was found as the dominant order, consisting 37.36% of the fish species aggregation and Cyprinidae was the most dominant family with 32.97% of the entire species assemblage. Twenty four piscine species (26.37%) were under threatened category in Bangladesh which subsumed 3 critically endangered (3.29%), 11 endangered (12.08%), and 10 vulnerable species (10.99%). Notably globally threatened Cirrhinus cirrhosus, Channa orientalis, and Wallago attu were available there. Fish population trends of 24.18% and 59.34% of the entire fish species was found in decreasing trends in global and national level, respectively. Leading intimidation to the fish diversity was indiscriminately overfishing, followed by fishing by dewatering of wetlands, katha fishing method, use of deprecated fishing gears, climate change, etc. Minimization of anthropogenic impacts, assuring the flux of water round the year, enactments of fish laws, installation and management of fish sanctuaries, and raising public awareness can be effective for the conservation of existing fisheries resources.

When it comes to financial transactions and counterfeit detection systems, banknote classification is crucial. In this paper, we propose an approach for the classification of Kazakhstan banknote images integrating Learning Vector Quantization (LVQ) with Statistical Texture Feature Extraction using the Gray Level Co-occurrence matrix (GLCM). Our methodology demonstrates effectiveness in accurately classifying banknote images, as evidenced by experimental results. The comprehensive testing scenarios show promising outcomes, with the combination of GLCM and Color Histogram under the LVQ algorithm achieving a high accuracy of 94.87 percent at distance 1 and 90°. These findings indicate the robustness and practical viability of the proposed method for authenticating banknotes.

The Pashmina and Barbari are two famous goat breeds found in the wide areas of the Indo-Pak region. Pashmina is famous for its long hair-fiber (Cashmere) production while Barbari is not-selected for this trait. So, the mRNA expression profiling in the skin samples of both breeds would be an attractive and judicious approach for detecting putative genes involved in this valued trait. Here, we performed differential gene expression analysis on publicly available RNA-Seq data from both breeds. Out of 44,617,994 filtered reads of Pashmina and 55,995,999 of Barbari which are 76.48% and 73.69% mapped to the ARS1 reference transcriptome assembly respectively. A pairwise comparison of both breeds resulted in 47,159 normalized expressed transcripts while 8,414 transcripts are differentially expressed above the significant threshold. Among these, 4,788 are upregulated in Pashmina while 3,626 transcripts are upregulated in Barbari. Fifty-nine transcripts harbor 57 genes including 32 LOC genes and 24 are annotated genes which were selected on the basis of TMM counts > 500. Genes with ectopic expressions other than uncharacterized and LOC symbol genes are Keratins (KRT) and Keratin Associated Proteins (KRTAPs), CystatinA&6, TCHH, SPRR4, PPIA, SLC25A4, S100A11, DMKN, LOR, ANXA2, PRR9 and SFN. All of these genes are likely to be involved in keratinocyte differentiation, sulfur matrix proteins, dermal papilla cells, hair follicles proliferation, hair curvature, wool fiber diameter, hair transition, hair shaft differentiation and its keratinization. These differentially expressed reported genes are critically valuable for enhancing the quality and quantity of the pashmina fiber and overall breed improvement. This study will also provide important information on hair follicle differentiation for further enrichment analyses and introducing this valued trait to other goat breeds as well.

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.

Technological advancements and innovations have profoundly changed the lives of people giving rise to smart environments, cities, and societies. As homes are the building block of cities and societies, smart homes are critical to establishing smart living and are expected to play a key role in enabling smart cities and societies. The current academic literature and commercial advancements on smart homes have mainly focused on developing and providing smart functions for homes to provide security management and facilitate the residents in their various activities such as ambiance management. Homes are much more than physical structures, buildings, appliances, operational machines, and systems. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, the roles of smart homes that would serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, rather fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public using over a hundred thousand research papers and nearly a million tweets. We develop a methodology using deep learning, natural language processing (NLP), and big data analytics methods and apply it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific, policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.

We live in a complex world characterised by complex people, complex times, and complex social, technological, and ecological environments. There is clear evidence that governments are failing at most public matters. The recent COVID-19 pandemic is a high example of global governance failure both at preventing such pandemics and managing the COVID-19 pandemic. It is time that all of us take responsibility and look into ways of collaboratively improving the governance of public matters, our matters. While there are many reasons for government failures, we believe the lack of information availability is a fundamental reason that limits the government’s ability to act smartly and allows the lack of transparency to creep into policy and action leading to corruption and failure. To this end, this paper introduces the concept of deep journalism, a data-driven deep learning-based approach for discovering multi-perspective parameters related to a topic of interest. We build three datasets (a newspaper, a technology magazine, and a Web of Science dataset) and discover the academic, industrial, public, governance, and political parameters for the transportation sector as a case study to introduce deep journalism and our tool DeepJournal (Version 1.0) that implements our proposed approach. We elaborate on 89 transportation parameters and hundreds of dimensions reviewing 400 technical, academic, and news articles. The findings related to the multi-perspective view of transportation reported in this paper show that there are many important problems seen by the public that industry and academia seem to not place their focus on. On the other hand, academia produces much broader and deeper knowledge on the subject such as a wide range of pollutions affecting the people and planet do not get to reach the public eye. Our deep journalism approach could find the gaps and highlight them to the public and other stakeholders.

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.

Heart is composed of muscle cells called cardiomyocytes, including a specialized population, named pacemaker cells, that form the Cardiac Conduction System (CCS), responsible for generating the action potential dictating heart contractions. Failure of the CCS system leads to cardiac arrhythmias requiring complicated therapies and often surgical implantation of electrical pacemakers. However, recent research focusses on development of novel therapies using biological pacemakers aiming to substitute electrical devices. While most signalling pathways and transcription factors involved in the development of the pacemaker cells are known, the upstream regulatory networks need to be predicted through computer-based databases, mathematical modelling as well as functional testing of the regulatory elements in vivo, indicating the need for further research. Here we summarise the current knowledge about the vertebrate myocardial CCS system and development of the pacemaker cells and emphasise areas of future research to clarify the regulation of muscle pacemaker cells and ease development of biological therapies.

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

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.

At present, sustainability and emerging technology are the most expressed issues in any supply chain management (SCM) sector. At the same time, pandemic makes consumers more concerned regarding health, and safe food with a sustainable way to access the current market. Thus, supervision and monitoring of product quality with symmetric traceability information in fresh food and fisheries SCM is significant. Research on food safety and traceability systems based on blockchain, internet of service (IoT), wireless sensor networks (WSN), and radio frequency identification (RFID) provides the solution of constancy from production to consumption. This review focused on the RFID-based traceability systems in fisheries SCM, which have been employed globally in the last fifteen years to ensure fish quality and security. Additionally, a summarized comparison study has presented different sectors’ traceability systems using RFID and their advantages over real-time applications. The outcome of this study will help future researchers to solve the crisis in terms of trust between consumers and the fisheries SCM. Thus, this review would be a guideline and solution for enhancing the reliability of RFID-based traceability in food SCM systems to ensure the integrity and reducing the opacity and asymmetry in the product information.

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.

Solar energy adoption worldwide has expanded exponentially due to a surge in international interest in producing clean energy and the declining cost of solar power plants and their technology. It is anticipated that by 2050, solar will have surpassed fossil fuels to become the primary source of energy. However, one of the main challenges associated with solar energy production is the instability of photovoltaic (PV) power generation because of weather changes. Short-term forecasting of the power output of photovoltaic systems is essential for efficient management of the power grid and energy markets. This paper aims to evaluate the ability of deep learning (DL) models to provide accurate forecasting of hourly global horizontal irradiance (GHI) using different sets of features, including weather and aerosol variables along with solar radiation components. The results show that the best forecast skills are achieved by the long short-term memory autoencoder (LSTM-AE) model.

We live in the information age and, ironically, meeting the core function of journalism – i.e., to provide people access to unbiased information – has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets, people’s skills and education, and businesses and industries from multi-generational perspectives. The Great Resignation and Quiet Quitting phenomena coupled with rapidly changing generational attitudes are bringing unprecedented and uncertain changes to labour markets and our economies and societies, and hence the need for journalistic investigations into these topics is highly significant. We combine big data and machine learning to create a whole machine learning pipeline and a software tool for journalism that allows discovering parameters for age dynamics in labour markets using LinkedIn data. We collect a total of 57,000 posts from LinkedIn and use it to discover 15 parameters by Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-parameters, namely Generations-Specific Issues, Skills & Qualifications, Employment Sectors, Consumer Industries, and Employment Issues. The journalism approach used in this paper can automatically discover and make objective, cross-sectional, and multi-perspective information available to all. It can bring rigour to journalism by making it easy to generate information using machine learning and can make tools and information available so that anyone can uncover information about matters of public importance. This work is novel since none of the earlier works have reported such an approach and tool and leveraged it to use LinkedIn media for journalism and to discover multigenerational perspectives (parameters) for age dynamics in labour markets. The approach could be extended with additional AI tools and other media.

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

A new subspecies of Sideroxylon mascatense (A.DC.) T.D.Penn. in the Western Hajar Mountains, differing from the type subspecies in the colour of the mature fruit, is described. The two colour forms are genetically isolated, showing that these two are distinct taxa.

The ASCE-EWRI reference evapotranspiration (ETo) equation is recommended as a standardized method for reference crop ETo estimation. However, various climate data as input variables to the standardized ETo method are considered limiting factors in most cases and restrict the ETo estimation. This paper assessed the potential of different machine learning (ML) models for ETo estimation using limited meteorological data. The ML models used to estimate daily ETo included Gene Expression Programming (GEP), Support Vector Machine (SVM), Multiple Linear Regression (LR), and Random Forest (RF). Three input combinations of daily maximum and minimum temperature (Tmax and Tmin), wind speed (W) with Tmax and Tmin, and solar radiation (Rs) with Tmax and Tmin were considered using meteorological data during 2003–2016 from six weather stations in the Red River Valley. To understand the performance of the applied models with the various combinations, station, and yearly based tests were assessed with local and spatial approaches. Considering the local and spatial approaches analysis, the LR and RF models illustrated the lowest rate of improvement compared to GEP and SVM. The spatial RF and SVM approaches showed the lowest and highest values of the scatter index as 0.333 and 0.457, respectively. As a result, the radiation-based combination and the RF model showed the best performance with higher accuracy for all stations either locally or spatially, and the spatial SVM and GEP illustrated the lowest performance among models and approaches.

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.

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.

Biodiversity has become an issue of global anxiety over the past decades due to its rapid decline worldwide. Bangladesh as one of the most densely populated countries in the world is no more exception. The country, although, was once very rich in biodiversity, during the last few decades as a consequence of the rapid reduction in forest area, urbanisation, habitat modification, unsustainable natural resources use and collection and overall climate change it has decreased alarmingly. Of late, the government, as a signatory of various regional and international conservation treaties, has taken various initiatives to improve country’s dwindling biodiversity. This paper reviews the present situation of biodiversity in Bangladesh, management trends and major causes of biodiversity loss. A separate statutory body is fundamental to ensure conservation, sustainable use and equitable sharing of benefits arisen from biodiversity in the country.

Despite of being an exceptionally biodiversity rich country, the forest coverage of Bangladesh is declining at an alarming rate. Declaration and management of protected areas in this regard is one of the efforts from government side to tackle the loss of biodiversity. The limited numbers of forest-protected areas (FPA), established to conserve the dwindling forest biodiversity of the country with high pressure on them for timber, non-timber forest products, and fuelwood - makes their management challenging. Moreover, most of the FPAs of the country declared only in the recent decades with very limited infrastructure, manpower and policy support for monitoring and governance. Some people-centred approaches for the management of FPAs and alternative livelihood and income generation subsidies although made available through a few project interventions, their number are still inadequate and performance remains less than satisfactory. This chapter provides a critical review of the FPAs of Bangladesh looking at their role in biodiversity conservation, management challenges, and key lessons from previous management interventions with recommendations for the future. It has been revealed that the FPA system of Bangladesh still poorly represents the diverse forest ecosystems with relatively small forest size and lack of corridors for the movement of wildlife. There are ample opportunities to render co-management of FPAs an effective strategy to minimize the conflicts in FPAs management in the country. It is, however, important to ensure the access of local forest-dependent people to different alternative income generating options that may adequately support their livelihoods.

A significant increase in persimmon production, combined with a higher percentage of fruits intended for export, led to the need to adapt post-harvest technology to ensure that the fruits reach the end consumer in optimal condition. Persimmon is a climacteric fruit, so the respiratory rate and ethylene production regulate its ripening during storage. Depending on the variety, it can be tart or impatient (initially). During mass harvesting, the pulp of persimmon, regardless of its variety, is usually very hard, but then it softens quickly, which creates problems during transportation. The fruit pulp of tart varieties of persimmon is usually lighter than that of tart varieties, but there are fewer filamentous fibers in the pulp of tart varieties that support its structure, therefore, during post-harvest aging, seedless astringent fruits soften faster than less tart fruits with 1-2 or more seeds. In addition, there are many intermediate varieties that change their food-tasting and technological properties depending on the degree of pollination in a single year. This encourages farmers to adhere to various strategies to reduce post-harvest losses. Persimmons are usually sold fresh or dried in the markets. In this article, we sought to compile an up-to-date and brief overview of the latest achievements in the persimmon drying technique, which may arouse interest among farmers and entrepreneurs seeking to implement their projects in this field of production activity.

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

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

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.

One of the most commonly known chronic neurodegenerative disorders, Alzheimer’s disease (AD), manifests the common type of dementia in 60–80% of cases. From a clinical standpoint, a patent cognitive decline and a severe change in personality, as caused by a loss of neurons, is~usually evident in AD with about 50 million people affected in 2016. The disease progression in patients is distinguished by a gradual plummet in cognitive functions, eliciting symptoms such as memory loss, and eventually requiring full-time medical care. From a histopathological standpoint, the~defining characteristics are intracellular aggregations of hyper-phosphorylated tau protein, known as neurofibrillary tangles (NFT), and depositions of amyloid β-peptides (Aβ) in the brain. The~abnormal phosphorylation of tau protein is attributed to a wide gamut of neurological disorders known as tauopathies. In addition to the hyperphosphorylated tau lesions, neuroinflammatory processes could occur in a sustained manner through astro-glial activation, resulting in the disease progression. Recent findings have suggested a strong interplay between the mechanism of Tau phosphorylation, disruption of microtubules, and synaptic loss and pathology of AD. The mechanisms underlying these interactions along with their respective consequences in Tau pathology are still ill-defined. Thus, in this review: (1) we highlight the interplays existing between Tau pathology and AD; and (2) take a closer look into its role while identifying some promising therapeutic advances including state of the art imaging~techniques.

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.

Hybrid power plants have recently emerged as reliable and flexible electricity generation stations by combining multiple renewable energy sources, energy storage systems (ESS), and fossil-based output. However, the effective operation of the hybrid power plants to ensure continuous energy dispatch under challenging conditions is a complex task. This paper proposes a dispatch engine (DE) based on mixed-integer linear programming (MILP) for the planning and management of hybrid power plants. To maintain the committed electricity output, the dispatch engine will provide schedules for operation over extended time periods as well as monitor and reschedule the operation in real-time. Through precise prediction of the PV and wind power output, the proposed approach guarantees optimum scheduling. The precise prediction of the PV and wind power are achieved by employing a predictor of Feed-Forward Neural Network (FFNN) type. With such a dispatch engine, the operational cost of the hybrid power plants, and the use of diesel generators (DGs) are both minimized. A case study is carried out to assess the proposed dispatch engine feasibility. A real-time measurement data pertaining to load, wind, and PV power outputs is obtained from different locations in the Sultanate of Oman. The real-time data is utilized to predict the future output power from PV and the Wind farm over 24 hours. The predicted powers are then used in combination with a PV-Wind-DG-ESS-Grid hybrid plant to evaluate the performance of the proposed dispatch engine. The proposed approach is implemented and simulated using MATLAB. The results of the simulation reveal the proposed Feed-Forward Neural Network’s (FFNN) powerful forecasting abilities. In addition, the results demonstrate that adopting the proposed DE can minimize the use of DG units and reduce plant running expenses.

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.

Food allergy has been recognised by the World Health Organisation as a chronic disease that could lead to fatality. The symptoms can mediate from mild to severe consequences. Some cases have reported that food allergy sufferers are bullied and are discriminated at workplace, thus increasing their stress level, apart from facing difficulty in consuming certain food due to allergy. This has an adverse effect on the quality of life amongst food allergy sufferers. Medical bills are increased when food allergy sufferers become unknowingly exposed to food allergen. Hence, this study investigated food allergens among food allergy sufferers in order to make their lives better. A total of 250 respondents participated in an online survey. Surprisingly, apart from chicken, soybean and other food allergens that have been listed under the Big Eight Allergens, the respondents mentioned some fruits that could trigger allergic reactions, such as rambutans and watermelons. Food allergy sufferers can be allergic to seafood and seafood products as well, such as shrimp paste or locally known as belacan, which refers to fermented shrimp paste cured under the sun. It is a common food ingredient used among Malaysians. The study findings offer practical insights, such as avoidance of food allergens amidst food allergy sufferers and help the government to carefully manage for menu planning. The knowledge contribution of this study reveals several specific food and fruits, as aforementioned, which appear to be common and nutritious food, yet harmful to food allergy sufferers.

Pseudomonas aeruginosa is a critical healthcare challenge due to its ability to cause persistent infections and the acquisition of antibiotic resistance mechanisms. Lack of preventive vaccines and rampant drug resistance phenomenon has rendered patients vulnerable. As new antimicrobials are in the preclinical stages of development, mining for the unexploited drug targets is also crucial. Here, we designed a chimeric vaccine against P. aeruginosa using a subtractive proteomics approach and identified nine unique enzymes as novel drug targets in PAO1 proteome. A total of five unique proteins were selected as potential vaccine candidates based on essentiality, extracellular localization, virulence, antigenicity, pathway association, protein-protein interaction analysis, hydrophilicity, and low molecular weight. These include two outer membrane porins OprF (P13794) and OprD (P32722), a protein activator precursor pra (G3XDA9), a probable outer membrane protein precursor PA1288 (Q9I456), and a conserved hypothetical protein PA4874 (Q9HUT9). These proteins were further analyzed using a reverse vaccinology approach to identify immunogenic and antigenic T cell and B cell epitopes. The best scoring epitopes qualifying for all set criteria were then further subjected to the construction of a polypeptide multi-epitope vaccine construct with cholera toxin B (CtxB) subunit as an adjuvant. The identified drug targets qualifying the screening criteria were: UDP-2-acetamido-2-deoxy-d-glucuronic acid 3-dehydrogenase WbpB (G3XD23), aspartate semialdehyde dehydrogenase (Q51344), 2-amino-4-hydroxy-6-hydroxymethyldihydropteridine pyrophosphokinase (Q9HV71), 3-deoxy-D-manno-octulosonic-acid transferase (Q9HUH7), glycyl-tRNA synthetase alpha chain (Q9I7B7), riboflavin kinase/FAD synthase (Q9HVM3), aconitate hydratase 2 (Q9I2V5), probable glycosyltransferase WbpH (G3XD85) and UDP-3-O-[3-hydroxylauroyl] glucosamine N-acyltransferase (Q9HXY6). For druggability and pocketome analysis crystal and homology structures of these proteins were retrieved and developed. A sequence-based search was performed in different databases (ChEMBL, Drug Bank, PubChem and Pseudomonas database) for the availability of reported ligands and tested drugs for the screened targets. These predicted targets may provide a basis for the development of reliable antibacterial preventive and therapeutic options against P. aeruginosa.

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

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

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

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

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