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.

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.

An in-silico WES approach using the Galaxy platform was adopted in the current study to predict the genetic basis of Premature Ovarian Failure (POF), where three affected patients in a Saudi Arabian family of seven, found associated with X-linked recessive mutations. The current analysis discovered 518,054 variants using FreeBayes variant caller that had 1,461,864 effects on variable sites in the genome revealed by SnpEff software. The causal genetic mutations were filtered and annotated with the ClinVar database using the GEMINI tool. This tool retained 369 pathogenic mutations harboring 130 genes. Among the total, 268 variants positioned on 69 genes are shared with three affected individuals, 61 variants on 23 genes are shared by any two of the affected individuals, and 40 of the variants on 38 genes are present in any one of the affected sample. Two mutations in one of the already POF-associated, POF1B gene were also observed e.g. (i) g.84563135T>A; p.M349L and (ii) g.84563194C>T; p.R329Q in the two affected individuals i.e. IV-I-C & IV-6 in the current data. This gene consists of 17 exons that span the region of >100 kb. The putative function of this gene in regulating the actin cytoskeleton due to homology with myosin tail and maintains a number of oocytes during fetal ovary development. In a nutshell, this Galaxy pipeline facilitates all-in-one to pinpoint not only the known pathogenic gene mutations for this disorder but few other novel genetic variants as well, whose gene-disease association may be validated by further experimental studies.

Mitochondrial Encephalohepatopathy (MEH) is an autosomal recessive neurodevelopmental disorder usually accompanied by microcephaly, white matter changes, cardiac and hepatic failure. Here, we applied the whole-exome sequencing (WES) framework on a trio family data with unaffected non-consanguineous parents and proband (neonate girl) with this inherited disorder. A total of 2,928,402 variants were observed with 2,613,746 SNPs, 112,336 multiple nucleotide polymorphisms (MNPs), 72,610 insertions, 113,207 deletions and 16,503 mixed variants. These variations are responsible for 82,813,631 effects on various genomic regions. Our pipeline uncovered candidate gene mutations from these variants and retained a handful of 5,277 variants harboring 3,598 genes, out of which, 8 genes codes for non-coding RNA while 178 genes are those with high impact severity. Among these 178 variants, 125 are de-novo variants that are not previously reported in the ClinVar database. Consistent to previous studies, the leftover high impact severity genes are involved in encephalopathy, Leigh syndrome, Charcot–Marie–Tooth disease, global developmental disorder, seizures, spastic paraplegia, premature ovarian failure, mitochondrial myopathy-cerebellar, ataxia-pigmentary, retinopathy syndrome, ocular and retinal degeneration, deafness, intellectual disability, cardiofacioneurodevelopmental syndrome etc. All these clinical features were also observed in the patient studied. The current analysis highlights and expands the genetic architecture of the MEH phenotype. Furthermore, this pipeline on trio family data significantly broadens the concept of its usefulness as a first-tier diagnostic method in the detection of complex multisystem phenotypic disorders.

Chronic Kidney Disease (CKD) is one of the worldwide health threats, kidneys may be affected by internal pathologies and environmental factors as well as a number of genes are also involved in the kidney dysfunctioning, so UMOD is one of these genes which has significant effect in regulating urea and creatinine level in blood and decreased excretion of uric acid which causes interstitial damages in kidney and may result CKD. Hence, the present study is envisaged to observe the association of this gene locus (r.1264T>C) with CKD in Pakistani population using ARMS-PCR genotyping, for the purpose, a total of 75 samples were investigated, out of which 45 were found homozygous wild-type, 21 heterozygous and 09 homozygous mutants. PLINK data analysis toolset showed that our sampled population does not obeys Hardy-Weinberg Equilibrium by χ² (2, N = 75) = 18.87, p = and Chi-square statistics with p = 1.40×10^-005 showing a significant genotypic association with the subject phenotype with an alternative allele frequency of 0.15 and 0.48 within cases & controls respectively. Furthermore, an odds-ratio of 0.2 predicts that the risk of wild-type allele (A) is 0.8 times higher in patient as compared to controls and mutant allele is 0.2 times preventive in patients. Additionally, a few bioinformatics tools e.g., ProtParam, PsiPred, TMHMM-2.0, Motif Scan, ScanProsite, NetPhos3.1, GPS PAIL2.0, NetOGlyc4.0, PRmePRed, PDB-RCSB and STRING were applied to predict different characteristics of Uromodulin protein including physicochemical properties, secondary/transmembrane structures, protein conserved domains and posttranslational modifications respectively. This pilot scale study provided a comprehensive insight of the association of aforementioned genetic variant with CKD to identify and evaluate the genetic risk factors along with functional effects of the subject variant which may be used as CKD prevention by adopting DNA screening and genetic counselling recommendations.

Whole genome pooled sequence data of 12 Pakistani Teddy goats is analyzed for positive selection signatures as their breed defining characteristics. Selection imprints left in the Teddy genome are unveiled by genomic differentiation after the successful paired-end alignment of 635,357,043 reads with (ARS1) reference genome assembly. Pooled-heterozygosity ( ) and Tajima’s D (TD) are applied for validation and getting better hits of selection signals, while pairwise F_ST statistics is conducted on Teddy vs. Bezoar (wild goat ancestor) for genomic differentiation. Annotation of regions under positive selection reveals 59 genes underlying production and adaptive traits. score ≥ 5 detected six windows having highest scores on Chr. 29, 9, 25, 15 and 14 that harbor HRASLS5, LACE1 and AXIN1 genes which are candidate for embryonic development, lactation and body height. Secondly, TD value of ≤ -2.2 showed 4 windows with very strong hits on Chr.5 & 9 harbor STIM1 and ADM genes related to body mass and weight. Lastly, F_ST analysis generated three strong signals with threshold ≤ 0.42 on Chr.12 & 5 harbor ITGB1 gene associated with milk production & lactation traits. Other significant selection signatures encompass genes associated with wool production, prolificacy, immunity and coat colors. In brief, this study identified the genes under selection in this Pakistani goat breed that will be helpful to refining future breeding policies and converging required productive traits within and across other goat breeds and to explore full genetic potential of this valued livestock species.

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.

This study compares the effects of lecture-based instruction and process-oriented guided inquiry learning (POGIL)-based instruction on the self-efficacy performance of Grade 12 students. A quasi-experimental, pretest-posttest design was adopted to investigate and assess the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes; Knowing, Applying and Reasoning (KAR). Two government high schools in Alain were selected as research sites, one for the boys and one for the girls. The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys), and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a circular motion unit in physics using POGIL-based instruction, while the control group was taught the unit using lecture-based instruction. The study's findings showed statistically significant differences between students of the control group and the treatment group in favour of the latter regarding their science performance, self-efficacy and science-related attitudes. Moreover, positive correlations were found between participants’ performance at the KAR test, self-efficacy and scientific attitudes towards scientific inquiry after the intervention. In conclusion, it is recommended to shift teaching towards POGIL-based instruction due to its positive impact on students’ performance and self-efficacy. It is also suggested to replicate the study to include government and private schools, elementary and high schools, teachers and advisors.

The aim of this study was to investigate the impact of Process Oriented Guided Inquiry Learning (POGIL)-based instruction versus lecture-based instruction on Grade 12 students’ performance in circular motion unit. A quasi-experimental, pretest-posttest design, the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes: Knowing, Applying and Reasoning (KAR). The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys) and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a unit of circular motion in physics using POGIL-based instruction while the control group was taught the unit using lecture-based instruction. The findings of the study showed statistically significant differences between students of the control group and the treatment group in favor of the later regarding their circular motion performance, suggesting the superiority of the POGIL in enhancing student understanding of the circular motion.

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.

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.

In this article, our main purpose is to establish a new extension of Mittag-Leffler function by using the known extended beta function $\mathbf{B}_{\omega}(a,b;p)$ introduced in [1]. It led to a novel extension of the applicability of Mittag-Leffler function that introduced them as distributions defined for a specific set of functions. We also, investigate some of its important properties, namely recursion relation, Mellin transform and differential formulas.

SIRT6 is a NAD+ dependent enzyme and stress response protein that has sparked the curiosity of a plethora of researchers in different branches of the biomedical sciences. A unique member of the known Sirtuin family, SIRT6 has several different functions in several different molecular pathways related to DNA repair, glycolysis, gluconeogenesis, tumorigenesis, neurodegeneration, cardiac hypertrophic responses and so on. Only in recent times however did the potential usefulness of SIRT6 come to light as we learned more about its biochemical activity, regulation, biological roles and structure [1]. Even until very recently, SIRT6 was known more for chromatin signaling but being a nascent topic of study, more information has been ascertained and its potential involvement in major human diseases namely, diabetes, cancer, neurodegenerative diseases and heart disease has been demonstrated. It is pivotal to explore the mechanistic workings of SIRT6 since future research may hold the key to engendering strategies, involving SIRT6, that may have significant implications for human health and expand upon possible treatment options. In this review, we are primarily concerned with exploring the latest understanding of SIRT6 and how it can alter the course of several life-threatening diseases that cripple today’s society such as processes related to aging, cancer, neurodegenerative diseases, heart disease and diabetes. In addition, SIRT6 has shown to be involved in liver disease, inflammation and bone related issues but more emphasis is given to the former. Lastly, any recent promising pharmacological investigations and study of potential therapeutic targets are also delineated in this review.

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.

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.

Even though Alzheimer’s disease (AD) is of significant interest to the scientific community, its pathogenesis is very complicated and not well-understood. A great deal of progress has been made in AD research recently and with the advent of these new insights more therapeutic benefits may be identified that could help patients around the world. Much of the research in AD thus far has been very neuron-oriented; however, recent studies suggest that glial cells, i.e., microglia, astrocytes, oligodendrocytes, and oligodendrocyte progenitor cells (NG2 glia), are linked to the pathogenesis of AD and may offer several potential therapeutic targets against AD. In addition to a number of other functions, glial cells are responsible for maintaining homeostasis (i.e., concentration of ions, neurotransmitters, etc.) within the central nervous system (CNS) and are crucial to the structural integrity of neurons. This review explores the: (i) role of glial cells in AD pathogenesis; (ii) complex functionalities of the components involved; and (iii) potential therapeutic targets that could eventually lead to a better quality of life for AD patients

Water scarcity, particularly in arid and semi-arid regions, poses a significant challenge to crop production, with drought being a prevalent factor limiting grain production globally. Enhancing agricultural water use efficiency is crucial for managing water resources effectively in these areas. Water resource management involves planning, developing, distributing, and optimizing the use of water resources. Water use efficiency management is also essential for crop growth in arid regions. Various agronomic and irrigation strategies can improve crop water use efficiency and drought tolerance. The primary goal of these strategies is to maximize irrigation efficiencies by applying the precise amount of water needed to restore soil moisture to the desired level. These sustainable practices not only reduce the cost of water and labor for farmers through reduced irrigation but also optimize the use of soil moisture storage. This review paper explores practical techniques to improve water use efficiency, highlighting the role of nutrients in enhancing water use efficiency in crop plants.

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.

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.

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.

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.

Evolution remains an incessant process in viruses, allowing them to elude host immune response and induce severe diseases, impacting the diagnostic and vaccine effectiveness. Predicting emerging viral genomes is crucial, particularly in diseases like dengue, where viruses disrupt host cells, leading to fatal outcomes. Deep learning has been applied to predict dengue fever cases; there has been relatively less emphasis on its significance in forecasting emerging Dengue Virus (DENV) serotype. While Recurrent Neural Networks (RNN) were originally designed for modeling temporal sequences, our proposed DL-DVE generative and classification model, trained on complete genome data of DENV, transcends traditional approaches by learning semantic relationships between nucleotides in a continuous vector space instead of representing contextual meaning of nucleotide characters. Leveraging 2000 publicly available DENV complete genome sequences, our Long Short-Term Memory (LSTM) based generative and Feedforward Neural Network (FNN) based classification DL-DVE model showcases proficiency in learning intricate patterns and generating sequences for emerging serotype of DENV. The generative model showed accuracy of 93% and the classification model provided insight into the specific serotype label, corroborated by BLAST search verification. Evaluation metrics such as ROC-AUC value 0.818, accuracy, precision, recall and F1 score all to be around 99.00%, demonstrated the classification model’s reliability.  Our model classified the generated sequences as DENV-4, exhibiting 65.99% similarity to DENV-4 and around 63-65% similarity with other serotypes, indicating notable distinction from other serotypes. Moreover, the intra-serotype divergence of sequences with a minimum 90% similarity underscored their uniqueness. We analyzed the conserved motifs in the genome through MEME Suite (version 5.5.5). Our research strives to contribute to the ongoing fight against the Dengue virus by offering predictive insights into its genomic evolution. Looking ahead, proactive predictive modeling before mutations occur holds potential for guiding vaccine design and diagnostic kit development.

The emerging concept of planetary health needs to be discussed in a more organized and sustainable way within the global public health and healthcare disciplines. Therefore, planetary health should be considered a cardinal component of the global academic framework for healthcare professionals. The availability of related curricula and courses is crucial to equip health professionals in this relatively new discipline of planetary health. In this review article, we aimed to explore published articles and online databases of courses to summarize the available planetary health education opportunities and discussions for health professionals, to identify the gaps in resource allocation and to suggest future recommendations. We observed a visible re-source inequity in global south with the lack of a universal planetary health module for healthcare professionals. Additionally, there is minimal inclusion of allied health disciplines in this learning process. We, therefore, recommend a dedicated network of motivated healthcare professionals and regional hubs with an agenda to ensure a comprehensive, uniform, and inclusive planetary health education curriculum and practice.

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.

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.

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.).

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.

Alphaviruses are arboviruses that cause arthritis and encephalitis in humans. Eastern Equine Encephalitis Virus (EEEV) is a mosquito transmitted alphavirus that is implicated in severe encephalitis in humans with high mortality. However, limited insights are available into its fundamental biology of EEEV and residue-level details of its interactions with host proteins. In recent years, outbreaks of EEEV have been reported mainly in the United States, raising concerns about public safety. This review article summarizes recent advances in the structural biology of EEEV based mainly on recent single particle cryogenic electron microscopy (cryoEM) structures. Together with functional analyses of EEEV and related alphaviruses, these structural investigations provide clues to how EEEV interacts with host proteins, which may open avenues for the development of therapeutics.

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.

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.

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.

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.

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.

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.

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.

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.

Two experiments were conducted to develop a Nano green manganese and assess its effect on grapevines. The first experiment was microbiological, where several incorporation strategies were utilized to enrich the yeast with manganese, as follows: (1) adding manganese to the liquid medium (growth phase), (2) adding after 24 h of incubation (non-growth phase). As the results, growth phase reduced the possibility of medium contamination. Manganese concentration in the yeast cells was increased as that of manganese sulphate in the medium. Manganese incorporation in yeast cells was 99.93% higher than that of the medium at 0.0195 g/l of manganese. Although the concentration of manganese in the medium raised the OD of yeast cell biomass, manganese sulphate had no passive influence on it. The second experiment was horticultural, done on Flame Seedless grapevines, since both active fresh and frozen yeast enriched with manganese resulted from the first experiment were sprayed twice a year at 10 or 20 cm3/l comparing with manganese sulphate, and manganese chelate. The results demonstrated that yeast extracts in both forms increased the yield and improved fruit properties. The most effective treatment on vegetative growth, yield and fruit properties was the frozen yeast enriched with manganese at 20 cm3/l.

Throughout the ages and times, the need to use everything that reflects women's beauty and helps them maintain their vision and health, and if the need to use the precious materials has been put in place, it is important that they produce the desired results without attention to the harmful chemicals and heavy metals that they contain. Over time, the toxicity of these heavy metals increases in our environment because of their long-term exposure to these pollutants, whether low or high-level in toxicity, animal-prednmost, environment, including air we breathe, water, food, etc. Cosmetics are one of these sources through which humans are exposed to heavy toxic metals. Heavy metals have been estimated in a number of previous studies, and in our study here, we aim to estimate the amount of cadmium metal and study it in various cosmetics such as Lipstick, Eye shadow, Face whitening cream. Two samples were taken from each of the locomoys and cadmium was estimated using the photometer of atomic absorption, one of the samples being expensive and the other low-cost, and purchased from the wholesale markets of Taiz City. Cadmium has been found prominently in these products and the highest rate found in the lowest cost-effective, Eye shadow, that the use of these cosmetic products exposes users to low- conc. heavy metals, which may pose a danger to their health. They are known to be clustered in their biological systems over time, resulting in an imbalance of body and environment. The results found that the low-priced color samples contained a higher concentration of cadmium than the high-price samples in low-priced samples, cadmium concentration was in order of eye shadow > lipstick = face whitening cream. The similar pattern are shown also for higher price product, which are lipstick > face whitening cream > eye shadow.

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.

The laser differential equations are used to transform them into identical coupled maps. Valuable results are deduced during analytical and numerical studies on cavity loss. Phase and spatiotemporal synchronized attractors are observed via quasi-chaos under a certain range of controlling parameters, and symmetry breaking of chaotic attractors due to collision with their basin boundaries, and transpire differently from the previous attractors. During the numerical simulation, it is found that the sequence of repeated strange attractors if the coupling strength further increases, which are orthogonal mirror images (the dynamics of the system is the same at different values of controlling parameters). Moreover, it can help us to predict future problems and their solutions based on current issues, if we develop this model in more general.

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.

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.

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.

Rice grassy stunt virus (RGSV) a member of Tenuivirus family, is very potent and destructive which effects rice crop in many countries, particularly China. Non coding RNAs have important functions in development and epigenetic regulation of gene expression in numerous organsisms. There is three type of small non coding RNAs have been found in eukaryotes, which are small interferring RNAs (siRNAs), microRNAs (miRNAs) and piwi interacting RNAs (piRNAs). Small RNAs (sRNAs) origination is from the infecting virus which is known as virus-derived small interfering RNAs (vsiRNAs), has responsibility for RNA silencing in plants. Virus-induced gene silencing (VIGS) is mainly dependent on RNA silencing (RNAi). Interestingly, RNA silencing happens in plants during viral infections. RNAi technique showed significant results in Nephotettix cincticeps. RNAi technique demonstrated the gene silencing of planthopper Nilaparvata lugens. The proteins P5, pcf4, Dnj, psn5, and pn6 act as potential movement proteins and serve as silencing suppressors for RGSV. VsiRNAs originate from dsRNA molecules which require Dicer-like (DCL) proteins, RNA dependent RNA polymerase (RdRP) proteins, and Argonaute (AGO) proteins. RdRP uses ssRNA for perfect RNA amplification process and can also be used for DCL dependent secondary vsiRNA formation. VSRs interfere with the movement of signals during silencing mechanism. Moreover, intercellular movement of viruses is facilitated by virus-encoded movement protein. RNAi is found in many eukaryotes which are related to transcriptional or post-transcriptional regulation by gene suppression. Transcription is bidirectional in ssDNA viruses which are originated from dsRNA molecules. In this review, we highlighted the biology of Rice grassy stunt virus and its insect vector and its silencing suppressors. This work will be helpful for plant virologists to understand the whole biogenesis mechanism for rice viruses especially RGSV.

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.

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.

Let $\mathcal{\mathcal{S}}_{\cos}^{\ast}$ denote the class of normalized analytic functions $f$ in the open unit disk $\mathbb{D}$ satisfying the subordination $\dfrac{zf^{\prime}(z)}{f(z)}\prec\cos z$. In the second section of this article we find the sharp upper bounds for the initial coefficients $a_{3}$, $a_{4}$ and $a_{5}$ and the sharp upper bound for module of the Hankel determinant $|H_{2,3}(f)|$ for the functions from the class $\mathcal{S}_{\cos}^{\ast}$. The first result of the next section deals with the sharp upper bounds of the logarithmic coefficients $\gamma_{3}$ and $\gamma_{4}$ and we found in addition the sharp upper bound for $\left|H_{2,2}\left(F_{f}/2\right)\right|$. For obtaining these results we used the very useful and appropriate Lemma 2.4 of N.E. Cho et al. [Filomat 34(6) (2020), 2061--2072], and the technique for finding the maximum value of a three variable function on a closed cuboid. All the maximum found values were checked by using MAPLE\texttrademark{} computare software, and we also found the extremal functions in each cases. All of our present results are the best ones and give sharp versions of those recently published in [Hacet. J. Math. Stat. 52, 596--618, 2023].

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.

3D reconstruction from a single 2D input is a classic problem in the field of computer vision. With the advancements in deep learning, the performance of 3D reconstruction has also significantly improved. The reconstruction task is more difficult for objects with no textures or complex deformations. This paper serves as a review of recent literature on 3D reconstruction from a single view, with a focus on deep learning methods from 2018 to 2021. Due to lack of standard datasets or 3D shape representation methods, it is hard make direct comparisons between all reviewed methods. However, this paper reviews different approaches for reconstructing 3d shape as depth maps, surface normals, point clouds and meshes; along with various loss functions and evaluation metrics used to train and evaluate these methods.

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.

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.

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.

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.

The rationale of this study is to explore underlying sustainability assessment constructs in the context of WCSM implementation as a key focus by manufacturing industries in the journey of achieving world-class status in their manufacturing operations. This study primarily intends to collect primary data from 143 plant managers and supervisors through the structured questionnaires at select manufacturing companies in south India on the basis of area-cum-purposive sampling technique. The survey questionnaire is designed exclusively to reflect the sustainability assessment dimensions in terms of social, economic and environmental issues to examine the impetus of WCSM implementation on the competitive advantage of manufacturing companies. The empirical findings of this study clearly revealed that sustainable manufacturing practices, sustainability programs, creation of sustainable products, safety for employee, community and product, operational efficiency and integration of sustainability goals in day-to-day manufacturing activities were considered to be the key indicators by the manufacturing firms in their never-ending journey towards becoming the world class. Moreover, the WCSM practices & techniques adopted by sample manufacturers have a substantial positive influence on the achievement of competitive advantage in their manufacturing and business operations.

Businesses use technological and social media marketing to respond to the Covid-19 pandemic. This study aims to understand better the factors that impact the spread of new technologies, the effectiveness of social media advertising, and the longevity of businesses. The study used a quantitative strategy based on partial least squares structural equation modeling to learn about the phenomenon of interest as much as possible. Dhaka, Bangladesh, small and medium-sized businesses participated in the survey. According to the findings, the popularity of internet and e-business technologies may be attributed to their apparent usefulness and ease of use. It is connecting internet/e-business technology, social media marketing, and the long-term success of small and medium-sized enterprises. Still, the expense factor was insufficient to prove that SMEs engage in social media advertising. Mediating effects between components may be better understood via internet/e-business technology and social media marketing, as shown by this study. This study is the first of its kind in Bangladesh and sheds light on the resilience of SMEs in the face of the Covivirus-19 pandemic, notwithstanding its focus on the capital city of Dhaka.

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.

Thalassemia is one of the most prevalent genetic disorders worldwide which has been previously found to have an association with several physiological and organ complications. Several studies found both its positive and inverse correlation with glomerular filtration rate (GFR). Therefore, in this meta-analysis, we tried to assess the accurate correlation of thalassemia with GFR. We searched in Google Scholar, PubMed, and ScienceDirect, and from the initial 96 articles we finally could include 15 studies. The quality and publication bias assessment confirmed that all the studies were of high to moderate quality with no publication bias. The main outcome of the mean difference (MD) was -6.45, 95%CI: -18.50, 5.60 (P<0.00001), which indicated a negative correlation of GFR with thalassemia. The sensitivity analyses found one study as a slight outlier and reanalyzing the data excluding that study MD was achieved as -13.90, 95%CI: -24.22, -3.59 (P<0.00001) which even strongly supports our main outcome. Our result determined that the GFR is generally higher in healthy people as compared to thalassemia patients.

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.

Stabilized un-doped Zinc Telluride (ZnTe) thin films were grown on glass substrates under vacuum using closed space sublimation (CSS) technique. A dilute copper nitrate solution (0.1/100 ml) was prepared for copper doping known as ion exchange process in the matrix of ZnTe thin film. The reproducible polycrystalline cubic structure of undoped and Cu doped ZnTe thin films with preferred orientation (111) was confirmed by X-rays diffraction (XRD) technique. Lattice parameter analyses verified the expansion of unit cell volume after incorporation of Cu species into ZnTe thin films samples. The micrographs of scanning electron microscopy (SEM) were used to measure the variation in crystal sizes of samples. The energy dispersive X-rays was used to validate the elemental composition of undoped and Cu-doped ZnTe thin films. The bandgap energy 2.24 eV of ZnTe thin film decreased after doping Cu to 2.20 eV may be due to the introduction of acceptors states near to valance band. Optical studies showed that refractive index was measured from 2.18 to 3.24 whereas thicknesses varied between 220 nm to 320 nm for un-doped and Cu doped ZnTe thin film respectively using Swanepoel model. The oxidation states of Zn⁺², Te⁺² and Cu⁺¹ through high resolution X-ray photoelectron spectroscopy (XPS) analyses was observed. The resistivity of thin films changed from ~10⁷ Ω-cm for undoped ZnTe to ~1 Ω-cm for Cu-doped ZnTe thin film, whereas p-type carrier concentration increased from to respectively. These results predicted that Cu-doped ZnTe thin film can be used as an ideal, efficient and stable intermediate layer between metallic and absorber back contact for the heterojunction thin film solar cell technology.

In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policy-makers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing upon established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale but also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

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.

This study comprehensively evaluates the DNA/RNA Defend Pro (DRDP) sample collection buffer designed to inactivate and stabilize patient samples. The primary objectives encompassed assessing DRDP's efficacy in ensuring sample stability, facilitating extraction-free polymerase chain reaction (PCR), and ensuring compatibility with rapid antigen testing.Ninety-five diagnostic nasopharyngeal swab samples tested for influenza A/B, RSV A/B, and/or SARS-CoV-2 were diluted 1/10 with DRDP and anonymized. Stability testing involved incubating 31 positive samples at 4 °C, 20 °C, and 37 °C for 7 days, with regular extraction-free nucleic acid amplification testing (NAAT).Results demonstrated that DRDP guaranteed viral RNA stability at all temperatures for influenza, SARS-CoV-2 and RSV showing stability up to 7 days at 4 °C. DRDP-diluted samples exhibited robust compatibility with direct PCR, as 72 out of 88 samples positive for cobas Liat also tested positive for direct PCR. Non-concordant results could be explained by the 200-fold lower input in the extraction-free NAAT.Compared to NAAT, rapid antigen testing demonstrated lower sensitivity, as expected, while maintaining perfect specificity. Clear cutoffs between positive and negative rapid antigen testing results were observed in both cobas Liat and direct PCR analyses, validating DRDP's compatibility with these techniques. In conclusion, DRDP is an effective stabilizing medium compatible with direct PCR and rapid antigen testing and shows great potential for optimizing diagnostic processes, particularly in resource-limited or time-sensitive scenarios.

The success of a software system is highly dependent upon its quality which is a very critical aspect and is the ultimate goal to be achieved. Defect Management is the most important process in ensuring software quality, it involves defect detection, defect categorization, defect prioritization, defect removal and defect verification. The main purpose of this process is to develop and produce defect free or least defected software systems of high quality. It plays a vital role in gaining customer confidence and satisfaction which is essential for the reputation of the organization developing the system. The central objective of this study is to explain the importance of defect management and its impact on the quality of the software along with various defect management techniques to support the objective.

All-solid-state lithium batteries (ASSLBs) using argyrodite electrolyte materials have shown promise for applications in electric vehicles (EVs). However, understanding the effects of pro-cessing parameters on the ionic conductivity of these electrolytes is crucial for optimizing battery performance and manufacturing methods. This study investigates the influence of electrolyte op-erating temperature, electrolyte operating pressure, electrolyte pelletization pressure, and electro-lyte pelletizing temperature on the ionic conductivity of Li6PS5Cl0.5Br0.5 argyrodite electrolyte (AmpceraTM, D50=10 µm). A specially designed test cell is employed for the experimental meas-urements, allowing for controlled pelletization and testing within the same tooling. The results demonstrate the significant impact of the four parameters on the ionic conductivity of the argy-rodite electrolyte. The electrolyte operating temperature has a more pronounced effect than oper-ating pressure, and pelletizing temperature exerts a greater influence than pelletizing pressure. This study provides graphs that aid in understanding the interplay between these parameters and achieving desired conductivity values. It also establishes a baseline for the maximum pelletizing temperature before undesirable degradation of electrolyte occurs. By manipulating the pelletizing pressure, operating pressure, and pelletizing temperature, battery engineers can achieve the desired conductivity for specific applications. The findings emphasize the need to consider operating conditions to ensure satisfactory low-temperature performance, particularly for EVs. Overall, this study provides valuable insights into processing and operating conditions for ASSLBs utilizing Li6PS5Cl0.5Br0.5 argyrodite electrolyte.

In this work, a highly efficient, molybdenum disulfide (MoS2) based near infrared (NIR) heterojunction photodetector is fabricated on a Si substrate using a cost-effective and simple drop casting method. A non-stoichiometric and inhomogeneous MoS2 layer with a S/Mo ratio of 2.02 is detected using energy dispersive X-ray spectroscopy and field emission scanning electron microscope analysis. Raman shifts are noticed at 382.42 cm-1 and 407.97 cm-1, validating MoS2 thin film growth with a direct bandgap of 2.01 eV. The fabricated n-MoS2/p-Si photodetector is illuminated with a 785 nm laser at different intensities, and demonstrate the ability of the photodetector to work in both regions, the forward biased and reverse biased from above 1.5 V and less than -1.0 V. The highest responsivity, R is calculated to be 0.52 A/W while the detectivity D* is 4.08 x 10^10 Jones for an incident light intensity of 9.57 mW/cm2. The minimum rise and fall times are calculated as 1.77 ms and 1.31 ms for an incident laser power of 9.57 mW/cm^2 and 6.99 mW/cm^2 respectively at a direct current bias voltage of 10 V. The demonstrated results are promising for the low-cost fabrication of a thin MoS2 film for photonics and optoelectronic device applications.

Compressive residual stresses in thin films can inhibit crack propagation under normal or sliding contact loading, with associated enhancement of the coating apparent toughness, load bearing capacity and wear resistance. This study investigates the influence of residual stress distributions on the thin film/substrate adhesion using a nanoindenter coupled with scanning electron microscope (SEM) investigations of indentation induced failure modes. Reactive and un-reactive magnetron sputtering with ion plating was used to coat a (100) silicon substrate with aluminum nitride (AlN) with and without an aluminum (Al) adhesion layer. The presence of an Al bond layer gives additional interfacial tensile stress because of the difference in thermal expansion coefficient. Additionally, a different magnitude of residual stresses in the AlN coating was achieved by changing the applied bias voltage onto the substrate. Wafer curvature method and incremental focused ion beam (FIB) milling, combined with high-resolution in situ scanning electron microscopy (SEM) imaging and full field strain analysis by digital image correlation (DIC), were used to measure the average and in-depth stress residual stress distribution in the produced coatings. The adhesion energy was then quantified by using a nanoindentation based model. Results demonstrate that the additional tensile residual stress in the aluminum adhesion layer decrease significantly the coating adhesion, even in presence of a higher compressive stress state in the AlN top-layer. Therefore, the coatings without Al-layer showed better adhesion because of a more homogeneous compressive residual stress in comparison with the coating having Al layer, even though both groups of coatings are produced under same bias voltage. Results are discussed, and some general suggestions are made on the correlation between coating/substrate property combination and the adhesion energy of multilayer stacks. The results suggested that Al bond-layer and inhomogeneous residual stresses affected the adhesion of AlN negatively to a substrate like silicon.

In this study, a novel porous geopolymer mortar (GP) was produced and tested experimentally. Industrial waste materials/by-products were used as constituents of the GP, along with dune sand. One sample was produced as a control sample for benchmarking. For the rest of the samples, 15%, 30%, and 45% by volume, the solid constituents were replaced with expanded polystyrene foam (EPS) beads. These mortar samples were heat cured to depolymerize the EPS to cause porosity inside the samples. Indoor experiments were conducted to evaluate the response of produced porous GP to high heat flux. The porous samples were able to reduce heat transmission across the opposite surfaces. Induced porosity resulted in a decrement in compressive strength from 77.2 MPa for the control sample to 15.8 MPa for 45% porous sample. However, the limit lies within the standards for partitioning walls in buildings and pavements in urban areas to absorb rainwater.

Pruning is an important part in controlling the branching, good fruit size, and prodctivity of fruit trees. and consequently, ‘Valencia’ orange trees were grown many years ago, the trees were suffering from a decline in productivity in the newly reclaimed lands after certain years of plantation. This study elucidated the effect of pruning severity on growth, yield and fruit quality of ‘Valencia’ orange trees. Four levels of pruning were applied on 35 years old ‘Valencia’ orange trees as follows: T1: Control (un-pruned), T2: Light pruning ( 25% of main branches were removed); T3: Moderate pruning (50% of main branches were removed) and T4: Heavy pruning: 75% of main branches were removed. The treatments were done in February 15th (for each season). The results indicated that pruning severity increased vegetative growth paraments such as shoot length and leaf area and decreased canopy volume of Valencia orange trees in comparison to control. Maximum yield was gained from 75% followed by 50% and 25% pruning severity treatment. The pruning treatments improve in fruit weight, size, firmness, juice content, TSS%, TSS/acid ratio and vitamin C content. In addition, pruning produced an increasing in fruit yield per tree as well as yield increasing %. Hence, it might be concluded that pruning at different severity applied in February can be used effectively on ‘Valencia’ orange trees to improve vegetative growth, fruit physical and chemical properties. Besides, increasing fruit yield by nearly 20% in comparison to control.

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.

There has been a rapid increase in the use of Marine Spatial Planning (MSP) throughout the world recently, partly due to the continued loss of marine biodiversity and habitat. The sustainability of marine resources is threatened in all regions of the world by major events such as climate change, marine pollution, overfishing, illegal, unreported and unregulated fishing both on the high seas and in country waters. Here, we present a comprehensive review and analysis of how economic information has been applied and used to inform decisions about MSP in British Columbia (BC), Canada and other similar jurisdictions. This focus for the paper was selected because important gaps remain in the literature in terms of incorporating economic questions into MSP. We first present different definitions of MSP, and then we extract useful lessons from MSP regimes with well–tested decision support tools, and use this to guide MSP implementation in BC. We finally present and discuss case studies from Australia, South Africa and Belgium. Our review suggests that applying economic information to support the design and implementation of MSPs would lead to better decisions. This in turn would foster livelihoods, attract finance, increase buy–in and advance United Nations Sustainable Development Goal 14: Life Below Water, thereby achieving Infinity Fish, i.e., ensuring that ocean benefits flow to humanity forever.

The treatment of petroleum refinery wastewater (PRWW) is of great interest in the field of industrial wastewater management. The wastewater contains a diverse concentration of contaminants such as oil and grease, petroleum hydrocarbons, phenol, ammonia, sulfides, as well as other organic and inorganic composites etc. The treatment of refinery wastewater has been treated through various processes including; physical, biological, chemical or hybrid methods which combine two or more techniques. Hence, the objective of this review is to summarize the recent research studies applied in the treatment of petroleum refinery wastewater using conventional, advanced as well as integrated treatment techniques. Furthermore, critically highlight the efficiencies and major limitations of each technique as well as prospects for improvement. Several conventional treatment techniques (basically, the physicochemical and biological processes) were discussed. In this context, advanced oxidation processes (AoPs) especially electrochemical oxidation and photocatalysis as well as the integrated/hybrid processes are found effective to remove the recalcitrant fraction of organic pollutants because of their various inherent mechanisms. These techniques could effectively remove COD and phenols concentration with an average removal efficiency exceeding 90%. Hence, the review also presented an elaborate discussion of the photocatalytic process as one of the advanced techniques and highlighted some basic concepts to optimize the degradation efficiency of a photocatalyst. Finally, a brief recommendation for research prospects is also presented.

Re-creation of the past of historical buildings sits at the intersection of the spatio-temporal manifestation of cultural memories, socio-cultural meanings, values and identity re-moulds and refines the existing understanding and sense of place. Digital technologies have become a popular tool in re-creation of the past by creating a new body of knowledge and historical discourse based on identifying the gaps within our written histories. Designers and policymakers around the world have been exploring various tools and technologies such as diachronic modelling yet there is a gap in evidence-based understanding regarding the actual functioning and success of application for place making. This paper, therefore, sets out to scrutinise the role of digital technologies in facilitating digital place making. To do so, it investigates the potential of a new “digital heritage” narrative in the revival of the lost architectural narrative of the Dennys Lascelles Wool Store, Geelong. The proposed paper aims to investigate the potential of a new “digital heritage” narrative and story-telling as a means towards digital place making framework. While exploring the new and unique capabilities provided by the digital narrative in capturing, simulating and disseminating ‘lost’ heritage it will further imbue a sense of place by connecting the everyday city dweller.

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

COVID-19 outbreak started in the Chinese city of Wuhan and spread around the globe in months due to its high contagious level. Hence, this disease posed a great challenge to researchers and mathematicians. Numerous mathematical models have been suggested to visualize the spreading speed and trends of this pandemic. In this paper, a comparison of a widely established and accepted method, the Susceptible-Exposed-Infected-Quarantined-Recovered-Death-Insusceptible (SEIQRDP) model with a newly-proposed fractional-order SEIQRDP is studied. Densely populated Countries of Asia (Pakistan, India, and Bangladesh) have been chosen as data sets and both algorithms have been applied to their data. The same comparison technique has also been used on the data of two polar countries, New Zealand and Russia, that validated our findings. Error comparison of both algorithms has been recorded in a tabulated form which shows multi-fold erroneous trends over WHO data of selected countries.

Alzheimer’s disease (AD) is one of the most prevailing neurodegenerative diseases, characterized by memory dysfunction and the presence of hyperphosphorylated tau and amyloid β (Aβ) aggregate in multiple brain regions, including the hippocampus and cortex. The exact etiology of AD has not yet been confirmed. However, epidemiological reports suggest that populations who were exposed to environmental hazards are more likely to develop AD than those who were not. Arsenic (As) is a naturally occurring environmental risk factor abundant in the Earth’s crust and human exposure to As predominantly occurs through drinking water. Convincing evidence suggests that As causes neurotoxicity and impairs memory and cognition although the hypothesis and molecular mechanism of As-associated pathobiology in AD are not clear yet. However, exposure to As and its metabolites leads to various pathogenic events such as oxidative stress, inflammation, mitochondrial dysfunctions, ER stress, apoptosis, impaired protein homeostasis, and abnormal calcium signaling. Evidence has indicated that As exposure induces alterations that coincide with most of the biochemical, pathological, and clinical developments of AD. Here, we overview existing literature to gain insights into the plausible mechanisms that underlie As-induced neurotoxicity and the subsequent neurological deficits in AD. Prospective strategies for the prevention and management of arsenic exposure and neurotoxicity have also been discussed.

Among others, road traffic, industrial emissions, commercial activities, smoking and cooking are considered as major contributing factors for the increasing levels of pollutants in atmosphere. High levels of potentially toxic metals and microbes in atmosphere, especially in indoor air, may pose serious threat to human health. Therefore, concentration and associated health risks of potentially toxic trace metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) and their risk to human health, and microbial load in indoor air was assessed using air condition (AC) filter dust samples collected from 5 locations representing residential, agricultural and industrial settings of Eastern Province, Kingdom of Saudi Arabia. The levels of trace metals varied considerably among sampling areas, with the highest levels of Cr and Cd recorded in the Industrial-area sites followed by the Agricultural and Urban-Residential sites. The highest levels of Pb and Fe were found in the Agricultural area sites followed by the Industrial and Urban-Residential area sites. The metals in dust sample, especially Cd, Cr and Pb, showed a considerable health risk through dermal pathway. Among the sites, the highest hazard quotient for these metals was found for Al-Qatif-Industrial areas sites and among the metals it was the highest for Cd. The cancer risk from the metals contained in AC filter dust was negligible. Samples collected from Agricultural and Industrial area sites were substantially contaminated with bacteria and fungi, respectively. Bacterial contaminants were mostly Gram Negative, with considerable antibiotic resistance and haemolytic activity. Thus, indoor air quality as assessed by AC filter dust depicted that a considerable health risk could be posed by the trace heavy metals and microorganisms for a long-term exposure. Furthermore, this study demonstrated that AC filters dust could be a unique and reliable test sample for the assessment of indoor environment.

Asia has formally entered the big data era thanks to the ongoing advancement of information technology. the company business management has discovered bright operations management based on technological advances, and the arrival of the big data era has improved work efficiency and brought about a number of economic benefits while also posing a number of challenges, such as the ongoing expansion of business scope. The business's current management model is unable to keep up with the demands of development as its scope continues to grow. The primary development trend for the future is the integration of intelligence technologies with enterprise business management, big data technology application in corporate business management promotes fairness, transparency, and justice across corporate development and consumer activities; it also facilitates the production of goods and the creation of value for products in the direction of socialization. In order to further deepen the understanding of artificial intelligence among relevant personnel, we aim to further elaborate the future growth direction of the application of artificial intelligence in the field of enterprise business management in this paper, which begins with an in-depth awareness of the current situation of the combined use of artificial intelligence technology and big companies’ business management. We then focus on the utilization path of artificial cognitive ability technology in businesses business management.

Cyanobacterial mass developments in eutrophic ponds and lakes are a major concern for lake management, as many cyanobacteria produce a huge variety of toxic secondary metabolites, e.g. microcystins. The aim of this research was to culture a strain of the cyanobacterium Microcystis sp strain BM25, to observe its biomass production and to isolate and purify protease inhibitors from this cyanobacterial biomass. Different secondary metabolites were isolated following a standard bioassay-guideline. Isolation was performed, with an enzymatic protease assay as bioassay. High performance liquid chromatography was used to identify different fractions of secondary metabolite from the strain BM25. Moreover, protease homogenates were isolated from Daphnia magna in order to test the inhibitors against naturally occurring major digestive proteases trypsin and chymotrypsin. It was measured that 60% MeOH and the 80% MeOH C18-SPE fraction inhibits chymotrypsin activity 98% (6 nmol pNA min^-1 mg^-1) and 99 % (4 nmol pNA min^-1 mg^-1), respectively. In contrast, trypsin activity was not inhibited by methanolic extracts of this cyanobacterium strain.

Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

The principal goal of this current study was to isolate a new potential probiotic strain to prepare contamination-free probiotic dahi in a dosage form with desired shelf life. To achieve our target, firstly, probiotic dahi was prepared by a probiotic strain, Lactobacillus acidophilus JB2CON (OM909067) (identified by 16S sequencing) isolated from natural dahi in Dhaka, Bangladesh. The isolate showed five probiotic features by in vitro test and had an antagonistic effect against five different pathogens. More interestingly, it inhibited Escherichia coli (diameter of zone of inhibition = 15.75±0.35) the most. In the time-kill analysis method, E. coli was killed totally within 5.50 hours of fermentation. Finally, the time duration of 14 days was an ideal shelf life, because the probiotic count increased but a slight change in pH did not affect the taste (sourness/acidity) of dahi. The most significant impact of this research is that the probiotic dahi of our experiment might be used as a cheap option for preventing pathogenic Escherichia coli-related foodborne diseases worldwide, especially in underdeveloped countries like Bangladesh.

Diarrhea and fever are prevalent childhood illnesses with potentially severe consequences, especially when they co-occur. This study investigates the prevalence and determinants of diarrhea, fever, and their coexistence among children under-five in Bangladesh. Data from the 2017-18 Bangladesh Demography and Health Survey (BDHS) were analyzed using multivariable stepwise logistic regression with backward selection. This study found that 5.0% for diarrhea, 34.0% for fever, and 3.0% for the coexistence of both illnesses. Common factors associated with childhood diarrhea and fever included the mother's education, her attitude toward wife-beating, and the child’s age (12-23 months). Diarrhea was associated with households with improved water sources and children in the Barisal division, while fever was linked to underweight children and those from more affluent backgrounds. The coexistence of both was significantly linked to underweight children, higher birth orders, and children from the Rajshahi division. Notably, child illnesses were associated with parental education, higher socio-economic status, and access to improved drinking water sources. Diarrhea affects one in 20 children, fever affects one in three, and the coexistence of both conditions affects one in 35 children in Bangladesh. The findings need further research and policy reviews to develop effective interventions and improve child health in Bangladesh.

The present work focuses on developing miniaturized, light weight electrochemical sensors for detection of chemical warfare agents (CWAs) like sarin and tabun simulants, i.e., diisopropyl fluorophosphate (DFP), and O,S-diethyl methyl phosphonothioate (O,S-DEMPT). Differential pulse voltammetry (DPV) was employed to examine the redox properties of capturing molecular probe CE2 with the nerve agent stimulants. Coupling of a portable potentiostat with the drone technology could allow on-situ and remote detection of analytes such CWAs to be realized, which can be crucially important to the national and global securities.

Deep neural networks (DNN) have successfully delivered a cutting-edge performance in several fields. With the broader deployment of DNN models on critical applications, the security of DNNs becomes an active and yet nascent area. Attacks against DNNs can have catastrophic results, according to recent studies. Poisoning attacks, including backdoor and Trojan attacks, are one of the growing threats against DNNs. Having a wide-angle view of these evolving threats is essential to better understand the security issues. In this regard, creating a semantic model and a knowledge graph for poisoning attacks can reveal the relationships between attacks across intricate data to enhance the security knowledge landscape. In this paper, we propose a DNN Poisoning Attacks Ontology (DNNPAO) that would enhance knowledge sharing and enable further advancements in the field. To do so, we have performed a systematic review of the relevant literature to identify the current state. We collected 28,469 papers from IEEE, ScienceDirect, Web of Science, and Scopus databases, and from these papers, 712 research papers were screened in a rigorous process, and 55 poisoning attacks in DNNs were identified and classified. We extracted a taxonomy of the poisoning attacks as a scheme to develop DNNPAO. Subsequently, we used DNNPAO as a framework to create a knowledge base. Our findings open new lines of research within the field of AI security.

In modern automobiles, Infotainment High-Performance Computing (HPC) systems play a vital role in enhancing the capabilities of drivers and passengers by providing advanced features consisting of music, navigation, communication, entertainment, etc. However, as the use of information technology in vehicles increases, it results in cybersecurity threats such as data breaches and the loss of sensitive information. To improve the security of the infotainment system in automotive vehicles, the research conducted threat modeling at the component level using Microsoft’s STRIDE tool and performed risk assessment by using SAHARA (Safety-Aware Hazard Analysis and Risk Assessment) and DREAD methodologies to evaluate associated risks. It provides a systematic representation of threats, associated risks, and generic mitigation strategies to counter cybersecurity attacks. Through the threat modeling process, 34 potential security threats were identified. The study also provided a comparative analysis to calculate the risk values of the threats to prioritize for treatment. These identified threats and associated risks need to be considered to avoid potential cyberattacks, before deploying the infotainment HPC system in real-world automotive vehicles.

COVID-19 pandemic in first seven months has led to more than 15 million confirmed infected cases and 600,000 deaths. SARS-CoV-2, the causative agent for COVID-19 has proved a great challenge for its ability to spread in asymptomatic stages and a diverse disease spectrum it has generated. This has created a challenge of unimaginable magnitude not only affecting human health and life but also potentially generating a long-lasting socioeconomic impact. Both medical sciences and biomedical research have also been challenged consequently leading to a large number of clinical trials and vaccine initiatives. While known proteins of pathobiological importance are targets for these therapeutic approaches, it is imperative to explore other factors of viral significance. Accessory proteins are one such trait that have diverse roles in coronavirus pathobiology. Here we analyze certain genomic characteristics of SARS-CoV-2 accessory protein ORF8, predict upon its protein features and review current available literature regarding its function. We have also undertaken review of ORF8 homolog ORF8ab from SARS-CoV with a purpose of developing holistic understanding of these proteins for reason that coronaviruses have been infecting humans repeatedly and might continue to do so. Despite low nucleotide and protein identity and differentiating genome level characteristics, there appears to be significant structural integrity and functional proximity between these proteins pointing towards their high significance. There is further need for comprehensive genomics and structural-functional studies to lead towards definitive conclusions regarding their criticality and that can eventually define their relevance to therapeutics development.